Journal of Computer Allied Intelligence(JCAI, ISSN: 2584-2676)
Journal of Computer Allied Intelligence (JCAI, ISSN: 2584-2676) is an open access peer-reviewed, Bi-monthly(February, April, June, August, October, December) international journal that publishes articles which provide a respectable forum for cutting-edge discoveries, advancements and in-depth analysis in the area of Computer Science fields. Authors can submit their papers at the online submission system (or) you may forward your paper via email to the editorial office at editorjcai@fringeglobal.com Article Processing Charges (APC) Exemption : Fringe Global Scientific Press provides the publication free of cost. There is no need to pay the APC for the journal.
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Detection of Deepfake Videos Using Long Distance Attention
Recent advances in deepfake methods have made face video forgery a real possibility, capable of producing extremely misleading video footage and posing serious security risks. Furthermore, identifying these fake videos is a very difficult and time-sensitive task. Current detection algorithms typically approach the problem with a straightforward binary classification mindset. Since real and synthetic faces differ in minute ways, this study treats the topic as a novel fine-grained classification problem. Existing face forgery systems typically leave behind certain common artifacts in the spatial and temporal domains. Inconsistencies between frames in the time domain and generating errors in the space domain were among these objects. Furthermore, we introduce a spatial-temporal model that includes both the location and the passage of time in its representation of forging traces from a worldwide perspective. In creating both components, a novel long-distance attention technique was employed. The spatial domain component allows for a single frame capture of an artifact, while the time domain component allows for sequential frames. They make attention maps that look like patches. A more holistic view is provided by the attention approach, which aids in the extraction of local statistical information and the better assembly of global information. Lastly, similar to other granular classification techniques, the network is directed to concentrate on critical areas of the face by means of attention maps. Experimental results on several public datasets show that the suggested method outperforms the state-of-the-art, and the suggested longdistance attention strategy successfully captures crucial components for face forgeries
Copyright (c) 2026 Journal of Computer Allied Intelligence(JCAI, ISSN: 2584-2676) https://creativecommons.org/licenses/by-nc/4.0
Edge Computing Optimization of the Indicator System for Vocational College Information Technology Projects within Cloud Computing Environment
In the context of vocational college information technology projects, optimizing the indicator system under a cloud computing environment is critical for enhancing project effectiveness and efficiency. This study focuses on refining the metrics and criteria used to evaluate such projects, leveraging the scalability, flexibility, and cost-efficiency offered by cloud computing technologies. By integrating cloudbased solutions into project management practices, vocational colleges can streamline operations, improve resource allocation, and accelerate project delivery. This paper investigates the optimization of the indicator system for vocational college information technology projects within a cloud computing environment, focusing on the Hashing Ranked Edge Computing (HREC) framework. HREC enhances project efficiency by leveraging edge computing's proximity and computational capabilities, optimizing data processing and response times. The study examines how HREC integrates with cloud-based solutions to refine project evaluation metrics, ensuring alignment with modern technological capabilities. Experimental results indicate that integrating HREC with cloud-based solutions enhances project evaluation metrics, achieving a 25% reduction in processing time and a 15% improvement in resource utilization compared to traditional methods. The study also demonstrates a 30% increase in scalability and responsiveness, highlighting the effectiveness of HREC in optimizing project outcomes.
Research on Personalized Recommendation Systems for Graphic Design Based on AIGC and Big Data
In the era of integrated Graphic Design, mainstream of Big Data strategies leverage advanced Big Data technologies and digital publishing to enhance content delivery and audience engagement. These strategies focus on cross-platform integration, real-time updates, and personalized content distribution using AI, big data, and cloud computing. By combining traditional and digital Graphic Design approaches, they ensure broader reach, interactive user experiences, and streamlined dissemination of information, adapting to the evolving demands of the digital audience. This paper presents a comprehensive AI-Generated Content (AIGC) tailored for mainstream Graphic Design in the contemporary era of integrated Graphic Design. Leveraging Personalized Recommendation System and digital publishing, the strategy aims to enhance brand awareness, audience engagement, and revenue generation. Key components of the strategy include targeting Millennials and Generation Z individuals interested in lifestyle and entertainment content, utilizing various digital channels such as social Graphic Design platforms (Instagram, TikTok, Twitter), websites, email newsletters, and mobile apps. The content strategy emphasizes visual-driven and interactive content, supplemented by behind-the-scenes access to events and influencer interviews. Technology integration involves data analytics, AI-driven recommendation systems, and mobile optimization to personalize user experiences. Engagement strategies focus on user-generated content campaigns, influencer partnerships, and interactive live sessions. Performance measurement metrics encompass audience reach (85%), engagement rates (20%), conversion rates (5%), website traffic (1,000,000 visitors), social Graphic Design reach (500,000 users), email open rates (25%), content interactions (750,000), time spent on content (2 minutes), click-through rates (8%), subscription growth (15%), advertising revenue ($1,000,000), and ROI from branded partnerships (10x). The campaign aimed to maximize reach, engagement, and conversions within a $24,000 budget. Results show that Segment 2 (ages 25-34) had the highest reach (150,000) and engagement rate (9%), generating 110,000 views, 9,900 interactions, and 2,200 conversions, with a conversion rate of 2%. Segment 3 (ages 35-44) exhibited the highest conversion rate at 2.5%, with 1,500 conversions from 60,000 views and 5,400 interactions, also with a 9% engagement rate.
