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Efficient Cardiovascular Health Monitoring System for Heart Disease Detection Based on machine learning Technique 2

Efficient Cardiovascular Health Monitoring System for Heart ...

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2nd international conference on Recent Trends in Microelectronics, Automation, Computing, and Communications Systems, ICMACC 2024

作者： Kumar, Kottu Santosh Sahoo, Ramesh Chandra Rout, Saroja Kumar Mohammad, Manzoor Jit, Biswa Ranjan Reddy, S. Eswar Kar, Bhabesh Ranjan Department of Information Technology Hyderabad India Manav Rachna International Institute of Research and Studies Faridabad India Hyderabad India School of Professional Studies DRIEMS UNIVERSITY Odisha Cuttack India Bhubaneswar India Cast and Crew LLC Hyderabad India

ISBN: (纸本)9798350366570

Heart diseases are a global leading cause of death, affecting nations universally. Early detection and machine learning assistance can mitigate mortality despite medical complexities. Hence, timely diagnosis is crucial to prevent heart attacks. This study aims to identify a machine-learning method that strikes a balance between computational efficiency and prediction accuracy when it comes to heart disease prognosis. This work proposes a model integrating various methods for effective heart disease prediction. Employing efficient data collection, pre-processing, and transformation, our model uses an ensemble approach that includes Six classification machine learning algorithms- SVM, DT, RF, LR, KNN, ANN, and PCA. The study identifies significant heart disease features, predicting the highest risk through machine learning. Experimental outcomes indicate prediction accuracy obtained by SVM, DT, RF, LR, KNN, ANN, and PCA. as a feature selection process are 91%, 90%, 86%, 91%, 88%, and 91%. Collaboration is vital for enhancing algorithm precision in clinical settings. The application of machine learning to cardiovascular health has the potential to transform continuous management, personalized treatment, and early diagnosis. Robust clinical tools for better patient outcomes and public health are ensured by addressing issues like data privacy and model interpretability and by fostering interdisciplinary collaboration. © 2024 IEEE.

关键词： Patient treatment

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ICREST 2021 - 2nd international conference on Robotics, Electrical and signal processing Techniques

ICREST 2021 - 2nd International Conference on Robotics, Elec...

引用

2nd international conference on Robotics, Electrical and signal processing Techniques, ICREST 2021

ISBN: (纸本)9781665415767

The proceedings contain 153 papers. The topics discussed include: designing of a handbag for women safety;a web based four-tier architecture using reduced feature based neural network approach for prediction of student performance;voice activated portable braille with audio feedback;transient stability enhancement of power system by virtual synchronous generator control of battery;blockchain-based peer-to-peer sustainable energy trading in microgrid using smart contracts;treatment of water algae by water surface discharge plasma;nicotine sensing by photonic crystal fiber in THz regime;electronic medical record data sharing through authentication and integrity management;improved machine learning based classification model for early autism detection;and an adaptive graph cut algorithm for spammer group detection from weighted one mode projection of bipartite graph.

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Basic Daily Activity Recognition with a Data Glove 10

Basic Daily Activity Recognition with a Data Glove

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10th international conference on Ambient Systems, Networks and Technologies (ANT) / 2nd international conference on Emerging Data and Industry 4.0 (EDI40)

作者： Maitre, Julien Rendu, Clement Bouchard, Kevin Bouchard, Bruno Gaboury, Sebastien Univ Quebec Chicoutimi 555 Blvd Univ Chicoutimi PQ G7H 2B1 Canada

Many people in the world are affected by the Alzheimer disease leading to the dysfunctionality of the hand. In one side, this symptom is not the most important of this disease and not much attention is given to this one. In the other side, the literrature provides two main solutions such as computer vision and data glove allowing to recognize hand gestures for virtual reality or robotic applications. From this finding and need, we decided to developed our own data glove prototype allowing to monitor the evolution of the dysfunctionality of the hand by recognizing objects in basic daily activities. Our approach is simple, cheap (similar to 220$) and efficient (similar to 100% of correct predictions) considering that we are abstracting all the theory about the gesture recognition. Also, we can access directly and easily to the raw data. Finally, the proposed prototype is described in a way that researchers can reproduce it. (C) 2019 The Authors. Published by Elsevier B.V. This is an open access article under the CC BY-NC-nd license (http://***/licenses/by-nc-nd/4.0/) Peer-review under responsibility of the conference Program Chairs.

关键词： Data glove Object recognition Basic daily activities signal processing machine learning

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A Review of Multimedia processing and its Application in MIDI 2

A Review of Multimedia Processing and its Application in MID...

