Macaw International Journal of Advanced Research in Computer Science and Engineering
The Macaw International Journal of Advanced Research in Computer Science and Engineering (MIJARCSE) (ISSN(O): 2455-4545) is a leading scholarly journal committed to publishing high-quality, peer-reviewed research in computer science and engineering. Established to disseminate cutting-edge advancements, MIJARCSE initially published monthly issues from 2015 to 2017 before transitioning to a half-yearly schedule in 2018. The journal covers diverse topics such as artificial intelligence, machine learning, data science, software engineering, network security, cloud computing, IoT, robotics, and computational theory. With a distinguished editorial team ensuring rigorous peer-review, MIJARCSE serves as a vital platform for researchers, academicians, and professionals to share innovative ideas and drive technological progress.
This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License (CC BY-NC 4.0). You may share and adapt the work for non-commercial purposes with appropriate attribution. For more details, visit https://creativecommons.org/licenses/by-nc/4.0/.
Safe-By-Design Clinical LLMs: Formal Risk Models and Silent-Trial Protocols for Workflow Integration
The rising adoption of Large Language Models (LLMs) in clinical decision-support environments has increased the need for effective safety frameworks so that uncertainties, operational risks, and bias in healthcare are addressed. The research investigates the application of safe-by design principle in clinical LLM development. This specifically aims towards the formal risk modelling and strategies of silent trail validation while integrating clinical workflow. To evaluate the topic, a systematic literature study was performed by applying PRISMA guidelines using Scopus, PubMed, Google Scholar, Web of Science, and IEEE Explore. The research targeted peer-reviewed publications that were released between 2021 and 2026. From the total, n=72 studies have met the inclusion criteria and were analyzed by applying quantitative synthesis and thematic evaluation. The outcomes recommend enabling safety in the clinical LLM systems that need a system-level method that integrates governance techniques, workflow compatibility, and technical reliability. The research shows contribution to the field by producing diverse safety strategies within a key framework and delivers guidance for healthcare enterprises, policymakers, and companies to deploy scalable AI solutions
Safe-by-design AI, Clinical LLMs, workflow integration, probabilistic risk analysis, workflow integration, formal risk modeling, EHR integration, AI governance, clinical decision support systems, model validation
Copyright (c) 2025 https://creativecommons.org/licenses/by-nc/4.0
Synthetic Clinical Cohorts from Generative Models: Utility-Privacy Tradeoffs, Validation Protocols, and Regulatory Compliance
The rapid development of electronic healthcare records and virtual health systems has produced valuable opportunities for data-driven healthcare research, yet effective privacy regulations continue to limit access to responsive patient data. Synthetic clinical information is produced with advanced deep learning approaches, and has emerged as a feasible solution to manage data accessibility with confidentiality. The systematic review investigates the current development in synthetic healthcare data generation, concentrating on data usage, validation methods, privacy risks, and regulatory issues. Following PRISMA rules and guidelines, 82 peer reviewed reports published between 2021 and 2026 were analyzed. The decisions indicate that generative adversarial networks are the most widely used models for synthesizing tabular clinical information and diffusion models are gaining traction in medical imaging applications. Nearly 74.4% of reports presented effective statistical similarity between the real and synthetic datasets and 59.8% indicates comparable predictive performance in MI (Machine Learning) tasks. Despite these promising results, only 25.6% addressed bias or fair issues and 40.2% of reports performed properly privacy risk assessments. Regulatory approaches like HIPAA and GDPR have remained cautious about synthetic data due to the possible re-identification risks. Overall, the review presents the need for effective and standardized validation frameworks, clear regulatory guideless and inclusive privacy auditing to ensure the ethical, safe, and scalable integration of synthetic clinical data in healthcare practices and research
Differential privacy, electronic health records, synthetic health data, generative adversarial networks, healthcare data governance
AI vs. AI in IoT Security: A Systematic Review of Autonomous Exploits and Defense Mechanisms
Artificial intelligence (AI) empowered defense systems have evolved as a unique response to cyber security challenges occurring in the Internet of Things (IoT) as conventional security measures are ineffective against autonomous and adaptive threat manifestations. The paper studies the association between AI-driven offensive and defensive methods for protecting smart equipment within the AI vs AI concept. This includes systematic review aligned with PRISMA insights implemented with peer-reviews from databases including Scopus, Web of Science, IEEE Xplore, and ScienceDirect. The study involved generation of the ultimate sample of n=62 domain-based studies and it highlights the urgent requirement to develop a scalable, adaptive and strong AI framework with capabilities to manage emerging challenges of IoT security. Future research needs to emphasize on improving generalized models for reducing false positives and unifying lighter AI solutions for resource limited devices
Artificial Intelligence, Internet of Things, Cybersecurity, AI-driven Intrusion Detection Systems, Deep Learning, Convolutional Neural Networks, Recurrent Neural Networks, Autoencoders, Adversarial Machine Learning, Reinforcement Learning, Generative Adversarial Networks
Copyright (c) 2026 https://creativecommons.org/licenses/by-nc/4.0
Socioeconomic Impacts of Mining: Examining Traditional Occupations, Household Incomes, and Community Perceptions
