Full Research Paper - NPS Australia Submission System

Segmentation-Guided Attention Transfer Learning for Breast Cancer Classification in Digital Mammography

1. An annotation-free attention-weighting pipeline combining CLAHE and ROI extraction into a pixel-level input attention map;
2. A two-phase fine-tuning protocol across three architectures under a patient/exam-level, group-aware split, closing a leakage gap present in an earlier image-level evaluation; and
3. A quantitative validation of the generated attention mask against radiologist-drawn ROI annotations (Dice/IoU), complementing the qualitative Grad-CAM++ analysis.

Uncovering Healthcare Concerns Among Bangladeshi Patients Seeking Medical Care in India Using BERTopic

Social media has become an important source of health information and peer support for patients seeking medical treatment. This study aimed to identify and characterize the major information needs, healthcare concerns, and discussion themes of Bangladeshi patients seeking medical treatment in India. This study examines the main discussion themes within two Facebook communities, “CMC Hospital Vellore Treatment Community Bangladesh” and “Bangladesh Treatment Community for Indian Treatment”. Data were scraped from publicly available Facebook posts (n = 1500) using web scraping. BERTopic was applied to analyze the data. This pattern yielded 28 interpretable topics, with coherence scores from 0.2990 to 0.8905. The results also found conversations regarding cardiovascular diseases, kidney diseases, neurological disorders, cancer, infertility, spinal disorders, ophthalmic disorders, liver diseases, gynecological disorders, and gastrointestinal diseases. Additionally, several other non-medical themes were noted, viz., treatment expenses, accommodation, travel, and procurement of medicines, as well as tourism planning, indicating the practical difficulties encountered by patients in a foreign country when seeking healthcare in India. The results reveal that in addition to being a source of medical information, these Facebook groups act as support networks in which patients turn for helpful advice such as what to expect, share personal experiences, and seek emotional support. The findings may assist researchers, clinical teams, and policymakers to gain insights into the information needs of cross-border patients, helping to enhance the provision of patient-centred healthcare services.

FairMoE-Surv: A Fairness-Gated Neuro-Symbolic Mixture-of-Experts for Equitable Clinical Risk Prediction with Cross-Cohort Validation

Abstract—Fairness-aware clinical risk models are often evaluated using aggregate metrics that may obscure disparities across
protected groups and provide limited evidence of generalizability.
We address both issues with FairMoE-Surv, a neuro-symbolic,
fairness-gated mixture-of-experts (MoE) architecture that combines a hybrid learned/symbolic router, clinically grouped mechanism tokens, momentum-updated population-prototype attention,
a top-2-of-6 MoE layer, demographic-parity and proxy-feature
consistency regularizers, and a calibration module. On N =
28,800 real, de-identified CIBMTR allogeneic hematopoietic-celltransplantation records spanning six self-reported race groups,
FairMoE-Surv achieves 89.2% AUROC, 88.9% accuracy, 88.4%
F1, 89.1% precision, 87.8% recall, and 88.7% concordance
index, outperforming CoxPH (83.8% AUROC), Random Survival
Forest (85.2%), DeepSurv (86.1%), DeepHit (84.8%), and MTLR
(86.9%). It also attains the lowest demographic-parity gap (9.6%
vs. 14.1–20.1%), equal-opportunity gap (9.1% vs. 15.3–23.6%),
and expected calibration error (2.8% vs. 4.1–6.7%) across all
six race groups. A five-component leave-one-out ablation shows
that every component contributes: removing the MoE layer
reduces AUROC to 84.6%, while removing the calibration module
reduces it to 86.9% but increases ECE to 3.5%. Retraining the
identical architecture on a disease-mismatched SEER breastcancer cohort (N = 4,024, three race groups) preserves FairMoESurv’s superiority but narrows its fairness margin from 27–46%
to 7–9%. These findings support cross-cohort retraining as
essential for validating fairness generalization.

SpearXAI-Org: Target-Context-Aware Benchmark and Explainable Hybrid Reference Framework for LLM-Assisted Spear-Phishing Detection

Our paper’s biggest contribution isn’t a smarter phishing-detector. In fact, we openly admit that our hybrid classifier itself doesn’t beat a simpler one in any measurable way. What we actually contributed is:

1. A better test set. We built 8,200 emails split into 5 clear types — normal email, generic scams, human-written targeted scams, AI-written targeted scams, and AI-reworded targeted scams. Most past research only asked “scam or not,” lumping AI-written attacks in with everything else. This lets us (and others) test tools specifically against AI-written phishing.
2. A more honest way to test these tools. We checked for common mistakes other studies make — like accidentally letting the same email (or a reworded copy of it) appear in both the “training” and “testing” piles, which makes a tool look smarter than it really is. We fixed that.
3. A real-world reality check. We showed that even a tool that scores 99% in a clean lab test would still flag mostly innocent emails as “false alarms” once used in a real company inbox, where actual scams are rare. This is a really important, often-ignored point.
4. Proof that “fancier” doesn’t always mean “better.” We tested whether combining two AI methods, or adding extra explanation features, actually improved accuracy — and found it mostly didn’t. That’s a useful, honest finding, since many papers overstate how much these extra bells and whistles help.

Bottom line: Our real contribution is a fair, well-tested “rulebook and test set” for judging future phishing-detection tools — not a new, better detector itself.

CMSA-Net: Cross-Modal Sparse Attention and Generative Representation Repair for Multimodal Classification Under Missing Inputs

Abstract—Heterogeneous clinical data streams—electronic
health records (EHR), computed tomography (CT), magnetic
resonance imaging (MRI), and wearable Internet-of-Things (IoT)
signals—offer complementary information for cardiovascular
risk stratification but are often incomplete at inference time.
We propose CMSA-Net, a novel Cross-Modal Sparse Attention
Network to integrate data from multiple modalities using a
transformer encoder with a specific missing modality mask
and top-K sparse cross-modal attention mechanism. In order
to deal with missing modalities arbitrarily, CMSA-Net has a
conditional variational auto-encoder based Generative Modality
Repair (GMR) mechanism that generates robust embeddings
for missing modalities along with a Monte-Carlo Dropout head
for accurate uncertainty quantification. On the standardized
multimodal dataset of 738 encounters, CMSA-Net delivers an
outstanding performance of 0.979 AUROC (bootstrapped 95%
CI: [0.976, 0.982]), 0.972 AUPRC, 0.909 F1, and 0.923 accuracy,
beating the best baseline, an early-fusion MLP, by 0.031 AUROC
and 0.058 F1. Comprehensive robustness analysis confirms invariance of performance across random missing modalities with
AUROC change by less than 0.001 with up to 70% modality
dropout. Moreover, five-fold cross-validation results in average
AUROC 0.983 ± 0.004 with well-calibrated expected calibration
error 0.040. These results establish CMSA-Net as a highly
effective, resilient architecture for robust clinical prediction under
missing-data conditions.

Deep Learning Based Breast Cancer Detection With Web Application

Breast cancer is one of the common cancers for
women in the world. It is also a leading cause of cancer-related
deaths.Early and accurate detection can improving the chances
of recovery for patients.In hand ,it also depends a lot of the
experience for the pathologist.Recent progress in AI and deep
learning has made computer diagnosis more effective.Neural
Networks or CNNs can automatically find patterns in complex
medical images.Deep learning methods have also shown promise
in classifying.ResNet-based models can learn features better
thanks to residual connections.In this study introducing a deep
learning system designed to detect breast cancer from images.The
system classifies images into two categories Malignant and
Benign.Our framework combines MobileNetV3 and ResNet50V2
to capture types of image features that work well together.Using
crossvalidation and Test Time Augmentation TTA to increase the
reliability of predictions.Apply Explainable Artificial Intelligence
or XAI techniques to highlight the parts of the image for the
decision.At the end building a web application to allow real-time
predictions using the trained model.

Leakage-Controlled Tomato Leaf Disease Classification Using Attention-Guided CNN and GCN Feature Fusion

Accurate tomato leaf disease recognition can be
overstated when visually repeated images occur across data
partitions or evaluation is restricted to familiar sources. This
study presents a leakage-controlled classification framework that
combines EfficientNetV2-S, the Convolutional Block Attention
Module (CBAM), an inductive Graph Convolutional Network
(GCN), and feature-level fusion. Starting from 31,450 cleaned
images representing 11 tomato leaf classes, a perceptual-hash
audit identified 1,237 repeated-image groups involving 2,539
images. After removing 24 cross-class conflicts and one corrupted
image, 31,425 images were partitioned group-wise into 21,990
training, 4,718 validation, and 4,717 held-out test samples, with
no verified SHA-256, perceptual-hash, or source-group overlap
across the partitions. The attention-enhanced network produced
1,280-dimensional image embeddings, while a cosine-neighbor
graph with k = 5 and an inductive graph network generated 128-
dimensional relational features.

Embedded Fail-Safe and Real-Time Communication Framework for HTTP-Based Wireless Mobile Robot Teleoperation

The research proposes an Adaptive Communication-Aware Autonomous Motion Termination (Adaptive-CAAMT) topology for HTTP-based wireless mobile robot teleoperation. Its key contribution is the integration of adaptive timeout supervision, network-quality-based motion degradation, heartbeat synchronization, timestamp validation, and autonomous fail-safe motor shutdown to improve communication resilience and robotic safety. Experimental results demonstrate 100% fail-safe shutdown success across tested fault conditions, with emergency stopping achieved in approximately 143–145 ms.

An Intelligent Software Project Risk Prediction Engine Using Machine Learning and Web-Based Decision Support Systems

The significant contribution of this research is bridging the gap between theoretical machine learning models and practical industrial application in software project management. It develops an end-to-end Intelligent Software Project Risk Prediction Engine using a Random Forest classifier and seamlessly integrates it into a real-time, Django-based Web Decision Support System (DSS). This integration provides project managers with an accessible, user-friendly platform to dynamically assess early-stage project risks based on 51 historical project features, overcoming the limitations of traditional manual assessments and standalone “black box” ML scripts.

MetaTrojan: Backdoor Attacks on Metadata-Conditioned Medical Diffusion Reconstructors

MetaTrojan is the first backdoor attack on a metadata conditioned medical diffusion reconstructor that operates purely through the text conditioning channel, requiring no pixel domain access, and is therefore structurally invisible to all existing image domain defenses. It demonstrates that a single poisoned metadata token, injected by one malicious actor during data curation, is sufficient to reliably hallucinate clinically plausible lesions at inference time, while remaining statistically indistinguishable from a benign model on clean inputs. A lightweight whitelist sanitization layer is proposed as a defense, reducing ASR from 100% to approximately 0% with no measurable cost to reconstruction fidelity (30.77 dB vs. 30.74 dB benign baseline).

A Deep Learning Framework for Cybercrime Topic Discovery and Sentiment Analysis in Bangladeshi Facebook Posts

This integrated approach provides a clearer understanding of cybercrime-related discussions and public perceptions in Bangladesh.

AgroNeuro Guardian An Integrated IoT-TinyML Framework for Autonomous Irrigation Control and Flood Prevention in Precision Agriculture

The AgroNeuro guardian is an IoT-based agricultural mon itoring and controlling prototype that was designed and de veloped using ESP32, sensors, actuators. The experiment conducted above proves beyond reasonable doubt that the water-level sensor, LDR, PIR sensor, servo motor, relay, and 5V water pump were successfully operated. Indeed, the water level sensor registered around 0% moisture level when it was kept in dry land and around 98% when it was kept in a wet condition. In addition, the LDR was successfully able to provide a particular digital output to operate the system. Most importantly, solar panels, TP4056 module, and a rechargeable battery were also successfully connected to the circuit. Thus, by and large, the prototype that was made is able to demonstrate the essential concept of agricultural monitoring and smart irrigation. However, there could be the following future implications, including the integration of Blynk remote monitoring system, cloud storage, intelligent irrigation, and machine learning for agricultural prediction.