引用

2nd international conference on Innovative Sustainable Computational Technologies, CISCT 2022

作者： Manan Verma, Gaurav Singh, Shalini Kukreti, Kamlesh Department of Electronics and Communication Engineering Dehradun India

ISBN: (纸本)9781665474160

The motive of Multimedia signal processing (MSP) is to integrate text, sound, image, and video information into a single communications channel, and to get high-quality communication. It also aims to provide an easy-to-use interactive platform to users worldwide. Musical Instrument Digital Interface (MIDI) is a digital language that allows the connection and interaction of multiple hardware and software instruments, computers and similar devices. This paper presents a review of multimedia processing and its application in MIDI technology to understand the processing of music signals, to find methods to extract desired parameters from those signals and convert them into digital MIDI data. The objective of this paper is to find existing research and scientific tasks performed by various researcher communities. Additionally, it seeks to discover novel uses for this technology in communications and to investigate uncharted territory in this area. Various collaborations of multimedia processing with technologies like Artificial Intelligence (AI), machine learning (ML), and Deep learning are currently under use and research. There are different types of multimedia, such as audio, video, text, graphics, etc. MIDI is mostly used for audio processing, but productive and efficient use of MIDI in other domains is still a challenge. This paper will help in finding new paradigm and possible solutions for the challenges of multimedia signal processing for MIDI applications. © 2022 IEEE.

关键词： Music

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A classification method of frequency hopping signal based on Convolutional Neural Network 2

A classification method of frequency hopping signal based on...

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2021 2nd international conference on machine learning and Computer Application, ICMLCA 2021

作者： Qian, Bo Wang, Pei-Sen Li, Yue Chen, Xi Institute of information science and technology Shenyang Ligong University Shenyang China

ISBN: (纸本)9783800757398

In order to solve the problem of low classification accuracy for frequency hopping signals under low signal-to-noise ratio, a classification method of frequency hopping signal is presented. By using STFT to gain time-frequency distribution features, and reducing noise component, the time-frequency distribution image of frequency hopping signal is got. By training convolutional neural network for time-frequency distribution image, the frequency hopping signals are classified. The simulation results show that the average recognition accuracy of frequency hopping signal reaches 90.61% when the signal-to-noise ratio is 0dB. Because of avoiding to use the large amount of sampling data directly to extract features, the method improves the processing efficiency. © VDE VERLAG GMBH · Berlin · Offenbach.

关键词： signal to noise ratio

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Deep learning based Framework for Underwater Acoustic signal Recognition and Classification 18

Deep Learning based Framework for Underwater Acoustic Signal...

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2nd international conference on Computer Science and Artificial Intelligence (CSAI) / 10th international conference on Information and Multimedia Technology (ICIMT)

作者： Wu, Hao Song, Qingzeng Jin, Guanghao Tianjin Polytech Univ Sch Comp Sci & Software Engn Tianjin Peoples R China

ISBN: (纸本)9781450366069

The recognition and classification are important to the research contents like underwater acoustic signal processing. Generally, the state-of-the-art signal recognition systems depend on feature extraction that is based on the knowledge or experience of experts, so that, it can efficiently represent target signatures. In contrast, this paper introduces a new framework based on deep learning methods to preprocess signals and extract features for the recognition and classification of underwater acoustic signals, which present the targets of ships and torpedoes. In our framework, signals are firstly transformed to spectral images of LOFAR, so that, those images can be extracted and classified by convolutional neural networks (CNN). The experimental results show that our deep learning based framework can obtain high classification accuracy in underwater acoustic signals case with the transformation to LOFAR spectrum. The accuracy of our best version reaches 97.22%, higher than those that use other networks, and achieved the expected objectives for real applications.

关键词： Underwater Acoustic signal LOFAR Spectrum Deep learning Recognition and Classification

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A Comparative Analysis of Ensemble Based machine learning Techniques for Diabetes Identification 2

A Comparative Analysis of Ensemble Based Machine Learning Te...

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2nd international conference on Robotics, Electrical and signal processing Techniques, ICREST 2021

作者： Taz, Nahid Hossain Islam, Abrar Mahmud, Ishrak Department of Electrical and Electronic Engineering Islamic University of Technology Gazipur Bangladesh

ISBN: (纸本)9780738130408

This manuscript develops a framework to deliver prediction of diabetes using the Ensemble-based machine learning techniques on Pima Indian Diabetes Dataset (PIDD). We investigate the performance measures of four distinct machine learning classifiers separately following by a soft voting classifier in order to enhance the performance metrics of Diabetes Identification. The classifications are estimated according to average classification accuracy, precision, recall, f1 score and ROC AUC of individual classifier models. LightGBM, XGBoost, AdaBoost and Random Forest classifiers are employed as distinct classifiers, and a Soft Voting classifier is further introduced to improve the robustness of performance metrics. Analyzed performance metrics results confirm that LightGBM has produced the optimum result as an individual classifier with accuracy score 94% and ROC AUC 95%, and Soft Voting classifier has additionally improved the complete structure to accuracy score 95% and ROC AUC 96%. © 2020 IEEE

关键词： Adaptive boosting

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Design of Wallace Multiplier Using Novel Approximate 4:2 Compressors 2

Design of Wallace Multiplier Using Novel Approximate 4:2 Com...