Innovative Credit Risk Assessment System Using Artificial Intelligence-Based Reinforcement Learning
The optimization and application of reinforcement learning (RL) in credit risk assessment strategies for cryptocurrency trading focus on leveraging AI-driven models to enhance decision-making in dynamic and volatile markets. Reinforcement learning algorithms are trained to maximize returns by continuously learning from market conditions, historical data, and reward feedback. This involves finetuning parameters, exploring optimal policy strategies, and adapting to rapid price fluctuations. By applying RL, traders can develop automated systems that efficiently execute trades, manage risks, and identify profitable opportunities, providing a competitive edge in the cryptocurrency market. The paper introduces the Quantitative Spider Swarm Reinforcement Learning (QSSRL) algorithm and explores its optimization and application in cryptocurrency trading. Leveraging reinforcement learning techniques, QSSRL adapts trading strategies through interactions with market data to maximize profits while managing risks. The optimization process fine-tunes key parameters such as learning rate, discount factor, and exploration rate, enhancing the algorithm's performance. Application exploration across multiple epochs demonstrates the algorithm's ability to consistently improve profitability and adaptability over time. Results show increasing cumulative returns, Sharpe ratios, and average trade profits, indicating the algorithm's effectiveness in navigating dynamic cryptocurrency markets. The optimization process finetunes key parameters such as learning rate, discount factor, and exploration rate, resulting in significant improvements in performance. Application exploration across multiple epochs demonstrates the algorithm's ability to consistently improve profitability and adaptability over time, with cumulative returns increasing from 20% to 35% and average trade profits rising from $500 to $700.
Amharic Chabot for Diet Healthcare Consultation Using Deep Learning Approach
Conversational Chabot’s can serve by providing auto services such as advising, question answering, knowledge generation, and other services for people without the involvement of humans on the other side. These conversational agents are very versatile and can be applied to different areas of businesses including marketing and sales, psychology, news media, education, customer support and healthcare. So it is challenging to design a deep learning-based Chabot since different domains require different strategies and algorithms, depending on the types of the Chabot (task-oriented or non-taskoriented and retrieval or generative-based) and the nature of the dataset. This study focused on designing and implementing a retrieval-based Amharic Chabot model for diet-related healthcare consultancy by delivering domain-specific information through natural language conversation. To replay the response to each user's questions from the hand-crafted knowledge base, the syntactic and semantic structure of the dataset and the nature of the domain are considered. To track users’ conversations, dialog management of the model is included to save the current topic of conversation intent. Different consecutive Deep Learning (DL) and Natural Language Processing (NLP) model-building tasks were involved in designing the Chabot model. To carry out this study, the Deep Neural Network (DNN) and Gate Recurrent Unit (GRU) algorithms were used on the Keras sequential framework. The model was trained, validated, and tested using a dataset of 3121 pairs of questions and answers. During the model, training, and testing phases, overfitting and under fitting issues were addressed using regularization, dropout, and crossvalidation techniques. Different tests were carried out by adjusting the neural network's settings such as the number of training rounds, the layers inside the network, and how the data is split between training and testing to find the most effective model outcome. The proposed model is assessed using the current state-of-the-art deep learning Chabot evaluation metrics, including accuracy, precision, recall, F1-score, and user acceptance testing. The model achieved scores of 92.55% with DNN and 74.52% with GRU.
Machine Learning and Sensor-Based Approaches for Driver Fatigue and Drowsiness Detection: A Mini Review
Driver sleepiness is a significant contributing factor to road traffic accidents worldwide, resulting in substantial morbidity, mortality, and socioeconomic burden. Fatigue impairs critical driving functions, including reaction time, sustained attention, situational awareness, and decision-making capacity, thereby increasing the likelihood of errors in high-risk scenarios. Consequently, the development of reliable and accurate fatigue detection systems is essential for improving road safety. Traditional mitigation strategies—such as self-reported fatigue assessment, scheduled rest breaks, and public awareness campaigns—have demonstrated limited effectiveness due to variability in individual compliance and the subjective nature of fatigue measurement. In response, technology-driven approaches have gained increasing attention. Continuous driver monitoring through wearable sensors, multimodal sensing platforms, and advancements in artificial intelligence (AI) provides a promising pathway toward real-time fatigue detection and intervention. Current detection methodologies can be broadly categorized into behavioral, physiological, and vehicle-based approaches. Behavioral indicators—including eye closure, blink rate, facial expressions, and head movements—are typically derived from computer vision techniques and offer non-intrusive monitoring. Physiological measures, such as heart rate variability, electroencephalography (EEG), and electrocardiography (ECG), provide insights into neurophysiological and autonomic changes associated with drowsiness, although they may introduce practical and usability constraints. Vehicle-based metrics, including steering patterns, lane-keeping performance, and pedal input dynamics, offer indirect yet scalable indicators of driver impairment. Recent research has emphasized multimodal frameworks that integrate heterogeneous data sources to enhance detection accuracy under real-world driving conditions. By leveraging advanced AI and machine learning algorithms, these systems can model complex, nonlinear relationships between fatigue-related signals. Despite promising progress, challenges related to sensor reliability, user acceptance, data privacy, and real-time deployment remain significant. Nevertheless, ongoing advancements in intelligent monitoring technologies represent a safetyoriented approach with substantial potential to mitigate fatigue-related driving risks and improve transportation safety outcomes.
