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A Review on EMG-based pattern Identification Methods for Effective Controlling of Hand Prostheses

A Review on EMG-based Pattern Identification Methods for Eff...

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international conference on Innovative Mechanisms for Industry Applications (ICIMIA)

作者： S. Ramkumar Dhanusha Rema T. Archana Devi K Elavarasi T Selvaganapathi S. Gokila Department of Computer Science Christ (Deemed to be University) Bangalore India Department of Food Technology Karpagam Academy of Higher Education Coimbatore India Department of Electrical and Electronics Engineering Karpagam Academy of Higher Education Coimbatore India Department of CSE Karpagam Institute of Technology Coimbatore India

The ability of amputees to do daily duties is significantly restricted by upper limb amputation. The myoelectric prosthesis uses impulses from the surviving muscles in the stump to gradually restore function to such severed limbs. Such myosignals are unfortunately tedious and challenging to gather and employ. The process of transforming it into a user control signal after it has been acquired often consumes a significant amount of processing resources. By modifying machine learning strategies for pattern recognition, the factors that influence the traditional electromyography (EMG)-pattern identification approaches may be significantly minimized. Although more recent developments in intelligent pattern recognition algorithms could discern between a variety of degrees of freedom with high levels of accuracy, their usefulness in practical (amputee) applications was less obvious. This review paper examined how well various pattern recognition algorithms for hand prostheses performed. Finally, we discussed the current difficulties and offered some suggestions for future research in our paper’s conclusion.

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An Application of Artificial intelligence for an Early and Effective Prediction of Heart Failure 3

An Application of Artificial Intelligence for an Early and E...

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3rd international conference on Latest Trends in Electrical Engineering and Computing Technologies, INTELLECT 2022

作者： Butt, Muhammad Owais Ur Rehman, Attique Javaid, Sabeen Ali, Tahir Muhammad Nawaz, Ali University of Sialkot Department of Software Engineering Sialkot Pakistan Gulf University for Sciences and Technology Department of Computer Science Mubarak Al-Abdullah Kuwait Gulf University for Sciences and Technology Department of Computer Science Al-Abdullah Kuwait

ISBN: (数字)9781665487061

ISBN: (纸本)9781665487061

The purpose of this study is to develop a reliable decision support system for predicting the survival of heart failure patients. Over time, heart disease (CVD) has become one of the most visible diseases in the world. The major factors of Heart failure are Sex, cholesterol, high blood pressure, stress, age, Exercise Angina, and Resting ECG. Many researchers have proposed several methods for early diagnosis on the bases of these features. However, due to the hereditary critique of heart disease and the life-threatening risks, it is important to improve the accuracy of the proposed techniques and methods. In this article, a machine learning framework with high accuracy is proposed for the effective diagnosis of heart failure. Specifically, the framework deals with handling missing values through the first Example filter. In the second stage, the data imbalance problem is solved through the Synthetic Minority Over-sampling Technique (SMOTE Upsampling). In the third step, the feature selection is done using (Optimized Feature Selection). The fourth is to normalize the data using the normalization technique, the fifth is to split the data into portions using split operators (30% and 70%). In the final step, the Decision Tree and K-Nearest Neighbor (KNN) classifiers are introduced for effective forecasting as these classifiers achieve the best accuracy (84.11%). The dataset validation has been performed in the background using four types of datasets. (i.e. Failure Prediction Dataset, Cardiovascular Disease, Stroke Prediction Dataset, heart disease). Comparative analysis proves that (Heart Failure Prediction) Dataset achieves better accuracy (84.11%) with fewer sets of features. © 2022 IEEE.

关键词： Forecasting

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Efficient Image Retrieval Technique with Local Edge Binary pattern Using Combined Color and Texture Features 1st

Efficient Image Retrieval Technique with Local Edge Binary P...

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1st international conference on Computational intelligence for Engineering and Management Applications, CIEMA 2022

作者： Sucharitha, G. Kalyani, B.J.D. Sekhar, Chandra G Srividya, Ch. Computer Science Engineering Institute of Aeronautical Engineering Telangana Hyderabad India

ISBN: (纸本)9789811984921

Image proceeding is foundation for pattern recognition and machine learning. The recent research focuses on image retrieval techniques with machine and deep learning methodologies. This paper recommends an other approach for extracting images based on combined characteristics of color and texture with edge binary pattern technique. The suggested descriptor first translates an RGB image into HSV color space. In this approach, the HSV color model makes use of the fundamental properties of color image as color, intensity, and brightness of an image. The hue (H) and saturation (S) components support to extract the color features, and the value component is more feasible to obtain the texture features;upon the value constituent of each image, local maximum edge binary patterns (LMEBPs) are used to find the relations among the pixels for every 3 × 3 matrix in order to extract texture features. Finally, all three histograms were used to extract the feature vector. The presented algorithm is experimented on two well-known color databases, Corel-10k and MIT-Visex. The comparative analysis of the proposed approach in terms of retrieval performance, with existing methods like CS-LBP, LEPSEG, LEPINV, has shown a substantial perfection in terms of precision and recall. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Local binary pattern

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Incremental approach for updating approximations of variable precision rough set based on dominance relations 3

Incremental approach for updating approximations of variable...