SeedNet: A Lightweight Fine-Tuned Deep Learning Framework for Plant Seedling Classification in Precision Agriculture

Developed a lightweight deep learning framework, EfficientNetB0, based on EfficientNet model, with transfer learning and selective fine-tuning to classify visually similar plant seedlings with accuracy. The proposed two-stage training technique is effective for domain adaptation and generalization, and achieves 95.40% test accuracy, 94.54% Macro-F1, and 99.90% micro-ROC-AUC on the V2 Plant Seedlings Dataset, without compromising the computation efficiency. The work highlights the possibility of practical and automated weed identification for precision sustainable agriculture.

Deep Learning-Based Gleason Grading of Prostate Cancer detection from Histopathology Images

The Gleason grading of prostate cancer is well
known for being subjective in clinical practice, interpathologist
disagreement frequently going above 30% because of persistent
stromal noise and morphological heterogeneity, as well as the
ongoing difficulty in identifying rare high-risk patterns such as
the cribriform subtype. In this study, we introduce the Hybrid
Swin-MIL Network, an automated 5-class Gleason grading (NC,
GG3, GG4, GG5 and GG4-C) framework which has been
assessed on the SICAPv2 benchmark dataset in order to address
these diagnostic problems. We have incorporated into our
pipeline an annotation-guided masking approach that
successfully shuts off the non-target background stroma so that
feature learning is focused solely on the malignant epithelial
structures while still preserving the surrounding benign
morphology. A new architecture combines an intrapatch token
attention pooling hierarchical Swin Transformer with a parallel
multi-scale CNN branch in order to capture the overall
glandular structure while at the same time keeping the fine
cellular texture. Five separate models were trained using a
patient-stratified five-fold cross-validation method and these
were combined by soft voting during inference. We achieved
95.55% accuracy, together with a 95.58% macro F1-score and
95.81% recall for the aggressive GG4-Cribriform subtype
across (12,081) previously unseen test patches using our
ensemble. Indeed, these results highlight the advantages of using
both selective input masking and dual-branch attention, and
cross-validation ensembling provides a robust, reproducible
route to fine-grained prostate cancer grading.

Beyond Reconstruction Fidelity: Task-Oriented Evaluation of Compact RGB-Derived Spectral Representations for Fruit Classification

Anchor-Preserving Coarse-to-Fine Hyperspectral Reconstruction from Mobile RGB–NIR Images

Constraint-Aware Solar MPPT Evaluation Using a Feasible-Power Reference and Pack-Terminal Energy

This paper introduces a constraint-aware evaluation framework for solar-battery MPPT. A pre-action, state-conditioned feasible-power reference distinguishes source-side tracking loss from intentional curtailment imposed by battery, protection, converter-current, and thermal limits, while pack-terminal energy measures useful delivered energy. Using equal-budget training-only tuning and independent paired uncertainty testing, 1,708 simulations of a nominal 30 W, 3S1P charger showed a positive mean IncCond-minus-P&O pack-energy difference across all four held-out profiles. The result is conditional on the modeled domain and does not claim universal superiority or hardware validation.

A Machine-Learning Surrogate Framework for Designing and Optimizing the Char Nizam Off-Grid Hybrid Power System in Bangladesh

1)A cost-minimal, reliable system design for Char Nizam: a feasible configuration (35.8 kW PV, two 10 kW turbines, battery, and diesel backup) that serves the load with < 0.1% unmet energy at an LCOE of $0.228/kWh, a 27.8% lower cost than a comparable grid search finds, demonstrably meeting objectives (i) and (ii).
2)A green, self-sufficient operating point: the design draws 50.6% of its energy from carbon-free renewables under an explicit renewable-share constraint, displacing diesel and its emissions while remaining fully islanded, meeting objectives (iii) and (iv).
3)The enabling method: a surrogate-and-search frame- work that replaces the exhaustive dispatch loop with a learned cost model. An open-source hourly simula- tor provides ground truth; a gradient-boosted surrogate predicts LCOE, renewable fraction, and feasibility in 0.13 ms (R2 ≥0.998, 36× faster than a full simulation); and differential evolution searches it, giving a measured O(g4) scaling gap and a measured 322× speedup over a g = 20 grid.
4)A justified model choice, and an audit of the framework’s own claims: we benchmark five regression families and select gradient boosting on the joint accuracy– latency criterion; separate the discretization advantage from the surrogate’s own contribution by running the same continuous search directly on the simulator, then close most of the remaining gap by adaptive refinement (7.1% → 3.4% for 100 extra simulations); cross-check the simulator against a published HOMER Pro study of this island; verify optimizer stability over 30 seeds; and decompose the LCOE band, which shows fuel price carries essentially all the risk.

Explainable Risk Detection in Blockchain Smart Contracts Using Natural Language Processing

This paper presents a lightweight machine learning framework for detecting six classes of Ethereum smart contract vulnerabilities directly from raw Solidity source code. Its main contribution is the combination of word and character-level TF-IDF features with domain-specific Solidity pattern counts, avoiding the need for bytecode compilation or graph construction. The study also uses a leakage-controlled SMOTE and vectorization pipeline, making the evaluation more methodologically reliable. Among six classifiers, LightGBM achieves the strongest performance, reaching 97.74% test accuracy and 97.07% cross-validated accuracy. A further contribution is the SHAP analysis, which connects influential model features to recognizable constructs such as .call(), delegatecall, block.timestamp, and block.number, making the predictions more interpretable for smart contract security analysis.

Evaluation Blind Spots in IEC 60870-5-104 Intrusion Detection: Beyond Attack-Only Metrics

We demonstrate that attack-only metrics under family-disjoint evaluation of IEC 60870-5-104 IDS can conceal significant false-positive rates: a 40% benign holdout reveals 5.8–10.5% FPR despite attack F1 > 0.99. We further show that 48–50% of false positives are shared across RF, XGB, and CNN, suggesting these errors stem from feature-space difficulty rather than classifier-specific overfitting.

Machine Learning for Literacy Rate Prediction in Bangladesh: Leveraging Engineered Socio-Economic Features and Explainable AI

This study presents a machine learning framework for predicting and analyzing literacy rates in Bangladesh, using a wide range of socio-economic and demographic data. The goal is to provide a practical statistical tool that helps explain the complex factors shaping national literacy. To gather useful information from the data such as GPD per capita and population density, we applied data preprocessing and feature engineering. We tested several regression models and among them the Random Forest regression model performed best achieving high prediction accuracy with a $R^{2}$ score of 0.992. A major contribution of this study is the use of Explainable AI (XAI) with SHapley Additive exPlanations (SHAP). This allowed us not only to predict literacy rates but also to understand how different factors influence the model’s results. The SHAP analysis showed that population patterns and economic indicators such as population density and gross national income (GNI) are the most important drivers of literacy outcomes. In summary, this study offers a highly accurate predictive model and also explains the complex relationships between different variables making it a valuable resource for both researchers and policymakers.

DART: An Analytical Failure-Attribution Study for Multimodal Edge Assistants

The significant contribution of this research is the development of DART, a five-layer Deployment-Aware Reliability Taxonomy for identifying where failures occur in multimodal edge assistants. It separates task-path failures from runtime/resource and connectivity-related exposure, enabling more precise reliability diagnosis than a single overall success rate. The framework is further supported by layer-specific metrics and a real case-study analysis using NORA.

Seeing Beyond the Fake: Detecting Deepfakes Using Deep Learning-Based Computer Vision

The main advantage of this research is the creation of the lightweight, leakage-aware spatial-temporal framework for deepfake detection. The work proposes a custom CNN with channel-wise attention, batch normalization, dropout and L_2 regularization, with approximately 511K trainable parameters. It also introduces frame-level detection to video-level analysis with an Attention-BiLSTM and fusion-based classification approach. One methodological contribution is the video-first data splitting to avoid data leakage by keeping all of the frames from the same video in either the training set or the test set. The study also includes a comparison of the spatial, temporal and fusion approaches, where the fusion approach attained 93.50% accuracy at the video level and 97.99% AUC.

SwinMLP-XAI: An Explainable Swin Transformer-Based Framework for Automated Detection of Tuberculosis in Chest Radiographs

This study introduces an explainable deep learning framework (SwinMLP-XAI) using a Swin-Tiny Transformer backbone to automate tuberculosis detection. It overcomes existing internal validation and interpretability issues by incorporating Integrated Gradients for clear attribution mapping, achieving 99.84% classification accuracy.

Beyond Image-Level Random Splits: A Leakage-Aware Reliability Assessment for Lightweight Jackfruit Leaf Health-State Recognition

This study presents a leakage-aware reliability evaluation framework for jackfruit leaf health-state recognition, addressing the risk of inflated performance estimates caused by image-level random splitting. It investigates how visually similar or related images distributed across training, validation, and test partitions can introduce hidden data leakage and compromise the reliability of deep learning evaluation.

To mitigate this issue, a conservative leakage-control visual grouping (LCVG) strategy is developed to enforce separation of visually related samples before model assessment. The proposed LCVG-safe evaluation protocol is systematically compared with conventional random splitting across multiple deep learning architectures, independent random seeds, and reconstructed dataset partitions to quantify the impact of leakage on reported performance.

Furthermore, this study provides a comprehensive reliability analysis through uncertainty estimation, calibration assessment, robustness evaluation under controlled corruptions, lightweight deployment analysis, and persistent error investigation. The findings highlight the importance of leakage-aware evaluation for developing trustworthy deep learning systems for practical agricultural applications.

Explainable 3D Swin Transformer for Tumor Mass Presence Classification and Quantitative Localization in Pancreatic CT: A Comparative Study

We compared six deep learning architectures for pancreatic tumor localization in CT imaging. Our 3D Swin Transformer achieved the best performance (93.49% accuracy, 0.973 AUROC), validated through 5-fold cross-validation and statistical testing, and produced more accurate Grad-CAM++ explainability results than a 3D ViT baseline.

Kolmogorov Arnold Network for Multiple Disease Classification: Interpretability and Faithfulness Evaluation

Using five clinical datasets, this study evaluates KANs for interpretable medical classification. It compares KAN with established ML models and evaluates its intrinsic spline-based explanations against SHAP, permutation importance, and Random Forest feature importance. A deletion-based faithfulness analysis further verifies whether the identified important features genuinely influence model predictions.

An Acoustic Dataset for Deep Learning-Based Non-Destructive Testing (NDT) of Brick Quality via Mel-Spectrogram Analysis

To determine the structural integrity of clay bricks, vision-based methods face many limitations. Another conventional testing method is to assess the impact sound of bricks using human subjective auditory perception. In this context, we present a comprehensive acoustic dataset of brick impact sounds to advance machine learning-based research in objective non-destructive testing (NDT). First, audio data were collected from three active brick kilns—MTB Brick Field, Amin Brick Field, and Trishal Brick Field. For each recording case, two bricks are struck together at a distance of 20-30 cm, and the resulting sound is recorded with a BOYA BY-M1 Pro II microphone. The dataset’s ground-truth labels were determined using preselected, factory-graded brick batches. The complete dataset contains a total of 1,426 distinct impact audio samples, classified into three quality grades: Grade A (465), Grade B (509), and Grade C (452). Second, the audio samples were converted to mel-spectrograms to accurately capture time-frequency acoustic features. This process included noise reduction and silent segment pruning. Third, machine learning-based classification experiments were conducted to evaluate the dataset’s performance. A classification accuracy of 82.74\% was achieved by applying a random forest classifier to the smoothed time-frequency features. These results indicate that the acoustic features present in brick impact sound can be mathematically separated into distinct quality grades. The presented dataset can serve as a useful basis for developing deep learning-based models and for non-destructive testing (NDT) research aimed at automatically assessing the structural quality of bricks.

XAI-Droid: An Explainable Hybrid Static and Dynamic Analysis Framework for Android Adware–Trojan Malware Detection

In this paper, XAI-Droid: Explainable Hybrid Machine Learning Framework for Android Adware–Trojan Malware Detection is proposed which is a combined桐approach that explores both the static and dynamic features of APKs for detection of Android Adware–Trojan malware. To enable accurate, transparent and reproducible malware analysis, the proposed framework offers a leakage controlled learning pipeline, ensemble-based classification and SHAP inspired explainability. XAI-Droid interprets the security decisions into identifiers of influential permissions, API characteristics and runtime behaviors, which can benefit practical Android malware screening and cyber defense applications.

