VFAST Transactions on Software Engineering
The VFAST Transactions on Software Engineering (ISSN(p):2411-6246 and ISSN(e):2309-3978) is a peer-reviewed scientific journal published by the VFAST-Research Platform. It was established in 2013 and covers the areas of software engineering and its applications. The aim of this journal is to provide an international platform for engineers and academicians all over the world to promote, share, and discuss various new issues and development in the field of software engineering and its application in Machine learning, Bioinformatics, Image processing, Robotics, Artificial Intelligence, and data science. The Journal has started 4 issues per year from 2021 (January-March, April-June, July-September, and October-December) Editor-in-Chief: Prof. Hejiao Huang, Department of Computer Science and Technology, Harbin Institute of Technology Shenzhen Graduate School, China Dr. Farooq Ahmad, Associate Professor, COMSATS Lahore, Pakistan <span class="csspropertycolor" style="box-sizing: inherit; color: red; font-family: Consolas, Menlo, 'courier new', monospace; font-size: 15px; font-style: normal; font-variant-ligatures: normal; font-variant-caps: normal; font-weight: 400; letter-spacing: normal; orphans: 2; text-align: start; text-indent: 0px; text-transform: none; white-space: normal; widows: 2; word-spacing: 0px; -webkit-text-stroke-width: 0px; background-color: #ffffff; text-decoration-thickness: initial; text-decoration-style: initial; text-decoration-color: initial;">Important Links REGISTER SUBMISSION LATEX TEMPLATE EDITORIAL TEAM Policy Document Indexing VFAST Transactions on Software Engineering (VTSE) is indexed in the following databases: SCOPUS Higher Education Commission (HEC), Pakistan – Y Category Google Scholar ResearchGate WorldCat ROAD / ISSN Portal Scilit
Authors who publish with this journal agree to the following terms: Authors retain copyright and grant the journal right of first publication with the work simultaneously licensed under a Creative Commons Attribution License (CC-By) that allows others to share the work with an acknowledgment of the work's authorship and initial publication in this journal. Authors are able to enter into separate, additional contractual arrangements for the non-exclusive distribution of the journal's published version of the work (e.g., post it to an institutional repository or publish it in a book), with an acknowledgement of its initial publication in this journal. Authors are permitted and encouraged to post their work online (e.g., in institutional repositories or on their website) prior to and during the submission process, as it can lead to productive exchanges, as well as earlier and greater citation of published work (See The Effect of Open Access). This work is licensed under a Creative Commons Attribution License CC BY
A DeepSpeech2-Inspired Convolutional Recurrent Framework for Low-Resource Urdu Speech Recognition
Low-resource language automatic speech recognition is a difficult task due to small annotated corpora, large speaker and phonetic diversity, and the lack of strong end-to-end metrics. The case of Urdu is especially significant because of the high number of speakers and the inability to provide high-performing open automatic speech recognition systems to date. The study presents an end-to-end Urdu speech-to-text model, built upon a DeepSpeech2-inspired convolutional recurrent neural network, which integrates a spectrogram-based acoustic modeling, bidirectional gated recurrent units, and Connectionist Temporal Classification to learn alignment-free transcription. This model was trained and tested on the Urdu subset of the Mozilla Common Voice corpus with 58,119 training utterances and 6,458 validation utterances and evaluated on a held-out test set. The proposed system has shown to converge consistently during training with a validation Word Error Rate of 21.29% and loss of 5.87 at epoch 478, and a final test Word Error Rate of 17.05, Sentence Error Rate of 34.72, and Word Information Loss of 0.41. The proposed model achieved better performance on the same evaluation setting compared with a reduced recurrent baseline, a transformer-based baseline, and a wav2vec2-style baseline, whose WERs were 23.84%, 19.62%, and 18.31%, respectively. Analysis of ablation also indicated that convolutional feature extraction, as well as deep bidirectional temporal modeling, are essential to performance, and error analysis revealed phonetic confusion, dialectal variation, noise, and high-speed speech as the most prevalent causes of recognition error. These results demonstrate that a well-tuned convolutional recurrent model can provide a competitive solution for Urdu automatic speech recognition under low-resource conditions and offers a reproducible reference point for future studies.
Copyright (c) 2026 VFAST Transactions on Software Engineering
Diabetes Prediction using Machine Learning Algorithms with Performance metrics and Holdout method on Egyptian dataset
Diabetes is a non-communicable disease affecting people of all ages worldwide, therefore, early detection using machine learning techniques is crucial. This study aims to predict diabetes using multiple machine learning algorithms, performance metrics, and holdout validation on an Egyptian dataset. The dataset was divided into four age groups, including paediatric, early adulthood, middle age, and geriatric. Ten algorithms were applied and validated using 80:20, 70:30, and 60:40 split ratios with accuracy, precision, and recall as evaluation metrics. Results showed that Random Forest, Extra Trees, and Support Vector Machine performed best in the paediatric group, while Gradient Boosting, Random Forest, and Support Vector Machine achieved superior performance in early adulthood, middle age, and geriatric groups. In contrast, Decision Tree, K-Nearest Neighbors, and AdaBoost consistently demonstrated lower performance. Further analysis reveals that classification performance varies significantly across age groups, with the middle age and geriatric groups achieving the highest accuracy above 0.99, followed by the paediatric group 0.98–0.99, while early adulthood exhibits comparatively lower performance due to increased class overlap. Confusion matrix results indicate strong diagonal dominance in higher-performing groups, reflecting better class separability, whereas performance heatmaps confirm that top models maintain a balanced trade-off between accuracy, precision, and recall with minimal variation across different data splits. Feature importance analysis shows that higher performing models rely on a small number of dominant predictors, particularly in the middle age and geriatric groups, while more distributed feature contributions in early adulthood reduce predictive effectiveness. Therefore, the findings demonstrate that ensemble methods provide robust and consistent performance, and that age-based dataset segmentation enhances classification accuracy and model stability.
A Two-Stage Noisy Pre-training and Fine-tuning Pipeline for Low-Resource Named Entity Recognition in Shahmukhi Punjabi
Named Entity Recognition (NER) for low-resource languages remains a critical challenge in natural language processing, particularly for scripts with limited annotated corpora. This paper addresses this challenge for Shahmukhi Punjabi, an underrepresented Perso-Arabic script used by millions in Pakistan. We propose a two-stage training pipeline that leverages a large-scale machine-labeled corpus generated by a Bagging-CRF ensemble to warm-start multilingual transformer models before fine-tuning on a small, gold-standard human-annotated dataset. We evaluate five state-of-the-art multilingual transformers, mBERT, XLM-R, mmBERT, RemBERT, and mDeBERTa-V3, under two experimental settings: (A) direct supervised fine-tuning on the human-labeled dataset, and (B) the proposed two-stage pipeline. The human-labeled dataset comprises 979 sentences and 25,221 tokens, while the larger machine-labeled corpus having 16,586 sentences and 336,502, both tokens covering 13 entity types. Experimental results demonstrate consistent improvements across all five models, mmBERT and RemBERT achieve the highest weighted F1 scores of 0.85 and 0.86 respectively. The most striking gains are observed for mDeBERTa-V3 (+0.21 F1, 39.6% relative) and XLM-R (+0.20 F1, 33.3% relative), demonstrating that the two-stage pipeline provides the greatest benefit to models with limited baseline performance on low-resource scripts. These results validate the effectiveness of noisy domain adaptation as a data augmentation strategy for low-resource NER in morphologically rich, right-to-left scripts.
A Simple and Reproducible Machine Learning Pipeline for Parkinson’s Disease Detection Using Smartwatch-Based Inertial Signals
Parkinson’s disease (PD) is a progressive neurological disorder characterized by motor symptoms such as tremor, rigidity, and bradykinesia. Wearable inertial sensors enable non-invasive and cost-effective assessment of motor abnormalities in real-world settings. Despite recent advances in deep learning, many existing approaches rely on complex architectures with limited interpretability and inconsistent evaluation protocols. This study proposes a simple and reproducible classical machine learning pipeline for PD detection using smartwatch-based inertial signals from the PADS dataset. Spectral and statistical features were extracted from accelerometer and gyroscope signals, and LASSObased feature selection was applied within a nested subject-level cross-validation framework to prevent data leakage. Several classiﬁers, including Logistic Regression, Support Vector Machine, Random Forest, CatBoost, and Multi-Layer Perceptron, were evaluated. The proposed pipeline achieved 79.26% balanced accuracy, 87.32% accuracy, and an F1-score of 0.92 for PD vs. healthy control classiﬁcation, while 67.15% balanced accuracy was obtained for the more challenging PD vs. differential diagnosis task. Feature analysis showed that PD vs. healthy control discrimination is dominated by tremor-related spectral and amplitude features, whereas variability-related features are more relevant for differential diagnosis. These results demonstrate that competitive performance can be achieved using a simple and interpretable pipeline, providing a practical alternative to more complex deep learning approaches.
