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2nd International Conference on Intelligent Data Communication technologies and Internet of Things, IDCIoT 2024

作者： Raman, Ramakrishnan Saravanan, K. Gokhale, Atul A Anitha, K. Ishwarya, M.V. Sujatha, S. Maharashtra Pune India R.M.D. Engineering College Department of Information Technology Tamil Nadu Chennai India Maharashtra Pune India School of Computing SRM Institute of Science and Technology Department of Computing Technologies Tamil Nadu Chennai India Agni College of Technology Artificial Intelligence and Data Science Department Tamil Nadu Thazhambur India Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Saveetha University Department of Biomedical Engineering Tamil Nadu Chennai India

ISBN: (纸本)9798350327533

Healthcare delivery and patient monitoring have been transformed by the fast rise of the Internet of Healthcare Things (IoHT). Cloud storage of sensitive health data creates privacy issues. Logistic Regression (LR) is used to ensure IoHT data sharing in this investigation. The cloud architecture allows efficient data exchange while protecting health information. LR is used to predict data sharing risk and make privacy choices. A varied collection of health measures and demographic data trains the model to generalize well to numerous contexts. The suggested system is tested extensively with Logistic Regression, which reliably identifies and mitigates privacy threats. Data accessibility and privacy are balanced by the model's high sensitivity and specificity. It also examines the model's interpretability to provide privacy prediction factors. This openness improves user trust and regulatory compliance. This technique is scalable for large-scale IoHT installations. The cloud-based IoHT data sharing challenge has a unique approach. Machine learning, especially Logistic Regression, protects privacy without reducing health data usefulness. This research increases healthcare data security in the connected device age. © 2024 IEEE.

关键词： Health Data Security Healthcare Analytics Patient Privacy Predictive Modeling Sensitive Information

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Optimizing Electric Vehicle Charging Networks Using Clustering Technique 2

Optimizing Electric Vehicle Charging Networks Using Clusteri...

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2nd International Conference on Computer, Communication and Control, IC4 2024

作者： Srividhya, V. Gowriswari, S. Vini Antony, N. Murugan, S. Anitha, K. Rajmohan, M. Meenakshi College of Engineering Department of Electrical and Electronics Engineering Tamil Nadu Chennai India Sri Sairam Engineering College Department of Instrumentation and Communication Engineering Tamil Nadu Chennai India R.M.D. Engineering College Department of Electronics and Communication Engineering Tamil Nadu Chennai India Saveetha Institute of Medical and Technical Sciences Saveetha University Saveetha School of Engineering Department of Biomedical Engineering Tamil Nadu Chennai India Srm Institute of Science and Technology School of Computing Department of Computing Technologies Kattankulathur Tamil Nadu Chennai India Hindustan Institute of Technology and Science Department of Electronics and Communication Engineering Tamil Nadu Chennai India

ISBN: (纸本)9798350387933

The increasing use of electric vehicles (EVs) highlights the need of a designed effectively charging network. This paper presents a novel way for EV charging by establishing a smart charging hub network using dynamic K-means clustering. The unpredictable nature of EV use patterns makes static clustering systems difficult to maintain up, which leads to inefficiency and load at charging points. The failure of current electric vehicle charging facilities to adapt to altering use patterns leads in unsatisfactory charging experiences for customers. These issues are exacerbated by charging station load mismatches and an inability to connect with renewable energy sources (RES). This paper attempts to address these issues by establishing a dynamic clustering approach and a synchronized hub network. The most important outcome is intelligent charging hub equipment that can quickly react to the specific charging demands of each specific EV. The system that is suggested employs a novel clustering method, organizes charging nodes, predicts demand via predictive analytics, employs RES, and encourages consumers to use environmentally conscious charging behaviours. Insights into the development of electric transportation are provided, which lays out a detailed plan for building a smart and environmentally friendly EV charging network. © 2024 IEEE.

关键词： K-means clustering

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Gender-Specific Speech Enhancement Architecture for Improving Deep Neural Networks Learning

Gender-Specific Speech Enhancement Architecture for Improvin...

