International Journal of Computational Intelligence and Control
The International Journal of Computational Intelligence and Control (IJCIC) (ISSN Print: 3106-6135 | ISSN Online: 3106-6143) is a peer-reviewed, open-access, quarterly journal published by the Institute of Insight Research (SMC–Private) Limited, registered with the SECP. It publishes high-quality research in Artificial Intelligence, Computational Intelligence, Control Systems, and Computer Science, promoting ethical publishing, rigorous peer review, and global research impact.
Institute of Insight Research [SMC – Private] Limited
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 Assistive Systems, Edge Computing, Internet of Things (IoT), Computer Vision, YOLO Object Detection, Machine Learning
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.
Geopolymer concrete, nano-silica, mechanical properties, microstructure, sustainable construction
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.
IDENTIFICATION OF PREGNANCY LOSS RISK FACTORS USING MACHINE LEARNING ALGORITHMS
Pregnancy loss, or spontaneous abortion, is defined as the loss of a fetus before the 20th week of gestation. According to the American College of Obstetricians and Gynecologists (ACOG), approximately 15–20% of clinically confirmed pregnancies result in pregnancy loss. This study utilized cross-sectional data from the Bureau of Statistics Punjab (BSP) to investigate the risk factors associated with pregnancy loss. Multiple machine learning algorithms—including Logistic Regression (LR), K-Nearest Neighbors (KNN), Linear Discriminant Analysis (LDA), Support Vector Machines (SVM), Naïve Bayes (NB), Regularized Naïve Classifier (RNC), Classification and Regression Trees (CART), Bernoulli Naïve Bayes (BNB), Passive Aggressive, and Extra Trees Classifier (ETC)—were applied to assess predictive performance. Among these, KNN achieved the highest accuracy at 91%, while all other algorithms exceeded 80% accuracy. Feature selection and importance analysis using LR, CART, and ETC identified the number of children ever born and place of delivery as the most influential factors affecting pregnancy loss risk.
Pregnancy loss, Risk factors, Machine learning, KNN, SVM, Decision Tree, Extra Trees Classifier
ADVANCES IN DEEPFAKE VOICE DETECTION: TECHNICAL APPROACHES, RISKS, AND ETHICAL PERSPECTIVES
Deepfake voice technologies represent a transformative advancement in artificial intelligence, particularly in speech synthesis and voice cloning. Leveraging deep learning architectures such as Generative Adversarial Networks (GANs) and autoencoders, these systems can produce highly realistic synthetic voices that closely resemble human speech. While offering benefits in domains such as accessibility, entertainment, and personalized services, deepfake voices also pose significant risks, including misinformation, identity theft, and cybercrime. This paper examines both the generation methods and detection strategies for synthetic speech, with a focus on neural network–based approaches to voice authentication and deepfake recognition. Furthermore, it discusses the ethical and legal challenges associated with deepfake voice technologies, emphasizing issues of consent, digital trust, and privacy. By critically reviewing recent advancements and proposing a structured framework for detection, this study seeks to contribute to the development of secure, transparent, and resilient solutions against malicious voice manipulation.
A DEEP LEARNING FRAMEWORK COMBINING CNN AND GRAPH ATTENTION NETWORKS FOR THYROID ULTRASOUND DIAGNOSIS
Thyroid disorders, including hypothyroidism and hyperthyroidism, are widespread endocrine conditions that pose serious health risks worldwide. Early diagnosis is essential to prevent complications, yet conventional diagnostic methods are often constrained by delayed results, dependence on human expertise, and limited accessibility in remote areas. To address these challenges, this study introduces a hybrid deep learning model that integrates Convolutional Neural Networks (CNNs) with Graph Attention Networks (GATs) for automated thyroid disease detection from ultrasound images. The proposed framework employs EfficientNet-B4 for spatial feature extraction and GAT layers to capture relational dependencies among features, thereby improving classification performance. Using the Algeria Ultrasound Thyroid Dataset (AUTD), the model achieved an accuracy of 92.48%, precision of 93.94%, recall of 92.48%, and an F1-score of 92.87%, significantly outperforming conventional approaches such as Sequential CNN with K-Means clustering (81.5% accuracy). Key contributions include dynamic graph construction for localized feature representation and advanced data augmentation to address class imbalance. Extensive experiments, confusion matrix evaluations, and multiclass ROC analyses validate the robustness and clinical applicability of the system. This work advances medical AI by offering a scalable, precise, and deployable solution for early thyroid disease detection. Future directions include exploring advanced attention mechanisms and integrating multimodal clinical data to further enhance diagnostic reliability.
