Full Research Paper - NPS Australia Submission System

BCViT++: Locality-Aware Large-Kernel Vision Transformer for Breast Cancer Histopathology Classification

Automated categorization of breast histopathology pictures can benefit computational pathology, however tissue appearance changes with optical magnification, and annotated datasets remain tiny for Vision Transformers (ViTs). In this study, we propose BCViT++, a magnification-specific framework for binary categorization of benign and malignant breast tumor pictures. This pipeline incorporates channel-wise principle component analysis (PCA) reconstruction with a variance threshold of at least 99%, training-set class balancing, geometric augmentation, Locality Self-Attention (LSA), and Large Kernel Attention (LKA). LSA masks token self-attention with a diagonal matrix and learns the attention temperature, while LKA refines value representations using depth-wise convolutions with a larger effective receptive field. All BreakHis experiments were done separately at 40×, 100×, 200× and 400×. We obtained an accuracy of 86.57%, 86.87%, 90.37% and 90.44% using a PCA-ViT baseline, respectively. The whole BCViT++ framework improved these accuracies to 93.53%, 91.17%, 96.79%, and 96.45% with absolute improvements of 6.96, 4.30, 6.42 and 6.01 percentage points, respectively. The best performance was achieved at 200× with accuracy of 98.55%, sensitivity of 96.79%, specificity of 96.80%, F1-score of 97.66% and MCC of 0.926. These results show consistent overall improvements over the PCA-ViT baseline for the studied image-level technique.

MVPR-PF: A Multi-View Peptide Representation and Probability Fusion Framework for Anticancer Peptide Prediction

Anticancer peptides (ACPs) are promising therapeutic candidates because of their selective activity against cancer cells and relatively low toxicity toward normal cells. However, experimental ACP identification is time-consuming and costly, motivating efficient computational screening methods. This study proposes Multi-View Peptide Representation and Probability Fusion (MVPR-PF), a hybrid machine and deep learning framework that integrates Binary Profile Features (BPF), Composition of $k$-Spaced Side-Chain Pairs (CKSSCP), a 20-dimensional pI-derived physicochemical property vector, and grouped 3-mer descriptors. XGBoost learns nonlinear relationships from the structured multi-view features, while a Bidirectional Long Short-Term Memory (BiLSTM) network captures bidirectional residue dependencies from the position-aware BPF representation. Their prediction probabilities are combined through weighted probability-level soft voting. Using stratified 10-fold cross-validation, MVPR-PF achieved 86.89% accuracy and 93.66% AUC on ACP740, and 85.83% accuracy and 91.89% AUC on ACP240. These headline numbers are consistent across all reported tables, the Discussion, and the Conclusion. These results demonstrate that combining complementary handcrafted and sequential representations provides effective computational ACP prediction.

Design of an Intelligent Solar-IoT Irrigation System for Sustainable Agriculture

The main contribution of this research is the integration of real-time automatic irrigation based on soil-moisture, ESP32 control, LoRa long-range communication, IoT monitoring into a single smart irrigation platform and dual axis solar power. The study also explores the water saving and operational cost benefits, suggesting its applicability for sustainable and efficient agricultural irrigation.

An Explainable Machine Learning Framework for Cyberbullying Detection in Social Media Comments: Hybrid Lexical–Sublexical Modeling with Global and Local Explanations

This research provides an explainable and high-performing cyberbullying detection framework by combining (1) hybrid lexical–sublexical text representation (TF-IDF with word-level and character-level) (2) optimized LinearSVC classification (3) explanation for decisions based on SHAP and LIME. The limitations of existing black-box detection models are overcome as the proposed approach reasoning about the predictions at the feature level in a transparent manner. The framework is evaluated using a custom dataset of 22,630 social media comments, and achieves a score of 97.77% accuracy, 98.04% F1-score, and 0.9976 ROC-AUC, with explanation faithfulness reported using token-deletion analysis. This research is making strides towards more trustworthy AI-assisted content moderation while also providing high predictive performance alongside interpretable decision explanations.

Hierarchical MLP for EEG- Based Decoding of Cognitive Load in Multi-Level n-Back Tasks

This paper contributes by performing following steps: (i) extracts a rich multi-domain EEG feature set spanning time, frequency, and time-frequency domains, (ii) applies rigorous non-parametric statistical selection to retain 241 informative channel–feature pairs, (iii) achieves high-accuracy binary and multi-class classification of n-back load levels, and (iv) complements classification with exploratory PLV and Time-Frequency Representation (TFR) analyses that illuminate the underlying neural mechanisms.

Leveraging AI-Driven Cloud Computing for Advanced Health Prediction Models

Modern healthcare systems are undergoing a paradigm shift from reactive clinical interventions to continuous, proactive risk prediction. However, deploying advanced predictive models in real-world clinical settings is severely constrained by heterogeneous data silos, computational latency, and the opaque black-box nature of complex algorithms. To overcome these challenges, this paper presents PERFORM (Patient Edge Resilient-Fast Optimized-Reliable Monitoring), a novel, multi-layered architectural framework that synergizes Artificial Intelligence (AI) with scalable hybrid Edge-Cloud computing infrastructure. The proposed framework operates across five interconnected layers: harmonizing disparate data streams (EHRs, imaging, IoMT telemetry, and multi-omics); dynamically orchestrating computational workloads between edge nodes and cloud environments to minimize processing latency; executing adaptive model selection; embedding Explainable AI (XAI) for transparent decision support; and maintaining continuous governed evolution via drift tracking and clinical feedback. By synthesizing data integration, low-latency execution, and audit-ready interpretability into an end-to-end system, this study provides a unified blueprint for deployable, safe, and clinically trustworthy risk prediction.

Interpretable High-Dimensional Advertisement Classification with Regularized Logistic Regression: A Numerically Stable Framework

High dimensional advertisement classification is hard due to the distribution of predictive information across disparate geometric and sparse lexical features, imbalanced class distributions, and the need for systems that explain their decisions in simple and reproducible ways. This work proposes an interpretable and numerically stable binary classification framework based on UCI Internet Advertisements dataset that has 3,279 observations and 1,558 predictive features. The model is a logistic regression trained with a self-written L2 regularization that fits within a leakage safe pipeline that contains stratified partitioning, training-only median imputation, and training-derived feature standardization. To avoid computational instability, numerical safeguards are used, such as sigmoid clipping and stabilized binary cross-entropy computation. The model achieves 97.56% test accuracy, 95.24% precision, 86.96% recall, 99.29% specificity, and a 90.91% F1-score. Training and validation losses are close and go to each other steadily, learning-curve analysis exhibits better generalization as sample size grows. A stable high-performance region is also identified through learning-rate sensitivity analysis. The framework is intrinsically interpretable, since the predictions are directly related to the coefficients that can be inspected. Overall, the results illustrate that a well-regularized linear model can provide good predictive performance, while maintaining transparency, numerical stability, reproducibility, and computational simplicity.

A Multi-Dataset Deep Learning and Sequential Pattern Mining Framework for Interpretable Disease Progression Tracking in Brain and Liver MRI

This study makes a significant contribution by proposing a novel hybrid framework that integrates deep learning (CNN/ResNet-18) with Sequential Pattern Mining (PrefixSpan) to bridge the gap between predictive accuracy and clinical interpretability in medical image analysis. The framework introduces a robust discretization technique that transforms continuous, high-dimensional deep imaging features into clinically meaningful symbolic states (Low, Medium, High), enabling the application of pattern mining without losing critical progression information. It further demonstrates a dual application of pattern mining by capturing temporal disease evolution in longitudinal data (LUMIERE) while also uncovering recurring spatial phenotypes in cross-sectional data (BraTS2021 and CirrMRI600plus). The framework’s generalizability is validated through comprehensive testing across three public MRI datasets covering both brain and liver diseases, achieving strong accuracies of 91.2%, 87.5%, and 85.1%, respectively. Ultimately, this research delivers clinically interpretable insights that allow healthcare professionals to understand and trust model outputs without specialized machine learning expertise, offering a practical, scalable, and explainable AI-based clinical decision support solution, particularly suited for resource-limited healthcare environments.

Secure Medical Image Communication using ECC-based Hybrid ROI-Aware Chaotic Cryptograpy

The principal contributions are as follows:
1. A multimodal database is constructed with the combination of CT, X-ray, ultrasound, and MRI images to examine encryption consistency across different images.
2. The prepared dataset is utilized to evaluate the statistical measures in encryption-decryption by the five widely used methods.
3. The communication perspective is also assessed in terms of their resistance to brute-force, differential, and known-plaintext attacks.
4. A weighted decision-oriented ranking approach is proposed to identify the most suitable method according to security, efficiency, communication suitability, and image-recovery performance.
5. A hybrid method is also proposed availing the best performances of different frameworks to check whether the method can show optimal performances in aforementioned criterion. The proposed method is a ROI-Aware ECC-Assisted Chaotic AES hybrid encryption framework. The method uses ROI-based selective encryption with ECC-based lightweight key establishment along with the logistic chaotic map (dynamic chaotic S-box) expansion technique.

Enhanced CBAM-LMSPNet: A Lightweight CNN with Learnable Multi-Scale Pooling for Apple Leaf Disease Classification

We propose a lightweight CBAM-enhanced CNN integrated with a novel Learnable Multi-Scale Pooling (LMSP) strategy for apple leaf disease classification. Unlike conventional transfer-learning approaches, the proposed model is trained from scratch and dynamically fuses fine-, medium-, and coarse-scale spatial features to capture complex lesion patterns. Evaluated using stratified 5-fold cross-validation, the proposed network outperforms six benchmark pretrained models, achieving 99.87% test accuracy and a 99.88% macro-F1 score. Furthermore, Grad-CAM++ visualizations demonstrate that the model consistently focuses on disease-relevant lesion regions rather than background areas, enhancing the interpretability of its predictions

Transformer Guardian 360: An IoT-Based Multi Parameter Condition Monitoring System for Distribution Transformers

This research presents an IoT-based multi-parameter transformer condition monitoring system that integrates voltage, current, oil temperature, oil level, humidity, and vibration sensing using an ESP32 microcontroller. A Transformer Health Index (HI) is developed by combining multiple operating parameters to provide an overall condition assessment and early fault warning. The system enables real-time monitoring through Blynk IoT, helping improve transformer reliability and support predictive maintenance.

A Systematic Review and Cross-Domain Taxonomy of Emerging Cybersecurity Threats and Zero-Trust-Based Defence Architectures

1. We develop a structured taxonomy of emerging cybersecurity attack vectors covering network and infrastructure attacks, IoT and next-generation communication threats, application and physical-layer attacks, and AI-enabled attack vectors.
2. We analyze existing studies using common criteria, including attack domain, methodology, evaluation environment, security mechanism, major findings, and reported limitations, enabling a consistent comparison across heterogeneous research areas.
3. We investigate how Zero Trust principles, including explicit authentication, least-privilege authorization, continuous monitoring, and dynamic policy enforcement, can complement modern attack detection and mitigation mechanisms.
4. We identify major research gaps and propose a conceptual adaptive cross-domain Zero Trust defence framework that connects attack detection, risk assessment, policy enforcement, decentralized trust, privacy-aware learning, and continuous security feedback.

