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Intelligent and Smart Health Care Monitoring System based on Deep Convolutional Neural Network Model

Intelligent and Smart Health Care Monitoring System based on...

Research Methodologies in Knowledge Management, Artificial Intelligence and Telecommunication Engineering (RMKMATE), International Conference on

作者： G. Anitha R. Sankar A. Vimala Juliet V. Dhivya B. Thiyaneswaran Ata Kishore Kumar Department of Electronics and Communication Engineering Saveetha School of Engineering SIMATS Chennai Department of Master of Computer Applications S.A. Engineering College Chennai Department of Electronics & Instrumentation Engineering SRM Institute of Science & Technology Chennai Department of ECE Vel Tech MultiTech Dr. Rangarajan Dr. Sakunthala Engineering College Chennai Department of ECE Sona College of Technology Salem Department of ECE School of Engineering Mohan Babu University (Erstwhile Sree Vidyanikethan Engineering College) Tirupati Andhra Pradesh

Remote health monitoring apps have revolutionized the healthcare industry as a whole with the advent of Internet of Things (IoT) technology. Strategies used to get insights through the analysis of lifestyle patterns as well as behaviors are also crucial to tailored healthcare and preventative services. Opportunities for both the investigation of illnesses and the forecasting of abnormal health situations are made possible by the use of intelligent data retrieval as well as categorization methods. The broad use of IoT has improved telemedicine capabilities and introduced more precise remote patient monitoring, both of which have had a favorable effect on the world at large. The impact of the Internet of Things (IoT) becomes deeper as it enhances healthcare infrastructure for patients, doctors, and hospitals. Through the usage of secure patient portals, healthcare providers have easier access to up-to-date patient information, allowing for more precise diagnosis and treatment. The prototype's potential for efficient healthcare monitoring is reinforced by the success of similar systems. In this research, we automated the AlexNet Model building process by experimenting with several approaches. In addition, a whole new method of diagnosis has been created. The experimental data shows that the suggested algorithm is superior to the state-of-the-art methods. The total accuracy of the suggested model is 97.13%, which is significantly higher than the accuracy of any of the other models compared. This demonstrates the model's efficiency and places it as a potentially game-changing breakthrough in healthcare.

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Transformative Sport science Using Social Media as a Collaboration Tool 7

Transformative Sport Science Using Social Media as a Collabo...

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7th International Conference on ICT for Smart Society, ICISS 2020

作者： Oktavia, Tanty Gaol, Ford Lumban Hosoda, Takaaki Syahir, Arsyan Bina Nusantara University School of Information Systems Information Systems Department Jakarta11480 Indonesia Bina Nusantara University Binus Graduate Program - Doctor of Computer Science Computer Science Department Jakarta11480 Indonesia Advanced Institute of Industrial Technology Decision Science Department Tokyo Japan Bina Nusantara University BINUS Graduate Program - Master of Information Systems Management Information Systems Management Department Jakarta11480 Indonesia

ISBN: (纸本)9781665404228

Sports science can have a significant and positive impact on coaches and athletes, as well as for the professional sports organization. According to this situation, more and more organizations are competing to implement sports science as the monitoring systems. Along with the development of technology, it becomes easier for humans to carry out their activities, including communication with each other. Advances in the field of communication technologies have led to the use of social networks to support all activity. With social media, all kinds of information that people want to search for can be found easily. Social networks have their uniqueness in their characteristics. Like LINE has free calling and video calling features, while Instagram has features like Instagram Story, where users can document their activities in the form of photos or recordings. By all the characteristics of social networks, it will support the activity of sports science. The methodology of this research focused on quantitative research based on the T-test approach. The result shows that social media involvement can significantly support collaboration in sports science, so it can increase people's motivation to interact with other participants in sport organizations. © 2020 IEEE.