MACHINE LEARNING APPROACHES FOR FAKE NEWS IDENTIFICATION AND CLASSIFICATION
With the rapid growth of social media, nearly 70% of people now rely on these platforms as their primary news source. However, social media has also become a major channel for spreading misinformation and fake news. This paper introduces a semantic-based approach for fake news identification, designed to capture the depth of misinformation and enable dynamic decision-making. The proposed Fake News Identification Ontology (FNIOnt) categorizes news content into fictitious classes and performs semantic analysis on dataset content. The extracted features are evaluated using three machine learning classifiers—Random Forest (RF), Logistic Regression (LR), and Long Short-Term Memory (LSTM). Experimental results demonstrate that the proposed framework outperforms existing methods, achieving a 99% accuracy rate. Despite these promising results, the adaptability of fake news creators presents an ongoing challenge. Techniques such as half-truths, subtle linguistic manipulation, and evolving writing styles make detection increasingly complex. Therefore, machine learning systems must evolve dynamically, learning from new data and adapting to emerging patterns of deception. Deep learning models—including Recurrent Neural Networks (RNNs), Convolutional Neural Networks (CNNs), and Transformers—further enhance detection by automatically capturing semantic and syntactic relationships in unstructured text. Unlike traditional models that rely on manual feature engineering, these neural approaches enable context- and sentiment-aware classification, strengthening robustness against nuanced manipulations. This study underscores the potential of combining semantic analysis with advanced machine learning and deep learning methods to build adaptive, accurate, and scalable systems for fake news detection.
A COMPARATIVE ANALYSIS OF CNN-BASED APPROACHES FOR PLANT LEAF DISEASE DETECTION
Automated plant disease detection has emerged as a vital component of precision agriculture, offering the potential to improve crop yield while reducing reliance on manual inspection. This study presents a comparative evaluation of convolutional neural networks (CNNs) for vegetable disease classification. A dataset of over 20,000 images, encompassing 15 disease categories and healthy samples, was employed to assess both a custom CNN model and transfer learning architectures. Four models were analyzed: a custom-designed CNN, VGG19, ResNet50, and Xception. The custom CNN achieved an accuracy of 87.50%, demonstrating the viability of lightweight models for resource-constrained applications. VGG19, leveraging transfer learning, improved performance to 89.52%, while ResNet50 achieved the highest accuracy of 94.86%, alongside strong precision, recall, and F1 scores, confirming its robustness and suitability for real-world deployment. In contrast, Xception underperformed, underscoring the importance of model selection and fine-tuning in plant disease recognition tasks. The findings emphasize the effectiveness of transfer learning, particularly with deep architectures like ResNet50, in delivering accurate and reliable disease detection. At the same time, the custom CNN offers a practical alternative for environments with limited computational resources. This study provides a foundation for future exploration into hybrid and ensemble models, deployment on edge devices, and techniques to address class imbalance for enhanced model robustness and scalability.
