The Air Journal of Artificial Intelligence (ISSN Print: 3106-7735 | ISSN Online: 3106-7743) is a quarterly, peer-reviewed, open-access journal published by the International Institute of Modern Research, a legally registered organization with the Securities and Exchange Commission of Pakistan (SECP Registration No. 0307037). The journal is dedicated to publishing high-quality research in Artificial Intelligence, Computational Intelligence, Control Systems, and Computer Science, with an emphasis on ethical publishing, rigorous peer review, and fostering research that has a global impact.
International Institute of Modern Research (SMC-Private) Limited
PROACTIVE FAULT DIAGNOSIS AND SELF-HEALING THROUGH AI-DRIVEN ANOMALY DETECTION IN SOFTWARE LOGS
Modern software systems generate vast volumes of logs, making manual analysis impractical for effective monitoring and fault diagnosis. This paper proposes an architecture leveraging advanced machine learning and deep learning techniques— such as LSTMs, Transformers, contrastive learning, and self-supervised learning— to detect anomalous patterns in logs for proactive fault diagnosis and self-healing. Traditional rule-based methods fall short in handling the complexity and scale of contemporary systems. Reinforcement learning and rule-based automation further enable fault correction, reducing system downtime. Evaluation across various log datasets using metrics like precision, recall, F1-score, and AUC-ROC shows Transformer-based models outperform traditional methods, albeit with higher computational demands. The proposed self-healing systems reduce downtime by up to 68.2%, highlighting AI’s potential to enhance system resilience. However, challenges remain in model interpretability, computational cost, and real-time deployment. Addressing these through lightweight models, explainable AI, and scalable deployment is key to advancing AI-driven anomaly detection in safety- critical systems. This work also offers a state-of-the-art review and outlines future research directions to improve practicality and scalability.
AI-powered anomaly detection, software logs, machine learning, deep learning, self-healing systems, log analysis, LSTM, Transformer models, contrastive learning, proactive fault diagnosis, reinforcement learning, explainable AI, system resilience, automated fault recovery, real- time anomaly detection
Cloud computing is an internet-based technology offering shared, flexible infrastructure as a service. It combines hardware and software delivered over the internet, gaining popularity due to its low cost, scalability, and adaptability. This research focuses on securing user data in cloud environments, particularly storage security and preventing unauthorized access through a proposed access control model. Existing models were reviewed, but many failed to meet the security needs of service providers, leaving data vulnerable to attacks. The proposed model addresses these gaps by enhancing data integrity, availability, and confidentiality, encouraging hesitant users to adopt cloud services with a clearer understanding of associated risks.
DESIGN AND ANALYSIS OF A SECURE STEGANOGRAPHIC COMMUNICATION SCHEME BASED ON RGB PIXEL ENCODING
This research proposes a practical and secure steganographic communication method using RGB pixel encoding combined with cryptographic protection. Message data is segmented into 3-bit, 3-bit, and 2-bit blocks, then embedded into the RGB channels of a digital image. A corresponding logarithmic (LOG) table is generated and encrypted using the Rijndael Managed algorithm to enhance confidentiality and defense against cryptographic attacks. Comparative analysis shows that this method preserves image quality with under 1% variation in pixel intensity, outperforming traditional techniques like Least Significant Bit (LSB) embedding. The approach ensures a robust, imperceptible, and secure communication channel, offering strong resistance to steganalysis.
Steganographic encryption, RGB pixel encoding, cryptographic security, Rijndael Managed algorithm, secure data hiding, image steganography, covert communication, digital forensics, LOG table, steganalysis resistance
INVESTIGATION OF BLACK HOLE ATTACKS IN WIRELESS SENSOR NETWORKS
As sensor technologies advance, various types of sensors are widely deployed to form networks such as MANETs, WBANs, and VANETs—categories of Wireless Sensor Networks (WSNs). These sensors collect and transmit data to head nodes or base stations, often facing numerous security threats, both active and passive. This paper focuses on performance-oriented, layer- oriented, and goal-oriented attacks, analyzing their impact and exploring corresponding countermeasures. Special attention is given to the investigation, detection, and prevention of black hole attacks in WSNs.
