Air journal of Artificial Intelligence
type: channel
description: 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.
publisher: International Institute of Modern Research (SMC-Private) Limited
language: en
publication name: Air journal of Artificial Intelligence
items: AN AI-BASED APPROACH TO PROACTIVE FAULT DIAGNOSIS AND SELF-HEALING THROUGH SOFTWARE LOG ANOMALY DETECTION
items: ACCESS CONTROL MODEL FOR CLOUD DATA
items: A NOVEL SECURE STEGANOGRAPHIC COMMUNICATION SCHEME BASED ON RGB PIXEL ENCODING: DESIGN AND ANALYSIS
items: A STYLISTIC ANALYSIS OF DEVIATION AND PARALLELISM IN E.E. CUMMINGS’ TRADITIONAL AND KINETIC POETRY
items: REVIEW OF ENERGY EFFICIENCY OPTIMIZATION TECHNIQUES IN C-RAN ARCHITECTURES
items: EMERGING TECHNIQUES IN DEEPFAKE VOICE DETECTION: METHODS, RISKS, AND ETHICAL IMPLICATIONS
items: A HYBRID CNN AND GRAPH ATTENTION NETWORK MODEL FOR THYROID ULTRASOUND DIAGNOSIS
items: COMPARATIVE ANALYSIS OF MACHINE LEARNING MODELS FOR FAKE NEWS IDENTIFICATION AND CLASSIFICATION
items: A STUDY OF CNN-BASED METHODS FOR PLANT LEAF DISEASE DETECTION: A COMPARATIVE ANALYSIS
items: EXPLORING AUTONOMY AND COLLABORATION IN AGENTIC AI SYSTEMS AND AI AGENTS: A COMPARATIVE FRAMEWORK
items: PROACTIVE FAULT DIAGNOSIS AND SELF-HEALING THROUGH AI-DRIVEN ANOMALY DETECTION IN SOFTWARE LOGS
items: DESIGN AND ANALYSIS OF A SECURE STEGANOGRAPHIC COMMUNICATION SCHEME BASED ON RGB PIXEL ENCODING
items: INVESTIGATION OF BLACK HOLE ATTACKS IN WIRELESS SENSOR NETWORKS
items: AN ANALYTICAL REVIEW OF ENERGY EFFICIENCY OPTIMIZATION IN C-RAN ARCHITECTURES
items: SMART ASSISTIVE SYSTEM FOR THE VISUALLY IMPAIRED USING COMPUTER VISION
items: INTELLIGENT VERTICAL HANDOVER DECISION FRAMEWORK FOR 5G NETWORKS VIA DATA MINING
items: ENHANCING GEOPOLYMER CONCRETE: MECHANICAL BEHAVIOR ANALYSIS WITH NANO-SILICA
items: DEVELOPMENT OF A LOW-COST LINKAGE-DRIVEN TWO-FINGER EXOSKELETON FOR HAND REHABILITATION
items: SYSTEMATIC ANALYSIS AND CLASSIFICATION OF FOODBORNE DISEASE OUTBREAKS
items: ADVANCEMENTS IN AFFECTIVE COMPUTING FOR SOCIAL ROBOTICS: A COMPREHENSIVE SURVEY
items: THE ROLE OF ARTIFICIAL INTELLIGENCE IN SMART GRIDS, POWER SYSTEMS, AND AUTOMATION WITHIN ELECTRICAL ENGINEERING
items: FEDERATED LEARNING FOR DISTRIBUTED NETWORK TRAFFIC ANOMALY DETECTION USING GRU MODELS
items: HOW CLIMATE CHANGE AFFECTS HUMAN HEALTH
items: LEVERAGING AI FOR ANOMALY DETECTION IN ELECTRICAL SYSTEMS AND NETWORKS TO PROTECT CRITICAL INFRASTRUCTURE FROM CYBERATTACKS
items: DESIGNING HIGH-PERFORMANCE PHOTOTRANSISTORS USING SEMICONDUCTOR NANOMATERIALS
items: CLOUD COMPUTING ADOPTION IN SMES: AN EMPIRICAL ANALYSIS USING PLS-SEM
items: ADVANCES IN MACHINE LEARNING: A SURVEY OF METHODS, MODELS, BENCHMARKS, AND INDUSTRY APPLICATIONS
items: OPTIMIZING PREFABRICATED CONSTRUCTION THROUGH SUPPLY CHAIN INTEGRATION AND ENTERPRISE ACCESS STANDARDS
items: EVALUATING THE ACCURACY AND EFFECTIVENESS OF FRAUD DETECTION SYSTEMS

