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5th International Conference on Smart Systems and Inventive Technology, ICSSIT 2023

作者： Ragavan, K. Ramalakshmi, R. Srirenganachiyar, V. Gnana Priya, G. Jeyageetha, K. Department of Electronics and Communication Engineering Ramco Institute of Technology Rajapalayam India Department of Computer Science and Engineering Ramco Institute of Technology Rajapalayam India

ISBN: (纸本)9781665474672

Smart healthcare monitoring is very essential for people who are in the Intensive Care Unit (ICU) in hospitals which provides continuous monitoring of health parameters, which cannot be afforded outside the hospitals. Because of the ever-increasing population of the world, smart health care has become increasingly crucial. The traditional patient-doctor consultation has lost its usefulness. The effective way of monitoring the patient's health within ICU has great potential to introduce Bluetooth Low Energy (BLE) and Message Queuing Telemetry Transport (MQTT) protocol. The patient's survival can be improved by the daily care of parameters such as heart rate, Hemodynamic, skin temperature, capillary refill, mental status, etc., which can be wirelessly monitored in smart way using BLE and MQTT. In addition, the Short Message Peer to Peer (SMPP) protocol could also be used for alerting the doctors if the sensor values go to a critical level as per the threshold value. It uses BLE and MQTT in monitoring the health of an ICU patient and also it will compare the ZigBee technology, which is already used in monitoring health parameters in following criteria low power, performance, cost, availability etc., with the proposed method. ZigBee will not support android version which is very important to access the smart phones. But the proposed method can support android version with low power consumption. © 2023 IEEE.

关键词： Intensive care units

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Machine learning based Parkinson’s disease detection and progression evaluation using gray and white matter segmentation from 3D MRI

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Journal of engineering and Applied Science 2024年第1期71卷 1-20页

作者： Islam, Nair Ul Khanam, Ruqaiya Department of Computer Science & amp Engineering Centre of Excellence for Artificial Intelligence in Medicine Imaging & ampForensics Sharda University Greater Noida India Department of Electrical Electronic & ampCommunication Engineering Centre of Excellence for Artificial Intelligence in Medicine Imaging & ampForensics Sharda University Greater Noida India

Parkinson’s disease (PD) is a movement-related neurological condition caused by the death of brain nerve cells that produce dopamine. T1 MR images were obtained from the Parkinson’s Progression Markers Initiative (PPMI) database. Data was collected at baseline, and at 48 months duration. SPM12 software was used to segment gray matter (GM) and white matter (WM) from the MR images. For the classification of PD, CNN and machine learning (ML) classifiers were used to train on the segmented GM and WM. The findings demonstrated that utilizing the segmented GM and WM obtained from MR images at 48 months had a better predictive ability than the data obtainment at the baseline. CNNs did not perform as well as the conventional ML algorithms, especially for the baseline data. This result is most likely due to the smaller dataset available for training the CNNs, as CNNs normally require more data for best performance. Overall, machine learning algorithms were able to distinguish between people with Parkinson’s disease and healthy controls by analyzing GM and WM in brain scans. The classifiers trained at different stages demonstrated varying degrees of accuracy, with the predictive accuracy for the 48-month dataset surpassing that of the baseline data by a significant margin. The accuracy rate for GM was 65.78% at baseline and improved significantly to 92.59% at 48 months. Similarly, the accuracy rate for WM was 60.52% at baseline and improved to 88.89% at 48 months. © The Author(s) 2024.

关键词： Magnetic resonance imaging

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Web of Science-Based Bibliometric Analysis Blockchain-Based Authentication in Cloud Environment

Web of Science-Based Bibliometric Analysis Blockchain-Based ...