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International Conference on Innovation and Intelligence for Informatics, computing, and technologies (3ICT)

作者： Soha A. Nossier Mhd Saeed Sharif Biomedical Engineering Department Medical Research Institute Alexandria University Alexandria Egypt Intelligent Technologies Research Group School of Architecture Computing and Engineering UEL London United Kingdom

ISBN: (数字)9798331533137

ISBN: (纸本)9798331533144

Deep learning techniques for speech enhancement rely on training a deep neural network to process noisy speech, regardless the gender of the speaker. However, research shows that the speech of male and female stimulates different parts in human brain, and that female speech requires more complex analysis. This implies that different processing is applied on the speech, based on the speaker gender. In this work, we argue that male and female speeches have different features that can help in the learning process of speech enhancement deep neural networks if the training is performed on male and female speech data, independently, and using two different deep neural networks, specifically implemented for enhancing the noisy speech signal of the target gender. This work presents a gender-specific speech enhancement architecture, which consists of a front-end binary classifier to detect the speaker gender. Based on the classifier decision, the noisy speech is enhanced using either a male or female speech enhancement model. One-stage and two-stage speech enhancement approaches are used to process male and female speeches, respectively. The results reveal that gender-specific speech enhancement has positive impact on the enhanced speech by deep neural networks. Additionally, the developed architecture achieved classifier accuracy 96.9% and 0.11 increase in Covl speech quality metric for the test data, in comparison to other best-performing networks.

关键词： Training Deep learning Technological innovation Computational modeling Noise reduction Computer architecture Speech recognition Speech enhancement Noise measurement Informatics

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Exploring smartphone usage patterns and perceived dependency: A cross-sectional study in Australia

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Acta Psychologica 2025年 257卷 105100页

作者： Nawaz, Saqib Mitchell, Matthew Linden, Tanya Bhowmik, Jahar Department of Computing Technologies School of Science Computing and Engineering Technologies Swinburne University of Technology Melbourne VIC Australia School of Computing and IS Faculty of Engineering and IT The University of Melbourne Melbourne VIC Australia Department of Biomedical Health and Exercise Sciences School of Health Sciences Swinburne University of Technology Melbourne VIC Australia

Smartphones have become indispensable in modern society, serving both everyday life and professional work. While they offer numerous advantages, their excessive use may cause adverse effects. Using a Uses and Gratifications Theory (UGT) framework, this descriptive cross-sectional study in Australia presents a comparative analysis of individual smartphone usage in relation to that of friends and family, alongside an examination of self-reported experiences of dependency related to smartphone use. A sample of 523 adults completed a self-report questionnaire assessing their smartphone usage, smartphone dependency, and the activities they frequently performed on their smartphones. The bivariate analysis conducted in this study revealed significant associations between self-reported smartphone use and various socio-demographic factors, including age, gender, parental status, and occupational status. Specifically, individuals aged 18–40, males, non-parents, and students reported higher levels of smartphone use compared to their respective counterparts. Regarding the most frequently performed smartphone activities, males and employed participants reported spending the majority of their time using social media and engaging in audio and video activities. The findings of this study also indicate that a significant portion of participants perceive their smartphone use as higher than that of their peers, reflecting a perceived compulsion to remain continuously connected and engaged. These findings show the influence of socio-demographic factors in understanding smartphone-related behaviours and highlight the need for targeted interventions and strategies addressing excessive smartphone use and dependency. © 2025 The Authors

关键词： Excessive smartphone use Higher smartphone use Problematic smartphone use Smartphone addiction Smartphone dependency Smartphone usage

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Cloud-Based Road Safety for Real-Time Vehicle Rash Driving Alerts with Random Forest Algorithm 3

Cloud-Based Road Safety for Real-Time Vehicle Rash Driving A...

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3rd International Conference for Innovation in Technology, INOCON 2024

作者： Presitha Aarthi, M. Reddy, Chilukala Mahender Anbarasi, A. Mohankumar, N. Ishwarya, M.V. Murugan, S. Shree Sathyam College of Engineering and Technology Department of Computer Science and Engineering Tamil Nadu Salem India Gitam University Department of Computer Science and Engineering Telangana Hyderabad India Srm Institute of Science and Technology School of Computing Department of Computing Technologies Chennai Tamil Nadu Kattankulathur India B.V. Raju Institute of Technology Department of Electronics and Communication Engineering Telangana Hyderabad India Agni College of Technology Artificial Intelligence and Data Science Department Tamil Nadu Chennai India Saveetha University Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Department of Biomedical Engineering Tamil Nadu Chennai India

