International Journal of Modern Computation, Information and Communication Technology
The International Journal of Modern Computation, Information, and Communication Technology (eISSN: 2581-5954) is a peer-reviewed, open-access journal that publishes original research and reviews spanning computational sciences, information systems, and advanced communication technologies. The journal emphasizes interdisciplinary work that bridges fields such as computer science, electrical and electronic engineering, applied mathematics, data science, and artificial intelligence. Its scope includes, but is not limited to, algorithms, cloud computing, cybersecurity, big data analytics, next-generation communication systems, and emerging digital technologies. By serving as a global platform for researchers and practitioners, the journal fosters innovative contributions that shape the future of modern computation and ICT.
Advancing Medical Imaging with Machine Learning Techniques by Utilizing IBM Watson Studio
IBM Watson Studio enhances medical imaging by integrating advanced machine learning techniques to improve diagnostic accuracy and clinical decision making. The platform leverages cloud based artificial intelligence to interpret and analyze complex medical images, enabling reliable identification of abnormalities and supporting early disease recognition. Deep learning algorithms facilitate automated feature extraction, pattern recognition, and image interpretation, improving diagnostic consistency while assisting radiologists in clinical workflows. The primary objective is to provide a dependable, data driven framework that delivers real time insights and predictive analytics for informed healthcare decisions. The cloud enabled architecture of IBM Watson Studio supports scalable data processing, seamless collaboration, and efficient management of large medical imaging repositories across healthcare environments. This intelligent integration promotes proactive, personalized, and efficient healthcare by enhancing diagnostic capabilities and improving patient outcomes. The Medical Imaging dataset is utilized to evaluate the proposed framework through comprehensive image analysis, image enhancement, and diagnostic performance assessment. The dataset incorporates evaluation metrics, image quality characteristics, edge detection attributes, noise reduction measures, and segmentation performance indicators, enabling systematic assessment of image processing quality, computational efficiency, and the effectiveness of machine learning models for medical image analysis.
Medical Imaging, Machine Learning, IBM Watson Studio, Diagnostic Accuracy, Data Privacy.
Copyright (c) 2026 https://creativecommons.org/licenses/by/4.0
5G-Based Low-Latency Control Framework for Autonomous Security Robots in Smart Cities
The growing demand for advanced city security has led to the development of autonomous security robots to be used in smart cities. It introduces a low-latency control architecture based on 5G to enhance other real-time monitoring, threat detection, and response efficiency. It is powered by 5G and edge computing, which achieves significant improvements in performance when compared to the traditional solutions. Experimental results show that there was reduction of end-to-end latency among areas to 310 ms as compared to 80200 ms in conventional networks, which facilitates faster communication and decision making. The suggested system exhibits a detection accuracy of 96% and a decreased false alarm rate of about 3%, so guaranteeing dependable monitoring. The maximum speed of transfer between data is 1200 Mbps, which makes it possible to transfer high-quality real-time video and sensor data processing. In addition, the reaction speed is reduced to 1-2 seconds, increasing the efficiency of a system in critical scenarios. The structure also supports scalable multi-robot collaboration with the system reliability of over 99.9%. The results confirm that the proposed methodology achieves a great deal of efficiency, accuracy, and responsiveness, which makes it fit the next-generation smart city security systems.
Autonomous Security Robots, Smart Cities, Edge Computing, Real-Time Surveillance, Robotics Control Framework.
Continuous Remote Cognitive Monitoring in Alzheimer’s Patients Through IoT Wearables
Alzheimer's disease (AD) is a progressive neurological condition marked by memory impairment, cognitive deterioration, and changes in behavior. Prompt identification and ongoing surveillance are essential for prompt interventions and individualized treatment. This research presents an IoT-enabled wearable solution for the remote cognitive assessment of Alzheimer’s patients. Five patients were observed over a five-week period using smartwatches, motion sensors, and wearable EEG equipment. Physiological and activity metrics indicated an average heart rate of 79 ± 2.3 bpm, daily step count of 4440 ± 420, and sleep efficiency of 86 ± 3%. Cognitive test performance shown that memory recall enhanced from 82% to 86%, attention span extended from 20 to 23 minutes, and problem-solving scores elevated from 75% to 79%. Real-time notifications were produced for atypical activity patterns, averaging 1.8 alerts per patient weekly. The method allowed caregivers to remotely monitor patients, identify early cognitive deterioration, and execute prompt therapies. The quantitative findings indicate that continuous IoT wearable monitoring delivers dependable physiological and cognitive evaluations, facilitating proactive, patient-centered treatment, minimizing hospital visits, and enhancing the overall quality of life for Alzheimer’s patients.
Alzheimer’s Disease, Cognitive Monitoring, Internet of Things (IoT), Wearable Devices, Remote Healthcare, Continuous Monitoring, Patient-Centered Care.