Refine Security Control Protocols for Block chain in Textile Industry Supply Chain Management
Cotton is a vital crop with numerous environmentally friendly applications in our daily lives, making it a globally significant agricultural commodity. Pakistan ranks as the world's third-largest producer and consumer of cotton, utilizing 15% of its land for cultivation. However, challenges like supply chain malpractice and opacity lead to economic losses for farmers, textile industries, and governments. To tackle these issues, a block chain-based framework for supply chain traceability and reliability in the cotton and textile industry has been developed. This innovative solution aims to create transparency and trust among supply chain participants. This solution introduces Cotton Coin (CC) for transaction tracking and utilizes smart contracts for monitoring trading and currency conversion. The Inter Planetary File System (IPFS) securely stores encrypted data of supply chain participants. The experimental results demonstrate the effectiveness of the suggested framework in comparison to existing supply chain projects. Key performance metrics, such as new block latency, transactions per minute, average gas charges, and transaction verification times, have shown significant improvements. These findings highlight the potential of this block chain-based solution to enhance supply chain transparency, security, and efficiency in the cotton and textile industry.
An Enhanced Deep Learning Approach for High-Dimensional and Complex Datasets
The increasing availability of extensive datasets with many features in contemporary scenarios imposes serious limitations on traditional machine learning and deep learning models due to such challenges as feature redundancy, noise sensitivity, and scalability restrictions. Therefore, this paper presents a powerful deep learning approach that, through its adaptive feature learning, integrated regularization, and efficient optimization strategies, is capable of overcoming these difficulties. The proposed method performs the feature selection and focus on the most informative features automatically, while the redundant and irrelevant ones are eliminated. As a result, the representational quality and generalization ability can be significantly enhanced. Extensive results obtained on benchmark as well as real-world datasets hardly any cases have been proposed to prove not only the superiority of the newly developed method over the baseline machine learning methods and even the latest deep learning models in terms of accuracy and robustness but also its application potential in the practice. Further, the component removal experiments provide support for the significance of the different parts of the design and the statistical significance tests justify the reliability of the performance gains observed. Also, the suggested technique results in improved computational performance, and as a result, it could be implemented in scenarios associated with a large amount of data and high-dimensional data processing. To sum up, their results highlight that the proposed model provides an effective and scalable solution to tackle complicated datasets.
Lightweight Texture-Based Classification of Huffaz Status from Structural MRI Using GLCM Correlation and Brodmann-Area VOIs
Previous neuroimaging studies suggest that intensive Quran memorization may be associated with structural brain differences, but simple and interpretable MRI-based biomarkers remain limited. This study investigated whether a single interpretable radiomics feature, gray-level co-occurrence matrix (GLCM) Correlation, extracted from predefined Brodmann Area (BA) volumes of interest (VOIs), is associated with Huffaz status.A cross-sectional case-control design was used involving 47 participants (23 Huffaz, 24 non-Huffaz). Structural MRI scans were pre-processed using a standard SPM pipeline to generate modulated, warped, and smoothed tissue-class images (smwc*.nii). Inferential analyses were restricted to five literature-driven candidate regions (BA22, BA24, BA32, BA40, and BA46) to reduce multiplicity. Best-subset logistic regression was performed with sex forced into all models and Bayesian Information Criterion (BIC) used for model selection. Bootstrap resampling was applied to assess feature-selection stability.BA46 GLCM Correlation emerged as the lowest-BIC predictor set. Bootstrap resampling showed higher selection stability for BA46 (approximately 0.68 selection frequency) than for the other candidate regions (approximately 0.13–0.15), supporting a reproducible single-region signal within the tested set. These findings suggest that BA46 texture organization, as captured by GLCM Correlation, may differentiate Huffaz from non-Huffaz after adjustment for sex.The modest sample size and lack of external validation limit generalizability. Nevertheless, the findings support the potential of an interpretable single-feature radiomics biomarker and motivate further validation in larger independent cohorts, as well as robustness analyses across preprocessing and discretization settings.
Object Detection: Surveillancing Cities for Citizens’ Safety and Protection Using Advanced Deep Learning Tools
Weapon incidents result in casualties and loss of precious human lives every year. According to the Center for Disease Control (CDC) report, over 18500 people died in year 2025 in the USA due to gun violence. It is a well-known fact that the death rate from weaponization is increasing significantly. Firearm‑related violence affects every aspect of human security, from personal safety issues like domestic violence and road rage to broader social and financial consequences. It can also escalate into large‑scale armed conflicts that cause massive violence and account for many deaths. It is evident to surveillance our cities and societies for weapon objects using artificial intelligence (AI) and deep machine learning tools. The existing machine learning tools are not efficient at detecting different kinds of weapons. In this research, the contribution is threefold. Our first contribution is that we thoroughly investigated weapon detectio systems that uses deep learning and can based automated weapon detection system that allows the system to detect multiple weapons at the same time such as knife, handgun, and rifle automatically. We have used You Only Look Once (YOLO) v4 and Convolutional Neural network (CNN) for our experimentation. We have obtained several weapon images which are publicly available and have developed the datasets. Secondly, we have compared the performance of our deep learning models on multiple combinations of datasets (fewer images to several thousand images. The experimental evaluation has shown that the YOLOv4 outperformed the CNN. Lastly, we have proposed a method to improve the accuracy of the CNN and this has been accomplished by adding N number of CNN layers N times where N = {1,3,5,...19}. This has resulted in reducing the complexity of the model and improving the accuracy and efficiency. The proposed model can be applied on surveillance systems, closed-circuit televisions and other object detection systems and many human lives can be saved by timely detection of weapons.
Vector+SQL Retrieval with Selectivity Workloads: Measuring Tail Latency and Quality under Filtered Top-K
We study Vector+SQL hybrid retrieval under structured filtering conditions and propose a reproducible Top-K evaluation framework for uniformly analyzing the quality and tail latency of different retrieval strategies. We construct controllable selectivity workloads on three text domains: arXiv, news, and movies, and compute exact ground truth within the same filter candidate set to ensure fair comparison. Experiments uniformly evaluate exact prefilter, ANN post-filter, two-stage probing, partition-aligned retrieval, and adaptive routing, and report Recall@20 and p50/p95/p99 latency. The results show that filter selectivity and predicate structure significantly affect the quality--latency trade-off in hybrid retrieval: exact prefilter has the highest accuracy but the largest tail latency; ANN post-filter is faster, but recall drops significantly under strict filtering; adaptive routing achieves more robust overall performance on the evaluated workloads. These results demonstrate that reporting only average latency is insufficient to reflect the true system cost of hybrid retrieval. Our report provides a systematic framework for evaluating, designing, and deploying hybrid retrieval strategies under filtering conditions, accounting for correctness, tail latency, and reproducibility.
Exploring Proximal Policy Optimization in ViZDoom: Training Agents for Complex Tasks with Hyperparameter Optimization
ViZDoom, a Reinforcement Learning (RL) research platform based on the classic first-person shooter game Doom, allows training and evaluating RL agents across a wide range of scenarios that vary in complexity. This research trains Deep Reinforcement Learning (DRL) agents using the modern Proximal Policy Optimization (PPO) algorithm, implemented through Stable-Baselines3 (SB3), across four different ViZDoom scenarios with increasing complexity: Basic, Defend the Center, Health Gathering, and Deadly Corridor. We analyze the impact of hyperparameter tuning on the PPO algorithm in these ViZDoom scenarios using the Optuna framework. While keeping in mind the complexity of the Deadly Corridor scenario, the advanced RL techniques like reward shaping and curriculum learning are employed to achieve the objective of this scenario. The agents are evaluated using the mean episodic reward and the mean episode length as performance metrics in each scenario. The results compare the PPO agents trained on default hyperparameters with the agents trained on optimized hyperparameters in each scenario. The findings demonstrate that hyperparameter optimization has a moderate impact in simple environments, resulting in a 3.9% and a 12.4% increase in the mean episodic rewards of Basic and Defend the Center scenarios, respectively, but shows significant gains in complex scenarios, achieving a 523.7% and a 203.7% improvement in Health Gathering and Deadly Corridor (skill level 5) scenarios, respectively. These findings provide insights into how DRL agents can be trained using the PPO algorithm in complex environments with multiple challenging tasks through appropriate hyperparameter optimization.