Plant Disease Detection Convolutional Neural Networks (CNNs)
Precision Agriculture Image Classification
COMPARING AUTONOMY AND COLLABORATIVE DYNAMICS IN AGENTIC AI SYSTEMS AND AI AGENTS
The rapid advancement of artificial intelligence has given rise to two closely related yet distinct paradigms: AI agents and agent-based AI systems. AI agents emphasize modular, task-specific automation, often enabled by large language models (LLMs), whereas agent-based systems extend these capabilities through multi-agent collaboration, dynamic reasoning, and persistent autonomy. This article provides a comparative analysis of the two paradigms from both theoretical and practical perspectives, synthesizing insights from foundational research. We distinguish their architectures, interaction models, and design objectives, and illustrate applications across healthcare, robotics, business automation, and digital ecosystems. Key challenges—including hallucination, coordination gaps, and accountability—are discussed alongside mitigation strategies such as ReAct loops, retrieval-augmented generation (RAG), and causal modeling. Beyond technical considerations, the study examines governance, ethical dimensions, and the broader restructuring of industries driven by agent-based technologies. Our contribution lies in proposing a unified framework and roadmap that clarifies terminology, aligns capabilities with real-world complexity, and supports the development of transparent, scalable, and reliable intelligent systems. This synthesis provides actionable guidance for researchers, policymakers, and industry leaders navigating the transition from automated tools to collaborative intelligent agents.
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
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.
C-RAN, Energy Efficiency, 5G, Resource Allocation, Sleep Mode, AI in Networks
IN-SITU ELEMENTAL ANALYSIS OF TOBACCO AND ASH USING LASER-INDUCED BREAKDOWN SPECTROSCOPY: HEALTH AND ENVIRONMENTAL IMPLICATIONS
Laser Induced Breakdown Spectroscopy (LIBS) is a rapid, cost-effective, and versatile technique for in-situ multi-elemental analysis with high resolution and low detection limits. Tobacco smoking significantly increases the risk of diseases such as cancer, heart disease, stroke, lung disorders, and COPD, largely due to the inhalation of toxic elements present in cigarette tobacco and ash. This study employs LIBS to detect harmful elements in tobacco and ash from four popular Pakistani cigarette brands, aiming to safeguard human health, agricultural safety, and environmental sustainability. Using a Q-switched Nd:YAG laser (1064 nm, 90 mJ, 10 ns pulse), the emission spectra revealed several elements including Fe, Ca, Mn, Sc, Ti, Cr, Sr, and Ni, with chromium (Cr), nickel (Ni), and strontium (Sr) identified as highly toxic. The presence of these metals in ash contributes to soil contamination and poses further health risks. Electron temperature (Te) was determined via the Boltzmann Plot Method, while electron density (Ne) was calculated using the Stark Broadening Method under local thermodynamic equilibrium conditions. These findings demonstrate LIBS as a powerful diagnostic tool for detecting toxic elements in solid samples, aiding in environmental protection and health risk assessment.
IMPROVING THERMAL AND COMBUSTION PERFORMANCE OF GAS WATER TANK GEYSERS USING BAFFLES
Enhancing the thermal and combustion performance of gas water tank geysers is vital for improving energy efficiency and reducing fuel consumption. This study evaluates seven baffle configurations—baseline (no baffle), strip, cylindrical, conical, finned conical, frustum, and bladed frustum—on the geyser’s performance. Experimental results show that structured baffles significantly improve heat transfer, optimize fuel-air mixing, and lower fuel usage. The bladed frustum baffle achieved the highest thermal efficiency at 69.03%, outperforming the conical baffle (61.24%) by 7.79% and reducing gas consumption by 8.4%. The frustum baffle also demonstrated notable gains, with a 5.83% increase in thermal efficiency (67.07%), a 6.49% reduction in gas consumption, and a 3.99% higher combustion efficiency (69%) compared to the conical baffle (65.01%), indicating superior heat retention and fuel utilization. However, the bladed frustum baffle showed a slight 0.61% drop in combustion efficiency (64.4%) due to increased stack temperature and altered combustion dynamics. Overall, the frustum baffle offers the best balance of enhanced thermal efficiency, stable combustion, and fuel savings, providing valuable insights for optimizing gas-fired heating systems in residential settings.
