Revolutionary Advances in Computing and Electronics: An International Journal
Topics of Interest The field of computing and engineering is witnessing rapid transformation, with emerging technologies and complex challenges that demand fresh perspectives and scholarly exchange. Revolutionary Advances in Computing and Electronics: An International Journal provides a platform for publishing original, peer-reviewed research that addresses both foundational and cutting-edge developments in subject engineering, specialised areas such as AI, cybersecurity, embedded systems, IoT, electronics design, robotics, quantum, communication networks, antenna 6G, sustainable engineering, and interdisciplinary applications. The journal promotes innovative algorithm design, system architecture, and real-world engineering solutions. Open Access Journal Revolutionary Advances in Computing and Electronics: An International Journal is a free, open-access journal committed to the global exchange of scientific knowledge. We believe that removing access and publication barriers helps accelerate the impact of research. All articles are available without subscription, and authors are not required to pay any publication or processing fees. Each submission undergoes rigorous single-blind peer review to ensure academic integrity and quality.
Adaptive Multi-Objective Task Scheduling in Cloud Computing Using Deep Reinforcement Learning and Hybrid Metaheuristic Optimization
The rapid development of cloud computing has posed a significant challenge for task scheduling, given the highly dynamic, diverse, and large-scale workloads. Most heuristic and metaheuristic scheduling algorithms fail to adapt to rapidly changing run-time conditions, and lead to sub-optimal resource utilization, longer makespan, higher energy usage and violation of Service Level Agreements (SLAs). This study proposes an adaptive multi-objective task scheduling system based on Deep Reinforcement Learning (DRL) combined with hybrid metaheuristic optimisation methods to overcome these restrictions. To enable independent decision-making in an unpredictable, fluctuating workload, the proposed approach dynamically learns optimal scheduling rules from real-time conditions in the cloud system using a Deep Q-Network (DQN). In addition, the integration of Genetic Algorithm and Particle Swarm Optimization (PSO) is performed to encourage convergence towards the optimal scheduling solution without the occurrence of local minima. The hybrid approach has been shown to find a good balance between exploration and exploitation in complex cloud environments. We further evaluate the practicality of the proposed framework through large-scale simulations of real-world cloud systems running Kubernetes and OpenStack with CloudSim. The performance is assessed based on key metrics such as makespan, resource utilisation, energy efficiency, throughput and SLA violation rate. Through experiments, it has been shown that the proposed method consistently outperforms the state-of-the-art scheduling strategies in execution time, energy efficiency, resource utilization, and fewer SLA violations. The study presents a scalable, intelligent, and self-adaptive scheduling paradigm suitable for effective, persistent resource management in extremely dynamic distributed computing environments for next-generation cloud infrastructures.
CloudSim, Deep Reinforcement Learning, Deep Q-Network, Genetic Algorithm, Kubernetes, OpenStack, Particle Swarm Optimization, Service Level Agreement, Clod Computing.
Copyright (c) 2026 Revolutionary Advances in Computing and Electronics: An International Journal https://creativecommons.org/licenses/by/4.0
A Study on Adopting Machine Learning to Develop Predictive Models for Diabetes
High blood sugar can harm the body's organs over time. In addition to issues with the kidneys, eyes, mouth, feet, and nerves, blood vessel damage can lead to heart attacks and strokes. Therefore, using machine learning to anticipate diabetes can assist physicians in making final decisions and initiating therapy. Models such as LightGBM, LDA, KNN, and neural networks were developed during this study. After evaluation, the neural network model performed well, achieving 90% accuracy, a balanced F1 score, and the maximum possible KS statistic of 0.8074. The dataset was pre-processed using techniques such as PCA, random undersampling, standard scaling, label encoding, and manual encoding. GridSearchCV was used to tune the models during training. In addition, the study underscores the real-world implications of implementing machine-learning-based diabetes prediction models in healthcare settings. These models can be trained using various preprocessing techniques, feature selection, and hyperparameter optimization methods, which further improve prediction accuracy and create a scalable framework for clinical decision support systems. The evaluation shows that neural networks can learn the complex, non-linear relationships present in patient data and have demonstrated greater sensitivity to early signs of diabetes. The integration can help healthcare providers to monitor patients in a more proactive way, create more customized treatment plans, and intervene on time, all of which will lead to better health outcomes and minimize the possibility of long-term diabetes complications.
