This paper studies a sustainable approach towards the fostering of Sustainable Entrepreneurial Intention among University Students in Bangladesh. Here, the focus is mainly on the Roles of Digital Skills, Entrepreneurship Education, and Innovation Orientation
We develop a unified multi-domain adaptation pipeline for adapting YOLO-World to heterogeneous medical and environmental imagery through annotation standardization, domain-specific fine-tuning, and multi-domain training. We systematically evaluate zero-shot, medical-only, and combined medical-environmental configurations, demonstrating substantial improvements in medical detection performance, with mAP@0.50 increasing from 0.60 to 0.89 and mAP@0.50:0.95 from 0.39 to 0.73. Furthermore, joint medical-environmental fine-tuning achieves 0.84 precision, 0.81 recall, and 0.87 mAP@0.50, demonstrating broader cross-domain generalization while retaining strong medical-domain performance. Finally, we provide a practical analysis of challenges such as small-object detection, occlusion, low contrast, prompt quality, and domain imbalance, while highlighting reproducibility considerations for future multi-domain open-vocabulary detection research.
This paper proposes S-GAME, a human-centered and sustainable gamified framework for developing university students’ English-speaking skills in resource-constrained contexts. It contributes by aligning game mechanics with measurable speaking outcomes, embedding accessibility, privacy, teacher control, and low-connectivity support into the system design, and presenting a multidimensional evaluation plan covering learning, engagement, equity, operational cost, and environmental impact.
Our contributions are as follows.
– We build a controlled harness that plants benign canary payloads in build output read by an open-source coding assistant, grades the assistant’s response on a four-state scale, and records every trial for reuse.
– We show that payload encoding drives success independently of wording. Carriage-return injection holds the attack at 80.0% while concealing it from a human reader; zero-width concealment drops it to 13.3% by breaking tokenization, with a non-deterministic residual that rules it out as a defense.
– We show that the assistant’s built-in filter keys on register: an overt directive is refused in every trial (0%), while the same instruction written as a build notice runs in 66.7%, and disabling confirmation lifts that only to 86.7%.
– We test two defenses and find that stripping control characters does nothing to the plain-text attack, while checking the log against the real build output stops it completely.
Abstract—The purpose of this research is to understand the relationship between hierarchical level and the leadership style of an organization’s executives and its budgetary system. This study will also investigate the influence of other behavioral factors (information asymmetry, procedural fairness) and employees’ organizational commitment. Managers at different levels are involved in the budgetary system, each with different responsibilities. Their leadership style in helping subordinates achieve targets also influences the process. The present study aims to understand this scenario in the context of manufacturing companies in Bangladesh. For this study, the term budgetary system’ is conceptualized in terms of subordinates’ participation. This study excludes the technical aspects of budgeting, as the current literature has extensively studied those variables. Data will be collected through a Likert-type questionnaire survey. The study will measure the constructs by adapting scales from earlier studies. A Structural Equation Model will be used to measure the relationships among the variables. The expected outcomes of the study are: executives with higher responsibility and a supportive leadership style encourage subordinates’ participation in the budgetary process; second, the relationship between management level and leadership style of executives with participation in the budgetary system is moderated by the extent of information asymmetry and procedural fairness of budgeting; and third, employees who participate in the budgetary sytem feel increased level of organizational commitment.
Index Terms—budget, participation, management level and leadership style of executives, manufacturing companies
This research presents an affordable, resilient multi-hazard early warning system engineered for flood-prone, low-resource regions like Bangladesh. Running on an ATmega2560 microcontroller, the system integrates water clearance, liquid submersion, combustible gas, and ambient light sensors to activate local hazard alarms in under 15 ms. To prevent communication blackouts when physical broadband connections fail, it features an autonomous dual-gateway engine that switches from wired Ethernet to cellular GSM in just 3.42 seconds. Crucially, the platform bridges rural populations using basic 2G feature phones through an edge-level parser that extracts location codes and trapped headcounts from simple SMS tokens (e.g., UNSAFE#FENI01#08) with 99.4% accuracy, without needing smartphones or internet access. These aggregated reports feed an operations dashboard that automatically dispatches targeted SMS alerts to rescue teams, healthcare units, and local administrators. With a total hardware cost under $45.00 USD, this design offers an inclusive, dependable, and life-saving disaster telematics solution.