Hybrid Convolutional Transformer Learning Utilizing Ordinal Sensitive Loss for Automated Grading of Diabetic Retinopathy
Diabetic retinopathy (DR) is a major cause of preventable blindness globally requiring precise and dependable automated grading systems to facilitate extensive screening initiatives. Recent deep learning techniques utilizing convolutional neural networks (CNNs) have yielded encouraging outcomes however, they predominantly concentrate on local lesion identification and frequently neglect to encompass the global retinal context. Furthermore, the majority of current methodologies regard diabetic retinopathy grading as a conventional multi-class classification issue, neglecting the ordinal characteristics of disease severity and the significant class imbalance present in clinical datasets. In this paper, we propose a hybrid convolutional-transformer learning framework with ordinal-sensitive loss for automated diabetic retinopathy grading. The suggested model combines a deep CNN backbone for strong local feature extraction with a lightweight transformer encoder that works on convolutionally down sampled feature maps to model long-range dependencies in a computationally efficient manner. This design reduces the quadratic complexity of self-attention while keeping the global context information needed to figure out how bad something is. To deal with class imbalance and penalties for misclassifying ordinal data, an ordinal-sensitive focal loss is used to make the model focus on clinically important mistakes. We test the framework on publicly available fundus image datasets using a wide range of performance metrics, such as accuracy, macro-F1 score, balanced accuracy, area under the ROC curve (AUC), and quadratic Cohen's kappa. Experimental results show that the suggested method consistently beats CNN-only baselines and standard cross-entropy-based training, getting better accuracy of 85%
Model Interpretability with XAI for Sarcastic Behavior Detection in Low-Resource Roman Urdu Language using Machine Learning and Ensembled Approaches
This work proposes a framework for sarcasm detection in Roman Urdu using machine learning and ensemble approaches. For dataset preparation, we extracted data from X and manually annotated a corpus of 11,320 tweets into two classes, i.e., sarcastic and non-sarcastic. Various classifiers were implemented using lexical features related to sarcastic patterns. The proposed preprocessing framework and ensemble-based model use language-related normalization strategies such as spelling variation standardization, and slang expansion to address orthographic variation and code-mixing within the text for sarcasm detection. Experimental findings show that ensemble learning models, especially Random Forest and XGBoost obtained the most accurate results with an accuracy of 87% and an F1-score of 88%, thus providing a reliable baseline on sarcasm detection using South Asian Roman Urdu language. These findings emphasize the potential of ensemble approaches to process and handle the complexity of sarcasm in low-resource code-mixed native languages. Furthermore, in order to enhance transparency and trust in the model's decisions, we employed the Explainable AI (XAI) technique, particularly LIME (Local Interpretable Model-agnostic Explanations), to interpret the predictions of our best-performing model. These findings emphasize the potential of ensemble approaches to process and handle the complexity of sarcasm in low-resource code-mixed languages, and XAI offers essential insights into the process of the model’s decision-making.
An Explainable Deep Learning Framework for Brain Tumor Detection Using MRI Images
Brain tumor detection using Magnetic Resonance Imaging (MRI) is a critical diagnostic procedure that demands high interpretability, accuracy, and efficiency. This paper presents a system of brain tumor classification that is based on interpretable deep learning model. The Convolutional Neural Network (CNN) used is a customized one trained on two publicly obtained Br35H dataset along with a four-class brain tumor MRI dataset. There are four-class brain tumor, glioma, meningioma, pituitary and no tumor. Image denoising, data augmentation, and normalization are applied using the methodology to enhance robustness and generalizability in models. Gradient-weighted Class Activation Mapping (Grad-CAM) and Local Interpretable Model-agnostic Explanations (LIME) are combined to help tackle the problem of deep learning model opacities. These devices visualise class-discriminating areas and important local superpixels enhancing clinical transparency. The proposed CNN has been experimentally demonstrated to achieve about 94% and 98% accuracy on the Br35H dataset and the multiclass brain tumor MRI dataset respectively. The accuracy, recognition, and the F1-scores are comparable across classes. The results indicate that the framework is capable of capturing the features, type of tumor, and generates interpretable visual data to be relevant to the clinical world. The paper presents a full and interpretable deep learning model in the diagnosis of brain tumors acquired through MRI. It helps close the high diagnostic accuracy/reliable model explainability gap.
FuzzyGreen: A Context-Aware Fuzzy Inference Framework for Carbon Footprint Reduction in Next-Generation Wi-Fi Network Infrastructure
The information and communication technology (ICT) sector contributes approximately 2--4\% of global carbon dioxide emissions, with wireless network infrastructure representing a significant yet underexplored opportunity for cleaner production interventions. Wi-Fi access points, operating continuously across billions of deployments worldwide, consume substantial energy while employing rigid power management mechanisms fundamentally inadequate for dynamic operational conditions. This paper introduces FuzzyGreen, a context-aware fuzzy logic-based power optimization framework designed to achieve measurable carbon footprint reductions in Wi-Fi network infrastructure. The proposed Mamdani-type fuzzy inference system processes five context variables—traffic load, channel utilization, user density, signal quality, and service requirements—to generate adaptive power scaling decisions. Comprehensive simulations across enterprise, high-density public venue, and residential deployment scenarios demonstrate that FuzzyGreen achieves 38–52% reduction in energy consumption compared to conventional approaches, translating to annual carbon dioxide equivalent savings of 47 kg per access point using global average emission factors. Scaled to the estimated 500 million global access points, the framework presents potential for avoiding 23.5 million tonnes of CO$_2$ emissions annually, contributing directly to Sustainable Development Goal~7 (Affordable and Clean Energy) and Goal~12 (Responsible Consumption and Production). The framework's computational efficiency (0.23 ~ms inference time) enables deployment on resource-constrained embedded hardware, positioning FuzzyGreen as a pragmatic cleaner production strategy for sustainable wireless network operations.
Research Trends on Sentiment Analysis and Imbalanced Data Handling in Fake Review Detection: A Systematic Literature Review
Fake reviews are deceptive evaluations that mislead customers rather than reflect genuine customer experiences. These reviews can damage the business's reputation by deceiving the customers, which then causes them to make poor decisions about what to buy and diminishes the trust that e-commerce platforms can have. Detecting fake reviews is crucial for e-commerce platforms to maintain their integrity, protect consumers, and uphold business reputations. Despite its importance, there is a paucity of comprehensive research addressing fake review detection through the lenses of Sentiment Analysis (SA) and imbalanced data handling. To bridge this gap, a systematic literature review uses Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines. This review analyzed 43 studies from the Scopus and Web of Science databases, covering the period from 2019 to 2024. Three primary themes emerged: SA levels, detection methods, and techniques for handling imbalanced data, which further branched into 28 sub-themes. The analysis revealed key trends such as a predominant focus on document-level SA, the application of machine learning approaches, and data resampling techniques to address imbalanced datasets. The review underscored the necessity for more research on aspect-level analysis and the development of combinational approaches, such as hybrid models, to enhance the accuracy and reliability of fake review detection. These insights provide valuable guidance for researchers, data scientists, and developers seeking to advance the field.
IoT Adoption for Agricultural Transformation in Developing Countries: Challenges and Opportunities - A case of Uganda
70% of Uganda's workforce is employed in agriculture, which accounts for 26.2% of the country's GDP and is a fundamental pillar of the country's economy. Nevertheless, the sector is increasingly hampered by the unpredictability of the climate, traditional farming inefficiencies, and serious information gaps that jeopardize local food security. The potential of Internet of Things (IoT) technology to address these issues through data-driven resource management and precision farming is examined in this paper. Primary data was gathered from 116 smallholder farmers in Eastern Uganda using a descriptive study approach and the Unified Theory of Acceptance and Use of Technology (UTAUT) framework. Performance Expectancy (Mean = 4.19) and Behavioral Intentions (Mean = 4.11) emerged as the strongest drivers in the quantitative results, indicating a high readiness for adoption. Additionally, 95.7% of respondents reported severe crop losses due to drought, confirming the urgent need for technical intervention. Despite this incentive, Facilitating Conditions such as technological infrastructure and government support scored far lower (Mean = 3.17), indicating a crucial structural barrier. The study offers a strategic framework for policymakers to use the suggested IoT-enabled soil productivity monitoring model to strengthen smallholder resilience and revolutionize Uganda's agricultural productivity, concluding that although farmer intent is high, sustainable adoption necessitates targeted investment in infrastructure and technical assistance.