Mining activities have significant implications for local communities, influencing traditional occupations, household incomes, and perceptions of development. This study investigates these impacts through an analysis of community responses and statistical tests. The findings reveal a divided perspective on mining's contributions, with many respondents expressing concerns over its detrimental effects on traditional livelihoods and the uneven distribution of economic benefits. While some perceive mining as a driver of infrastructure development and employment, skepticism remains regarding its overall value to the community. The study highlights the importance of addressing these concerns through inclusive policies, equitable benefit-sharing mechanisms, and sustainable practices. The results contribute to the growing discourse on balancing the socioeconomic and environmental impacts of mining in resource-dependent regions, offering actionable insights for policymakers and stakeholders
Mining impacts; socioeconomic effects; Traditional livelihoods; Household incomes; Community perceptions; Sustainable development
Copyright (c) 2025 P Rajender, G. Kamal Kant Sharma, Inumula Satyanarayana, DVNS Pavan Kumar (Author) https://creativecommons.org/licenses/by-nc/4.0
Real-Time Dynamic Face Identification Using One-Shot Learning Models
Real-time face identification remains a critical challenge in computer vision, especially in open-set environments where new identities can appear without prior training data. Traditional face recognition systems often fail to generalize in such scenarios due to their dependence on large-scale, fully supervised datasets and frequent retraining. This study aims to develop a real-time, one-shot learning-based framework capable of identifying individuals dynamically using a single image per identity. The proposed system integrates a lightweight ResNet-18-based embedding network with contrastive learning to generate compact face descriptors. Face detection is handled by YOLOv8-Face, and similarity matching is performed using cosine distance over a dynamic reference set. The model is trained and evaluated on the VGGFace2 dataset, simulating real-world conditions including occlusion, side profiles, and open-set testing. A comprehensive experimental setup was implemented on GPU hardware using PyTorch and Python, with 5-fold cross-validation to ensure reliability. The system achieved a maximum accuracy of 97.5% for well-lit frontal faces and maintained 82.1% accuracy for unseen identities. It delivered an average inference time of 35.3 ms per frame with throughput up to 31 FPS. Compared to baseline Siamese and Triplet models, the proposed method improved F1-score by 4.2% and reduced latency by over 20%. ROC-AUC analysis yielded a score of 0.94, confirming strong classification capability. The framework demonstrates robust performance under real-time constraints, making it ideal for surveillance, access control, and edge-based AI applications. Future work will explore adaptive similarity thresholds and online reference updating for greater autonomy.
Real-time face identification, One-shot learning, Contrastive loss, VGGFace2, ResNet-18, YOLOv8-Face, ROC-AUC, Open-set recognition.
Enhanced Financial Forecasting: Advancing Stocks And Cryptocurrency Predictions
Financial markets, particularly cryptocurrencies and high-volatility stocks, exhibit dynamic and nonlinear behaviors influenced by both quantitative indicators and public sentiment. Traditional models often fail to adapt to these complexities, resulting in poor forecasting performance and limited real-time applicability. This study proposes a hybrid deep learning framework that integrates time-series modeling and sentiment analysis to improve forecasting accuracy for both stock and cryptocurrency price movements. The model combines Long Short-Term Memory (LSTM) networks and Transformer encoders to capture sequential and contextual dependencies. It processes historical price data, technical indicators (e.g., RSI, MACD, EMA), and real-time sentiment embeddings derived from FinBERT using the FinancialPhraseBank v1.0 dataset. Extensive preprocessing, feature engineering, and multi-source data integration were employed. The model was trained on data from Yahoo Finance and Binance, and evaluated using RMSE, MAE, R² Score, and Directional Accuracy across multiple configurations and input resolutions. The proposed model outperformed ARIMA, SVM, CNN-LSTM, and Transformer-only baselines. It achieved an RMSE of 9.1, MAE of 7.6, R² Score of 0.91, and directional accuracy of 89.3%. Performance improved further when sentiment and technical features were integrated, and hourly forecasting outperformed daily resolution. The model showed robust generalization under multiple evaluation scenarios. The hybrid framework demonstrates strong potential for deployment in algorithmic trading, portfolio optimization, and financial advisory systems. Its ability to process multimodal inputs and adapt to sentiment shifts offers a scalable and accurate solution for modern financial forecasting challenges.
ial Forecasting, Cryptocurrency Prediction, LSTM, Transformer, Sentiment Analysis, FinBERT, Time-Series Prediction, Deep Learning, FinancialPhraseBank.
An Effective Deep Learning Model for Hand Shadow Puppet Recognition
Hand shadow puppetry, a culturally significant form of storytelling, involves forming recognizable shapes through hand gestures projected as shadows. Despite its artistic relevance, automatic recognition of shadow puppets remains a challenging task due to varying lighting, finger articulation, and shadow distortion in real-world conditions. This study aims to design and implement a lightweight, high-accuracy deep learning model capable of real-time hand shadow puppet recognition for interactive and educational applications. A novel convolutional neural network architecture, termed ShadowNet, was proposed and evaluated using the LeapGestRecog dataset. The dataset underwent preprocessing and augmentation to simulate real-world variability. The model was developed using TensorFlow with depthwise separable convolutions, batch normalization, and dropout to ensure efficiency and generalization. A 5-fold cross-validation strategy was employed to validate robustness. Performance was benchmarked against state-of-the-art models including MobileNetV2, ResNet18, and VGG16.ShadowNet achieved an accuracy of 96.3%, a precision of 0.961, and an F1-score of 0.962, outperforming all comparison models. It maintained a real-time inference speed of 25 frames per second (FPS) with a model size of only 6.5 MB, making it suitable for edge-device deployment.The proposed system offers a scalable solution for real-time hand shadow puppet recognition. Its lightweight design and strong performance open opportunities for deployment in interactive learning environments, gesture-based interfaces, and digital heritage preservation systems.
Shadow Puppetry, Deep Learning, Gesture Recognition, Real-Time Inference, CNN, Human-Computer Interaction.