IOT based Over and Under Voltage Protection for Automated Silo Storage System Using LOGO PLC

Automated silo storage systems need predictible protection in the event of abnormal supply-voltage conditions, as well as continuous supervision of environmental parameters. Typically, conventional implementations require another protection and monitoring device, which adds complexity and expense to systems. This paper introduces a common PLC based architecture which includes over and under voltage detection, multi-stage protection of motors, temperature and humidity measurement and alarm handling in a single Siemens LOGO! controller. Transportation of measurements to the protection layer is done locally so that the protective actions are not dependent on the communication layer. The Modbus-based interface, Node-RED and Blynk platform is used for remote supervision, visualisation and event monitoring. The control logic developed worked well in identifying the high and low voltage tested conditions and then starting the programmed protection sequence and the appropriate environmental alarms were generated. The communication pathway also passed on representative operating information to the remote dashboard. The results show that a low-cost and integrated protection and monitoring system for automated silo storage systems is viable; however, hardware validation is needed for full field-level evaluation.

A Lightweight Hybrid CNN–DBN Framework for Lung Cancer Classification Using CT Images

The current research work proposes an automated lightweight hybrid CNN-DBN framework for the classification of lung cancer from the CT images. The framework consists of CLAHE for enhancing images, CNN for spatial feature extraction, DBN for hierarchical representational learning and Logistic Regression for classification that ensures efficient and accurate diagnosis. A low-confidence sample refinement strategy is presented which filters out the samples that the model does not predict accurately, and therefore improves the accuracy of the prediction without re-training the model. The proposed framework yield 98.80% classification accuracy, which shows the efficacy of using both supervised and unsupervised feature learning in medical image analysis. The use of lightweight AI-based CAD systems for accurate and efficient lung cancer screening were emphasized.

LsEyeNet: A Hybrid Deep Feature Fusion Framework for Multi-Class Ophthalmic Disease Recognition from Retinal Fundus Images

LsEyeNet, a hybrid deep feature fusion model is proposed for multi-class ophthalmic disease recognition from fundus images. The study evaluates supervised and self-supervised deep learning models in a comprehensive fashion in a common framework. The proposed framework using combined complementary feature representations of ConvNeXtBase and EfficientNetB3 attains improved classification results, such as classification accuracy of 97.80% and F1-score of 0.96. It also investigates the potential of self-supervised learning using SimCLR and shows the advantage of feature fusion in building a reliable, efficient and accurate AI supported ophthalmic screening framework.

A Decentralized and Privacy-Adaptive Ride-Sharing Framework Using Blockchain and Differential Privacy

This work proposes a decentralized and privacy-preserving ride-sharing system that allows for secure multi-provider mobility data aggregation with the support of a trust management system based on blockchain, a differential privacy framework, an OAuth 2.0 authorization system and a hybrid storage system. The proposed approach addresses existing systems’ shortcomings by offering privacy-preserving ride discovery, easy consent management, and secure sharing of location information while maintaining the service’s utility. Prototype evaluation shows the feasibility of the framework by demonstrating location perturbation of ~80 meters at ε = 0.025 and acceptable performance for moderate workloads.

Deep Learning-Based Ordinal Severity Grading of Mustard Flea Beetle Infestation from Plant Images

Damage to mustard caused by feeding by flea beetles is an important constraint to mustard production, especially during early stand establishment when feeding may cause a reduction in plant vigor and development. However, in contrast to the detection of pests and the determination of overall damage, the gradation of severity of flea beetle infestation has not been thoroughly studied. This research proposes an automated grading system for the severity of a mustard flea beetle infestation using a deep learning approach and four ordered classes: healthy, mild, moderate and severe. The framework includes source aware data partitioning, redundancy control, imbalance-aware learning, ordinal severity modelling, and comparison of five CNN architectures. The accuracy, balanced accuracy, macro-F1, Quadratic Weighted Kappa (QWK) and ordinal mean absolute error (MAE) are used for performance evaluation. The highest individual accuracy is obtained by ConvNeXt-Tiny with 94.93%, the best balanced accuracy is 0.7341, the best macro-F1 is 0.6866, the best QWK is 0.9295 and the best ordinal MAE obtained is 0.0510. The performance is further enhanced to 95.26% accuracy, 0.6930 macro-F1 and 0.9351 QWK by using prediction-level ensemble averaging. The mean of the three-fold cross validation is 0.6296 ± 0.0028 (source grouped). The activation energy using Grad-CAM analysis is focused in the estimated leaf areas only 72.1%, whereas the mean leaf area index is 38.8%. The results indicate that deep learning has the potential to be used for automated multi-level flea beetle infestation grading and can be used as a starting point for further validation in different field conditions.

An Explainable Transfer Learning Framework Based on EfficientNetB3 for Multi-Class Brain Tumor Classification from MRI Images

Accurate and interpretable brain tumor classification from magnetic
resonance imaging (MRI) remains challenging due to substantial
variations in tumor appearance and the limited interpretability of
deep learning models. This paper presents an explainable transfer
learning framework based on EfficientNetB3 for four-class brain tumor
classification, covering Glioma, Meningioma, Pituitary Tumor, and No
Tumor. The framework combines ImageNet-based transfer learning,
training-time data augmentation, selective backbone fine-tuning with
frozen Batch Normalization layers, and a customized classification
head with regularization. The model is evaluated on 7,153 MRI images
using a stratified 70%/15%/15% train-validation-test split. On the
held-out test set, the proposed framework achieves 99.16% accuracy,
with weighted precision, recall, and F1-score of 99.17%, 99.16%, and
99.16%, respectively. Only nine misclassifications are observed among
1,073 test images. Furthermore, Grad-CAM visualizations provide
qualitative insights into the image regions contributing to the model’s
predictions. The results demonstrate that the proposed framework
combines high multi-class classification performance with
model-level interpretability, providing a promising foundation for
explainable AI-assisted brain tumor analysis.

Towards Edge Artificial Intelligence for Predicting Antimicrobial Resistance in Mycobacterium tuberculosis

The main research contribution of this paper is the creation of a lightweight machine learning method. This can run on edge devices to predict resistance in Mycobacterium tuberculosis using whole‑genome sequences. Existing methods usually need features that use a lot of computing power or need prior knowledge of resistance variants. The new model instead uses a 256‑dimensional 4‑mer frequency vector fed into a Random Forest classifier. This change cuts the feature space by orders of magnitude while retaining strong predictive ability. The result is a tool that works well in hospitals in low‑ and middle‑income countries where computers are slow and internet is weak. An average ROC‑AUC of 0.820 is achieved across 20 antibiotics.

Unsupervised EEG-Based Subgroup Discovery in Alzheimer’s Disease Using Spectral and Temporal Features

This study contributes an unsupervised, subject-level EEG framework for identifying reproducible electrophysiological subgroups within Alzheimer’s Disease. Unlike conventional AD classification approaches, it integrates multi-domain EEG features, PCA-based dimensionality reduction, K-Means clustering, bootstrap consensus stability analysis, and split-half validation to ensure that the discovered subgroups are both meaningful and reproducible. This provides a systematic approach for characterizing intra-AD heterogeneity and may support future research on disease progression and personalized clinical strategies.

Quantum-Resilient Self-Healing Cybersecurity Architecture for IoT-Enabled Smart Grids Using Federated Intelligence and Digital Twins

In this paper, the authors present a novel quantum-resilient self-healing cybersecurity system for smart grids with IoT technology, combining post-quantum security, federated intelligence, and digital twin technologies. The proposed framework facilitates distributed and private threat detection and ongoing monitoring of the grid’s behavior to detect any anomalous activities. Self-healing dynamically isolates adversely affected components and re-establishes secure operations with minimal service disruption. The architecture offers a scalable and adaptive security solution that helps establish the resilience of the smart grid to traditional and new quantum-related cyber attacks.

UGPDA and BDAI: Robust Medical Semi-Supervised Learning Under Varied Labeled Ratios and Class-Imbalance Profiles

Semi-supervised learning (SSL) holds huge potential for medical image classification by using vast pools of unlabeled data to reduce the burden of expert annotation. Standard SSL frameworks like FixMatch experience performance degradation when deployed under real-world clinical environments because of data scarcity and class imbalance. In these environments, unfiltered pseudo-labels create confirmation bias, resulting in the model learning the majority classes. To overcome these limitations, we present a unified framework integrating two novel modules: Uncertainty Guided Pseudo-Label Acceptance (UG- PDA) and Belief Distribution Augmentation Invariance (BDAI). UGPDA acts as a targeted entropy gate that checks the validation status of weak-view predictions using Shannon entropy to filter out high-uncertainty samples. On the other hand, BDAI preserves the full soft probabilistic target distributions (belief distributions) of accepted samples. It utilizes an asymmetric Kullback-Leibler divergence constraint to align strongly augmented views without forcing minority class nuances into rigid one-hot targets. By testing on chest X-ray dataset under varied labeled ratios (5%, 10%, 15%, and 20%) shows that our approach significantly outperforms state-of-the-art baselines. At an extreme 5% labeling within a native 3 : 1 imbalanced distribution, our framework achieves an F1-Score of 0.7521, an AUC-ROC of 0.9523, and a clinical Recall sensitivity of 0.9974. This establishes robust diag- nostic stability where traditional techniques undergo catastrophic class collapse.

A Comprehensive Benchmark of Recurrent, Convolutional, and Attention-Based Models for Radar Emitter Classification Under Photon-Starved Conditions

The identification of radar scanning patterns from
pulse amplitude sequences is a critical function in electronic in
telligence and electronic warfare. While deep learning has proven
highly effective for this task under additive white Gaussian noise,
its performance in photon-starved sensing environments remains
largely unexplored. This work presents a systematic benchmark
of eight deep learning architectures—LSTM, BiLSTM, GRU,
xLSTM, CNN, TCN, InceptionTime, and Transformer—under
identical quantum-noise-limited conditions. A physically realistic
Poisson noise model, incorporating pulse amplitude distortions,
missing pulse events, spurious outliers, and frequency-dependent
gain variations, is used to generate nine distinct noise scenarios.
Each architecture is trained and evaluated over ten indepen
dent runs with consistent preprocessing and training protocols.
Experimental results show that all models achieve 86–89%
accuracy under classical conditions but suffer a significant
degradation to 47–60% accuracy under quantum noise. Among
the evaluated architectures, InceptionTime achieves the highest
mean quantum accuracy (56.48%), while Transformer exhibits
the lowest (45.82%). Statistical analysis reveals no significant
performance differences across architectures, indicating that the
observed degradation is architecture-independent and fundamen
tally driven by photon-counting statistics. These findings establish
a clear performance bound for amplitude-only classification
under quantum-limited sensing and underscore the urgent need
for phase-aware or coherent detection strategies in future radar
emitter classification systems.

Provable Moving-Target Defense for Cyber-Physical Networks: From the Security–Performance Frontier to No-Regret Defense

The paper makes CPS moving-target defense quantitative and provable. It formalizes randomization as a game with an explicit physical-cost term and proves a security–performance frontier J⋆(p) attained by interpretable capped water-filling — a full convex curve rather than the single equilibrium point of prior grid-MTD work. Generalizing the idealized exact-match model to a graded confusion kernel yields the paper’s sharpest result: feasibility only above val(K), a matrix-game security floor that no physical budget can buy past (≈ 0.21 on the grid). A no-regret mirror-descent defender then reaches that frontier against an adaptive attacker with unknown payoffs, the novelty being the reduction rather than the standard √T rate. Because cost and security both derive from the same IEEE 14-bus physics, the price of security is stated in megawatts.

