International Journal of Computational Intelligence and Control
type: channel
description: International Journal of Computational Intelligence and Control (IJCIC) is a quarterly, open-access, double-blind peer-reviewed research journal committed to advancing scholarly work in Artificial Intelligence (AI) and Computer Science. The journal welcomes high-quality submissions across all domains of AI and computer science, with a focus on disseminating original, rigorous, and impactful research that contributes meaningfully to these interdisciplinary fields. All manuscripts are evaluated based on originality, technical depth, and significance of contribution. The journal follows a double-blind peer-review process, which is undertaken within one (01) month of submission. Authors are formally notified of editorial decisions, accompanied by constructive and timely feedback, ensuring a transparent, efficient, and academically rigorous publication workflow. IJCIC is published under the auspices of the Institute of Insight Research [SMC – Private] Limited, a duly incorporated legal entity registered with the Securities and Exchange Commission of Pakistan (SECP) under Registration No. 0306665.
publisher: Institute of Insight Research [SMC – Private] Limited
language: en
publication name: International Journal of Computational Intelligence and Control
items: ADVANCES IN DEEPFAKE VOICE DETECTION: TECHNICAL APPROACHES, RISKS, AND ETHICAL PERSPECTIVES
items: A DEEP LEARNING FRAMEWORK COMBINING CNN AND GRAPH ATTENTION NETWORKS FOR THYROID ULTRASOUND DIAGNOSIS
items: MACHINE LEARNING APPROACHES FOR FAKE NEWS IDENTIFICATION AND CLASSIFICATION
items: DETECTING PLANT LEAF DISEASES USING CNN MODELS; A COMPARATIVE STUDY
items: BRINGING AUTONOMY AND COOPERATION TOGETHER: A COMPARISON OF AGENTIC AI SYSTEMS AND AI AGENTS
items: AI-BASED ANOMALY DETECTION IN SOFTWARE LOGS FOR PROACTIVE FAULT DIAGNOSIS AND SELF-HEALING
items: ACCESS CONTROL MODEL FOR CLOUD DATA
items: SECURE STEGANOGRAPHIC COMMUNICATION USING RGB PIXEL ENCODING AND CRYPTOGRAPHY
items: INVESTIGATION OF BLACK HOLE ATTACKS IN WIRELESS SENSOR NETWORKS
items: OVERCOMING ENERGY EFFICIENCY CHALLENGES IN C-RAN: A REVIEW OF RECENT ADVANCES AND SOLUTIONS
items: IN-SITU ELEMENTAL ANALYSIS OF TOBACCO AND ASH USING LASER-INDUCED BREAKDOWN SPECTROSCOPY: HEALTH AND ENVIRONMENTAL IMPLICATIONS
items: IMPROVING THERMAL AND COMBUSTION PERFORMANCE OF GAS WATER TANK GEYSERS USING BAFFLES
items: IMPACT OF AGILE PRACTICES ON SOFTWARE DEVELOPMENT SUCCESS: A STRUCTURAL EQUATION MODELING STUDY
items: COMPARATIVE PERFORMANCE ANALYSIS OF TEXTURED AND UNTEXTURED TURNING INSERTS IN DRY MACHINING OF AL 7075
items: HOW CLIMATE CHANGE AFFECTS HUMAN HEALTH
items: DESIGN OF AN INTELLIGENT VTOL DRONE WITH FLYSKY TRANSMITTER AND PIXHAWK CONTROLLER FOR HIGH-RISK RESCUE AND MILITARY MISSIONS
items: COMPARATIVE ANALYSIS OF PHYSIOLOGICAL AND HISTOLOGICAL EFFECTS OF E-CIGARETTE VS. CONVENTIONAL SMOKING IN ALBINO MICE
items: FUZZY LOGIC-BASED WEED DETECTION USING IMAGE PROCESSING FOR SMART AGRICULTURE
items: MACHINE LEARNING SYSTEM FOR PREDICTING ROBOTIC HAND FINGER MOVEMENTS USING A SMART GLOVE
items: DEEPFAKE VOICE DETECTION: METHODS, RISKS, AND ETHICAL CHALLENGES
items: IMPROVING THYROID ULTRASOUND DIAGNOSIS USING A HYBRID CNN-GAT MODEL
items: FAKE NEWS DETECTION AND CLASSIFICATION USING MACHINE LEARNING

