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3rd International Conference on Smart Generation Computing, Communication and Networking, SMART GENCON 2023

作者： Ghule, Vijaykumar R. Agrawal, Ankur Kumar Kumar, Jambi Ratna Raja Mondal, dipannita Hemelatha, S. Preetha, M. Vishwakarma Institute of Information Technology Department of Artificial Intelligence & Data Science Pune India Mangalayatan University Department of Management Aligarh India Genba Sopanrao Moze College of Engineering Balewadi Department of Computer Engineering Maharashtra Pune India Dr. D.Y. Patil College of Engineering and Innovation Artificial Intelligence and Data Science Department Talegaon India Karpagam Academy of Higher Education Department of Computer Science Engineering Coimbatore India Prince Shri Venkateshwara Padmavathy Engineering College Department of Computer Science and Engineering Chennai India

ISBN: (纸本)9798350319125

The emergence of synthetic intelligence (AI) has substantially revolutionized the way records is accessed and optimized. With AI-powered equipment and algorithms, groups are capable of efficiently method and manipulate large amounts of data, main to upgrades in overall performance and decision-making. This paper pursuits to evaluate the impact of AI on statistics get entry to optimization performance, discussing both the potential blessings and challenges that include its integration. One of the major blessings of using AI for information get entry to optimization is its capability to automate and streamline obligations. AI-powered structures can analyze and categorize massive datasets at a quicker and extra correct rate than human beings, liberating up time and assets for different obligations. This results in stronger efficiency and productivity in statistics control methods. Further, AI also can enhance the accuracy and best of facts access. thru system learning and predictive analytics, AI structures can identify styles and correlations in information, providing treasured insights for decision-making. This now not simplest improves the speed at which records is accessed, however also the reliability and relevance of the data. However, there are also demanding situations associated with the combination of AI in facts get admission to optimization. one of the fundamental worries is the potential bias in algorithms, which can result in erroneous or discriminatory selection-making. it's far vital for groups to carefully display and review the algorithms utilized by AI structures. © 2023 IEEE.

关键词： accuracy discriminatory predictive structures treasured

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Ensuring Secure Cloud Communication with MedTech devices Using a Public Key Infrastructure 8

Ensuring Secure Cloud Communication with MedTech Devices Usi...

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8th IEEE International Conference on Computational System and Information Technology for Sustainable Solutions, CSITSS 2024

作者： Harshitha, K. Hitesh, S. Mamatha, K.R. Parimi, Praagnya Roy, Sandip Kumar Sharan, Preeta The Oxford College of Engineering Department of Electronics and Communication Engineering Bangalore India Bms institute of Technology and management Department of Electronics and Communication Bangalore India Pes University Department of Computer Science Engineering Bangalore India S P Jain School of Global Management Dubai United Arab Emirates The Oxford College of Engineering Department of Electronics and Communication R&d Bangalore India

ISBN: (纸本)9798331505462

In the current landscape of digital healthcare, the integrity of communication between health device applications and central data repositories is paramount. To safeguard this data exchange cryptographic measures are employed. Health devices act as clients, gathering and transmitting patient information to a central server. It undergoes processing, is securely stored, shielded by potent encryption protocols like AES and RSA. The objective of this research is to leverage characteristics of Public Key Infrastructure (PKI) in protecting medical data transfer. This involves the application of the RSA algorithm to fortify the communication pathways between health device applications and the server. By reinforcing these communication conduits, the current work posits that we can substantially reduce the hazards linked to data compromises and cyber incursions. The emphasis of the current study is on the healthcare domain, thereby enhancing patient confidentiality and bolstering confidence in digital health solutions. Key performance indicators used are time for encryption and CPU usage. The improved encryption performance achieved is 0.0125 ms/byte. The optimized CPU utilization for the same data size (8 bytes) is 91.97%. © 2024 IEEE.

