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4th International Conference on Soft Computing for Security Applications, ICSCSA 2024

作者： Elango, S. Mohanaprakash, T.A. Siva, D. Gowtham, A. Sasikumar, A.N. Sunitha, T. Department of Computer Science and Engineering SOET CMR University Bengaluru India Department of Computer Science and Applications SRM Institute of Science and Technology Chennai India Madanapalle Institute of Technology & Science AP India Department of Computer Science and Engineering Panimalar Engineering College Chennai India Department of Artificial Intelligence & Data Science Saveetha Engineering College Chennai India

ISBN: (纸本)9798331515720

The evolution of human civilization has been intrinsically linked to advancements in technology, leading to the development of multiple languages as mediums of communication. However, this linguistic diversity poses significant challenges in cross-cultural communication. Despite numerous efforts and achieving near state-of-The-Art results in language translation, a universal solution remains elusive. This research study proposes an advanced approach to machine translation by utilizing server-side machine learning to enable seamless communication across various languages. By sending GET or POST requests through JQuery, users can efficiently translate text. The process is structured into three key steps: speech recognition (conversion of spoken words to text), translation using machine learning algorithms, and speech synthesis (conversion of translated text to audio). This approach aims to enhance real-Time translation capabilities, offering a more effective and user-friendly solution for overcoming language barriers. © 2024 IEEE.

关键词： Adversarial machine learning

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Autism Detection using InceptionV3 and Security using Blockchain 8

Autism Detection using InceptionV3 and Security using Blockc...

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8th International Conference on Electronics, Communication and Aerospace Technology, ICECA 2024

作者： Annie Silviya, S.H. Seetha, R. Raja, S. Sriman, B. Rupesh, S. Gopala Krishnan, J. Rajalakshmi Institute of Technology Computer Science Engineering Chennai India Vellore Institute of Technology Computer Science Engineering and Information Systems Vellore India Panimalar Engineering College Computer Science and Engineering Chennai India Institute of Science and Technology Computer Science and Engineering Vel Tech Rangarajan Dr Sagunthala R&d Chennai India Rajalakshmi Institute of Technology Computer and Communication Engineering Chennai India

ISBN: (纸本)9798350367904

The mental health and well-being of children are critical components of their overall development and future success. In India, only 1 in 6 8 children are diagnosed with autism, since monitoring and addressing the mental health issues among children is a challenge because of various factors. Studies have proven that autism detection can be made possible with the assessment of specific facial features that are used as primary markers for the proposed model. The proposed solution aims to leverage technological advancements, such as deep learning, for detecting autism in children using the aforementioned markers. The model gathers data on the children's facial features and predicts any underlying mental disorder. Since the application collects and analyzes sensitive information from the users, an additional security feature is implemented in the proposed solution using blockchain. Key features of the solution include standardized assessment tools, real-time tracking capabilities, personalized interventions, and seamless communication channels between parents, caregivers, and mental health professionals. © 2024 IEEE.

关键词： Diseases

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Cyber intrusion detection using dual interactive Wasserstein generative adversarial network with war strategy optimization in wireless sensor networks

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Multimedia Tools and Applications 2025年第18期84卷 19223-19253页

作者： Anusha, N. Tapas Bapu, B.R. S, Selvakumaran Vijayaraj, A. Ramesh Kumar, C. P, Raji Department of IoT School of Computer science and Engineering VIT Tamil Nadu Vellore India Department of Electronics and Communication Engineering S.A.Engineering College Tamil Nadu Chennai India Department of Artificial Intelligence and Data Science Rajalakshmi Institute of Technology Tamil Nadu Chennai India Department of Information Technology R.M.K Engineering College R.S.M Nagar Tamil Nadu Kavaraipettai India Department of Electronics and Communication Engineering Panimalar Engineering College Tamil Nadu Chennai India Department of Electronics and Communication Engineering Saveetha Engineering College Tamil Nadu Chennai India

Wireless sensor network (WSN) is one of the essential components of a multi-hop cyber-physical system comprising many fixed or moving sensors. There are many common attacks in WSN, which can quickly harm a WSN system. In this manuscript, Dual Interactive Wasserstein Generative Adversarial Network (DIWGAN) with war strategy optimization (WSO) algorithm is introduced for detecting intrusion in WSN. Initially, the input data is attained from the NSL-KDD and CICIDS2017 datasets. The data is then passed onto Morphological filtering and Extended Empirical wavelet transformation (MFEEWT) based pre-processing. After that, the filtered data is sent to the DIWGAN, which detects the intrusion and classifies the fraud data in the network. Additionally, the WSO algorithm is introduced to enhance the classification parameters of DIWGAN. By then the performance of the proposed DIWGAN-WSO-IDS methodology is evaluated using Python Platform. Also, the performance of the proposed methodology is calculated using various metrics like accuracy, precision, recall, f-measure, mean square error, and attack detection rate. Thus, the proposed technique has attained 29.87%, 31.7%, and 26.8% higher accuracy, 23.54%, 20.09%, and 26.98% higher recall, 12.76%, 10.96%, and 6.7% higher precision. And 6.85%, 4.56%, and 8.56% lower MSE than the existing techniques like BFSGSRF, MHICA-SSA, and CNN-LSTM methods. © The Author(s), under exclusive licence to Springer science+Business Media, LLC, part of Springer Nature 2024.