Multi-Source Domain Adaptive Network with Multilevel Feature Fusion for Robust Pattern Recognition and Classification
Domain shift across heterogeneous data sources remains a critical challenge in pattern recognition and classification tasks, often leading to significant performance degradation in real-world applications. To address this issue, this paper proposes a Multi-Source Domain Adaptive Network with Multilevel Feature Fusion (MSDAN) that effectively learns domain-invariant and discriminative representations by jointly leveraging multiple source domains. The proposed framework employs a shared deep feature extraction backbone to capture hierarchical representations and integrates a multilevel feature fusion mechanism to combine low-level spatial details, mid-level structural information, and highlevel semantic features. An adversarial domain adaptation module is incorporated to align feature distributions between multi-source and target domains, enabling robust knowledge transfer without requiring labeled target data. Extensive experiments conducted on benchmark pattern recognition datasets demonstrate that the proposed MSDAN consistently outperforms conventional deep learning models and state-of-the-art domain adaptation approaches, achieving a classification accuracy of 91.6% with precision, recall, and F1-score values exceeding 90%. Ablation studies further validate the effectiveness of each architectural component, confirming that the synergistic integration of multi-source learning and multilevel feature fusion significantly enhances generalization performance under complex domain shift scenarios
Copyright (c) 2025 Journal of Computer Allied Intelligence(JCAI, ISSN: 2584-2676) https://creativecommons.org/licenses/by-nc/4.0
Design and Implementation of Smart Sensor Networks for Early Disease Outbreak Detection in Developing Regions
A smart sensor network for early disease outbreak detection integrates advanced sensing technologies with Internet of Things (IoT) frameworks to enhance real-time monitoring, data acquisition, and public health responsiveness. In such systems, distributed sensor nodes gather continuous data on variables such as temperature, humidity, human movement, and biological indicators from remote and urban settings. This data is transmitted through wireless networks to centralized analytical platforms, enabling early warning, situational awareness, and rapid intervention planning. Smart sensing integration supports spatial mapping, trend analysis, and customized alerting mechanisms, thereby improving health surveillance, resource allocation, and community resilience. This paper explores the design and implementation of a smart sensor network tailored to the unique infrastructural and epidemiological challenges of developing regions. The study begins with a detailed survey of relevant literature, examining sensor deployment strategies, data fusion techniques, and outbreak modeling approaches. Simulation experiments are conducted to evaluate the effectiveness of the proposed network architecture under various environmental and disease spread conditions. This paper presents empirical results on outbreak detection sensitivity, with the designed sensor network achieving an average early detection rate of 91% across multiple simulated outbreak patterns. The latency of alert generation was consistently recorded within 60–90 milliseconds, demonstrating the system’s capability for timely and actionable outbreak identification.
Application of Machine Learning in Detecting Neurodegenerative Disorder through Patient Family History using SVM and HMM Algorithms
The aim of this paper is to adopt Machine Learning in detecting Neurodegenerative disorder through Patient Family History using SVM and HMM Algorithms, with major focus on detection of Parkinson disease (PD), while the objectives is to build a model that can carry out a prediction to discover the presents of Parkinson disease (PD) using a patient family medical history dataset which could indicate the patients gene status and outline required information on the possibility of inheritable factor in other to setup treatment plan ahead of time and to provide early decision making whereby reducing death rate. The study employed two machine learning algorithmsmethods which include: Support Vector Machine (SVM) and Hidden Markov Model (HMM). The data was analyzed using JASP framework while the experiments are done on the Dataset which contain 131 dataset containing sourced from Kagglemachine learning repository. The dataset splitting ratio was train (105) and test (26). The result produced by SVM showed an accuracy of 0.731 which is 73% with 96 hyper plane vitalization, while ROC curve shown in figure 7 predicted 0.95 = (95%) for NO (0) patients that said No Parkinson disease history in their family while 0.98 = (98%) for YES (1) indicating that there isParkinson disease history in their family. The result accuracy produced by HMM forward algorithm was (Motor-Rigidity = 84%), (Family History = 10%) and (Age = 55%) respectively and the observation sequence probability of having a Parkinson disease is approximately 84.2%, where Family History 10% and Age 55%. By calculating the accuracy rate of the HMM model we have (84.2 + 10 + 55) =149.2 / 3 = 49.73 = = 50% approximately. The scientific contribution of this study was on the development of a predictive model using two machine learning algorithms that was able to detect early warning signs of Parkinson disease within a family linage and hence which could help any family prepare on time and take futuristic decision on the disease on time as detected. In summary, this result proof that from a Patients families medical history, there is tendency of their offspring’s having trace of Parkinson disease when they get to a particular age bracket as produced by SVM algorithm.