IMPACT OF AGILE PRACTICES ON SOFTWARE DEVELOPMENT SUCCESS: A STRUCTURAL EQUATION MODELING STUDY
This study investigates the impact of Agile methodologies on software development success, focusing on the mediating roles of team collaboration and communication efficiency. Using Structural Equation Modeling (SEM) with Smart PLS, data from 300 software professionals across various industries were analyzed. The study examined Agile frameworks such as Scrum, Kanban, and Extreme Programming (XP) and assessed their effects on software quality and project success. Results reveal that Agile methodologies significantly enhance software quality and project outcomes while improving communication efficiency and fostering better team collaboration. Additionally, both team collaboration and communication efficiency mediate the relationship between Agile practices and software development success. These findings offer valuable insights for software firms and project managers to optimize Agile implementation and improve project management strategies.
COMPARATIVE PERFORMANCE ANALYSIS OF TEXTURED AND UNTEXTURED TURNING INSERTS IN DRY MACHINING OF AL 7075
Surface texturing is a proven method to enhance the tribological performance of cutting tools, reducing friction and wear by minimizing contact area and forming micro-lubricant reservoirs. In machining operations, factors such as tool geometry, material, and cutting parameters significantly affect both tool wear and workpiece quality. This study compares the performance of untextured and textured (parallel grooved, perpendicular grooved, and dimpled) tungsten carbide inserts, including a titanium-coated variant, in the dry machining of Al-7075. An L20 (5×4) Taguchi orthogonal array was used for experimental design. Tool wear and surface roughness were measured using a CMM and a Surftonic surface meter, respectively. Analysis through Pareto coefficients, main effect plots, contour plots, and surface plots revealed that the parallel grooved textured insert outperformed all others, demonstrating the lowest surface roughness and tool wear. These findings support the effectiveness of surface texturing in improving machining performance for aluminum alloys.
This study investigates the application of machine learning algorithms for detecting and preventing cyberattacks. Models including Decision Trees, Support Vector Machines (SVM), Random Forests, and Neural Networks were evaluated using datasets related to network traffic, intrusion detection, and malware classification. Data preprocessing techniques—such as cleaning, feature selection, and class balancing—were employed to enhance model performance. Among the tested models, Neural Networks achieved the highest performance across accuracy, precision, recall, and F1-score metrics, followed by Random Forests. The findings underscore the effectiveness of machine learning in identifying complex attack patterns and enhancing real-time cybersecurity systems.
DESIGN OF AN INTELLIGENT VTOL DRONE WITH FLYSKY TRANSMITTER AND PIXHAWK CONTROLLER FOR HIGH-RISK RESCUE AND MILITARY MISSIONS
The development of intelligent unmanned aerial vehicles (UAVs) has significantly advanced capabilities in high-risk rescue and military operations. This paper presents the design and implementation of a smart Vertical Take-Off and Landing (VTOL) drone, equipped with a FlySky Transmitter and Pixhawk Controller to support both manual and autonomous flight. The system incorporates advanced navigation and control algorithms for stable performance in dynamic, complex terrains. The FlySky Transmitter ensures reliable communication, while the Pixhawk Controller handles flight stabilization, sensor integration, and mission automation. Key features include obstacle detection, real-time video streaming, and emergency response protocols. Extensive flight testing demonstrates the drone’s reliability and adaptability in critical scenarios such as search and rescue and military surveillance. The study highlights the platform’s potential for future integration with AI technologies and discusses both the technical and ethical considerations for deploying smart UAVs in high-risk environments.
COMPARATIVE ANALYSIS OF PHYSIOLOGICAL AND HISTOLOGICAL EFFECTS OF E-CIGARETTE VS. CONVENTIONAL SMOKING IN ALBINO MICE
This study compares the physiological and histological effects of e-cigarette (T1) and conventional cigarette (T2) exposure in albino mice over nine weeks. Both groups showed decreased hemoglobin levels, with T1 exhibiting higher white blood cell and platelet counts. Testosterone was elevated in T2, while glucose and estrogen levels increased in T1 but decreased in T2. Histological analysis revealed myocardial damage, glomerulosclerosis, liver cell ballooning, and lung emphysema in both groups, more severe in T2. Both exposures caused weight loss, especially in males. Findings suggest that while both vaping and smoking adversely affect health, e-cigarettes may cause greater immune and hormonal disruption alongside significant blood and tissue damage.
