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.
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
Copyright (c) 2025 https://creativecommons.org/licenses/by/4.0
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
Privacy of Big Data on the Internet of Things
There has been a proliferation in Internet of Things (IoT) solutions, goods, and services in the markets during the last several years. All these approaches will collect massive volumes of user and environmental data. The goal of the Internet of Things is to increase knowledge and provide services to end customers. Data from "things" on the Internet of Things (IoT) may be stored locally on devices or uploaded to a remote server in the cloud. The true values of data collections occur during large-scale data processing and aggregations when insights are revealed. However, user privacy concerns may arise from such a process. Some of the major threats to privacy in IoT are discussed in this article, along with some potential solutions and areas for further studies and developments. In addition, present a few of the active research initiatives that tackle IoT privacy concerns.
Big data, privacy, big data storage, big data collection, Internet of Things
Copyright (c) 2023 S Kalpana Devi, R Thenmozhi, D Sathish Kumar https://creativecommons.org/licenses/by/4.0
Investigation of Cyber Security Attacks and Security
Information security and cybersecurity are often used interchangeably; however, the latter recognizes the importance of humans as a target and an extra factor in the security process. However, including the ethical aspects of society in a discussion on cyber security has far-reaching implications. Several models and frameworks have been created to help with cyber security. The ideas of cybersecurity, including its infrastructure, workforce, and data pertaining to computerized personal information protection, are also introduced. In this article, look back at the history of efforts to counteract these dangers and discuss the strengths and weaknesses of these approaches. The paper also includes suggestions for more study.
Cyber security, frameworks, workforces, threats, techniques
Copyright (c) 2023 A KathijaNasreen, N Senthamilarasi, R Monisha https://creativecommons.org/licenses/by/4.0
Medical Information System to Improve Quick Access Using Anti-Collision Algorithm
In addition to allowing for real-time monitoring of assets and personnel, Radio Frequency Identification (RFID) technology also facilitates quick, accurate access to patient records for medical providers. Despite optimistic predictions, RFID implementation has lagged significantly. Patient outcomes, operational efficiency, and treatment quality may all be enhanced via the use of RFID technologies in healthcare systems using anti-collision algorithms. The healthcare business is rapidly adopting it as a useful solution since it tackles crucial concerns, including patient safety, asset management, and regulatory compliance. The objectives of improving patient care, increasing operational efficiency, and addressing significant issues within the healthcare system—are congruent with the use of RFID technology in healthcare. The goal is to improve healthcare delivery in terms of efficiency, security, and quality by using RFID capabilities. This research methodology-based literature review examines RFID's potential healthcare uses. The goal is to determine the present possibilities, possible advantages, and hurdles to adoption. According to research, RFID is effective and helpful for asset monitoring and patient identification, as reported by most healthcare practitioners. The high price tag, technical constraints, and patient privacy concerns all work against widespread RFID use in healthcare. Better-built RFID systems are required to boost the adoption and appropriate usage of RFID in healthcare, even though RFID gives healthcare practitioners benefits to enhance clinical practice. The result shows that the Passive tag contains 69% and the active tag contains 31%.
Health care, Quick access, Antennas, Medical information systems, Radio Frequency Identification and Anti-collision algorithm
Copyright (c) 2023 P V V Raghava Sharma, D Sai Prasanna, Grandhi Sirisha https://creativecommons.org/licenses/by/4.0
Sentiment Analysis Using Deep Learning Techniques
Massive troves of information, including users' perspectives, feelings, ideas, and disputes regarding various social events, goods, brands, and politics, are produced by various online platforms, such as social networks, forums, review sites, and blogs. Readers, product sellers, and legislators all pay close attention to the opinions offered by internet users. Sentiment analysis has garnered a lot of interest because of its ability to evaluate and organize the unstructured type of data seen in social media. The term "sentiment analysis" is a method of text organization used to categorize the conveyed mindset or emotions as either "negative," "positive," "neutral," "positive," "negative," "thumbs up," "thumbs down," etc. The difficulty with sentiment analysis is that there is not enough labeled data in the NLP sector. Since deep learning models are useful because of their automated learning potential, this problem has been solved by combining sentiment analysis with deep learning. The current research on using deep learning models like deep neural networks to address sentiment analyzing challenges such as sentiment categorization, cross-lingual issues, textual/visuals analysis, product reviews analysis, etc., are summarized.