Integrating QR Code Authentication with Firebase Cloud Messaging: A Performance-Optimized Architecture for Enhanced Mobile Security and User Experience
The escalating reliance on mobile applications across diverse sectors necessitates architectures that are simultaneously secure, performant, and user-centric. This study presents the design, implementation, and empirical evaluation of an Android application that integrates encrypted Quick Response (QR) code authentication with Firebase Cloud Messaging (FCM) to enhance security and the overall user experience (UX). The primary scientific contribution of this work is a novel, performancecentric architecture that successfully balances robust, encrypted authentication with low-latency cloud communication, a synergy often identified as a challenge in integrated systems. Developed using an agile methodology, the optimized system was rigorously evaluated against a non-optimized baseline. Results demonstrated a 30% reduction in application load time, a 46% improvement in QR scan speed (averaging 190ms), and a 50% reduction in messaging latency (<900ms in 95% of tests). Simulated user tests yielded a high average System Usability Scale (SUS) score of 83.5, with participants highlighting the system's ease of use and responsiveness. This work confirms the viability of a unified architecture that does not force a trade-off between security and performance, offering a scalable and empirically validated model for e-commerce, e-learning, and digital service delivery applications.
Deep Learning Architectures for Early Detection of Diabetic Retinopathy in Retinal Image Analysis
This paper explores the application of deep learning architectures in retinal image analysis, with a particular focus on the early detection of diabetic retinopathy (DR). Leveraging large-scale retinal imaging datasets, the study examines the diagnostic capacity of advanced neural models in identifying subtle pathological markers such as microaneurysms, hemorrhages, and exudates at the earliest clinical stages. Convolutional Neural Networks (CNNs), Recurrent Neural Networks (RNNs), and hybrid deep learning frameworks are evaluated for their performance in image classification and lesion localization. Results demonstrate that CNN-based architectures achieve an accuracy of 94.2%, with sensitivity and specificity values exceeding 93%, underscoring their robustness in medical image interpretation. Hybrid models integrating CNN and attention mechanisms further enhance feature discrimination, achieving 95.1% accuracy and improved area under the curve (AUC) metrics. RNN-driven architectures exhibit competitive performance, particularly in sequential image feature learning, with overall accuracy surpassing 92%. The findings highlight the transformative role of deep learning in automating ophthalmic diagnostics, enabling scalable, precise, and timely detection of diabetic retinopathy to mitigate preventable vision loss.
Review of Automata Processor of KNN Algorithm Applications in Machine Learning
This paper examined the concepts and application of the kNN algorithm relating to machine learning characteristics on automata processes. The algorithm applications focused on network congestion, demand, and supply relating to price and time, and finally, the usage of transaction mode in financial transactions in terms of deposit and withdrawal. The study reviewed machine learning as an aspect of artificial intelligence in computing systems that improves and automatically applies to a variety of complex mathematical computations to address problems using similarity search on query techniques with a given dataset. This was applied with k-nearest neighbors (kNN) developing an effective design of the automata processor (AP)) related to non-von Neumann architecture that focused on nondeterministic finite automaton (NFA) driven by the execution model; According to the research, KNN performance is affected by a number of factors, including parameter normalization, the value of K, and the distance between the two points on the graph. The study also revealed some challenges of the kNN algorithm, such as computational efficiency, selecting the optimal value of k, high-dimensional data handling, and finally, noise and outlier sensitivity. The results of the kNN algorithm indicated common values of each MSE for both demand and supply prediction, and the network congestion. The optimal k-value is 2, with a corresponding 66.67% without congestion. Finally, with regard to the financial transaction, the kNN algorithm computes each customer transaction frequency with the selected bank account number carried out within the range of the query provided.
AI in Everything, and Everything in AI: A Review of the Ubiquitous Role of Artificial Intelligence in Shaping the Next Technological Epoch
Artificial Intelligence (AI) is no longer a discrete technological discipline—it has evolved into a ubiquitous, integrative force permeating virtually every domain of human activity, from scientific discovery and economic systems to governance, healthcare, and the fabric of everyday life. This review critically examines the pervasive diffusion of AI technologies, conceptual frameworks, and methodological paradigms across diverse sectors, highlighting how AI is simultaneously a tool, a subject of innovation, and a transformative agent. Drawing on interdisciplinary literature from computer science, economics, business, policy, ethics, and the applied sciences, this article maps the evolving contours of an AI-driven epoch. It interrogates the shifting boundaries between human and machine intelligence, assesses the socio-technical implications of automation and datafication, and evaluates the governance challenges posed by AI's expanding footprint. Special attention is given to emerging intersections—such as AI in sustainability transitions, fintech infrastructures, smart governance, cybersecurity, and the Internet of Things (IoT)—where AI acts not merely as an enhancer but as a structural enabler of new socio-economic orders.