Lesion-Aware Graph-Augmented Deep Framework for Pancreatic Cancer Detection from CT
Because pancreatic cancer lesions are tiny, iso-attenuating, and partially obscured by surrounding arteries and ducts, early detection is still challenging. The end-to-end pipeline proposed in this study includes lesion-aware feature fusion, hybrid U-Net tumour masking, radiomic texture mining, physics-aware pre-processing, a MobileViT backbone with a lightweight graph head, and post-hoc probability calibration. It makes use of the 1,418 high-resolution DICOM slices (typically 512×512) from the Pancreatic CT Images collection. Slice-level performance is 0.942 for accuracy, 0.936 for macro-F1, 0.972 for AUROC, and 0.969 for AUPRC, according to the held-out test set. The accuracy increases to 0.960, the macro-F1 increases to 0.957, the AUROC increases to 0.986, and the AUPRC increases to 0.983 when the data is aggregated at the patient level. The Expected Calibration Error decreases from 0.031 to 0.009 when the temperature is scaled, improving the accuracy of risk assessments. With a Dice score of 0.82±0.09 (median 0.84; HD95 7.3 mm), the segmentation branch is likely to get worse as the tumours get smaller than a centimetre. Lesion-aware attention yields the greatest gains in calibration and discrimination, according to ablations (ΔAUROC −1.4 points, ΔECE +0.012 upon removal). There are also some advantages to radiomics (-0.3 points) and graph (-0.7 points). The site shift is minimal (worst site: AUROC 0.958), and the AUROC decreases steadily under robustness stress (motion S3: 0.940; noise S3: 0.945; bias-field S3: 0.949). The sensitivity and specificity of a Youden-optimal threshold are 0.927 and 0.935, respectively. The sensitivity and specificity of a screening operating point are 0.965 and 0.881, respectively. With throughput (about 28 ms each slice; 1.48 s per study) and 98.5% coverage following quality gates, the system can be used in real-world scenarios. According to the findings, pancreatic cancer can be detected on CT images using calibrated, lesion-aware, graph-augmented fusion that is efficient and low in processing overhead.
Designing Intelligent Software Systems for Early Detection of Chronic Mycobacterial Lung Disease
Early detection of mycobacterial lung disease is tough because we have a lot of different clinical data and old diagnostic methods are not very good. This paper is about a healthcare software system that was made using a careful and organized approach. The chronic mycobacterial lung disease software system puts together data collection getting the data ready and analysis that uses intelligence. The chronic mycobacterial lung disease software system looks at chest X-ray pictures and clinical information using a kind of neural network. We tried it out with 5,200 examples and it was very good. It got the answer 95.1 percent of the time it was precise 94.6 percent of the time it caught most of the cases 95.8 percent of the time it had a good balance of precision and recall with a score of 95.2 percent and it was very good at telling the difference between things with a score of 0.97. When we compared it to models it did better every time. The results show that we need to use kinds of data and plan the software carefully to make chronic mycobacterial lung disease software systems that are reliable and easy to use. The chronic mycobacterial lung disease software system is very important, for mycobacterial lung disease diagnosis and treatment.
Designing Inclusive AI for Aging Populations: A Conceptual Agentic Framework for Lifelong Learning
Global demographic changes are also changing the educational focus, where the aged will number over two billion by 2050. Despite progress in innovative artificial intelligence (AI) in education, most frameworks are developed for younger learners, who are underserved. This paper introduces the Agentic AI framework for elderly learners, a conceptual model which makes seniors active, autonomous learners throughout their life. The framework entails the combination of three mutually supporting layers, i.e., emotion-sensitive mentorship, where learning interactions are personalized to emotional states; gamified discovery learning, where learning engages curiosity and maintains motivation; or reflective knowledge reservoir, where knowledge is anchored by memory and cognitive resilience. A systematic review of recent literature suggests that although each of these domains has been independently validated, there is no common framework to address integration of the domains in elderly learners. The paper formulates three research questions and corresponding hypotheses and calls them onto the proposed layers and a roadmap for research. This roadmap progresses to consolidation on the theoretical level to prototype development, pilot testing, and large-scale validation to ensure that it is scalable and inclusive. The discussion not only introduces some theoretical contributions such as the extrapolation of the affective computing, gamification and reflection to the field of elderly learning, but also introduces some practical implications such as design solutions such as empathetic AI mentor, age-friendly gamification and age-friendly reflection. There is also focus on societal effects, which help to indicate how the framework can contribute to cognitive health, social inclusion and fair digital participation. Limitations such as the lack of empirical testing is noted along with directions of future research. The combination of discontinuous strands into a unified model provided through the Agentic AI framework provide a stringent basis to create inclusive learner-centered AI systems that will allow the elderly adults to excel in the digital era.
An Ensemble Deep Learning Framework for Automated Multi Class Skin Lesion Classification Using ConvNeXt-Tiny, EfficientNetV2-S, and MobileNetV3
Skin cancer is one of the fastest rising malignancies in the world, where early and accurate diagnosis plays a decisive role in the survival of patients. Even though the performance of convolutional neural networks (CNNs) in dermoscopic image analysis has been impressive, three main issues that hinder their application in the clinical setting remain: severe imbalance in classes, inter-class visual similarity, overfitting, and lack of interpretability. In this paper, a three-stream ensemble deep learning model that combines ConvNeXt-Tiny, EfficientNetV2-S, and MobileNetV3-Large has been proposed to classify automated multiclass skin lesion on the HAM10000 dataset. Balanced stratified sampling strategy (BalancedDataGen) is used in order to take care of class imbalance without synthetic oversampling in order to have equal contribution of classes in the training. More successful feature diversity and generalization are further improved with the help of adversarial Albumentations-based data augmentation. Each backbone model is fine-tuned separately based on label smoothing, dropout regularization, early stopping, and adaptive learning rate scheduling. The probability-level ensemble averaging is done to give final predictions. The post-hoc explainability of the results is offered to improve clinical interpretability, and ensemble saliency maps created through SmoothGrad allow seeing lesion-specific discriminatory areas consistently. Experimental findings indicate that the proposed ensemble is more accurate, robust and minority-class recognizing than individual models, and it has a test accuracy of 89.72%. The framework provides a good, interpretable and computationally efficient solution to automated dermoscopic diagnosis to fit low-resource clinical settings.
Public Perception of Chinese Language Education in Saudi Arabia: A Keyword-Enhanced Aspect-Based Sentiment Analysis of Social Media Discourse
The increasing popularity of Chinese as a second language in Saudi Arabia offers a unique chance to study and explore public perceptions and opinions through computational methods. Natural Language Processing (NLP) is one such method, widely adopted in research for this type of analysis. It offers techniques to extract insights from large volumes of unstructured data. However, sentiment analysis on multilingual, culturally specific online discourse remains a challenging task in NLP. The aim of this research work is to address the problem of accurately detecting sentiments and topics in contexts influenced by culture; we discuss the adoption of Chinese language education in Saudi Arabia. For this, we implemented a transformer-based sentiment analysis model on a custom domain-specific dataset with LDA for topic modeling. In this way, we identified key thematic clusters related to globalization, education, and cultural exchange. The research results indicate that topics associated with globalization carry the most positive sentiment, reflecting optimistic public attitudes toward linguistic expansion. This work contributes to the field of applied NLP by demonstrating the feasibility of sentiment and topic modeling in low-resource, culturally diverse environments and contexts.
Enhancing Traffic Sign Recognition in Rough Weather to Reduce Traffic Accidents
Background: Automated traffic sign recognition is a technology that helps in detecting and understanding the traffic signs on the road. These signs may include stop signs, speed limits and some other warning signs. These signs are important in maintaining road safety and helping drivers obeying the traffic rules. Machine learning and computer vision together have advanced in this field and many methods have been proposed for automated traffic sign detection and recognition. Despite these advancements, there are still many challenging situations including occlusion, varying lighting conditions, difficult environmental conditions and sign variations which still need attention. Methods: We use Arabic traffic sign dataset to train EfficientNetB3 architecture with attention mechanism to classify the traffic sign under the unique visual and linguistic complexities as well as diverse environmental conditions. We also improved the dataset by augmenting and adding extra images to cover actual scenarios like fog, heavy rain, low light etc. making it stronger for testing and future research. Results: Our trained model achieved the accuracy of 99.61\% in testing better than the compared to the baseline CNN-Resnet model. Conclusion: In this research we addressed the existing limitations and sets a benchmark for the effective and efficient classification of Arabic traffic signs, particularly in challenging weather conditions.
Automatic Number Plate Recognition Using Deep Learning Under Night time and Low-Illumination Conditions
Intelligent traffic management relies heavily on the recognition and localization of license plate numbers of moving vehicles, making it a critical task in this field. Numerous methods have been proposed to automate this procedure, utilizing computer vision and image processing algorithms to extract the number and characters from the detected license plate in surveillance photos and videos. However, these methods have primarily focused on daytime photographs and films, neglecting the challenges posed by difficult weather conditions or dim lighting settings. As a result, identifying the position of license plates and interpreting the characters from them remains an understudied area, particularly in low-light environments and night time photography. In response, we present a Night Time number plate detector and recognizer model in this paper. The model begins with a YOLOv5-based detector that has been trained to detect license plates in dark and hazy vehicle photos, generating a polygon bounding box around the number plate. The second phase of the process comprises an improvement module, where the retrieved picture of the license plate undergoes a variety of filters. Lastly, Easy OCR is employed to read the characters on the license plate. Our experimental results demonstrate that training the detector on dark and low illumination photographs, along with precise bounding box generation, significantly improves detection and recognition accuracy. Specifically, our model achieved a mAP score of 97%, highlighting the efficacy of our approach. In conclusion, our Night Time number plate detector and recognizer model represents a significant step forward in the recognition and localization of license plate numbers, particularly in low-light conditions. Our approach provides a powerful and effective tool for intelligent traffic management systems, and we believe that our results will pave the way for further research in this field.