AN AI-BASED APPROACH TO PROACTIVE FAULT DIAGNOSIS AND SELF-HEALING THROUGH SOFTWARE LOG ANOMALY DETECTION
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) 2026
date: 2026-06-30
publication date: 2026-06-30
starting page: 55
ending page: 74

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) 2026
date: 2026-06-28
publication date: 2026-06-28
starting page: 35
ending page: 54

A NOVEL SECURE STEGANOGRAPHIC COMMUNICATION SCHEME BASED ON RGB PIXEL ENCODING: DESIGN AND ANALYSIS
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
creator: Sana Cheema
rights: Copyright (c) 2026
date: 2026-06-27
publication date: 2026-06-27
starting page: 22
ending page: 34

A STYLISTIC ANALYSIS OF DEVIATION AND PARALLELISM IN E.E. CUMMINGS’ TRADITIONAL AND KINETIC POETRY
type: item
description: Creativity and innovation have increasingly shaped literary expression in the modern era, making unconventional thinking essential in a rapidly evolving world. This study examines the interplay between poetic structure and creative discourse through a stylistic lens, focusing on foregrounding features such as deviation and parallelism. Using Leech’s (1969) Advanced Stylistics framework, a comparative analysis is conducted on E.E. Cummings’ selected poems l(a (1958) and was (1954), representing traditional and kinetic poetry, respectively. The findings reveal that deviation and parallelism are strategically employed to foreground thematic elements, capturing the reader’s attention. While traditional poetry features occasional deviations, kinetic poetry consistently utilizes structural deviation to emphasize meaning, often with greater attention to visual representation. This study enriches the understanding of how stylistic devices shape interpretation and highlights thematic focus in Cummings’ poetry.
subject: Articles
  topic: section
subject: Kinetic Poetry, Linguistic Deviation, Lexical Choices, Syntactic Structure, Advanced Stylistics.
  topic: keywords
creator: Khalid Khan
creator: Qasim Khan
creator: Wasim Khalil
rights: Copyright (c) 2026
date: 2026-06-28
publication date: 2026-06-28
starting page: 12
ending page: 21

REVIEW OF ENERGY EFFICIENCY OPTIMIZATION TECHNIQUES IN C-RAN ARCHITECTURES
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) 2026
date: 2026-06-28
publication date: 2026-06-28
starting page: 1
ending page: 11

EMERGING TECHNIQUES IN DEEPFAKE VOICE DETECTION: METHODS, RISKS, AND ETHICAL IMPLICATIONS
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, speech synthesis, voice cloning, Generative Adversarial Networks (GANs), synthetic speech detection, voice authentication, digital trust, ethical implications.
  topic: keywords
creator: Muhammad Bilal Ikram
creator: Ahsan Arif
creator: Asad Riaz
rights: Copyright (c) 2026
date: 2026-03-31
publication date: 2026-03-31
starting page: 46
ending page: 60

A HYBRID CNN AND GRAPH ATTENTION NETWORK MODEL 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, CNN, GAT, Ultrasound images
  topic: keywords
creator: Muhammad Moeed Raza
creator: Muhammad Ali
rights: Copyright (c) 2026
date: 2026-03-31
publication date: 2026-03-31
starting page: 35
ending page: 45

COMPARATIVE ANALYSIS OF MACHINE LEARNING MODELS 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, Fake News, Spreading of news, Symmetric analysis, Ontology, Machine Learning
  topic: keywords
creator: Faisal Shahzad
creator: Nazia Azim
rights: Copyright (c) 2026
date: 2026-03-31
publication date: 2026-03-31
starting page: 22
ending page: 34

A STUDY OF CNN-BASED METHODS FOR PLANT LEAF DISEASE DETECTION: A COMPARATIVE ANALYSIS
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), Transfer Learning Deep Learning, ResNet50, VGG19, Xception, Precision Agriculture Image Classification, Edge Computing
  topic: keywords
creator: Dr. Sara Khan
rights: Copyright (c) 2026
date: 2026-03-31
publication date: 2026-03-31
starting page: 11
ending page: 21