AN ANALYTICAL REVIEW OF ENERGY EFFICIENCY OPTIMIZATION IN C-RAN ARCHITECTURES
Cloud Radio Access Networks (C-RAN) represent a modern shift in cellular network architecture by centralizing baseband processing units (BBUs) in cloud data centers while deploying distributed remote radio heads (RRHs) at cell sites. Although C-RAN offers improved scalability, flexibility, and centralized network management, it also introduces significant energy efficiency challenges due to high power consumption in BBU pools, multiple active RRHs, and substantial cooling demands in central facilities. This paper reviews key energy efficiency challenges in C-RAN, particularly under dynamic traffic loads and idle power scenarios. It also explores recent advancements such as dynamic BBU pooling, RRH sleep mode techniques, AI-based traffic prediction, and the integration of renewable energy sources. Findings suggest that substantial energy savings can be achieved without compromising network performance by deploying these strategies across different layers of the network. The paper concludes with future research directions aimed at enhancing C-RAN energy efficiency in the context of evolving 5G and beyond-5G networks.
SMART ASSISTIVE SYSTEM FOR THE VISUALLY IMPAIRED USING COMPUTER VISION
This study presents an Intelligent Assistive Device that integrates Artificial Intelligence (AI), Computer Vision, and the Internet of Things (IoT) to support individuals with visual impairments. The system is designed to overcome limitations of existing solutions by providing an efficient, user-friendly, and cost-effective device tailored to the needs of visually impaired users. Core functionalities include obstacle detection, recognition, and distance measurement to facilitate independent navigation, supplemented by features such as face and currency recognition, wet floor and fall alerts, live location tracking, text reading assistance, guardian monitoring, and emergency dialing. These functions are implemented iteratively to empower users to perform daily tasks with minimal external support. The device architecture combines IoT and computer vision technologies, employing Raspberry Pi and AI-based methods. A critical review of current assistive tools informed both the identification of their shortcomings and the integration of effective design elements into the proposed system. User-centric considerations—such as wearability, portability, and lightweight design—were incorporated, along with a cost analysis to ensure affordability without compromising functionality. Overall, the proposed system enhances independence, safety, and quality of life for visually impaired individuals while reducing the social and economic burdens associated with visual disabilities.
INTELLIGENT VERTICAL HANDOVER DECISION FRAMEWORK FOR 5G NETWORKS VIA DATA MINING
Seamless vertical handover (VHO) is essential for ensuring continuous connectivity and high Quality of Service (QoS) in 5G heterogeneous networks. However, variations in network behaviors and protocols complicate VHO decision-making, often resulting in higher latency and service disruptions. This paper proposes a data mining-based VHO decision framework for 5G networks that leverages historical handover data to optimize mobility management. Using multivariate regression and Analysis of Variance (ANOVA), the framework identifies critical parameters such as signal strength, bandwidth, jitter, latency, packet loss, and coverage. Simulations conducted in the NetNeuman environment demonstrate that the proposed approach outperforms baseline algorithms by reducing latency, improving handover success rates, and enhancing overall network performance. Real-time decision-making, supported by historical insights, enables the framework to better meet user demands, thereby improving both reliability and user experience. The study also highlights the potential of integrating advanced machine learning methods for adaptive and predictive mobility management in future 6G networks. This work contributes to the development of intelligent, data-driven handover mechanisms vital for achieving ultra-reliable low-latency communication and seamless mobility in next-generation wireless systems.
ENHANCING GEOPOLYMER CONCRETE: MECHANICAL BEHAVIOR ANALYSIS WITH NANO-SILICA
The widespread use of Portland cement concrete contributes significantly to carbon emissions, creating a need for sustainable alternatives such as geopolymer concrete (GPC). Despite its potential, GPC often shows inconsistent mechanical properties, particularly in early-age strength, limiting its broader application. Nano-silica has proven effective in cementitious composites, yet its influence on GPC, especially in terms of dosage optimization and microstructural behavior, remains underexplored. This study examines the effect of nano-silica (0–4% by fly ash weight) on the mechanical and microstructural properties of GPC. Mechanical tests (compressive, tensile, and flexural strength) and microstructural analyses (SEM, XRD) were conducted, supported by statistical evaluation using ANOVA, Tukey HSD, and regression. The results indicate that 3% nano-silica achieved the highest compressive strength (39.66 ± 0.91 MPa), a 24.2% increase over the control, while tensile and flexural strengths improved by 32.4% and 24.2%, respectively. Microstructural analysis revealed denser matrices with lower porosity (8.5% vs. 12.5% in control). Workability decreased with increasing dosage, with the optimum balance observed at 2–3%, beyond which agglomeration occurred. Overall, nano-silica enhances the mechanical performance and microstructure of GPC, offering a cost-effective and sustainable pathway for low-carbon infrastructure.