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2023 International Conference on Advanced Computing and communication Technologies, ICACCTech 2023

作者： Kaur, Geet Kiran Kaur, Ranjit Singh, Jaswinder Punjabi University Department of Computer Science Engineering Patiala India Punjabi University Department of Electronic and Communication Patiala India

ISBN: (纸本)9798350380880

Cloud computing stores consumer data on third-party servers and provides high-quality, high-performance services. Innovative cloud computing offers flexible infrastructure and simple access, but security challenges persist. Blockchain design uses distributed ledgers to store transaction information across network nodes. Consensus processes verify all system transactions and prevent data changes. Literature study and bibliometric assessment are used to evaluate blockchain-based authentication methods from 2018 to 2023. We evaluate the amount and quality of empirical evidence on blockchain-based authentication and access control solutions. The experts believed that this study topic is vital and may assist future scholars in connecting information and technology. This research may interest blockchain technology researchers and practitioners interested in notable people, subjects, and historical events. Journal articles, conference papers, and book chapters have a worldwide impact on research communities, according to this bibliometric analysis. R bibliometric studies and their methods are described in this article. Visualization clarifies many cloud authentication research topics. © 2023 IEEE.

关键词： Authentication

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Collective Diagnostic Prototypical in Internet of Medical Things for Depression Identification using Deep Learning Algorithm

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Recent Patents on engineering 2024年第9期18卷 24-33页

作者： Selvarajan, Shitharth Thirumalaisamy, Manikandan Manoharan, Hariprasath Devendiran, Ramkumar Lakshmi Narayanan, S. Kumar, Ashwani Department of Computer Science Kebri Dehar University Kebri Dehar250 Ethiopia School of Computer Science Engineering and technology Bennett University U.P India Department of Artificial Intelligence and Machine Learning Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Tamil Nadu Chennai India Department of Electronics and Communication Engineering Panimalar Engineering College Poonamallee Tamil Nadu Chennai600 123 India School of Computer Science and Engineering VIT-AP UNIVERSITY Andhra Pradesh Amaravathi522237 India Gojan School of Business and Technology Chennai India Department of Computer Science and Engineering School of Engineering and Technology Sharda University Greater Noida India

Background: The majority of wearable technology that is present in various patents for Internet of Medical Things (IoMT) health monitoring systems is introduced to recognize various bodily indicators. The enumerated patents indicate that monitored values are sent to a central server, where they are all treated by experts at the appropriate moment. Therefore, a new patent technique by expanding the use of wireless devices, has been discovered that such communication technologies can recognize specific depression traits and mood swings. Objectives: The major objective of the proposed method is to analyze the disputes that arise in the characteristics of an individual by observing the leveling periods that are identified from the processed image. In addition, the rate of data transfer in case of any dispute is maximized therefore recognition problem is solved at a minimized distance. Further, the steady state probability values are achieved at low delay thus minimizing the dropout packets in the monitored system using IoMT and LSTM. Methods: A balanced record with four distinct parameters—such as livelihood, self-reliance, correlation, and precision—is employed with the projected model on IoMT for depression identification. As a result, high data transfer rates and low distance separation are used to process the identification framework. Additionally, by combining an original matrix representation with the input feature set using LSTM, a novel framework with great efficiency is created. Results: In order to assess the results of IoMT using LSTM, four situations are split apart and their probability ratios are calculated. The results of each situation are then contrasted with the current methodology, and it is found that when there is a low dropout ratio, depression in a person is quickly diagnosed. Conclusion: The comparison analysis demonstrates that the proposed method, when compared to the current method, offers the best-compromised outcomes at roughly 64%. © 2024 B

关键词： Data transfer

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Optimizing Self-Healing Distribution Networks with Smart RMUS

Optimizing Self-Healing Distribution Networks with Smart RMU...