关键词： Sports

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Automatic COVID-19 Prediction with Comprehensible Machine Learning Models

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Open Public Health Journal 2024年 17卷

作者： Puttegowda, Kiran Sunil Kumar, D.S. Mallu, Sahana Vijay, C.P. Ravi, Vinayakumar Sushmitha, B.C. Department of Electronics and Communication Engineering Vidyavardhaka College of Engineering Karnataka 570002 India Department of Master of Computer Application Acharya Institute of Technology Karnataka 560107 India Department of Electronics and Communication Engineering ATME College of Engineering Karnataka 570028 India Department of Computer Science Engineering (AI&ML) Vidyavardhaka College of Engineering Karnataka 570002 India Center for Artificial Intelligence Prince Mohammad Bin Fahd University Khobar 34754 Saudi Arabia Department of Electronics and Communication Engineering ACS College of Engineering Bengaluru India

Background: The development of technology has a significant impact on every aspect of life, whether it is the medical industry or any other profession. The potential of artificial intelligence has been demonstrated in data processing and analysis is used to inform decisions in the field of health care. The most crucial action is the early detection of a life-threatening illness to stop its development and spread. Highly contagious COVID-19 is a disease that requires immediate attention as it has spread globally. There is a need for a technology that can be utilised to detect the virus because of how quickly it spreads. With the increased use of technology, we now have access to a wealth of COVID-19-related information that may be used to learn crucial details about the virus. Objective: The objective of the work is to develop comprehensible machine learning models for the automatic prediction of COVID-19. These models aim to accurately predict the likelihood of COVID-19 infection based on relevant input features, such as symptoms, demographics, and potential diagnostic tests or imaging results. Methods: In this work, we mainly aimed to assess how well different machine learning methods might predict COVID-19 situations. In order to do this, we thoroughly evaluated a variety of widely used classifiers in machine learning. Popular algorithms like the random forest, k-nearest neighbour, and logistic regression were all included in our analysis. Results: To assess the performance of our suggested algorithms using different machine learning techniques, we used an open-source dataset in the study. Our algorithms performed better than other models that are currently in use, which is noteworthy. The high degree of precision in predicting COVID-19 instances is demonstrated by our remarkable accuracy of 96.34%. We also obtained a good F1 score of 0.98 for our models, indicating the strength and efficiency of our method in obtaining metrics for both recall and precision. Concl

关键词： COVID-19 Diagnosis k-nearest neighbour Machine learning Neural networks Random forest prediction

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An Improved Network Segmentation Performance in Lesion Segmentation based on Mask R-CNN

An Improved Network Segmentation Performance in Lesion Segme...

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International conference of Electronics, Communication and Aerospace technology (ICECA)

作者： T. Thivya C. Kalaivanan N. Juliet Vengadapathiraj M G. Mary Valantina V. Mohanavel Department of Information Technology Velammal Engineering College Chennai Department of Electrical and Electronics Engineering Sona College of Technology Salem Department of Master of Computer Application S.A Engineering College Chennai Department of Electronics and Communication Engineering Rajalakshmi Instittue of Technology Chennai Department of Computer Science and Engineering Saveetha School of Engineering SIMATS Chennai Centre for Materials Engineering and Regenerative Medicine Bharath Institute of Higher Education and Research Chennai Tamil Nadu India Department of Mechanical Engineering Chandigarh University Mohali Punjab India

ISBN: (纸本)9781665482721

Computational intelligence has grown increasingly common in the analysis of healthcare data in recent years. The deep learning algorithms, in instance, have such a number of fruitful implications in the analysis of medical images. This research study has presented a double awareness deep convolution network-based system for end-to-end healthcare lesion classification. Several fully supervised and poorly supervised extracted features are included in this framework. The issue of the high cost of pixel-level labels with lesions in the medical pictures is alleviated by the weakly supervised segmentation module's use of bounding-box labels of lesion regions, which allows for correct lesion segmentation to be accomplished. In addition to this, a dual attention mechanism has been included for the purpose of improving the network's capacity for visual feature learning. The dual attention mechanism, which consists of both channel and spatial attention, may assist in drawing the network's attention to the extraction of features from significant locations. It is possible for it to considerably minimize the gaps between ground-truth labels and pseudo labels, in comparison to the current standard approach of weakly supervised segmentation using pseudo labels. Final results show that our proposed approach beat the competitors on to an oral disease collection, while our approach was extended to incorporate dermatological disease categorization in the ultimate analysis.