Real-Time Health Monitoring and Predictive Analytics for Chronic Disease Management
The given Health Monitoring System based on an IoT is supposed to improve the control over chronic diseases in the framework of constant monitoring and analysis of main physiological indicators. This system consists of a wearable/portable health monitoring device, a data transmission component, cloud, data analytics engine, and interfaces to the system. The analytical engine of the system is based on such anomaly detection algorithms as Isolation Forest and such predictive algorithms as VARIMA model based on the collected/ created data, we perceived that the physiological variables showed much variability among the simulated ten patients in terms of glucose levels, blood pressure, heart rate, SpO2, cholesterol, etc. Isolation forest algorithm succeeded well in detecting anomalies in these parameters which suggested health risks. The VARIMA model also gave future predictions of trends and this is essential to real-time prevention of diseases
IoT, Health Monitoring, Chronic Diseases, Isolation Forest, VARIMA, Wearable Technology, Data Analytics, Cloud Infrastructure, Predictive Analytics, Non-AI Detection
Hybrid and Adaptive Optimization for Entanglement Preservation under Composite Noise in Quantum Systems
As quantum computing systems progress into the noisy intermediate-scale quantum (NISQ) era, ensuring reliable computation in the presence of various noise sources remains a critical challenge. This paper presents a novel Hybrid-Adaptive error mitigation framework that synergistically integrates Quantum Error Correction (QEC), Dynamical Decoupling (DD), and Feedback Control techniques to enhance system fidelity, robustness, and entanglement lifetime under diverse noise conditions. Through extensive simulations and analytical modeling, we demonstrate that the proposed method outperforms individual strategies across key performance metrics. Specifically, the Hybrid-Adaptive model sustains higher fidelity levels, shows prolonged entanglement lifetimes up to 23 steps, and maintains low standard deviation in performance across dephasing, thermal, and amplitude noise types. Furthermore, it offers superior fidelity gains per unit resource cost, highlighting its efficiency and scalability. Our results underscore the potential of hybrid architectures in building noise-resilient quantum systems and pave the way for future explorations involving learning-based and real-time adaptive quantum error mitigation techniques.
Quantum Computing, Hybrid Error Mitigation, Quantum Error Correction (QEC), Dynamical Decoupling (DD), Feedback Control, Fidelity, Entanglement Lifetime, NISQ Devices, Noise Robustness, Quantum System Optimization, Adaptive Control, Quantum Coherence, Fault-Tolerant Quantum Computation.
Copyright (c) 2023 https://creativecommons.org/licenses/by-nc/4.0
Evaluating AI-Driven Financial Interventions: Behavioral Impacts on Savings, Risk Diversification, and Bias Reduction
This paper evaluates the behavioral and financial effectiveness of AI-driven personalized financial interventions delivered through the Adaptive Financial Advisory Network (AFAN). The study investigates the impact of machine learning-based recommendations on key user behaviors, including savings rates, investment diversification, and cognitive biases such as loss aversion and overconfidence. Using a pre-post intervention design and behavioral metrics derived from financial transactions and psychological assessments, the system’s influence was quantitatively analyzed. Results indicate that users demonstrated improved savings discipline, enhanced portfolio balance, and measurable reductions in behavioral biases after engaging with the AI advisory system. These findings support the hypothesis that integrating AI with behavioral economics can lead to more rational and effective financial decision-making. The study contributes evidence that adaptive, personalized interventions can enhance financial well-being and reduce cognitive distortions in digital finance environments.
Financial behavior, AI-driven intervention, savings rate, portfolio diversification, behavioral bias, loss aversion, overconfidence, personalized finance, behavioral economics, financial decision-making.
Copyright (c) 2024 https://creativecommons.org/licenses/by-nc/4.0
Profiling Financial Personalities Using Multimodal Data: Integrating Transactions, Sentiment, and Psychometrics
This paper presents a multimodal framework for profiling financial personalities by integrating three distinct data sources: transactional records, psychometric assessments, and social media sentiment. Traditional financial advisory systems often rely solely on historical spending or demographic data, failing to capture the full complexity of user behavior and psychological disposition. In contrast, this approach constructs enriched user profiles that account for behavioral patterns, cognitive traits, and emotional attitudes toward money. The proposed system uses clustering algorithms and feature fusion techniques to identify latent user segments and develop interpretable financial personality profiles. Experimental results demonstrate that multimodal integration enhances user differentiation, increases predictive accuracy, and provides a deeper foundation for personalized financial guidance. These findings suggest that behaviorally informed, data-driven profiling can significantly improve the precision and ethical alignment of intelligent financial technologies.
Multimodal learning, financial personality profiling, psychometrics, sentiment analysis, transactional data, user segmentation, behavioral finance, data fusion, clustering, personalized financial advisory.
Adaptive Financial Advisory Networks - A Machine Learning Approach to Personalized Financial Behavior Prediction
This paper presents the design and implementation of the Adaptive Financial Advisory Network (AFAN), an intelligent system that employs machine learning to predict user financial behaviors and deliver real-time, personalized financial recommendations. By integrating supervised, unsupervised, reinforcement, and few-shot learning models, the system dynamically evolves with user interactions and financial contexts. Results show that AFAN achieves high accuracy in behavior prediction (85%), successfully segments user profiles using clustering (silhouette score: 0.75), and adapts to new users with few-shot learning (80% accuracy). The study demonstrates the value of adaptive AI in transforming static financial guidance into dynamic, user-centric advisory experiences.