Sentiment analysis, Deep Learning, Social Networks, and NLP
Copyright (c) 2023 B Praveen Kumar, J Umamageswaran, A. V. Kalpana, R Dhanalakshmi https://creativecommons.org/licenses/by/4.0
Network Intrusion Detection for IoT Security
The proliferation of IoT devices can be seen in every region of the world. The Dyn cyber-attack of 2016 revealed several vulnerabilities in modern intelligent networks. The safety of IoT devices is now paramount. The security of IoT is compromised when infected Internet-connected Things are deployed as botnets, but the whole Internet ecosystem is also at risk because of the potential for exploitation of these Things (smart gadgets). Mirai virus infiltrated security camera and brought the Internets to a grinding’s halts with DDoS assaults. Both the complexities and varieties of security attacks route have increased in recent years. Therefore, it is vital to study approaches in the context of the Internets of Things to discover and avoid or detect fresh assaults. This study analyzes the state of the art in IoT DNN (Deep Neural Networks) network security and provides a classification of the risks and issues faced by these networks. Since NIDSs are our major focus, this study examines the available NIDS implementation tools, datasets, and opensource and free network sniffer software.
Copyright (c) 2023 D Lekha, M Ahsan Shariff, R Monisha, H Anwar Basha https://creativecommons.org/licenses/by/4.0
Power Quality Optimization in Microgrids Using Genetic Algorithm and Reinforcement Learning
In the micro grid (MG) system, the power quality enhancement is established using a new control algorithm in DSTATCOM device as presented in this paper. A micro grid is one of the best ways to solve the before mentioned problems. A MG is specified as a distributor cluster (DGs) and loads that provide heat and power in the region by a central controller. A new technique will mitigate issues like reactive power, harmonics, voltage sag, voltage swell. To resolve the above problems, calculation-based intelligence methods for intuitive energy quality reduction and transient solution problems have been found to be the most efficient. In the proposed system, the unbalanced voltage can be compensated through the voltage source inverter by using the control method of genetic algorithm-based reinforcement learning. The power quality issue
Micro grid, DSTATCOM, power quality improvement, controller, power quality issues
Copyright (c) 2023 J Shalini Priya, K Rajesh, Sumathi Franklin, K Gunasekaran https://creativecommons.org/licenses/by/4.0
Virtual Security Zones for Students Tracking in Schools to Avoid Child Abuse Using IoT
Children's health in the school system has now become a crucial problem for students at the university campus. The school / college and parents would like to know whether the candidate is in the classroom or not. The suggested device that tracks and updates it to the authority as he or she crosses a specified boundary line of a pre-defined area to control the location of the attached individual. It comes from simulated areas of RFID. Wireless Sensor Network (WSN) & Radio Frequency Identification are two essential communicational advancements with a range of uses that have infinite promise for the future. However, there have also been parallel developments in RFID and sensor networks. The scientific group is not paying attention to the convergence of RFIDs and wireless sensor networks. The first subject in this article is RFID, ZigBee and GSM Communication. Several existing systems & systems suggested evaluated with various application styles as regards technologies & deployment of RFID & GSM integration. To order to create a user interface framework, the proposed design outlined to detail. This device lets school and university administrators deter students from going over an invisible fence alerting school officials via SMS or via Smartphone IoT apps.
RFID, Wireless Sensor, Transmitter, Receiver, Networks.
Copyright (c) 2022 J Shirisha, B Prasad, D.P Sangeetha, R Sabitha https://creativecommons.org/licenses/by/4.0
Machine Learning based Detection Technique to Predict the Survival of Patients with Chronic Kidney Diseases
There are currently many people all around the world who are suffering from chronic kidney infections. Today, everyone is attempting to be health-conscious, even though, owing to overwork and a hectic schedule, one only pays attention to one's health when symptoms appear. A few factors, for example, dietary habits, temperature, and expectations for daily luxuries, cause large numbers of people to be afflicted unexpectedly and without knowledge of their condition. Finding persistent kidney disease is often intrusive, costly, time-consuming, and dangerous. The main reasons why many people die without receiving care are especially in many developing countries since resources are few. As a result, early diagnosis and recognition of illness remains important, particularly in non-industrialized countries where illnesses are typically studied in late stages. However, if it does not show any symptoms at all, or if it does not show any disease-specific symptoms, it is very difficult to find &predict the disease type, detect and prevent such a disease, and this could lead to permanent health damage as well as the formation of new diseases, but machine learning can be a hope, as it is the best way for prediction and disease analysis. We will utilize data from CKD patients with 14 variables, as well as several machine learning approaches such as Decision Tree, SVM, and CNN model. To create an efficient machine learning model with the highest accuracy (by comparing several machine learning models) in predicting whether or not a person has CKD and, if so, how severe it is.