关键词： Representation learning Deep learning Visualization Costs Medical services Feature extraction Lesions

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Analyzing Robotics and computer Integrated Manufacturing of Key Areas Using Cloud Computing

Analyzing Robotics and Computer Integrated Manufacturing of ...

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International Conference on Contemporary Computing and Informatics (IC3I)

作者： Awaneesh Gupta Anil Kumar Dixit K. Suresh Kumar C Lavanya M. Kalyan Chakravarthi Durgaprasad Gangodkar Department: Master of Computer Applications United Institute of Management Prayagraj Dr. AKTU Lucknow Uttar Pradesh India Department of LCD Uttaranchal University Dehradun India Department of MBA Panimalar Engineering College Varadarajapuram Poonamallee Chennai Department of CSE Bannari Amman Institute of Technology Sathyamangalam School of Electronics Engineering VIT - AP University Amaravati Andhra Pradesh India Department of Computer Science & Engineering Graphic Era Deemed to be University Dehradun Uttarakhand India

The rising customer demands are fulfilled by the industrial sector through increased output and expedited shipping. The objectives or satisfied through the manufacturers by using cloud technologies. Tiers of manufacturing sectors adopt with the proposed technology in the future for maintaining the connection shows with the supply chain as well as customers. Cloud computing is referred as abstraction of storage, computation and infrastructure of network altogether as the platform which allows application deployment in speedy manner and dynamic scaling as well. The crucial area in cloud computing is self-service that can allow users to fill the web-form easily and quickly. A novel technical approach to job execution and resource sharing compared to conventional industrial robots is provided by Smart Manufacturing Environments, which uses cloud robotics. SME can be built up on the combination of cloud computing and industrial robots. The main focus of the proposed study is to successfully enabling the manufacturing services on demand which is provided over the internet. Manufacturing services with Cloud-based equipment can also be used by companies attempting to convert Collaborative Robots, Pa-as-you go business models, and production of increasing the productivity of production facilities, raising the caliber of products, enhancing producers’ capacity to adapt to shifting consumer needs, and entering new markets.

关键词： Cloud computing Service robots Scalability Computational modeling Supply chains Software systems Manufacturing

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IoT based Power and Water Theft Detection in Urban Cities

IoT based Power and Water Theft Detection in Urban Cities

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Sustainable Communication Networks and Application (ICSCNA), International Conference on

作者： Praful V. Nandankar G.N.R. Prasad M. Lakshminarayana Mr. Doddabasappa N G. Satish Ramya Maranan Assistant Professor Department of Electrical Engineering Government College of Nagpur Maharashtra India Sr. Asst. Professor Department of Master of Computer Application Chaitanya Bharathi Institute of Technology (A) Hyderabad India Assistant Professor Department of Medical Electronics Engineering M S Ramaiah Institute of Technology Karnataka India Assistant Professor School of Electrical and Electronic Engineering REVA University Bengaluru Karnataka India Associate Professor Department of Electrical and Electronics Engineering Sree Dattha Institute of Engineering and Science Sheriguda Ibrahimpatnam Hyderabad Telangana India Assistant Professor Department of Research and Innovation Saveetha School of Engineering SIMATS Chennai India

Many people in metropolitan areas have experienced problems with energy and water shortages. Some people used modern technologies and advancements to theft water and power illegally. Because the planet absolutely needs both water and power. Currently, the Internet of Things (IoT), smart meters, smart pipelines, and smart homes are being heavily integrated to replace traditional systems for detecting theft activities. This can be digitizing both the meter readings and the data that has been gathered. Wireless technology may be used to collect and analyze the data, which helps to reduce the need for physical labor. In this project, we suggested creating an IoT-based system for water and power theft detection. The proposed project aims to design a system to monitor the amount of power and water used by the people and indicates if water and electricity theft activities happen. This research study offers a user interface system to help people to monitor whether anyone is stealing their water or power. This study employs a hardware like voltage sensors, current sensors, flow sensors, and additionally, the Arduino IoT cloud to enable remote control and monitor the water and power usage.