DEVELOPMENT OF A LOW-COST LINKAGE-DRIVEN TWO-FINGER EXOSKELETON FOR HAND REHABILITATION
Hand rehabilitation plays a crucial role in restoring motor function in individuals with neurological impairments such as stroke, and robotic-assisted therapy has demonstrated significant therapeutic benefits. However, the practical application of existing hand exoskeletons is limited by high costs and complex designs, particularly in healthcare systems with constrained financial and technical resources. Current research lacks a verified system that provides essential finger mobility through a low-cost, simple, and easily fabricable structure. This study focuses on the mechanical design and preliminary evaluation of a hand exoskeleton that employs a planar linkage system to guide the index and middle fingers. A detailed CAD model was developed to ensure accurate component dimensions and proper assembly alignment. The prototype was fabricated using laser-cut acrylic linkages, actuated with standard servo motors, and transmitted motion via a bevel gear pair. Control was implemented through an Arduino microcontroller, programmed to drive finger trajectories according to predefined flexion-extension angles. The design demonstrates mechanical simplicity, with a six-bar linkage system constructed from readily available components forming a precise therapeutic motion unit. The complete prototype weighs approximately 100 grams, with material costs under $50 USD, and requires no specialized parts for assembly. Performance testing confirmed controlled finger flexion and extension, verifying mechanical integrity, actuation reliability, and electronic responsiveness. These results highlight the potential for a mechanically feasible and economically accessible hand exoskeleton, offering a framework for expanding rehabilitation access in resource-limited settings. The study indicates that affordable robotic mechanisms can reliably facilitate finger mobilization, supporting improved recovery outcomes for patients with hand paralysis or post-stroke motor deficits.
SYSTEMATIC ANALYSIS AND CLASSIFICATION OF FOODBORNE DISEASE OUTBREAKS
Foodborne diseases, primarily resulting from the consumption of contaminated food and beverages, pose significant public health risks. Timely identification and classification of foodborne disease outbreaks are essential to mitigate illness and mortality. This study aims to rapidly identify causative agents to enhance food safety and prevent disease-related consequences. Through analysis of the dataset, key outbreak patterns were identified, including trends in the frequency of outbreaks by year, food type, location, and affected species. Classification of these outbreaks was performed using Decision Tree, Naïve Bayes, and Random Forest algorithms. Experimental results demonstrate the effectiveness of the proposed approach in accurately identifying and classifying outbreak patterns, providing valuable insights for disease prevention and food safety management.
ADVANCEMENTS IN AFFECTIVE COMPUTING FOR SOCIAL ROBOTICS: A COMPREHENSIVE SURVEY
This study examines the evolving domain of Human-Robot Interaction (HRI) within workplace environments, focusing on the complex interplay between humans and robots in professional contexts. With the increasing integration of artificial intelligence (AI), particularly generative AI, into organizational workflows, understanding HRI dynamics has become critical for businesses, society, and ethical governance. This paper synthesizes existing literature to provide a comprehensive overview of current trends, challenges, and future research directions in workplace HRI. It explores how advancements in AI, including natural language processing and adaptive interaction, are transforming human-robot collaboration, communication, and cohabitation in professional settings. Emphasis is placed on the role of trust, anthropomorphism, and social intelligence in promoting effective HRI, drawing insights from psychology, computer science, and sociology. The review also considers the impact of generative AI technologies, such as ChatGPT, on creating more natural and human-like interactions, while addressing associated ethical concerns. Additionally, the study highlights how effective workplace HRI can enhance employee well-being, operational efficiency, and innovation. By identifying gaps in current research, this review offers guidance for future studies on long-term user adaptation, AI decision-making interpretability, and the development of ethical frameworks for responsible AI deployment in professional environments.
Affective Computing, Socially Interactive Robots
THE ROLE OF ARTIFICIAL INTELLIGENCE IN SMART GRIDS, POWER SYSTEMS, AND AUTOMATION WITHIN ELECTRICAL ENGINEERING
This research examines the transformative impact of artificial intelligence (AI) on electrical engineering applications, focusing on smart grids, power systems, and industrial automation. It explores how machine learning, deep learning, and related AI techniques enhance grid management, improve power system stability, optimize resource allocation, and streamline automation. Quantitative analysis of multiple case studies reveals a 37% increase in fault prediction accuracy, 22% reduction in energy consumption, and 45% decrease in system downtime. The study offers a comprehensive framework for utilities and industrial operators to integrate AI solutions, addressing key challenges in reliability, sustainability, and resilience of modern electrical systems.