关键词： Electronic health record

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Toward Personalization of User Preferences in Partially Observable Smart Home Environments

IEEE Transactions on Artificial Intelligence

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IEEE Transactions on Artificial Intelligence 2023年第3期4卷 549-561页

作者： Suman, Shashi Rivest, Francois Etemad, Ali Queen's University Department of Electrical and Computer Engineering KingstonONK7L 3N6 Canada Royal Military College Department of Mathematics and Computer Science KingstonONK7K 7B4 Canada

The technologies used in smart homes have recently improved to learn the user preferences from feedback in order to enhance the user convenience and quality of experience. Most smart homes learn a uniform model to represent the thermal preferences of users, which generally fails when the pool of occupants includes people with different sensitivities to temperature, for instance, due to age and physiological factors. Thus, a smart home with a single optimal policy may fail to provide comfort when a new user with a different preference is integrated into the home. In this article, we propose a Bayesian reinforcement learning framework that can approximate the current occupant state in a partially observable smart home environment using its thermal preference and, then, identify the occupant as a new user or someone who is already known to the system. Our proposed framework can be used to identify users based on the temperature and humidity preferences of the occupant when performing different activities to enable personalization and improve comfort. We then compare the proposed framework with a baseline long short-term memory learner that learns the thermal preference of the user from the sequence of actions that it takes. We perform these experiments with up to five simulated human models each based on hierarchical reinforcement learning. The results show that our framework can approximate the belief state of the current user just by its temperature and humidity preferences across different activities with a high degree of accuracy. © 2020 IEEE.

关键词： Reinforcement learning

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A Novel Framework for Efficient Handover Mechanism in d2d Communication Using Networking Principles 4

A Novel Framework for Efficient Handover Mechanism in D2D Co...

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4th International Conference on Smart Electronics and Communication, ICOSEC 2023

作者： Jayanthi, S. Raja, Mandapati Jackson, Beulah Jayasree, S. Nithya, A. Jeneetha Jebanazer, J. R. M. D Engineering College Department of Electronics and Communication Engineering Tamil Nadu Chennai India CMR Engineering College Department of Electronics and Communication Engineering Telangana India Vel Tech Rangarajan Dr. Sagunthala R and D Institute of Science and Technology Department of Electronics and Communication Engineering Tamil Nadu Chennai India Prince Shri Venkateshwara Padmavathy Engineering College Department of Chemistry Tamil Nadu India R. M. K College of Engineering and Technology Department of Computer Science and Engineering Tamil Nadu Chennai India Panimalar Engineering College Department of ECE Tamilnadu Chennai India

ISBN: (纸本)9798350300888

Software defined Networking (SdN) technology utilizes software to manage and alter the current networks to improve the networking and interfacing capabilities. It does this by providing a split between the control plane and the data plane. On the SdN controller, each function is run as an application. This paper suggests a structure made up of a unified RAN and an SdN-based core network. For mobile terminals to access the services of the network operator, as well as the Internet and other cloud-based apps, all radio access points in the RAN are connected with the aid of OpenFlow interfaces. decentralized Content delivery Networks (CdN) manage mobility without requiring data to pass via the core network, which minimizes signaling. To gauge the signaling burden, traffic steering between the framework's different entities is examined. The suggested work establishes criteria based on a combination of fundamental factors, such as Signal Strength, distance, and Speed, to assure service continuity of mobile nodes in heterogeneous networks. The parametric analysis of the handover is contrasted with that of the current designs. The suggested framework also provides ProSe capability for a select number of desirable mobile devices in the network and is configured for d2d connection. © 2023 IEEE.

关键词： Heterogeneous networks

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Hidden Markov Model with Machine learning-Based Black hole Attack Identification in Wireless Sensor Networks

Hidden Markov Model with Machine Learning-Based Black hole A...

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2023 IEEE International Conference on Intelligent and Innovative Technologies in Computing, Electrical and Electronics, ICIITCEE 2023

作者： Balasubadra, K. Shiny, X.S.Asha Pramila, P.V. Solainayagi, P. Maniraj, S.P. R.M.D Engineering College Department of Information Technology Tamil Nadu Chennai India Department of Computer Science and Engineering Telangana Hyderabad India Department of Computer Science Engineering Tamil Nadu Chennai India Aarupadai Veedu Institute of Technology Department of Computer Science and Engineering Tamil Nadu Paiyanoor India SRM Institute of Science and Technology Department of Computer Science and Engineering Tamil Nadu Chennai India

ISBN: (纸本)9781665462631

Wireless Sensor Network (WSN) is lying to several attacks because the Black hole is problematic to distinguish and protect. Impostor internments and re-programs block the packets rather than transmitting and obtaining them at the base station (BS) in a black hole attack. Consequently, any data that arrives in the black hole area is taken and cannot reach the recipient, triggering high packet losses and decreasing network performance. To overcome this problem, a hidden Markov model with machine learning (HMMl)-based black hole attack identification in WSN is introduced. This approach uses the Hidden Markov Model (HMM) to detect the blackhole node in the WSN. This model examines the shortest routes between the sender and the recipient to discover the black hole route. This model contains states as well as the observation sequence. Forward and received count and reply of non-adjacent nodes are used for verifying the shortest routes. Here, the supervised and unsupervised training machine learning methods decide for detecting black hole nodes. © 2023 IEEE.