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Enhanced Particle Swarm Optimization based Node Localization Scheme in Wireless Sensor Networks

Enhanced Particle Swarm Optimization based Node Localization...

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Augmented Intelligence and Sustainable Systems (ICAISS), International Conference on

作者： Shashank Singh R. Poonkuzhali G. Nithya R. Anil Kumar J. Kartigeyan S. Ramya Department of Computer Science and Engineering Integral University Lucknow Uttar Pradesh India Department of Information Technology K.S.Rangasamy College of Technology Tiruchengode Tamilnadu India Department of Electronics and Communication Engineering Malla Reddy Engineering College for Women Maisammaguda Secunderabad Telangana India Electronics and Communication Engineering Aditya College of Engineering and Technology Surampalem Andhra Pradesh India Department of Electrical and Electronics Engineering J.B.Institute of Engineering and Technology Hyderabad Telangana India Master of Computer Applications M.Kumarasamy College of Engineering Thalavapalayam Karur Tamilnadu India

ISBN: (纸本)9781665489638

Wireless sensor networks (WSNs) are widely examined recently because of their various applications in processes which has to scatter over a vast region. sensor's locations in WSNs generally must be known. WSN comprises great quantity of sensors thereby installing global position system devices in all of them does not accept. Some of the anchor nodes with well-known locations are generally utilized. Forecasting sensor node locations from radio strength signal index are regarded as a hard optimization issue. This article introduces an Enhanced Particle Swarm Optimization based Node Localization Scheme (EPSONLS) in WSN. The presented EPSONLS technique mainly aims to determine the location of the unknown nodes in the network. To do so, the presented EPSONLS model applies the PSO algorithm to identify the node position. In addition, the EPSONLS model determines the node location with the goal of minimizing localization error and time. An extensive-ranging experimental examination is performed and the outcomes are investigated under distinct prospects. The simulation outcome demonstrated the betterment of the EPSONLS model over recent approaches.

关键词： Location awareness Wireless sensor networks Sensor systems Sensors Indexes Particle swarm optimization Forecasting

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How to design the biofeedback training for the rehabilitation of PTSD patients?

How to design the biofeedback training for the rehabilitatio...

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Cognitive sciences, Genomics and Bioinformatics (CSGB)

作者： Olga Bazanova Alexander Zakharov Irina Larkova Tatiana Maryanovskaya Andrey Melnikov Sargylana Ermolaeva Ekaterina Nikolenko Igor Shirolapov Laboratory of Computer systems of Biofeedback The Research Institute of Molecular Biology and Biophysics Dolgoprudny Russia Educational and Scientific Center for Humanities and Social Science The Moscow Institute of Physics and Technology Novosibirsk Russia Research Institute of Neurosciences Samara State Medical University Samara Russia Laboratory of psychophysiological support Lugansk Republican Center for Emergency Medical Care and Disaster Medicine Lugansk Russia Mechanic-Mathematic Department Novosibirsk National Research State University Novosibirsk Russia Department of Physiology The Russian University of Sport «GTSOLIFK» Moscow Russia Master's Degree in Applied Data Analysis in the Medical Field The Moscow Institute of Physics and Technology Dolgoprudny Russia Department of Clinical Neuroscience Behavior and Neurotechnology Federal State Budgetary Scientific Institution “Scientific Research Institute of Neurosciences and Medicine” Novosibirsk Russia