Robust Fabric Defect Classification Using Deep Learning with Grad-CAM Explainability

Most fabric-defect papers report accuracy from one train-test split, so there’s no way to know if it holds on a different slice of data. This work pairs five-fold stratified cross-validation with a fully separate held-out test set across five CNN architectures under identical conditions, something none of the compared prior work does. EfficientNet-B3’s 98.26% test accuracy tracked its cross-validation estimate closely, not a lucky split. Grad-CAM visualizations suggest the model attends to actual defect regions rather than background shortcuts, feeding into a practical 0.85-confidence rule for routing uncertain cases to human review.

Machine Learning Approaches to Child Malnutrition Prediction in Bangladesh: A Dual-Round BDHS Analysis with Concept Drift Detection and Web-Based Screening

Bangladesh’s child-malnutrition profile presents a paradox: stunting declined by 7.7 percentage points between BDHS 2017-18 and 2022, whereas wasting increased by 3.2 percentage points. No previous ML study has explained this divergence while also developing a deployable screening tool. This paper presents NutriGuard BD, integrating BDHS data (n = 10,705 children under five), a community survey of 52 mothers, and five key-informant interviews with paediatricians. We benchmark seven classifiers-LR, RF, GB, XGBoost, LightGBM, CatBoost, and MLP-with a novel Composite Vulnerability Index (CVI) and a leakage-free pipeline (SMOTE on training folds only, Optuna Bayesian optimisation, Youden’s J threshold tuning). CatBoost achieves the best dietary quality AUC (0.636; CV-AUC 0.632); after threshold optimisation, LightGBM achieves clinical recall of 0.769 (stunting) and 0.714 (underweight). Critically, all six non-MLP classifiers collapse to near-random wasting prediction in 2022 (AUC 0.479-0.497; 2017-18 baseline AUC=0.935)-the first architecture-independent surveillance-level evidence of concept drift in BDHS modelling. SHAP identifies wealth index (v190) as the dominant predictor (mean |ϕ| = 0.377 Spearman ρ > 0.97 cross-architecture). Iron-course completion is only 11.5%; its sole significant predictor is iron receipt in pregnancy (p = 0.018, ϕ = 0.292). NutriGuard BD delivers these models as a bilingual (English/Bangla) Django web application for community health workers with zero patient-data persistence.

Murnitin: An Explainable, Adversarially Robust and Privacy-Preserving Academic Integrity Framework for the LLM Era

Current AI detectors like Turnitin act as black boxes, frequently mislabel non-native English writing and fail against simple evasion tricks while storing student papers in private databases. In this paper, we present Murnitin, an open framework that addresses these issues directly. We combine sentence-level explainable metrics (perplexity and burstiness) with real-time defense against zero-width and homoglyph attacks. Crucially, we introduce keystroke process verification to confirm authentic human drafting, which cuts the false positive rate on non-native English essays down to 2%. Finally, we implement zero-knowledge cryptographic hashing so institutions can verify document similarity without ever keeping raw student text on central servers.

Two Public CKD Benchmarks Are Not Independent: A Reproducible Audit of Leakage, Cohort Separability, and Pseudo-External Validatio

This work provides record-level evidence that two widely-used public CKD benchmark datasets (the UCI 2015 Indian release and the 2020 Bangladeshi release) are not independent: exact-match bipartite record linkage shows all 200 patients in the 2015 dataset also appear in the 2020 dataset, contradicting their documented separate provenance. The paper demonstrates that models trained on one and “externally validated” on the other are not measuring transportability but rather leakage — near-ceiling ROC-AUC performance (up to 1.000) is shown to result from post-diagnosis outcome-correlated variables rather than genuine predictive power, dropping to a more realistic 0.909 once such variables are removed. The study contributes a reusable, dataset-agnostic auditing methodology (a provenance gate, feature taxonomy, and leakage-prevention framework) for detecting duplicate contamination and incorporation bias in clinical prediction benchmarks generally.

Ensemble Deep Learning with Test-Time Augmentation for Breast Ultrasound Classification on BreastMNIST

Our main contribution was a methodical testing of three different models of heterogeneous ensemble CNN–Transformer—ConvNeXt-Tiny, EfficientNetV2-S and Swin-Tiny—using test-time augmentation (TTA) with soft voting, all optimized for the BreastMNIST classification task. The ensemble performs with a test accuracy of 92.31%, higher than that of the best individual model, and class-wise error and inter-model agreement analyses are included to give better estimates of prediction reliability. Critically, the paper formalizes this contribution as an empirical ensemble evaluation rather than a new learning algorithm.

Lung-Ensemble: A Secure Multi-Stage Diagnostic Pipeline for Multi-Class Respiratory Disease Detection using a Stacked Ensemble and Explainable AI

Abstract—Lung diseases continue to be one of the fundamental
health threats for a significant portion of the global population.
Respiratory health and lung function, however, face severe threats
from various infections that significantly increase mortality rates.
In addition, there is a growing need for rapid detection and
accurate diagnosis of these diseases to prevent severe clinical
complications. In this paper, we have explored and analyzed
several state-of-the-art deep learning models for multi-class lung
disease detection using a comprehensive dataset of chest Xrays. From the models we tested, we noticed that MobileNetV2
and VGG16 gave the best individual results. To leverage these
strengths, we propose a secure, multi-stage diagnostic pipeline
called Lung-Ensemble. This framework utilizes a binary ”Gatekeeper” classifier to filter out non-medical images, securing
the system against erroneous processing, before passing valid
X-rays to a feature-fusion based ensemble utilizing VGG16,
DenseNet121, and MobileNetV2. Our proposed model (LungEnsemble) shows a significant improvement, achieving a final
accuracy of 98.33%.

CEEC: Mixed-Precision EfficientNetB3 Transfer Learning for Fine-Grained Endemic Bird Classification and Biodiversity Monitoring

Abstract—Identifying bird species is crucial for biodiversity
conservation, yet many of the 25 endemic Indian species are
visually similar and hard to distinguish. Fine-grained classification is challenging due to high intra-class variability in
pose, lighting, and background, coupled with subtle inter-class
differences in plumage and morphology. Despite growing use
of deep learning for avian identification, large-scale, balanced
datasets and tailored models for Indian endemic species remain
scarce. This paper presents a deep learning framework trained
on a curated dataset of 37,500 images spanning 25 endemic
Indian bird species. Our contributions include: (i) a balanced,
annotated dataset; (ii) a reproducible pipeline with systematic
data augmentation and mixed-precision training; and (iii) a
novel EfficientNetB3-based architecture (CEEC) optimized for
fine-grained classification. The CEEC model employs a frozen
EfficientNetB3 backbone pretrained on ImageNet, combined with
a custom classification head incorporating batch normalization,
dropout, and dense layers to reduce overfitting while preserving discriminative features. Among evaluated models, including
custom CNNs, ResNet152V2, and EfficientNet variants, CEEC
achieved superior performance: 98.74% test accuracy, 0.05487
test loss, and 0.9873 F1-score.

GateDistill-NPK: A Novel Reject-Aware Distillation Framework for Rice-Leaf Macronutrient-Deficiency Diagnosis

Deficiencies of nitrogen (N), phosphorus (P), and potassium (K) constitute a leading yet largely correctable cause of rice-yield loss, but operational diagnosis remains constrained by scarce agronomic expertise and by destructive laboratory assays that do not scale to smallholder cultivation. Prevailing image classifiers entangle two decisions that ought to be separated, namely whether a leaf is a deficient paddy leaf and, conditionally, which nutrient is depleted, and they provide no mechanism for rejecting the out-of-domain imagery that field acquisition unavoidably yields. This work introduces GateDistill-NPK, a reject-aware cascade that decouples detection from differential diagnosis. A first-stage EfficientNet-B0 gate assigns each image to a deficient, healthy, or non-paddy outcome, converting out-ofdomain inputs into an explicit rejection rather than a spurious nutrient label. Only deficient images advance to a compact Scratch-Fusion MobileNetV2 student (SF-MV2), which fuses a pre-trained MobileNetV2 backbone with a from-scratch convolutional branch and is optimized by temperature-scaled distillation from a ResNet-50 teacher; a deterministic rule then maps the prediction to a fertilizer prescription. Evaluation uses a threesource corpus of 8,037 images, deliberately assembled so that each gate category originates from an independent acquisition domain. The gate attains 94.35% test accuracy with a 0.9732 micro-averaged area under the curve (AUC), while SF-MV2 delivers 87.83% accuracy, 0.9181 balanced accuracy, and a 0.9789 AUC using only 2.72 M parameters, an 8.8× compression that preserves accuracy comparable to its teacher. These results establish that reject-aware gating and cross-capacity distillation can be unified within one edge deployable pipeline for trustworthy in-field nutrient diagnosis.

Secure Federated Learning for Brain Tumor Segmentation Under Non-IID and Missing-Modality Conditions

This paper presents a lightweight (<1M-parameter) federated 3D U-Net for multi-region (WT/TC/ET) brain tumor segmentation under realistic hospital conditions — non-IID client data and a per-client missing MRI modality — evaluated with a modality-aware Dice/HD95 protocol. We show that ignoring a client's missing modality at evaluation time inflates pooled Dice by 0.075–0.12 points, a bias not disclosed in prior federated segmentation work. We also validate a full secure-deployment stack (TLS, ECDSA node authentication, SecAgg+) on real gRPC connections, and demonstrate that a gradient-inversion attack recovering a 15.51 dB PSNR patch from a raw update drops to −66.03 dB once SecAgg+ masking is applied — an 81 dB gap quantifying secure aggregation's practical privacy benefit.

The Evolution of Cyber Attack Vectors: From Network Exploits to Autonomous AI Threats

1. A unified taxonomy of cyber attack vectors covering network, cloud, IoT, CPS, human-centric, and AI/autonomous-agent attacks.
2. A cross-domain comparative analysis highlighting how cyber attacks have evolved from isolated vulnerabilities to interconnected and intelligent attack scenarios.
3. A research roadmap identifying key challenges and future directions for securing AI-driven and autonomous digital ecosystems.

A Graph-Based Framework with Patient Similarity Clustering, Imbalance-Aware Learning, and Multi-Level Explainability for Reliable Stroke-Risk Prediction

This research presents a reliable and interpretable framework for stroke-risk prediction by combining patient similarity graphs, clustering, graph attention networks, and imbalance-aware learning. Patients are represented as nodes in a mutual k-nearest-neighbor similarity graph, while Ward-linkage agglomerative clustering is used to capture community-level risk patterns. A two-layer Graph Attention Network with focal loss and an F2-based decision threshold is then used to improve the detection of stroke-positive patients, with particular emphasis on reducing false negatives. The framework also integrates GNNExplainer, SHAP, and counterfactual analysis to provide explanations at the patient, neighbor, and cluster levels. Experimental results show that the proposed GAT with agglomerative clustering provides stronger recall-oriented performance than the evaluated alternatives, while cross-validation, cluster stability, and explanation-consistency analyses provide additional evidence of reliability.

UniPedFormer: A Unified Single-Stage Vision Transformer for Joint Pedestrian Detection and Trajectory Prediction

We describe a monocular, pedestrian-specific single-stage architecture in which one transformer encoder serves both detection and short-horizon trajectory prediction, and we position it explicitly against the existing joint perception-and-prediction literature rather than around it.

We introduce a Spatial-Temporal Decoder that composes pedestrian self-attention, scene cross-attention, and temporal self-attention inside each decoder layer, so that crowd context, visual grounding, and motion history are mixed before either head reads the query.

We show that a four-term objective combining focal, smooth-$L_1$, ADE, and FDE losses converges without the loss balancing problems that often affect multi-task training, and we report the resulting behaviour honestly, including the synthetic nature of the benchmark and the metrics it does not support.