Forecasting Independent Evolution Possibilities of Code Clones

This paper is the first to forecast whether a code clone fragment’s next change will propagate to its sibling copies (dependent evolution) or happen alone (independent evolution), predicted before the change is made using only prior revision history. Existing approaches predict consistency only at copy-and-paste time, cover only copy-paste clones, and rely on shuffled cross-validation that leaks future data into training. We instead consider every clone fragment in a system, predict a recurring modification-level event across the clone’s entire lifetime, and enforce a strict walk-forward protocol that retrains after every revision with no look-ahead. Each fragment is described by 52 features capturing its edit history, pairwise coupling with siblings, and how that coupling decays over time. Across 33,003 modification events in four open-source C and Java systems, our best classifier reaches MCC 0.705 and balanced accuracy 0.850. All datasets and tools are publicly released.

Reputation over Geometry: A Validation-Guided Robust Federated Defense for Imbalanced Drone Intrusion Detection

The paper’s main contribution is a reframing of the aggregation criterion: it replaces the geometric question (“is this update an outlier?”) with a behavioral one (“does this update improve rare-class recall on a balanced probe?”). This dissolves the imbalance–robustness tension, since an honest rare-class client and a poisoner are indistinguishable by position but separable by behavior. RAVEN operationalizes this as probe scoring → tempered-softmax reputation → blended aggregation, paired with a federated-native Balanced-Softmax objective. Empirically it holds minority recall at 0.770 under strong ALIE collusion where Krum collapses to zero, with the margin widening as the attack strengthens.

The second, arguably more durable contribution is negative: the probe-optimal white-box adversary drives every evaluated defense to zero minority recall, and the natural geometric fix is shown to be structurally unavailable, since honest non-IID clients reach 0.99 cosine similarity against colluders’ 1.00. This converts what could have been a method-specific weakness into a boundary result on the entire aggregation-defense class, and points future work toward signals outside the update geometry — secret rotating probes, system-level attestation.

BAGC-FL: Benign-Anchor Gradient Correction for Imbalance-Aware Federated Intrusion Detection in IoMT Networks

The significant research contribution of this study is the BAGC-FL framework, a server-side Benign-Anchor Gradient Correction mechanism that mitigates the adverse effects of severe class imbalance and label-skewed non-IID data in federated IoMT intrusion detection. Unlike conventional FedAvg and class-weighted training, BAGC-FL selectively removes the gradient component that increases benign-anchor loss while preserving the original model architecture, local BCE objective, and communication protocol. Its effectiveness is demonstrated through a controlled E0–E2 ablation, IID/non-IID evaluation, paired statistical testing, and post-hoc SHAP/LIME analysis.

Lightweight and Explainable AI-Driven Intrusion Detection for IoT Networks: A Resource-Efficient Machine Learning Framework with SHAP-Based Interpretation

This paper presents a lightweight intrusion detection framework for IoT networks that combines machine learning-based detection with explainable AI analysis. The study evaluates several low-complexity classifiers, including Logistic Regression, Decision Tree, Random Forest, Gaussian Naive Bayes, and LightGBM, to identify a suitable model for resource-limited IoT environments. The proposed approach incorporates feature optimization, leakage-aware preprocessing, class balancing, and memory-efficient data handling to reduce computational requirements while maintaining detection capability. In addition, SHAP-based analysis is applied to explain the factors influencing intrusion decisions, improving the transparency of the detection process. The framework is evaluated using the CICIoT2023 DDoS-ICMP Fragmentation dataset, demonstrating how lightweight machine learning models can be used for accurate and interpretable IoT security monitoring.

AI-Enhanced Digital Twin with Hardware-in-the-Loop Deployment for Real-Time Lithium-Ion Battery State-of-Health Estimation

This work presents an AI-enhanced battery Digital Twin combining an aging-adaptive equivalent circuit model, an SOH-coupled Unscented Kalman Filter, and a systematic seven-model comparison for SOH prediction, validated not only through leave-one-battery-out cross-validation on the NASA dataset but also through live deployment on a physical hardware-in-the-loop sensor rig with closed-loop fan actuation. Unlike most reported battery Digital Twins, which are validated purely in simulation, this system demonstrates that a physics-informed, ML-benchmarked architecture can be deployed and cross-checked on real sensor hardware in real time.

ExplainYOLOv11: A Robust and Interpretable YOLOv11 Framework for Real-Time Traffic Sign Detection in Autonomous Vehicles

A new YOLOv11 traffic sign detection framework for autonomous driving and intelligent transportation systems (ITS) with an explanation is introduced. The proposed approach not only enhances the accuracy of the detection system but also incorporates the possibility of evaluating the robustness of the system and performing a computational efficiency analysis, along with the ability to explain why the system made the decision it did. The framework undergoes systematic testing and benchmarking against the existing YOLOv5 – YOLOv11 with the same experimental conditions and under challenging real-world conditions such as low-light, noise and occlusion. To increase transparency in safety-critical applications, the proposed model will include Grad-CAM and attention-based explanations. The YOLOv11 model that has been developed achieves excellent detection results with an mAP@0.5 of 0.989 and has been adeptly deployed through a real-time web-based deployment prototype, showcasing the model’s practicality.

A Hybrid Modified DES–Keccak Cryptographic Framework with Dynamic S-Box Design for Secure Data Confidentiality and Integrity Verification

This research presents a hybrid Modified DES–Keccak cryptographic framework that combines enhanced symmetric encryption with hash-based integrity verification in a unified architecture. The proposed method improves the conventional DES structure by introducing a 128-bit block and key mechanism, a round-dependent dynamic S-box generation approach using Keccak-256 hashing, and enhanced Feistel-based encryption operations. Unlike traditional DES variants that rely on fixed substitution tables and separate integrity mechanisms, the proposed framework integrates dynamic substitution and ciphertext authentication within a single design. The contribution of this work lies in strengthening confidentiality through improved cipher randomness and confusion while providing an additional layer of tamper detection using Keccak-256-based hash verification. The framework is experimentally evaluated in terms of computational performance, security characteristics, and integrity detection capability, demonstrating its potential as a secure alternative for modern data protection applications.

PULSE-ELF: Temporal Channel Learning and Low-Overhead Feedback for Integrated Sensing and Communication in Commodity Wi-Fi

In this paper, we propose PULSE-ELF, a lightweight Integrated Sensing and Communication (ISAC) system whereby commodity Wi-Fi hardware enhances both the sensing capability and communication efficiency. To tackle the challenges in them, the work proposes to use temporal CFR learning to capture the dynamics of channel variations to perform robust sensing, and introduce a select input low-overhead channel feedback method to reduce the channel feedback cost by sending informative parts of channels only. To achieve this, the proposed framework combines temporal feature extraction, few-shot adaptation, and an efficient channel reconstruction, all of which are required for the practically deployable ISAC framework, relying on the ubiquitous Wi-Fi infrastructure. Through the experimental results, we show that the proposed approach can achieve more than 99% sensing accuracy and up to 96% reduction in feedback overhead, which is effective for scalable and resource-efficient Wi-Fi sensing and communication.

Analysis of Various Machine Learning Model Performances for Epilepsy Prediction

The paper presents a comprehensive comparative framework demonstrating the critical impact of feature engineering (specifically chi-square and p-value statistical testing) on classical machine learning classifiers for clinical epilepsy type prediction. By bridging raw clinical patient attributes with proper data normalization, the study successfully elevates Support Vector Machine (SVM) accuracy from a baseline of 47% up to 80%, matching the overall top-performing Random Forest and Logistic Regression models at 81% accuracy

Paddy Seeds Classification using Machine Learning Models for Public Health

The paper establishes an automated computer vision classification baseline for public health and quality control across ten distinct rice cultivars utilizing transfer learning. By training and testing deep state-of-the-art models (GoogleNet, ResNet-50, and VGG-16) on automated photographic seed datasets provided directly by the Bangladesh Rice Research Institute (BRRI), the framework achieves a stable classification accuracy up to 78% without relying on time-consuming manual feature extraction.

Machine Learning Applications in the Classification of Dates

his work presents an experimental deployment of deep learning architectures (Custom CNN, GoogleNet, and ResNet-50) to mitigate multi-class classification challenges caused by data imbalances in highly similar qualitative food items. The study maps real-world nutritional variances across seven types of commercial dates, proving that GoogleNet delivers the highest robust validation accuracy of 98.57%, making it an effective baseline tool for precision agriculture and food tracking.

Elevating Skin Cancer Diagnosis: A Study of Various Machine Learning Approaches

The paper introduces a highly competitive multi-branch hybrid deep learning network integrating pre-trained EfficientNetB3, DenseNet121, and a customized Multi-Head Attention module to capture complex spatial linkages in dermoscopic skin lesions. By merging multi-scale textural features and global contextual correlation maps, the system successfully circumvents subjective diagnostic biases, capturing complex visual details to deliver a balanced 92.6% overall accuracy on the HAM10000 dataset for binary screening.

A Comparative Deep Learning Approach: CNN And Hybrid Algorithms For Lung Cancer Classification In Histopathology Images

The study designs a novel hybrid deep learning model (CNN-VGG16-ViT) that simultaneously resolves local tissue morphology extraction challenges and global multi-scale visual dependencies in histopathology samples. Evaluated against a highly balanced multi-class dataset of 15,000 images, this integrated local-global feature representation outperforms standard custom CNN baselines by boosting classification accuracy from 96.03% to 98.47% while yielding a robust micro-average AUC of 0.9913.

A Closed-Loop Supply Chain Model for End-of-Life Electronics under Carbon Tax Policy

Significant research contributions of this manuscript are as follows:
•Developed an integrated closed-loop supply chain (CLSC) model for end-of-life electronic products, combining forward and reverse logistics within a unified framework.
•Introduced distributor-level reintegration of repaired and recycled products, enabling recovered products to re-enter the forward supply chain earlier than in conventional models.
•Formulated a multi-period, multi-echelon Linear Programming (LP) model incorporating suppliers, plants, distributors, retailers, return-processing, repair, recycling, and secondary-market facilities.
•Integrated a comprehensive carbon-tax mechanism by applying CO₂-related costs to transportation across all forward and reverse logistics links.
•Combined economic and environmental considerations by minimizing total supply-chain cost while accounting for transportation emissions and revenue from scrap recovery.
•Demonstrated model applicability through a city-based case study, involving multiple suppliers, plants, distributors, retailers, customer zones, and reverse-logistics facilities.
•Established model robustness through sensitivity analysis of customer demand, carbon-tax rate, and emission factor, showing stable and predictable cost responses.
•Provided quantitative evidence on carbon-tax impacts, showing only a small increase in total supply-chain cost under the specified case-study conditions.

Interpretable Short-Term Electricity Demand Forecasting for the PGCB Grid Using Feature Fusion

The main contribution of this study is a feature-engineered short-term electricity demand forecasting framework that integrates historical demand, temporal/calendar, and weather information, combines rigorous leakage-aware chronological evaluation with machine learning optimization, and provides interpretable predictions using SHAP analysis for the PGCB grid.