FUZZY LOGIC-BASED WEED DETECTION USING IMAGE PROCESSING FOR SMART AGRICULTURE
Image processing for weed detection is an emerging technology with the potential to revolutionize modern agriculture by enabling precise weed identification and targeted pesticide application. This paper presents a system combining image capture, processing, and fuzzy logic-based decision-making to determine optimal herbicide dosage and application areas. Using MATLAB, grassy weed regions are identified from field images, while the fuzzy logic system analyzes weed coverage and patch data to recommend precise treatment levels. Amid growing global food demand and resource limitations, such advanced, sustainable methods are vital for improving crop management and reducing environmental impact.
MACHINE LEARNING SYSTEM FOR PREDICTING ROBOTIC HAND FINGER MOVEMENTS USING A SMART GLOVE
This research proposes a machine learning-based method to predict robotic hand finger movements using a Smart glove equipped with Light Dependent Resistor (LDR) sensors. Real-time finger flexion and extension data are captured via an ESP-WROOM-32 microcontroller interfaced through Arduino IDE and Jupyter. The multi-output regression problem of predicting simultaneous finger movements is addressed using a K-nearest neighbors regressor, optimized by Root Mean Square Prediction Error (RMSPE). Implemented in real-time, the system successfully synchronizes the robotic hand’s finger movements with the Smart glove inputs, enhancing control precision, reducing latency, and improving user experience. This approach holds promise for advancing artificial limb control and remote robotic surgery applications.
DEEPFAKE VOICE DETECTION: METHODS, RISKS, AND ETHICAL CHALLENGES
Deepfake voice technology, powered by deep learning models like GANs and autoencoders, enables highly realistic synthetic speech. While useful in entertainment and accessibility, it also raises serious concerns around misinformation, identity theft, and cybercrime. This paper reviews generation methods and neural network–based detection techniques for deepfake voices, emphasizing voice authentication and synthetic speech recognition. It also discusses ethical and legal issues related to consent, privacy, and digital trust. The study proposes a detection framework to strengthen defenses against malicious voice manipulation.
IMPROVING THYROID ULTRASOUND DIAGNOSIS USING A HYBRID CNN-GAT MODEL
Thyroid disorders like hypothyroidism and hyperthyroidism are widespread and require early diagnosis to avoid serious complications. Traditional diagnostic methods often suffer from delays, expert dependence, and limited access in remote areas. This study proposes a hybrid deep learning model combining EfficientNet-B4 for spatial feature extraction and Graph Attention Networks (GATs) for capturing feature relationships in thyroid ultrasound images. Trained on the Algeria Ultrasound Thyroid Dataset (AUTD), the model achieved 92.48% accuracy, 93.94% precision, 92.48% recall, and a 92.87% F1-score, outperforming traditional methods such as CNN with K-Means clustering. Key innovations include dynamic graph construction and advanced data augmentation to address class imbalance. Comprehensive evaluation through confusion matrices and ROC curves supports its clinical reliability. The approach offers a scalable and accurate solution for early thyroid disease detection, with future work focusing on improved attention mechanisms and integration with broader clinical data.
FAKE NEWS DETECTION AND CLASSIFICATION USING MACHINE LEARNING
With over 70% of news consumed via social media, the spread of misinformation poses a significant challenge. This study introduces a semantic-based fake news detection model, FNIOnt, which analyzes news content by dividing it into fictitious categories and applying logical inference through an ontology framework. The processed content is then classified using machine learning models—Random Forest, Logistic Regression, and LSTM—with LSTM achieving the highest accuracy of 99%, outperforming traditional methods. As misinformation tactics evolve, including the use of half-truths and subtle manipulation, detection models must adapt accordingly. Deep learning approaches such as RNNs, CNNs, and Transformers demonstrate strong capabilities in understanding the semantic and syntactic structure of text, enabling more accurate classification of real versus fake news even under nuanced manipulation.