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9th International Conference on Communication and Electronics Systems, ICCES 2024

作者： Umanandhini, d. Karthika, S. Chakradhar, Vankadari Teja, Mopidevi Sumanth Teja, Mallela Saisurya Vel Tech Rangarajan Dr. Sagunthala R&d Institute of Science and Technology Department of Computer Science & Engineering Tamil Nadu Chennai India

ISBN: (纸本)9798350377972

Millions of people across the world suffer from various mental health disorders, although they do not receive timely support. Stigma and constricted resources act as barriers to healing despite people universally struggling with anxiety, depression, and other such conditions. In contrast to this view, this challenge affects individuals, their families, their communities, and the entire world at large. We should imagine a world in which access to mental health support is a human right where every person able to get care when they need it. Our objective is to improve the mental health of service users through an AI-powered chatbot that can autopilot emergency patients and pre-assesses patient conditions. The innovation makes all clinicians free from workload pressures, allows them to engage in very specialized clinical tasks. The chatbot operates tirelessly and responds instantaneously, hence reducing pressures through traditional referral systems. We reviewed various mental health apps across them, common therapeutic approaches: tracking (39 apps), meditation (37 apps), relaxation techniques (82 apps), CBT (26 apps) and information (21 apps). These apps generally provide good usability, rich features, personalization, and affordability. However users want improvements in usability, credibility, security, and a diversity of features and personalization settings. Also consumers look out for information and features on social support and emergency support. Control over the app functions is also important, allowing users to personalize the settings to individual needs to maximize satisfaction. We adopt evidence-based psychotherapeutic practices emphasizing cognitive behavioral therapy. In trying to make uniformity and reliability, we adopt practices according to guidelines, IAPT manual and NICE recommendations. Our chatbot obtains routine outcome measures, that data drives our methods. © 2024 IEEE.

关键词： Electronic health record

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Empowering Agility: Unleashing Performance Potential with Power BI in IT Project Management 16

Empowering Agility: Unleashing Performance Potential with Po...

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16th IEEE International Conference on Computational Intelligence and Communication Networks, CICN 2024

作者： Sengupta, Arijit Aziz, Faizul Symbiosis Centre for Information Technology Pune India Vel Tech Rangarajan Dr.Sagunthala R&d Institute of Science and Technology Chennai India

ISBN: (纸本)9798331505264

This paper examines the incorporation of Power BI in managing IT project performance and flexibility, which has brought a paradigm shift in managing organizations' projects. It also looks into how Power BI facilitates project agility by providing means to display status using dynamic dashboards and integration with project management tools, financial applications, and operation databases. Another aspect where Power BI is utilized is when it comes to risk forecasting to prevent risks in the future. Automated reports enhance communication to ensure stakeholders are presented with timely and relevant visual reports to make relevant decisions. Power BI decreases data silo and improves contingency strategy transitions, as well as quickens project responsiveness through cross-departmental collaboration. Ultimately, it enhances the project management cycle by increasing flexibility and productivity and enhancing coordination, thus becoming an essential application in the dynamic IT environment. © 2024 IEEE.

关键词： Project management

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Security Enhancement for the 5G Network Concerns

Security Enhancement for the 5G Network Concerns

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2023 International Conference on System, Computation, Automation and Networking, ICSCAN 2023

作者： Yazhinian, S. Kathirvel, N. Nithiya Snega, V. devarasu, Neelamegam Sankar, M. School of Computing Vel Tech Rangarajan Dr. Sagunthala R&d Institute of Science and Technology India Manakula Vinayagar Institute of Technology Department of Ece India Vel Tech Rangarajan Dr. Sagunthala R&d Institute of Science and Technology Department of Ece India

ISBN: (纸本)9798350315127

Fifth era (5G) is the up and coming age of versatile interchanges with low inactivity and high information throughput. As far as these elements, 5G organizations ought to ensure incredible levels, of All clients' security and security to be safeguarded. Subsequently, scientists painstakingly assessed protection and security answers for 5G clients. 5G remote organizations offer a modern idea that tackles issues influencing past ages of correspondences. Of most noteworthy worry to numerous scientists in the cell phone industry is client protection, which is the drawn out Worldwide Versatile Endorser Identifier (IMSI) and the transient membership identifier Impermanent Portable Supporter Identifier and Cell radio Organization Fleeting Identifier (TMSI and C-rNTI). This article investigates accessible information on client security, broadened protection, and protection settings in 5G organizations. Also, it examines a large part of the examination on client character in Id, pagination, and site refreshes. This paper talks about different security issues in 5G organizations utilizing open IMSI or impermanent identifiers (like TMSI and C-rNTI) contrasted with IMSI that spills client protection. This record likewise analyzes the accessible data on client personality and security, featuring the central matters, issues, issues and future proposals and issue arrangements. © 2023 IEEE.