30-Day-Ahead Load Forecasting for the Rajshahi Zone of the Bangladesh Power System: A Linear-Base Boosted BiLSTM Approach

This paper develops a 30-day-ahead daily peak load forecasting framework for the Rajshahi zone of the Bangladesh Power System, a horizon that governs maintenance scheduling, fuel procurement and outage planning but has received far less attention than day-ahead forecasting, and almost none for supply-constrained tropical grids.

Three contributions are made. First, a regularised linear stage is fitted before gradient boosting, so that the 4.2% per year growth trend is carried by a learner that can extrapolate it; this removes an extrapolation bias worth 0.69 percentage points against plain XGBoost on identical features. Second, supply-side outages are screened by a local V-drop criterion so that load shedding is not learned as demand, and no reported score is computed against an imputed target. Third, the worth of the target-date weather forecast is measured rather than assumed, at 2.80 percentage points, which is larger than the spread across the twenty-five models compared and identifies forecast-weather quality rather than model choice as the dominant lever at this horizon.

Evaluation is on a strictly chronological 2025 hold-out year against twenty-four benchmarks, with Diebold-Mariano tests, four-fold rolling-origin validation and seed-averaged deep models.

EndoStack-XAI: A Hybrid and Explainable Machine Learning Framework for Endometriosis Prediction

This study proposes EndoStack-XAI, a leakage-safe two-level stacking ensemble for interpretable endometriosis prediction using demographic, reproductive, symptom-related, laboratory, and clinical features. The framework combines RBF-SVM, Random Forest, and Extra Trees as base learners with Logistic Regression as a meta-learner trained on leakage-controlled out-of-fold predictions. SHAP and LIME are further integrated to provide global and local model interpretability while maintaining strict separation of the held-out test set from model development.

Physics and Communication-Aware Remote Terminal Unit Cyberattacks Detection and Classification for Smart Energy Grids

Paper contributions
(i) A causal feature set combining robust temporal statistics, three-phase power consistency, spatial RTU disagreement, and communication evidence;
(ii) Explicit fault-versus-cyber rules whose triggers have engineering meaning;
(iii) Stateful persistence that prevents rolling-baseline adaptation from prematurely clearing sustained events; and
(iv) An attack-wise evaluation in which every waveform and timeline explicitly identifies normal operation, the normal electrical fault, and each cyberattack.

Explainable AI-Based Thyroid Diagnosis Classification Using SHAP-Guided Feature Reduction

In this study, we developed an explainable machine learning framework for benign–malignant thyroid diagnosis classification using structured tabular data. We evaluated seven classifiers through a consistent preprocessing and class-imbalance pipeline.
CatBoost achieved 82.93% test accuracy, 69.92% balanced accuracy, and 55.41% F1-score. Our three-fold cross-validation produced 82.69% mean accuracy and confirmed the close performance of the leading boosting models.We used SHAP to identify eight influential predictors, and the corresponding Top-8 representation preserved the principal classification metrics of the original 14-feature model. Our central contribution is therefore a compact and interpretable feature representation rather than a new learning algorithm.

AI-Enhanced Smart Grid Integration for Solar Energy: A Comprehensive Bibliometric Analysis

Solar energy is now a large part of the smart grid. Its variability makes forecasting and stability harder. Artificial intelligence (AI) and machine learning (ML) are widely used to manage these problems. This paper maps that literature. We analyzed 273 Scopus-indexed documents published between 2010 and 2024. VOSviewer and Biblioshiny were used for keyword co-occurrence, citation, bibliographic coupling, cocitation, co-authorship and thematic analysis. The results reveal an exponential growth in publications (28.64% annual growth rate), with China, India, and the United States as the leading contributors. Three clusters were found: smart grid and solar fundamentals, AI-based energy management, and deep learning for solar forecasting. Smart power grids and solar energy appear as basic themes. AI and energy management remain niche but well developed. Keyword trends show a shift from shallow neural networks to deep and reinforcement learning. The review outlines influential works, collaboration patterns and open gaps for future research in AI-enabled solar smart grid integration.

BreastSegX: A Pipeline for High-Fidelity Breast Ultrasound Lesion Segmentation

The main contributions of this work
are summarized as follows:
• We pre-trained EfficientNet-B4 [6] on 3,369 external
breast ultrasound images to improve feature representa-
tion before fine-tuning on the BUSI dataset.
• The model was trained using 5-fold cross-validation with
class balancing and data augmentation, then evaluated on
a held-out 10% test set (78 images).
• For post-processing, we applied morphological closing
and connected-component analysis to reduce noise and
smooth tumor boundaries.
• Finally, we proposed specific evaluation metrics for “Nor-
mal” scans to avoid division-by-zero errors and to report
all classes consistently.

EXPLORING POSSIBLE ADOPTION FACTORS OF mHealth APPLICATIONS AMONG WOMEN IN BANGLADESH

The study extends the base UTAUT model by incorporating seven additional independent variables — Technology Anxiety, Habit, Trust, Personal Innovativeness in IT, Privacy, Price Value, and Resistance to Change , that are not part of the original framework, tailoring it specifically to the mHealth context. It also modifies the moderating structure by removing Gender (since the sample is women-only) and introducing Education as a new moderator alongside Age, Experience, and Voluntariness of Use.Using PLS-based structural equation modeling on survey data from 491 women, the study empirically identifies which factors significantly drive adoption and which do not, offering a more nuanced picture than the base UTAUT model alone.

Evaluating Synthetic Oversampling Strategies for Imbalanced Thyroid Disease Classification with SHAP-Based Explainability: A Comparative Study Using Ensemble Classifiers.

Here are the main points in simple terms:

The Problem: The thyroid dataset had very few sick patients (only 3.7%), making it hard for normal AI models to spot the disease.

The Solution: The authors tested different ways to create smart, artificial data for rare sick cases and combined them with 5 popular machine learning models.

Best Result: Combining Random Forest with ADASYN or SMOTE worked best—it caught 98% of sick patients (up from 90%) without making many false alarms.

Key Finding: While creating artificial data helps the model catch more sick cases, statistical tests showed it doesn’t fundamentally make the model smarter at ranking risk.

Medical Check (SHAP): Using an explainability tool (SHAP), they proved the AI is making decisions based on real medical blood markers (like TSH, T3, and T4), not random noise from the fake data.

An Empirical Study on the Impact of Authorship on the Bug-proneness of Code Clones

A common technique in software development is code cloning, which allows for the reuse of pre-existing code. However, frequently it comes at the cost of more difficulties and unnoticed flaws (bugs). The effect of code clones on software quality has been thoroughly examined in the existing studies, but the relationship between clone authorship and bug proneness has received less attention. Code fragments cloned by developers other than the original author are intuitively more likely to contain bugs than those cloned by the original author. However, there is no empirical evaluation of this intuition. In order to bridge this research gap, we have conducted an empirical study to analyze the impact of clone authorship on the bug-proneness of code clones. For the purpose of our study, we have mined a particular clone evolutionary pattern from thousands of revisions of our subject systems. The pattern helps us discover those clone fragments that were created by one programmer through copy/pasting another programmer’s code. We have analyzed the bug-proneness of such clones. According to our analysis on five diverse subject systems, if a programmer copy/pastes and reuses another programmer’s code, the copy/pasted code fragment is more likely to contain bugs compared to a code fragment which was created through copy/pasting by the original programmer. Our research provides actionable suggestions regarding the cloning activities of programmers towards minimizing bugs in code clones.

Comparative Analysis and Shunt-Reactor Mitigation of the Ferranti Effect in 132-kV Overhead and Underground Transmission Lines

This work develops a unified MATLAB/Simulink framework to evaluate and directly compare the Ferranti effect in 132-kV overhead lines (OHL) and underground cables (UGC) under identical operating conditions. The study quantifies that underground cables exhibit nearly eight times higher voltage rise and require approximately twenty times greater shunt-reactor compensation than equivalent overhead lines. These findings offer planning and protection engineers a quantitative, technology-specific framework for allocating reactive-power compensation in hybrid OHL-UGC sub-transmission corridors.

OmniCortex: A Hybrid Framework for Explainable Multimodal Brain Tumor Classification

The significant research contribution of this study is the development of OmniCortex, a hybrid ResNet50–CNN framework for automated brain tumor classification using multimodal CT and MRI images. The proposed framework introduces a spatial tensor reconstruction layer to preserve spatial information and address cross-modal feature alignment limitations, while a custom CNN refinement head further enhances the high-level features extracted by ResNet50. The model was evaluated on 9,618 CT and MRI images and achieved 98.23% testing accuracy, outperforming the evaluated baseline architectures without requiring artificial data balancing or manual radiomic feature engineering. Furthermore, the integration of Grad-CAM provides visual interpretability by demonstrating that predictions are focused on clinically relevant tumor regions rather than background artifacts, thereby improving the transparency and potential clinical utility of the proposed system.

Multi-Class MRI Brain Tumor Classification Using a Hybrid EfficientNet-Attention Architecture

The significant contribution of this research is the development of EMA-BrainNet, a lightweight hybrid deep learning framework that integrates an EfficientNet-B0 convolutional backbone with a Multi-Head Attention (MHA) mechanism for accurate multi-class brain tumor classification. Unlike conventional CNN-based approaches that primarily focus on local features, the proposed framework simultaneously captures detailed anatomical patterns and long-range spatial dependencies, enabling more effective discrimination among glioma, meningioma, pituitary tumor, and healthy brain MRI images. The study further introduces a systematic preprocessing pipeline involving CLAHE, standardized image resizing, Z-score normalization, and class-preserving data augmentation to improve feature quality and model generalization. Experimental evaluation on a balanced dataset of 8,000 MRI images demonstrates that EMA-BrainNet achieves 99.97% training accuracy, 98.88% validation accuracy, and 99.38% test accuracy, outperforming the evaluated baseline models and recent competing approaches. In addition, the integration of Grad-CAM provides visual explanations of the model’s decisions by highlighting clinically relevant tumor regions, improving the interpretability of the automated diagnostic system. Overall, the research contributes an efficient, accurate, and interpretable framework with strong potential for computer-aided brain tumor diagnosis.

Traffic Demand Regulation by an Intelligent Management System: A Case Study in Dhaka

Traffic congestion in urban cities is acute, resulting in extensive delays, elevated fuel usage, and pollution due to mismanaged signals and unregulated traffic systems. These inhibitions primarily stem from ungoverned signal timing and unpredictable traffic patterns. An Intelligent Traffic Control System (ITCS) is developed here as a pilot study by utilizing field-observed traffic data from Gulshan-2 Circle, Dhaka. Machine learning (ML) techniques were used to recreate a daylong traffic demand profile from data obtained in specific time periods, while preserving temporal variability and realistic demand features. Three ways of synchronized traffic control are offered. First, a Traffic Demand Regulation Model (TDRM) based on incentives is proposed to transfer part of the trips in the peak periods to the nearby off-peak periods. Second, staggered scheduling is proposed for academic institutions, offices, and commercial organizations by altering opening and closing times to lessen simultaneous travel demand. Third, an Adaptive Traffic Signal Control (ATSC) technique is proposed to reduce the queue length and delay by dynamically adjusting the signal timing according to the traffic conditions. Mathematical models for TDRM, staggered scheduling, and ATSC have been built and assessed inside a MATLAB simulation environment. Comparative simulations of baseline and intervention scenarios show that transferring only 1-10% of peak period demand to off-peak periods may significantly reduce congestion and coordinated control methods can cut fuel consumption and CO2 emissions by about 10-20%. Integrated demand management, scheduling, and adaptive control can improve urban traffic efficiency under realistic heterogeneous urban traffic conditions and support sustainable transportation in Dhaka, contributing to SDGs 11 and 13.