Enhancing Security and Privacy in Radio Frequency Identification Systems Using Symmetric Cryptosystem Algorithm
Privacy and security vulnerabilities in RFID systems remain a pressing concern as the technology sees wider adoption for automatic identification and real-time tracking. These vulnerabilities are not purely technical, addressing them involves interconnected economic, ethical, and social challenges alongside the technological ones. This work presents recent investigations into RFID privacy and security flaws, with a focus on developing solutions that are affordable, practical, reliable, scalable, adaptable across organizations, and durable, while still meeting RFID's core privacy and security requirements. A central obstacle in meeting these requirements is that RFID tags typically operate under severe resource constraints such as limited power, memory, and processing capacity — which restricts the cryptographic techniques that can be applied. In cryptography, information is encoded so that only an intended recipient can decipher it; for RFID, this study addresses the constraint problem by optimizing cryptographic processes specifically for resource-limited devices, aiming to make effective use of available computing power while minimizing any negative impact on device functionality. The proposed cryptographic approach was evaluated for its computational efficiency and its impact on RFID device operation under real-time conditions. Results show that the proposed system achieves an average processing time of 0.12 seconds, indicating that meaningful security gains can be achieved without significantly compromising device responsiveness. It is important to note that outcomes may vary depending on implementation specifics, the comprehensiveness of the security measures applied, and the system's ability to adapt to evolving security threats. This study synthesizes the sociological and technological context behind existing proposals for protecting individual privacy and RFID system integrity and is intended as a resource for both generalist readers and domain experts.
Radio Frequency Identification System, Authentication, Privacy, Security and Symmetric Cryptosystem Algorithm.
Blockchain-Secured Big Data Platform for Sustainable Soil Resource Optimization and Agricultural Safety
This study proposes a new version of a big data platform integrating blockchain technology for the management of sustainable soil resources and agricultural security. The solution offers a new way to collect data using IoT, decentralized big data storage, and blockchain technology to ensure safe, transparent, and reliable management of agricultural data. The real-time soil, meteorological and environmental data is collected, validated and processed, resulting in valuable information and actions for farmers and stakeholders. The results regarding quantitative data are that the improvement is deemed to be remarkable after the system adoption. The soil moisture level increased by 17%, while the nitrogen and phosphorus levels increased by 45% and 50%, respectively, which contributed to increased soil fertility. Water usage reduced by 29%, fertilizer usage reduced by 31%, energy usage reduced by 22% with improved utilization of the resources. Such cuts offer an economic saving and contribute to environmental sustainability. Moreover, crop production rose by 2.8 - 3.9 tons per acre, while the number of failed crops dropped from 18% to 9%, confirming improved agricultural security and productivity. By introducing blockchain, the system ensures integrity, transparency, and safe access of data, promoting trust within the system among its users. The recommended platform can be seen as a method that can help in better management of soil resources, sustainable agriculture and as a flexible platform for modern agriculture.
Blockchain, Big Data Analytics, Smart Agriculture, Soil Resource Optimization, Agricultural Safety, Internet of Things (IoT), Precision Farming.
An Adaptive Social Media Ecosystem Using IoT and Deep Learning for Personalized User Interaction and Well-Being Optimization
Social media companies can now personalize user experiences by adapting to users’ emotional and cognitive states through IoT devices and advanced machine learning algorithms. This study develops a wearable IoT-based adaptive social media system that employs deep learning to enhance user interactions and overall well-being. Smartwatches and fitness trackers are used to detect heart rate, physical activity, and sleep patterns in real time, while mobile sensors track user interactions and application usage. Advanced deep learning models, including Convolutional Neural Networks (CNNs) and Long Short-Term Memory (LSTM) networks, are applied to collect and analyze these data to identify emotional patterns. The system estimates users’ cognitive load and emotional states to dynamically adapt content delivery and interaction strategies. When stress is detected, the system curates content to reduce anxiety and adjusts notification frequency to prevent cognitive overload. Sentiment analysis is applied to user-generated content to further refine content recommendations. A continuous feedback loop enhances model accuracy and system personalization based on user responses. Through these mechanisms, the proposed system aims to improve user satisfaction and mental well-being by delivering personalized social media experiences. This study contributes to the advancement of emotion-aware digital environments and demonstrates how IoT and machine learning technologies can enhance online user interactions.
Social Media Ecosystem, Convolutional Neural Networks (CNNs), Long Short-Term Memory (LSTM) Networks, Personalized Content, Internet of Things (IoT).