关键词： 5G mobile communication systems

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Animal Intrusion detection System using SIFT Features and Transfer Learning with MobileNetV2 3

Animal Intrusion Detection System using SIFT Features and Tr...

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3rd International Conference on Innovative Mechanisms for Industry Applications, ICIMIA 2023

作者： Chandralekha, E. Muzammil Ali, A. ritesh, V. Srinivasan, Muthu Kumar CSE Vel Tech Rangarajan Dr.Sagunthala R&D Institute of Science and Technology Avadi Chennai India AI&DS Vel Tech Rangarajan Dr.Sagunthala R&D Institute of Science and Technology Avadi Chennai India

ISBN: (纸本)9798350343632

Agriculture's impact on India's economy is substantial, offering employment for a significant part of the population and contributing to livelihoods. It plays a crucial role in ensuring food security, providing industrial raw materials, and supporting exports. Yet, agricultural challenges arise from animal-related issues like crop damage, livestock attacks, disease transmission, and farming infrastructure disruptions. These problems hinder productivity, necessitating sustainable farming strategies. deep learning advancements enhance animal intrusion detection by autonomously analyzing data patterns for accurate identification of wildlife, thus improving farm security. A unique intrusion detection model is introduced, utilizing architectural augmentation and transfer learning. The model, built on MobileNetV2 architecture, successfully detects intruders across diverse image categories, bolstered by a data generator and custom layers. Effective optimization techniques are employed in model construction, and the trained model is stored for future intrusion detection use. © 2023 IEEE.

关键词： Animal Intrusion detection Convolutional Neural Network data Augmentation MobileNetV2 Transfer Learning

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detection of Sleep Apnea using Cat Swarm Optimization with TabNet versus CNN Model

Detection of Sleep Apnea using Cat Swarm Optimization with T...

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2024 International Conference on Integration of Emerging technologies for the digital World, ICIETdW 2024

作者： Kujani, T. Arivubrakan, P. Usha, V. Alex david, S. Umamaheswari, r. Sathya, T. Vel Tech Rangarajan Dr.Sagunthala R&D Institute of Science and Technology Computer Science and Engineering Tamilnadu Chennai India Vel Tech Rangarajan Dr.Sagunthala R&D Institute of Science and Technology Department of Computer Science and Engineering Chennai India Sri Sairam Engineering College Computer Science and Business Systems Tamilnadu Chennai India

ISBN: (纸本)9798331541170

Sleep apnea is a sleep disease that is often an undetected cause of death. It is generally diagnosed with polysomnography, which can be costly and uncomfortable. To solve this issue, we suggest a website for detecting sleep apnea that uses user-provided data such as the Berlin questionnaire, Epworth Sleepiness Scale (ESS), body measurements, Body Mass Index (BMI), age, and gender. The purpose of this work is to categorize sleep apnea based on the Apnea-Hypopnea Index (AHI) as normal, mild, moderate, or severe. This classification is carried out using a deep learning model, TabNet which suits the best to process the text input. It could be useful for people who feel they have sleep apnea, allowing them to take the essential steps toward a formal diagnosis and therapy. However, this aids in early diagnosis by indicating the stage of the disease, and users are recommended to check with healthcare specialists for a definitive diagnosis. For feature selection, nature-inspired Cat Swarm optimization is applied. The TabNet is trained on a dataset that includes these characteristics as well as the AHI, a common metric for determining the severity of sleep apnea. The algorithm uses these inputs to forecast the likelihood of having sleep apnea: normal, mild, moderate, or severe. The developed TabNet Model is compared with the Convolution Neural Network (CNN) and the results are showcased. To summarize, this approach is a revolutionary technique for sleep apnea detection using Cat Swarm Optimization. It promises to give a more accessible and cost-effective solution for people who feel they have this illness by utilizing user-generated data and deep learning algorithms. © 2024 IEEE.

关键词： Sleep research

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Bitcoin Price Predictive dynamics Using Machine Learning Models 15

Bitcoin Price Predictive Dynamics Using Machine Learning Mod...