EXPLORING AUTONOMY AND COLLABORATION IN AGENTIC AI SYSTEMS AND AI AGENTS: A COMPARATIVE FRAMEWORK
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, Agentic AI, Large Language Models (LLM), Dynamic Reasoning, Autonomous System, Retrieval Augmented Generation (RAG), Ethical AI, Human AI Collaboration, Scalable Intelligent System
  topic: keywords
creator: Dr. Aman Ullah
creator: Mir Wali Shah
creator: Syeda Arfa Quddusi
rights: Copyright (c) 2026
date: 2026-03-31
publication date: 2026-03-31
starting page: 1
ending page: 10

PROACTIVE FAULT DIAGNOSIS AND SELF-HEALING THROUGH AI-DRIVEN ANOMALY DETECTION IN SOFTWARE LOGS
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) 2026
date: 2025-12-31
publication date: 2025-12-31
starting page: 70
ending page: 89

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
  topic: keywords
subject: Deployment Models
  topic: keywords
subject: Data Security
  topic: keywords
subject: Consumer Service
  topic: keywords
subject: Trust Provider
  topic: keywords
creator: Waseema Batool
rights: Copyright (c) 2026
date: 2025-12-31
publication date: 2025-12-31
starting page: 51
ending page: 69

DESIGN AND ANALYSIS OF A SECURE STEGANOGRAPHIC COMMUNICATION SCHEME BASED ON RGB PIXEL ENCODING
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
creator: Sana Cheema
rights: Copyright (c) 2026
date: 2025-12-31
publication date: 2025-12-31
starting page: 38
ending page: 50

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 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) 2026
date: 2025-12-31
publication date: 2025-12-31
starting page: 12
ending page: 37

AN ANALYTICAL REVIEW OF ENERGY EFFICIENCY OPTIMIZATION IN C-RAN ARCHITECTURES
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
  topic: keywords
subject: Energy Efficiency
  topic: keywords
subject: 5G
  topic: keywords
subject: Resource Allocation
  topic: keywords
subject: Sleep Mode
  topic: keywords
subject: AI in Networks
  topic: keywords
creator: Muhammad Awais
rights: Copyright (c) 2026
date: 2025-12-31
publication date: 2025-12-31
starting page: 1
ending page: 11

SMART ASSISTIVE SYSTEM FOR THE VISUALLY IMPAIRED USING COMPUTER VISION
type: item
description: 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.
subject: Articles
  topic: section
subject: Intelligent Assistive Systems
  topic: keywords
subject: Edge Computing
  topic: keywords
subject: Internet of Things (IoT)
  topic: keywords
subject: Computer Vision
  topic: keywords
subject: YOLO Object Detection
  topic: keywords
subject: Machine Learning
  topic: keywords
creator: Sofia Shafiq
creator: Irsha Qureshi
rights: Copyright (c) 2025
date: 2025-09-30
publication date: 2025-09-30
starting page: 50
ending page: 70

INTELLIGENT VERTICAL HANDOVER DECISION FRAMEWORK FOR 5G NETWORKS VIA DATA MINING
type: item
description: 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.
subject: Articles
  topic: section
subject: Vertical handover
  topic: keywords
subject: data mining
  topic: keywords
subject: 5G networks
  topic: keywords
subject: multivariate regression
  topic: keywords
subject: mobility management
  topic: keywords
creator: Younus khan
creator: Aftab Ahmed Soomro
creator: Obaidullah
rights: Copyright (c) 2025
date: 2025-09-30
publication date: 2025-09-30
starting page: 41
ending page: 49

ENHANCING GEOPOLYMER CONCRETE: MECHANICAL BEHAVIOR ANALYSIS WITH NANO-SILICA
type: item
description: 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.
subject: Articles
  topic: section
subject: Geopolymer concrete
  topic: keywords
subject: nano-silica
  topic: keywords
subject: mechanical properties
  topic: keywords
subject: microstructure
  topic: keywords
subject: sustainable construction
  topic: keywords
creator: Shahzaib Khan
rights: Copyright (c) 2025
date: 2025-09-30
publication date: 2025-09-30
starting page: 25
ending page: 40