The ARIS paper makes four significant research contributions:
1. Closed-Loop Governance Architecture It proposes the first IoT–Digital Twin–AI pipeline that converts real-time ecological sensing into adaptive tourism permit decisions (Sensor → Data → Twin → AI → AGCI → Governance), moving beyond static dashboards to actionable governance.
2. Socio-Ecological Digital Twin Unlike conventional environmental twins, ARIS deploys three coupled sub-twins—Ecological, Tourism (agent-based), and Governance—creating a bidirectional feedback loop where tourism pressure is modelled as an endogenous system variable rather than external forcing.
3. AGCI as a Computational Governance Index The Adaptive Governance Capacity Index mathematically operationalises ecological state (E, R) against tourism pressure (I) and seasonal stress (S) to compute daily adaptive permit ceilings, replacing fixed carrying capacity with dynamic, data-driven thresholds.
4. Human-in-the-Loop AI Decision Support The AI layer (prediction, scenario, optimisation, recommendation agents) is explicitly designed as decision-support, not decision-authority —ensuring algorithmic transparency and institutional human oversight, which is critical for governance ethics in developing-country contexts.
The proof-of-concept demonstrates that under high tourism pressure (1,200/day), AGCI declines from 0.35 to 0.18 within 45 simulated days, triggering progressive restrictions—validating the framework’s capacity to prevent ecological overload without requiring permanent bans.
Small-scale food processing industries in
Rwanda and Malawi face persistent challenges in quality
control, energy efficiency, and supply chain management.
Traditional quality assessment methods are manual,
labour-intensive, and error-prone, leading to high
rejection rates, increased waste, and operational
inefficiencies, while the absence of real-time monitoring
results in excessive energy consumption and production
delays. This paper presents the design and development of
an integrated system that combines Artificial Intelligence
(AI) and the Internet of Things (IoT) to enhance
manufacturing efficiency, reduce waste, and improve
product quality. An AI-powered machine vision pipeline
built on Convolutional Neural Networks (CNNs) and
YOLOv8 performs real-time defect detection and
classification of maize grains, while IoT sensors monitor
machine health, environmental conditions, and energy
usage on the production line. Long Short-Term Memory
(LSTM) networks applied to sensor time-series data
forecast equipment failures, enabling predictive rather
than reactive maintenance. The system is deployed on edge
AI hardware, allowing on-site inference without
dependence on continuous cloud connectivity. Field
context motivates the work: post-harvest maize losses in
sub-Saharan Africa range between 15 and 30 percent,
roughly 25 percent of maize grains in Malawi are affected
by aflatoxin contamination, and 18 percent of processed
maize flour samples in Rwanda failed national quality
standards. Manual inspection processes approximately 100
kg of maize per hour against more than 1,000 kg per hour
for the automated system, a tenfold efficiency gain. The
solution is designed for affordability and scalability across
small and medium-sized enterprises, contributing to food
safety, waste reduction, and industrial competitiveness in
Rwanda and Malawi
This research presents a video-authentication method that combines visual, temporal, quality, and motion information to detect video manipulation while remaining tolerant to normal compression and format changes. It can identify frame insertion, deletion, and replacement, report the affected time interval, and mark unclear cases as uncertain instead of making an unreliable decision. ECDSA signatures and hash chaining also protect the stored reference records from unauthorized modification. Evaluated on 54 videos using six-source leave-one-source-out testing, the method achieved 83.33% accuracy, 100% precision, and 80% F1-score.
This research presents a two-stage model compression framework that combines structured channel pruning, safe pointwise incremental pruning, and knowledge distillation to optimize MobileNetV2 for sustainable edge-based crop disease detection. The proposed approach achieves 99.43% test accuracy while reducing model size by 73.84%, parameters by 23.53%, MACs by 21.86%, CPU inference latency by 26.39%, and inference energy and estimated CO₂ emissions by 28.43% compared with the baseline.