FEDERATED LEARNING FOR DISTRIBUTED NETWORK TRAFFIC ANOMALY DETECTION USING GRU MODELS
This work introduces a novel GRU-based federated learning approach for anomaly detection in network traffic. Our decentralized method effectively addresses privacy concerns and constraints that prevent centralized servers. Supported by extensive experiments and comparisons, it demonstrates strong performance in detecting anomalies across distributed environments.
Rising demand for sustainable materials has spurred advances in next-generation biodegradable polymers, offering eco-friendly solutions to reduce environmental pollution. This study reviews recent innovations focusing on renewable resources like plant polysaccharides, proteins, and microbial biopolymers to create materials with customizable mechanical properties and controlled degradation. Nanotechnology and bio-based additives enhance stability, strength, and biodegradability, enabling applications in packaging, agriculture, medicine, and textiles. Advances in processing—such as 3D printing and green chemistry—support scalable, energy-efficient production. These polymers reduce fossil fuel reliance and accelerate natural degradation, helping tackle plastic waste. Despite challenges in cost, scalability, and end-of-life management, ongoing research aims to optimize performance and environmental compatibility. The paper highlights emerging trends, lifecycle assessments, and prospects for integrating biodegradable polymers into circular economies, marking a critical step toward a sustainable, resilient planet.
LEVERAGING AI FOR ANOMALY DETECTION IN ELECTRICAL SYSTEMS AND NETWORKS TO PROTECT CRITICAL INFRASTRUCTURE FROM CYBERATTACKS
As reliance on critical infrastructure grows, so does its vulnerability to sophisticated cyber-attacks that traditional security measures struggle to counter. This paper explores how Artificial Intelligence (AI) and Machine Learning (ML) enhance threat forecasting and mitigation in these systems. AI/ML excel in real-time processing of large datasets, detecting patterns indicative of cyber threats. The study investigates advanced AI techniques—supervised, unsupervised, and reinforcement learning—for intrusion detection, vulnerability assessment, and threat prediction. Key challenges such as adversarial attacks, privacy issues, and data quality are analyzed. Explainable AI (XAI) is highlighted as essential for transparency and trustworthiness in deploying ML-based security solutions in critical infrastructure. The integration of AI-driven threat intelligence with existing cyber frameworks supports real-time threat mitigation and adaptive response strategies. AI-powered Industrial Control Systems (ICS) demonstrate reduced downtime, financial losses, and infrastructure failures. Findings confirm that AI and ML foster proactive cybersecurity, improving resilience against cyber threats. The paper concludes with recommendations for future research on robust AI defenses, standardized data protocols, and interdisciplinary collaboration among AI experts, cybersecurity professionals, and infrastructure managers, aiming to safeguard vital services and maintain operational stability.
DESIGNING HIGH-PERFORMANCE PHOTOTRANSISTORS USING SEMICONDUCTOR NANOMATERIALS
Background: Phototransistors are vital in optoelectronics for their high sensitivity and broad spectral response, yet improving key metrics like responsivity, detectivity, and response time remains challenging. Semiconductor nanomaterials—with tunable band gaps, high carrier mobility, and strong light interaction—offer promising solutions. Objective: This study designs and fabricates high-performance phototransistors using GaN-ZnO solid solution nanowires and WS₂–InGaZnO heterojunctions, investigating how band gap engineering, synthesis conditions, plasmonic enhancement, and heterostructure formation affect device performance. Methods: GaN-ZnO nanowires with varying ZnO content were synthesized and deposited on Si/SiO₂ substrates, forming phototransistor channels. Performance was evaluated across synthesis temperatures and durations. Ag nanoparticles were added for plasmonic enhancement. WS₂–InGaZnO heterojunctions were fabricated using CVD and sputtering. Devices employed bottom-gate architectures with Ti/Au electrodes. Key parameters—photocurrent, responsivity, detectivity, response time, and stability—were measured. Results: Band gap tuning from 3.4 eV (GaN) to 2.6 eV (Zn-rich) improved visible light absorption. Optimal synthesis at 850°C achieved responsivity of 95.3 A/W and detectivity of 2.1 × 10¹¹ Jones. Ag nanoparticle decoration enhanced responsivity to 131.7 A/W and reduced response time to 5.9 ms. WS₂–InGaZnO heterojunctions showed superior performance with 122.5 A/W responsivity, 3.8 × 10¹¹ Jones detectivity, and 91.7% stability over 50 cycles. Conclusion: Band gap-engineered nanowires, plasmonic enhancement, and heterojunction integration significantly improve phototransistor performance, paving the way for next-generation broadband, high-sensitivity photodetectors for optoelectronic applications.