SimSighs: Benchmarking and Explaining Transformer Models for Bengali Telecom Complaints

The increasing amount of customer feedback in
Bengali presents a significant problem for telecom operators.
Analyzing and classifying such data takes a lot of time and does
not always produce accurate results. This article introduces
SimSighs, an improved and relabeled benchmark dataset for
Bengali telecom complaint classification. We aim to address the
problems caused by inconsistencies in existing public datasets,
merge classes with similar meanings, and balance the number of
samples across all classes. We also evaluate twelve different
models, ranging from traditional machine learning to deep
learning and state-of-the-art transformer architectures. Our
experiments demonstrate that models based on transformer
architecture, especially Bangla-BERT, achieve the best results
in terms of F1-score, reaching 92.22%. We apply Explainable
AI (XAI) to understand which parts of the input text contribute
most to a model’s decision. The visualization technique, Layer
Integrated Gradients, helps identify patterns and words that a
model uses to classify a complaint based on its category. Overall,
our research emphasizes the importance of high-quality datasets
and the application of XAI methods for developing dependable
and accurate NLP systems for customer feedback analysis.

A Comparative Study of Machine Learning Algorithms for Nationwide Air Quality Index Prediction in Bangladesh

The contribution of this paper to the research is that it provides a nationwide and multiregion framework for AQI forecasting in Bangladesh, filling in a critical gap within the literature. In particular:

1. Geographic scale – While previous research done in Bangladesh only used a few cities or regions for AQI prediction, this study has utilized a dataset consisting of 103 cities/regions for approximately 25 years (2000-2025).
2. Chronological split for time-series validity – This framework has utilized a chronological and region-wise train-test split and region-wise lag feature engineering as opposed to randomized split and non-region-wise lag feature engineering, providing a better simulation of forecasting.
3. Comparative evaluation between models – This framework sets a benchmark for evaluating the models in a consistent environment using the same evaluation metrics (RMSE, MAE, MAPE, and R²) at a national level and at a regional level explicitly.
4. Flexible framework – It provides foundations for future implementation of XGBoost, LightGBM, GRU, Transformer, graph models, and even IoT-based real-time implementation.

Overall, the paper specifically says that the experimental results of the study are illustrative, not measurements obtained during the model training process. Therefore, the actual contribution of this work is the framework and methodology themselves.

Ink Meets Intelligence: A Computer Vision Based Approach for Bengali Handwritten Character Recognition

This research presents a practical and lightweight approach to Bengali handwritten character recognition. We developed an 84-class dataset containing 168,528 images of vowels, consonants, and compound characters by combining newly collected handwriting samples with the BanglaLekha-Isolated dataset. We also compared a custom CNN-MobileViT model with an adapted MobileNetV3-Small model. The best model achieved 94.71% test accuracy while using only 1.12 million parameters, making it suitable for devices with limited computational resources. In addition, we introduced a confusion-aware loss to give greater attention to visually similar characters and used Grad-CAM and LIME to explain which parts of each character influenced the model’s predictions.

A Spatio-Temporal Deep Learning Approach for Edge Deployable Camera-Based Detection of Cardiac Arrest Symptoms Using Generative-AI Based Synthetic Data

This research develops an edge-deployable camera-based system for detecting visible cardiac-arrest-related symptoms using Generative-AI-based synthetic video data and limited real recordings. Four spatio-temporal deep learning models were comparatively evaluated, achieving up to 98.11% accuracy, while a lightweight CNN-LSTM model was deployed on a Raspberry Pi 4 for real-time symptom monitoring and alert generation.

e, Who Must Not Be Named: Cryptanalysis of LCG-Based Randomness in Guillou-Quisquater

This work presents a lattice and polynomial-based attack on the Guillou–Quisquater identification protocol when its ephemeral randomizers are generated by a known-parameter LCG. The attack exploits short integer relations among challenges to eliminate the secret-dependent term, recover the initial LCG state through polynomial GCD computation, and subsequently recover the secret, without requiring the public exponent(e) during the recovery procedure.

Explainable Machine Learning Framework for Depression Risk Prediction

Depression is a significant mental health issue and early recognition of those who are vulnerable may aid in timely intervention. In this study, an explainable machine learning framework for predicting depression risk based on demographic, socioeconomic, behavioral, clinical, examination and laboratory variables is presented. The data was also preprocessed and selected using feature selection techniques and class imbalance was corrected from the training set with SMOTEENN. Nine machine learning models were evaluated, including Logistic Regression, Decision Tree, Random Forest, K-Nearest Neighbors, Support Vector Machine, Naïve Bayes, Extra Trees, Gradient Boosting, and XGBoost. Overall, XGBoost performed best, with an accuracy of 82.60% and a ROC-AUC of 0.760. To make the model more interpretable and to find influential factors related to the depression risk, SHapley Additive exPlanations (SHAP) was used. The framework offers global and local explanations, making predictions more transparent. The results suggest that effective predictive modeling coupled with EAI can be used to facilitate a more reliable and interpretable evaluation of depression risk and could be part of future healthcare decision-support systems.

DIAsm: A Bilingual AI-Assisted Mobile Health Application for Type 2 Diabetes Self-Management in Bangladesh

This research presents DIAsm, a deployed bilingual English–Bangla Android application that integrates multiple Type 2 diabetes self-management functions within a single platform designed for the Bangladeshi context. Its main contribution is the combination of health monitoring, reminders, lifestyle and nutritional support, diabetes education, risk assessment, wellness utilities, locally relevant food information, and a grounded conversational assistant. The assistant employs a hybrid RAG pipeline incorporating rule-based intent classification, multilingual semantic retrieval, metadata filtering, heuristic reranking, and constrained local LLM-based response rewriting. Evaluation included a 50-query technical benchmark and a post-deployment study with 65 users, demonstrating the feasibility and positive perceived usability of this integrated approach.

Multivariate Modeling of Employee Retention Using Reward, Work-Life Balance, and Work Environment Factors in Malaysia’s Industrial Equipment Sector

Employee retention has become a strategic necessity in human resource management, specifically in the engineering and industrial distribution sectors. Specialized skills are rare and essential for operational continuity, customer service reliability, and profitability. The purpose of this study is to explore the
empirical factors that affect employees’ exit from Sime Darby Industrial (SDI) Sdn Bhd, a franchisee distributor of Caterpillar heavy machinery in Malaysia, with a high rate of employees’ voluntary turnover that has been observed to be over 40% for the
past decade. Based on the three theories, a quantitative deductive investigation was performed by administering a 35-item survey to N = 60 mid-level technical executives, engineers, diagnostic specialists, and managers (with a 50% return rate) from a target population of N = 120 that represents the extent of the sample. The extent of the sample to be used in the investigation was N = 120, but a 50% return rate of N = 60 mid-level technical executives, engineers, diagnostic specialists, and managers was achieved for a quantitative deductive investigation, founded on the three theories. Construct reliability (α), normality diagnostics (skewness/kurtosis z-scores), Pearson correlation, and Ordinary Least Squares (OLS) multiple linear regression were used for the analysis of the empirical data in IBM SPSS 23. The results show that reward and recognition have a statistically significant positive direct effect on employee retention (r = 0.323, p = 0.012; regression β = 0.425, t = 2.620, p = 0.011). By contrast, both Work-Life Balance (r = 0.173, p = 0.186) and Work Environment (r = 0.042, p = 0.750) have weak direct bivariate impacts but
strong, statistically significant bivariate correlations with the composite intervening psychological construct (Job Satisfaction, Organizational Commitment, and Employee Loyalty) (r = 0.546 and r = 0.658, respectively; all p ¡ 0.001). The intervening construct, in turn, significantly contributes to retention (r = 0.311,
p = 0.016). The overall regression model is statistically significant (F(3, 56) = 3.109, p = 0.034, R² = 0.143). Proposed to create a strategic retention architecture in 5 pillars for the industrial machinery businesses.

User Interface Evolution of YouTube (2005-2026): A Longitudinal Analysis from HCI Perspective

YouTube has evolved from a simple video-sharing website into a global social-media platform used by billions of viewers across diverse languages, abilities, and levels of digital experience. This study examines how its user interface changed from 2005 to July 2026 and what those changes imply for usability and user control. A longitudinal archival dataset of 22 YouTube homepage snapshots was collected from the Wayback Machine. Semiotic analysis traced changes in navigation, thumbnails, feedback mechanisms, account identity, and search, while a usability evaluation rated seven archive-visible dimensions on a 0-5 ordinal scale at representative checkpoints. Supporting structural properties of the archived HTML were also examined to identify implementation-level changes. The results identify five broad time periods: YouTube moved from explicit, text-heavy navigation and community ratings toward visual discovery, persistent identity, personalization, recommendation-driven feeds, and AI-assisted interaction. The descriptive HCI score increased from 2.86 in 2005 to 4.43 in July 2026, mainly through stronger consistency, efficiency, feedback, and accessibility support; user control improved more modestly as content discovery became increasingly algorithmically mediated. The findings suggest that successful interface evolution depends not only on adding functionality but also on preserving understandable signifiers, predictable interaction, and meaningful user agency.

An Autonomous Detection Approach for Isolated Drones of the Swarm in 3D Environments

. Swarm drones generally used to perform a specific task on Target of Inter-est(ToI). Any members(drones) of the swarm can be outlying at any time during execution time of its assigned task. If any drone outlays during the execution time, performance of the swarm may be degraded and goal may not be achieved. For this reason, detection of isolated drone is very much important. In this paper we propose an autonomous framework to detecting these isolated drones in 3D environment. At first, we collect boundary drones using Gift Wrapping Algo-rithm. But all boundary drone may not be connected at least one hop neighbor. To overcome this limitation, we offered an algorithm. This algorithm will restrict the connectivity between two boundary drone with their communication range and produce a concave polyhedron. Then we tested very drone autonomously wheth-er they lay within the area produced by concave polyhedron. If they lay in the polyhedron, it is treated as in well topology. otherwise treated as an isolated drone. We evaluate our experiment in python language in google colab environ-ment and got 100% detection accuracy.

CoopShield: An IoT-Based Multimodal Environmental Monitoring and Safety System for Smart Poultry Farming

The main research and findings of CoopShield are connected with the creation of an inexpensive and low-latency multimodal automation system, which is capable of reducing risks for the poultry farming site in real time. The device can regulate the temperature, humidity, ammonia gas level, number of PPM in the air, and flock commotion noise as a sign of stress and bad conditions. It uses a single microcontroller (ESP32) to process this information and perform relay operations for the actuators. The main unique aspect of the developed solution is the implementation of a closed-loop system with events triggered in real-time for each particular actuator to reduce the danger for the flock. Additionally, the data from sensors are continually sent through Bluetooth to Blynk for plotting and additional analysis in the cloud. Thus, the created device is an easy-to-use smart technology for farming that provides safety for animals and automation of the process.

Above Chance, Below the Baseline: A Majority-Anchored, Case-Grouped Ablation for Thyroid Ultrasound Segmentation and Report-Derived Suspicion Classification

Thyroid ultrasound computer-aided diagnosis is routinely reported as a segmentation Dice score and a classification F1 score, with little scrutiny of what drives either number on small, imbalanced, multi-image-per-patient benchmarks. We supply that scrutiny through two matched-protocol ablations on TN3K (segmentation) and DDTI (classification) under one Holm–Bonferroni-corrected statistical protocol. On TN3K, three ImageNet-pretrained encoder–decoder architectures (U-Net, U-Net++ and FPN, ResNet-34 encoder) exceed a from-scratch U-Net by 0.0670–0.0758 Dice under 3-fold cross-validation (adjusted p ≤ 0.0054), while the pretrained architectures separate only partially from one another: U-Net++ exceeds FPN (adjusted p = 0.024) but not U-Net (adjusted p = 0.103). A multi-axis ablation confirms pretraining (−0.0858 Dice) and augmentation (−0.0228 Dice) matter (adjusted p = 0.039 each); loss form and resolution do not reach significance. On DDTI, a deterministic majority-class predictor is a mandatory row in every table: all four backbones (ResNet50, DenseNet121, EfficientNet-B0, ViT-B/16) score below it on F1 (best 0.8896 vs. 0.9035) and accuracy (best 0.8162 vs. 0.8246), yet all exceed it on Matthews correlation coefficient (0.1541–0.3072 vs. 0.0000) and balanced accuracy (0.5660–0.6506 vs. 0.5000); no pairwise difference survives correction (best raw p = 0.040, adjusted p = 0.240). Replacing a naive image-level split with a case-grouped one moves F1 by −0.1439 but Matthews correlation coefficient by only −0.0143 and AUC by +0.0029. Re-running two identical configurations gives repeat-run spreads of 0.0494 Matthews correlation coefficient, 0.2217 F1 and 0.0420 Dice, larger than most measured effects. We further disclose a pixel-level ROC-AUC of 0.9804 against a Dice of 0.7568, and a Youden threshold saturating at 1.0000 with expected calibration error 0.1324.