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2023 International Conference on Power Energy, Environment and Intelligent Control, PEEIC 2023

作者： Nidhya, M.S. Sharma, Himanshu Gupta, Ashish Department of Computer Bangalore India Noda Institute of Engineering and Technology Department of Electronics and Communication Engineering Uttar Pradesh Greater Noida India Tula's Institute Department of Computer Science and Engineering Dehradun India

ISBN: (纸本)9798350357769

Self-healing (SH) smart grids provide vital intelligence characteristics for timely remedial operations in the case of network failures. Intelligent sensors, smart devices, and contemporary communication technologies are all included into the SH design. Owing to the high cost of SH components, especially the computerized ring main block (SRMU), deployment must be optimized to provide best practices at lowest possible cost. In light of numerous economic concerns, this research presents a unique technique for figuring out the ideal number and placements of SRMUs inside power distribution networks. One important consideration for determining the sites of SRMUs is the connection price for on-grid solar energy (PV) installations. To determine the required number of SRMUs, the technique of nonlinear programming NLP (natural language optimization is used, with a focus on cost/benefit analysis-a crucial consideration for Distribution Corporations (DISCOs). In order to maximize network operating efficiency, the best SRMU sites are also chosen using a combination of integer linear programming (it) optimization, which accounts for the expenses of losses, energy not provided (ENS), and PV disconnection. Interest rates and cable failure rates are taken into account in the process. The Egyptian energy distribution network and an experiment for control center construction using SRMU s are also introduced in the paper. Simulation results showing the effectiveness of the suggested strategy are shown together with application of this approach to a modified IEEE 37-node identified as test feeder of a section of a particular district infrastructure in South Cairo, consisting of 158 nodes with dispersed PV power facilities. © 2023 IEEE.

关键词： distribution networks electricity mixed-integer linear programming (MILP) nonlinear programming (NLP) optimization photovoltaic (PV) plants reliability and cost-effectiveness Self-healing Smart Ring Main Units (SRMUs)

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Optimizing Restroom Resources: A Smart Toilet Paper Dispensing System using Reinforcement Learning Algorithm 10

Optimizing Restroom Resources: A Smart Toilet Paper Dispensi...

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10th International Conference on communication and Signal Processing, ICCSP 2024

作者： Sasikala, D. Lakshmi, V. Vidya Jayanthi, L.N. Muthukumarasamy, S. Joy Winnie Wise, D.C. Thirumaraiselvan, P. Department of Biomedical Engineering Tamil Nadu Namakkal India Velammal Engineering College Department of Electronics and Communication Engineering Tamil Nadu Chennai India Department of Commerce Tamil Nadu Chennai India S.A.Engineering College Department of Computer Science and Engineering Tamil Nadu Chennai India Rajalakshmi Institute of Technology Department of Computer Science and Engineering Chennai Tamil Nadu Thandalam India Adhiparasakthi Engineering College Department of Electronics and Communication Engineering Tamil Nadu Melmaruvathur India

ISBN: (纸本)9798350353068

Public restroom resource management may be enhanced with the help of the study's innovative proposal. Using IoT and RL-based technology, a system is put in place to deal with the ongoing issue of insufficient toilet paper supply in public restrooms. Through the use of Internet of Things (IoT) sensors embedded in the dispensers, the system continuously assesses the stock of toilet paper and transmits that data to a central control platform. An automatic reordering mechanism is initiated to streamline the process of distributing extra supplies when the quantity of toilet paper reaches a certain level. This article delves into the smart bathroom system's design, implementation, and benefits. It addresses the potential operational efficiency, savings, and user convenience that may result from reducing the number of human checks required and the number of disruptions caused by empty dispensers. It also highlights the system's commitment to sustainability and resource conservation initiatives. By suggesting an achievable approach to the management of bathroom resources based on the Internet of Things, this research adds to the optimization of public facilities. This method decreases operating costs while also improving user experiences. © 2024 IEEE.

关键词： Internet of things

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An Effective Cyber Security Threat Detection in Smart Cities Using Dueling Deep Q Networks 4

An Effective Cyber Security Threat Detection in Smart Cities...