Deep Research Agent: An AI-Powered System for Automated Research Paper Analysis and Citation-Based Answer Generation
The rapid growth of scholarly publications makes it difficult for researchers and students to locate relevant evidence, compare findings, and produce source-grounded answers efficiently. This paper presents Deep Research Agent, an AI-powered system for the automated analysis of research papers and the generation of citation-based answers. The system ingests uploaded PDF research papers, extracts and cleans the text, segments the content into retrievable passages, generates semantic embeddings using Sentence-BERT, stores the vectors in ChromaDB, and combines dense retrieval with BM25 keyword matching. Retrieved evidence is passed to a large language model via citation-aware prompts, ensuring that generated answers remain linked to the source passages. A Neo4j knowledge graph layer supports exploration of entity and topic relationships, while a Streamlit interface provides an accessible workflow for uploading papers and asking research questions. Compared with generic RAG assistants, the proposed system focuses specifically on local scholarly-paper analysis, hybrid retrieval, explicit evidence mapping, and citation-grounded responses. The revised manuscript also discusses practical deployment issues, including document quality, retrieval latency, privacy, evaluation, and scalability.
Deep Research Agent, Retrieval-Augmented Generation, Large Language Models, Citation-Aware Generation, Scholarly Retrieval, ChromaDB, Neo4j.
Intelligent Software Fault Prediction Using Machine Learning and Neural Network Techniques
The investigative methodology includes a careful process that begins with data cleaning and feature selection. Uses 10-fold cross-validation, which means it basically takes 10ths of the data and tests it to see if it's consistent. A number of machine learning algorithms are evaluated, including Decision Tree, Naive Bayes, Support Vector Machine, Random Forest, and Multi-Layer Perceptron. The classic criteria accuracy, precision, recall and F1 score are used to evaluate all. When the models were completed, the results showed that models that use several techniques (e.g., Random Forest) and/or neural networks (e.g., Multi-Layer Perceptron) generally outperform simpler models. Among them, the Multi-Layer Perceptron is best suited for predictions, and the Random Forest is strong across all types of datasets. These discoveries indicate that machine learning can be used to estimate software failure time. Based on the research involved and the results it yielded, it is suggested that further investigation be undertaken into the possibility of combining the methods and emphasizing learning-based approaches. In this work, a methodology utilising data preprocessing, followed by feature selection and ten-fold cross-validation, is used to obtain a fair assessment. The performance of various classifiers, including Decision Tree, Naïve Bayes, Support Vector Machine (SVM), Random Forest, and Multi-Layer Perceptron (MLP), is assessed using four popular metrics: accuracy, precision, recall, and F1-score. The findings show that the ensemble and neural network outperform traditional classifiers for some data sets. The Multi-Layer Perceptron achieves the best predictive accuracy, while the Random Forest shows consistent and reliable performance. This suggests that machine learning has potential applications that go beyond simply predicting software failure in an early and efficient manner and explores further in the realm of hybrid and learning-focused approaches.
Software Fault Prediction, Machine Language, Neural Network, Software Metrics, Software Quality
AI-Based Fake News Detection Using NLP and Machine Learning Techniques: A Review
Digital communication channels have been agents of change, radically reshaping how information is created, engaged with, and disseminated within societies. This democratisation of content has allowed users around the world to gain greater power, but it has also led to the easy spread of artificial stories, known as fake news. These traditional fact-checking methods cannot keep up with the pace and scale of misinformation online, so there is a need for independent computational infrastructure that can automatically assess textual credibility at scale. This paper aims to provide a comprehensive theoretical framework for developing an AI-driven fake news detection system based on the principles of classical Machine Learning (ML) and Natural Language Processing (NLP). Empirical studies are excluded here, with emphasis on the theoretical, mathematical and linguistic aspects needed to design such a system. A full theoretical framework is developed from the characterisation of the datasets, text pre-processing, and the TF-IDF vectorisation. The authors examine in detail four classification algorithms: Logistic Regression, Decision Tree, Random Forest and Gradient Boosting; they are explored in terms of their mathematical formulation, their suitability to high-dimensional sparse text data and their ability to adhere to linguistic patterns typical of deceptive content. This paper does not present empirical findings but rather lays the groundwork for future experiments, performance benchmarking, and deployment in misinformation detection applications in a structured framework for conceptualization.