Adaptive Financial Advisory; Machine Learning; Behavioral Prediction; Personalized Finance; Reinforcement Learning; Few-Shot Learning; Financial Technology (FinTech); User Segmentation; Intelligent Advisory Systems; Supervised and Unsupervised Learning
Copyright (c) 2022 https://creativecommons.org/licenses/by-nc/4.0
Entropy Scaling and Resource Trade-offs in Noisy Multi-Dimensional Quantum Systems
Understanding the behavior of entanglement entropy under noisy conditions is fundamental to building reliable quantum systems. This paper investigates the scaling and dynamics of entanglement entropy across one-dimensional (1D) and two-dimensional (2D) quantum architectures, with a focus on quantifying the impact of noise and evaluating the performance of various error mitigation strategies. Through simulation-based analysis, we examine how system dimensionality influences entropy growth, collapse thresholds, and scalability. Our results reveal that 2D systems, despite offering richer entanglement, exhibit faster entropy accumulation and earlier collapse under noise compared to their 1D counterparts. We also compare Quantum Error Correction (QEC), Dynamical Decoupling (DD), and Feedback Control in terms of entropy gain versus mitigation cost, establishing a resource-efficiency trade-off for each strategy. The findings highlight the advantages of context-dependent hybrid strategies for optimizing entropy suppression. This study contributes to the development of scalable and robust quantum architectures by providing a systematic framework for entropy monitoring and mitigation under realistic noise environments.
Quantum Entanglement, Entropy Scaling, Noise Resilience, 1D Quantum Chain, 2D Quantum Grid, Quantum Error Correction, Feedback Control, Dynamical Decoupling, Entropy Collapse, Mitigation Cost, Quantum System Topology, Decoherence, Quantum Information Theory, Adaptive Quantum Strategies, Quantum Robustness.
Benchmarking Quantum Error Correction, Dynamical Decoupling, and Feedback Control for Entanglement Preservation
Quantum entanglement serves as a foundational resource for quantum computing, communication, and sensing. However, in practical implementations, entangled states are highly vulnerable to decoherence induced by environmental noise, especially in Noisy Intermediate-Scale Quantum (NISQ) systems. This paper presents a comprehensive comparative analysis of three leading error mitigation strategies—Quantum Error Correction (QEC), Dynamical Decoupling (DD), and Feedback Control—focusing on their effectiveness in preserving entanglement fidelity under dephasing, amplitude damping, and thermal noise. Simulations were conducted on systems of up to 100 qubits, using consistent benchmarks for entanglement lifetime, fidelity degradation, and statistical robustness across noise types. Our results reveal that while QEC delivers superior fidelity at low noise levels, it suffers rapid degradation with increasing decoherence and high resource overhead. DD offers efficient protection under structured noise but shows sensitivity to pulse timing and noise variability. Feedback Control consistently outperforms both methods in entanglement preservation, exhibiting the highest fidelity retention, longest entanglement lifetime, and lowest variance across conditions. The findings offer actionable insights into the deployment of noise-resilient control techniques for near-term quantum technologies.
Quantum entanglement, error mitigation, quantum error correction, dynamical decoupling, feedback control, quantum fidelity, decoherence, NISQ, quantum noise, entanglement lifetime, quantum robustness, fidelity gain, quantum simulations, noise resilience, quantum system design.
Real-Time Detection of Road Damage and Lane Lines for Autonomous Driving Using Attention-Augmented Deep Learning
Accurate detection of road surface damage and lane boundaries is essential for the safe operation of autonomous vehicles and advanced driver-assistance systems (ADAS). Traditional vision-based approaches often fail under challenging visual conditions, such as occlusions, faded markings, or poor illumination, limiting their reliability in dynamic, real-world scenarios.This study proposes a real-time dual-stream perception framework that integrates an anchor-free YOLOv8 model for road damage detection and a lightweight Vision Transformer (ViT-lite) pipeline for lane boundary segmentation. The system processes live camera feeds, combining Canny edge-based preprocessing with patch-wise attention encoding to improve spatial continuity and structural accuracy in lane detection. The road damage detection model is trained on a hybrid dataset comprising RDD2022 and custom urban road imagery, while the lane detection module utilizes samples from TuSimple, CULane, and custom annotated images with augmented edge masks.Experimental results show that the proposed architecture achieves a mean Average Precision (mAP) of 85.2% for road damage detection, outperforming YOLOv4 by 13.8%, and yields a lane segmentation IoU of 78.6%, with an F1-score of 82.1%, surpassing traditional edge-based and CNN-only methods. Real-time inference is maintained at ~16 FPS on NVIDIA Jetson Xavier NX using ONNX and TensorRT optimizations.This work contributes a deployable, robust perception system for autonomous driving that balances accuracy and efficiency, with strong potential for integration into embedded real-time navigation and infrastructure monitoring solutions.
Pothole detection, YOLO, CNN, Lane detection, Autonomous driving, Pavement distress.