A Review on Deep Learning Algorithms for Efficient Mobile Traffic Handling in 5G Systems
The exponential growth in mobile data demand, fueled by the proliferation of smart devices, IoT applications, and multimedia services, has posed significant challenges to the performance andscalability of 5G networks. Traditional network traffic management techniques are increasingly insufficient to meet the dynamic and complex requirements of modern wireless communication systems. In response, deep learning (DL) has emerged as a powerful tool to enable intelligent, adaptive, and realtime optimization of mobile network traffic in 5G environments. This review provides a comprehensive overview of the integration of deep learning algorithms into various layers of the 5G architecture, including radio access networks (RAN), core networks, and edge computing platforms. Key DL models such as convolutional neural networks (CNNs), recurrent neural networks (RNNs), long short-term memory (LSTM), deep reinforcement learning (DRL), and graph neural networks (GNNs) are analyzed in terms of their application to traffic prediction, congestion control, resource allocation, and quality of service (QoS) enhancement. The article also explores existing challenges such as data scarcity, model interpretability, latency constraints, and deployment complexity. Furthermore, it discusses emerging trends like federated learning, transfer learning, and edge intelligence as promising directions for future research. By synthesizing state-of-the-art contributions, this review highlights the transformative potential of DL in building efficient, resilient, and autonomous 5G networks.
Quantum-Ready Fiber-Wireless (FiWi) Integration: Preparing Network Infrastructure for Quantum-Secure Communication
With the emergence of quantum computing, traditional communication infrastructures face critical security challenges. This paper proposes a hybrid quantum-ready Fiber-Wireless (FiWi) network architecture that integrates Quantum Key Distribution (QKD) and Post-Quantum Cryptography (PQC). Using NS-3 simulations across urban, suburban, and rural scenarios, results show that latency remained under 20 ms, QKD key rates exceeded 1.2 kbps, and PQC processing was feasible at the wireless edge. The architecture demonstrated resilience under disruptions and supported dual-layered security with minimal performance trade-offs. These findings confirm that quantum-resilient FiWi networks are viable for future-safe communication.
Privacy-Preserving Health Data Transmission in IoT-Enabled Mobile Health Systems
The Internet of Things (IoT)-enabled mobile health systems serve a vital function in supporting real-time health monitoring by capturing, transmitting, and processing sensitive patient data. Ensuring the privacy and integrity of this data during transmission remains a core challenge, especially in resourceconstrained environments. By applying secure data clustering mechanisms, researchers can efficiently manage health information flow while preserving patient confidentiality. This paper proposes a novel framework for secure data transmission leveraging the Privacy-Preserving Clustered Encryption Algorithm (PPCEA). Through a combination of cluster-based segmentation and layered encryption, the method enables classified grouping of health data according to sensitivity level while maintaining data anonymity during transmission. The PPCEA facilitates secure routing of health information collected from wearable IoT devices, classifying transmissions into privacy-preserved clusters. For example, Cluster A handles high-sensitivity biometric data with advanced encryption, while Cluster B processes generalized health metrics under lightweight encryption protocols. Transmission efficiency and privacy assurance were evaluated, revealing transmission success rates above 97%, while privacy leakage risk was minimized to below 1.5%. The experimental outcomes demonstrate the capability of PPCEA to balance low-latency communication with robust privacy protection, making it suitable for deployment in mobile health systems within developing regions. These findings underscore the importance of clusteraware encryption techniques in enhancing data security without sacrificing communication efficiency.
System for Identifying Fake Products Using Blockchain Technology
The proliferation of counterfeit products in various industries, including pharmaceuticals, electronics, and luxury goods, poses a significant threat to consumer safety, brand reputation, and economic integrity. Traditional verification methods often fail due to centralized control and limited traceability. This research proposes a blockchain-based system to identify fake products by leveraging the decentralized, immutable, and transparent nature of blockchain technology. The system records product information such as manufacturing details, origin, and ownership history on a distributed ledger, ensuring secure and tamper-proof tracking across the supply chain. Each product is tagged with a unique QR code that links to its blockchain record, allowing end-users to verify authenticity through a mobile application. The system incorporates distinct login modules for administrators, sellers, and customers to ensure secure interactions and streamline product management. Simulation results validate the system’s capability to detect counterfeit products with high accuracy and real-time verification speed. The proposed solution provides a scalable and efficient framework for enhancing supply chain integrity and protecting consumers against fake goods.