Copyright (c) 2025 https://creativecommons.org/licenses/by/4.0
Enhancement of Railway Passenger Information Systems Using Temporal Fusion Transformers and IoT Data
The integration of Temporal Fusion Transformers (TFTs) with IoT-driven data streams presents an effective approach for improving Railway Passenger Information Systems (PIS). This study introduces an innovative framework that utilizes real-time data from IoT sensors, including GPS, environmental, and ticketing systems, to predict train schedules, delays, and other significant events. A predictive model based on TFTs is developed to process and analyze temporal data, thereby enhancing prediction accuracy. Quantitative results indicate that the proposed system achieves a 15% improvement in delay prediction accuracy compared with conventional machine learning models, along with a 20% reduction in root mean square error (RMSE). Moreover, passenger wait times decrease by an average of 10% due to improved prediction accuracy. The platform supports real-time updates, enabling more dynamic and responsive passenger notifications. This study highlights the significant potential of integrating IoT data with TFTs to enhance operational efficiency, reduce delays, and improve the overall passenger experience in modern railway systems. The findings suggest that incorporating TFTs with IoT data can substantially transform railway Passenger Information Systems, leading to improved service reliability and passenger satisfaction.
Passenger Information System, Temporal Fusion Transformers, Predictive Analytics, Passenger Satisfaction, Transportation Systems.
On-Chain Transaction Anomaly Detection Using BlockSecAnalyzer for Blockchain Forensics
Blockchain ecosystems require continuous surveillance to detect anomalous on-chain transactions and mitigate illicit activities such as fraud, money laundering, and unauthorized smart contract interactions. This study presents BlockSecAnalyzer, an analytical framework designed for on-chain transaction anomaly detection to support blockchain forensics. The proposed approach analyzes transaction patterns to identify deviations from normal behavior, including abnormal transaction volumes, rapid fund movements, and atypical smart contract interactions. BlockSecAnalyzer integrates machine learning techniques with statistical modeling to generate transaction-level alerts and actionable forensic insights. Real-time monitoring enables early identification of suspicious activities, while continuous analysis supports proactive threat mitigation strategies. Experimental evaluation demonstrates the effectiveness of the proposed framework in enhancing the security and resilience of blockchain networks against fraudulent and malicious operations.
BlockSecAnalyzer, On-Chain Transaction Detection, Blockchain Forensics, Anomaly Detection, Threat Monitoring.
Advances in Weather Forecasting Using Machine Learning Techniques
Weather forecasting plays a crucial role in numerous sectors, ranging from agriculture and transportation to disaster management. Traditional meteorological models, while valuable, often face challenges in accurately predicting complex and dynamic weather patterns. This research paper explores the integration of machine learning (ML) techniques into weather forecasting to enhance predictive accuracy and reliability. The study begins by providing an overview of the limitations of conventional numerical weather prediction models and emphasizes the need for innovative approaches. It introduces a weather forecasting system based on machine learning, utilizing Decision Tree, Support Vector Machine, Random Forest, K-Nearest Neighbors, Gradient Boosting, Logistic Regression, and Naïve Bayes algorithms. The paper discusses the development and training of ML models using large datasets to capture intricate relationships among atmospheric variables. Among the evaluated models, Gradient Boosting achieves the highest predictive accuracy by effectively capturing nonlinear relationships and minimizing prediction errors. Performance evaluation demonstrates that integrating multiple machine learning techniques provides a stable, reliable, and scalable solution for short- to medium-term weather forecasting.
Weather Forecasting, disaster management, Machine Learning, Atmospheric variables, Meteorological Data
An Enhanced Feed-Forward Neural Network for Gastrointestinal Stromal Tumor Detection in CT Images
Gastrointestinal stromal tumors (GISTs) are rare soft-tissue neoplasms that can be life-threatening if not detected at an early stage. Accurate and automated analysis of computed tomography (CT) images plays a crucial role in supporting timely diagnosis and treatment planning. This study proposes an enhanced feed-forward artificial neural network (IFFANN)–based classification framework for the automatic detection of GISTs from CT images. A curated dataset comprising GIST-positive and non-GIST CT scans was assembled from multiple sources and annotated by expert radiologists. The proposed network integrates optimized feature learning through convolutional layers followed by a feed-forward classification stage to improve discrimination between pathological and normal cases. The model was trained and evaluated using cross-validation to ensure robustness and reduce overfitting. Experimental results demonstrate that the proposed IFFANN achieves high accuracy, sensitivity, specificity, and F1-score in distinguishing GIST cases from non-GIST samples. The findings indicate that the proposed approach can effectively support computer-aided diagnosis systems and assist radiologists in improving diagnostic accuracy and efficiency. Further validation of larger and more diverse datasets will strengthen the generalizability of the proposed method.
Gastrointestinal Stromal Tumor, CT Images, Deep Learning, Convolutional Neural Network, Medical Imaging.