Transfer Learning-Enhanced CNN Framework for Human Activity Recognition in Real-Time Rehabilitation Monitoring
This paper introduces a novel deep learning framework for rehabilitation motion tracking that addresses critical limitations in current clinical practice, including subjective assessment and lack of real-time feedback. The proposed Rehab Motion Tracker leverages a 1D Convolutional Neural Network (CNN) architecture enhanced with transfer learning to achieve accurate exercise classification while minimizing computational overhead. Trained on the PAMAP2 and OPPORTUNITY datasets, the system demonstrates superior performance with 97.2% (±0.8%) accuracy and 97.1% (±0.7%) F1-score, outperforming conventional ConvLSTM (93.4%) and DenseNet121 (80.0%) models while maintaining 57% lower latency than comparable CNN-BiLSTM implementations. The architecture's efficiency (1.2M parameters, 89MB RAM usage) enables deployment on edge devices, with clinical trials across 112 participants validating its robustness to real-world challenges: sensor displacement (±5 cm causing only 4.7% accuracy degradation), demographic diversity (93.7% accuracy for stroke patients), and environmental noise (1.8% accuracy loss under 10% Gaussian noise). A modular design incorporating real-time biofeedback and telehealth integration has shown measurable clinical impact, reducing therapist review time by 62% and improving patient adherence by 21% in pilot deployments at three rehabilitation facilities. The system's performance remains clinically viable (>95% accuracy threshold) across age groups (20-70 years) and mobility levels, though limitations persist in classifying rare movements (<50 samples) and consumer-grade sensor compatibility (+3.2% error). This work advances rehabilitation technology by bridging the gap between computational efficiency and therapeutic precision, with future research directions including multi-modal sensor fusion and adaptive personalization algorithms for chronic condition management. The complete implementation is available for research collaboration, offering a scalable solution for modern physical therapy applications
rehabilitation monitoring, CNN, transfer learning, real-time feedback, telehealth integration, wearable sensors
CNN-Based Deep Learning Framework for Real-Time Plant Leaf Disease Detection Using GAN and Cloud Integration
The agricultural sector faces significant threats from plant diseases, which cause substantial crop losses, reduce productivity, and affect global food security. Traditional disease detection methods are often time-consuming, subjective, and infeasible for large-scale deployment. This study aims to develop a robust, scalable, and highly accurate deep learning-based system for early detection and classification of plant diseases using image data. A Convolutional Neural Network (CNN) model was trained and evaluated using the publicly available PlantVillage dataset comprising 54,303 labeled images across 38 disease classes from crops such as tomato, potato, and bell pepper. The model architecture included convolutional, pooling, and fully connected layers optimized using the Adam optimizer and categorical cross-entropy loss. Data augmentation techniques (e.g., rotation, flipping, cropping) were applied to improve generalization. To further enhance performance, a Generative Adversarial Network (GAN) was employed to synthesize additional training samples. Cloud-based deployment enabled real-time image processing and classification. Performance was evaluated using accuracy, precision, recall, and F1-score metrics under varying environmental conditions. The GAN-augmented CNN model achieved an accuracy of 97%, precision of 96%, recall of 98%, and F1-score of 97%, outperforming baseline CNN and ANN models. The system demonstrated strong performance across crops and maintained efficiency with real-time image batch processing. However, challenges such as performance degradation under low-light and noisy conditions (10–15% drop in accuracy) were observed. The proposed system offers an effective solution for early, automated plant disease detection, enabling timely intervention, reducing pesticide use, and supporting sustainable agriculture. Future work will focus on enhancing model adaptability through transfer learning and expanding cross-crop generalization
IoT-driven model, urban wildlife monitoring, real-time data processing, smart cities, predictive analytics, environmental monitoring, sustainability
SQL Injection Detection via Graph Neural Networks and Query Dependency Graphs
SQL injection (SQLi) remains one of the most critical threats to web applications, allowing attackers to manipulate backend databases and compromise sensitive data. Despite extensive research, traditional detection techniques—including rule-based filters and sequence-based machine learning models—often fail to generalize against obfuscated or zero-day attack vectors. This study introduces a scalable and robust SQLi detection framework that leverages the structural semantics of SQL queries through Graph Neural Networks (GNNs). The proposed method converts each query into a Query Dependency Graph (QDG), where nodes represent syntactic elements and edges encode structural and semantic relationships. These graphs are processed using a two-layer Graph Convolutional Network (GCN), followed by mean pooling and softmax classification. The model is trained on a diverse dataset of over 25,000 queries sourced from CICIDS 2017, OWASP WebGoat, and synthetically generated attack payloads. Real-time scalability is achieved through seamless integration with Elasticsearch for indexing and log analysis. Experimental results show that the GNN model achieves 98.74% accuracy, 99.35% recall, and a 98.73% F1-score, significantly outperforming LSTM, CNN, and rule-based baselines. Furthermore, the system demonstrates strong resilience to adversarial query manipulation while maintaining an inference latency of just 28 ms per query. These findings confirm that GNNs, when paired with QDG-based structural modeling, provide an effective and scalable solution for real-time SQL injection detection in modern web application environments.
SQL Injection Detection, Graph Neural Networks (GNNs), Query Dependency Graphs (QDGs), Web Application Security, Adversarial Robustness, Real-Time Threat Detection
AlertDriveNet - A Deep Learning Model for Predicting Driver Distraction
Driver distraction is a leading cause of traffic accidents, contributing significantly to global road fatalities. Traditional monitoring systems often fail to provide real-time detection and lack the sophistication to accurately classify complex driver behaviors. This study proposes AlertDriveNet, a deep learning-based framework for predicting driver distraction using vision-based analysis. The model employs transfer learning with a pre-trained VGG-16 network for spatial feature extraction, combined with a CNN-2D architecture and LSTM integration to capture temporal dependencies. The system is trained and evaluated on the State Farm Distracted Driver Detection dataset comprising 25,907 images across ten distraction classes. To enhance model generalization, data augmentation techniques such as flipping, zooming, and brightness adjustments were applied. The framework includes Grad-CAM for visual explanation of predictions, increasing transparency and trust in model decisions. Experimental results indicate that the VGG-16 model achieved an accuracy improvement from 92% to 95%, while CNN-2D improved from 88% to 92% post distraction-aware training. Loss values decreased from 0.60 to 0.55 for VGG-16 and from 0.70 to 0.65 for CNN-2D, confirming better convergence. Inference times remained below 20 milliseconds per image, supporting the feasibility of real-time deployment in intelligent driver assistance systems. The proposed model demonstrates high accuracy, efficiency, and interpretability, offering a practical solution for enhancing road safety through automated distraction detection.
