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Prediction of Heart Disease Using Machine Learning Algorithms 2

Prediction of Heart Disease Using Machine Learning Algorithm...

2nd IEEE International Conference on Interdisciplinary Approaches in technology and Management for Social Innovation, IATMSI 2024

作者： Burle, Rushikesh Gaurkhede, Sanskruti Dewangan, Leelkanth Pacharaney, Utkarsha Datta Meghe Institute of Higher Education and Research Artificial Intelligence and Machine Learning Faculty of Engineering and Technology Mahartashtra Wardha442001 India Datta Meghe Institute of Higher Education and Research Computer Science and Design Faculty of Engineering and Technology Mahartashtra Wardha442001 India Datta Meghe Institute of Higher Education and Research Artificial Intelligence and Data Science Faculty of Engineering and Technology Maharashtra Wardha442001 India

ISBN: (纸本)9798350360523

In India, heart disease claims the lives of about one person every day. The human body's most vital organ is the heart. The heart has two primary purposes: first, it gathers blood from body tissues and pumps it to the lungs;second, it gathers blood from the lungs and pumps it to all body *** disease has claimed the lives of many people. Predicting that disease at the appropriate moment is crucial, for this reason. Making use of machine learning Additionally, illnesses are easily identified and anticipated. It also covers the use of many algorithms on the Heart Disease Dataset. The dataset used in this research was obtained from Kaggle. Analyzing fundamental machine learning algorithms for the prediction of heart disease, such as Random Forest, K-Nearest Neighbours, Decision Tree Classifier, and Support Vector Machine, is the primary goal of this study. Our Acquired Model for The Decision Tree Classifier has a score of 79.0%. And the Random Forest Classifier has an 84.0% score. With a linear kernel, the Support Vector Classifier scores 83.0%. Next, K Neighbours Classifier~ outperformed all previously employed methods, achieving the maximum score of {87%} with ~8 nearest result. RF (Random Forest), K-Nearest Neighbours Algorithm, and Decision Tree, together with Python programming and Visual Studio, which excels at both data and code. © 2024 IEEE.

关键词： Heart

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ML for Sustainable Solutions: Applications in Renewable Energy Optimization and Climate Change Prediction 4

ML for Sustainable Solutions: Applications in Renewable Ener...

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

作者： Awachat, Arya Dube, Ananya Chaudhri, Shivnath Datta Meghe Institute of Higher Education and Research Faculty of Engineering and Technology Department of Artificial Intelligence and Machine Learning Maharashtra Wardha442001 India Datta Meghe Institute of Higher Education and Research Faculty of Engineering and Technology Department of Computer Science and Design Maharashtra Wardha442001 India

ISBN: (纸本)9798331523923

This paper delves into the transformative potential of ML in addressing some of the world's most pressing challenges in optimizing renewable energy and mitigating climate change. How ML algorithms work to improve efficiency within solar and wind energy systems, smart grids, and energy storage technologies is revealed by large data sets and predictive analytics. As ML improves the forecasting of energy, minimizes waste, and allows for better grid integration, its contribution to cutting carbon emissions significantly is immense. In addition, it facilitates climate adaptation by accurately predicting extreme weather occurrences and climate variability. Challenges facing the approach include data paucity, computational cost, and integration with older systems. Interdisciplinary collaboration and newer ML models tackle these challenges well. The study recognizes the significant role of machine learning in creating a sustainable environment and notes its ability to actually reconcile environmental, economic, and social objectives toward a robust and greener future. © 2025 IEEE.

关键词： Clean energy

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Formation Control of Heterogeneous Multi-Agent Systems Under Fixed and Switching Hierarchies

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IEEE Access 2024年 12卷 97868-97882页

作者： Shamrooz Aslam, Muhammad Bilal, Hazrat Chang, Wen-Jer Yahya, Abid Anjum Badruddin, Irfan Kamangar, Sarfaraz Hussien, Mohamed China University of Mining and Technology Artificial Intelligence Research Institute Xuzhou221116 China University of Science and Technology of China Department of Automation Hefei230026 China Department of Marine Engineering Keelung202 Taiwan Botswana International University of Science and Technology Department of Electrical Computer and Telecommunications Engineering Palapye Botswana King Khalid University College of Engineering Department of Mechanical Engineering Abha Saudi Arabia King Khalid University Faculty of Science Department of Chemistry Abha61413 Saudi Arabia