Rationale for this review is the pressing medical and social problem of the posttraumatic stress disorder (PTSD) treatment. It is known that biofeedback technology is one of the most effective methods of treatment and rehabilitation for emotional profile disorders. In order to develop biofeedback aimed to PTSD rehabilitation, we conducted a literature search on this problem. However, it appears that at the moment there is not enough unambiguous data on this type of biofeedback design. The pathogenesis of PTSD is closely related to impaired efficiency of sensorimotor integration (SMI). The effectiveness of therapy for psychosomatic disorders in patients with PTSD can be increased by restoring normal sensorimotor integration. The review examines various autonomic, electrophysiological and postural markers of destroyed sensorimotor integration (SMI) in individuals with PTSD. We have found that the most informative indicators of SMI in norm include: an increase in EEG power in the individually determined high-frequency alpha subrange, a decrease in the speed of body sway and energy demands to maintain a vertical posture, and a decrease in EMG activity of muscles not involved in cognitive or psychomotor performance. We intend to use these indicators in diagnostic purposes and to develop neurofeedback technology for SMI recovery in patients with PTSD.

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Energy aware clustering for biomedical wireless sensor networks

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AIP Conference Proceedings 2023年第1期2831卷

作者： G. Vennira Selvi N. Rathna R. Felista Sugirtha Lizy S. Prabavathy P. Aurchana Department of Computer Science and Engineering GLOBAL Institute of Engineering and Technology Vellore India Department of Physics A.P.C. Mahalaxmi College for Women Thoothukudi India Department of Computer Science A.P.C. Mahalaxmi College for Women Thoothukudi India Department of Master of Computer Applications Sri Manakula Vinayagar Engineering College Pondicherry. India

Biomedical Wireless Sensor Networks (BWSN) is major technique for Health Care applications for providing quality of life with minimum cost. To enforce the quality of medical care provided to the citizen, such networks should be integrated with existing network infrastructures. The requirement of quality of service should be concentrated on development stages and are monitored for the duration of network operation. The life time of the network is most relevant feature for ensuring the quality of medical care requirements. In order to increases the network lifetime, maintaining the remaining energy of each sensor node with reliable communication link throughout the network. An energy aware clustering for biomedical wireless sensor network (EACBWSN) is proposed for increasing the network lifetime by minimizing the energy consumption among sensor nodes and provides a reliable communication link between sensor nodes. The proposed algorithm efficiently prolong the lifetime of the network when compared with existing algorithm.

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Innovations in Image Forensics: Designing a Smart Learning Based Methodology for Forgery Detection over Digital Images

Innovations in Image Forensics: Designing a Smart Learning B...

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Pioneering Developments in computer science & Digital Technologies (IC2SDT), International Conference on

作者： G. Ramkumar K. Pithamber C.H. Srilakshmi S. Rathika B Anni Princy Vijay Anand Kandaswamy Department of ECE Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences (SIMATS) Saveetha University Chennai Tamil Nadu India Department of Electronics and Communication Engineering MLR Institute of Technology Dundigal Hyderabad India Department of Computer Science and Business Systems R.M.D. Engineering College Kavaraipettai Tamil Nadu India Department of Master of Business Administration Prince Shri Venkateshwara Padmavathy Engineering College Chennai Tamil Nadu India Department of Artificial Intelligence and Machine Learning Panimalar Engineering College Chennai Tamil Nadu India Department of Electronics and Communication Engineering R.M.K. Engineering College Kavaraipettai Tamil Nadu India

ISBN: (数字)9798350365016

ISBN: (纸本)9798350365023

Detecting forgery in digital images is crucial for several reasons. Firstly, with the widespread use and accessibility of image editing tools, individuals can easily manipulate digital images, raising concerns about the authenticity and integrity of visual information. This study introduces a novel approach, termed SFVGG, for detecting forgery in digital images by leveraging a smart learning methodology. The SFVGG method integrates the robust features of SIFT, the clustering capabilities of Fuzzy C-Mean clustering, and the deep learning process of VGG19. Through a synergistic combination of these techniques, the proposed forgery detection system achieves a comprehensive and effective solution. The methodology was implemented using Python, showcasing its practical applicability. Remarkably, the SFVGG framework demonstrates a high accuracy of 94.5%, underscoring its effectiveness in identifying forged content within digital images. This research represents a significant advancement in digital image forgery detection, offering both a sophisticated methodology and practical implementation.

关键词： Deep learning Visualization Technological innovation Accuracy Digital images Digital forensics Educational technology Forgery

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