Mathematical Modelling of the Impact of Developer Experience Metrics on the Duration of the Release Cycle in Full-Stack Projects
Modern DevOps practices are increasingly emphasizing Developer Experience metrics as critical factors influencing the efficacy of software delivery. However, despite significant advancements in this direction, the challenge of constructing a causal model that elucidates the impact of Developer Experience metrics on the speed of releases remains unresolved. This study aims to formalize and empirically evaluate the causal relationship between various indicators of development experience (such as cognitive load and technical frustration) and the speed of the release cycle within DevOps environments. A simulation model was developed for this analysis, embodying a prototypical CI/CD infrastructure characterized by either a microservices or serverless architecture within a cloud framework. Bayesian Causal Modeling was employed, facilitating the exploration of how Developer Experience variables influence the time parameters of the release cycle. The model encompasses seven variables, comprising five key Developer Experience metrics alongside two control parameters (architectural and organizational levels). Within the simulation framework, data was generated based on the principles of Web Application Architecture, incorporating contemporary methodologies of Full Stack Development and RESTful API interfaces. The findings reveal a statistically significant causal relationship between cognitive load and technical frustration and an increase in the duration of the deployment cycle. For instance, the probability of exceeding a release duration of 14 days increases from 21% to 83% in case of slow CI/CD integration, and accordingly from 18% to 72% when utilizing low-quality tools. This underscores the feasibility of integrating Developer Experience metrics into Engineering Decision Support Systems within DevOps processes. The results obtained from this study can be leveraged to enhance DevOps monitoring strategies, prioritize interventions within development workflows, and further advance analytical platforms aimed at managing the quality of the developer experience.
A Novel Agentic AI and DQN Approaches for Unmanned Drone Route Combinatorial Optimisation in Distributed Edge Cloud Networks
This study presents a novel agentic artificial intelligence (Agentic-AI) framework, combined with Deep Q-Network (DQN) reinforcement learning, to solve the combinatorial optimization problem of unmanned drone route planning, specifically designed for smart-city surveillance and road–traffic assessment. The system is designed to integrate autonomous decision-making, context-aware sensing, and dynamic route adaptation to efficiently manage large-scale urban monitoring tasks. To evaluate real-world management of large-scale urban monitoring tasks efficiently in the Karachi Smart City environment in various metropolitan zones, including Saddar, Nazimabad, Gulshan-e-Iqbal, North Karachi, Clifton, Korangi and adjoining high-density regions. These areas exhibit computationally and communication-intensive congestion patterns. Communication-intensive congestion patterns and irregular road conditions make them ideal for testing adaptive, multimodal data, such as vehicle density, traffic flow ratio, road surface deterioration, and broken-road occurrences, while interacting with distributed edge nodes for real-time processing and data exchange. The simulation testbeds show that the proposed methods have higher accuracy of data collection by 98%, processing time by 31% and minimize the resource consumption as compared to existing studies.
Development and Statistical Evaluation of a Deep Learning Framework for Real Time Tissue Classification in Robotic Surgery
Minimally invasive surgery and robotic assisted procedures are increasingly preferred over traditional open surgeries because they offer faster recovery times and reduce postoperative complications. However, these techniques require precise force application to prevent tissue overstress. The lack of reliable real time tissue recognition limits a surgeon’s ability to apply appropriate force according to tissue type, thereby increasing the risk of injury. This study proposes a framework that applies deep learning and object detection techniques to classify tissues in real time and support safer force modulation in robotic systems. As a proof of concept, the system distinguishes fat, muscle, and skin tissues using GoogLeNet, YOLOv8, and YOLOv10. Skin images were collected from 30 individuals following informed consent, while fat and muscle samples were processed to create a dataset comprising 1,800 augmented images. The GoogLeNet architecture achieved training and test accuracies of 93% and 97.2%, respectively. The YOLOv8 model demonstrated strong performance, achieving a mean average precision (mAP) of 94.7% at IoU = 0.5, with an inference time of 28 ms. YOLOv10 achieved an mAP@0.5 of 96.2% with a latency of 22 ms. The NMS-free architecture of YOLOv10 resulted in a 21% reduction in inference time compared to YOLOv8, along with a 1.5% improvement in accuracy. A statistically significant difference among the evaluated models was confirmed using analysis of variance (ANOVA), with a significance threshold of p < 0.001, indicating that YOLOv10 demonstrated superior performance under the evaluated experimental conditions.
Hybrid Deep Learning Approaches Enable Intrusion Detection System for Zero-Day Phishing Detection
These days, The website Uniform Resource Locator (URL) is widely used for accessing and navigating online information. However, the rise of AI-generated fake content, scams, counterfeit URLs, and other cyberattacks has significantly increased phishing-related threats. These fake URLs are difficult to identify because phishing links often resemble legitimate URLs. Consequently, both known and unknown (zero-day) phishing attacks remain difficult to detect in practice.This paper presents a hybrid deep learning–based intrusion detection system capable of detecting both known and zero-day phishing URLs. The objective is to provide users with absolute URLs while protecting them from fake ones. Another goal is to design an adaptive intrusion detection system (IDS) that combines static analysis, signature-based detection, and heuristic methods to identify phishing URLs. The proposed method, named the Zero-Phishing Convolutional Neural Network and Long Short-Term Memory (ZP-CNN-LSTM) algorithm, consists of three distinct schemes. For instance, static analysis rules are based on regular expressions, signatures for convolutional neural networks (CNNs), and zero-day detection using LSTM and autoencoders. We tested our project in the VR and AR research laboratory on 2 million testbed URLs, determined whether they were real or fake with respect to phishing, and predicted their phishing patterns. Results show that the proposed method achieves 98% higher accuracy than existing phishing detection methods in practice.
Copyright (c) 2025 VFAST Transactions on Software Engineering
Vehicle to Everything (V2X) and Edge Computing: A Secure Federated learning-based Lifecycle for UAV-Assisted Vehicle Network: A Survey
The Internet of Vehicles (IoV), integrated with edge computing, revolutionizes communication in smart cities. This paper investigates Unmanned Aerial Vehicle (UAV)-assisted vehicle networks, addressing the growing complexity of connected vehicle systems. The proposed system enhances efficiency, security, and scalability by incorporating Vehicle-to-Everything (V2X) communication, edge computing, and UAV deployment. Federated Learning (FL) creates a secure, privacy-preserving lifecycle for UAV-assisted V2X networks. UAVs serve as edge computing nodes, extending network coverage, performing real-time data acquisition, and enabling fast processing and decision-making. FL supports collaborative vehicle-UAV model training while ensuring data protection. The system lifecycle involves data acquisition, FL-based model development, and secure communication during system adjustments. Scalability, latency, and energy efficiency challenges are explored with proposed solutions. The research demonstrates how UAV-assisted V2X networks improve traffic management, autonomous driving, and emergency response, contributing to safer and more efficient transportation systems.
Hybrid Model for Real-Time Mobile Snatching Detection in Video Surveillance Using Time-Distributed CNN and Attention-Based LSTM
We propose a Hybrid approach that consolidates Time Distributed CNNs with Attention-Embedded LSTM network model for identifying mobile theft activities from video surveillance. Gadget snatching incidents seems to be increasing a little too rapidly globally, and another step has been taken by the police in Pakistan as they now possess around 1,700 mobile phone data in the effort of halting this. We propose a model to tackle this challenge by combining temporal relation modeling ability of LSTMs and the spatial feature extraction power of CNNs.An attention mechanism that directs focus to salient cues in video sequences enhances its effectiveness. The system was trained and tested with a real-life dataset of snatching events that were reported on social media. The results of the test show that our method works because it is 96.45\% accurate.The research presented here highlights the potential of social media platforms as effective instruments for crime prevention and identification, thereby advancing the field of artificial intelligence-driven crime detection. We want to make the algorithm's source code and dataset public so that more people can use it and do more research in this area.