Chronic Kidney Disease, machine learning, deep learning, sequential model, Neural network
Copyright (c) 2022 J Sofia Bobby, C. L Annapoorani, A Jerusha Renae, E. T Suruthi, K Shineka https://creativecommons.org/licenses/by/4.0
Machine Learning and Embedded Sensor-Based Flood Monitoring System
Asphyxiation and flooding will have a profound adverse effect on humanity and its framework. Choking may occur as the patient moves to a deeper area or may be caused by a patient's underlying medical condition. Floods are catastrophic events typically caused by waterway flow, weather patterns, or unnatural climatic effects resulting from extreme weather conditions. From now on, IoT, driven by sensory innovation, will help effectively mitigate this impact on humanity. This is a test guide for everyone to avoid the risk of suffocation and can help people avoid disasters like floods. This study offers IoT devices equipped with sensors and looking at systems to judge the flood level, the customer situation, and water level, even while the consumer is in the water. Then, using the portable software to become aware and inform the consumer. AI calculations were performed to determine the quantity order.
Safety, Flood, Machine learning, Embedded system, Early warning system
Copyright (c) 2022 M Rajkumar, S Omkumar, N Vikram, Viyyapu Lokeshwari Vinya https://creativecommons.org/licenses/by/4.0
A Statistical Feature Data Mining Framework for Students' Progress over Gender Variations
Gender differences in academic progression are examined in this article using two novel indicators. Two indicators are used to show how long students have studied above the minimal amount of time needed to graduate. We examine these characteristics using Kaplan–Meier techniques, Expedited Fault Timeframe, Regression trees, Multiple Regression analysis tree branches, and a redesigned Gender Parity Index. It is possible to analyse data that has been censored as well as add other factors into the study using this unique mix of statistical methodologies. Gender disparities in university education may be studied using a combination of survival analysis and the Accelerated Failure Time (AFT) model, according to data from a Greek as well as Italian institution. It is possible to quantify the relevance of these influencing factors using data mining approaches such as survival trees and multivariate regression trees. The proposed indicators concurrently use the Multivariate Regression Tree technique, which considers more than one consistent outcome variable. Gender plays an essential part in this research, as women outperform males in terms of these new metrics by completing their studies in a shorter amount of time and with a greater level of performance, regardless of other student characteristics. The findings were improved across the board when using a gender parity index that was tweaked.
Data Mining, Regression Tree, Statistical Analysis, Student Data, Learning Management System (LMS).
Copyright (c) 2022 R Dhanalakshmi, A.V Kalpana, J Umamageswaran, B Praveen Kumar https://creativecommons.org/licenses/by/4.0
A Data Analytic Model for Remote Sensing Data Using Data Mining Techniques
"Big Data" is a term used to describe the massive quantity of real-time data generated by the digital world's remote sensing assets. Data from remote sensing is far more complex than it appears at first glance; thus, retrieving relevant information in an effective manner can lead a system to severe computing issues, such as analysis and storage wherever data are remotely acquired. Accordingly, a design specification that allows for both real-time and offline data processing is needed considering the foregoing considerations. So, in this research, we offer a real-time data mining framework for remote sensing satellites. Ubiquitous Big Data collecting, processing, and decision-making units are the three key components of the suggested architecture. Using the suggested architecture, only valuable data will be divided up, balanced out, and processed in parallel. Consequently, real-time remotely sensed Big Data employing Earth observatory systems may be efficiently analysed using data mining approaches. As an additional benefit, the suggested architecture includes the capacity of retaining raw data to undertake offline analysis on huge dumps, if necessary. Big data from Earth observation satellites are analysed using Hadoop for land and water areas.
Data mining, big data, decision making, remote sensing, data analysis
Copyright (c) 2022 M Anand, S Babu https://creativecommons.org/licenses/by/4.0