ADVANCES IN DEEPFAKE VOICE DETECTION: TECHNICAL APPROACHES, RISKS, AND ETHICAL PERSPECTIVES
type: item
description: 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.
subject: Articles
  topic: section
subject: Deepfake voice
  topic: keywords
subject: speech synthesis
  topic: keywords
subject: voice cloning
  topic: keywords
subject: Generative Adversarial Networks (GANs)
  topic: keywords
subject: synthetic speech detection
  topic: keywords
subject: voice authentication
  topic: keywords
subject: digital trust
  topic: keywords
subject: ethical implications
  topic: keywords
creator: Muhammad Bilal Ikram
creator: Ahsan Arif
creator: Asad Riaz
rights: Copyright (c) 2025
date: 2025-09-30
publication date: 2025-09-30
starting page: 48
ending page: 62

A DEEP LEARNING FRAMEWORK COMBINING CNN AND GRAPH ATTENTION NETWORKS FOR THYROID ULTRASOUND DIAGNOSIS
type: item
description: 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.
subject: Articles
  topic: section
subject: Thyroid Disease Detection
  topic: keywords
subject: CNN
  topic: keywords
subject: GAT
  topic: keywords
subject: Ultrasound images
  topic: keywords
creator: Muhammad Moeed Raza
creator: Muhammad Ali
rights: Copyright (c) 2025
date: 2025-09-30
publication date: 2025-09-30
starting page: 37
ending page: 47

MACHINE LEARNING APPROACHES FOR FAKE NEWS IDENTIFICATION AND CLASSIFICATION
type: item
description: 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.
subject: Articles
  topic: section
subject: Social media
  topic: keywords
subject: Fake News
  topic: keywords
subject: Spreading of news
  topic: keywords
subject: Symmetric analysis
  topic: keywords
subject: Ontology
  topic: keywords
subject: Machine Learning
  topic: keywords
creator: Faisal Shahzad
creator: Nazia Azim
rights: Copyright (c) 2025
date: 2025-09-30
publication date: 2025-09-30
starting page: 23
ending page: 36

DETECTING PLANT LEAF DISEASES USING CNN MODELS; A COMPARATIVE STUDY
type: item
description: 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.
subject: Articles
  topic: section
subject: Plant Disease Detection Convolutional Neural Networks (CNNs)
  topic: keywords
subject: Transfer Learning Deep Learning
  topic: keywords
subject: ResNet50
  topic: keywords
subject: VGG19
  topic: keywords
subject: Xception
  topic: keywords
subject: Precision Agriculture Image Classification
  topic: keywords
subject: Edge Computing
  topic: keywords
creator: Dr. Sara khan
rights: Copyright (c) 2025
date: 2025-09-30
publication date: 2025-09-30
starting page: 11
ending page: 22

BRINGING AUTONOMY AND COOPERATION TOGETHER: A COMPARISON OF AGENTIC AI SYSTEMS AND AI AGENTS
type: item
description: 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.
subject: Articles
  topic: section
subject: AI Agents
  topic: keywords
subject: Agentic AI
  topic: keywords
subject: Large Language Models (LLM)
  topic: keywords
subject: Dynamic Reasoning
  topic: keywords
subject: Autonomous System
  topic: keywords
subject: Retrieval Augmented Generation (RAG)
  topic: keywords
subject: Ethical AI
  topic: keywords
subject: Human AI Collaboration
  topic: keywords
subject: Scalable Intelligent System
  topic: keywords
creator: Dr. Aman Ullah
creator: Mir Wali Shah
creator: Syeda Arfa Quddusi
rights: Copyright (c) 2025
date: 2025-09-30
publication date: 2025-09-30
starting page: 1
ending page: 10

AI-BASED ANOMALY DETECTION IN SOFTWARE LOGS FOR PROACTIVE FAULT DIAGNOSIS AND SELF-HEALING
type: item
description: 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.
subject: Articles
  topic: section
subject:
  topic: keywords
  value: 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
creator: Rizwan Iqbal
rights: Copyright (c) 2025
date: 2025-06-30
publication date: 2025-06-30
starting page: 73
ending page: 93

ACCESS CONTROL MODEL FOR CLOUD DATA
type: item
description: 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.
subject: Articles
  topic: section
subject: Cloud Computing, Deployment Models, Data Security, Consumer Service, Trust Provider
  topic: keywords
creator: Waseema Batool
rights: Copyright (c) 2025
date: 2025-06-30
publication date: 2025-06-30
starting page: 52
ending page: 72