关键词： Wireless sensor networks

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Transforming Fetal Health Monitoring: Advanced Machine Learning Innovations for Accurate Prognosis

Transforming Fetal Health Monitoring: Advanced Machine Learn...

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IEEE Punecon

作者： Nancy Jenifer A. L. Jabasheela Kavitha Subramani Vinmathi M. S Sathiya V Maheswari M Computer Science and Engineering Panimalar Engineering College Chennai

ISBN: (数字)9798331527822

ISBN: (纸本)9798331527839

Early detection of pregnancy-related illnesses, such as congenital heart defects, neural tube defects, Down syndrome, and Inborn Errors of Metabolism (IEM), is vital for ensuring the health of both the fetus and the expectant mother. Leveraging advanced ML techniques-specifically Decision Trees, KNN, and Random Forest-can significantly enhance the accuracy of predicting these conditions. Decision Trees provide a transparent, interpretable model that visualizes decision-making through a series of binary splits, enabling healthcare professionals to understand the rationale behind predictions. In contrast, KNN operates on the principle of proximity, classifying new data points based on the 'k' nearest neighbors in a multidimensional space, making it effective in scenarios with complex, non-linear relationships. Meanwhile, Random Forest improves predictive performance by aggregating the outputs of multiple Decision Trees, thus mitigating overfitting and enhancing reliability. This ensemble approach draws on the diversity of trees, each trained on random subsets of data, allowing it to capture intricate interactions among variables. Together, these ML techniques utilize extensive historical data to autonomously predict pregnancy-related conditions, forming a sophisticated decision support system that complements traditional screening methods. By integrating these advanced algorithms, healthcare providers can achieve more accurate early diagnoses, ultimately improving maternal and fetal health outcomes and paving the way for targeted interventions.

关键词： Technological innovation Accuracy Medical services Predictive models Nearest neighbor methods Decision trees Reliability Prognostics and health management Random forests Diseases

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Information Centric Networking Based Cooperative Caching Framework for 5G Communication Systems

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computers, Materials & Continua 2024年第9期80卷 3945-3966页

作者： R.Mahaveerakannan Thanarajan Tamilvizhi Sonia Jenifer Rayen Osamah Ibrahim Khalaf Habib Hamam Department of Computer Science and Engineering Saveetha School of EngineeringSaveetha Institute of Medical and Technical SciencesChennai602105India Department of Computer Science and Engineering Panimalar Engineering CollegeChennai600123India Department of Computer Science and Engineering Sathyabama Institute of Science and TechnologyChennai600119India Department of Solar Energy Al-Nahrain Research Center for Renewable EnergyAl-Nahrain UniversityJadriyaBaghdad64074Iraq Faculty of Engineering Universitéde MonctonNew BrunswickE1A3E9Canada Hodmas University College Mogadishu521376Somalia Bridges for Academic Excellence Tunis1002Tunisia School of Electrical Engineering University of JohannesburgAuckland ParkJohannesburg2006South Africa

The demands on conventional communication networks are increasing rapidly because of the exponential expansion of connected multimedia *** light of the data-centric aspect of contemporary communication,the information-centric network(ICN)paradigm offers hope for a solution by emphasizing content retrieval by name instead of *** 5G networks are to meet the expected data demand surge from expanded connectivity and Internet of Things(IoT)devices,then effective caching solutions will be required tomaximize network throughput andminimize the use of ***,an ICN-based Cooperative Caching(ICN-CoC)technique has been used to select a cache by considering cache position,content attractiveness,and rate *** findings show that utilizing our suggested approach improves caching regarding the Cache Hit Ratio(CHR)of 84.3%,Average Hop Minimization Ratio(AHMR)of 89.5%,and Mean Access Latency(MAL)of 0.4 *** a framework,it suggests improved caching strategies to handle the difficulty of effectively controlling data consumption in 5G *** improvements aim to make the network run more smoothly by enhancing content delivery,decreasing latency,and relieving *** improving 5G communication systems’capacity tomanage the demands faced by modern data-centric applications,the research ultimately aids in advancement.

关键词： Information-centric networking caching schemes 5G communication non-negative matrix factorization(NMF) weighted clustering algorithm

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Advancing Personalized Recommendation Systems with a Groundbreaking Collaborative Filtering Algorithm Driven by Machine Learning 2

Advancing Personalized Recommendation Systems with a Groundb...