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15th International Conference on Computing Communication and Networking technologies, ICCCNT 2024

作者： Geetha Jenifel, M. Anita Jasmine, r. Umanandhini, d. Srm Institute of Science and Technology Data Science and Business Systems KTR Campus Chengalpattu India Vel Tech Rangarajan Dr. Shagunthala R&d Institute of Science and Technology India

ISBN: (纸本)9798350370249

Cryptocurrency markets, exemplified by the notable volatility in Bitcoin prices, have become pivotal arenas for financial exploration and investment. In response to this, our research undertakes a comprehensive comparison of diverse machine-learning models to predict Bitcoin prices. The models scrutinized include Linear regression, ridge regression, decision Tree, random Forest, Support Vector Machine (SVM), K-nearest neighbors (KNN), and Neural Networks. This study evaluates and contrasts these models based on performance metrics such as Mean Absolute Error (MAE), Mean Squared Error (MSE), and r -squared. This work encompasses the collection and preprocessing of historical Bitcoin price data, the engineering of pertinent features, and the division of the dataset into training and test sets. Each machine learning model is meticulously trained on the training set, allowing for the tuning of hyperparameters to optimize predictive capabilities. Subsequently, the models' performance is systematically assessed on the test set, providing valuable insights into their accuracy and reliability. The scope of this research is delimited by a specific timeframe, focusing on historical Bitcoin price data up to 15/11/2023. By addressing these objectives, this research aspires to guide investors, researchers, and analysts in navigating the intricate landscape of cryptocurrency investment decisions. The findings contribute to an enhanced understanding of the nuanced strengths and limitations inherent in different machine learning models when applied to the volatile context of Bitcoin price prediction. Ultimately, this research aims to facilitate more informed and strategic decision-making processes in the cryptocurrency market. © 2024 IEEE.

关键词： Adversarial machine learning

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3d printing with natural fiber composites: a review for advanced manufacturing applications

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Interactions 2024年第1期245卷 1-12页

作者： Karthik, K. Kolappan, S. ramesh, V. Muthukumarasamy, S. Department of Mechanical Engineering Vel Tech Rangarajan Dr.Sagunthala R & ampD Institute of Science and Technology Tamil Nadu Chennai600062 India

3d printing has transformed manufacturing by enabling the creation of complex shapes with high precision and minimal material waste. However, the widespread use of non-renewable, petroleum-based plastics in 3d printing raises environmental concerns due to their non-biodegradability and high carbon emissions. The use of natural fiber composites, combining natural fibers like hemp, flax, jute, and sisal with bio polymers or other sustainable matrix materials, has emerged as a more eco-friendly alternative offering strength, light weight, and sustainability. This article explores the viability of using these natural fiber composites for various 3d printing techniques such as fused deposition modeling, direct ink writing, and stereo lithography. It analyzes the strengths and limitations of each method for fabricating natural fiber composite parts. Potential applications span industries like automotive, aerospace, construction, and consumer goods, where these materials can serve as environmentally-friendly replacements for traditional plastics. The environmental and economic benefits of 3d printing with natural fiber composites are highlighted, including lower emissions, utilization of renewable resources, and support for local economies. While challenges like moisture sensitivity, uneven fiber distribution, and limited bio polymer choices are acknowledged, the article underscores the urgent need for further research to improve the functionality and print ability of these materials. Integrating natural fiber composites into 3d printing represents a significant stride towards sustainable manufacturing, presenting an opportunity to minimize environmental impact while producing high-performance components across diverse industries. © The Author(s), under exclusive licence to Springer Nature Switzerland AG 2024.

关键词： Hemp fibers

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NMF based Speech Enhancement using CNN 15

NMF based Speech Enhancement using CNN

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15th International Conference on Advances in Computing, Control, and Telecommunication technologies, ACT 2024

作者： Jessintha, d. Palas, P. Bini Berakhah, F. Stanley Kumar, r. Jeen retna Easwari Engineering College Department of Electronics and Communication Engineering Chennai India Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology Department of Computer Science Engineering Chennai India Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology Department of Electronics and Communication Engineering Chennai India