A Trend-Based IoT Monitoring and Automated Cleaning Framework for Solar PV Systems Under Dust and Salinity Conditions

Dust, humidity, and salt deposition are some of the challenges faced by PV systems in coastal areas that can impact the output of the electrical system and make them more prone to maintenance. Typical maintenance methods are guided by manual cleaning or by monitoring a single parameter only and can lead to unnecessary cleaning and/or delayed maintenance. A smart PV monitoring and automated cleaning system for coastal area based on Internet of Things (IoT) is proposed in this paper. The proposed system includes voltage, current, dust and salinity sensors, an autonomous solar-panel cleaning robot and an ESP32 controller. The Trend Based Cleaning Algorithm compares the PV voltage it expects, the measured PV voltage, a performance loss per unit time, salinity level, and the time since the last cleaning operation. Cleaning is started only when the pre-set performance-loss and salinity requirements are met. A 7 day simulation and hardware experiments were performed. The salinity losses were from 2.1% to 5.0% and the performance losses were 4.2% to 9.8% measured in the hardware. The results of the simulation and hardware showed to be in good agreement the trends, differences were due to the environmental conditions, sensor tolerances and measurement noise. The automated cleaning cycle was about 8-12 minutes, which is less time than manual cleaning takes, which is reported as 30-40 minutes; the project reports a 70% reduction in maintenance labour. The findings show that such a comprehensive system (environmental sensing, electrical performance analysis, IoT monitoring and autonomous cleaning) for PV maintenance in a challenging coastal environment is feasible.

LLM-Assisted Service Migration and Resource Allocation for Dynamic Cloud-Edge Computing

Dynamic cloud–edge computing environments require continuous service placement and resource allocation under changing workloads, user mobility, network conditions, and heterogeneous computing resources. Conventional heuristic approaches provide efficient decision making but may lack adaptability, while deep reinforcement learning (DRL) approaches can require substantial training and computational overhead. This paper proposes an \textit{LLM-Hybrid Service Migration and Resource Allocation (SMRA)} framework that combines Large Language Model (LLM)-based candidate generation with heuristic optimization and multi-objective cost evaluation. The framework represents the current system state using workload, latency, available resources, energy consumption, bandwidth, and migration-related information, which are provided to the LLM to generate candidate migration strategies. In parallel, a heuristic module generates analytically derived candidate strategies. The feasible candidates from both modules are evaluated using a unified cost function incorporating service latency, energy consumption, and migration overhead, and the minimum-cost strategy is selected for execution. A simulation-based evaluation involving mobile users and edge servers compares the proposed framework with heuristic, DRL-based, and random baseline approaches. The reported results show reductions of approximately 29% in average service latency, 17% in energy consumption, and 22% in total system cost compared with the heuristic baseline. The findings demonstrate the potential of combining LLM-based contextual candidate generation with deterministic optimization for adaptive service management in dynamic cloud-edge environments.

DriftErase-HAR: Ledger-Anchored Federated Unlearning for Multimodal Human Activity Recognition under Asynchronous Sensor Drift

The paper proposes DriftErase-HAR, a novel federated unlearning framework designed to address the intertwined challenges of sensor drift and data deletion in multimodal human activity recognition. The framework introduces a compact, drift-adaptive backbone using mask-aware multimodal encoding, gated low-rank adapters, and coverage-aware attention-based fusion. It further presents a three-stage unlearning mechanism combining partition-weighted ledger rollback, distribution-matching data scrubbing, and fairness-aware model repair, supported by a hash-chained update ledger for auditability. Experiments on the WISDM dataset demonstrate that DriftErase-HAR substantially reduces membership-inference leakage while improving recognition accuracy and preserving client-level fairness. Moreover, it achieves near-identical parameterization to the retraining oracle with 22.79× fewer optimizer steps, demonstrating an effective trade-off between privacy, utility, fairness, auditability, and computational efficiency.

The Mediating Role of Perceived Dealer Pricing Fairness in the Relationship Between Technology Readiness and Solar Home System Adoption: Evidence from Rural Communities

This study provides strong empirical evidence from rural communities in Hatiya, Noakhali, Bangladesh, demonstrating that Optimism (OPT) and Innovativeness (INN) significantly and positively influence Solar Home System Adoption Intention (SHA), whereas Discomfort (DIS) and Insecurity (INS) do not exert significant effects. Importantly, Perceived Dealer Pricing Fairness (PDP) significantly and positively influences SHA and mediates the relationships between OPT and SHA and between INN and SHA. These findings establish fair dealer pricing as a critical market-based mechanism through which positive technology-related dispositions can translate into stronger SHS adoption intention, offering a context-specific contribution to renewable-energy adoption research in geographically remote rural communities.

Digital Twin-Driven Framework for Predictive Cyberattack Detection and Autonomous Resilience in IoT-Enabled Smart Grids

This research proposes a Digital Twin-driven framework for predictive cyberattack detection and autonomous resilience in IoT-enabled smart grids. The key contribution is the integration of real-time Digital Twin modeling, AI-based cyberattack prediction, and autonomous response mechanisms to continuously monitor grid behavior, identify anomalous activities, and mitigate threats before they cause significant disruption. The framework enables adaptive threat intelligence, real-time state synchronization, and self-healing responses, improving the security, resilience, reliability, and operational continuity of smart-grid infrastructures against evolving cyberattacks.

RETINO GUARDAI:ANEFFICIENTNETMODELANDDEEPLEARNING FRAMEWORKFOR EXPLAINABLE,ANATOMICALLY-AWAREDIABETIC RETINOPATHY SCREENING

Diabetic Retinopathy (DR) remains one of the lead
ing preventable causes of vision loss among working-age adults
with diabetes, and international screening guidelines recommend
regular fundus examination to catch referable disease before
irreversible damage occurs. Manual grading, however, is slow,
requires a trained ophthalmologist, and does not scale to the
size of most diabetic populations, which motivates automated
screening from retinal fundus photographs. This work presents
RetinoGuard AI, an integrated screening pipeline built around a
fine-tuned EfficientNet-B4 backbone for binary DR classification,
reaching 91.4% validation accuracy on the APTOS 2019 Blind
ness Detection dataset with a sensitivity of 92.1% and specificity
of 90.8% on referable cases. Beyond classification, the system
contributes two further components that most published DR
classifiers do not provide: first, a Grad-CAM based explainability
module that produces a pixel-level saliency map showing exactly
which parts of the fundus image drove the prediction; and
second, a Five-Zone Retinal Region Activation Analysis that
aggregates that saliency map over five anatomically named
zones- the macula, optic disc, superior arcade, inferior arcade,
and periphery- condensing a dense heatmap into a small
set of clinically meaningful numbers a reviewer can scan in
seconds. All three components are wired into a single session
authenticated Streamlit web application, so that classification,
visual explanation, and zone-level risk summary are produced
end-to-end from one uploaded image, without a separate offline
analysis step.

A Comparative Study of Spatial, Frequency-Domain, and Color-Channel Preprocessing Filters for Fundus-Based Cataract Detection

Evaluation of Significance of Rechargeable Battery with Special Emphasis on Lithium-ion Technology

This research provides a comprehensive evaluation of rechargeable battery technologies, with particular emphasis on lithium-ion batteries, covering their evolution, operating principles, performance characteristics, applications, challenges, and future prospects. It highlights the critical role of lithium-ion technology in renewable energy integration, portable electronic devices, electric vehicles, and other emerging applications, while identifying key challenges related to safety, cost, raw materials, environmental impact, and sustainability. The study also emphasizes the potential of AI and advanced materials to accelerate the development of safer, more efficient, and sustainable next-generation battery technologies

Ensemble Deep Learning Framework for Calibrated and Explainable Diabetic Retinopathy Grading on RetinaMNIST

The importance of this study is that it aims to shift the focus from achieving grading accuracy in DR towards a more reliable, clinically meaningful and interpretable grading system. The proposed four-model ensemble obtained a QWK score of 0.813 and macro-AUC of 0.908, which are better than the previously reported baseline results on RetinaMNIST and achieved better ordinal and discriminative results.

Most importantly, the study merges the ordinal-aware evaluation with the explainability, calibration, uncertainty analysis and structured error analysis of Grad-CAM. This not only provides an assessment of the accuracy of the model, but also the validity of the level of confidence, whether the model targets clinically relevant retinal lesions, and where potentially harmful errors (e.g. under-grading PDR) may arise.

Thus, the key contribution is a more holistic perspective on the assessment of AI-based DR grading, focusing not just on accuracy, but also on clinical trust and risk awareness.

Rethinking Noise Shaping in Differentially Private Federated LoRA: Why Isotropic Perturbation Wins.

Differentially private federated fine-tuning with
LoRA typically perturbs adapter updates as homogeneous vectors, overlooking the asymmetric effects of its low-rank factors.
Because the effective update is (α/r)BA, perturbations in A
and B are amplified according to the norm of the complementary factor. We formalize this asymmetry through a block-wise
Gaussian mechanism with separate clipping thresholds and noise
scales, subject to ∑︁g Cg2/s2 g = 1/σ2, which preserves the privacy
guarantee of isotropic noise. Although the proposed shaping reduces modeled noise energy, experiments show that it consistently
degrades utility. We identify three causes: degenerate single round error modeling under standard LoRA initialization, crossround cancellation between perturbation and gradient repair,
and update-direction distortion introduced by per-block clipping.
Across twelve paired experiments at identical (ϵ, δ), isotropic
noise consistently outperforms shaping, with gains of 3.02 and
7.28 percentage points on attention and feed-forward projections,
respectively. These results support isotropic perturbation as the
stronger baseline for private federated LoRA.

A Coupled Index Machine Learning Optimization Framework for River Water Quality Assessment and Control: The Halda River, Bangladesh

The maintenance of a river suitable for fish spawning demands three separate abilities that need to be separately developed: a defendable description of water quality in terms of a scalar parameter, a technique for its economical and continuous estimation, and a rule of converting it into action. This paper integrates the three into a single framework and applies it to the case of the Halda River, the only remaining natural spawning ground for Indian major carps in Bangladesh. The formula of a weighted arithmetic water quality index (WQI) is modified so that the subindex for DO is normalized by the saturation value depending on temperature; the principal term of the resulting index is then equivalent, up to rescaling, to the deficit variable of a Streeter–Phelps oxygen balance, making the index and the load allocation model commensurate in terms of the state variable. Applying the WQI to 18 station-seasons at six stations and three seasons, one obtains: the lower tidal reach at Mohora scores 92.4 (Very poor) during monsoon season and 109.3 (Unsuitable) after monsoon, the impounded reach above the Bhujpur rubber dam remains Excellent (6.3–12.9), and the seasonal average value decreases from 63.6 in monsoon to 34.3 in winter. Seven predictor variables are then learned for predicting the index value without oxygen data using four simple inputs. Extreme randomised tree model with leave-one-out cross validation achieves R² = 0.715 and RMSE = 14.66 index units, logistic regression classifier classifies acceptable and Poor-or-worse water quality states with 100% cross validated accuracy, and the predictive estimator recovers 87.1% of the index variation with 12 out of 13 alert months identified in a 36-month synthetic deployment scenario. The 5000-sample Monte-Carlo simulation analysis shows that the assimilative capacity of Mohora exceed with probability 1.00 in both wet seasons, which requires a 27–43% load reduction. An analytical solution demonstrates that the required reduction is independent of the reaeration-to-deoxygenation rate ratio. Constrained optimisation of aeration and sediment control gives the minimum cost action for Excellent class, and the oxygen rather than the turbidity becomes the binding control.

Explainable Hybrid Feature Fusion Network for Automated Skin Cancer Detection from Dermoscopic Images

A fair and explainable hybrid framework integrating EfficientNetV2L deep image features, handcrafted computer
vision descriptors, and patient metadata for multi-class
skin lesion classification.
• A preprocessing pipeline incorporating hair artifact removal and data augmentation to improve image quality
and address class imbalance.
• Integration of LIME and SHAP for local and global
interpretation of model predictions.
• Fairness-aware evaluation across estimated skin-tone
groups.