The major research contribution of RSSDL-IDS is a unified seven-stage intrusion detection framework that addresses several limitations of conventional IDS simultaneously: limited zero-day detection, dependence on labelled data, fixed anomaly thresholds, adversarial vulnerability, concept drift, and lack of interpretable alerts. It integrates these capabilities into one pipeline and empirically evaluates their individual and combined effects through ablation experiments.
This work proposes a hybrid attendance monitoring framework that combines mandatory fingerprint authentication with continuous CCTV-based facial recognition using FaceNet embeddings and an SVM classifier. Unlike conventional attendance systems that only verify a student at check-in, the proposed framework continuously measures classroom presence from video frames and determines final attendance using a predefined presence threshold. The dual-stage approach helps reduce proxy attendance while also identifying early departure or prolonged absence. The framework integrates face detection, preprocessing, FaceNet-based feature extraction, SVM classification, frame-level presence logging and automated attendance decision-making into a single practical system for classroom attendance monitoring.
Automated symptom-checkers built on tabular fea-
ture vectors cannot model the relational structure inherent in
medical knowledge. We propose an Explainable Heterogeneous
Graph Convolutional Network (H-GraphConv) with Severity-
Weighted Edges for automated clinical diagnosis. Edge weights
combine empirical symptom prevalence with validated clinical
severity scores, and a Multi-Type Heterogeneous Graph incorpo-
rates Disease, Symptom, Anatomical Region, and Lab Test node
types. A Latent Feature Attribution module delivers mathemat-
ically exact, per-symptom explanations without approximation,
while a Graph-Retrieval Augmented Generation (GraphRAG)
pipeline powered by Google Gemini generates clinician-facing
diagnostic narratives grounded in GNN-derived graph facts. On a
4,920-record dataset covering 41 diseases and 131 symptoms, the
model attains 95.6% link-prediction accuracy, 95.7% F1-score,
and AUC-ROC of 0.9412. Under simulated symptom-dropout
noise of 0–30%, Top-5 disease-ranking accuracy reaches ≈33%,
which is 2.7× the random baseline.
Index Terms—Graph Neural Networks, Explainable AI, Latent
Feature Attribution, Link Prediction, Heterogeneous Graphs,
Medical Diagnosis, Symptom Checker, Clinical Decision Support,
GraphRAG, PyTorch Geometric.
This conceptual paper develops a comprehensive framework for understanding savings behavior among private university students in Bangladesh. Drawing on established theoretical perspectives—Theory of Planned Behavior, Life Cycle Hypothesis, and Financial Socialization Theory—the paper identifies seven key determinants of student savings behavior: financial literacy, financial attitude, peer influence, parental socialization, income/allowance, lifestyle/expenditure patterns, and self-control. The unique socio-economic context of Bangladesh, characterized by rapid economic growth, expanding private higher education, and evolving financial systems, creates distinct challenges and opportunities for student financial behavior. The proposed conceptual model integrates individual cognitive factors, social influences, and contextual variables to explain savings behavior among this population. The framework offers practical implications for policymakers, educational institutions, and financial service providers seeking to promote financial well-being among young adults in developing economies. The paper concludes by acknowledging limitations and proposing directions for future empirical research to validate and refine the conceptual model.
The study proposes a transfer learning-based explainable hybrid framework for early cervical cancer detection for women in Bangladesh. The study is ongoing and plans to combine Bangladeshi Pap-smear data, CNN-based image features, and clinical risk factors. Various transfer learning models such as VGG, ResNet, InceptionV3, DenseNet and EfficientNet will be evaluated and SVM, Random Forest and XGBoost will be considered for classification.
In addition, it is planned to explain the decision of the model using Grad-CAM, SHAP and LIME and apply focal loss, SMOTE/ADASYN and GAN-based techniques to deal with class imbalance . Overall, the study aims to combine transfer learning, hybrid CNN-ML, multimodal XAI and Bangladeshi local data to create an interpretable and effective cervical cancer screening framework.