Benchmarking Deep Learning Models for IoMT Intrusion Detection with McNemar Testing and Explainable Artificial Intelligence

The main contribution of this study is not a new architecture but a clearer picture of what existing ones actually achieve on IoMT traffic. By training seven deep-learning models under a single disclosed protocol on CICIoMT2024 and comparing them with McNemar testing under Holm correction, we show that the differences between architectures, while statistically significant across all twelve pairwise comparisons, are often practically small once the size of the test partition is taken into account. More importantly, the results expose a gap that weighted reporting conceals: the best multiclass model reaches 98.01% weighted F1 but only 58.81% macro F1, and fails to detect the rare Recon Ping Sweep class at all. Since much of the surrounding literature reports weighted or overall accuracy alone, this gap suggests that current performance figures for IoMT intrusion detection may be considerably more optimistic than per-class behaviour warrants. LIME explanations of selected predictions, together with an explicit note that the feature-sequence reshaping used by six of the models carries no genuine temporal meaning, are included to keep the interpretation of these results appropriately bounded.

LeukemiaCellNet: A Patient-Aware Multi-Scale CNN–Transformer Framework with Attention-Based Aggregation for Leakage-Free AML Cytomorphology Classification

In this work, LeukemiaCellNet is a patient-aware multi-scale CNN–Transformer framework for leakage-free AML cytomorphology classification. The proposed method fuses a multi-scale CNN with a global Transformer to extract the local morphological features and global contextual features, respectively, to represent the complementary attributes of cells. To integrate cell-level features, an attention aggregation mechanism at the patient level is introduced, and consistency regularization is used to enhance the stability of prediction in different patient samples. Patient-disjoint evaluation addresses the limitation of information leakage and allows obtaining a more accurate evaluation of the use of AI for AML classification in clinical decision support.

Assessment of Near-Field and Far-Field Electromagnetic Exposure in Daily-Use Wireless Devices

This research provides a unified comparative analysis of near-field and far-field electromagnetic exposure from common wireless devices. It evaluates both SAR and power density, compares the calculated exposure levels with ICNIRP and IEEE safety standards, and highlights the influence of distance, frequency, and device power. The study also provides practical safe-use recommendations to improve public awareness of electromagnetic exposure.

A Dual-Band Rectenna with High RF-to-DC Conversion Efficiency for Ambient Wireless Power Harvesting

1) A compact 50 mm × 45 mm dual-band antenna is
presented, with a simulated reflection coefficient below
−23 dB at 2.44 GHz and 4.71 GHz on low-cost FR-4,
and radiation efficiencies of 89% and 78–80% at the two
bands, respectively.
2) A dual-band T-junction matching network is designed
to simultaneously transform the 50 Ω antenna-port
impedance into the complex, frequency- and power-
dependent impedance of a voltage-doubler rectifier.
3) A complete rectenna is designed and verified through
harmonic-balance circuit simulation, achieving peak RF-
to-DC conversion efficiencies of 90% at 2.44 GHz and
88% at 4.71 GHz, on par with or exceeding recently
reported rectenna designs.

Impedance-Based Stability Boundary for Two-Stage Solid-State-Transformer EV Fast Chargers

Two-stage SST fast charger is appealing for high power EV charger since it provides regulated front-end converter followed by another DAB isolated stage, however, the common dc link can cause instability even if both front-end converter and DAB are individually stable. This paper will delve into the interaction through impedance-based analysis and present the closed-form expressions for the DAB input and PFC output impedances by cascading their PI-controlled dynamics. The minor-loop gain obtained is utilized to determine a realistic stability limit of a 10~kW 800/400~V charger. The nominal design has been validated to have a 10.4~dB interaction margin and $|T|_{\max}=0.301$. The minimum capacitance of the dc-link depends on the converter: $C_{dc,pfc}>12.55~\mu\mathrm{F/kW}\times P_0$; for the nominal $C=414.5~\mu$F, the converter is operated at $3.32\times$ above the minimum capacitance. A sweep of the PFC equivalent controller reveals that the critical bandwidth of the PFC voltage-loop is around 6~Hz, which indicates that the cascaded bandwidth must not be reduced too much or the cascade will be unstable. The accuracy of the predicted boundary is verified by an independent 13-state nonlinear model , whose deviations in critical capacitance and boundary frequency are 0.01\,\% and 2.9\,\% respectively . Furthermore, the boundary is validated through nonlinear free-response and switching-level impedance verifications.As shown, the results indicate that after crossing the instability boundary, the value of $\min_{\omega}|1+T|$ may be misleading whereas the value of $|T|_{\max}$ and the Nyquist plot encirclement remain reliable indicators of interaction.

X-STREAM-IDS: An Explainable Streaming-Aware Deep Learning based Intrusion Detection System for IoT

The proposed X-STREAM-IDS makes the following key contributions:
1. Develops a dataset-aware multi-file attack taxonomy for the BoT-IoT dataset to support controlled multi-class intrusion labeling.
2. Introduces a robust feature encoding mechanism for handling heterogeneous and noisy IoT traffic.
3. Implements mini-batch online learning with noise-based regularization to simulate streaming traffic and reduce overfitting.
4. Integrates SHAP and LIME to provide interpretable and transparent intrusion detection decisions.

Brain Tumor MRI Classification Using Hybrid Model

• Implementation and evaluation of NeuroVision, a ResNet-
18 + Transformer-encoder hybrid, for four-class brain tu-
mor MRI classification, using genuine ImageNet transfer
learning for the CNN backbone.
• A controlled, within-notebook comparison against a fine-
tuned ResNet-18-only baseline trained with identical data
splits, optimizer, and hyperparameters.
• Identification and correction of a shared-dataset
transform-aliasing defect in the data pipeline, with a
description of the corrected implementation.
• A positioning of the results against recent (2026) open-
access literature on CNN, wavelet-enhanced CNN, Vision
Transformer, and 3D volumetric methods for brain tumor
MRI analysis.

An Explainable ML Framework for Five Customs Fraud Type Predictions Using XGBoost and SMOTE-Based Class Balancing

Customs fraud causes significant revenue losses and undermines fair international trade. Common fraud types include undervaluation, misclassification, false declaration of origin, and smuggling, yet most existing machine learning approaches only predict whether a declaration is fraudulent, without indicating the fraud type or explaining the prediction. This paper presents a multiclass and explainable machine learning framework for customs fraud detection. A dataset of 14,873 customs import declarations was extended from binary fraud labels to five classes: no fraud, false origin, undervaluation, misclassification, and smuggling. The framework applies missing-value handling, domainbased feature engineering, SMOTE-based class-imbalance treatment, and an ensemble feature-selection method combining ChiSquare, ANOVA, Recursive Feature Elimination, and SHAP. Several models were benchmarked under different preprocessing configurations and tuned via hyperparameter search. XGBoost achieved the best performance, with 96.0% accuracy, macroF1 of 0.840, and macro-AUCROC of 0.947. SHAP, LIME, and counterfactual explanations were integrated to make the predictions interpretable.

ASD-Predict: A Multi-class Explainable Autism Disorder Severity Prediction Using LightGBM and SMOTE

Timely assessment of autism disorder (ASD) severity
is significant for proper disease diagnosis and healthcare of a
person or child. While a sizable amount of machine learning
research has focused on autism screening, most of this work uses
a limited number of classifiers and seldom offers a methodical
comparison of learning (ML and DL) techniques for multiple
type of autism disorder severity assessment. In order to evaluate
autism severity across four levels, this study introduces ASDPredict, an adaptive learning framework that uses machine
and deep learning-based methods. To improve data quality and
model dependability, the framework employs comprehensive data
preparation techniques with encoding and feature scaling. Seven
predictive machine learning models (RF, XGBoost, LightGBM,
MLP, RNN, ANN and LSTM) are developed and assessed.
Stratified cross-validation, proper feature selection, and parameter tuning are conducted along with explainability results. The
experimental results highlighted that the suggested LGBM-based
framework offers an accuracy of 98.09 percent and outperforms
the traditional background work’s performance.

An AI-Enhanced Four-Class Stacking Ensemble Framework for Explainable Income Tax Fraud Detection Using Calibrated NBR IT-10B Structure

Revenue collection is severely hampered by income
tax fraud, especially in developing nations like Bangladesh. The
National Board of Revenue (NBR) has to deal with issues including severely skewed fraud statistics and a dearth of instruments
that can recognize various forms of fraud from the nation’s own
tax return system. The majority of current machine learning
research is mostly on binary fraud detection and offers scant
justification for their forecasts. Based on Bangladesh’s IT-10B
individual tax return structure and the 2025–2026 tax slabs, this
paper suggests a four-class income tax fraud classification scheme
that covers No Fraud, Underreporting, Inflated Deductions, and
False Credits. With the assistance of a practicing tax attorney,
a dataset of 2,498 records was created from anonymized tax
files and verified. Experts in the field were consulted in order to
further verify the fraud typology guidelines. A weighted Borda
aggregation of RFECV, LightGBM feature significance, Pearson
correlation, and Mutual Information was used to decrease the
initial 62 features produced by the proposed framework to
25. To solve class imbalance, SVMSMOTE was solely used on
training data. A pruned stacking ensemble was used to merge
five models (SVM, Random Forest, ANN, CatBoost, and Logistic
Regression), with Logistic Regression serving as the meta-learner.
The suggested model obtained a macro F1-score of 0.9502 and
an accuracy of 96.53% on the locked test set.It fared better
than the soft-voting ensemble (0.9272), the best individual model,
SVM (macro F1: 0.9343), and four re-implemented literature
baselines tested on the same dataset and test split. The model’s
general behavior and class-specific predictions were explained
using SHAP and LIME.

ASD a new era begins

research has focused on autism screening, most of this work uses
a limited number of classifiers and seldom offers a methodical
s performance.