ISBN: (纸本)9798350381931

The cloud-based road safety technology introduced in this research improves real-time vehicle behavior monitoring and warns for rash driving. It uses cloud computing to gather and interpret data from onboard car sensors and other traffic monitoring equipment. Powerful machine learning technology the Random Forest (RF) algorithm analyses this data intelligently. The RF algorithm is trained on a varied array of driving metrics to detect rash driving tendencies. Real-time data streams may be processed efficiently and scalable on the cloud, enabling speedy decision-making for rash driving detection. A network sends alerts to authorities and nearby vehicles. The system is adaptable to different road conditions, resistant to sensor data noise, and able to enhance rash driving detection accuracy via model updates. Cloud architecture makes the system available worldwide. With its focus on real-time notifications utilizing the RF algorithm, this cloud-based road safety system may reduce the hazards of reckless driving and avoid accidents. © 2024 IEEE.

关键词： Cloud computing architecture

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Real-time Agrochemical Management Solutions using Cloud computing and K-Nearest Neighbors Algorithm 8

Real-time Agrochemical Management Solutions using Cloud Comp...

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

作者： Sivanesan, T.M. Mohankumar, N. Shibu, N.V. Nandagopal, V. Rajasekaran, B. Srinivasan, S. Vel Tech Rangarajan Dr.Sagunthala R&d Institute of Science and Technology Department of Computer Science and Engineering Tamil Nadu Chennai India Nagpur Campus Maharastra Pune India Srm Institute of Science and Technology School of Computing Department of Computing Technologies Kattankulathur Tamil Nadu Chennai India Mohan Babu University School of Engineering Department of Electrical and Electronics Engineering Andhra Pradesh Tirupathi India Vinayaka Missions Kirupananda Variyar Engineering College Department of Electronics and Communication Engineering Tamil Nadu Salem India Saveetha University Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Department of Biomedical Engineering Tamil Nadu Chennai India

ISBN: (纸本)9798350367904

Optimizing pesticides to ensure crop health and productivity while limiting environmental effect is one of the many issues faced by modern agriculture. In this research, it provides a cloud-based, K-Nearest Neighbors (KNN) algorithm-based real-time agrochemical management system. The proposed method allows for effective data processing and analysis by using the computing power of cloud platforms. Environmental factors, crop type, soil properties, and historical data are used to inform the KNN algorithm's prediction of appropriate pesticide application rates. To provide the most current insights and suggestions, the system incorporates real-time data from a variety of sources, including sensors, satellites, and weather predictions. Enhanced crop yields, decreased costs, and less environmental impact may result from farmers making informed decisions about the use of agrochemicals via continuous monitoring and analysis. The system can be easily deployed and adjusted to many agricultural contexts because of the scalability and flexibility of cloud computing. This method provides a viable strategy for improving pesticide management practices, which in turn promotes sustainable farming and increases food safety. © 2024 IEEE.

关键词： Smart agriculture

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Diagnosis of Alzheimer's disease and Mild Cognitive Impairment using Rethinking and Deep Neural Networks 24

Diagnosis of Alzheimer's disease and Mild Cognitive Impairme...

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24th International Conference on Digital Signal Processing, DSP 2023

作者： Kostopoulos, Evangelos Diamantaras, Konstantinos Chouvarda, Ioanna Tsolaki, Magda Department of Information and Electronic Engineering International Hellenic University Sindos57400 Greece Lab of Computing Medical Informatics and Biomedical Imaging Technologies School of Medicine Aristotle University of Thessaloniki Thessaloniki54124 Greece Greek Association of Alzheimer's Disease and Related Disorders Thessaloniki54643 Greece

ISBN: (纸本)9798350339598

The early diagnosis of Alzheimer's Disease (AD) and its prodromal stage, Mild Cognitive Impairment (MCI), are important for doctors to treat patients as soon as possible. In this paper we focus exclusively on the use of EEG for the Machine Learning based diagnosis of MCI and AD. The choice of EEG is made because this modality is relatively inexpensive, non-invasive and easily available even outside the clinical environment. Unlike the majority of ML algorithms for AD diagnosis which treat EEG as an 1-D signal we propose a 2-D approach. We first split the EEG channel signals into fixed length segments and then we reshape them into 2-D images used to train a powerful VGG-16 deep Convolutional Neural Network model. In order to improve the performance of the VGG-16 model and avoid overfitting, we employed a powerful pruning method called *** study two experimental tasks: (a) classifying segments into AD/MCI or Health Control (HC) categories (b) classifying subjects (patients and healthy individuals) into the same categories. Our results showed that the proposed method can be used as a tool to diagnose AD at early stages with accuracy over 98.8. © 2023 IEEE.