The swift expansion of electric vehicles (EVs) and
renewable generation is basically changing distribution networks ,
from boring passive power-delivery setups into flexible , data-
driven smart grids. Still, if EV charging is not coordinated, it can
push up the evening peak demand, make voltage deviations get
worse, overload feeders , and also drive operating costs higher,
mainly in renewable-rich areas. So in this paper we offer a
degradation-aware vehicle-to-grid (V2G) coordination approach
for EV fleets inside a future smart distribution network that is
calibrated for Bangladesh. The idea is to treat an EV fleet as an
aggregated distributed energy resource, and then schedule both
grid-to-vehicle (G2V) and V2G power exchanges using an
aggregator-based control scheme. For the experiments,
simulations are carried out on an IEEE 33-bus radial feeder,
together with a Bangladesh-calibrated daily demand pattern.
Dhaka solar photovoltaic inputs come from NASA POWER, and
the stress case is built as a 2035 future scenario. That scenario
assumes 450 equivalent EVs, 2.40 MW of PV capacity, 4.05
MWh of daily EV energy demand, plus an aggregate charging cap
of 2.60 MW and an aggregate V2G cap of 1.35 MW. Altogether,
six operating cases are tested: no EV, uncontrolled charging,
price-only smart charging, rule-based V2G, degradation-blind
V2G, and the proposed degradation-aware V2G strategy. The
results are pretty clear. With uncontrolled charging the peak grid
import climbs to 7.80 MW and the smallest bus voltage drops to
0.8193 pu. Meanwhile, the proposed strategy cuts the peak grid
import by 42.09% , raises the minimum voltage to 0.8883 pu, and
reduces total daily operating cost by 4.44% , all while keeping
unnecessary battery cycling under check. Overall, these findings
suggest that battery-aware EV fleet coordination can help with
peak-load reduction, better voltage profiles, and real cost savings
in renewable-integrated distribution systems. That said, even with
this, feeder reinforcement and reactive-power support might still
be necessary once electrification gets extremely high.
The global effort to bridge the digital divide requires wireless solutions that are not only high-performing but also sustainable, secure, and resilient to local infrastructure challenges. This is particularly true in developing nations like Bangladesh, where issues such as power instability (load shedding) and shared bandwidth congestion can severely degrade the user experience. This paper proposes the “Aakash Deep” Hub, a futuristic, consumer-focused Li-Fi framework designed specifically to address these challenges. We move beyond traditional performance metrics to evaluate the system’s viability through a series of high-fidelity Python simulations grounded in the context of a typical Bangladeshi home. The study demonstrates, with quantitative reasoning, how Li-Fi’s inherent properties, when enhanced by AI, create a superior consumer product. Our simulated results show that the system’s high energy efficiency ensures connectivity survives through multi-hour power cuts where traditional Wi-Fi fails. We demonstrate how an AI-driven Quality of Service (QoS) engine prevents buffering and lag in a congested, multi-user family environment. Furthermore, we visualize Li-Fi’s inherent physical-layer security, a critical advantage for user privacy. A final techno-economic analysis reveals that while the initial hardware cost may be higher, the total cost of ownership and the vastly improved reliability and user experience present a compelling value proposition. This research concludes that an AI-enhanced, sustainable Li-Fi framework is a highly consistent and viable solution for the next generation of home networking in Bangladesh and other developing markets.
Nanoparticle cytotoxicity prediction can help with early safety screening, though a reliable evaluation is kinda hard when datasets have repeated nanoparticle identities, plus the toxicity labels are very skewed. In this work we put forward a leakage-audited machine-learning pipeline that aims to classify nanoparticle cytotoxicity by using physicochemical, exposure, and biological-response descriptors. The curated dataset has 308 records and 22 engineered features across three toxicity categories, yet the rarest category only has two records, and it does not show up in the frozen, held out test split. We then combine Random Forest and LightGBM models using logistic regression stacking. All preprocessing and resampling are done inside the training partitions, so it stays leakage-checked. For a fair like for like comparison, every model is tested with nanoparticle-grouped cross validation, using macro F1, balanced accuracy, and per class metrics. We also include a supplementary analysis based on the frozen split, but we treat the overall numbers as partial, two-class style evidence, not as a complete three-class validation. Feature importance and case level explanations point to how much biological-response, exposure, and physicochemical variables contribute. Overall the results suggest the pipeline can be made auditable for screening, but they also underline that we still need bigger and more balanced datasets, and independent external validation, before anyone should claim broad generalization or regulatory readiness.