DEVELOPMENT OF A LOW-COST LINKAGE-DRIVEN TWO-FINGER EXOSKELETON FOR HAND REHABILITATION
type: item
description: 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.
subject: Articles
  topic: section
subject: Hand Exoskeleton
  topic: keywords
subject: Rehabilitation Robotics
  topic: keywords
subject: Physiotherapy
  topic: keywords
subject: Assistive Devices
  topic: keywords
subject: Post-Stroke Recovery
  topic: keywords
subject: Low-Cost Design
  topic: keywords
subject: Linkage Mechanism
  topic: keywords
subject: Servo Motor Actuation
  topic: keywords
subject: Frugal Innovation
  topic: keywords
creator: Muhammad Waleed Iqbal
creator: Umair Ashfaq
rights: Copyright (c) 2025
date: 2025-09-30
publication date: 2025-09-30
starting page: 13
ending page: 24

SYSTEMATIC ANALYSIS AND CLASSIFICATION OF FOODBORNE DISEASE OUTBREAKS
type: item
description: 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.
subject: Articles
  topic: section
subject: Classification
  topic: keywords
subject: Foodborne
  topic: keywords
subject: Outbreaks
  topic: keywords
subject: Causative Agents
  topic: keywords
subject: Naïve Bayes
  topic: keywords
subject: Decision Tree
  topic: keywords
subject: Random Forest
  topic: keywords
creator: Resham Naz
creator: Shahid Mehmood
creator: Ali Haider
rights: Copyright (c) 2025
date: 2025-09-30
publication date: 2025-09-30
starting page: 1
ending page: 12

ADVANCEMENTS IN AFFECTIVE COMPUTING FOR SOCIAL ROBOTICS: A COMPREHENSIVE SURVEY
type: item
description: 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.
subject: Articles
  topic: section
subject: Emotionally Intelligent Robots
  topic: keywords
subject: Human-Robot Collaboration
  topic: keywords
subject: Affective Computing, Socially Interactive Robots
  topic: keywords
subject: Robotic Companions
  topic: keywords
subject: Artificial Intelligence
  topic: keywords
subject: Machine Learning
  topic: keywords
creator: Ramez Atif
creator: Sundas Abbas
creator: Dr. Muhammad Shakir
rights: Copyright (c) 2025
date: 2025-06-30
publication date: 2025-06-30
starting page: 75
ending page: 101

THE ROLE OF ARTIFICIAL INTELLIGENCE IN SMART GRIDS, POWER SYSTEMS, AND AUTOMATION WITHIN ELECTRICAL ENGINEERING
type: item
description: 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.
subject: Articles
  topic: section
subject: Artificial Intelligence
  topic: keywords
subject: Smart Grids
  topic: keywords
subject: Power Systems
  topic: keywords
subject: Industrial Automation
  topic: keywords
subject: Machine Learning
  topic: keywords
subject: Deep Learning
  topic: keywords
subject: Neural Networks
  topic: keywords
subject: Fault Prediction
  topic: keywords
subject: Energy Optimization
  topic: keywords
subject: Demand Response
  topic: keywords
subject: Renewable Integration
  topic: keywords
subject: Predictive Maintenance
  topic: keywords
creator: Jawaria Aslam
rights: Copyright (c) 2025
date: 2025-06-30
publication date: 2025-06-30
starting page: 59
ending page: 74

FEDERATED LEARNING FOR DISTRIBUTED NETWORK TRAFFIC ANOMALY DETECTION USING GRU MODELS
type: item
description: 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.
subject: Articles
  topic: section
subject: Federated Learning
  topic: keywords
subject: GRU-Based Models
  topic: keywords
subject: Network Traffic Anomaly Detection
  topic: keywords
subject: IoT Security
  topic: keywords
subject: Privacy-Preserving
  topic: keywords
subject: Edge-IIoTset Dataset
  topic: keywords
subject: Distributed Computing
  topic: keywords
creator: Hamad Riaz
rights: Copyright (c) 2025
date: 2025-06-30
publication date: 2025-06-30
starting page: 45
ending page: 58

HOW CLIMATE CHANGE AFFECTS HUMAN HEALTH
type: item
description: 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.
subject: Articles
  topic: section
subject: Sustainable Materials
  topic: keywords
subject: Green Innovation
  topic: keywords
subject: Biodegradable Polymers
  topic: keywords
creator: Abdul Wahhab
rights: Copyright (c) 2025
date: 2025-06-30
publication date: 2025-06-30
starting page: 18
ending page: 44