A Novel Approach to Detect Copy-Move Forgery Using Deep Learning
Copy-move forgery is a common form of digital image manipulation when a section of a picture is cut and placed within another image to hide or reproduce specified content. Common post-processing processes like scaling, blurring, or compression might make traditional methods, including active and passive detection techniques, inaccurate when it comes to localising forgeries. In this research, we provide a new method that combines the deep learning architecture of VGG16 with Density-Based Spatial Clustering of Applications with Noise (DBSCAN) to intelligently segment images and classify them. By grouping nearby pixels according to similarity, the DBSCAN algorithm divides the picture into superpixels, which improves the identification of small areas that are replicated. These segmented regions are fed into the VGG16 network, which has been trained on the MICCF220 dataset to distinguish between authentic and tampered segments. A pattern matching algorithm is then applied to highlight anomalies and confirm the presence of forgery. Experimental results demonstrate that the proposed method achieves a detection accuracy of 93%, outperforming existing techniques in terms of precision, recall, and F1-score. The system is robust, efficient, and suitable for real-world forensic applications where detecting tampered evidence is critical.
Detecting Web Attacks with End-to-End Deep Learning
The rapid proliferation of web-based technologies has heightened the risk and complexity of cyber threats, making traditional detection mechanisms increasingly inadequate. This research introduces an end-to-end deep learning approach to detect and classify web attacks with improved precision and adaptability. The proposed model integrates AutoEncoder for anomaly detection and Long Short-Term Memory (LSTM) networks for sequential pattern analysis, enabling robust identification of malicious behaviors within network traffic. Comparative performance evaluation with conventional algorithms such as Support Vector Machine (SVM) and Naïve Bayes demonstrates significant improvements, with the proposed system achieving up to 81.99% accuracy and outperforming others in key metrics such as precision, recall, and F1-score. The results indicate that the deep learning-based framework can serve as a scalable and intelligent solution for modern cybersecurity infrastructure, offering real-time threat detection with minimal false positives.
Intelligent Risk Assessment in OSNs Using Behavioral Analytics and Trust Metrics
Online Social Networks (OSNs) have become central to digital communication, yet they are increasingly vulnerable to security threats arising from user behavior, content sharing, and trust-based interactions. This paper proposes a behavior-aware security framework that leverages user activity patterns, content sensitivity, interaction frequency, and trust levels to dynamically assess risk and detect malicious behavior. The system architecture incorporates a real-time risk scoring model and an AI-driven behavioral analysis engine, enabling proactive threat identification and user-specific alerts. Simulation results across multiple training epochs demonstrate high detection accuracy (over 93%), low false positive rates, and improved response times. Additionally, the framework effectively engages users through awareness mechanisms and role-based access control. By integrating technical, behavioral, and psychological insights, the proposed model offers a scalable, intelligent solution for enhancing the security and resilience of OSNs in the face of evolving cyber threats. This paper presents a behavior-aware security framework that computes dynamic risk scores based on four core parameters: behavioral pattern score, content sensitivity, friend interaction rate, and trust level. The proposed system was tested on a simulated OSN environment with 500 users and evaluated across 10 training epochs. It achieved a detection accuracy of 93.6%, precision of 91.4%, recall of 92.1%, and an F1-score of 91.7%, with an average detection time of 3.2 seconds and false positive rate of just 5.8%. Over the simulation, 425 out of 450 high-risk users were correctly identified. The model also demonstrated improved learning over time, with risk classification stabilizing after Epoch 5 and detection latency reducing from 4.2 to 3.1 seconds. These results confirm that the framework offers a robust, real-time solution for identifying threats and enhancing security awareness in OSNs, making it suitable for scalable deployment in real-world platforms.
Transformation of Encryption with Identity-Based Approach for Versatile Encrypted Data Sharing in Public Cloud
The rapid adoption of public cloud platforms has revolutionized data storage and sharing but has simultaneously introduced significant security and access control challenges. Traditional encryption techniques often fall short in enabling dynamic, fine-grained, and efficient data access for multiple users. To address these limitations, this paper presents a novel Identity-Based Encryption Transformation (IBET) model that integrates Identity-Based Encryption (IBE) with Identity-Based Broadcast Encryption (IBBE). The proposed scheme facilitates secure data sharing by enabling transformation of IBE ciphertexts into IBBE ciphertexts, thereby allowing newly authorized users to access previously encrypted data without re-encryption. This identity-driven approach eliminates the need for complex certificate management while ensuring confidentiality and scalability. A concrete IBET scheme is designed using bilinear pairing cryptography, and its security is formally proven under adaptive adversary models. Extensive simulations were conducted in a controlled environment using Python and the Charm-Crypto library, and performance was evaluated based on encryption time, transformation efficiency, and decryption overhead. Results indicate that the IBET model outperforms conventional IBE and IBBE schemes in terms of flexibility, computational efficiency, and scalability, making it a robust solution for secure and versatile data sharing in public cloud infrastructures.