CLOUD COMPUTING ADOPTION IN SMES: AN EMPIRICAL ANALYSIS USING PLS-SEM
Cloud computing offers SMEs affordable and scalable IT solutions, making it a transformative technology for their operations. This study investigates how perceived usefulness, cost efficiency, and security concerns influence cloud adoption in SMEs, with organizational readiness acting as a mediator. Using a quantitative research design, data were collected via a 7-point Likert scale from 209 IT managers, system administrators, and decision-makers in Karachi-based SMEs. Structural Equation Modeling (SEM) via SmartPLS revealed that perceived usefulness and cost efficiency positively impact cloud adoption, while security concerns serve as a deterrent. Organizational readiness significantly mediates these relationships. The findings provide valuable insights for SME decision-makers and policymakers, emphasizing the need for improved IT infrastructure and greater managerial involvement to foster cloud adoption.
Digital Transformation lipid peroxidation
ADVANCES IN MACHINE LEARNING: A SURVEY OF METHODS, MODELS, BENCHMARKS, AND INDUSTRY APPLICATIONS
This study examines the fundamentals of Machine Learning (ML), a key branch of Artificial Intelligence (AI) that enables systems to learn and adapt from data without explicit programming. It outlines core ML types and approaches, emphasizing their ability to improve with experience. The paper also traces ML’s evolution—from statistical learning theory to modern neural networks—highlighting how big data and computational advances have accelerated its growth. Finally, the study explores ML applications across sectors such as healthcare, finance, and retail, showcasing its role in solving complex problems, enhancing decision-making, and driving industry transformation.
OPTIMIZING PREFABRICATED CONSTRUCTION THROUGH SUPPLY CHAIN INTEGRATION AND ENTERPRISE ACCESS STANDARDS
This study explores supply chain integration in the prefabricated building industry, focusing on upstream–downstream enterprise relationships. Using Importance-Performance Analysis, it identifies key risks across the project lifecycle. Findings show that high costs and policy shifts dominate early stages, while risk management is weakest during manufacturing and construction. Challenges in standardization, quality control, and technology adoption are also highlighted. The study underscores the role of government policies and industry standards in driving integration and proposes strategies such as establishing unified standards, investing in workforce training, and adopting a lifecycle project management approach.
EVALUATING THE ACCURACY AND EFFECTIVENESS OF FRAUD DETECTION SYSTEMS
Financial fraud, waste, and abuse cost the global economy an estimated $5.4 trillion annually, with digital payment systems becoming increasingly vulnerable. This study systematically evaluates modern fraud detection and prevention systems across major financial institutions, focusing on accuracy, scalability, and adaptability. Using a mixed-methods approach, the research assessed detection algorithms on four key dimensions: accuracy, efficiency, adaptability to new threats, and feasibility. Hybrid models combining supervised learning and unsupervised anomaly detection outperformed traditional rule-based systems, achieving 92.7% accuracy versus 78.3%. Graph-based deep learning models proved especially effective against organized fraud, reducing false positives by 34% and increasing true positives by 27%. As real-time, high-volume transactions rise, detection systems must scale accordingly. A classification framework is introduced, mapping systems by algorithm type, fraud category, and performance metrics. Key challenges identified include adversarial threats, real-time computational limits, and evolving fraud tactics. The study proposes a next-gen detection architecture featuring real-time adaptability, explainable AI, and cross-institutional data sharing—potentially reducing fraud losses by up to 41% when deployed at scale.