Protection and Authentication of DICOM Images with Iris Recognition
Protection of medical images over the internet is a challenging task regarding its communication and usefulness. This work proposes a novel mechanism for the protection of DICOM images. The technique provides the facility to embed the iris (image) as a watermark to protect its integrity. After retrieval of the watermark, the mechanism is so robust that it recognizes the iris efficiently and correctly. Similarly, the system protects the Region of Interest (ROI) before the extraction of watermark as it is quite difficult to find volume, visualize or analyze the ROI. The technique not only protects the watermark but also gives the protection to ROI, which may be different in various medical images. Experimental results provide a deep view about the robustness of the technique in real time against both the intentional and unintentional attacks. The empirical results also show that after extraction of the watermark, the system has the capability to accurately find the volume, visualize and analyze the ROI.
Explainable BERT Models for Rumor Detection on Social Media
The rapid spread of unverified information and rumors across social media platforms poses serious risks to public health, economic stability, and societal trust. Researchers developed specific machine learning and deep learning models for automated rumor detection because of the increasing challenge that these platforms posed. The existing automated systems operate through uninterpretable black box operations thus users find it hard to trust these systems or understand how they work. This study addresses these limitations by developing explainable framework for rumor detection through transformer-based models along with advanced Large Language Models (LLMs). The proposed research evaluates and compare the performance outcomes between fine-tuned BERT-based models BERT, RoBERTa, DistilBERT, and ALBERT with cutting edge Large Language Models (LLMs) LLaMA3, DeepSeek R1, and Mistral. These models are applied to the PHEME dataset, a corpus contains actual Twitter posts that have already received labels as either rumors or unverified statements or non-rumors. Data preprocessing includes cleaning tweet text, extracting engagement metrics, and user features. BERT performs best in the fine-tuned context by achieving the highest accuracy (82.9%) and the RAG-based LLMs showed less effective in the zero-shot evaluation; thus, the observed discrepancy indicates variations in training paradigms rather than inherent architectural superiority. Explainability is achieved using Local Interpretable Model-Agnostic Explanations (LIME), which visualizes influential features behind predictions. The findings highlight a trade-off between LLM flexibility and transformer precision, offering a scalable, interpretable solution for trustworthy rumor detection and content moderation.
Distinguishing Human-Generated and AI-Generated Academic Writing: A Machine Learning Benchmark Study
The rapid adoption of large language models (LLMs) such as ChatGPT has raised critical questions about authorship, originality, and integrity in academic writing. Unlike conventional plagiarism testing tools, AI-generated or AI-rephrased text can preserve the original meaning and context of the text while modifying the writing style, making it challenging to detect using standard similarity checks. This study addresses this challenge by creating a domain-specific corpus of postgraduate-level academic texts. The corpus contains 22,520 samples, equally divided between human-written text and AI-rephrased text. All samples were preprocessed and represented using two common techniques: TF-IDF and Word2Vec. The dataset was evaluated using well-known machine learning and deep learning models, including Logistic Regression, Support Vector Machines, Recurrent Neural Networks, and transformer-based models BERT and T5. The results show that linear and sequential models provide low baseline performance, with accuracy between 50-54%. While BERT significantly outperforms the other models, achieving 83% precision along with a high recall rate. Confusion matrix analysis further shows that traditional models tend to overpredict AI authorship, whereas BERT demonstrates strong reliability in distinguishing between human-written and AI-generated text. The results show that transformer-based models are more effective for authorship verification in academic settings. They also emphasize the trade-offs among interpretability, computational cost, and predictive performance. In general, this study offers some important recommendations for the creation of credible, transparent, and domain-sensitive AI detectors for academia.
The Secure GPS Tracking Data for transportation in Distributed Environments
Efficient and secure tracking of the public transport has become a critical issue in smart cities, especially in urban areas like Karachi which has high population. Current systems are usually characterized by slowness, unreliability in data transmission and susceptibility to security risks including services by unauthorized users and information alteration. In an attempt to solve these problems, this paper introduces a Secure Transporting Tracking Method one of the (SSTM) which is a new AI-based system of real-time GPS tracking of the public transport in the distributed environment. The SSTM incorporates a secure transmit of GPS data, AIs, location prediction, and efficient vehicle-passenger matching to make it more accurate, less time-consuming, and data integrity-insured. The model has been applied to and tested in several places all around Karachi, such as Gulberg, Nipa, Gulshan-e-Iqbal and Sachal. The outcomes of simulations prove that SSTM is faster than the current approaches, such as the traditional GPS and chatbot-enhanced ones, in processing, encryption/decryption, and tracking. The research brings in a safe, large scale, and smart transport tracking platform specific to smart cities with the possible use in real-time fleet management and secure passenger safety systems.
Complexity Analysis of LLM-Generated Recursive Code: A Systematic Evaluation
Programming is an essential skill, but it can be difficult for beginners, especially when it comes to logical concepts like recursion. Despite the development of many computational and pedagogical methods to simplify programming, recursion remains a challenging topic to understand, implement, and debug. Artificial intelligence has led to the development of large language models (LLMs), such as ChatGPT, Gemini, and DeepSeek that can generate programming source code. Various studies have analyzed the quality of code produced by LLM. However, the complexity of the recursive code generated by these models has not been studied. This study compared and analyzed recursive Python programs generated by Gemini (2.5 Pro), DeepSeek (V3.1) and ChatGPT (GPT-5) in an attempt to fill this gap. For the study, 250 programs generated by each model were examined using Halsted and cyclomatic complexity metrics. The results showed that ChatGPT produced less complex code, indicating easier recursion, while DeepSeek produced more complex programs due to higher Halstead and cyclomatic complexity scores. Gemini programs have a medium level of difficulty. The Kruskal-Wallis test was used to further analyze the data, and it revealed significant differences between the recursive code generated by ChatGPT, DeepSeek, and Gemini. Overall, the study found that each LLM has a distinct pattern: ChatGPT emphasizes simplicity, Gemini takes a balanced approach, and DeepSeek's generated code promotes clarity but suffers from complexity. More comprehensive analysis will be conducted in the future by expanding the dataset and including larger language models.
AI-Driven Deep Learning for Lung Segmentation in CT Imaging
With the remarkable growth in the field of artificial intelligence; fully automated diagnostic systems are no longer a dream. Recent advancements in medical imaging have paved an early and accurate pulmonary complications. This study presents an updated approach of lung nodule segmentation from CT (computed tomography) scans using an improved UNet architecture. The performance of model is enhanced with ResNet-34 and EfficientNet backbone. After segmenting, feature extraction and subsequent classification is performed to classify the type of chest cancer. A publicly available dataset comprising 1,000 CT scan images available on Kaggle is used. The core of our segmentation model is built upon the U-Net architecture, renowned for its efficacy in biomedical image segmentation tasks. Implemented using Python and TensorFlow within the Google Colab environment, the model trains to accurately classify 3 types of chest cancers. The performance is evaluated by metrics such as the Intersection over Union (IoU) to assess segmentation quality. To further enhance the model's robustness and generalizability, UNet architecture integrated with hybrid ResNet-34 and EfficientNet-B4 backbone encoder which achieves a dice score of 0.66 with better performance on small nodules i.e., 0.58. The hybrid backbone ensures efficient feature extraction, contributing to improved segmentation results. The proposed model employed multiphase optimization which further enhances its potential for AI-driven solutions in medical diagnostic. The automated segmentation and classification can aid clinicians in the diagnosis and detection of pulmonary abnormalities, including COVID-19.
Breast Cancer Classification Using Gene Expression Profile Data with a Deep Learning Framework
Breast cancer is a harmful disease that is dangerous to human life. It causes a continuous increase in the overall death rate. It is a challenging task for machine learning models to classify gene expression profile data because of its complex nature. These machine-learning models took a lot of time and consumed larger data during their training, which resulted in inconsistent accuracy. The main purpose of this research is to build a deep neural network model that can accurately classify cancerous and non-cancerous patients by using gene expression profile datasets of breast cancer. This study presents a novel deep learning framework specifically designed for the classification of breast cancer using gene expression profile data. Unlike conventional machine learning approaches that require extensive preprocessing and manual feature extraction, the proposed model automatically learns discriminative features from high-dimensional genomic data. The proposed research implies a highly accurate model having precision (0.91), recall (1.00), and F1 score (0.95) with 95% accuracy. The macro average and weighted average both indicate that the model performs well on average also which is helpful for medical practitioners for early breast cancer prognosis.
Multi-class Offensive Language Detection in Roman Urdu
Automated systems for detecting hate speech play a crucial role in combating the proliferation of hateful content, especially as social media user bases continue to grow. Recent research efforts have focused on creating datasets for this purpose, but the majority have been designed for English, leaving low-resource languages like Roman Urdu with limited resources. To enhance hate speech identiﬁcation in Roman Urdu, various machine and deep learning models were trained on a publicly available dataset of Roman Urdu tweets (RUSHOLD). For multi-class classiﬁcation, both machine and deep learning techniques were employed, while restricting binary classiﬁcation to deep learning methods. Given the dataset’s class imbalances, particularly with some classes having fewer instances, SMOTE was employed to address this disparity. The ﬁndings indicated that the developed machine learning model outperforms the deep learning model in terms of recall, as well as key metrics such as F1 and Macro F1.