SECURE STEGANOGRAPHIC COMMUNICATION USING RGB PIXEL ENCODING AND CRYPTOGRAPHY
type: item
description: 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.
subject: Articles
  topic: section
subject:
  topic: keywords
  value: Steganographic encryption, RGB pixel encoding, cryptographic security, Rijndael Managed algorithm, secure data hiding, image steganography, covert communication, digital forensics, LOG table, steganalysis resistance
rights: Copyright (c) 2025
date: 2025-06-30
publication date: 2025-06-30
starting page: 38
ending page: 51

INVESTIGATION OF BLACK HOLE ATTACKS IN WIRELESS SENSOR NETWORKS
type: item
description: 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.
subject: Articles
  topic: section
subject: Wireless sensor network
  topic: keywords
subject: WSNs attack
  topic: keywords
subject: Blackhole Attack
  topic: keywords
subject: Performance-oriented attack
  topic: keywords
subject: WSNs security
  topic: keywords
subject: Active attack
  topic: keywords
creator: Syed Muhammed Nouman Qadir
rights: Copyright (c) 2025
date: 2025-06-30
publication date: 2025-06-30
starting page: 13
ending page: 37

OVERCOMING ENERGY EFFICIENCY CHALLENGES IN C-RAN: A REVIEW OF RECENT ADVANCES AND SOLUTIONS
type: item
description: 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.
subject: Articles
  topic: section
subject: C-RAN, Energy Efficiency, 5G, Resource Allocation, Sleep Mode, AI in Networks
  topic: keywords
creator: Muhammad Awais
rights: Copyright (c) 2025
date: 2025-06-30
publication date: 2025-06-30
starting page: 1
ending page: 12

IN-SITU ELEMENTAL ANALYSIS OF TOBACCO AND ASH USING LASER-INDUCED BREAKDOWN SPECTROSCOPY: HEALTH AND ENVIRONMENTAL IMPLICATIONS
type: item
description: 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.
subject: Articles
  topic: section
subject: LIBS
  topic: keywords
subject: Tobacco
  topic: keywords
subject: Cigarette Ash
  topic: keywords
subject: Toxic Elements
  topic: keywords
subject: Mc Whirter Criteria
  topic: keywords
creator: Nimra Shahzad
rights: Copyright (c) 2025
date: 2025-03-31
publication date: 2025-03-31
starting page: 68
ending page: 81

IMPROVING THERMAL AND COMBUSTION PERFORMANCE OF GAS WATER TANK GEYSERS USING BAFFLES
type: item
description: 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.
subject: Articles
  topic: section
subject: Water tank Geyser
  topic: keywords
subject: thermal efficiency
  topic: keywords
subject: combustion efficiency
  topic: keywords
subject: baffles
  topic: keywords
subject: burner
  topic: keywords
creator: Faiq Said
rights: Copyright (c) 2025
date: 2025-03-31
publication date: 2025-03-31
starting page: 53
ending page: 67

IMPACT OF AGILE PRACTICES ON SOFTWARE DEVELOPMENT SUCCESS: A STRUCTURAL EQUATION MODELING STUDY
type: item
description: 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.
subject: Articles
  topic: section
subject: Agile methodologies
  topic: keywords
subject: software quality
  topic: keywords
subject: project success
  topic: keywords
subject: team collaboration
  topic: keywords
subject: communication efficiency
  topic: keywords
subject: Smart PLS
  topic: keywords
subject: structural equation modeling
  topic: keywords
creator: Dr. Ali Raza Rang
rights: Copyright (c) 2025
date: 2025-03-31
publication date: 2025-03-31
starting page: 41
ending page: 52

COMPARATIVE PERFORMANCE ANALYSIS OF TEXTURED AND UNTEXTURED TURNING INSERTS IN DRY MACHINING OF AL 7075
type: item
description: 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.
subject: Articles
  topic: section
subject: AI 7075Alloy
  topic: keywords
subject: Textured Turning Inserts
  topic: keywords
subject: Un-Textured Turning Inserts
  topic: keywords
subject: Dry Machining
  topic: keywords
subject: Tool Wear
  topic: keywords
creator: Muhammad Toffique
rights: Copyright (c) 2025
date: 2025-03-31
publication date: 2025-03-31
starting page: 27
ending page: 40

HOW CLIMATE CHANGE AFFECTS HUMAN HEALTH
type: item
description: 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.
subject: Articles
  topic: section
subject: Machine learning
  topic: keywords
subject: algorithms, detect
  topic: keywords
subject: prevent
  topic: keywords
subject: cyberattacks
  topic: keywords
creator: Dr. Anwar Ali Sanjrani
rights: Copyright (c) 2025
date: 2025-03-30
publication date: 2025-03-30
starting page: 16
ending page: 26