ISBN: (纸本)9798331300579

Convolutional Neural Networks (CNNs), are adept at learning complex patterns in data and can effectively denoise speech signals by mapping noisy spectrograms to clean ones. NMF, on the other hand, is a signal processing technique that can decompose a spectrogram into additive components, including a noise profile. By estimating the noise profile from the noisy speech spectrogram, NMF enables the separation of noise from the desired signal. Combining deep learning with NMF leverages the strengths of both approaches, allowing for robust and efficient speech enhancement even in challenging noisy environments. First, noisy and clean speech samples are loaded and pre-processed, including trimming or padding to a fixed length and computing magnitude spectrograms. A CNN-based deep learning model is then defined and compiled, with two convolutional layers for feature extraction from input spectrograms followed by dropout layers for regularization. This model is trained using the noisy spectrograms as input and their corresponding clean spectrograms as target output. Meanwhile, NMF is applied to estimate the noise profile from the magnitude spectrogram of the noisy speech signal, separating noise from the signal. Finally, the trained model is used to denoise the spectrogram, and the enhanced audio is reconstructed and saved. This approach combines the strengths of CNNs for learning complex mappings and NMF for noise estimation, resulting in improved speech enhancement performance. © Grenze Scientific Society, 2024.

关键词： Non-negative matrix factorization

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Energy-Efficient Green Information Centric Networking for Future Wireless Communications

International Journal of Interactive Mobile Technologies

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International Journal of Interactive Mobile technologies 2024年第23期18卷 149-158页

作者： Girirajan, S. Vinoth Kumar, S. Karthick, M. Saju raj, T. Ponnaian, Geetha Department of Computing Technologies School of Computing SRM Institute of Science and Technology Kattankulathur Tamil Nadu India Department of CSE Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology Tamil Nadu Chennai India Department of Computer Science and Engineering Nandha College of Technology Tamil Nadu Erode India Department of Computer Science and Engineering Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology Tamil Nadu Chennai India Department of Computer Science and Engineering Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Saveetha University Tamil Nadu Chennai India

Energy-efficient green information-centric networking (EEGICN) is proposed in this paper for advancing future wireless communication networks by addressing the challenge of energy consumption. This model can adapt the power consumption of network nodes to optimized values according to the associated link utilization. The model aimed to reduce energy consumption and increase network performance and stability. The EEGICN model incorporates efficient routing mechanisms, content caching strategies, and energy-intelligent communication protocols to improve resource utilization across the network infrastructure. EEGICN minimizes large data transmission, reduces power consumption, and reduces network congestion. Popular resources are cached in key network locations, and proximity data is exchanged to achieve this. The model also includes dynamic power management algorithms that adapt to changing traffic demand and network conditions to provide consistent performance across a variety of operational scenarios. In comparison to current wireless network systems that employ various forms of cache, the evaluation findings demonstrated that EEGICN can increase network efficiency by dramatically lowering the number of hops and energy consumption. Future networks may find this application to be a quick and easy way to transmit content. © 2024 by the authors of this article.

关键词： energy efficiency (EE) future wireless communications green information-centric networking information-centric networking (ICN) wireless network

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Suicidal risk Prediction Using Catboost Classifier: A Comparative Study of Six Machine Learning Classifiers 1

Suicidal Risk Prediction Using Catboost Classifier: A Compar...

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1st International Conference for Women in Computing, InCoWoCo 2024

作者： Prasher, Shikha Nelson, Leema Pappa, Kanmani Chitkara University Institute of Engineering & Technology Punjab India Vel Tech Rangarajan R&D Institute of Science and Technology Department of Electronics and Communication Engineering Chennai India

ISBN: (纸本)9798331518943

This study employs machine learning classifiers, including Logistic regression, random Forest Classifier, Extra Trees Classifier, XGB Classifier, LGBM Classifier, and CatBoost Classifier, to predict suicidal mental health. By employing a thorough methodology, this research endeavours to ascertain the classifier that is most efficient in performing this crucial duty. CatBoost Classifier demonstrated superior performance among the classifiers that were assessed, attaining a training accuracy of 84% and a testing accuracy of 73%. The findings highlight the effectiveness of the classifier in reliably differentiating between cases of depression and Suicide, showcasing its potential usefulness in initiatives aimed at preventing suicide. Although there was a marginal decline in test accuracy when compared to the accuracy achieved during training, the CatBoost Classifier still exhibited robust predictive capabilities, underscoring its dependability in practical situations. The results of this research make a valuable contribution to the field of mental health predictive modelling and underscore the criticality of employing machine learning methods in the realm of suicide prevention. Additional investigation is necessary in order to verify the CatBoost Classifier's scalability and efficacy across a wide range of populations and contexts. © 2024 IEEE.

关键词： Contrastive Learning

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