Impact of Artificial Intelligence on Workforce Engagement and Retention in Digital Human Resource Management
The rapid digital transformation in Human Resource Management (HRM) has accelerated the adoption of Artificial Intelligence (AI) to optimize workforce engagement, enhance retention strategies, and drive talent management innovations. This study examines the impact of AI-driven HR systems on key performance indicators such as employee satisfaction, voluntary turnover, and HR operational efficiency. Empirical data collected from over 15 multinational organizations, encompassing 12,000 employee records across diverse industries, indicate that AI-powered HR solutions improve workforce engagement by 42%, reduce voluntary turnover rates by 27%, and increase HR process efficiency by 35%. AI-driven sentiment analysis enhances dissatisfaction detection by 33%, enabling proactive interventions. Additionally, AI-based predictive hiring models demonstrate 85% accuracy in forecasting employee attrition, compared to 72% for traditional HR models. The study employs a hybrid AI framework integrating logistic regression and Long Short-Term Memory (LSTM) networks to analyze employee engagement trends, job satisfaction fluctuations, and retention patterns over time. By leveraging AI-powered chatbots, predictive analytics, and intelligent automation tools, HR processes are optimized, reducing administrative workloads by 38% and improving response times from 20 hours to 1.5 hours. Comparative analysis with conventional HR approaches highlights AI’s superior ability to personalize career development plans and detect high-risk attrition cases, ensuring timely interventions. Despite these advantages, AI adoption in HRM presents challenges such as bias in decision-making, lack of transparency in AI-driven recommendations, and employee concerns regarding data privacy. This study addresses these challenges by proposing a scalable, explainable AI (XAI) framework that ensures fairness, transparency, and ethical compliance in AI-driven HRM systems. The research findings provide valuable insights for organizations aiming to integrate AI into HR strategies while fostering trust, employee engagement, and long-term workforce sustainability. By bridging the gap between academic research and real-world HR applications, this study offers actionable recommendations for optimizing AI-driven talent management in the modern digital era.
AI in HRM, Workforce Engagement, Employee Retention, Predictive Analytics, HR Automation, Digital Transformation.
Copyright (c) 2025 M.D Rehaman Pasha, Syed Ahmed Salman, Amiya Bhaumik https://creativecommons.org/licenses/by/4.0
Machine Learning Based Efficient Framework for Industrial Motor Condition Monitoring
Industrial motors play a crucial role in modern manufacturing and energy systems, where unexpected failures can result in significant downtime and financial losses. Therefore, effective condition monitoring is essential to ensure operational reliability and cost efficiency. This study presents a machine learning–based approach for industrial motor condition monitoring, capable of accurately detecting faults and predicting potential failures at an early stage. The proposed method leverages features extracted from vibration, current, and temperature data, followed by optimized classification using supervised machine learning algorithms. Experimental results demonstrate that the approach achieves high accuracy while reducing computational costs, outperforming conventional techniques. Furthermore, the system is scalable and adaptable to various motor types and operating conditions, making it suitable for real-time monitoring applications. Overall, this work contributes to improving industrial motor reliability, reducing maintenance costs, and advancing predictive maintenance strategies.
Industrial motor, condition monitoring, machine learning, fault detection, predictive maintenance, vibration analysis, real-time monitoring
Copyright (c) 2024 https://creativecommons.org/licenses/by/4.0
Stability Oriented Clustering in Vehicular Ad Hoc Networks Through Machine Learning
Vehicular Ad Hoc Networks (VANETs) are vital for enabling intelligent transportation systems through real-time vehicle-to-vehicle communication. However, maintaining stable network topologies remains a major challenge due to high mobility and frequent topology changes. This study introduces a machine learning–based clustering method designed to establish robust VANET topologies. The proposed approach employs supervised learning models to dynamically identify potential cluster heads based on factors such as mobility patterns, relative velocity, node density, and connection duration. By optimizing cluster formation, the method minimizes frequent re-clustering, strengthens connectivity, and reduces communication overhead. Simulation results demonstrate that the machine learning–based clustering approach significantly improves cluster stability, packet delivery ratio, and communication reliability compared to conventional clustering techniques. Overall, this research highlights the effectiveness of intelligent, data-driven strategies in managing VANET dynamics, thereby enabling reliable vehicular communication and enhanced road safety.
Vehicular Ad Hoc Networks (VANETs), Deep Learning, Cluster Head Selection, Mobility Prediction, Intelligent Transportation Systems (ITS), Reliable Topologies
Efficient Sequential Pattern Discovery with PrefixSpan for Large-Scale Data Analytics
The exponential growth of big data has intensified the demand for scalable association rule mining techniques capable of uncovering meaningful sequential patterns. This study evaluates the PrefixSpan algorithm, which recursively constructs frequent patterns from prefixes, thereby reducing the search space and enhancing efficiency in large-scale applications. Experimental analysis demonstrates its capability to discover frequent itemset and sequential dependencies across synthetic datasets, with visualization matrices highlighting strong associations and recurring sequences. The algorithm achieves significant efficiency gains by focusing on relevant postfixes, enabling practical use in domains such as market basket analysis, bioinformatics, and social network analysis. Results further indicate that PrefixSpan effectively balances computational performance and scalability, supporting incremental updates for dynamic environments while minimizing memory overhead. Comparative analysis with alternative approaches shows PrefixSpan’s superior adaptability and reduced computational demands, though challenges remain in handling extremely large datasets and non-contiguous pattern discovery. Quantitative evaluation demonstrates that PrefixSpan identifies frequent itemset with high occurrence counts, extracts sequential dependencies across multiple sequences, and achieves measurable efficiency improvements in scalability and reduced search space compared to Apriori and SPADE, while facing performance trade-offs in extremely large-scale data processing.