URL-Only Phishing Detection Based on Structural and Character-Level Lexical Fusion Using TF-IDF and LightGBM

Phishing URLs are commonly used for credential
theft and online fraud. This study presents a URL-only phishing
detector using the raw URL and class label from the PhiUSIIL
dataset. The method combines LightGBM trained on 38 fixed
structural lexical features with Logistic Regression trained on
character-level TF-IDF URL representations. Component configurations, fusion weights, and classification thresholds are selected
through inner validation. Five registrable-domain/IP-disjoint
outer folds ensure that URLs from the same registrable domain or
IP-address group do not cross training–test boundaries. Across
234,604 cleaned URLs, the method achieved 99.81% accuracy,
99.96% precision, 99.59% recall, and a 99.78% F1-score from
pooled held-out predictions. All comparison models also received
inner-validation configuration and threshold selection. Holmadjusted exact paired McNemar tests showed significant paired
correctness advantages over six of seven comparison models; the
small numerical advantage over Extra Trees was not statistically
significant. The complete artifact averaged 7.60 MB, with batch
scoring of 0.191 ms per URL. In a contextual comparison with
selected PhiUSIIL studies, the proposed method achieved 0.02
percentage points higher accuracy than the highest reported
result, although evaluation protocols differ

Complexity-Aware Explainable Deep Learning for Skin Cancer Classification: A Dynamic Explanation Selector for Adaptive XAI on Dermoscopic Images

Skin cancer is among the most rapidly increasing malignancies worldwide, and early, accurate diagnosis is critical to improving patient survival. Conventional diagnosis relies on visual examination by dermatologists, a process that is time-consuming, subjective, and highly dependent on clinical expertise. Automated detection is further complicated by the inherent variability of dermoscopic images, including ambiguous lesion boundaries, heterogeneous colors and shapes, low contrast, and artifacts such as hair and shadows. This paper presents an automated, explainable deep learning framework for skin cancer classification from dermoscopic images. Three convolutional neural network architectures, including ResNet50, DenseNet121, and EfficientNetB3 are evaluated using transfer learning and data augmentation on the benchmark HAM10000 dataset, with EfficientNetB3 achieving the strongest performance among the three, reaching an overall test accuracy of 92% (weighted F1-score 0.92). To address the opacity of deep learning predictions, the framework integrates three Explainable AI (XAI) techniques: Score-CAM, Saliency Maps, and LIME and also introduces a Dynamic Explanation Selector (DES) that automatically identifies the most suitable explanation method for a given image based on a computed visual complexity score derived from edge density, texture contrast, color variation, and entropy. Feature separability across lesion classes is further examined using t-SNE visualization. Experimental results demonstrate that the proposed framework achieves high classification accuracy alongside interpretable, clinically meaningful visual explanations, positioning it as a promising decision-support tool for dermatological practice.

Deep Learning-Driven Detection of Temporal Lobe Seizures Using EEG Signals

Epileptic seizure detection using electroencephalogram (EEG) signals has received considerable attention because of the potential of automated systems to support continuous neurological monitoring and clinical decision-making. Among focal epilepsies, temporal lobe epilepsy is particularly important because the temporal lobe is a common site of focal seizure activity. However, many existing EEG-based approaches process all available channels simultaneously, potentially introducing redundant information and reducing the interpretability of the resulting models. This paper investigates a region-specific deep learning approach for automated seizure detection using EEG signals obtained from the temporal lobe. The study is derived from a broader brain-region-based seizure detection framework in which EEG channels are grouped according to anatomical regions and informative channels are selected using the Lion-Mapped Chaotic Whale Optimization (LMC-WWO) algorithm. The selected EEG signals are normalized, filtered, artifact-reduced, segmented, and transformed using Gramian Angular Summation Field (GASF) and Continuous Wavelet Transform (CWT) representations. Four deep learning architectures, namely Long Short-Term Memory (LSTM), One-Dimensional Convolutional Neural Network (CNN1D), Temporal Convolutional Network-1 (TCN1), and Temporal Convolutional Network-2 (TCN2), are evaluated for temporal-lobe seizure classification. A weighted ensemble of CNN1D and TCN2 is also investigated. Experimental results show that the LSTM model achieves the highest temporal-lobe classification accuracy of 97.62%, outperforming CNN1D (93.45%), TCN1 (91.67%), TCN2 (72.02%), and the weighted ensemble (78.57%). The findings indicate that temporal-lobe EEG signals contain sequential characteristics that can be effectively captured by recurrent temporal modeling. The study demonstrates the potential of region-specific EEG analysis for developing accurate and clinically interpretable seizure detection systems

A Lightweight Knowledge-Distilled CNN for Real-Time Waste Classification on Edge Devices

The exponential rise in solid waste generation, coupled with inefficient manual sorting practices, has intensified the need for automated waste classification systems that can operate reliably within resource-constrained Internet of Things (IoT) infrastructures. While deep convolutional neural networks (CNNs) have demonstrated strong performance in waste classification tasks, their substantial computational and memory requirements render them unsuitable for deployment on low-power edge devices commonly used in smart bin applications. To address this gap, we propose a lightweight CNN architecture trained via knowledge distillation to enable accurate, real-time waste classification on edge hardware. We first benchmarked thirteen pretrained CNN architectures, including VGG, ResNet, DenseNet, MobileNetV2, InceptionV3, Xception, EfficientNet, and NASNet variants, to identify the most effective teacher model. ResNet152 achieved the highest classification accuracy of 98.02\% and was subsequently employed to guide the training of a compact student network through a distillation-based learning framework. The proposed student model, trained under teacher supervision, achieved a classification accuracy of 93.35%, a substantial improvement over the 86.44% obtained when trained from scratch without distillation, while maintaining a significantly smaller parameter footprint suitable for embedded deployment. Experimental results confirm that the distilled model preserves much of the teacher’s discriminative capability while drastically reducing inference latency and memory overhead. The classifier categorizes waste into biodegradable, non-biodegradable, and background classes, making it directly applicable to intelligent smart bin systems for automated, real-time waste sorting in smart city and environmental monitoring applications, contributing toward sustainable and scalable waste management infrastructure.

A Dual-Branch Framework for Livestock Disease Screening Using Multi-Repository Visual Datasets and Symptom-Based Intelligence

• We design a seven-stage harmonization pipeline that consolidates four heterogeneous bovine disease repositories
into a single validated six-class dataset, addressing taxonomy standardization, annotation validation, duplicate
removal, class balancing, and cross-split data leakage
verification.
• We train a YOLOv8m-based multi-class bovine disease
detector on 11,804 harmonized images, achieving an
mAP@50 of 0.7136, and analyze its behavior across
confidence thresholds and disease classes.
• We develop a rule-based symptom assessment module,
built on a veterinary-derived disease–symptom pathology
matrix, that generates ranked differential diagnoses from
farmer-reported clinical observations without requiring
any image input, and we characterize its reliability and
failure modes under simulated ambiguous reporting

Predicting Solar PV Power Under Real-World Conditions: A Comparative Evaluation of Interpretable Machine Learning Models

Solar power generation prediction plays a vital role in improving the stability of the power grid, energy management, and the integration of renewable energy with the existing power grid to achieve the required objectives. Due to its inherent variability and climate dependent nature, solar photovoltaic output poses a huge challenge for grid operators and energy planners such as the capacity of the energy producers to predict output with high accuracy and the capacity of the grid to adjust to the nonlinear relationship between environmental factors and PV output. In this study, the authors compare four popular machine learning models: Linear Regression, Decision Tree, Random Forest and Extreme Gradient Boosting (XGBoost) to predict solar power generation from a publicly available data set from the solar plant in South Asia. The data set consists of 68,778 samples with attributes such as irradiation, ambient temperature, module temperature and the target is the AC power. Experiments conducted in the same way for all models: Train/Test split 80/20, no shuffling of time series. The performance of the models was evaluated using Mean Absolute Error (MAE), Root Mean Squared Error (RMSE), and Coefficient of Determination (R²). The experimental results prove that the model of Random Forest has the best results with an MAE of 34.700, RMSE of 53.261, and an R² of 0.976, which is quite close to the results of XGBoost with an R² of 0.974. As expected, with the basic physics of solar power generation, feature importance analysis showed that the irradiation was the most important predictor accounting for about 97% of the model’s predictive power. The irradiation and AC output power were correlated and found to be highly positive (0.98). The results indicate that the ensemble methods are not only accurate in predicting the results but also computational efficient and easy to interpret, thus they are suitable for use in resource-limited settings, like developing countries.

FedPdM: Privacy-Preserving Federated Learning for Remaining Useful Life Prediction Across Heterogeneous Industrial Systems

• We have introduced a unified DP-FL framework for
multi-domain RUL prediction that combines FedProx
with formal (ε, δ)-differential privacy;
• We have conducted a cross-architecture benchmark of five
deep learning models under federated non-IID conditions
on three industrial datasets;
• We have proposed three new DP-FL evaluation metrics,
plus evidence that personalized federation consistently
closes the gap to the centralized upper bound.

Web-based Explainable Deep Learning Framework for Skin Disease Detection

We have developed a controlled comparative framework
for evaluating six CNN and Transformer architectures on
the HAM10000 dataset under a consistent experimental
protocol.
• We have employed a two-phase transfer learning strategy
consisting of initial feature extraction with frozen backbones
followed by selective fine-tuning using a reduced learning
rate.
• We have incorporated random oversampling to address the
substantial class imbalance in the HAM10000 dataset and
improve the representation of minority disease categories
during training.
• We have integrated GradCAM++ into the classification
pipeline to provide class-specific visual explanations of
model predictions.
• We have evaluated the models using accuracy, macroprecision, macro-recall, macro-F1 score, macro-AUC, and
inference time to provide a comprehensive performance
comparison.
• We have integrated the classification and explainability
components into a responsive web-based application to
demonstrate an accessible prototype for dermoscopic image
analysis.

Universe Analytics: A Dynamic University Ranking Platform Through Real-Time Interactive Analysis

We have proposed an interactive university ranking platform that enables real-time customization of ranking criteria through an explainable weighted scoring framework.
• We have integrated personalized university matching,
interactive analytical visualization, and AI-assisted counseling into a unified decision-support platform.
• We have provided a transparent ranking mechanism
that reveals criterion-level score contributions and allows
users to analyze the impact of different weighting strategies on ranking outcomes.
• We have developed a secure web-based architecture that
supports real-time ranking updates and role-based man-
agement of institutional data

Explainable AI Based Parkinson’s Disease Detection From Voice Data Using Machine Learning

Development of a robust preprocessing and acoustic
feature extraction pipeline for Parkinson’s voice analysis.
• Comprehensive comparative evaluation of seven machine
learning models and one CNN-based deep learning model
for Parkinson’s Disease detection.
• Identification of Random Forest as the best-performing
classifier with an accuracy of 84.93% and ROC-AUC of
0.8906.
• Integration of SHAP and LIME to enhance model transparency and support clinical interpretation

LLM Web Agent Security for Indirect Prompt Injection Analysis through Action-Level Evaluation

• We formalize two measurable quantities – the Action-Silent
Compliance Gap (δAS ) and the Context Contamination
Probability (γCS ) – that decouple textual compliance from
tool-invocation compliance at each trajectory step.
• We introduce ALEF, a five-stage measurement pipeline that
maps IPI objectives to tool-invocation goals, varies structural
page representation, executes stateful five-page trajectories,
audits both text and action layers, and evaluates execution-
layer defenses.
• We report a large-scale study (300 payloads, 8 LLMs, 5 rep-
resentations, N = 47,812 valid runs) showing action-level
compliance systematically exceeds text-level compliance for
high-stakes tasks, that the gap is largest for state-of-the-art
(SOTA) closed models, and that representations suppressing
textual compliance do not proportionally suppress action
compliance.
• We quantify cross-page context contamination, including
delayed action hijacking on clean downstream pages, and
empirically evaluate three execution-layer defenses.

IoT-Driven Refreshable Braille Architecture Utilizing Vision-Based OCR and Cloud Storage

This paper introduced an IoT-enabled architecture for Braille reading that is based on vision-based OCR, cloud synchronization, embedded processing, and tactile Braille creation. The designed technology uses the ESP32-S3 CAM to read printed documents, FastAPI web service with Tesseract OCR to get text out of it, and Firebase Realtime Database to synchronize this information. The ESP32-S3 processes this information, creating six-dot patterns from the retrieved characters, which it then renders into Braille dots by controlling six push-pull solenoids through a DRV8833 driver. Interaction with LCD and navigation buttons was also supported by the device. As well as Android application, which allows to upload other types of content – images and voice

Global AI Governance Without the Giants: Market Access as Middle-Power Leverage

The article contributes to AI regulation in three ways. It shifts AI governance from its source to access gateways. While source control remains important, it is often beyond the reach of most jurisdictions. It proposes a coalition-based market-access regime to strengthen the leverage of AI-exposed jurisdictions. Last, and equally consequential, it proposes a coalition-wide, shared infrastructure to support AI governance.