关键词： Diagnosis

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Few-Shot Learning for Early Detection of Laryngeal Cancer with Explainable AI 8

Few-Shot Learning for Early Detection of Laryngeal Cancer wi...

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8th International Conference on Electronics, Materials engineering and Nano-Technology, IEMENTech 2025

作者： Pradhan, Ujan Prerna Aditya, M.N. SRM Institute of Science & Technology Department of Computing Technologies Chennai India SRM Institute of Science & Technology Department of Biomedical Engineering Chennai India SRM Institute of Science & Technology Department of Electronics and Communication Engineering Chennai India

ISBN: (纸本)9798331520762

Early diagnosis of laryngeal cancer significantly relates to prognosis and treatment. This work presents a classification model that uses very few-shot learning where classification between healthy and early-stage cancerous tissues can be done accurately even with very limited data. The proposed approach relies on an expert-curated dataset of 1,320 image patches of size 100x100 pixels, which suggests promising potential for real-world applications, especially in strictly limited amounts of annotated medical images. This approach uses Explainable AI (XAI) to increase the transparency of the model and eventually enhance clinical trust in the predictions obtained with that model. This technique has the promise for early detection, but it also has high accuracy and further allows clinical thinking into the decision mechanism of the model. That is a future direction for developing the refined version of such a technique to develop robustness and application in different clinical settings. © 2025 IEEE.

关键词： Mammography

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Creating a Real Time Public Transport Information System utilizing GPRS Technology

Creating a Real Time Public Transport Information System uti...

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Trends in Material Science and Inventive Materials (ICTMIM), International Conference on

作者： R. S. Amshavalli Arun Shankar Aarav Kannan Jayakumar J. Kalaivani Dept of Computer Science and Engineering Sathyabama Institute of Science and Technology Chennai India Dept of Biomedical Engineering SRM Institute of Science and Technology Chennai India Dept of Computing Technologies SRM Institute of Science and Technology Chennai India

ISBN: (数字)9798331501488

ISBN: (纸本)9798331501495

Efficient public transportation systems are critical to sustainable urban development, yet they often face challenges such as delayed operations, inadequate fleet management, and limited real-time visibility for passengers. This research addresses these issues by proposing a real-time public transport information system utilizing GPRS technology, which combines GPS and GSM modules to improve the operational efficiency of public transport systems. The system provides real-time vehicle location tracking, SMS notifications when a vehicle starts, and allows passengers to request vehicle location updates or remotely control the vehicle via SMS commands. By integrating affordable components such as Arduino Uno, SIM800L GSM modules, and NEO-6M GPS modules, this system is cost-effective, scalable, and operates without the need for continuous internet connectivity. This feature makes it particularly suitable for areas with limited network infrastructure. The outcome of this research demonstrates that the proposed system offers a feasible, reliable, and affordable solution to enhance passenger convenience, improve operational control, and facilitate scalable fleet management, making public transport more efficient and safer.

关键词： GSM Materials science and technology Solids Market research Real-time systems Reliability Remote control Public transportation Information systems Global Positioning System

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Few-Shot Learning for Early Detection of Laryngeal Cancer with Explainable AI

Few-Shot Learning for Early Detection of Laryngeal Cancer wi...

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International Conference on Electronics, Materials engineering and Nano-Technology (IEMENTech)

作者： Ujan Pradhan Prerna M.N. Aditya Department of Computing Technologies SRM Institute of Science & Technology Chennai India Department of Biomedical Engineering SRM Institute of Science & Technology Chennai India Department of Electronics and Communication Engineering SRM Institute of Science & Technology Chennai India

ISBN: (数字)9798331520762

ISBN: (纸本)9798331520779

关键词： Technological innovation Accuracy Explainable AI Transfer learning Training data Predictive models Data models Few shot learning Medical diagnostic imaging Cancer

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