PrefixSpan Algorithm, Association Rule Mining, Big Data, Sequential Patterns, Data Mining Techniques
Analyzing Diabetes Risk Factors Using Support Vector Machine Algorithm
Diabetes mellitus is a persistent metabolic condition that presents considerable health issues globally. Active prediction and risk assessment are essential for mitigating problems and enhancing patient outcomes. This research examines diabetes risk variables with the Support Vector Machine (SVM) algorithm, an effective supervised machine learning method appropriate for classification applications. Diverse clinical and lifestyle characteristics, such as age, body mass index (BMI), glucose levels, blood pressure, and familial history, are used as input features. The SVM model is developed and evaluated to categorize people as diabetic or non-diabetic with high precision. Performance assessment is conducted with criteria accuracy, recall, and F1-score. The results indicate that SVM proficiently detects critical risk variables and delivers dependable predictions, underscoring its potential as a decision-support instrument in healthcare for the early identification of diabetes.
Diabetes Mellitus, Support Vector Machine (SVM), Machine Learning, Classification, Prediction Model, Healthcare Analytics.
Artificial Neural Networks in Smart Stadiums: Optimizing Crowd Flow, Safety, and Fan Engagement
Modern sports entertainment increasingly relies on cutting-edge technologies to enhance audience engagement and ensure stadium safety. This work discusses the integration of smart stadium systems powered by Artificial Neural Networks (ANNs) to improve spectator experience, security, and crowd management. IoT sensors deployed across stadium zones capture real-time data on crowd density, environmental conditions, and spectator behavior. This information is processed through ANN-based predictive modeling and decision-making algorithms to generate actionable insights. A key focus of this approach is dynamic fan engagement. By analyzing preferences, historical interactions, and real-time context, ANNs enable personalized services for fans through their devices, including customized notifications, offers, and immersive augmented reality experiences. At the same time, ANNs enhance safety by continuously monitoring IoT sensor data streams to detect anomalies and risks. For example, they can identify overcrowded areas or unusual temperature fluctuations and automatically alert security and emergency systems to safeguard visitors and staff. Furthermore, ANNs provide valuable support for crowd management and resource optimization. By leveraging attendance statistics, crowd dynamics, weather forecasts, and transportation updates, stadiums can make informed, real-time decisions. This enables adaptive control of seating arrangements, concession stand operations, and entry/exit flows, ultimately improving both efficiency and the overall fan experience.
Smart Stadium, Fan Engagement, Artificial Neural Networks (ANNs), Internet of Things (IoT), Crowd Management.
Patient Health Monitoring in Dementia Using K-Nearest Neighbor Model
Dementia is a progressive neurological condition that severely affects memory, behavior, and everyday functioning, necessitating ongoing surveillance of patients' health state. Conventional healthcare methods often lack timely treatments owing to restricted real-time data analysis. This paper presents a patient health monitoring system using the K-Nearest Neighbour (K-NN) classification model to identify and assess essential health metrics in dementia patients. The system amalgamates sensor-derived physiological data, including heart rate, blood pressure, and activity levels, to categorize patient health situations into normal, warning, or critical categories. The k-NN method is used for its simplicity, versatility, and efficacy in managing non-linear medical data. Experimental findings indicate that k-NN provides dependable accuracy in forecasting health hazards, hence assisting carers and healthcare professionals in their decision-making processes. This methodology enhances patient safety, individualized treatment, and proactive disease management in dementia monitoring.
Dementia, Patient Health Monitoring, k-Nearest Neighbor, Machine Learning, Health Risk Prediction, Personalized Care, Physiological Data
Parallel and Adaptive Graph Growth Methods for Shortest Path Computation in Big Data
Shortest path computation is a critical task in domains such as transportation, communication, and social networks, but conventional algorithms often struggle with the complexity and dynamism of big data environments. This study explores the use of graph growth algorithms to enhance shortest route calculations by incrementally expanding and adapting graph structures. The proposed framework emphasizes incremental construction, dynamic updating, and parallel processing to minimize computational overhead while maintaining adaptability to evolving datasets. Real-world applicability is demonstrated through models including random growth, preferential attachment, and community-based clustering, which collectively enhance scalability, fault tolerance, and system reliability. By integrating graph growth methods into shortest path analysis, the approach offers improved efficiency and responsiveness for large-scale applications such as logistics, healthcare, and finance. Quantitative evaluation confirms that graph growth techniques reduce computational overhead, maintain scalability across networks of increasing size, and achieve measurable improvements in pathfinding accuracy and processing efficiency, while still requiring significant resources in highly dynamic contexts.