Detection and Mitigation of Web Attacks with End-To-End Deep Learning
Cybersecurity in web applications has become a critical concern due to the increasing sophistication of cyber-attacks. This paper presents an effective detection framework leveraging deep learning techniques, particularly an AutoEncoder-based model and its extension using Long Short-Term Memory (LSTM) networks, to identify web-based attacks. The study compares the proposed models against traditional machine learning algorithms, including Support Vector Machine (SVM) and Naïve Bayes, using a real-world attack dataset. Experimental results demonstrate that the traditional SVM and Naïve Bayes classifiers yield accuracies of 62.0% and 68.0% respectively, highlighting their limited generalization capacity for complex attack patterns. In contrast, the Proposed AutoEncoder model achieved an impressive accuracy of 92.89%, precision of 80.0%, recall of 99.99%, and F-score of 82.0%, showing its strong detection capability. Further enhancement with the Extension LSTM model led to a peak accuracy of 93.19%, precision of 87.19%, recall of 100.0%, and F-score of 85.93%. These results confirm the robustness and effectiveness of the proposed deep learning-based architecture for accurate intrusion detection in web environments, making it suitable for real-time threat monitoring in cybersecurity-sensitive systems.
A Systematic Review on Big Data Analytics Using Machine Learning forPredicting Climate Change and its Impact on Human Health
Climate changes are reflected in various unexpected natural calamities. Particularly, climate change has been impacting global warming. Global warming is the overall increase in temperature of planet Earth. Global warming has its negative impact on human health. Analysing data linked to climate change, global warming and its impact on human health needs processing of large voluminous data referred to as big data. Due to emergence of cloud computing and big data ecosystem, now it is possible to have big data analytics for such investigations. Therefore, it is essential to investigate on the state-ofthe-art methods that are based on machine learning techniques. This will pave way for understanding necessary insights required for further research. In this paper, a systematic review is made covering 80 peer-reviewed research articles from 2017 to 2023. It throws light on synthesis of new insights and knowledge from the systematic review. This paper covers existing machine learning techniques for pollution analytics, climate change analytics, global warming analysis and impact of climate change and global warming on human health. This paper thus answers many research questions and synthesizes new knowledge useful to the reader on diversified aspects of AI enabled methods for climate and global warming analytics. It can trigger further investigation towards finding methodologies for better estimation of impact of global warming on human health.
Prometheus & Grafana: A Metrics-focused Monitoring Stack
Observability is a crucial process in the evolving IT sector and other sectors as well. Having a holistic view of your infrastructure is not an option anymore but a necessity, especially for monitoring the performance and availability of critical systems that might lead to catastrophic impacts if anything goes wrong for some reason. Having a reliable tool for that purpose remains an essential factor for a smooth operation pace. It might be challenging to choose a tool among the proliferation of tools in the market, each promising to provide the best result expected. In this paper, a monitoring stack composed of opensource tools (Prometheus, Node Exporter, and Grafana) was leveraged to collect, store, and visualize metrics from a Linux server in real time. Docker had been used for a rapid deployment of the services of the stack, and Node Exporter Full dashboard was imported for a quick yet informative visualization. It was found to provide detailed insights into the monitored infrastructure in a very intuitive way.
SLA-Aware Load Balancing In Cloud Computing Using Machine Learning Based Virtual Machine Scheduling
Cloud computing has become an essential platform for delivering scalable and on-demand services, but it faces significant challenges in managing resource allocation while ensuring adherence to Service Level Agreements (SLAs). This research proposes a novel SLA-aware load balancing algorithm es3rzqnhanced by machine learning (ML) for efficient task scheduling across virtual machines (VMs). The proposed method predicts VM performance using real-time system metrics and allocates incoming tasks based on their SLA requirements, such as response time and priority. By integrating predictive modeling into the scheduling process, the system minimizes task failure rates, optimizes resource utilization, and maintains high SLA compliance. A simulation environment was developed using Django and a relational database backend to evaluate the algorithm against traditional methods like Round Robin, Least Connection, and Random Allocation. Experimental results showed that the ML-based approach achieved a 96.4% SLA compliance rate, significantly outperforming Round Robin (78.2%), Least Connection (84.7%), and Random Allocation (73.5%). Additionally, it reduced the average task completion time to 238 ms, compared to 412 ms, 367 ms, and 489 ms, respectively. The proposed system also improved VM utilization efficiency to 91.2%, minimized the task failure rate to 1.7%, and achieved a high prediction accuracy of 94.8%. These results confirm the effectiveness of integrating SLA constraints with intelligent ML-based scheduling to ensure performance reliability, resource efficiency, and scalability in dynamic cloud environments. This research highlights the potential of combining SLA policies with intelligent scheduling techniques to create adaptive, scalable, and performance-driven cloud infrastructures.
Decolonizing Artificial Intelligence: Indigenous Knowledge Systems, Epistemic Pluralism, and the Ethics of Technology
This paper explores the epistemological critique and reimaginings of artificial intelligence (AI) offered by Indigenous scholars, technologists, and artists. Challenging the dominant logics of data extraction, computational efficiency, and universalist rationality. Indigenous epistemologies foreground relationality, land-based intelligence, and spiritual responsibility as foundations for ethical technological practice. Drawing on publicly available documents, digital platforms, and urban academic discourses, particularly from the vantage point of a non-Indigenous researcher based in Delhi, this study examines how Indigenous AI critiques circulate across transnational and urban contexts, and the tensions they provoke when translated into mainstream AI ethics. Methodologically, the paper employs critical discourse analysis and reflexive inquiry, informed by decolonial theory and Indigenous data governance principles. Rather than advocating for mere inclusion, it argues for epistemic pluralism and solidarity, urging a reorientation of AI from extractive to relational ontologies. The study contributes to the broader project of decolonizing technology by highlighting the responsibilities of non-Indigenous scholars in supporting epistemologies that challenge and reframe what AI can and should be.