Cellular senescence kind a contributes to age
related tissue dysfunction , but the senolytics right now they don’t really have that context specificity, and also safety can be kind of shaky. Here we show a computational framework for designing and screening senescence responsive synthetic gene circuits, using generative AI plus a risk aware evaluation layer. The whole workflow mixes together several bits, like: (i) a tissue aware senescence expression atlas built from harmonized single cell RNA‑seq datasets ; (ii) a senescence state classifier trained on atlas derived features, with explicit train/validation/test splits, (iii) a constraint aware conditional sequence generator aimed at regulatory DNA, (iv) a logic compiler that turns Boolean specs (AND/NOT) into genetic parts, (v) biophysical simulation of circuit dynamics through chemical reaction network ODEs, and (vi) a safety scoring module that tries to penalize OFF state leakiness, off target activation, and cellular burden. For candidate selection, we also lean on explainability tools—motif enrichment and SHAP—plus distribution free uncertainty quantification using conformal prediction, so risk sensitive picks are not just based on point estimates. We report dataset identifiers, model architectures, training hyperparameters, baseline comparisons, and ablation studies, and we include confidence intervals. All figures are numbered, referenced in text, and paired with statistical summaries. This study is explicitly computational, so no wet lab work or in vivo validation is claimed. Instead, we provide an auditable reproducible design protocol and a ranked set of candidate circuits meant for future pre-clinical evaluation. Overall, the work clarifies what methodological requirements and evaluation standards should look like for generative AI assisted senolytic circuit design.
The present study provides empirical evidence of pedestrian risk behaviour at an unsignalized mid-block crossing in Rome. It measures the level of unsafe crossing practices and shows that approximately one third of pedestrians crossed without looking in both directions and almost one quarter were distracted by mobile phones. The study also adds an age-related dimension, showing that young pedestrians are disproportionately affected by mobile phone distraction and that unsafe crossing behaviours increase significantly during peak hours.
An important result of the study is the strong impact of the group size on the pedestrian behaviour. Pedestrians in larger groups were significantly less likely to assess traffic conditions independently, showing a tendency toward herd-like behaviour. To summarise, the findings suggest that behavioural and situational factors are stronger predictors of pedestrian risk than demographic characteristics.
This study develops a novel bio-composite from banana stem fiber and coconut particulate filler by the use of two food grade natural binders such as okra mucilage and rice gum paste as fully plant based alternatives to synthetic resins. Through direct qualitative comparison, okra mucilage was found superior to rice gum paste in rigidity, surface quality and fiber binder adhesion. It’s also retaining strong adhesion under water and oil exposure. The work establishes a locally sourced low cost and biodegradable composite system aligned with circular economy principles and UN SDGs 9, 12, and 13 laying the foundation. This is a continuous process, the strength of the newly formed composite is yet to confirm, however the composite shows better adhesion.
As Generative Artificial Intelligence (Generative AI) gains traction, it is poised to offer
innovative solutions for teaching and learning mathematics, particularly in the form of intelligent tutoring systems. But most current AI tutoring systems are designed to output
answers rather than to help students through a sequence of reasoning and problem-solving
steps. For this reason, their contribution to the development of critical thinking is limited,
a very important goal of mathematics education. In addition, current prompt engineering
techniques tend to be optimized for a single prompt rather than offering a complete pedagogical structure based on validated educational theory.
This study aims to develop a structured prompt framework for a Generative AI tutoring
system for facilitating critical thinking in the learning of mathematics in Secondary School
Certificate (SSC). The framework combines the problem solving approach of Pólya with
prompts representing the four stages of the problem solving process: understand the problem, make a plan, execute the plan, look back. Prompts for learners’ guidance, evaluation of
their responses, adaptive feedback, and advancing to the next stage are included as distinct
sections in each stage to facilitate the learners’ active reason and reflective learning.