Few-Shot Hybrid Time Series and Large Language Model Framework for Air Leak Fault Diagnosis in Pressurized Pneumatic Systems

This study presents a novel few-shot multimodal framework for air-leak fault diagnosis in pressurized pneumatic systems, integrating learned multivariate time-series representations with large language model (LLM) based reasoning. The key contribution is the coupling of a convolutional autoencoder Prototypical Network with instruction-tuned LLMs through fixed and confidence weighted probability fusion, enabling complementary temporal and statistical information to be exploited under limited labeled data. The framework was validated on real-world MetroPT-3 compressor telemetry and achieved 99.0% accuracy, 99.1% precision, 99.0% F1-score, and 1.000 AUC with the Phi-3.5-mini hybrid. Moreover, confidence weighted fusion improved probability estimation, demonstrating that the proposed integration can enhance both fault discrimination and prediction reliability in few-shot industrial diagnosis.

A Multimodal Framework for Bangla Fake News Classification through Cross-Attention Fusion

We have developed a multimodal framework that integrates BanglaBERT, CLIP, AI-generated image detection,
and metadata through cross-attention fusion for Bangla
fake news detection.
• We have introduced a structured credibility mechanism
combining headline-body consistency, source credibility,
and virality features for improved fake news detection.
• We have evaluated the framework on 3,601 Bengali news
samples, including a held-out set of 491 unseen articles
to assess generalization.

An Explainable AI Approach for Bus Safety Risk Classification in Data-Scarce Transport Networks

Bus overtaking and reckless driving are important
road-safety concerns in data-scarce low- and middle-income
cities. This paper develops a machine-learning and explainable-AI
(XAI) framework for Dhaka, Bangladesh, where structured bus
telematics, digital driver logs, mechanical-condition histories, and
bus-specific incident labels are not readily available. The central
contribution is the Dhaka Bus Driving Risk Dataset (DBDRD), a
domain-grounded synthetic benchmark containing 12,000 records
and 43 variables across six feature groups. Its generation
process combines context-specific distributions, explicit conditional
dependencies, a 12-component literature-calibrated risk score,
Gaussian perturbation, percentile-based ordinal labeling, and
controlled missing-at-random noise. Random Forest, XGBoost,
LightGBM, and radial-basis-function SVM were evaluated using
a leakage-controlled 70/15/15 split and cross-validated tuning.
XGBoost achieved the strongest overall test performance with
76.4% accuracy, 0.761 macro F1, 0.767 weighted F1, and 0.937
one-versus-rest ROC-AUC. SHAP analysis identified operational
risk, aggression, compliance risk, and speed excess as the dominant
predictors and revealed compound behavioral-mechanical effects
in Critical-risk cases. The results establish a reproducible synthetic
benchmark and interpretable modeling pipeline, while external
validity remains contingent on future validation with real Dhaka
bus data.

Automated Bug Detection, Root-Cause Theme Discovery, and Solution Recommendation Using Large Language Models

Issue tracking repositories aggregate thousands of natural language bug reports every month, yet screening them into genuine defects, identifying probable root-cause themes, and recommending possible fixes still consumes a disproportionate share of maintenance effort. This paper introduces a comprehensive pipeline that integrates sentence embeddings, dense retrieval, unsupervised clustering, gradient boosted classification, and a retrieval augmented open weight large language model (LLM) to support three interrelated software maintenance tasks: (i) distinguishing between bugs and non bugs, (ii) discovering exploratory root-cause themes, and (iii) recommending solutions in natural language. The system’s performance is assessed using 30,511 cleaned bug reports sourced from four long lived, structurally diverse open source projects in the GitBugs corpus: Apache Cassandra, Apache Hadoop, Mozilla Firefox, and Apache HBase. A Sentence Transformer/FAISS retrieval module reaches an overall nDCG@5 of 0.669, while a LightGBM classifier trained on embeddings, metadata, and structural text flags detects genuine bug reports with 0.8558 test accuracy, an F1-score of 0.9084, and a ROC-AUC of 0.8855; a soft ensemble with a TF-IDF baseline pushes F1 to 0.9100. In an evaluation of 100 held out reports, a retrieval augmented Qwen2.5-1.5B-Instruct model generates root cause hypotheses and fix suggestions. 99 outputs yield parseable bug verdicts, of which 53.5% agree with the resolution derived labels. SHAP explanations expose the metadata and semantic signals on which the classifier depends, and per project confidence intervals show that performance is markedly uneven across repositories. All code, figures, and result artifacts are generated programmatically to support reproducibility.

Early Detection of Traffic Congestion Before Breakdown: A Spatiotemporal Warning Framework for Intelligent Transportation Systems

Proactive traffic management requires more than
identifying congestion after it has formed. This study develops
a multi-horizon early-warning framework for detecting freeway
congestion before confirmed traffic breakdown using real-world
detector data from the Caltrans Performance Measurement
System. The PeMS08 benchmark contains 17,856 five-minute
observations from 170 detectors with traffic flow, occupancy,
and speed measurements. A sensor-adaptive congestion definition
combines speed below 75% of detector-specific free-flow speed,
elevated occupancy, and at least 15 minutes of persistence.
A 30-minute clear period is additionally required before each
onset. Chronological train, validation, and test partitions are
used to prevent temporal leakage. Four classifiers are evaluated
for warning horizons of 5, 10, 15, 20, and 30 minutes. Extra-
Trees is selected using validation precision-recall area under the
curve (PR-AUC). On the held-out test period, it achieves ROCAUC/
PR-AUC values of 0.930/0.825 at 5 minutes and 0.841/0.627
at 10 minutes. Performance decreases with longer lead time,
while useful predictive information remains detectable at 15–30
minutes. Interpretation and ablation analyses show that speed
dynamics, occupancy buildup, and neighboring-detector conditions
provide the dominant warning information. The results
support a 10-minute horizon as a practical compromise between
actionable lead time and predictive reliability for proactive
intelligent transportation systems.

Developing and Validating a Decision Analytics Maturity Model for Human Resource Management Using CFA and Machine Learning

This study proposes and validates the Decision Analytics Maturity Model for HRM (DAMM-HR). The five maturity stages of the model are reactive reporting, diagnostic insight, predictive foresight, prescriptive optimization, and autonomous decisioning. Firstly, it offers a theoretical contribution by establishing decision embeddedness as a key aspect of HR analytics maturity. Second, it does offer an empirical contribution by conducting multi-phase validation via confirmatory factor analysis, ordinal regression, and qualitative text analysis based on data from 217 HR analytics
practitioners. Third, it provides a practical diagnostic tool to help HR leaders assess their organizations’ maturity and identify areas for further development.

Data Leakage and the Limits of Attention: A Significance-Tested Benchmark for Solar Panel Fault Detection

We present a reproducible, leakage-free benchmark evaluated on two complementary PV
inspection datasets: a ground-level RGB surface-fault dataset and a large-scale aerial thermal anomaly dataset. We systematically compare several attention mechanisms integrated with an EfficientNet-B0 backbone under a rigorous evaluation
protocol based on 5-fold cross-validation, multiple random seeds, and statistical significance testing. Across both modalities, no attention module yields a statistically significant improvement over the plain backbone, while a controlled experiment shows that augmenting before splitting inflates test accuracy by approximately ten percentage points. The findings highlight the importance of leakage-free evaluation and statistical validation, and establish a reproducible benchmark for fair comparison of future PV fault detection methods.

Beyond SOH: A Composite Degradation Index with Uncertainty-Aware SOC Estimation for EV Batteries

Conventional battery health indicators such as State of-Health (SOH) capture only capacity fade, while neglecting operational stress, round-trip efficiency losses, and model-derived uncertainty. All of these features accelerate aging in real world electric vehicle (EV) environments. Extending our prior uncertainty-aware State-of-Charge (SOC) estimation work, this paper introduced a novel degradation index named the Unified Degradation Score (UDS), a Ridge-regularized composite metric integrating Depth-of-Discharge (DOD), SOH, C-rate, Round-Trip Efficiency (RTE), and Monte Carlo (MC) Dropout epistemic uncertainty. Validated on the CALCE CS2-36 dataset
over 729 controlled aging cycles, the underlying FT-Transformer achieved MAE 0.599% and RMSE 0.846% during early-life operation. Beyond cycle 250, epistemic uncertainty rose systematically, functioning as an automatic model-derived degradation signal.The UDS achieved a Pearson correlation of r = −0.975 with SOC prediction error, outperforming SOH alone (r = −0.938).A closed-loop BMS charging simulation demonstrated that UDS triggered current throttling preserved 79.0% remaining useful life (RUL) at cycle 729, surpassing conventional SOH-step control (77.2%) and unmitigated fixed-rate operation (64.1%).

Why Fixed-Weight Fusion Fails: A Non-Circular Evaluation of LSTM–DBSCAN–XGBoost for Predictive Maintenance

This paper’s real contribution isn’t the LSTM+DBSCAN+XGBoost architecture itself — those are established methods. It’s a methodological finding, made visible only by a non-circular evaluation protocol:
Fixed-weight fusion of a forecaster, an anomaly detector, and a health classifier looks catastrophic (macro-F1 0.592 vs. 0.913 for the best single branch) but that’s almost entirely a measurement artifact, not a real weakness of fixed weights. Once you fuse at the probability level instead of collapsing everything to a scalar first, the same fixed weights recover to 0.892. 0.298 of the 0.322-point gap comes from throwing away class information (scalarization); only 0.024 comes from the weights being fixed rather than learned (statistically confirmed, p=0.003).

Twenty-Five Years of Air Quality in Bangladesh: Trends, Seasonality, and Spatial Pollution Regimes

This study provides a comprehensive national-scale analysis of air quality in Bangladesh by examining 25 years of hourly data covering over 3.19 million records across 103 cities. It reveals that the apparent flat long-term national trend is an artifact of network expansion diluting severe pollution in hotspots like Dhaka and Narsingdi, which worsen at a rate of 1.84 AQI points per year. Furthermore, the study identifies strong, statistically significant seasonality with peaks in January and lows in July, maps a clear urban-rural gradient showing air quality improvement with distance from the capital, categorizes cities into four distinct pollution regimes via K-means clustering, and establishes that particulate matter PM2.5 and PM10 serve as the primary driver of overall AQI variation.

Advanced Analysis of Amplitude Modulation and Demodulation Techniques for Enhanced Signal Processing in Analog Communication Systems

mplitude Modulation (AM) remains a
fundamental technique in analog
communication systems, essential for
transmitting information over long
distances. This paper presents an
in-depth analysis of AM and its
demodulation processes, focusing on the
impact of varying message and carrier
frequencies on signal fidelity and
modulation efficiency. Using a hands-on
experimental approach, AM signals were
generated and observed under controlled
conditions, followed by a detailed
examination of the demodulation process.
Results indicate that the modulation index
significantly influences the amplitude and
quality of the transmitted signal, while
sideband frequencies play a crucial role in
maintaining signal integrity. The study
further explores challenges encountered
in maintaining optimal signal modulation
and discusses potential enhancements to
conventional AM techniques. These
findings provide valuable insights into
improving analog signal processing, with
implications for enhancing communication
reliability in AM-based systems.

Low-Cost Water Level Indicator Using Logic Gates for Enhanced Water Management

Water scarcity and tank overflow pose significant
challenges in urban Bangladesh, leading to substantial water
wastage. This paper presents a cost-effective water level indicator
designed for domestic water management, utilizing a single IC
74HC04 NOT gate, two BC547 transistors, LEDs, and resistors.
The system leverages water’s conductivity to detect mid and
upper water levels, providing visual indications via LEDs at a
total cost of approximately 300 Taka ( 2.46 USD). Compared to
alternative solutions like ultrasonic sensors ( 1200 Taka) or float
switches ( 950 Taka), the proposed design offers a 3–4 times cost
reduction, making it accessible for low-income households and
educational applications. Implemented on a portable breadboard
setup, the system ensures reliability, with stable LED indications
verified through experimental testing. This work provides a simple, affordable solution tailored to resource-constrained settings,
promoting efficient water management in urban environments.