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4th IEEE International Conference on Mobile Networks and Wireless communications, ICMNWC 2024

作者： Stanly Jayaprakash, J. Kodati, Sarangam Kanchana, A. Al-Farouni, Mohammed Ramachandra, A.C. Mahendra Institute of Technology Department of Computer Science and Engineering Namakkal India Cvr College of Engineering Department of Information Technology Hyderabad India Mahendra Engineering College for Women Department of Computer Science and Engineering Namakkal India College of Technical Engineering The Islamic University Department of Computers Techniques Engineering Najaf Iraq Nitte Meenakshi Institute of Technology Department of Electronics and Communication Engineering Bengaluru India

ISBN: (纸本)9798350352931

In recent days, smart city technologies are developing rapidly with the incorporation of IoT devices and AI-driven systems has modernized and thus facilitated for enhancing productivity, sustainability, and connectivity. The motive is to mitigate the rising cyber threats which are affecting the interrelated organizations of smart cities. Despite advancements in threat detection technologies, challenges persist, including real-time anomaly detection in vast data streams, managing resource constraints on IoT devices, and ensuring adaptability against evolving attack vectors. Therefore, this research suggests a novel approach by using Dueling Deep Q Networks (DDQN) method. Initially, the data is collected from the dataset and then Inter Quartile Range (IQR) is imposed at the preprocessing stage for detection of outliers. Then, extraction of optimal features is done by using Term Frequency-Inverse Document Frequency (TF-IDF) and Recursive Feature Elimination (RFE) which transforms the data into statistical vectors and eliminates the least significance feature respectively. Finally, the obtained optimal features are detected using DDQN method as it has the ability to detect effectively with respect to the state of action. The proposed DDQN has resulted with accuracy of 99.32% when compared with Improved Bacterial Foraging Optimization using optimum deep learning for Anomaly detection (IBFO-ODLAD). © 2024 IEEE.

关键词： Cyber attacks

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Building Safer Work Environments for Hazard Detection in Construction Sites with IoT and Cloud Server 10

Building Safer Work Environments for Hazard Detection in Con...

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10th International Conference on communication and Signal Processing, ICCSP 2024

作者： Azath, H. Sivanantham, E. Suganthi, B. Swarnalatha, E. Suresh, M. Elango, B. Vit Bhopal University School of Computing Science and Engineering Bhopal India Rajalakshmi Institute of Technology Department of Electronics and Communication Engineering Tamil Nadu Chennai India Rvs College of Engineering and Technology Department of Electronics and Communication Engineering Tamil Nadu Coimbatore India Guru Nanak Institute of Technology Department of Electronics and Communication Engineering Hyderabad Telangana Ibrahimpatnam India Vinayaka Missions Kirupananda Variyar Engineering College Department of Computer Science and Engineering Tamil Nadu Salem India Adhiparasakthi Engineering College Department of Electronics and Communication Engineering Tamil Nadu Melmaruvathur India

ISBN: (纸本)9798350353068

This research explores real-time Internet of Things (IoT) methods to track and mitigate construction site hazards. The construction industry must innovate due to its high accident rate to protect workers. It uses IoT to create a system that watches the construction site and discovers real-time safety issues. The system uses sensors across the construction site to monitor worker location, equipment operation, ambient conditions, and structural integrity. In real-time, the IoT identifies potential threats from this data. Workers, supervisors, and other users receive timely alerts about dangers, enabling quick and focused mitigation. The system's proactive approach to building site safety reduces accidents, injuries, and deaths. With real-time IoT solutions, potential hazards can be discovered early and addressed quickly. Data collection and analysis allow the system to improve safety protocols and practices over time. It highlights the importance of real-time IoT technologies in construction site safety management. IoT devices improve hazard tracking and identification and safety culture by fostering proactive actions and informed decision-making. As buildings embrace digital transformation, this study enables safer and more efficient procedures. © 2024 IEEE.

关键词： Risk management

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Brain Imaging and GRU-CNN Hybrid Framework for Alzheimer's Disease Progression Analysis 4

Brain Imaging and GRU-CNN Hybrid Framework for Alzheimer's D...