Graph Growth Algorithms, Shortest Path Calculations, Big Data, Computational Efficiency, Scalability
Distributed Graph Processing with Depth-First Search in Apache Hadoop
The rapid expansion of large-scale datasets has highlighted the importance of scalable graph processing methods within distributed computing environments. Apache Hadoop, through its integration of the Hadoop Distributed File System (HDFS) and MapReduce, provides a foundation for handling such challenges. This study explores the incorporation of Depth-First Search (DFS) into Hadoop for efficient big data graph processing. The work outlines the design of Hadoop-compatible graph structures and a MapReduce-based DFS framework optimized for large-scale traversal. Advanced implementations, including iterative, randomized, and parallel DFS, are evaluated for their impact on execution efficiency, resource allocation, and scalability. The proposed integration enables applications in web graph analysis, computational biology, and social network exploration, while also providing a generalized foundation for adapting other graph algorithms within Hadoop. Quantitative evaluations demonstrate DFS’s ability to process large adjacency matrices, efficiently traverse graphs of up to 56–101 vertices, and highlight performance trade-offs in terms of execution time, memory handling, and scalability compared with sequential DFS, confirming the benefits of distributed parallelization in Hadoop-based environments.
Apache Hadoop Depth-First Search, Big Data, Graph Processing, Scalability.
Advanced Vulnerability Detection and Exploitation in Web Applications Using Burp Suite
Web applications remain exposed to a wide range of vulnerabilities, creating persistent risks of exploitation. This study applies Burp Suite’s advanced modules such as Scanner, Intruder, Repeater, Spider, and Collaborator to evaluate their effectiveness in identifying and exploiting web flaws. Experimental assessments reveal that Burp Suite consistently detects between 75 and 95 vulnerabilities per scan, with 30–45 confirmed as exploitable issues. The platform demonstrates accuracy by maintaining a low false positive rate of 4–7 cases while completing scans within 42–55 minutes, ensuring practical usability for real-world assessments. Notably, the Scanner module achieved the highest detection capability, identifying up to 140 distinct vulnerabilities, while the Repeater and Intruder modules proved critical in refining and exploiting complex weaknesses. Severity analysis shows that 60–75% of findings were classified as high risk, underscoring the importance of timely remediation. These results highlight Burp Suite’s dual strength in comprehensive detection and targeted exploitation, confirming its role as a powerful tool for proactive web application security.
Web Vulnerabilities, Burp Suite, Security Testing, Exploitation Techniques, Vulnerability Analysis
Assessing Symantec Endpoint Protection for Multi-Device Cybersecurity in Modern Networks
Endpoint devices such as laptops, desktops, and mobile systems remain primary entry points for cyberattacks, necessitating robust, multi-layered security solutions. This study evaluates Symantec Endpoint Protection (SEP) as a comprehensive defense platform, integrating antivirus, intrusion prevention, firewall management, and real-time monitoring into a unified solution. SEP’s performance was assessed through malware detection trends over sev eral months and its success rates across diverse threat categories. Results demonstrate consistent protection effectiveness, with virus detection rates reaching up to 98% on laptops and robust defense against phishing and network-based intrusions across all device types. The analysis also reveals diminishing performance against advanced and emerging threats, highlighting the need for continual innovation in endpoint security. Beyond quantitative performance, SEP’s adaptability, integration with machine learning, and centralized administration tools position it as a practical choice for both enterprise and personal environments. The findings confirm that SEP delivers significant reductions in attack surfaces while maintaining minimal system performance impact, thereby validating its role as a critical component of contemporary cybersecurity infrastructures.
Endpoint Security, Symantec Protection, Malware Defense, Cyber Threats, Device Protection
Copyright (c) 2024 J Lenin, T Kumanan https://creativecommons.org/licenses/by/4.0
Developing a Cloud-Based Restaurant Recommendation System Using User Experience
By giving individualized eating alternatives based on user preferences and contextual data, a cloud-based restaurant recommendation system employing Term Frequency - Inverse Document Frequency (TF-IDF) promises to improve customer happiness. The TF-IDF algorithm is used to analyze user evaluations, restaurant attributes, and eating patterns to find crucial decision-making elements. This method effectively analyzes massive datasets on a scalable cloud platform to provide real-time, reliable suggestions. Designing of an intelligent system that adjusts to user preferences, enhances decision-making, and builds loyalty via a customized eating experience is studied. Combining natural language processing with TF-IDF improves restaurant classification and ranking by extracting useful insights from textual reviews. This method provides scalability, enabling the system to serve varied populations and changing culinary trends, enhancing eating ecosystem engagement and pleasure.