Fake News Detection, Natural Language Processing, TF-IDF, Logistic Regression, Decision Tree, Gradient Boosting, Random Forest, Text Classification
Explainable Robustness Against Localized Malware Obfuscation: A Hybrid CNN-ViT Approach with Comparative Attention Analysis
The rapid proliferation of obfuscated and zero-day malware variants poses a critical challenge to modern cybersecurity defences. Traditional Convolutional Neural Network (CNN) classifiers, while effective on clean binary images, exhibit catastrophic vulnerability when adversaries apply localised packing or byte-level scrambling to evade detection. In this paper, a hybrid deep learning architecture that combines a lightweight CNN feature extractor with a Vision Transformer (ViT) encoder to achieve explainable robustness against localised malware obfuscation. The CNN extracts hierarchical texture patterns from grayscale binary images, while the ViT models long-range spatial dependencies through multi-head self-attention across 196 image patches. Crucially, this approach injects Spatial Dropout (nn.Dropout2d) into the CNN layers and employs Focal Loss to handle severe class imbalance, forcing the model to learn from partially corrupted feature maps during training itself. Evaluated on the Malimg benchmark (9,339 samples, 25 families), the hybrid model achieves 93.80% accuracy with a Macro AUC of 0.9980. Under simulated obfuscation with 30% local byte noise, the hybrid architecture maintains 81.82% accuracy, while an equivalent pure CNN baseline using the same CNN feature extractor collapses to 52.62%, a gap of 29.20 percentage points. Through comparative attention heatmaps, this paper provides visual, interpretable proof that while CNN activations scatter across noise artifacts under obfuscation, the ViT’s self-attention dynamically shifts to focus on persistent global structural payloads, explaining why the architecture survives where CNNs fail.
Anomalous Activity Recognition Using Pose Estimation and Incremental Learning
Automated surveillance is based on the observation of abnormal human behaviors in video streams. In practice, this task remains challenging because abnormal events are inherently rare and difficult to capture in large-scale annotated datasets. Consequently, most supervised approaches depend heavily on labeled instances of anomalies and often exhibit limited generalization in real-world environments, where novel or previously unseen behaviors may arise. To address this weakness, a pose-based anomaly detection system is proposed, which learns patterns of normality human motion. The system does not learn about specific abnormal activities; instead, it learns normal patterns of behaviour and identifies significant deviations as anomalies. Video frames are processed to extract human skeletal key points using the YOLO-Pose model, which predicts 17 body joints per person. Keypoints are normalized with respect to the bounding box coordinates to achieve scale and position invariance. Derived features of spatial and temporal motion the skeleton representation record the movement dynamics and the body posture. The distribution of normal poses is reflected in an Incremental One-Class Support Vector Machine (SGDOneClassSVM), which is trained only on normal samples. The experiments on the ShanghaiTech Campus dataset show that the proposed method achieves a detection accuracy of 91.0% and operates at about 21 frames per second.
Deep Learning-Based Prediction Model for Monkeypox Detection
Monkeypox is an emerging zoonotic viral disease caused by the Monkeypox virus (MPXV), which gained global attention following the unprecedented 2022 outbreak. The rapid spread of the disease across multiple continents highlighted the urgent need for efficient, scalable, and accessible diagnostic tools. This study proposes a deep learning-based prediction model for the automated detection of Monkeypox from skin lesion images, aimed at supporting early diagnosis, particularly in resource-constrained and rural healthcare settings. The proposed system employs a U-Net-based segmentation framework to isolate infected lesion regions, followed by Convolutional Neural Network (CNN) classifiers trained on a curated dataset of over 300 labelled images encompassing Monkeypox, Chickenpox, and healthy skin samples. To enhance model robustness and generalization, preprocessing techniques including noise reduction, normalization, and data augmentation were systematically applied. Several state-of-the-art transfer learning architectures, including AlexNet, VGG16, ResNet, InceptionNet, and MobileNetV2, were evaluated and benchmarked under uniform training conditions. The model demonstrates promising classification performance and holds significant potential for deployment as a mobile or web-based diagnostic aid, bridging critical gaps in healthcare accessibility worldwide.