PSL SignBank: A Multimodal Machine Readable Dictionary for Pakistan Sign Language
The deaf community in Pakistan faces significant communication barriers due to the absence of standardized, machine-readable resources for Pakistani Sign Language (PSL). To address this challenge, SignBank for Pakistani Sign Language (PSL SignBank) has been developed as a machine-readable dictionary to preserve and promote PSL. The corpus includes 300 commonly used English words. Each word is translated into Urdu and encoded using HamNoSys notation language-independent phonetic transcription system. Each entry of dictionary integrate multiple modalities including English word, Urdu translation, HamNoSys vector representation, human signer video, and avatar-generated animation via SiGML (Signing Gesture Markup Language) rendering. The development of corpus involved systematic video recording with deaf participants from multiple institutions. This procedure was followed by the team of three sign language experts and two interpreters who verified gestural accuracy in the shape, movement, and location parameters of the hand. Compared to traditional video-based dictionaries, PSL SignBank achieved approximately 95% storage reduction with HamNoSys notation requiring around 1 KB per sign versus 1 MB for video and supports scalable sentence-level translation through the concatenation of machine-readable notations. The avatar-based rendering system was validated against human signer videos which confirmed accurate gesture reproduction for both static and dynamic signs. This work establish a foundational infrastructure for computational PSL applications that include text-to-sign translation systems, sign language recognition models, and educational platforms. PSL SignBank represents a critical advance towards accessibility, digital inclusion, and empowerment of Pakistan's deaf community and it also provide a replicable framework for under-resourced sign language documentation globally.
Integrating COBIT 2019 with Zero Trust Architecture: A Strategic Approach to GRC in Cybersecurity
This paper is the first to show a shared security governance model that combines COBIT 2019 for strategic oversight and ZTA for tactical, real-time enforcement. This model addresses the poor performance of traditional GRC and perimeter security that cannot handle distributed clouds and sophisticated insider attacks. We posit that the objective of COBIT strong governance could be operationalized with ZTA’s “never trust, always verify” principles. The present study illustrates the integration by linking COBIT’s process domains (EDM, APO, BAI, DSS, MEA) to the core ZTA pillars (Identities, Devices, Networks, Applications, Data). This mapping provides confirmation that ZTA is a practical mechanism for control assurance that enables organizations to take a strong, risk-sensitive, and compliant posture within a changing digital environment.
Machine and Deep Learning Approaches for Alzheimer's Disease Classification with EEG Signals and MRI Images
Alzheimer’s disease (AD) is a progressive neurodegenerative disorder and the main cause of dementia globally, making early and accurate diagnosis crucial for early and effective treatment. Traditional diagnostic approaches, such as MRI and EEG-based diagnosis, have limitations due to their high cost, lengthy execution times, and the need for specialized clinical expertise. Recent advances in machine learning (ML) and deep learning (DL) offer new prospects for automating and improving AD detection by extracting discriminative structures from multimodal biomedical data. This study proposes a smart system for Alzheimer’s disease classification based on the MRI images. Various deep learning models such as VGG16, InceptionV3, and ResNet50 were used for MRI-based classification. Various preprocessing, enhancement, and feature selection techniques were applied to enhance data quality and model performance. Experimental results show that VGG16 achieves the best accuracy of 98% on MRI images followed by the Inception 3 model. The RestNet50 model performed worst compared to these two models with respect to accuracy, F1 score and Recall. The proposed work shows the potential of using advanced learning algorithms to achieve robust and scalable early diagnosis of Alzheimer’s disease, thereby assisting clinicians in reducing misdiagnosis and improving patient treatment outcomes.
Quantifying the Impact of Data Quality on Deep Learning Models
ML models heavily rely on the quality of the data, but researchers are more focused on model optimization rather than on data refinement. The paper assesses the effect of mislabeled information on the accuracy of image classification with the Convolutional Neural Network (CNN) and Artificial Neural Network (ANN) models. Our dataset consisted of 1,000 images of plants in five classes, and we progressively mislabeled the dataset with different ratios (5%, 10%, 15%, and 20%). CNN had a 91% accuracy on clean data compared to ANN, which had 84%. The addition of only 5% mislabeling decreased CNN accuracy by 8% (to 83%) and ANN accuracy by 5% (to 79%). CNN accuracy decreased to 73% and ANN to 67% at 20% mislabeling, which indicates that CNN is more robust to label noise. The AQUAVS method of mislabeled instance detection and removal enhanced accuracy (e.g., 83% to 86% on 5% mislabeled), but did not restore baseline performance. Moreover, the recovery of the baseline accuracy involved the addition of considerable clean data: 100 images for 5% mislabeling and 200 images for 20% mislabeling. These results indicate that data quality is far more important than data quantity, and it is important to have strict data quality criteria in ML processes.
A Review on Network Intrusion detection systems based on Machine Learning, Deep Learning and Blockchain for IoT-based healthcare systems
The fast development of the Internet of Things (IoT) in the healthcare sector has raised the issues of data privacy, security, and system integrity. This review deals with a narrative-comparative study of machine learning and deep learning-driven intrusion detection systems (IDS), blockchain-based security architectures, and cryptographic methods to protect IoT-based healthcare settings. The review summarizes the recent studies with the help of synthesis of the findings and mentions the strengths and limitations of each method: While ML/DL-based IDS offer high detection accuracy, they suffer from high false-positive rates and by computational resources; but scalability and latency remain significant challenges and cryptographic methods ensure confidentiality but have to be optimized to address the limited resources of IoT devices. The review has identified substantial research gaps, including the need to have lightweight real-time security models, standardized healthcare specific IoT databank, and enhanced integration of hybrid security frameworks. This work adds the synthesized comparison of methods available to date and presents the perspectives of the research in the future to create an efficient and scalable and multi-layered security solution of IoT-based healthcare systems.
Scalable Hybrid Deep Learning-Based Architecture for Glaucomatous and Healthy Eye Classification in Retinal Fundus Images
Glaucoma remains one of the leading preventable causes of irreversible blindness worldwide, with early detection being essential for preserving vision. This study presents a hybrid deep learning framework that integrates VGG16 and ResNet-50 architectures to improve the classification of glaucoma severity using retinal fundus images. A balanced dataset of 2,081 images was utilized, with data augmentation and the Synthetic Minority Over-sampling Technique (SMOTE) applied to address class imbalance and enhance model generalization. All images were normalized and resized to 224 × 224 pixels, and training was conducted for 50 epochs with a batch size of 32, resulting in approximately 14.7 million trainable parameters. The proposed hybrid model achieved an average accuracy of 83%, surpassing standalone VGG16 (80%), ResNet-50 (70%), and EfficientNet-B0 (51%), underscoring the benefit of combining hierarchical and residual feature extraction. In addition, it achieved a precision of 82%, recall of 81%, and an F1-score of 82%, with a final loss value of 0.36. Quantization-aware training was employed to optimize computational efficiency, reducing the average prediction time to 95 milliseconds per image and enabling near real-time deployment in low-resource clinical environments. While some misclassifications were observed due to close visual similarities among glaucoma stages, the proposed approach demonstrates strong potential as a scalable and efficient solution for automated glaucoma screening and early detection.
AI-driven Physics Informed Neural Network for Daily Temperature Forecasting with Constraint Aware Learning and Explainable Feature Attribution
The daily mean temperature prediction is essential to implement agricultural adaptation and disaster risk reduction strategies in countries with varied climatic regions like Pakistan. Traditional machine learning methods often had difficulty complying with thermodynamic constraints, limiting their practicality for temperature estimation. The study proposed a Physics-Informed Neural Network (PINN), which integrates data and governing thermal principles to overcome these limitations. Integrating thermal equilibrium conditions within the loss function inherently consolidates the thermodynamic coherence of the construction. The high-resolution meteorological data from the Badin, Dadu and Rohri observation networks are used to build domain-specific features. These include diurnal excursions of temperature and humidity, a cyclic year encoding and wind-humidity ratios that capture the nonlinear mesoscale thermodynamics of the system. The PINN shows strong predictive ability as compared to a benchmark linear regression, some ensemble algorithms, and feedforward networks (R2 = 0.9975 Badin, 0.9974 Dadu, 0.9949 Rohri). SHAP and LIME, used in feature importance quantification, help to identify temperature drivers. In Badin, wind regimes have the most influence, whereas in Dadu and Rohri, lingering time trends have the most impact. With a focus on physical plausibility and explainable AI, the proposed methodology combines the probabilistic advantages of statistical learning with the constraint-based approach of atmospheric physics. This leads to resilient and spatially flexible predictions of temperature in data-scarce regions. As the study shows, PINNs could become a game-changing operational meteorological forecast technology when observational networks are weak or lacking altogether.