关键词： Hidden Markov models

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Smart Tracking and Surveillance Systems for Automated delivery Robots in Food and E-Commerce Systems 2

Smart Tracking and Surveillance Systems for Automated Delive...

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2nd International Conference on Self Sustainable Artificial Intelligence Systems, ICSSAS 2024

作者： Vimala Ithayan, J. Ranganathan, Chitra Sabapathy Vinola, C. Venkatesh, G. Meenakshi, R. Muthulekshmi, M. Vel Tech Rangarajan Dr. Sagunthala R&d Institute of Science and Technology Department of Computer Science and Engineering Tamil Nadu Chennai India Mphasis Corporation Tamil Nadu Chennai India Sri Sairam College of Engineering Department of Computer Science and Engineering Karnataka Bengaluru India Koneru Lakshmaiah Education Foundation Department of Computer Science and Engineering Andhra Pradesh Vaddeswaram India Chennai Institute of Technology Department of Computer Science and 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

ISBN: (纸本)9798350368413

This research study provides an innovative approach to creating smart monitoring and surveillance systems for autonomous delivery robots in the food and e-commerce industries. The system aims to increase the effectiveness and security of delivery operations by integrating ultrasonic sensors, radio frequency (RF) modules, cameras, remote controls, and speaker technologies. The robot can recognize obstacles and avoid them due to the use of an ultrasonic sensor. The system includes an RF module to provide seamless connection and remote control. This allows the robot and the central control unit to exchange data in real-time, simplifying remote monitoring, navigational corrections, and operational management. The camera module monitors the robot's surroundings while recording real-time video visually. Operators or managers are given a remote control so users can monitor and manage the robot's motions, observe the live video feed, and make smart decisions during the delivery process. The system utilizes a speaker to provide voice-based alerts and notifications. The work advances delivery services in the food and e-commerce industries by enhancing operational efficiency, security, and safety. The performance of the system is analysed in terms of response times, reliability, and accuracy rates and a comparative analysis is also made to show the effectiveness of the system. © 2024 IEEE.

关键词： Ultrasonic sensors

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Exploration of Pretrained Models used in Automatic Speech Recognition System: A Review and Analysis 6

Exploration of Pretrained Models used in Automatic Speech Re...

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6th International Conference on Recent Trends in Advance Computing, ICRTAC 2023

作者： Suhasini, S. Pandiaraj, A. Ramshankar, N. R. M. D. Engineering College Department of Computer Science and Engineering India Srm Institute of Science and Technology Department of Computing Technologies Chennai Kattankulathur India

ISBN: (纸本)9798350394702

Automatic Speech Recognition (ASR) systems have made significant strides in recent years, we have advent of deep learning and the availability of large-scale pretraining methods. This research paper presents a comprehensive review and analysis of leveraging pretrained models for ASR systems. It explores the benefits of using pretrained models, examines various architectures and techniques, discusses challenges and limitations, and suggests future directions for research and development in this field. © 2023 IEEE.

关键词： deep learning

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Machine learning Classifiers to decrease diabetic Patients Probability of Hospital Readmission 4

Machine Learning Classifiers to Decrease Diabetic Patients P...

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4th International Conference on Electronics and Sustainable Communication Systems, ICESC 2023

作者： Thenappan, S. Ramshankar, N. Hemavathy, N. Subashree, V. Manjul, Ravi Ranjan Rajeswari, J. Department of Electronics and Communication Engineering Vel Tech Rangarajan Dr.Sagunthala R&D Institute of Science and Technology Chennai India R. M. D. Engineering College Department of Computer Science and Engineering Tamilnadu Chennai India Velammal Engineering College Department of Electronics and Communication Engineering Tamilnadu Chennai India Ramchandra Chandarvansi Institute of Technology Department of Mechanical Engineering JharkhandBishrampur Palamu India Agni College of Technology Department of Electronics and Communication Engineering Tamilnadu India

ISBN: (纸本)9798350300093

diabetes is a chronic condition that affects millions of individuals around the globe. Unfortunately, patients with diabetes have a significant risk of hospital readmission following discharge. Using machine learning classifiers, proposed a strategy to reduce the risk of readmission for 768 diabetes patients. This utilized a dataset containing patient demographic information, medical history, and laboratory results which are collected from Pima India dataset. To predict the risk of readmission, and train several Machine learning models, including logistic Regression, Random Forest, and Support Vector Machines. The research indicates that the Random Forest model has the maximum degree of precision, at 97%. Age, glucose level, Hba1c levels, and comorbidities have been identified as the most significant factors contributing to the risk of readmission. On the basis of these findings, this recommends a personalized approach to diabetes management that takes individual patient characteristics and risk factors into account. This strategy could reduce the likelihood of readmission for diabetic patients and enhance their overall health. © 2023 IEEE.