AI-Based Document Analysis and Question Answering System
The exponential growth of textual data, in the form of corporate documents, reports, and research papers, in the current digital era has increased the need for intelligent systems that can automatically comprehend documents. Analysing these documents by hand is ineffective and time-consuming. In order to extract valuable insights from documents, this study provides an AI- Based document analyzer with a question-answer system that makes use of Natural Language Processing approaches. Users can submit text or PDF files to the system, which then extracts content, generates succinct summaries, identifies keywords, and enables interactive question-answering. Python is used to build the architecture, which is then serverless deployed on Amazon Web Services (AWS) utilising Amazon S3 and Amazon EC2, and for the question-answering system, GEMINI is used. By reducing reading time and providing immediate access to pertinent information, the suggested solution increases productivity. It is affordable, scalable, and suitable for business, education, and research.
Artificial Intelligence, Natural Language Processing, Amazon Web Services, Simple Storage Service, Application Programming Interface, Question Answering, Portable Document Format.
Cardiovascular Disease Prediction through Machine Learning: A Comparative Study of Ensemble Techniques
Cardiovascular disease is a worldwide health issue that necessitates enhancements in the evaluation of risks and early identification methods. Several lethal heart illnesses have been transmitted to humans through various recognized mechanisms. According to a figure from the World Health Organization (WHO), over 17.9 million individuals in the country perish annually, representing 32% of global mortality. The increasing annual population poses a significant challenge in terms of early-stage diagnosis and treatment. Machine learning approaches have proven highly useful in the realm of healthcare. The study employed the "heart dataset" obtained from Kaggle. The implementation was carried out using the Open editor and the Python programming language. This work aims to calculate and assess the precision of ensemble machine learning algorithms in predicting cardiac disease, along with other performance metrics. The study examined seven proven models, including Gradient Boosting, AdaBoost, XGBoost, CatBoost, Light GBM (boosting techniques), and Random Forest and Extra Tree Classifier (bagging algorithm). These models were tested and trained using the heart dataset. 80% of the dataset will be allocated for training, while the remaining 20% will be used for testing. This study examines machine learning methods to determine the optimal model for predicting cardiac disease. Upon assessing multiple models, we determined that XGBoost achieves the highest accuracy and F1-score, with values of 87.50% and 88.44% respectively, surpassing the performance of other models we employed.
Copyright (c) 2025 Revolutionary Advances in Computing and Electronics: An International Journal https://creativecommons.org/licenses/by/4.0
AI-Based Intelligent Framework for Enhancing Phishing Attack Detection
Phishing has become one of the most widespread and quickly evolving cyber threats that capitalise on human and system-based vulnerabilities in emails, websites and SMS. Conventional defence controls, such as blacklists, rule-based filters, and signature matching, cannot identify advanced, obfuscated, and zero-day phishing attacks. Recent developments in Artificial Intelligence (AI) and classical machine learning have shown promise for scalable and interpretable phishing detection. The research paper proposes a lightweight and real-time phishing detection framework based on TF-IDF textual feature extraction and Naive Bayes and Random Forest classification. The model incorporates the discriminative value of terms in URLs, email messages, and SMS messages, and thus it can accurately separate legitimate and malicious communication. Evaluation of experimental phishing benchmark datasets demonstrates that high classification, inference speed and generalization with low computational cost can be achieved. The simplicity and openness of the framework render it applicable to academic settings, resource-constrained systems, and real-time deployment and it provides an efficient alternative to other more complicated deep learning models. The contribution of this work to the development of accessible, interpretable, and adaptable AI-based phishing detection systems for contemporary cybersecurity issues is significant.