Modern information technology is driving the rapid development of the information and automation industry through the combination of computer technology, communication technology, and control technology. Inspired by nature's cluster movement, such as birds, people proposed the concept of Multi-Agent Systems (MASs). This paper deals with the formation control problem for a class of heterogeneous multi-agent systems with time-delays. First, the formation control of heterogeneous multi-agent systems with communication delay in fixed and switching topologies is investigated. A heterogeneous multi-agent system model with input delay and communication delay is established. Furthermore, based on this model, the formation control protocol is designed and the stability of the proposed control protocol in fixed and switched topologies is analyzed. The first- and second-order multi-agent control system was formulated for both fixed and switched topology, respectively. Then, using the new LMI toolbox, the feasibility of the parameters is obtained and the example is created by MATLAB. The simulation results show that the proposed formation control protocol with delay in this paper can make the systems converge to the expected value under fixed and switched hierarchies. © 2013 IEEE.

关键词： Topology

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Facial expression recognition in videos using hybrid CNN & ConvLSTM

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International Journal of Information technology (Singapore) 2023年第4期15卷 1819-1830页

作者： Singh, Rajesh Saurav, Sumeet Kumar, Tarun Saini, Ravi Vohra, Anil Singh, Sanjay Department of Electronic Science Kurukshetra University Kurukshetra India CSIR-Central Electronics Engineering Research Institute Pilani 333031 India Department of Computer Science Birla-Institute of Technology and Science Pilani 333031 India

The three-dimensional convolutional neural network (3D-CNN) and long short-term memory (LSTM) have consistently outperformed many approaches in video-based facial expression recognition (VFER). The image is unrolled to a one-dimensional vector by the vanilla version of the fully-connected LSTM (FC-LSTM), which leads to the loss of crucial spatial information. Convolutional LSTM (ConvLSTM) overcomes this limitation by performing LSTM operations in convolutions without unrolling, thus retaining useful spatial information. Motivated by this, in this paper, we propose a neural network architecture that consists of a blend of 3D-CNN and ConvLSTM for VFER. The proposed hybrid architecture captures spatiotemporal information from the video sequences of emotions and attains competitive accuracy on three FER datasets open to the public, namely the SAVEE, CK + , and AFEW. The experimental results demonstrate excellent performance without external emotional data with the added advantage of having a simple model with fewer parameters. Moreover, unlike the state-of-the-art deep learning models, our designed FER pipeline improves execution speed by many factors while achieving competitive recognition accuracy. Hence, the proposed FER pipeline is an appropriate candidate for recognizing facial expressions on resource-limited embedded platforms for real-time applications. © 2023, The Author(s), under exclusive licence to Bharati Vidyapeeth's institute of computer Applications and Management.

关键词： 3D convolutional neural networks (3D-CNN) Convolutional LSTM (ConvLSTM) Long short-term memory (LSTM) Video-based facial expression recognition (VFER)

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Interference Cancellation by Using Viterbi Algorithm for Space Base AIS System 2nd

Interference Cancellation by Using Viterbi Algorithm for Spa...

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2nd International Conference on Signal and Data Processing, ICSDP 2022

作者： Parmar, Naina Darji, Mohini Trivedi, Dhruvi Department of Computer Science and Engineering Devang Patel Institute of Advance Technology and Research Charotar University of Science and Technology Gujarat Changa India Charotar University of Science and Technology Gujarat Changa India

ISBN: (纸本)9789819914098

Automatic Identification systems are becoming more popular and more likely to be incorporated Collison minimization in navigational frame-works. The recently developed satellite automatic identification system outperforms the previously used Terrestrial Automatic Identification system in terms of precision. GMSK is used to govern the message in a space-based automatic identification system. Over a self-organized TDMA channel, the regulated message of the automatic identification system (AIS) is then sent and received among boats and satellite Automatic ID Systems. Due to message cover, the conventional single-axis spacecraft Automatic ID System receiver failed to decode the message precisely. In this paper, the implementation of an interference cancellation method corrects the covered AIS message and decodes the obtained data. Very high-speed description language is used to design the receiver, transmitter, and interference cancellation. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Time division multiple access

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A Comprehensive Study on Parkinson's Disease Detection Through Machine Learning Techniques

A Comprehensive Study on Parkinson's Disease Detection Throu...