Implementation of an FM Transmitter Circuit for FM Radio Signal Communication

This paper presents the design and implementation
of a three-stage FM transmitter for short-range audio broadcasting within the 88-108 MHz FM band. The system consists of
three primary stages: pre-amplification, carrier generation, and
power amplification. Initially, the weak audio signal is amplified
by Q1, a NPN transistor, to a level suitable for modulation. In
the next stage, Q2, configured as a Colpitts oscillator, generates
a stable high-frequency carrier signal. This carrier is modulated
by the audio signal in the FM stage, where the frequency of
the carrier is varied in proportion to the amplitude of the audio
input, encoding the audio information.The modulated signal is
then amplified by Q3 in the power amplification stage, ensuring
sufficient strength for short-range transmission. The amplified
FM signal is passed to the antenna, which broadcasts the signal
for reception by FM radios. The system was validated using
MATLAB simulations, which modeled the frequency response,
power spectral density (PSD), and the effects of Additive White
Gaussian Noise (AWGN) and multipath interference on the signal.
The Proteus simulation environment was used to further verify
the circuit’s performance in real-world conditions. The results
demonstrate that the transmitter is capable of handling signal
interference, maintaining frequency stability, and delivering a
robust FM signal for broadcasting.This work provides a novel
approach to FM transmission, integrating theoretical analysis,
simulation models, and practical validation. It addresses common
challenges in wireless communication, such as signal clarity, frequency drift, and interference, making it a valuable contribution
to the field of short-range FM broadcasting and educational
applications

Wideband Antenna Design and Performance Analysis Using CST Simulations

This paper has presented the design and analysis
of a wideband rectangular-shaped antenna using FR-4 as the
substrate, optimized for ultra-wideband (UWB) applications. The
antenna’s performance has been measured using the reflection
coefficient (S11) through 3 GHz to 11 GHz, with CST simulations
which have shown an excellent impedance match, achieving a
minimum S11 of −24.84 dB at 9.44 GHz and −20.95 dB at 4
GHz, indicating good impedance matching at lower frequencies.
Notably, S11(5) has demonstrated near-perfect impedance matching with S11 = −48 dB at 9 GHz, making it suitable for UWB and
advanced wireless systems. When the surface current at 4 GHz
and 9 GHz has been analyzed, it has been found to be evenly
spread out at 5 GHz for broad radiation and concentrated at
9 GHz for higher directivity. These results have demonstrated
how well the antenna has performed over a wide range of
frequencies. This paper has demonstrated the antenna’s superior
impedance matching and wideband performance, applicable in
indoor positioning, short-range communication, radar sensing,
high-speed data transmission, through-wall radar, and medical
or security imaging, thereby underscoring the significance of
simulation-based design in advanced wireless technologies

Adaptive Filtering Techniques for Signal Enhancement in Modern Communication Systems

Signal transmission is central to modern communication systems because reliable, high-fidelity data transfer
depends on it. As demand for higher data rates and stronger performance grows, quantization noise an unavoidable by-product
of analog-to-digital conversion (ADC) has become an obstacle.
Traditional static noise-shaping filters can reduce this noise but
are ill suited to changing signal and channel conditions. This
paper presents an Adaptive Sigma-Delta Modulator (ASDM), a
software configurable scheme that adjusts its noise-shaping filter
in real time to the prevailing signal and channel state. By refining
the modulation process, the ASDM lowers quantization noise
and thereby raises the Signal-to-Noise Ratio (SNR). Simulation
studies show that, under dynamic conditions, the ASDM outperforms conventional Sigma-Delta Modulators (SDMs), delivering
marked gains in dynamic range, stability, and system performance. The design also integrates adaptive filtering including
the Least-Mean-Squares (LMS) algorithm to enable continuous
rate and phase-aware adjustments that improve signal quality
in real time. Beyond performance, the approach offers practical
benefits: higher power efficiency, simpler hardware, and robust
handling of non-stationary signals. These attributes make the
ASDM a strong candidate for high-resolution ADCs, wireless
communication systems, and other applications requiring precise
signal processing. This dynamically responsive strategy points
toward stronger, cost effective, and adaptive solutions for nextgeneration communication technologies.

Regime-Aware Temporal Convolutional–Gated Recurrent Forecasting of Hydrogen Production in Wave-Driven PEM Electrolysis

This research introduces RA-TCGN, a compact regime-aware temporal convolutional-gated recurrent model for multi-step hydrogen-production forecasting in wave-powered PEM electrolysis. It establishes a leakage-safe, experiment-level evaluation showing that one-step accuracy is misleading and demonstrates that operating-regime conditioning reduces pooled LOEO RMSE by 28.6%. External testing confirms improved forecasting and substantially lower extreme errors while revealing regime-specific deployment limitations.

Beyond Internal Validation: Cross-Dataset Generalization of Machine Learning and Deep Learning Models for Oral Cancer Image Classification

The contribution of this study is the systematic evaluation of the cross-dataset generalization of oral cancer image classification models, moving beyond conventional within-dataset validation. By evaluating ML and DL models across two independently collected datasets using bidirectional external validation, the study quantifies the generalization gap and demonstrates that high internal performance may not translate to unseen datasets. The study further integrates the selected model into a web-based screening prototype, connecting model evaluation with practical accessibility.

A Retrieval-Augmented Generation Framework for Cancer Health Question Answering Using Semantic Filtering and GPT-4o-mini

This work presents a lightweight, domain-specific RAG framework for cancer QA that uses semantic embedding-based topic filtering to construct a focused 752-pair corpus from MedQuAD. It demonstrates substantial performance gains over BM25 (64.1% MRR improvement) and non-RAG GPT-4o-mini (10.4% BERTScore improvement), with expert evaluation confirming higher safety and accuracy. The ablation studies systematically identify optimal retrieval depth (k=4) and embedding model, while the framework is designed for scalable, non-diagnostic consumer health information access.

Q-Learning-Based Degradation-Aware BESS Management for Agricultural Microgrids

Battery Energy Storage System (BESS) integration
improves reliability and sustainability in agricultural microgrids,
but inefficient control accelerates degradation and can itself
compromise reliability. We propose a reliability-constrained,
degradation-aware BESS management strategy using Q-learning.
During development we found that an unconstrained action space
lets a Q-learning agent inflate its apparent cycling reduction
by withholding discharge from demand– a reward-hacking
failure mode leaving 4.7–14.9 percentage points more demand
unserved than the baseline. We resolve this by guaranteeing
discharge whenever the battery can physically supply it, and
training the agent only to control charging intensity during
solar surplus. Evaluated on one year of real 15-minute field
data from an agricultural microgrid in Jashore, Bangladesh,
across three agricultural seasons with a chronological, leakage
free 80/20 train/test split and validated across 3 independent
training seeds, the controller matches a same-dataset greedy
baseline’s service reliability to within 0.03% of total demand
while reducing cumulative battery cycling by 44.4–54.1% and
cumulative degradation damage by 66.2–69.3% (mean ± std) on
held-out data. A companion ablation confirms the unconstrained
variant inflates its apparent reduction to 77.8–95.2% only by
sacrificing reliability. A complementary multi-objective (NSGA
II) analysis and a benchmark of four supervised SoC/RUL fore
casting models (best R2=0.915, LSTM) are also reported. Results
indicate reliability-first reward design is essential for trustworthy
degradation-aware reinforcement learning in agricultural BESS
applications.

An Explainable and Probability-Calibrated AdaBoost Framework for Type-2 Diabetes Risk Prediction Using SMOTETomek

Type-2 Diabetes Mellitus (T2DM) is a growing
public health challenge, particularly in low- and middle-income
countries where early risk identification remains critical. This
study proposes an explainable and probability-calibrated Ada
Boost framework for T2DM prediction using a Bangladesh-based
clinical dataset of 1,065 women. Six machine learning classifiers
were evaluated with four imbalance-handling strategies under
stratified 10-fold cross-validation. AdaBoost with SMOTETomek
achieved the best performance and was further optimized using
isotonic probability calibration. The calibrated model achieved
an accuracy of 0.939, recall of 0.999, F1-score of 0.961, and
AUC of 0.905 on an independent test set. Calibration significantly
improved probability reliability, reducing the Brier score from
0.1618 to 0.0620 and Expected Calibration Error from 0.3194 to
0.0542. SHAP analysis identified fasting glucose, systolic blood
pressure, age, and number of pregnancies as the most influential
predictors, while Decision Curve Analysis demonstrated positive
clinical net benefit across relevant threshold ranges. The proposed
framework integrates imbalance correction, calibrated predic
tion, explainability, and clinical utility assessment, providing a
transparent approach for diabetes risk stratification. External
validation on larger multi-center cohorts is required before
clinical deployment.

Explainable Lightweight Deep Learning for Plant Leaf Disease Detection Using MobileNet and Grad-CAM

This research proposes an explainable lightweight deep learning framework for plant leaf disease detection using MobileNetV2 and Grad-CAM. The proposed approach achieves high classification performance while reducing computational complexity, making it suitable for mobile and resource-limited agricultural applications. MobileNetV2 is compared with EfficientNetB0 and ResNet50 based on accuracy, precision, recall, F1-score, model parameters, and inference time. Grad-CAM is integrated to provide visual explanations by highlighting disease-relevant regions of leaf images, improving model transparency and reliability.

A Trust-Weighted Blockchain Framework Coupling AI-Driven Risk Scoring with Bounded Sybil-Resistant Stake Economics

1.A source-degeneracy diagnostic for fake-news benchmarks. We show that reported detection accuracies above 99% are largely an artifact of benchmark construction: on ISOT the source field alone predicts the label with 100% accuracy, a bag-of-words model matches deep encoders, and a source-disjoint split is therefore impossible in principle.
2.A rigorous four-protocol, leakage-controlled evaluation. On 61,199 items we progress from an in-domain split (97.6%) through lexical scrubbing and cross-corpus transfer (down to 61.7%) to leave-one-publisher-out cross-validation, where the mean per-publisher AUC of the label-mixed hyperpartisan outlets is near chance (≈0.52), reported with Hanley–McNeil confidence intervals.
3.Evidence that calibration does not transfer under distribution shift. A source-fitted temperature removes far less calibration error than a target-fitted one, and the divergent GossipCop temperature collapses predictions toward 0.5, showing that single-scalar temperature scaling cannot repair severe shift but correctly detects that the score is uninformative.
4.A remediation architecture whose safety does not depend on the classifier. The calibrated score allocates scarce human adjudication rather than delivering verdicts; an oracle-review upper bound and an imperfect/adversarial five-voter jury simulation both lift a below-baseline classifier (65.3%) to a safe system (82.8–97.3%).
5.A bounded, Sybil-resistant bonding curve. A logarithmically bounded Integrity Bond compresses the novice-to-elite entry-cost ratio from 10,001× to 14.0×, invariant to the risk multiplier, while preserving the security invariant that every fresh identity posts the maximum bond.
6.A deployed, security-tested ERC-1155 contract with a burst-detection correction. We identify and fix an ordering flaw in which writing the probation flag inside the reverting transaction silently discards it, and report on-chain gas, batching savings (35.5% per item), and slashing-funded verification, with deployment claims scoped to a permissioned/Layer-2 setting.

History-based Improvement of the Co-change Suggestion Capability of FLeCCS

FLeCCS is a technique that suggests code fragments likely to need a matching change (co-change candidates), based only on how similar two fragments look on the page. It never checks whether fragments have actually changed together in the past, so it misses real candidates that happen to be worded differently. This paper introduces RT-FLeCCS, which mines a system’s own revision history as method-level association rules using Tarmaq, keeps only the rules whose support and confidence clear chosen thresholds, and merges the result into FLeCCS’s suggestions. Across four open-source systems (774 to 1700 revisions each, in C and Java), RT-FLeCCS raises recall over plain FLeCCS by 53% to 170% and F-score by 33% to 90%, while giving up almost nothing in precision. A support threshold of 1 paired with a confidence threshold of 0.5 gives a reliable general-purpose setting across all four systems.