DRIG-Net: A Dynamic Renewable Interaction Graph Framework for Latent Operating State Analysis

This paper presents a framework to construct a Dynamic Renewable Interaction Graph (DRIG) to characterize renewable operating dynamics. We used autoencoder-based representation learning to extract latent embeddings from the data, and we assessed the learned latent representations using reconstruction loss, variance analysis, and downstream performance on an Extreme Gradient Boosting (XGBoost) model. The Principal Component Analysis(PCA) of the latent representations reveals the structure of the renewable operating state (ROS). K-means clustering identifies the operating regimes among the ROS. Subsequently, utilizing the latent embedding and ROS, the DRIG was constructed which models the temporal dynamics within Markov modeling technique to investigate regime persistence and seasonal dynamics analysis. Finally, an explainable AI technique was applied to XGBoost for SHAP analysis, which identifies the dominant factors driving changes in the latent ROS. Experimental results demonstrated that the proposed autoencoder-based extracted latent embeddings learns most informative representations with low reconstruction error. The proposed framework offers a new perspective on renew- able–demand interactions and interprets meaningful seasonal variation in renewable availability and renewable operating-state dynamics.

A Machine Learning-Based Framework for Detection and Classification of Spam and Malicious Textual Content on X Using Large-Scale Social Media Data

This study presents a hybrid CNN-BiLSTM deep learning approach for detecting spam accounts and harmful content on X. The proposed method combines textual features with account-level metadata and compares its performance with traditional machine learning algorithms, including Logistic Regression, Naïve Bayes, SVM, and Random Forest. Experimental results on a dataset of 50,000 tweets achieved 97% accuracy, 96% precision, 96% recall, and 96% F1-score. The findings demonstrate that the proposed hybrid approach effectively improves spam and harmful content detection while providing a reliable and scalable solution for enhancing social media security.

Infrastructure-Independent Smart Medication Alert System with GSM-Based Caregiver Escalation and Non-Blocking Acknowledgment

Medication non-adherence is a persistent healthcare
challenge, particularly among elderly individuals and patients
receiving long-term therapy, often resulting in reduced treat-
ment effectiveness and serious health complications. This paper
presents a low-cost standalone Smart Medicine Alert System
based on an Arduino Uno microcontroller. The system integrates
a DS3231 RTC, a 16×4 I2C LCD, a 4×4 keypad, LEDs, an
active buzzer, and a SIM900A GSM module. A non-blocking
timing algorithm based on the millis() function provides a
15-second grace period for user acknowledgment, after which
unacknowledged reminders automatically trigger SMS and voice-
call notifications to predefined contacts. Experimental evaluation
over a continuous 72-hour period demonstrated stable system
operation with an average SMS delivery latency of 4.19 s,
remaining below the 5 s target threshold. The proposed system
provides a simple, affordable, and reliable medication reminder
solution for elderly individuals, patients with chronic diseases,
and caregivers.

Investigation of Electromagnetic Energy Absorption and Thermal Response of Layered Biological Tissues

A Comprehensive Structural Analysis of Zero-Day Attack Detection: Machine Learning Paradigms, Performance Trends, and Open Research Challenges

Zero-day attacks, a persistent issue to traditional cybersecurity systems due to lack of predetermined signatures and behavioral profiles at the time of compromise, are the focus of this survey. The studied methods can be categorized into eight types by methodology: empirical and conceptual frameworks, systematic literature reviews, supervised machine learning and deep learning, unsupervised anomaly detection, vision transformer-based techniques, explainable AI, real-time hybrid detection, and vulnerability-prioritization and ransomware specific architectures. Data suggests zero-day vulnerabilities can remain unaddressed for an extensive length of time, but the likelihood of exploiting them increases considerably after public disclosure. Detected performance results span from the around 90% in the early KDD based methods up to over 99% in some explainable MLP based methods. This cannot be taken at face value, as evaluation is plagued by heterogeneity of datasets, features, attack categories and training configurations used among studies. Challenges that arise consist of encrypted and obfuscated traffic, outdated benchmarks, class imbalance, high latency, false positive rate, poor explainability and evaluation methods not reflective of a zero-day environment. In many instances it seems evidenced, by an increase in family holdout testing, zero-day hit rate and analysis through Wasserstein distance, rather than the standard random-split accuracy. Future research areas have been suggested towards achieving intelligent, transparent and effective real-time zero-day systems that handle out-of-distribution data with ease.

Enhanced Climate Forecasting in Northeastern Bangladesh using Hybrid SARIMA-ANN Models

This study is highly significant as it introduces an advanced hybrid seasonal ARIMA-ANN model to forecast climatic variables—rainfall, and temperature —in the Sylhet and Sreemongol region of Bangladesh. Accurate and reliable climate forecasts are critical for a variety of industries, particularly agriculture, public health, and disaster management, which are directly impacted by weather patterns. The hybrid model’s ability to capture both linear and nonlinear trends is an important advantage over standard forecasting methods. This study supports sustainable agricultural practices by giving precise forecasts, allowing farmers to better organize their activities and prevent potential losses due to severe weather conditions. Enhanced weather predictions also allow for better planning for extreme weather occurrences, lowering the danger to human lives and infrastructure. Furthermore, the insights acquired from this study will assist policymakers, enabling them build effective adaptation and mitigation plans to climate change. Overall, the study’s findings will enhance socio-economic resilience and promote sustainable development in Sylhet and Sreemongol regions, ensuring that communities are better equipped to handle the challenges posed by climate change.

Aqua Monitor: A Decoupled LoRa-to-Cloud IoT Architecture for Low-Cost Multi-Pond Water Quality Monitoring

The significant research contribution is the development of Aqua Monitor, a low-cost, Wi-Fi-independent multi-pond water-quality monitoring architecture that combines point-to-point 433 MHz LoRa communication, edge-side MAD/IIR signal filtering, physics-informed DO estimation, and a dual Firebase (Realtime Database + Firestore) cloud architecture. The system enables real-time and historical monitoring of multiple ponds at a hardware cost of approximately 9,550 BDT (USD 77.64).

Comparative Analysis of Resource-efficient Benchmarking and Observation of Neural and Machine learning models with Emission evaluation for Diagnostic datasets

This paper introduces CARBON-MED, the first unified emission-aware
benchmarking study for classical ML models across six heterogeneous medical
diagnostic datasets. We propose three novel evaluation metrics (the
Carbon-Efficiency Score (CES), Stability Index, and Emission-Aware Performance
Index (EAPI)) that jointly assess predictive performance and carbon footprint.
Our findings demonstrate that lightweight, GPU-optimized models (XGBoost, KNN)
achieve state-of-the-art diagnostic accuracy with near-negligible CO₂ emissions,
establishing a principled and reproducible benchmark for sustainable clinical AI.

When Technostress Drains and Autonomy Protects: How Organizational Support Sustains Employee Well-Being in Hybrid Work

The study contributes by empirically demonstrating how and under what conditions technostress affects employee well-being in hybrid work. It identifies perceived organizational support as a mediating mechanism and work autonomy as a buffering job resource, thereby extending Conservation of Resources Theory to explain employee well-being in technology-driven hybrid work environments.

Cost–Reliability Optimization of Marine-Integrated Hybrid Renewable Energy Systems for Kutubdia

The lack of grid connection and variability of renewable energy resources make it essential that remote island electrification is backed up by reliable and cost-effective energy solutions. In this paper, a multiobjective optimization framework for a marine-integrated hybrid renewable energy system (HRES) is presented for the complete external-grid interruption at Kutubdia, Bangladesh. A 2025 synchronized solar, wind, wave, tidal and literature-sourced load profile is input into an hourly simulation framework, which is then used to optimize four PV–wind–marine–BESS architectures. A mixed discrete–continuous multiobjective particle swarm optimization (MOPSO) is used to minimize an annualized referenced technology-cost metric and the loss of power supply probability (LPSP) and enforce cyclic annual state-of-charge closure. The chosen base trade-off solutions go from 9.979\% LPSP for S1 to 0.546\% LPSP for S4, 0.834 to 4.774~MUSD/yr cost metric and 544.46 to 29.77~MWh/yr EENS. Robustness tests including optional wave, and extending PV, wind and BESS-power limits, indicate optimal configurations are dependent on the architecture definition and design boundaries. The cost–reliability trade-off in the planning of HRES for an islanded application is evaluated systematically within the proposed framework without using objective aggregation with weighting factors.

An Interpretable Deep Learning Framework for Diabetic Foot Ulcer Classification Using Multi Optimizer Training and Genetic Algorithm Selection

This research introduces an interpretable deep learning framework for diabetic foot ulcer (DFU) classification by integrating a custom CNN architecture with multi-optimizer training, Genetic Algorithm (GA)-based model selection, and Grad-CAM explainability. Unlike conventional approaches that rely on manually selected optimizers and black-box predictions, the proposed framework systematically evaluates seven optimizers and automatically selects the best-performing model using evolutionary optimization. The selected AdamW-based CNN achieved 95.24% accuracy and 98.97% ROC-AUC on an independent test set while providing visual explanations through Grad-CAM to highlight clinically relevant ulcer regions. The framework offers a lightweight, accurate, and transparent AI solution to support reliable DFU screening and clinical decision-making.

Physical Layer Security Analysis of RIS-assisted Hybrid FSO/RF Networks for Secure IoT Communications under Potential Eavesdropping Attacks

In this research, a RIS-assisted hybrid FSO/RF system is proposed to enhance the security of IoT networks in the presence of an eavesdropper. Analytical expressions for key secrecy performance metrics, including the secrecy outage probability and effective secrecy throughput are derived. Numerical results are further presented to investigate the effects of several system parameters on network security, providing valuable design insights for the development of secure IoT networks.

Statistically Validated Benchmarking of Federated Learning Optimizers for Binary Intrusion Detection in Imbalanced IoMT Traffic

This work provides the first controlled, imbalance-aware comparison of five federated optimization algorithms (FedAvg, FedSGD, FedProx, FedAdam, and FedDyn) for binary intrusion detection in IoMT networks, using a single shared CNN-LSTM-ResNet backbone so that performance differences reflect the aggregation strategy alone. Evaluating each method under both IID and controlled label-skew non-IID client partitions on the CICIoMT2024 benchmark, we show that FedAvg delivers the strongest minority-class-aware performance in both settings, while FedDyn is most robust to label skew—revealing that the best optimizer depends on whether absolute performance or robustness is prioritized. We further demonstrate why accuracy is misleading under severe class imbalance (FedSGD reaches 98.996% accuracy yet fails entirely on the minority class), and we validate all findings with stratified paired bootstrap intervals and exact McNemar tests with Holm correction, alongside a matched centralized baseline on an identical frozen test set.