Driver Distraction Detection, CNN-2D, VGG-16, Transfer Learning, State Farm Dataset, Real-Time Monitoring, Grad-CAM, Deep Learning, Automotive Safety
Stochastic Gradient Descent Intrusion Detection for Wireless Sensor Networks Attacks Detection System Using Machine Learning
Wireless Sensor Networks (WSNs) are highly susceptible to cyber-attacks due to their decentralized architecture and limited computational resources, posing serious challenges for real-time security. Traditional Intrusion Detection Systems (IDS) often fall short in such environments due to their heavy processing requirements and inability to adapt to evolving threats. This study presents a lightweight and scalable IDS framework tailored for WSNs, optimized using Stochastic Gradient Descent (SGD) to improve efficiency and detection accuracy. The proposed system integrates preprocessing steps such as normalization, SMOTE-based class balancing, and dimensionality reduction using Principal Component Analysis (PCA). It utilizes supervised learning models—Support Vector Machines (SVM) and Artificial Neural Networks (ANN)—trained with SGD for adaptive learning on benchmark datasets including NSL-KDD, UNSW-NB15, and KDD Cup 99. Evaluation metrics include accuracy, precision, recall, F1-score, and ROC-AUC, supported by 5-fold cross-validation for reliability. Experimental results show that the SGD-optimized ANN achieves a peak accuracy of 94.2%, with an F1-score of 92.1%, outperforming Random Forest and XGBoost models. Additionally, the detection rates for rare attacks like U2R and R2L improved by over 15% compared to traditional training approaches. The framework also demonstrates a 30% reduction in training time and memory usage, validating its applicability for real-time WSN deployment. This work contributes a robust and resource-efficient solution for IDS in WSNs, offering high performance without compromising scalability or adaptability
Wireless Sensor Networks (WSNs), Intrusion Detection System (IDS), Stochastic Gradient Descent (SGD), Support Vector Machines (SVM), Artificial Neural Networks (ANN), NSL-KDD, PCA, Real-time Security, Cybersecurity, Machine Learning
Leveraging Convolutional Neural Networks for Non-Invasive Detection of Vitamin Deficiencies in Dermoscopy Images
Vitamin deficiencies such as those of Vitamin B12, D, and A are widespread and often underdiagnosed due to the reliance on invasive and costly blood tests. These deficiencies manifest visibly in the skin through signs like pallor, dryness, and pigmentation changes, which can be captured through dermoscopic imaging. This study aims to develop a deep learning-based framework for the non-invasive detection of vitamin deficiencies using dermoscopy images, enabling accessible and early nutritional diagnostics. A domain-informed synthetic dermoscopy dataset was constructed to simulate dermatological features associated with Vitamin B12, D, and A deficiencies, along with healthy skin. A custom lightweight Convolutional Neural Network (CNN) was designed and trained on this dataset. The architecture comprises three convolutional blocks followed by a fully connected layer and softmax output, optimized using the Adam optimizer and categorical cross-entropy loss. The dataset was split using a 70-15-15 train-validation-test scheme and further evaluated using 5-fold cross-validation. The proposed model achieved a classification accuracy of 92.7%, with a macro-averaged F1-score of 0.921 across four classes. Compared to existing models applied in similar non-lesional medical imaging tasks, the model demonstrates superior performance with lower computational complexity, making it suitable for real-time deployment on mobile devices. This work introduces a novel application of CNNs for non-invasive vitamin deficiency detection using dermoscopy, marking a shift from lesion-focused dermatological AI to systemic health diagnostics. The proposed system offers significant potential for telemedicine, community screening, and mobile health applications, particularly in resource-constrained environments.
Vitamin Deficiency Detection, Dermoscopy Imaging, Convolutional Neural Networks, Non-Invasive Diagnostics, Synthetic Medical Dataset, Mobile Health Applications.
Machine Learning for Euro-to-Dollar Exchange Rate Forecasting: Buy/Sell Decision System
Accurate forecasting of foreign exchange (Forex) rates, particularly the Euro-to-US Dollar (EUR/USD) pair, remains a complex challenge due to the market’s non-linear behavior, high volatility, and sensitivity to macroeconomic factors. Traditional statistical models often fail to capture these dynamic patterns, limiting their effectiveness in real-world trading environments. This study aims to develop a machine learning-driven forecasting framework integrated with a decision-making system to generate actionable Buy/Sell signals for EUR/USD trading. The proposed system utilizes a hybrid approach, combining traditional models (Linear Regression, Random Forest) and a deep learning-based Long Short-Term Memory (LSTM) network. Historical EUR/USD data spanning from 2010 to 2024 was used, enriched with technical indicators such as RSI, MACD, and Bollinger Bands. A rule-based decision engine translates predicted rate movements into Buy/Sell/Hold signals, and the system’s performance is validated through backtesting and statistical evaluation. The LSTM model achieved a Mean Absolute Percentage Error (MAPE) of 0.94% and Root Mean Squared Error (RMSE) of 0.0210, outperforming both traditional models. The decision engine produced a 65.4% trading signal accuracy and a Sharpe Ratio of 1.42, indicating strong risk-adjusted returns. This research contributes a scalable, interpretable, and data-driven Forex forecasting system that bridges predictive analytics and trading execution, offering significant potential for deployment in algorithmic trading and decision support applications.
EUR/USD forecasting, machine learning, LSTM, Forex trading, technical indicators, decision support system.
Blockchain-Enabled Product Tokenization and CNN-Based Counterfeit Detection for Secure Supply Chain Verification
The global surge in counterfeit products poses a critical threat to consumer safety, brand reputation, and economic stability. Traditional anti-counterfeiting methods, including physical labels and centralized verification systems, have proven increasingly vulnerable to tampering, replication, and limited traceability. This study aims to design and implement a robust, end-to-end system that combines machine learning-based product classification with blockchain-enabled authenticity verification through smart contract-based tokenization. The proposed approach integrates a lightweight Convolutional Neural Network (CNN) trained on a publicly available fake product detection dataset containing 2,364 images. The model achieved an average accuracy of 94.2%, with a precision of 92.7%, recall of 93.1%, and F1-score of 92.9% across 10-fold cross-validation. Each verified genuine product is minted as a unique ERC-721 token on the Ethereum blockchain, with associated metadata retrievable via QR-code-based verification. The system demonstrated a True Verification Rate (TVR) of 95.3%, a False Acceptance Rate (FAR) of 4.1%, and an average verification latency of 1.42 seconds, while maintaining minimal gas costs on the Goerli test net. The study introduces a scalable, tamper-proof, and transparent product authentication framework suitable for industries such as pharmaceuticals, fashion, and electronics. By bridging intelligent classification with decentralized verification, this research advances the field of anti-counterfeit technology and lays a foundation for real-world blockchain-integrated supply chain systems.