DESIGN OF AN INTELLIGENT VTOL DRONE WITH FLYSKY TRANSMITTER AND PIXHAWK CONTROLLER FOR HIGH-RISK RESCUE AND MILITARY MISSIONS
type: item
description: 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.
subject: Articles
  topic: section
subject: Intelligent VTOL Drone
  topic: keywords
subject: FlySky Transmitter
  topic: keywords
subject: Pixhawk Controller
  topic: keywords
subject: Autonomous Navigation
  topic: keywords
subject: High-Risk Operations
  topic: keywords
creator: Saifullah Zadran
rights: Copyright (c) 2025
date: 2025-03-31
publication date: 2025-03-31
starting page: 1
ending page: 15

COMPARATIVE ANALYSIS OF PHYSIOLOGICAL AND HISTOLOGICAL EFFECTS OF E-CIGARETTE VS. CONVENTIONAL SMOKING IN ALBINO MICE
type: item
description: 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.
subject: Articles
  topic: section
subject: E-cigarettes, conventional smoking
  topic: keywords
subject: hematology
  topic: keywords
subject: histopathology
  topic: keywords
subject: systemic toxicity
  topic: keywords
creator: Muhammad Saleem Khan
rights: Copyright (c) 2024
date: 2024-12-31
publication date: 2024-12-31
starting page: 65
ending page: 80

FUZZY LOGIC-BASED WEED DETECTION USING IMAGE PROCESSING FOR SMART AGRICULTURE
type: item
description: 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.
subject: Articles
  topic: section
subject: Fuzzy logic
  topic: keywords
subject: Smart Agriculture
  topic: keywords
subject: Membership Function
  topic: keywords
subject: MATLAB
  topic: keywords
subject: Input variable
  topic: keywords
subject: Output Variable
  topic: keywords
creator: Muhammad Sajjad
rights: Copyright (c) 2024
date: 2024-12-31
publication date: 2024-12-31
starting page: 54
ending page: 64

MACHINE LEARNING SYSTEM FOR PREDICTING ROBOTIC HAND FINGER MOVEMENTS USING A SMART GLOVE
type: item
description: 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.
subject: Articles
  topic: section
subject: LDR Sensor
  topic: keywords
subject: Finger Movement Machine Learning
  topic: keywords
subject: Multioutput Regression Problem
  topic: keywords
subject: K-Nearest Neighbours
  topic: keywords
subject: Real-Time Integration
  topic: keywords
creator: Engr. Burhan Aslam
rights: Copyright (c) 2024
date: 2024-12-31
publication date: 2024-12-31
starting page: 43
ending page: 53

DEEPFAKE VOICE DETECTION: METHODS, RISKS, AND ETHICAL CHALLENGES
type: item
description: 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.
subject: Articles
  topic: section
subject: Deepfake voice
  topic: keywords
subject: Speech synthesis
  topic: keywords
subject: Voice cloning
  topic: keywords
subject: Generative Adversarial Networks (GANs)
  topic: keywords
subject: Autoencoders
  topic: keywords
subject: Synthetic speech detection
  topic: keywords
subject: Voice authentication
  topic: keywords
subject: Misinformation
  topic: keywords
subject: Digital trust
  topic: keywords
subject: Ethical implications
  topic: keywords
subject: AI-generated speech
  topic: keywords
subject: Identity theft
  topic: keywords
creator: Ayesha Khalid
rights: Copyright (c) 2024
date: 2024-12-31
publication date: 2024-12-31
starting page: 26
ending page: 42

IMPROVING THYROID ULTRASOUND DIAGNOSIS USING A HYBRID CNN-GAT MODEL
type: item
description: 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.
subject: Articles
  topic: section
subject: Thyroid Disease Detection
  topic: keywords
subject: CNN
  topic: keywords
subject: GAT
  topic: keywords
subject: Ultrasound images
  topic: keywords
creator: Iftikhar Alam
rights: Copyright (c) 2024
date: 2024-12-31
publication date: 2024-12-31
starting page: 15
ending page: 25

FAKE NEWS DETECTION AND CLASSIFICATION USING MACHINE LEARNING
type: item
description: 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.
subject: Articles
  topic: section
subject: Social media
  topic: keywords
subject: Fake News
  topic: keywords
subject: Spreading of news
  topic: keywords
subject: Symmetric analysis
  topic: keywords
subject: Ontology
  topic: keywords
subject: Machine Learning
  topic: keywords
creator: Kashif Liaqat
rights: Copyright (c) 2024
date: 2024-12-31
publication date: 2024-12-31
starting page: 1
ending page: 14