LEVERAGING AI FOR ANOMALY DETECTION IN ELECTRICAL SYSTEMS AND NETWORKS TO PROTECT CRITICAL INFRASTRUCTURE FROM CYBERATTACKS
type: item
description: 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.
subject: Articles
  topic: section
subject: Artificial Intelligence (AI)
  topic: keywords
subject: Machine Learning (ML)
  topic: keywords
subject: Cybersecurity
  topic: keywords
subject: Critical Infrastructure
  topic: keywords
subject: Threat Prediction
  topic: keywords
subject: Anomaly Detection
  topic: keywords
subject: Explainable AI (XAI)
  topic: keywords
subject: Industrial Control Systems (ICS)
  topic: keywords
subject: Real-Time Threat Mitigation
  topic: keywords
subject: Adversarial Attacks
  topic: keywords
creator: Shakir Bukhari
rights: Copyright (c) 2025
date: 2025-06-30
publication date: 2025-06-30
starting page: 1
ending page: 17

DESIGNING HIGH-PERFORMANCE PHOTOTRANSISTORS USING SEMICONDUCTOR NANOMATERIALS
type: item
description: 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.
subject: Articles
  topic: section
subject: Phototransistor
  topic: keywords
subject: GaN-ZnO nanowires
  topic: keywords
subject: WS₂–InGaZnO heterojunction
  topic: keywords
subject: plasmonic enhancement
  topic: keywords
subject: photoresponsivity
  topic: keywords
subject: semiconductor nanomaterials
  topic: keywords
creator: Salman Khan
rights: Copyright (c) 2025
date: 2025-03-31
publication date: 2025-03-31
starting page: 83
ending page: 92

CLOUD COMPUTING ADOPTION IN SMES: AN EMPIRICAL ANALYSIS USING PLS-SEM
type: item
description: 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.
subject: Articles
  topic: section
subject: Cloud Computing Adoption
  topic: keywords
subject: SMEs
  topic: keywords
subject: Perceived Usefulness
  topic: keywords
subject: Security Concerns
  topic: keywords
subject: Cost Efficiency
  topic: keywords
subject: Organizational Readiness
  topic: keywords
subject: PLS-SEM
  topic: keywords
subject: Digital Transformation lipid peroxidation
  topic: keywords
subject: animal model
  topic: keywords
creator: Naeem Akbar Channar
rights: Copyright (c) 2025
date: 2025-03-31
publication date: 2025-03-31
starting page: 72
ending page: 82

ADVANCES IN MACHINE LEARNING: A SURVEY OF METHODS, MODELS, BENCHMARKS, AND INDUSTRY APPLICATIONS
type: item
description: 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.
subject: Articles
  topic: section
subject: Algorithms
  topic: keywords
subject: Artificial Intelligence
  topic: keywords
subject: Data
  topic: keywords
subject: Finance
  topic: keywords
subject: Healthcare
  topic: keywords
subject: Industry Applications
  topic: keywords
subject: Retail
  topic: keywords
subject: Machine Learning
  topic: keywords
subject: Neural Networks
  topic: keywords
subject: Statistical Learning
  topic: keywords
creator: Mir Sajjad Hussain Talpur
rights: Copyright (c) 2025
date: 2025-03-31
publication date: 2025-03-31
starting page: 57
ending page: 71

OPTIMIZING PREFABRICATED CONSTRUCTION THROUGH SUPPLY CHAIN INTEGRATION AND ENTERPRISE ACCESS STANDARDS
type: item
description: 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.
subject: Articles
  topic: section
subject: Prefabricated building
  topic: keywords
subject: Supply chain integration
  topic: keywords
subject: Risk management
  topic: keywords
subject: Standardization
  topic: keywords
creator: Shasha Xie
rights: Copyright (c) 2025
date: 2025-03-31
publication date: 2025-03-31
starting page: 43
ending page: 56

EVALUATING THE ACCURACY AND EFFECTIVENESS OF FRAUD DETECTION SYSTEMS
type: item
description: 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.
subject: Articles
  topic: section
subject: Evaluating the Accuracy and Effectiveness of Fraud Detection Systems
  topic: keywords
creator: Fawad Naseer
rights: Copyright (c) 2025
date: 2025-03-31
publication date: 2025-03-31
starting page: 23
ending page: 42