Counterfeit Detection, Blockchain, Smart Contracts, Product Tokenization, Convolutional Neural Networks, Supply Chain Verification.
Enhanced Hybrid ML Framework for Smart Grid Attack Detection
The increasing integration of information and communication technologies into smart grid infrastructures has significantly improved efficiency, automation, and reliability. However, this digital transformation has also introduced new vulnerabilities, making smart grids increasingly susceptible to sophisticated cyberattacks such as Denial-of-Service (DoS), false data injection, and replay attacks. This study aims to develop a robust, hybrid machine learning framework for intelligent intrusion detection in smart grids, addressing the limitations of existing single-model approaches in terms of generalization, real-time performance, and adaptability. The proposed framework combines three complementary models—Random Forest (RF) for static pattern classification, Long Short-Term Memory (LSTM) networks for temporal sequence modeling, and Autoencoders for unsupervised anomaly detection. Experiments were conducted on three benchmark datasets: NSL-KDD, CICIDS2017, and SWaT, covering both network-level and process-level cyberattacks. All models were trained under uniform preprocessing conditions, and their performances were evaluated using standard classification metrics and detection time analysis. The hybrid model achieved an accuracy of 96.42%, precision of 95.88%, recall of 95.55%, and F1-score of 95.71% on the NSL-KDD dataset, outperforming baseline models including SVM, RF, and LSTM. Average detection time was maintained between 4–5 ms/sample, validating its suitability for near real-time deployment. The results demonstrate the proposed framework’s effectiveness in enhancing cybersecurity for smart grids, offering a scalable, accurate, and computationally efficient solution for modern power systems.
Smart Grid Security, Intrusion Detection, Machine Learning, Hybrid Model, Cyberattack Detection, Real-Time Monitoring.
Non-Invasive Blood Group Classification via Fingerprint Biometrics Using Vision Transformers
This study addresses the limitations of conventional serological blood group identification—which, while highly accurate (>99%), is invasive, time-consuming, and resource-intensive—by proposing a non-invasive, deep learning-based alternative using fingerprint biometrics. The objective was to develop and evaluate a robust blood group classification system leveraging state-of-the-art Vision Transformer (ViT) models. A curated dataset of 6,000 fingerprint images spanning eight major blood groups and an additional 'Unknown' category was employed. Preprocessing involved resizing, normalization, and augmentation, followed by model training using a ViT-Base architecture pretrained on ImageNet-21k and fine-tuned on the fingerprint dataset. Optimization strategies included the Adam optimizer with learning rate decay, dropout regularization, and early stopping. The ViT model achieved a training accuracy of 97.84% and a validation accuracy of 92.52%, significantly outperforming baseline models such as MobileNet (77.56%) and ResNet+RNN (75.57%). The system demonstrated F1-scores exceeding 0.90 for all primary classes, with O+ achieving the highest at 0.96, though performance was relatively lower for underrepresented classes like AB− and 'Unknown'. These findings affirm the viability of ViT for real-time, non-invasive blood group classification, particularly in point-of-care or resource-constrained settings. Despite limitations in dataset diversity and model sensitivity to rare classes, the framework presents a promising advancement toward deployable, biometric-based medical diagnostics.
Blood group detection, Fingerprint analysis, Deep learning, MobileNet, ResNet, RNN, Vision Transformers, Biometric identification, medical diagnostics.
SC-GAN: A Style-Conditioned Generative Adversarial Network for High-Quality Artistic Image Generation
Artistic image generation has emerged as a significant challenge in artificial intelligence (AI), requiring models that can synthesize visually appealing and diverse artworks while maintaining stylistic coherence. This study proposes a novel Style-Conditioned Generative Adversarial Network (SC-GAN) aimed at generating high-quality, stylistically consistent, and controllable artistic images. The SC-GAN integrates adversarial, perceptual, and style losses to ensure both visual realism and artistic fidelity. It leverages the WikiArt dataset, containing over 25,000 images across 10 diverse artistic styles, for training and evaluation. The model's performance is benchmarked against state-of-the-art architectures, including CycleGAN, Pix2Pix, and StyleGAN2, using metrics such as Fréchet Inception Distance (FID), Learned Perceptual Image Patch Similarity (LPIPS), and style classification accuracy. The results demonstrate that SC-GAN outperforms existing models with an FID of 16.32 and an LPIPS of 0.210, indicating superior realism and perceptual quality. Additionally, SC-GAN shows remarkable diversity, with high precision and recall scores, alongside competitive inference speed. This work introduces a robust framework for the controllable and diverse generation of artistic images, with implications for creative AI applications in fields such as digital art, content creation, and entertainment. The model's ability to generate stylized, high-quality outputs makes it a significant advancement in the intersection of AI and art.
StyleGAN, Generative Adversarial Networks, Artistic Image Generation, Style Transfer, Perceptual Loss, SC-GAN.