Evaluating the Accuracy and Effectiveness of Fraud Detection Systems
AI-DRIVEN SIMULATION OF CYBER ATTACKS AND DEFENSES: A GENERATIVE APPROACH TO THREAT MODELING
Cyber risk awareness is essential in today’s evolving threat landscape, where traditional detection methods like signature- and rule-based systems fall short against advanced, emerging attacks due to their reactive nature. To stay ahead, organizations must adopt proactive defense strategies. This paper explores the use of Generative AI—specifically models like GANs and VAEs—to simulate realistic cyberattacks and defense scenarios. These models can generate novel, previously unseen attack patterns, aiding in the assessment and enhancement of cybersecurity systems. By simulating threats such as APTs across the attack lifecycle, generative AI enables a shift from reactive to proactive threat modeling, aligning security capabilities with the growing sophistication of cyber threats.Generative AI
OMPARATIVE CNN MODELS FOR PLANT LEAF DISEASE DETECTION
Automated plant disease detection has gained traction in precision agriculture for its potential to boost crop yields and reduce manual effort. This study explores vegetable disease classification using convolutional neural networks (CNNs), utilizing a dataset of over 20,000 images across 15 disease categories and healthy classes. Four models were evaluated: a custom CNN, VGG19, ResNet50, and Xception. The custom CNN achieved 87.50% accuracy, demonstrating the potential of lightweight models for resource-limited scenarios. VGG19 performed better with 89.52%, while ResNet50 outperformed all with 94.86% accuracy and strong precision, recall, and F1 score. Xception underperformed, emphasizing the importance of model architecture and tuning. The results highlight the effectiveness of transfer learning, especially with deep models like ResNet50, and the viability of custom CNNs in constrained environments. Future work will explore class-specific errors, edge-device optimization, and techniques to address class imbalance for improved robustness.
INTEGRATING AUTONOMY AND COLLABORATION: DISTINGUISHING AGENTIC AI SYSTEMS FROM AI AGENTS
The evolution of AI has brought forth two interlinked paradigms: AI agents, which execute predefined, task-specific actions, and agentic AI systems, which coordinate multiple agents to autonomously set goals, plan, and act within dynamic environments. This article offers a comparative overview—covering architecture, interaction models, and applications in domains such as healthcare, robotics, business automation, and digital ecosystems—while addressing challenges like hallucination, coordination failures, and accountability. We discuss mitigation strategies including ReAct loops, retrieval-augmented generation (RAG), and causal modeling, and explore governance and ethical implications. Our contribution is a unified framework that aligns terminology, bridges theoretical and practical gaps, and guides the development of scalable, transparent, and goal-oriented intelligent systems.
DATA MINING-DRIVEN VERTICAL HANDOVER DECISION FRAMEWORK FOR 5G NETWORKS
Effective and seamless vertical handover (VHO) is essential for sustained connectivity and high QoS in 5G heterogeneous networks. However, varying network behaviors and protocols complicate VHO decisions, leading to latency and service disruptions. This paper proposes a data mining-based VHO decision framework that leverages historical handover data using multivariate regression and ANOVA to identify key parameters such as signal strength, bandwidth, jitter, latency, packet loss, and coverage. Simulations in NetNeuman demonstrate improved network performance, reduced latency, and higher handover success rates compared to baseline methods. Real-time decisions based on historical trends enhance user experience and network reliability. Future integration with advanced machine learning could enable adaptive and predictive handovers for 6G networks, supporting ultra-reliable, low-latency communication.
EFFECT OF NANO-SILICA ON GEOPOLYMER CONCRETE STRENGTH
Portland cement concrete contributes significantly to carbon emissions, highlighting the need for sustainable alternatives like geopolymer concrete (GPC). However, GPC often shows inconsistent mechanical strength, especially at early ages. This study investigates the effect of nano-silica (0–4% by fly ash weight) on the mechanical and microstructural properties of GPC. Compressive, tensile, and flexural strength tests, along with SEM and XRD analyses, were conducted. Statistical tools (ANOVA, Tukey HSD, regression) revealed that 3% nano-silica yielded the highest compressive strength (39.66 ± 0.91 MPa), with notable improvements in tensile (32.4%) and flexural (24.2%) strengths. Microstructural analysis showed reduced porosity (8.5% vs. 12.5% in control). However, workability decreased with higher dosages. Optimal performance was observed at 2–3% dosage, balancing strength and handling. These findings demonstrate nano-silica's potential to enhance GPC performance and contribute to low-carbon, sustainable construction.
IDENTIFICATION AND CLASSIFICATION OF FOODBORNE DISEASE OUTBREAKS
Foodborne diseases are primarily caused by consuming contaminated food or beverages. Timely identification and classification of outbreaks are crucial to reducing illness and death. This research aims to rapidly detect causative agents to enhance food safety. Using dataset analysis, patterns were identified based on year, food type, location, and species. Classification was performed using Decision Tree, Naïve Bayes, and Random Forest algorithms. Experimental results demonstrate the effectiveness of the proposed approach in identifying and classifying outbreak patterns.