Cloud-Based Recommendation System, Term Frequency - Inverse Document Frequency Algorithm, Personalized Dining Experience, User Engagement, Natural Language Processing
Copyright (c) 2024 Palaniraj Rajidurai Parvathy https://creativecommons.org/licenses/by/4.0
Hootsuite for Cloud-Based Monitoring of Consumer Social Media in Business
Social media influences customer behavior, making real-time monitoring crucial for firms trying to boost engagement and brand visibility. Hootsuite, a cloud-based social media management software, tracks customer interactions, analyses trends, and optimize marketing campaigns. Hootsuite's cloud capabilities will let organizations measure customer sentiment and interaction across numerous platforms for effective social media monitoring. Use automated analytics, sentiment monitoring, and scheduling to simplify social media operations and improve reaction methods. Real-time customer behavior, brand perception, and market trends are used to improve data-driven decisionmaking. Businesses can easily combine social media data, automate content posting, and monitor KPIs using Hootsuite's cloud-based analytics integration. Actionable insights from AI-powered sentiment analysis and trend identification enable adaptive marketing and consumer interaction. Scalable cloud storage allows large-scale data collecting and processing with minimum infrastructure. Automating procedure reduces human labor and boosts marketing productivity. Deeper AI-driven analytics, predictive consumer behavior modelling, and seamless interaction with upcoming social platforms may boost cloud-based social media monitoring in business intelligence and digital marketing.
Social Media Monitoring, Consumer Insights, Cloud-Based Analytics, Sentiment Analysis, Business Optimization
Copyright (c) 2024 Chitra Sabapathy Ranganathan https://creativecommons.org/licenses/by/4.0
Quantum Artificial Intelligence Prediction Using Generative Adversarial Networks
There has been a significant change in QAI with the introduction of Quantum Generative Adversarial Networks (QGAN), Quantum Variational GAN (QVGAN), Quantum-enhanced GAN (QEGAN), and Quantum Wasserstein GAN (QWGAN). QGAN brings the power of GANs into the quantum world, paving the way for the creation of quantum data distributions that can then be used to facilitate both quantum data augmentation and synthesis. QVGAN optimizes the parameters of quantum circuits by drawing on variational concepts to improve quantum generative modeling. QEGAN utilizes quantum hardware to speed up adversarial training, which in turn speeds up the production of quantum data. Aligning the distribution of quantum data is QWGAN's main goal, since this helps quantum datasets converge. Collectively, these quantum GAN variations open new possibilities for QAI by facilitating the production, manipulation, and alignment of quantum data distributions. QGANs and variations can break through classical barriers and push forward quantum data-driven applications in fields as diverse as quantum chemistry, quantum machine learning, and quantum cryptography by harnessing the power of quantum computing. QGAN variants' quantum benefits highlight their importance in pushing the limits of quantum information processing.
Quantum GAN, Quantum Variational GAN, Quantum-enhanced GAN, Quantum Wasserstein GAN, Foresight, Quantum Artificial Intelligence, Generative Adversarial Networks, Quantum Data Generation, Quantum Data Manipulation, Quantum Information Processing
Copyright (c) 2024 T.R Ganeshbabu, R Praveena https://creativecommons.org/licenses/by/4.0
Predictive Hive Health Management using IoT and Linear Regression for Beekeeping and Pollinator Conservation
Bee numbers have declined worldwide, raising worries about pollinator conservation and its effects on agriculture and ecosystems. The research develops a predictive hive health monitoring system using Internet of Things (IoT) technologies and linear regression to promote sustainable beekeeping and pollinator conservation. The system uses beehive IoT sensors to collect real-time data on environmental parameters, hive conditions, and bee behavior. The method uses linear regression to forecast hive conditions and possible difficulties, including disease outbreaks, insect infestations, and environmental stresses. Beekeepers may use constant monitoring and analysis to detect problems and take action to keep their colonies healthy and productive. The system's predictive capabilities enable preventative interventions to reduce colony collapse disorder and other threats, preserving pollinator populations. The proposed system gives beekeeper real-time input and decision assistance, enabling proactive hive management. IoT technology also improves system scalability and accessibility, allowing remote beehive monitoring and administration across several sites.