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4th International Conference on Sentiment Analysis and Deep Learning, ICSADL 2025

作者： Selvam, L. Dhamotharan, R. Krishnan, R. Santhana Francis Raj, J. Relin Sankar, G. Ram Yamini, G. Srm Madurai College for Engineering and Technology Department of Computer Science and Engineering Pottapalayam Sivaganga India Kalasalingam Academy of Research and Education Department of Computer Science and Information Technology Krishnankoil India Scad College of Engineering and Technology Department of Electronics and Communication Engineering Cheranmahadevi India Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Department of Electronics and Communication Engineering Chennai India Velammal College of Engineering and Technology Department of Computer Science and Engineering Madurai India Vel Tech Rangarajan Dr. Sagunthala R&d Institute of Science and Technology Avadi Department of English Chennai India

ISBN: (纸本)9798331523923

Alzheimer's disease detection and classification using deep learning techniques offer significant advancements in early diagnosis and progression analysis of this neurodegenerative disorder. This approach leverages various medical imaging modalities such as MRI, PET, CT, and fMRI, alongside clinical data like cognitive test results and biomarkers, to assess brain changes associated with Alzheimer's. The focus is on classifying the disease into three stages: Mild, Moderate, and Severe, using advanced machine learning models. Deep learning techniques, such as EfficientNet B7 for 2D imaging and 3D CNNs for volumetric data, enable efficient feature extraction from medical scans. Additionally, Recurrent Neural Networks (RNNs) with GRU layers are used to capture temporal patterns from longitudinal data, improving predictive accuracy. Hybrid models, combining CNNs with attention mechanisms, further enhance classification. The model uses a multi-class classification approach with Softmax activation to predict the likelihood of each stage of Alzheimer's disease. Performance evaluation includes key metrics such as accuracy, precision, recall, F1 score, and AUC (Area Under the Curve). AUC can be calculated in two ways: individual AUC scores for each disease stage or a combined AUC for overall classification. This analysis provides a comprehensive understanding of the model's effectiveness in distinguishing between different stages of Alzheimer's. The results can be integrated into clinical workflows to assist healthcare professionals in diagnosing and monitoring Alzheimer's disease progression, offering a potential path toward more efficient and early intervention strategies. © 2025 IEEE.

关键词： Recurrent neural networks

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An Effective Public-key Authenticated Searchable Encryption for Protecting Sensitive data in Healthcare IoT

An Effective Public-key Authenticated Searchable Encryption ...

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2023 International Conference on Evolutionary Algorithms and Soft Computing Techniques, EASCT 2023

作者： Shanthi, M. Navaneetha, M. Kartheek, G.C.R. Parameshachari, B.D. Cmr Institute of Technology Department of Computer Science and Engineering Bengaluru India Gopalan College of Engineering and Management Department of Computer Science and Engineering Bangalore India Nitte Meenakshi Institute of Technology Department of Electronics and Communication Engineering Bengaluru India

ISBN: (纸本)9798350313413

The practice of conveying medical records to either a Third-Party Cloud Service (TCS) for backups has become increasingly popular in recent decades at least as the healthcare Internet of things has expanded. However, when used in many environments, a few little PAEKS schemes also have security issues. People proposed a novel, efficient, but also effective PAEKS scheme in this article. The Diffie-Hellman pow consensus concept is used in this scheme to generate a secret key known only to the receiver and to the sender. We show that our scheme is completely risk-free against inside keyword various attacks in several co-environments when the Cloudera Diffie-Hellman assumption is used. From the analysis, it shows that proposed method achieved better results in terms of accuracy (96.8%), precision (95.2%), recall (95.8%) f1-score (95.5%), memory utilization (80 MB) and execution time (0.7 sec) respectively. © 2023 IEEE.

关键词： Attribute-Based Encryption Data Preprocessing Healthcare IoT Public-Key Authenticated Searchable Encryption Query Processing Searchable Encryption

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