关键词： Hospitals

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logistic Regression for Personalized Hypertension Risk Profiling in Cloud-Integrated Healthcare Systems

Logistic Regression for Personalized Hypertension Risk Profi...

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2024 Asian Conference on Intelligent Technologies, ACOIT 2024

作者： Saravanan, S. Ramya, S. Monikapreethi, S.K. Kavididevi, Venkatesh Kovarasan, Rajesh Kambattan Sujatha, S. Department of Electrical and Electronics Engineering Muthayammal Engineering College Tamil Nadu Namakkal India Department of Cyber Security Paavai Engineering College Tamil Nadu Namakkal India Department of Electronics and Communication Engineering Rajalakshmi Institute of Technology Tamil Nadu Chennai India Department of Information Technology Vardhaman College of Engineering Telangana Hyderabad 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 Biomedical Engineering Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Saveetha University Tamil Nadu Chennai India

ISBN: (纸本)9798350374933

Advanced analytics for personalized chronic disease risk profiles are becoming possible as healthcare systems combine cloud-based technology. This work applies logistic regression (lR) models to cloud-integrated healthcare systems for personalized hypertension risk profiling. A large dataset of heterogeneous patient records was used to train and evaluate the lR model, including clinical characteristics and lifestyle variables. To assess the lR model's predictive accuracy in identifying high-risk hypertension patients and to provide a framework for personalized risk stratification that can be seamlessly integrated into cloud-based healthcare systems. The model's performance was rigorously cross-validated and compared to risk assessment methods. The lR approach predicted hypertension risk better than older techniques. Healthcare professionals and patients found the cloud integration of the personalized risk profiling system convenient, scalable, and accessible. It shows that cloud-based analytics may improve preventive healthcare by personalizing treatments to risk profiles. lR techniques can optimize personalized hypertension risk management in cloud-integrated systems, advancing data-driven healthcare. © 2024 IEEE.

关键词： Cloud Integration Healthcare Systems Hypertension Risk Personalized Medicine Predictive Modeling

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A Comparative Analysis of denoising PCG Signals using Adaptive Wiener-Kalman Filtering

A Comparative Analysis of Denoising PCG Signals using Adapti...

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International Conference on Computation System and Information Technology for Sustainable Solutions (CSITSS)

作者： A. Pandiaraj N. Ramshankar A. Gnanasekar Arunchandran R Azariah John B S Febin C Department of Computing Technologies SRM Institute of Science and Technology Kattankulathur Chennai Department of Computer Science and Engineering R.M D. Engineering College Department of Computer Science and Engineering R.M D. Engineering College kavaraipettai Computer Science and Engineering R.M.D Engineering College Chennai India

ISBN: (数字)9798331505462

ISBN: (纸本)9798331505479

Cardiovascular disease (CVd) is the group of disorder occurs on heart and blood vessel, and they are coronary heart disease, cerebrovascular disease, rheumatic heart disease, etc. Particularly people under the age of 70 are mostly affected by this disease. Earlier cardiologist analyse phonocardiography (PCG) signals to know the condition of heart. Because of the undesirable noise present in PCG signal makes complication in diagnosis by experts. This study analyse and denoising heart sound using five different techniques namely Short-Time Fourier Transform (STFT), Empirical Mode decomposition (EMd), Recurrent Neural Network (RNN), weiner filter (WF), adaptive Wiener-Kalman filtering (AWKF). There are lot of noise removal techniques available as discussed in the literature, failed to achieve higher performance in denoising audio signal. The developed denoising techniques were applied on various pathological heart sounds and on heart sound recorded in a noisy environment. The AWKF filtering has great impact on removing distortion and interference present on the signal. This method reserves the major characteristics of heart beat sound. The experimental results showed that AWKF acquired higher SNR, PSNR, lower RMSE and great denoising effect.

关键词： Heart Adaptation models Recurrent neural networks Filtering Heart beat Computational modeling Noise reduction Predictive models Phonocardiography Signal to noise ratio

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