Privacy‑Enhanced Federated Learning Framework for Intrusion Detection in Smart IoT Environments
The rapid proliferation of the Internet of Things (IoT) has given rise to Smart Environments where sensors, actuators and embedded systems interact seamlessly to provide automation and convenience. Recent reports estimate that the number of connected IoT devices reached 14.4 billion in 2022 and continues to grow despite supply‑chain disruptions. Unfortunately, this connectivity also exposes critical infrastructures to malware, botnets and other cyber‑attacks. Conventional intrusion detection systems (IDS) are often ill‑suited for resource‑constrained IoT nodes because they require centralised data collection, violating privacy regulations and incurring excessive bandwidth consumption. Federated learning (FL) has emerged as a promising paradigm to overcome these limitations by enabling collaborative model training directly on edge devices. However, FL alone does not guarantee privacy, model updates may leak sensitive information and naive aggregators remain vulnerable to single points of failure. This work proposes a privacy‑enhanced federated learning framework for anomaly and malware detection in Smart IoT environments. The contributions of this research paper is threefold: (1) a hierarchical FL architecture that distributes computation across edge, fog and cloud tiers, incorporating differentially private noise to model updates is designed; (2) a multi‑agent intrusion detection algorithm that trains lightweight deep models locally using real traffic data while a fog‑level coordinator performs secure aggregation is developed; and (3) extensive experiments on modern IoT intrusion datasets to evaluate detection accuracy, communication overhead and resource consumption is done. The results show that the proposed framework achieves comparable accuracy to centralised training while substantially improving privacy and resilience.
Risk-Oriented Taxonomy of Android App Assessment Models
Android application security research has been dominated by the malware-detection paradigm, in which either benign or malicious labels are assigned to applications. Although effective in terms of large-scale screening, this binary perspective is not effective to capture the diverse risks brought about by modern Android applications, such as privacy leakage, financial abuse, intrusive tracking, and misuse of system resources. This work provides a structured review of the research efforts on Android app assessment models from a risk-oriented perspective. Rather than proposing new techniques for detection, this review synthesizes the existing literature and organizes prior work through a multi-dimensional taxonomy with respect to risk modelling granularity, risk semantics, evidence sources, learning paradigms, and interpretability. This paper reveals important trends, constraints, and unresolved issues by analysing how current approaches conceptualise and depict risk. The purpose of the article is to outline the shift from malware detection to thorough application risk assessment and to provide a comprehensive reference for future Android security research.
PowerGNN: Using Spatial Intelligence for Autonomous Grid Resilience
The increasing amount of extreme weather conditions and the increasing complexity of cyber-physical threats pose a significant threat to the resilience of modern power distribution systems. The conventional approaches to restoration rely on centralised control and information flow across the globe and therefore, are susceptible to failure at one point and delays in the event of a major disaster. This paper presents a decentralised Multi-Agent Reinforcement Learning (MARL) algorithm with Graph Neural Networks (GNNs) to restore the power grid in real time and independently. The inductive bias of GNNs allows every substation agent to learn to jointly plan switching actions and power dispatch depending on local topological characteristics and neighbour messages, eliminating the need to have a central supervisor. The suggested framework is assessed using some IEEE standard test systems under various load stressors, including N-k contingencies, blackouts in communication, and adversarial False Data Injection (FDI) attacks. The methodology is aimed at creating a policy that is agnostic to grids and puts forward the priority of restoring critical loads, but assuring voltage stability by the use of localised spatial intelligence. This work shows the theoretical and practical benefits of moving from complex centralised optimisation to a scalable, O(N) decentralised graph-inference model. By demonstrating that learned policies can transfer across different topologies, this research offers a strong foundation for the next generation of self-healing, “dark-start” resilient smart grids that can effectively handle the challenging environment of post-disaster recovery.
The Growing Threat of Fake Job Postings: A Review of Machine Learning and NLP-Based Detection Approaches
This review paper discusses the Fake Job Post Detection, spotlighting machine-learning, deep-learning, and natural-language-processing tools built to shield young job seekers from con artists and keep the hiring scene honest. To ground the discussion, an in-depth survey of past studies maps the techniques and trends that now dominate the field. The main contribution is a side-by-side comparison of existing systems, spelling out where each shines and where it falls short. The paper goes on to summarize the key hurdles and opportunities investigators still face. A pressing call for larger, cleaner datasets, closer industry partnerships, and fraud alerts that fire in real time runs through the review. By weaving these threads together, the article offers a fresh perspective on what is known today and sketches concrete directions for future studies and policy action in the fast-changing area of fake-job-ad detection.