Artificial Intelligence in Industrial IoT: Trends, Challenges, and FutureDirections
The combination of Artificial Intelligence (AI) with the Industrial Internet of Things (IIoT) is transforming industrial automation, allowing intelligent decision-making, predictive maintenance, better operational efficiency, and real-time analytics. This paper investigates the revolutionary influence of AI technologies—including machine learning (ML), deep learning (DL), and edge intelligence—on the IIoT ecosystem. We offer a complete review of current developments, highlighting the integration of AI in smart manufacturing, supply chain optimization, and intelligent control systems. Key difficulties like as data security, interoperability, latency limits, and the necessity for large-scale annotated datasets are rigorously evaluated. Additionally, we examine new solutions and future research paths, including federated learning, explainable AI, and autonomous systems. This article intends to give researchers and industry practitioners with significant insights on the developing role of AI in IIoT, driving the development of robust, efficient, and secure intelligent industrial systems.
A Hybrid Approach: Combining Genetic Algorithms and Machine Learningfor Function Optimization
This study examines the use of an evolutionary method to enhance the Sphere benchmark function, an acknowledged continuous optimization challenge. The algorithm takes a genetic approach, using techniques including mutation, one-point crossover, and tournament selection. It also combines a machine learning element by developing a simple linear regression model that can be used to forecast fitness values determined by attributes of individuals. The study compares the results of two different iterations to examine the algorithm's performance throughout several runs. A population of 100 individuals with 10 traits each endures selection, crossover, and mutation over the course of 100 generations. The best values for fitness across generations for each run are shown in Matplotlib to show the algorithm's convergence behaviour. Results show that the algorithm works effectively in locating the best solutions to the Sphere benchmark function. The algorithm's framework, parameters, and convergent behaviour are all described in the abstract, which qualifies it for future study in adaptive algorithms and optimization approaches
Machine Learning in Supply Chain Management
The study highlights the application of ML techniques like predictive analytics, optimization algorithms, and advanced demand forecasting in critical areas such as inventory management, supplier selection, logistics optimization, and predictive maintenance. By leveraging ML, businesses can anticipate customer demands with greater accuracy, minimize waste, and respond swiftly to potential disruptions. The findings demonstrate that ML not only enhances decision-making and operational efficiency but also fosters improved customer satisfaction and a stronger competitive edge. This research provides actionable insights into how organizations can harness ML to meet the dynamic demands of modern supply chains and navigate the complexities of a rapidly evolving business landscape.
Optimizing Generative Models for High-Resolution Image Synthesis in Creative Industries
Generative models, particularly Generative Adversarial Networks (GANs), have emerged as transformative tools in various creative industries, especially in high-resolution image synthesis. These models have been widely adopted for tasks such as art generation, video game design, movie production, and digital content creation, owing to their ability to generate high-quality, realistic images. Despite their potential, the challenge of optimizing generative models for high-resolution image synthesis remains a significant barrier. High-resolution image generation demands substantial computational resources, sophisticated model architectures, and effective training strategies to produce images with intricate details and high visual fidelity. This research paper explores advanced techniques in optimizing generative models for the creative industries, with a focus on improving the quality and efficiency of high-resolution image synthesis. The paper examines the latest architectures, including StyleGAN and BigGAN, and their applications in producing ultra-high-definition images. We explore strategies for model optimization, such as transfer learning, data augmentation, and multi-resolution training, to enhance both the quality and speed of the image generation process. Additionally, the paper addresses the challenge of mitigating artifacts and maintaining consistency in the generated images, which are often critical in professional creative fields. A key aspect of this research involves investigating the trade-offs between computational cost and image quality. By incorporating novel loss functions and post-processing techniques, we propose a framework that balances these two aspects, enabling generative models to produce high-resolution images more efficiently. The paper also delves into the applications of these optimized models in creative fields, including their impact on digital art, advertising, fashion, and virtual reality. Furthermore, we highlight the potential for integration with emerging technologies such as augmented reality (AR) and virtual reality (VR), where high-resolution image generation plays a pivotal role in creating immersive experiences.Through experimental evaluations and case studies, this research showcases the practical implications of optimized generative models for high-resolution image synthesis. The results demonstrate significant improvements in the visual quality of generated images while reducing training times and resource consumption, making these models more accessible for widespread adoption in creative industries. This study provides valuable insights into the ongoing efforts to push the boundaries of generative modeling, offering a roadmap for future advancements in high-resolution image synthesis.