Hive Management, Beekeepers, Linear Regression, Pollinator Conversation, Internet of Things
Copyright (c) 2024 R Monisha, S Prabhu, Harish, RM Sivagama Sundari, Indumathi N https://creativecommons.org/licenses/by/4.0
Identifying and Classifying Toxic Comments in Cloud Using Amazon Web Services Comprehend Technology
Growing concerns about toxic remarks on digital platforms need effective detection and categorization systems to make the internet safer. Amazon Web Services (AWS) Comprehend automates dangerous material discovery and categorization using cloud-based Natural Language Processing (NLP). The goal is to increase automatic content moderation and detection accuracy using AWS Comprehends deep learning. The goal is to use sentiment analysis and entity identification to categorize harmful comments into specified categories for real-time content filtering and reduce human moderation. To identify toxic language, minimize false positives, and enable proactive action, a scalable and adaptable solution is needed. AWS Comprehend classifies abusive or improper language by analyzing user-generated content context and sentiment using powerful NLP models. Cloud-based deployment supports high-volume text analysis with low latency and easy interaction with varied digital ecosystems. Using AWS's scalable infrastructure, poison detection may be continually optimized to respond to changing language trends and damaging talk. This strategy reduces toxic remarks, enforces content management, and promotes ethical communication on online platforms while adhering to platform standards, improving digital safety.
Toxic Comment Detection, Amazon Web Services Comprehend, Natural Language Processing, Content Moderation, Cloud Technology
Copyright (c) 2024 R Meenambigai, N Saranya Devi https://creativecommons.org/licenses/by/4.0
With the proliferation of e-commerce platforms and social media sites, businesses across the globe now produce massive amounts of data daily. Safe data transmission via the Internet relies on several factors, but information security is the most fundamental of them. As society continues its transition into the digital information era, network security challenges are also rising to the forefront. The growing number of people using the Internet makes it a prime target for malicious hackers. Computer and network security, i.e., the crucial challenges, need its implementation. These harmful nodes are a systemic problem. It may make use of other nodes' resources while keeping its own secure. This article presents a high-level introduction to Network Security and the many cryptographic approaches that may be used to strengthen Network Security.
Network Security, Cryptography, Cyber-attacks, Information security, Internet
Copyright (c) 2023 J Umamageswaran, B Praveen Kumar, R Dhanalakshmi, A V Kalpana https://creativecommons.org/licenses/by/4.0
Big Data Analytics with Security Implementation Using Data Mining Algorithms
Big Data Analytics (BDA) is going to be discussed using data mining algorithms in this paper. Develop security in BDA fields for data security to achieve confidential storage. Big Data refers to data sets that are very vast, very complicated, and very dynamic and that have many independent sources. Big data is quickly implemented in all research and engineering fields, including physical, biological, and biomedical science, thanks to rapid developments in networking, data storage, and data gathering capacities. From a data mining point of view, this study gives a theorem characterizing the aspects of the Big Data revolution and suggests a Big Data processing model. This data-driven paradigm considers security and privacy concerns, data mining and analysis, and the modeling of user interests. Here, takes a look at a few of the knottier aspects of big data and the data-driven approach, and calculate the precisions, recalls, and measures, which are 90% of different data mining algorithms.
Data mining, Data abstraction, Data management, Big Data, Data mining algorithms.
Copyright (c) 2023 R Kiruthika, P Devi, S Vijayasarathi , R M Asha https://creativecommons.org/licenses/by/4.0
Today, when discussing the societal significance of robotic applications, the first example that usually comes up is the deployment of assistive technology in healthcare settings. Despite concerns regarding their technical preparedness and the absence of realistic use scenarios in regular nursing practice, so-called caring robots are touted as solutions to the nursing problem. Investigate the relationship between social robots and medical assistance. Three areas of activity—innovations policy, care organizations, and robotics engineering—are examined to illustrate how the two are mutually accessible. Begin by examining the "logics" of care robotics as they are discussed in the context of European innovation policy, then reveal the tensions that have been building up between care robotics and healthcare organizations over time and finally demonstrate how care scenarios are being employed in robotics research and development. Based on this analysis, it provides a three-pronged criticism of healthcare robots that highlights the political, historical, and social context in which robotics care for the elderly operates.
Robotics, automation, artificial intelligence, health care
Copyright (c) 2023 R Monisha, M Jaiganesh, L Sujatha, I Shanmugapriya https://creativecommons.org/licenses/by/4.0
One of the biggest issues with the Internet today is phishing emails, which can be both frustrating and costly for businesses and customers. There are several methods for identifying and blocking phishing emails, but none of them are entirely effective. This article provides a comprehensive overview of the present level of knowledge on these types of assaults. This study is the first large-scale examination of how to prevent phishing emails. Provide a survey of the current methods used to identify phishing emails at various points in the attack lifecycle, with an emphasis on machine learning approaches. These filtering strategies are analyzed and compared with one another. This sheds light on the nature of the issue, the range of possible solutions, and the likely future of research.
Phishing email, Classifiers, Authentication, Network-level protection, Filtering
Copyright (c) 2023 R Monisha, Veto Dey, N Jayapriya, I Shanmugapriya https://creativecommons.org/licenses/by/4.0