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2nd IEEE International Conference on Advances in Information Technology, ICAIT 2024

作者： Anbalagan, E. Sasikumar, S. Kumar, M. Guru Vimal Paramesh, J. Sriram, K.P. Professor Department of Computer Science and Engineering Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Saveetha University Chennai Professor Department of Computer Science and Engineering Saveetha Engineering College Chennai Associate Professor Department of Computer Science and Engineering Vel Tech Rangarajan Dr.Sagunthala R&D Institute of Science and Technology Professor Department of Computer Science and Engineering Mohamed Sathak A.J. College of Engineering Ekattu Siruseri Chennai India Assistant Professor Information Technology St.Joseph's Institute of Technology Chennai India

ISBN: (纸本)9798350383867

By introducing this collaborative filtering algorithm, which is dependent on machine learning that can be used in enhancing the user-based recommendation systems, this paper is trying to achieve more advanced personalized recommendation systems. Utilizing recent innovative designs of networks together with relevant contextual information, the algorithm provides high accuracy recommendation, scalability, and aids in improving user experience than traditional approaches. The model is shown to perform exceedingly well in all respects by its being subjected to a thorough assessment and compared with the baseline and enhanced models. It joins ranks with the best in all the indices of performance like precision, recall, F1-score, mean average precision and user satisfaction. In addition, hyperparameter tuning, and scalability metrics, so ending efficiency for model configuring and deployment recommendation models which could deal with large-scale datasets and offer users timely suggestions are of vital importance. To summarize, these results highlight the disruptive character of the latest machine learning algorithms that might create a new reality of recommendation systems with the trend of the time of individualization of relationships in the digital world. ©2024 IEEE.

关键词： Collaborative filtering

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Information sharing over social media analysis using centrality measure

Lecture Notes on Data Engineering and Communications Technol...

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Lecture Notes on Data engineering and Communications Technologies 2021年 66卷 25-34页

作者： Ashvitha, K.P. Akshaya, B. Thilagavathi, S. Rajendiran, M. Department of Computer Science and Engineering Panimalar Engineering College Chennai India

Instagram and Twitter are popular social media in India. This exploration plans to comprehend the client’s behavior and strong/weak relationship inside the social media and to find the range of interaction in the social network. UCINET is an analytic tool for social media networks which examines the client highlights and floating inspiration of these four environments. To measure the centrality three markers such as degree centrality, closeness centrality and betweenness centrality are widely used. Clients obtained the most astounding estimation of centralities are mostly used in social media by people, and the least centrality is used the least. The recurrence to utilize four web-based lives is comparable as per the results of three centralities. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2021.

关键词： Social networking (online)

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Deep Learning based Human Activity Recognition in Smart Home 4

Deep Learning based Human Activity Recognition in Smart Home

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4th IEEE International Conference on Data engineering and Communication Systems, ICDECS 2024

作者： Anbazhagan, K. Swamy, G. Janani, R. Farakte, Amarsinh Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Department of Computer Science and Engineering Chennai India Vignana Bharathi Institute of Technology Department of Computer Science and Engineering Andhra Pradesh India Panimalar Engineering College Department of Computer Science and Business Systems Chennai India Sant Gajanan Maharaj College of Engineering Department of Electronics and Telecommunication Engineering Maharashtra India

ISBN: (纸本)9798350393354

Human activity recognition (HAR) in the context of smart homes has attracted considerable interest because to its potential to increase residents' quality of life, safety, and energy efficiency. This study dives deeper into the use of deep learning (DL) algorithms for human activity recognition in a smart home scenario. A realistic approach for distinguishing distinct human behaviors like as walking, sitting, cooking, and sleeping is provided by the combination of artificial intelligence and sensor technology. This study offers FCN-LSTM, a revolutionary technique for HAR in smart homes that combines a Fully Convolutional Network (FCN) architecture with a Long Short-Term Memory (LSTM). The proposed FCN-LSTM model capitalises on the strengths of both architectures, combining the FCN's ability to capture specific information spatially with the LSTM's experience modelling temporal correlations. This hybrid method efficiently captures both spatial and temporal variability inherent in human activities, overcoming the HAR issues. The FCN-LSTM model is trained on a huge dataset acquired from sensors deployed in a smart home setting, which includes a wide range of actions encompassing ordinary daily routines. This method is used to the Wireless Sensor Data Mining (WISDM) dataset, which was produced from data collected from a large number of people who engaged in six different activities: walking, sitting, downstairs, running, standing, and upstairs. According on the experimental data, the FCN-LSTM model outperforms established approaches and standalone systems. The results show that the FCN-LSTM algorithm performs well in recognizing human actions. The FCN-LSTM algorithm achieves a testing precision of 95.99%, with recall and f-measure of 97.11% and 99.99%, respectively. © 2024 IEEE.