An Explainable Ensemble Machine-Learning Framework for Predicting ICU Mortality in Critically Ill Patients

1. A 71-feature clinical feature-engineering pipeline built from 22 raw ICU variables.
2. An ensemble architecture combining XGBoost,LightGBM, CatBoost, HistGradientBoosting,ExtraTrees, and Random Forest.
3. An optimized weighted ensemble with logisticregressionstacking to fuse predictions from complementary base learners.
4. A preprocessing and class-balancing pipeline usingmedian imputation, SMOTE,RobustScaler, and F1-driven threshold tuning.

An Intelligent Multi-Scale Lesion Attention Transformer Framework with Retrieval-Augmented Farmer Guidance for Onion Leaf Disease Diagnosis

Onion diseases have been a significant hurdle for crop production, resulting in huge crop losses. It is necessary to detect and identify diseases quickly and correctly for effective disease management. This paper proposes a deep learning framework for identifying onion leaf diseases automatically. Our data collection methodology involves capturing images of onions grown in the field under various conditions in Faridpur, Bangladesh. We used our collected images for classifying diseases through the proposed method.Our proposed framework utilizes EfficientNetB4 along with a lesion attention mechanism and multi-scale feature fusion by transformers to increase feature extraction and classification capability of the network. To make our model more transparent, we have used the XAI approach to explain our prediction using a Grad-CAM visualization technique to help in understanding which region is important for predicting the infection. Furthermore, our model suggests a treatment strategy based on RAG guidance module. As per experimental results, our proposed model performs with 98.65% classification accuracy.

Robust Federated IIoT Intrusion Detection Under Byzantine Label-Poisoning Attacks: Class-Wise and Client-Wise Analysis on DataSense

The main contribution is empirical, not methodological. The paper shows that global metrics can hide a targeted attack completely. Under Bruteforce→Benign label poisoning, accuracy stays near 94% for all three aggregators. But Bruteforce recall drops to 4.11% under FedAvg. Trimmed Mean recovers it to 47.24%, and FedCARR to 84.65%. The paper backs this with a reproducible 10-client, 8-class non-IID DataSense setup, and with class-wise, client-wise, ROC, and paired statistical diagnostics on one locked experimental grid. The practical takeaway is that robust FL for IIoT intrusion detection must be evaluated per class and per client, since aggregate scores alone will not reveal the failure.

PP-GBD: Privacy-Preserving Graph Neural Botnet Detection in Encrypted Traffic

This work proposes PP-GBD, a unified privacy-preserving framework for botnet detection in encrypted traffic using graph neural networks and payload-independent metadata. Its key contribution is a formally calibrated fixed-slot graph-level differential privacy mechanism with explicit add/remove adjacency and sensitivity, integrated with federated learning and an HE-oriented polynomial GCN design. The study also provides a reproducible controlled benchmark and quantifies the privacy–utility trade-offs across non-private, differentially private, polynomial, and federated models.

LAUC-RiskNet: Leakage-Aware and Interpretable Learning for Rule-Driven Mental-Health Labels

The significant contribution of this research is an audit-then-ablate framework that identifies and quantifies deterministic target leakage in behavioral mental-health datasets. LAUC-RiskNet combines interpretable threshold learning, contextual encoding, adaptive fusion, probability calibration, and uncertainty estimation. Crucially, ablation shows that the apparent predictive performance arises mainly from the label-generation rule rather than independent clinical signals, enabling more transparent and trustworthy evaluation.

Evaluating Arterial Network Resilience Post-Mass Transit Integration: A Hybrid Simulation and Empirical Corridor Study

Widening roads offers only a partial solution to the traffic gridlock that plagues rapidly developing megacities. We evaluate a 3.18-km (1.98-mi) section of Kazi Nazrul Islam Avenue, Dhaka, before and after the integration of MRT Line 6, AI-enforced access management, and semi-automatic signal control, and estimate the associated change in corridor delay and CO₂ emissions. We investigate the impact of integrating high-capacity rail with technology-driven surface management. We use a hybrid approach, comparing a validated 2018 microscopic simulation that documented severe delays under manual police control with new 2026 field trajectory data. Recent developments include the introduction of Mass Rapid Transit Line 6, deployment of Artificial Intelligence (AI) cameras for lane monitoring, and implementation of a semi-automatic Intelligent Transportation System (ITS) developed by the Bangladesh University of Engineering and Technology (BUET). Despite permanent lane narrowing from MRT viaduct pillars, observed peak-hour car speeds rose from a 5.2 km/h simulated baseline to 12.3 ± 0.5 km/h (n = 15; +136%). Because classified volume counts were unavailable, the relative contributions of operations management and MRT-induced demand shift cannot be separated and are discussed in §IV-D. The increase in peak-hour car speeds was 136 percent, moving from a baseline of 5.2 kilometers per hour to an empirically measured 12.31 kilometers per hour. Averaging 17.43 kilometers per hour, two-wheelers outperformed other modes of transportation. We also examined the new variances observed at key intersections in signal cycles, ranging from 180-second cycles to as long as 260 seconds during heavy traffic. Data indicates that manual ITS overrides are an essential component of boundary metering, which restricts traffic to some extent and prevents the internal network from completely locking up. In the end, our findings indicate that traffic volume is not solely determined by the physical lane capacity. The combination of overhead transit and access controls backed by technology can be instrumental in eliminating latent delay and maintaining the speed of urban highways.

Novel Attacks Arrive Diffuse: Rethinking Few-Shot Open-Set Enrollment for ROS Intrusion Detection

The paper’s central contribution is showing that single-prototype enrollment in few-shot open-set recognition has a geometric ceiling that more labels cannot lift — novel-class F1 actually declines with support size, and an oracle prototype built from all 4,326 novel flows matches the 100-shot result exactly, ruling out sampling error. The diagnosis generalizes beyond ROS: because the episodic loss never shapes the unseen class, it arrives 2–4× more diffuse than trained classes and its mean falls inside a rival class basin. The fix is cheap and follows directly — enrolling three k-means sub-prototypes instead of one raises mean novel-class F1 from 0.694 to 0.862 with no retraining.

Interface-Dependent Current and Voltage Losses in a Lead-Free ZnSe/FASnI3/Cu2O Perovskite Solar Cell: A SCAPS-1D Study

This work examines a Glass/ITO/ZnSe/FASnI3/Cu2O/Au lead-free perovskite solar cell in SCAPS-1D, degrading the two absorber interfaces independently rather than together. The Cu2O/FASnI3 interface is shown to be the costlier one to degrade, taking 1.981 mA/cm2 of photocurrent and 5.02 percentage points of efficiency against 0.483 mA/cm2 and 3.41 percentage points at FASnI3/ZnSe, a photocurrent ratio of 4.10 at the highest defect density modeled. This ordering is the reverse of the ETL-side dominance reported for other lead-free absorbers, and the 0.80 eV electron barrier at Cu2O offers a plausible explanation, since electrons generated near that face must traverse the remaining absorber thickness before extraction at ZnSe. The device is also run under separate front and rear one-sun illumination, which leaves the photovoltage and fill factor nearly unchanged and concentrates the loss in photocurrent; because the modeled rear electrode is opaque Au, that result measures the electrical symmetry of the layer sequence rather than bifacial performance.

Calibrated Federated Intrusion Detection for Drone Swarms under Non-IID Feature Skew

For the protection of critical infrastructure with
drone swarms, an intrusion detection system must be able
to detect intrusion without concentrating sensitive telemetry.
Evaluation leakage, which has been commonly used in prior
federated learning (FL) based intrusion detection approaches, has
been shown to lead to high discrimination scores, but these prior
works have overlooked the probability calibration aspect and
have resulted in overconfident alerts under non-IID feature skew.
We propose an uncertainty-aware intrusion detection system
that is trained using federated learning and tested under a
leakage-safe protocol, comprised of chronological holdout splits,
cross-split duplicate hashing, and feature hardening. With the
ISOT Drone Intrusion dataset, we create a setting of ten clients
with a feature-skew distribution, and compare four aggregation
strategies (FedAvg, FedProx, FedNova, FedDyn) using operating
points found through Youden’s J statistic. Accuracy and Macro
F1 are used to measure the predictive quality, while Expected
Calibration Error (ECE) after post-hoc temperature scaling is
used to measure the reliability, and SHAP is employed as a
behavioral inspection tool. The upper bound is given by a
centralized Random Forest with Macro F1 = 0.9896. When the
skew is severe, FedAvg achieves 98.60% accuracy and Macro
F1 = 0.9844 and temperature scaling makes the ECE 0.0027
thus allowing selective alerting to reduce false alarms. The
federated model is also better at withstanding skew, as shown in
explainability analysis, in that it is more agile than centralized
models with respect to spatial identifiers. These results apply to
the particular dataset and construction of the non-IID system
and may not represent all shifts in deployments; however, they
suggest that an FL system can provide near-centralized UAV
intrusion detection with reliable human-in-the-loop deferral.

HemaStack: Harnessing Heterogeneous Stacking with Neural Meta-Learning and Dual Explainability for Cost-Effective Dengue Diagnosis

Dengue fever in Bangladesh is a critical public
health issue, particularly in the Dhaka area, with over 30,000
cases reported in 2025, highlighting the need for quick and
reliable diagnostic tools. While routine Complete Blood Count
(CBC) parameters are easily available, traditional diagnosis relies
on NS1 antigen testing, which is expensive and susceptible to false
negatives in later stages of infection. To overcome this limitation,
we propose HemaStack, a stacking ensemble framework for
binary dengue classification using routine hematological blood
parameters. A dataset of 1,037 patients from various tertiary
hospitals in Dhaka, Bangladesh, from June to August 2025, was
preprocessed using label encoding, IQR-based outlier removal,
StandardScaler normalization, and SMOTE balancing. HemaS
tack integrates five base learners—Random Forest, Extra Trees,
XGBoost, LightGBM, and SVM—with an MLP meta-learner
using passthrough feature augmentation, benchmarked against
eight baseline classifiers under 10-fold cross-validation and an
independent test set. HemaStack attained an accuracy of 98.59
± 0.98%, an F1-score of 98.59 ± 0.97%, and a ROC-AUC of
0.9966 ± 0.0037; on the held-out test set it achieved an accuracy
of 97.57%, a recall of 99.38%, an F1-score of 98.45%, a ROC
AUC of0.9852, and an MCCof0.9292, outperforming all baseline
models. SHAP and LIME analyses identified Lymphocytes (%),
Platelet Count, and Hematocrit as the most important features.
The results indicate that HemaStack enables detection of dengue
in a timely, cost-effective, and interpretable manner without
specialized diagnostic tests, which could be useful in the resource
limited healthcare system in Bangladesh and other endemic areas.

A Noble Technique of Patient Data Handling for Bangladesh.

The significant research contribution is the development of a secure, interoperable, and scalable patient information management system that integrates heterogeneous healthcare databases through a middleware layer. The system reduces patient-data fragmentation, enables efficient retrieval of comprehensive records, protects sensitive information, and supports improved healthcare and telemedicine services, particularly in remote areas.

RSANet: An Advance in Oral Cancer Prediction using Residual Network with Soft-Attention Mechanism

Oral cancer is still a major worldwide health problem, especially in South and Southeast Asia, with late detection and variability in diagnosis leading to poor survival. Histopathological investigation is the gold standard of diagnosis but is time-consuming, subjective and pathologist dependant. We propose RSANet in this work, a deep learning framework integrating a ResNet50 backbone with a soft-attention mechanism for discriminating normal tissue from oral squamous cell cancer (OSCC) in histological pictures. The residual backbone encodes hierarchical morphological patterns, while the attention mechanism distributes higher weights to selected spatial regions and provides visual maps to check model focus. RSANet obtained an accuracy of 97.56%, a sensitivity of 98.33% and an F1-score of 97.75% on the held-out test set. The ResNet50 baseline acquired an accuracy of 91.40%, for comparison, equal to an absolute gain of 6.16 percentage points. Our results show that using soft attention improves classification performance in the experimental setup we analyzed. RSANet is a promising computer-assisted OSCC classification system that might support pathologists in making diagnostic decisions, however independent validation in multicenter setting is needed prior to its possible clinical use.

Paper Type: Full Research Paper