The study is guided by Design and Development Research (DDR) methodology consisting of four phases namely problem identification by literature review, design of framework,
design of prototype and evaluation by expert opinion. A prototype of AI mathematics tutor based on the proposed framework is built, and the framework is assessed by experts in
computer science and mathematics education through questionnaires and semi-structured
interviews. The assessment is on the clarity, pedagogical relevance, usability and potential
of the framework to support critical thinking.
This inquiry offers a pedagogically informed prompt model incorporating the principles of mathematics learning and of prompt engineering for Generative AI. The proposed
framework offers concrete design considerations for designing AI tutoring systems to support structured mathematical thinking and critical thinking, especially in the context of SSC
mathematics learning.
This work investigates the classification of brain tumors from MRI scans using a variety of deep learning architectures, such as Light U-Net, DenseNet121, Attention U-Net, and Custom Convolutional Neural Network (CNN). To increase accuracy, an ensemble-based classifier was created with a weighted soft voting technique. Explainable AI (XAI) approaches, like Gradient-weighted Class Activation Mapping (Grad-CAM), were used to improve clinical applicability by visualization. With an accuracy rate of 97.93\% in differentiating between gliomas, meningiomas, pituitary adenomas, and healthy patients, the ensemble model behaved better than single structures. Heatmaps produced by Grad-CAM showed important areas affecting model predictions, matching radiological characteristics and boosting confidence in the outcomes.
Code comments play a critical role in ensuring
the comprehensibility, maintainability, and collaborative devel-
opment of software systems. However, in Artificial Intelligence
and Machine Learning (AI/ML) projects, where experimentation,
rapid prototyping, and iterative model development are cen-
tral, commenting practices are often inconsistent or insufficient.
To support systematic research on this issue, we introduce
ComML, a real-world dataset for analyzing code comments in
AI/ML systems. ComML comprises 50 AI/ML repositories written
in Python, having 16,110 Python files, 3,314 classes, 111,950
functions, 146,264 code blocks, 5,662,809 lines of source code,
and 449,962 lines of comments. The dataset was constructed
by cloning repositories from GitHub and extracting structured
code blocks using Python’s Abstract Syntax Tree parser. Each
code block was automatically classified into one of nine AI/ML
workflow stages. Then we checked the frequency and quality
of the comments inside the code. Along with raw code and
comments, ComML provides computed metrics for comment
density, readability, and consistency. Our initial analysis reveals
notable documentation gaps, with the Model Evaluation Stage
missing comments in 74.09% of cases. This dataset enables
researchers to study commenting behavior across AI/ML de-
velopment phases and supports the design of automated tools
for improving comment quality and documentation practices in
machine learning software.
This work contributes a lightweight, training-free method for detecting hallucinations in large language models that requires no model retraining, no access to model internals, and no specialized hardware, running on a single consumer GPU. By decomposing self-consistency into token, entity, and semantic granularities and fusing them with interpretable per-model weights, it delivers competitive detection while avoiding the computational and energy cost of gradient-based or white-box approaches. We further show that the most effective signal varies with a model’s tuning regime, and we release the full evaluation pipeline to support reproducible, resource-efficient hallucination research.
The proposed
model achieved a mean Average Precision at IoU 0.50 (mAP50) of 0.93, which represents a significant improvement over established benchmarks —a 9.4% relative increase over YOLOv5 (0.85 mAP50) and a 13.4% relative increase over Faster R-CNN (0.82 mAP50). It also demonstrated high precision (0.91) and recall (0.92), indicating robust performance in accurately identifying and localising fractures with a low rate of false positives and negatives.
The proposed
model achieved a mean Average Precision at IoU 0.50 (mAP50) of 0.93, which represents a significant improvement over established benchmarks —a 9.4% relative increase over YOLOv5 (0.85 mAP50) and a 13.4% relative increase over Faster R-CNN (0.82 mAP50). It also demonstrated high precision (0.91) and recall (0.92), indicating robust performance in accurately identifying and localising fractures with a low rate of false positives and negatives.