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2024 International Conference on Intelligent Systems for Cybersecurity, ISCS 2024

作者： Burle, Rushikesh Gaurkhede, Sanskruti Fulkar, Bhushan Gundewar, Swapnil Pacharaney, Utkarsha Verma, Prateek Datta Meghe Institute of Higher Education and Research Artificial Intelligence and Machine Learning Faculty of Engineering and Technology Mahartashtra Wardha442001 India Datta Meghe Institute of Higher Education and Research Computer Science and Design Faculty of Engineering and Technology Mahartashtra Wardha442001 India Datta Meghe Institute of Higher Education and Research Artificial Intelligence and Data Science Faculty of Engineering and Technology Mahartashtra Wardha442001 India

ISBN: (纸本)9798350375237

One of the biggest issues with worldwide public health is Parkinson's disease (PD). It is usually known that about are around one million Americans who have Parkinson's disease, whereas there are approximately five million cases of the condition globally. As a result, it's critical to identify Parkinson's disease early on and make the appropriate treatment plans. These days, machine learning is essential for analysing problems in real time and offering answers thanks to its widely used algorithms. These days, machine learning algorithms are used in the healthcare industry to identify patients' health problems. This research went into great detail about the methods used using algorithms that use machine learning to classify Parkinson's disease. A fault in the brain cells that generate dopamine, a necessary chemical for synapses to connect with one another, is the cause of Parkinson's disease. The brain's dopamine-producing neurons are in charge of control, flexibility, and event familiarity. When 60-80% of these cells disappear, inadequate dopamine is produced, which causes Parkinson's disease symptoms to manifest. In this case, the Random Forest, Decision Tree, K-Nearest Neighbours, and Support Vector Machine techniques were used to identify the illness. Compared to Decision Tree and Support Vector Machine, Random Forest and K-Nearest Neighbours offer the best performance. © 2024 IEEE.

关键词： Support vector machines

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QoS Based Efficient Link and Consistent Routing in Wireless Sensor Network

QoS Based Efficient Link and Consistent Routing in Wireless ...

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

作者： Sivakumar, S. Anusuya, R. Nagaraju, V. Narendruni, Lakshmi Priya Thamizhamuthu, R. Department of Computer Science Tamil Nadu Perambalur India Modern Institute of Technology and Research Centre Department of Computer Science Engineering Rajasthan Alwar India Department of Computer Science Engineering Tamil Nadu Chennai India Nalla Malla Reddy Engineering College Department of Computer Science Engineering Telangana Hyderabad India SRM Institute of Science and Technology Department of Computing Technologies Tamil Nadu Chennai India

ISBN: (纸本)9781665462631

Wireless sensor networks, also known as WSN s, are an essential component for a wide variety of applications, particularly those in which a sizeable number of nodes are scattered all over the region being watched. Evaluation of the accuracy and quality of each connection is necessary for WSN s. When the mapping models between the physical layer and link quality characteristics are used, it is possible to forecast the outcomes, which comes with the advantages of greater vigilance and lower overhead. Traditional link quality estimator parameters, on the other hand, are unable to capitalize on connection quality information and instead deliver inconsistent routing. Simultaneously, the shortest data packet routing action is considered the most energy utilization, and it creates inconsistent routing. QoS-based Efficient Link and Consistent Routing (QLCR) in WSN are proposed to solve these issues. Here, the node Received Signal Strength Indication, Consistency Achievement, Forecasted Consistency, Communication Rate, and Left-over Energy parameters are used to measure the prediction of the link QoS factor. The forward nodes are chosen from the nodes with the greatest QF score. A thorough evaluation of the link's quality may guarantee that data will be sent, improve network performance, and extend the lifespan of the network. The Wireless Sensor Network, often known as WSN, is an important component for a variety of applications in which a sizeable number of nodes are spread out throughout the region being monitored. © 2023 IEEE.

关键词： Sensor nodes

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An Anonymous IoT-Based E-Health Monitoring System Using Blockchain technology

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IEEE Systems Journal 2023年第2期17卷 2422-2433页

作者： Samuel, Omaji Omojo, Akogwu Blessing Mohsin, Syed Muhammad Tiwari, Prayag Gupta, Deepak Band, Shahab S. Department of Computer Science Osara264103 Nigeria Edo State University Uzairue300281 Nigeria Shestco Applied Mathematics and Simulation Advanced Research Centre Abuja186 Nigeria Comsats University Islamabad Department of Computer Science Islamabad45550 Pakistan Aalto University Department of Computer Science Espoo02150 Finland Department of Computer Science and Engineering Maharaja Agrasen Institute of Technology Delhi110086 India Research Advisor Ucrd Chandigarh University GharuanPunjab Mohali140413 India National Yunlin University of Science and Technology Future Technology Research Center Douliou64002 Taiwan