关键词： Long short-term memory

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Intelligent Drug Release System Development and Evaluation via Support Vector Machine Algorithm

Intelligent Drug Release System Development and Evaluation v...

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2024 International Conference on Innovation and Intelligence for Informatics, Computing, and Technologies, 3ICT 2024

作者： Kalyani, G. Jayaraman, Venkatesh Sathish Kumar, P.J. Surendran, R. Balamurugan, K.S. Karpaga Vinayaga College of Engineering and Technology Tamil Nadu Chengalpattu India Department of Computer Science and Engineering Chennai Institute of Technology Chennai India Department of Computer Science and Engineering Panimalar Engineering College Chennai India Department of Computer Science and Engineering Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Chennai India Department of Electronics and Communication Engineering Karpaga Vinayaga College of Engineering and Technology Tamil Nadu Chengalpattu India

ISBN: (纸本)9798331533137

This study proposes a unique sensor plus machine learning system designed to enhance patient care and targeted medicine administration, with a specific focus on controlling Cryptococcosis, in the search for more precise and proactive healthcare solutions. This research had contributed to the conceptualization, design and implementation of system which integrates constant sensor data monitoring and machine learning algorithms to anticipate and prepare for any health problems. Over the course of a day, Support Vector Machines (SVM), Artificial Neural Networks (ANN), and Random Forest (RF) were used to analyse sensor data from 10 patients with Cryptococcosis. The sensor data gathered from these people included information on their body temperature, blood pressure, oxygen saturation, heart rate, blood sugar levels, and ECG measurements. The models executed flawlessly, achieving F1 scores for accuracy, recall, and precision. SVM won with a precision score of 97.6%, followed by ANN with a score of 95.3% and RF with a score of 92.1%. The models' capacity to get rid of misclassifications and provide precise predictions needed for right away action was demonstrated by the confusion matrices that went along with them. This discovery, which might revolutionise how patients are treated for difficult-to-treat illnesses like cryptococcosis, is an encouraging first step in the direction of proactive healthcare management. The sensor and machine learning system offers a patient-centered, data-driven approach that keeps up with the changing landscape of healthcare. ©2024 IEEE.

关键词： Support vector machines

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Evaluation of CatBoost for Diabetes Prevention in Comparison to XGBoost: To avert mortality by developing an AI model capable of predicting the onset of diabetes

Evaluation of CatBoost for Diabetes Prevention in Comparison...

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2024 IEEE International Conference on Electronic Systems and Intelligent Computing, ICESIC 2024

作者： Gifta Jerith, G. Jagadeesh, S. Ponmathi Jeba Kiruba, P. Mahesh, C. Anantha Babu, S. Samsudeen Shaffi, S. Hyderabad India Vel Tech Rangarajan Dr. Sagunthala R and D Institute of Science and Technology Department of Computer Science and Engineering Chennai India Saveetha Engineering College Department of Computer Science and Engineering Chennai India Panimalar Engineering College Department of Information Technology Chennai India GITAM University GITAM School of Technology Department of Computer Science and Engineering Bangalore India

ISBN: (纸本)9798331522988

Diabetes is a metabolic condition defined by consistently elevated blood glucose levels. Hyperglycemia is characterized by heightened hunger, thirst, and urination. Numerous consequences may arise from untreated diabetes. Diabetic ketoacidosis, hyperosmolarity, hyperglycemia, and even mortality may occur as acute consequences. Heart disease, stroke, chronic renal failure, foot ulcers, and eye impairment are some of the worst long-term consequences. A total of 463 million individuals were found to have diabetes in 2019, according to the worldwide diabetes organisation. Assuming predictions come true, that number will soar to 700 million by 2045, up 578 million by 2030. It has modern medical applications, allowing for the rapid and precise diagnosis of illness. Thus, we may use machine learning to avert mortality by developing an AI model capable of predicting the onset of diabetes;to determine which algorithm is most suited for this task, we will compare the CatBoost and XGBoost methods. In the end, the research compared two diabetes prediction models, XGBoost and CatBoost, using a variety of health indicators from the dataset. Using a real-world dataset obtained from Kaggle, our suggested system is trained and built using Python. Performance assessment criteria such as precision, recall, F1score and accuracy measurements are used to thoroughly analyse our methods. In comparison to XGBoost's 88.33% accuracy rate, 85.25 percent precision, 85.63 percent recall, and an F1 Score of 86.50. CatBoost's 91.08% percent accuracy, 85.25 percent precision, 85.63 percent recall, and 87.38 F1 Score. CatBoost marks it as the most successful ensemble approach. © 2024 IEEE.

关键词： Prediction models

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