Smart Detection of Credit Card Fraud Using Machine Learning
The increase in digital financial transactions has dramatically increased credit card fraud, which now poses significant threats to consumers as well as financial institutions. This study provides a comparative study of a variety of machine learning models for credit card fraud detection using a real life, imbalanced dataset. We compared four machine learning models—Logistic Regression (LR), Random Forest (RF), XGBoost, and Artificial Neural Network (ANN)—in terms of their ability to detect fraudulent transactions accurately. We used the Synthetic Minority Over-sampling Technique (SMOTE) for preprocessing and balancing the datasets, but tested the machine learning models on the original imbalanced set to adequately represent real-world performance. The experimental results of this study indicate that the ANN and XGBoost models were the most accurate and had the highest recall and F1-score, with the ANN performing the best in most corrective metrics. The feature importance plot shows that there are some PCA variables, such as V14 and V17, that are relevant to fraudulent activity. The research provides evidence of the capabilities of ensemble and deep learning models in performing fraud detection tasks, especially after careful preprocessing and addressing problems related to the dataset imbalance. This study also provides a computationally sound and useful methodology that could be utilized in developing intelligent fraud detection systems in the financial sector.
CNN-LSTM Powered Network IDS for Adaptive Cyber Defence
A Network IDS (NIDS) is devised to examine network traffic to detect signs of malicious behaviour or violations of protection policies. It is an essential piece of equipment in the fight to improve cybersecurity via early threat detection and response. Existing machine learning approaches, though efficient, are generally bogged down by significant manual feature engineering, which restricts their flexibility to adapt to dynamic attack scenarios. Deep learning approaches, with the ability to automatically learn high-level features, provide a robust alternative to designing effective IDS. This work proposes a novel hybrid deep learning architecture to synergistically integrate CNN and LSTM networks for tackling the complexity of network intrusion detection. The CNN module performs well in detecting spatial patterns of network traffic data, and the LSTM module incorporates temporal dependencies to facilitate exhaustive analysis of sequential attack patterns. To improve model efficiency and avoid overfitting, batch normalization and dropout layers are strategically integrated in the architecture. The model is extensively tested on three diverse datasets, CIC-IDS2017, UNSW-NB15, and NSL-KDD, covering a broad range of contemporary attack types. Experiments are performed for binary and multiclass classification tasks, and performance metrics are evaluated based on a confusion matrix. Key performance metrics, like false alarm rate, accuracy, F1 score and detection rate, define the model’s performance in intrusion detection with high accuracy while avoiding a high false positive rate. The outcome proves the model’s robust performance across diverse network environments, varying from wired to wireless networks, and its applicability in detecting known and novel threats. By tapping the power of automated feature extraction and sophisticated neural network design, the work critically contributes to a scalable and efficient solution to existing network security, opening the door to real-time, adaptive intrusion detection across complex digital terrain.
Tampering with Truth: Designing a Tool to Undermine Digital Evidence Recovery
The internet and technology advancements have given rise to too much comfort for both legitimate and malicious users or cyber criminals. Therefore, if these advancements are misused deliberately by the offenders, it can result in many harmful consequences, such as the prevention of services for benign or legitimate users. It is very difficult to investigate and prosecute any electronic crime because investigators need to build their cases or perform the investigation according to the evidence left by the computer criminals on the system. These days, computer criminals or adversaries are very much aware of the Computer Anti-forensic tactics and methods. Criminals apply such anti-forensic techniques to impede the whole digital investigation process efficiently and successfully. Such forensic investigation processes affected by Anti-forensic measures are too expensive and time-consuming to carry out. Numerous anti-forensic techniques can be used by anti-forensic practitioners to thwart the investigation process. Therefore, intruders try to hide or wipe out the evidence from the compromised system so that they cannot fall into the hands of Forensic examiners. Adversaries can try their very best to protect their evidence so that they can use any anti-forensic techniques for the same. In this study, we have proposed an Anti-Forensic tool for destructing the evidential files stored on the hard disk. Our proposed tool works under the controlled environment of the Secondary extended file system of the Linux distribution. This tool clears out all the inodes which actually store the metadata of the files and folders on the file system, so clearing the inodes corrupts the file system structure in the internal structure of the file system. Detection of such activity performed by the anti-forensic practitioner is possible by the Forensics software or Forensic Investigator during the Investigation. Still, it will not be possible for the analyst to recover the file content as the file will no longer be accessible, as its inode entries have already been cleared out by the intruder. In this research work, we have also compared our proposed tool with similar existing tools and found that none other than the proposed one could clear out all the inode entries of the evidential files on the File System.