A Novel Approach toward Windspeed Forecasting using an Advanced Deep Learning Framework with Explainable AI
Accurate wind speed forecasting is vital for optimizing renewable energy deployment and for advancing our understanding of climate dynamics. Traditional machine-learning approaches often neglect the fundamental physical principles driving atmospheric processes, which limit their robustness and ability to extrapolate beyond the training domain. This investigation introduces an innovative PINN architecture that integrates deep learning techniques with established meteorological theory to improve both predictive fidelity and interpretative clarity. The framework embeds a temperature-sensitive physical constraint directly within the optimization objective. This formulation guarantees that the predictions remain consistent with thermodynamic equilibrium. Structured as a four-layer sequential network with 13 inputs, two hidden (64 neurons each), and a single output unit, the PINN outperformed eight competitive baseline architectures ranging from Bayesian ridge regression to gradient boosting and various hybrid architectures trained on a suite of handcrafted covariates, including wind-shear terms, mean humidity, and temperature-interaction derivatives derived from multi-year records spanning the climatically distinct locales of Badin, Dadu, and Rohri. Observed reductions in mean-squared error and mean absolute error were dramatic, and the coefficient of determination rose to an impressive 0.99. Furthermore, the application of XAI techniques, specifically SHAP and LIME, identified temperature and humidity as the dominant predictors, corroborating the physical consistency of the model while ensuring operational transparency for users. This study establishes an integrative linkage between data-driven learning methodologies and established domain expertise, resulting in a robust and interpretable decision-support tool for both energy system planning and climate impact assessment.
Advances in Multilevel Encryption Techniques: A Comprehensive Review of Hyperchaotic Neural Networks, Quantum-Inspired Approaches, and Data Hiding Mechanisms
The growing sophistication of cyber threats and the limitations of traditional cryptographic methods have necessitated the development of advanced encryption frameworks. This paper presents a comprehensive review of multilevel encryption techniques, focusing on three key dimensions: hyperchaotic neural networks, quantum-inspired encryption (QIE), and advanced data hiding mechanisms. Hyperchaotic neural networks, characterized by their high-dimensional chaotic systems and dynamic adaptability, generate unpredictable key sequences to enhance resistance against brute-force and statistical attacks. Quantum-inspired encryption leverages principles such as superposition and entanglement to design lightweight, scalable cryptographic frameworks that operate on classical systems, offering high entropy and robust security for IoT and real-time applications. Additionally, adaptive data hiding techniques, including neural network-based steganography and hyperchaotic embedding, ensure imperceptibility and resilience against compression and detection. This review consolidates state-of-the-art advancements, comparing the performance, scalability, and application of these techniques across domains such as healthcare, IoT security, multimedia protection, and cloud storage. The integration of these approaches into multilevel frameworks is highlighted, along with their potential to address computational, scalability, and security challenges posed by modern cyber threats. Future research directions are identified, emphasizing the development of hybrid techniques, energy-efficient algorithms, and robust implementations for emerging applications in cybersecurity and beyond.
An Explainable Deep Learning Framework for Automated Classification of Ocular Diseases in a Big Data Environment
Ocular diseases such as cataracts, glaucoma, age-related macular degeneration, and diabetic retinopathy remain major contributors to global visual impairment and blindness, where early detection is critical for effective intervention. While color fundus photography is widely used for retinal screening, manual interpretation is time-consuming and prone to error, highlighting the need for automated, accurate, and interpretable diagnostic solutions. In this study, we propose a deep learning based framework for the automated classification of ocular diseases and normal cases using medical images. The framework incorporates a comprehensive preprocessing pipeline and leverages a scalable Apache-powered big data environment for efficient feature extraction and model training on large-scale datasets. A Convolutional Neural Network (CNN) was proposed and benchmarked against state-of-the-art architectures including VGG19, ResNet50, and GoogLeNet, achieving superior performance with an accuracy of 97%, precision of 93%, recall of 97%, and F1-score of 93%. To enhance interpretability and clinical trust, Gradient-weighted Class Activation Mapping (Grad-CAM) was integrated, generating heatmaps that highlight the most discriminative retinal regions influencing predictions. The proposed approach not only achieves high diagnostic accuracy but also ensures transparency, scalability, and clinical relevance, making it a promising step toward real-world deployment of explainable AI systems in ophthalmology and broader healthcare applications.
Sentiment-Aware Summary Generation for User Reviews Using Deep Learning Models
User reviews are a valuable source of information for potential buyers. User reviews provide valuable feedback that reflects genuine customer experiences, enabling future buyers to make more informed decisions. Reviewing every single comment can be a challenging, lengthy, and overwhelming process. Text summarization helps by providing concise summaries, allowing users to quickly grasp key points from multiple reviews. Additionally, sentiment analysis extracts subjective information, such as overall opinions, strengths, weaknesses, and recommendations, helping potential buyers in making informed decisions. This study proposes the XLNet model for sentiment analysis of user reviews and LED (Longformer Encoder-Decoder) for text summarization of user reviews. XLNet is a pre-trained transformer model that captures bidirectional relationships between words to improve sentiment analysis, while LED is a transformer-based model designed for efficient text summarization of long documents by using a sparse attention mechanism. The results show that XLNet achieves high accuracy in sentiment classification on the Amazon Fine Food Reviews dataset of 50K reviews, while LED generates effective and concise summaries, achieving the highest ROUGE and METEOR scores among the tested summarization models. To ensure robust evaluation, multiple metrics, including ROUGE, METEOR, MoverScore, and BERTScore-F1, were applied across models, providing both lexical and semantic perspectives on summarization quality. By performing both sentiment analysis and text summarization within a single framework, this approach efficiently extracts meaningful insights from large datasets, streamlining the decision-making process for users.
A Comparative Study of Object-Oriented, Procedural, and Functional Programming Paradigms in Microservice Architecture
It is noted here that the microservices architecture has changed the whole paradigm of software engineering so that it permits building systems that could be distributed, scalable, and maintainable. Object-Oriented Programming (OOP) still dominates the design paradigms in industry. However, there has been revived interest in evaluating it in light of Procedural Programming (PP) and Functional Programming (FP) paradigms with respect to the evolving nature of software architecture. This paper covers a comprehensive comparative analysis of these three paradigm types in relation to microservice-based system design. In this case, each of the paradigms is applied in almost the exact architectural requirements upon a real-world e-commerce domain model, and finally, the evaluation was made on the basis of modularity, scalability, maintainability, and operational efficiency. Our finding is that, although OOP provides a balanced mix between abstraction and modularity beneficial for service-based architecture, FP is about minimizing mutable state through immutability and pure functions; hence the reduction of race conditions and making concurrency safer in distributed environments. Procedural programming is quite efficient for small-scale operations, but it faces serious hurdles in establishing service modularization and maintainability. In terms of such an ideal model supported by the current literature (2020-2025). This paper argues that OOP will be the most relevant paradigm for microservices, with Functional Programming reaping its benefits and promising alternatives for cloud-native or event-driven scenarios. In conclusion, this study narrates the call for hybrid approaches-the approaches that will utilize the strength of all paradigms to fulfill the ever-evolving software needs.
A Supervised Intrusion Detection System Leveraging Machine Learning for Secure Smart Education in the Internet of Education (IoEd)
The Internet of Education (IoEd) is an emerging field of IoT that combines Internet of Things (IoT) technologies with various learning environments such as smart classrooms, LMS, and web applications to improve educational productivity more efficiently and effectively. However, the integration of IoT Technologies within the educational environment, led to significant security risks, like different cyber-attacks, especially due to their limited processing capacity, which makes traditional defenses like firewalls and VPNs less effective. A single vulnerability within an IoT device can compromise the integrity of the entire network. Thus, this study highlights the urgent need to secure IoT-enabled academic systems to support sustainable educational advancement. This study proposes an Intrusion Detection System (IDS) based on Machine Learning, that identifies abnormal activities and potential cyber threats in an academic network, using a recently curated dataset, IoEd-Net. Leveraging Python packages like scikit-learn, TensorFlow, pandas, and NumPy. Six ML classifiers are used; Logistic Regression, Decision Tree, AdaBoost, Random Forest, ANN, and KNN. The experiment was done into two ways, 70/30 train-test split had achieved the highest accuracy of 98\% for Random Forest. The same highest accuracy was obtained in a second run with 5-fold cross-validation, by contributing to the IoEd security by developing an Intrusion Detection system (IDS) that can predict unseen network activities. It gives valuable analysis into network behavior, also enhancing privacy and security in an IoEd networks.