1) To improve identification performance, we proposed a multimodal framework that integrates medical imaging and clinical textual data.
2) Introduction of a classification token mechanism to enhance feature representation and determination with Vision Transformer.
3) Finally, we implement hyperparameter optimization techniques to improve model efficiency, generalization, and overall performance.
The significant research contribution of this project is the development of a web-based platform that integrates real-time taxi booking, ride-sharing, and volunteer ride services tailored for Fiji. This innovative solution addresses key challenges in Fiji’s urban transportation, such as traffic congestion, vehicle overuse, and lack of affordable transport for low-income individuals. By promoting environmental sustainability, fostering community engagement through volunteer rides, and leveraging secure online payment systems, this platform contributes to enhancing mobility and reducing greenhouse gas emissions in a unique socio-economic context.
This study is significant for several reasons. First, it provides a scalable and automated solution for detecting and analyzing advertisements within video content, which is typically labor-intensive when done manually. Second, it offers insights into the relationship between advertisements and video content, which can have profound implications for advertisers seeking to improve targeting strategies and for content creators aiming to optimize sponsored ad placements within their videos. Third, the research lays the groundwork for future advancements in content-based advertising, where the alignment between ad messaging and content themes can be refined using advanced natural language processing (NLP).
The significant research contribution of this project is the development of a web-based platform that integrates real-time taxi booking, ride-sharing, and volunteer ride services tailored for Fiji. This innovative solution addresses key challenges in Fiji’s urban transportation, such as traffic congestion, vehicle overuse, and lack of affordable transport for low-income individuals. By promoting environmental sustainability, fostering community engagement through volunteer rides, and leveraging secure online payment systems, this platform contributes to enhancing mobility and reducing greenhouse gas emissions in a unique socio-economic context.
This study compares the performance of object detection models through data augmentation with a severe weather dataset.
The goal of this project is to predict customer behavior from a large real-world e-commerce dataset using tree-based machine learning modeling techniques that will employ decision tree, random forest, and gradient boosting. Each of the models will be evaluated and compared to determine which of the three is the best model for predicting customer behavior.
The goal of this project is to predict customer behavior from a large real-world e-commerce dataset using tree-based machine learning modeling techniques that will employ decision tree, random forest, and gradient boosting. Each of the models will be evaluated and compared to determine which of the three is the best model for predicting customer behavior.
The aim of the study is to develop a suitable algorithm for interference minimizing in 5G and satellite communication networks coexistence employing Nakagami-m and Shadowed Rician models. Based on this aim, the following research contributes to:
1-Develop a suitable theoretical strategy that evaluates interference scenarios for co-existence between5G and satellite communication networks.
2-Develop an algorithm based on Nakagami-m and Shadowed Rician models for interference minimization in the co-existence between 5G and satellite communication networks.
We analyze more than 400,000 tweets posted between 6-21 February 2023, and explore different use cases of Twitter networking site aftermath of the quake series. We carry out descriptive analysis of the tweets distribution, and analysis on hashtag agenda setting property. Topic distribution both in hashtags and tweet content is investigated.
Unmanned aerial vehicles (UAVs) are being successfully used in a variety of applications, including agriculture, search and rescue operations, surveillance systems, and mission-critical services, thanks to some technological and practical advantages, such as high mobility, the ability to extend wireless coverage areas, or the capacity to reach locations inaccessible to humans. In contrast, attacks against drones, as opposed to traditional cyberattacks, typically happen as a result of serious design flaws and a lack of wireless security protection methods. The study examines lightweight asymmetric cryptographic algorithms for secure Internet of Drones (IoD) communication, addressing cybersecurity
challenges within this emerging technology. It evaluates RSA, ElGamal, DiffieHellman, and Elliptic Curve Cryptography (ECC), focusing on their suitability for IoD through comparative analysis on calculation time, memory usage, key size, and security. The goal is to contribute to developing robust, efficient, and secure communication protocols for IoD, promoting growth while mitigating risks. This research is pivotal for the advancement of IoD security, exploring the application of these cryptographic techniques to ensure secure, efficient operations within the IoD framework.