Thyroid Disease Prediction Using Machine Learning
This paper investigates the application of machine learning for thyroid disease prediction, focusing on improving early diagnosis and accurate classification of conditions such as hypothyroidism, hyperthyroidism, and thyroiditis. Present systems often encounter challenges, including limited generalizability due to imbalanced datasets, insufficient inclusion of significant clinical features, and reliance on traditional algorithms that fail to capture complex relationships. To address these issues, the study adopts a robust methodology involving data preprocessing, feature selection through Recursive Feature Elimination, and training machine learning models such as Logistic Regression, Random Forest, and Support Vector Machines. The models were evaluated using performance metrics, including accuracy, precision, recall, and F1-score, to ensure comprehensive validation. The findings reveal that the proposed system achieves a predictive accuracy of approximately 94.5%, with precision and recall values exceeding 90% across most conditions. Features like TSH, T3, and T4 levels were identified as the most critical predictors. Additionally, the study demonstrated a 12% improvement in recall for rare conditions like thyroiditis by addressing dataset imbalances through advanced preprocessing techniques. The system's scalability and interpretability make it a valuable tool for assisting clinicians in making accurate and timely diagnoses. Future work emphasizes integrating multi-regional datasets, incorporating real-time data from wearable devices and electronic health records, and exploring deep learning approaches for even greater accuracy and adaptability. These enhancements aim to bridge existing gaps and further refine thyroid disease prediction models.
Thyroid Disease Prediction, Machine Learning, Feature Selection, Accuracy, Clinical Diagnosis, Dataset Imbalance, Future Enhancements
Smart Timetable Generation using Genetic Algorithm
The research focuses on developing a smart timetable generation system using genetic algorithms to address inefficiencies in traditional scheduling methods. The key objective is to create a scalable, conflict-free, and user-friendly system that optimizes resource utilization and adapts to dynamic constraints. Present systems often face challenges such as overlapping schedules, poor resource management, and heavy reliance on manual effort, leading to errors and dissatisfaction among stakeholders. The proposed methodology leverages a genetic algorithm to evaluate and improve schedules iteratively, handling hard constraints like room capacities and instructor availability, as well as soft constraints like time preferences. The system incorporates a web-based interface, allowing real-time customization and user interaction. Findings indicate that the model achieves up to 95% conflict resolution and improves resource utilization by 90%, with efficient execution times of 10.8 seconds for datasets exceeding 2000 records. This performance significantly outperforms existing systems, which often struggle with scalability and fail to effectively balance constraints. Achievements of the study include high adaptability, scalability, and an interactive user interface, making the system suitable for academic institutions of varying sizes. However, limitations remain, such as computational challenges with very large datasets and reduced flexibility under extreme resource constraints. These limitations suggest potential for future work, including integrating machine learning for dynamic constraint handling and enhancing the interface for multi-user collaboration. The study demonstrates how combining optimization algorithms with modern technologies can transform traditional scheduling into a smarter, more efficient process
Genetic Algorithm, Timetable Optimization, Resource Utilization, Conflict Resolution, Web-Based System, Scheduling Efficiency, Dynamic Constraints
Securing Cloud Data Under Key Exposure Innovative Techniques for Robust Data Protection
This paper introduces a Blockchain-Based Key Management System (BKMS) to overcome the limitations of traditional centralized key management systems, which often face issues like single points of failure, limited scalability, and non-compliance with data protection regulations such as GDPR and HIPAA. These challenges result in security vulnerabilities, high operational costs, and difficulties in maintaining accurate and tamper-proof audit trails. The proposed BKMS leverages blockchain’s decentralized architecture to eliminate single points of failure, while smart contracts automate key lifecycle operations, including key creation, distribution, rotation, and revocation. Additionally, the integration of AI-driven anomaly detection enhances the system’s ability to identify potential threats proactively. The methodology involves using blockchain for secure and transparent key storage, with smart contracts ensuring regulatory compliance and efficiency. Scalability tests demonstrate that the BKMS achieves up to 50% higher cost efficiency and maintains consistent transaction throughput under increasing workloads compared to traditional systems. It also ensures full compliance auditability with immutable and detailed logs, addressing the gaps in conventional solutions. The findings indicate that the proposed BKMS significantly improves security, scalability, and operational efficiency while reducing dependency on centralized systems. Achievements include enhanced compliance capabilities, reduced human intervention through automation, and effective anomaly detection. However, the system's limitations, such as scalability trade-offs under extreme workloads and the need for better interoperability with legacy systems, highlight opportunities for future improvement
Blockchain, Key Management System, Smart Contracts, Compliance, Scalability, Anomaly Detection, Auditability
Predicting Heart Diseases with Advanced Machine Learning Algorithms for Improved Diagnostic Accuracy
Heart disease remains a leading cause of global mortality, necessitating innovative approaches for early diagnosis and prevention. This study proposes an advanced machine learning framework designed to enhance the predictive accuracy and reliability of heart disease diagnosis. The objectives of this research include addressing the limitations of existing systems, such as suboptimal accuracy, difficulty in handling complex medical data, and lack of model interpretability. Techniques like Random Forest, XGBoost, and Deep Neural Networks are leveraged to create a robust and scalable predictive model. Current systems often face challenges including imbalanced datasets, inadequate feature selection, and limited adaptability to diverse population data. The proposed methodology integrates comprehensive data preprocessing steps, advanced feature engineering techniques, and hyperparameter tuning to overcome these issues. Using the Heart Disease Dataset, the model achieved a notable accuracy of 96.2%, significantly outperforming existing benchmarks like Gradient Boosting and hybrid systems, which achieve up to 95%. Findings reveal that combining ensemble methods with deep learning not only improves prediction accuracy but also enhances the model’s reliability and clinical utility. Achievements include the development of an explainable AI framework suitable for real-world applications, addressing critical issues such as data imbalance and complex feature relationships. Future work will focus on incorporating real-time data streams, exploring wearable device integration, and testing the model on more diverse datasets to improve generalizability. This study underscores the transformative potential of AI in revolutionizing healthcare diagnostics and improving patient outcomes
Heart disease prediction, machine learning, Random Forest, XGBoost, Deep Neural Networks, explainable AI, accuracy improvement, healthcare diagnostics