CQI Prediction in 5G Networks Using a Mobility-Aware Random Forest Model

1. Introducing a random forest-based machine learning model for CQI estimation that incorporates the velocity of the user along with traditional radio parameters SNR, RSRP, and RSRQ.
2. An extensive comparison of the proposed model against 9 benchmark algorithms, demonstrating superior performance in metrics like RMSE and R², validating its effectiveness over state-of-the-art methods.
3. A comprehensive ablation study that shows the impact of including velocity in CQI estimation, showing an almost 13% decrease in RMSE, statistically validating its importance.
4. The model’s robustness has been validated by showing consistent performance across both low- and high-velocity scenarios.
5. Local Interpretable Model-Agnostic Explanations (LIME) has been employed to explain the decision-making process.
6. Beyond error metrics, the real-world impact of the proposed model has been assessed using feedback overhead analysis and noise robustness, showing the RF-predicted CQI yields better fairness and a robust outcome compared to traditional SINR-to-CQI mapping.

Curvature-Regularized Graph Tractography for Reproducible Multi-Site Diffusion MRI Connectomes

This paper introduces a step-invariant minimum radius-of-curvature constraint for graph-based diffusion-MRI connectome construction, applied during beam-search path enumeration over an orientation graph. Prior graph tractography controls path smoothness with a fixed minimum-angle threshold that must be re-tuned to voxel resolution; expressing the criterion as a radius of curvature instead makes it step-length invariant and adds a single interpretable parameter with no per-dataset tuning. On 229 subjects across five independent acquisition sites, with reconstruction, atlas, and seeding held fixed, the constraint reduces pooled connectome edge-count variability by 19 percent, driven by a 35 percent reduction in the between-site component (cross-site harmonization), and it is more reproducible than both deterministic and probabilistic streamline tractography. The method is therefore a construction-time complement to post-hoc statistical harmonization: a simple, interpretable geometric prior that improves the cross-site consistency of graph-based connectomes, a first-order requirement for reliable multi-site diffusion-MRI studies.

Sustainable Power Supply for Cellular Base Transceiver Stations Using Solar PV–Battery Systems

Cellular Base Transceiver Stations (BTSs) require reliable and uninterrupted power supply to keep these stations operating continuously. The regular power outages and reliance on diesel generators add to the operating costs, fuel usage and greenhouse gas emissions, and traditional terrestrial photovoltaic (PV) systems require a lot of land and supporting infrastructure, which are not suitable for densely populated areas in Bangladesh. This paper introduces an integrated PV–battery system using the tower structure of BTS to replace the independent PV support structure, and no additional land will be needed, which is the tower-integrated PV–battery system. A 9.84~kWp PV system was designed and evaluated with the existing battery backup under the climatic condition of Bangladesh. This study explores the electrical performance, energy generation, economic viability and environmental advantages of the proposed configuration, taking into account the extra structural reinforcement needed for tower mounting PV system. The simulation results show that the proposed system can effectively reduce the consumption of grid electricity, the operation of diesel generator, improve the utilization of renewable energy, and reduce the operating costs of the whole life cycle with the estimated payback period of about 4 years. In addition, the proposed configuration can offer the necessary carbon dioxide emission reduction of ~11-12 tCO2 for the year and can utilize the existing telecom infrastructure to the maximum extent. The results show that tower integrated PV systems can be a technically feasible, economically viable and scalable solution to enhance the sustainability and resilience of the Bangladesh BTS power system, as well as other power systems in other countries sharing similar energy and land challenges.

Maximum Power Point Tracking Techniques for Solar PV Systems: Performance Analysis of Different Control Strategies

Solar photovoltaic output is nonlinear and changes with irradiance, temperature, load, and partial shading, so a maximum power point tracking controller must balance speed, energy capture, ripple, and implementation burden. An evidence-weighted performance analysis of 16 documents supplied, from 15 different studies, after duplicate screening, is presented in this paper. Without mixing incompatible power ratings or test profiles, conventional and adaptive methods, global-search and hybrid optimizers, prediction-based controllers and reinforcement learning are compared. The evidence clearly demonstrates that under uniform conditions simple local procedures are still appealing, while partial shading needs to be explicitly addressed by including the global-search or regime-switching features in the procedure. Hybrid controllers, which divide fast local regulation and global search, reported the best hardware supported results and methods that learned were promising with respect to adaptation but had to deal with training and validation costs and with computational costs. The following information is introduced: a control-evidence matrix, an all-source synthesis table, a validation-maturity assessment, and a minimum reporting framework. The main results are that controller selection should be condition and evidence aware: there is no single best controller and only numerical performance makes sense in a defined array, converter, disturbance profile, metric definition and validation platform.

Elevation Thresholds for Monsoon Flood Risk Stratification in Rohingya Refugee Camps: A Validated Observational Study

This research develops a lightweight, physics-motivated elevation-based framework for stratifying monsoon flood risk in Rohingya refugee camps. Using eight years of flood records, it validates a 3 m elevation threshold, achieves 90.2% temporal validation accuracy, and identifies 11 high-risk camps containing 276,510 people. The framework requires no model training, GPU, or internet connection, offering a practical and low-cost tool for humanitarian preparedness and pre-positioning of aid.

A Lightweight Geospartial Framework for Monsoon Flood Risk Stratification in Rohingya Refugee Camps: Temporal, Spatial, and External Validation

This research develops a lightweight, physics-motivated elevation-based framework for stratifying monsoon flood risk in Rohingya refugee camps. Using eight years of flood records, it validates a 3 m elevation threshold, achieves 90.2% temporal validation accuracy, and identifies 11 high-risk camps containing 276,510 people. The framework requires no model training, GPU, or internet connection, offering a practical and low-cost tool for humanitarian preparedness and pre-positioning of aid.

Empirical Assessment of CSS Techniques: Efficiency, Reliability, and Throughput Trade-offs

As the number of wireless communication services increases exponentially, there is an acute shortage of available frequency spectrum. Allocation of spectrum statically and conventionally leads to the inefficient use of spectrum because some licensed frequency bands become underutilized, while the other unlicensed bands may be congested. A solution proposed in such cases is Cognitive Radio (CR), which offers dynamic spectrum allocation based on intelligent spectrum sensing. There are different techniques of sensing, and one such technique is Cooperative Spectrum Sensing (CSS) in which the information about sensing is shared by multiple CR terminals. In this paper, we analyze the performance of various cooperative spectrum sensing schemes in a cognitive radio environment, viz., centralized, distributed, relay aided and external cooperative sensing. We conduct performance comparison in terms of important factors, namely probability of detection (Pd), probability of false alarm (Pf) and Signal-to- Noise Ratio (SNR). Also, we explore the bandwidth-efficient techniques of cooperative sensing using censoring and quantization techniques. The simulations clearly show that cooperative sensing outperforms non-cooperative sensing by a fair margin but at the cost of little bandwidth efficiency.

CNN-Based Automatic Modulation Classification for Cognitive Radio: Benchmarking Against Fourth- Order-Cumulant Feature Classification

After a cognitive radio (CR) determines that a channel is occupied, identifying the primary user’s modulation scheme — automatic modulation classification (AMC) — supports interference-aware coexistence, standards identification, and physical-layer security. This paper presents a simulation-based benchmark of a one-dimensional convolutional neural network (CNN) operating directly on raw
in-phase/quadrature (I/Q) samples against the classical fourth order- cumulant feature classifier, for six modulation schemes
(BPSK, QPSK, 8PSK, 16QAM, 64QAM, CPFSK) under AWGN with random carrier-frequency offset and phase. On a held-out test set of 3,240 examples spanning −10 to +15 dB SNR, the proposed CNN reaches 81.1% accuracy at +15 dB and 57.5%overall, compared with 47.8% and 26.7% for the cumulant based classifier, a gap that is largest at moderate-to-high SNR and consistent with the modern automatic-modulation classification literature. The paper further reports per-class accuracy, confusion matrices at low and high SNR, model complexity comparison, and a genuine limitation: random carrier phase substantially degrades discrimination among
phase- and amplitude-based schemes (QPSK, 8PSK, 16QAM) relative to the constant-envelope, phase-agnostic classes (BPSK, CPFSK). All results are produced by Python-based Monte Carlo signal simulation and model training (NumPy, scikit-learn, TensorFlow/Keras); no captured RF data were used.

Belief-State Q-Learning for Dynamic Channel Selection in Cognitive Radio Networks: A Simulation Study

Dynamic spectrum access requires a secondary user (SU) to repeatedly choose which of several primary-user (PU) channels to sense and access under channel occupancy that is correlated in time (a “restless” process) and a cost for switching channels. This paper presents a simulation-based performance evaluation of channel-selection policies for cognitive radio (CR) networks, modeling each channel as an
independent two-state (idle/busy) Markov chain. As its principal contribution, the paper implements and benchmarks a belief state Q-learning agent — which discretizes a continuously propagated per-channel occupancy belief into a tabular state and learns a channel-selection policy with no knowledge of the
underlying transition probabilities — against four baselines (Random, Round-Robin, frequentist ε-greedy, and UCB1) and a model-based myopic belief policy given the true channel statistics. Averaged over 12 independent runs of 20,000 time slots with 5 heterogeneous channels, the proposed Q-learning agent achieves 74.6% steady-state throughput, versus 71.1% for UCB1, 69.2% for ε-greedy, and 66.7%/67.3% for Random/Round-Robin, and comes within 4.1 percentage points of the model-based myopic policy (77.8%). The paper further quantifies cumulative regret against a genie oracle, the throughput/switching-rate trade-off as a function of an explicit channel-switching cost, sensitivity to the number of channels,
the effect of the exploration schedule on convergence, and a state-space-coverage limitation of the tabular approach. All results are produced by Python-based Monte Carlo simulation (NumPy); no physical RF measurements were taken.

Binary and Multiclass Intrusion Detection in IoMT Networks: Comparative Machine Learning with McNemar Testing and XAI

The significant contribution of this research is the comprehensive evaluation of five machine-learning models for binary and 15-class IoMT intrusion detection, complemented by McNemar statistical testing and LIME-based explainability. The study demonstrates the superior performance of Decision Tree while highlighting the hidden weaknesses of minority-class detection through macro-averaged metrics.

A Class-Wise Error and Misclassification Analysis of Deep Learning Models for Industrial IoT Intrusion Detection

This paper’s principal contribution is to demonstrate, on the DataSense IIoT benchmark, that overall accuracy is an insufficient basis for evaluating intrusion-detection models and to provide a class-wise, error-oriented evaluation framework that surfaces deployment-critical failures which aggregate metrics conceal—most notably the asymmetric binary error profile of the best model (485 missed attacks versus only 144 false alarms) and the directional multiclass confusions (Recon→Benign and DDoS→DoS) together with the systematically weak Bruteforce class. Beyond this, the work delivers a controlled, uniform comparison of nine deep-learning architectures (feed-forward, recurrent, convolutional, hybrid, and residual) under a single leakage-controlled pipeline, validates the observed performance differences statistically through Cochran’s Q and Holm-adjusted pairwise McNemar tests rather than treating them as incidental, and applies SHAP and LIME at the appropriate interpretive scope (global and class-selective attributions alongside local per-prediction explanations), framed explicitly as associational rather than causal. Collectively, these contributions show that class-wise behavior and specific misclassification patterns—not aggregate accuracy alone—should guide the assessment of deep intrusion-detection models for the Industrial Internet of Things.

Paper Type: Full Research Paper