The Internet of Things (IoT) has made it possible for health institutions to have remote diagnosis, reliable, preventive, and real-time decision-making. However, the anonymity and privacy of patients are not considered in IoT. Therefore, this article proposes a blockchain-based anonymous system, known as GarliMediChain, for providing anonymity and privacy during COVID-19 information sharing. In GarliMediChain, garlic routing and blockchain are integrated to provide low-latency communication, privacy, anonymity, trust, and security. Also, COVID-19 information is encrypted multiple times before transmitting to a series of nodes in the network. To ensure that COVID-19 information is successfully shared, a blockchain-based coalition system is proposed. The coalition system enables health institutions to share information while maximizing their payoffs. In addition, each institution uses the proposed fictitious play to study the strategies of others in order to update its belief by selecting the best responses from them. Furthermore, simulation results show that the proposed system is resistant to security-related attacks and is robust, efficient, and adaptive. From the results, the proposed proof-of-epidemiology-of-interest consensus protocol has 15.93% less computational cost than 26.30% of proof-of-work and 57.77% proof-of-authority consensus protocol, respectively. Nonetheless, the proposed GarliMediChain system promotes global collaborations by combining existing anonymity and trust solutions with the support of blockchain technology. © 2007-2012 IEEE.

关键词： COVID-19

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Cognitive Radio-Based Spectrum Sensing for Dynamic Spectrum Access in Sensor Ad Hoc Networks 15

Cognitive Radio-Based Spectrum Sensing for Dynamic Spectrum ...

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

作者： Geetha, B. Kansal, Amit Sudhamsu, Gadug Gawali, Piyush P. Amudha, A. Samrat, Bhavuk Prince Shri Venkateshwara Padmavathy Engineering College Department of Science and Humanities Chennai127 India Quantum University Research Center Quantum University Uttar Pradesh India School of Engineering and Technology Computer Science and Engineering Karnataka Bangalore India Vishwakarma Institute of Technology Department of Computer Engineering Pune India Karpagam Academy of Higher Education Department of Electrical and Electronics Engineering Coimbatore641021 India Chitkara University Institute of Engineering and Technology Chitkara University Centre for Interdisciplinary Research for Business and Technology Punjab India

ISBN: (纸本)9798350370249

Cognitive Radio-primarily based Spectrum Sensing (CRSS) is a crucial generation for permitting dynamic spectrum access in sensor ad hoc networks (SAHNs). This era allows customers to successfully make use of to be had spectrum whilst keeping off interference from existing customers. CRSS makes use of sensing and optimization strategies to identify free frequency bands to enable verbal exchange in SAHNs. Moreover, the CRSS additionally consists of protocols and techniques to locate and defend against malicious behavior, which include spectrum jamming and false sensing reports. Subsequently, CRSS may be blended with superior technology, such as multi-agent structures and system gaining knowledge of, to create an efficient and sturdy gadget for dynamic spectrum access. © 2024 IEEE.

关键词： Frequency Generation Malicious Permitting Primarily Spectrum Techniques technology

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Using Blue Whale technology: An ML Edge Self-Adaptable Vehicle Slowdown Earliest Warning Information System 8th

Using Blue Whale Technology: An ML Edge Self-Adaptable Vehic...

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8th International Conference on Microelectronics Electromagnetics and Telecommunications, ICMEET 2023

作者： Murali, N. David, D. Beulah Research Scholar Department of Computer Science and Engineering Saveetha School of Engineering SIMATS Tamilnadu Chennai India Department of Data Analytics Institute of Information Technology Saveetha School of Engineering SIMATS Tamilnadu Chennai India

ISBN: (纸本)9789819784219

Human life is challenged by this main work’s ultimate goal of reducing accidents and ensuring life safety due to the enormous growth of vehicles and those based on safety. Here, cases of suspected drunk driving, reckless driving, etc., are present. In this scenario, vehicles are traveling at full speed and irresponsibly using technology on all roads. An early warning system is used to stop auto accidents. In order to accommodate the city's steadily increasing automobile population, intelligent traffic systems must be designed in a resourceful and sustainable manner using all available technology in both light and dark. The advanced ML's work on unexpected traffic flow and all road conditions resulted in significant real-time changes in the flow of traffic for every significant issue that arose. Here, the Blue Whale technology makes use of clever intelligence to quickly identify, detect, and modify warning systems. The primary objective of the study was to train CNN/BWT on the collected data sets in order to operationalize the unsupervised approach for the prediction of critical situations. Additionally, it makes an effort to clarify the situation so that guests can feel more at ease and help them decide on transfers in time-sensitive situations. IOT is a practical way to locate the nearest object using machine learning methods that are accurate and apply received data sets, resulting in significant findings that support object recognition. The paperwork fully focuses on improving driving knowledge and offering new approaches for quick object recognition. The Fast Warning System Initiative, which aims to increase daytime safety and security, is providing a prior warning system. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Backward wave tubes

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