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2nd international conference on Paradigm Shifts in Communications Embedded Systems, machine learning and signal processing, PCEMS 2023

作者： Jonnalagadda, Srinivas Pavan Avutapalli, Ram Kumar Bobbadi, Venkata Jayasri Pranitha Bagati, Keerthi Kumar, Gundugonti Kishore V.R. Siddhartha Engineering College Department of Electronics and Communication Vijayawada520007 India

ISBN: (纸本)9798350310719

In this study, we suggested an approximation multiplier that employs an approximate 4-2 compressor and is energy-efficient. When compared to the current designs, the suggested compressor has a small area. The results of simulations reveal that the suggested approximation multipliers display a reasonable decrease in Mean Error Distance, Mean Relative Error Distance, Normalized Mean Error Distance, compared to multiplier that is designed with exact compressors. The Power, Delay and Area of multipliers developed with this approximate compressor is superior to that obtained with previously suggested approximate compressors, according to implementation results in 90nm CMOS technology. © 2023 IEEE.

关键词： Energy efficiency

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Wireless signal Service Type Identification Based on Convolutional Neural Network 2

Wireless Signal Service Type Identification Based on Convolu...

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IEEE 2nd international conference on Electronic Information and Communication Technology (ICEICT)

作者： Tian, Haomin Yin, Liang Yang, Xiaofeng Li, Shufang Yu, Haoyang Beijing Univ Posts & Telecommun Beijing Lab Adv Informat Networks Sch Informat & Commun Engn Beijing Peoples R China Beijing Univ Posts & Telecommun Beijing Key Labratory Network Syst Architecture & Sch Informat & Commun Engn Beijing Peoples R China Beijing Univ Posts & Telecommun Beijing Peoples R China

ISBN: (纸本)9781538692981

Nowadays, with the birth of 5G and the Internet of Things, more and more business signals have emerged. The identification of signal service types has become a hot research topic. Whether it is suitable for daily life in the military field, there is a wide application prospect. Using the power spectrum data of the wireless signal, the characteristics of the power spectrum waveform are captured to identify the type of traffic of the wireless signal. This paper proposes the use of convolutional neural networks to extract and classify the wireless signal power spectrum data. After hundreds of iterative training, it can achieve an ideal recognition effect.

关键词： signal recognition convolutional neural network deep learning feature extraction

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Overview of machine learning for Bioengineering EEG signal processing 2

Overview of Machine Learning for Bioengineering EEG Signal P...

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2nd international conference on Intelligent Cyber Physical Systems and Internet of Things (ICoICI)

作者： Dhande, Siddhi Kamble, Aahash Gundewar, Swapnil Babu, Naathi Naresh Kumar, Praveen Datta Meghe Inst Higher Educ & Res Dept Comp Sci & Med Engn Wardha India Datta Meghe Inst Higher Educ & Res DU Dept Artificial Intelligence & Data Sci Wardha Maharashtra India Datta Meghe Inst Higher Educ & Res Dept Artificial Intelligence & Machine Learning Wardha India

ISBN: (纸本)9798331540661;9798331540678

Electroencephalography (EEG) is a well-established method in neuroscience and bioengineering that offers valuable information about brain function. Recent technological advancements have led to more complex EEG recordings, which necessitate more advanced analysis techniques. machine learning has emerged as an asset in EEG signal processing, enabling novel approaches to comprehension and utilization of brain activity. This article provides an in-depth analysis of the intersection of machine learning and EEG signal processing in bioengineering applications. This text presents a comprehensive overview of EEG data analysis techniques, focusing on the key steps of acquisition, preprocessing, and feature extraction. It highlights the challenges and strategies used to extract valuable information from raw EEG recordings. Additionally, it surveys various machine learning algorithms, including classic and modern deep learning methods, demonstrating their effectiveness in analyzing EEG signals and opening new frontiers in the field of bioengineering. This paper explores the growing connection between machine learning and EEG signal processing, examining how they work together to enhance healthcare, neurotechnology, and our knowledge of the brain. By studying existing research and cutting-edge developments, this study intends to focus on this synergistic relationship and its importance in these fields.

关键词： EEG signal processing machine learning algorithms Bioengineering applications Neurotechnology

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