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3rd international conference on Information Technology and Management Innovation, ICITMI 2014

作者： Li, Yan Hou, Jia Jia Liu, Xiao Qing Key Lab of Machine Learning and Computational Intelligence College of Mathematics and Computer Science Hebei University Baoding China

ISBN: (纸本)9783038352396

Variable precision rough set (VPRS) based on dominance relation is an extension of traditional rough set by which can handle preference-ordered information flexibly. This paper focuses on the maintenance of approximations in dominance based VPRS when the objects in an information system vary over time. The incremental updating principles are given as inserting or deleting an object, and some experimental evaluations validates the effectiveness of the proposed method. © 2014 Trans Tech Publications, Switzerland.

关键词： Rough set theory

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Classification of Motor Imagery EEG Signals with CSP Filtering Through Neural Networks Models 17th

Classification of Motor Imagery EEG Signals with CSP Filteri...

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17th Mexican international conference on Artificial intelligence (MICAI)

作者： Virgilio Gonzalez, Carlos Daniel Sossa Azuela, Juan Humberto Espino, Elsa Rubio Ponce Ponce, Victor H. Inst Politecn Nacl Ctr Invest Comp Av Juan de Dios Batiz & M Othone de Mendizabal Mexico City 07738 DF Mexico Tecnol Monterrey Escuela Ingn & Ciencias Av Gen Ramon Corona 2514 Zapopan Jalisco Mexico

ISBN: (纸本)9783030044916;9783030044909

The paper reports the development and evaluation of brain signals classifiers. The proposal consisted of three main stages: organization of EEG signals, feature extraction and execution of classification algorithms. The EEG signals used, represent four motor actions: Left Hand, Right Hand, Tongue and Foot movements;in the frame of the Motor Imagery Paradigm. These EEG signals were obtained from a database provided by the Technological University of Graz. From this dataset, only the EEG signals of two healthy subjects were used to carry out the proposed work. The feature extraction stage was carried out by applying an algorithm known as Common Spatial pattern, in addition to the statistical method called Root Mean Square. The classification algorithms used were: K-Nearest Neighbors, Support Vector machine, Multilayer Perceptron and Dendrite Morphological Neural Networks. This algorithms was evaluated with two studies. The first one aimed to evaluate the performance in the recognition between two classes of Motor Imagery tasks;Left Hand vs. Right Hand, Left Hand vs. Tongue, Left Hand vs. Foot, Right Hand vs. Tongue, Right Hand vs. Foot and Tongue vs. Foot. The second study aimed to employ the same algorithms in the recognition between four classes of Motor Imagery tasks;Subject 1 - 93.9% +/- 3.9% and Subject 2 - 68.7% +/- 7%.

关键词： EEG signals Motor Imagery Common Spatial pattern RMS One vs Rest Pair-Wise Dendrite Morphological Neural Network Multilayer Perceptron

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3rd international conference on Information and Communication Technology for Intelligent Systems, ICTIS 2018

3rd International Conference on Information and Communicatio...

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3rd international conference on Information and Communication Technology for Intelligent Systems, ICTIS 2018

ISBN: (纸本)9789811317460

The proceedings contain 147 papers. The special focus in this conference is on Information and Communication Technology for Intelligent Systems. The topics include: Review of EEG signal classification;How to reverse engineer ICS protocols using pair-HMM;classification of plants using invariant features and a neural network;CSTeller: Client or server is a question;geographical zone-based cluster head for routing in urban vehicular network;Congestion management by Static Var Compensator (SVC) using power world simulator;combining user-based and item-based collaborative filtering using machine learning;Predictive monitoring system using K-NN, QDC classifiers of physiological data;Recommender system based on OSN data analytics;automation of process evaluation of saccharification of wheat starch followed by fermentation of glucose to prepare bioethanol using digital image processing;Early detection of ransomware by indicator analysis and WinAPI call sequence pattern;a novel integrated framework based on modular optimization for efficient analytics on twitter big data;study of controller techniques for precision positioning;an effective content-based strategy analysis for large-scale deduplication using a multi-level pattern-matching algorithm;tamil sign language translator—An assistive system for hearing- and speech-impaired people;aspect-based sentiment analysis of students’ feedback to improve teaching–learning process;fair mechanisms for combinatorial reverse auction-based cloud market;timbre-vibrato model for singer identification;a range-based adaptive and collaborative localization for wireless sensor networks;Image edge detection techniques using sobel, T1FLS, and IT2FLS;An optimal cryptographic approach for addressing security breaches to build resilient WSN;detection of mimicry attacks on speaker verification system for cartoon characters’ dataset;smart helmet for safety driving.

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Model of utterance and its use in cooperative response generation 3rd

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3rd international conference on Human-Computer Interaction, EWHCI 1993

作者： Yamada, Koichi Mizoguchi, Riichiro Harada, Naoki Nukuzuma, Akira Ishimaru, Keiichi Furukawa, Hiroshi Laboratory for International Fuzzy Engineering Research Siber Hegner Building 3FL 89-1 Yamashita-cho Naka-ku Yokohama-shi231 Japan The Institute of Scientific and Industrial Research Osaka University 8-1 Mihogaoka IbaragiOsaka567 Japan

ISBN: (纸本)9783540574330

A cooperative response model is proposed for interactive intelligent systems that recognizes user intentions and makes cooperative responses. Though many models developed so far have shown that they can achieve some form of cooperative responses, the coverage of each model is limited. In this paper, we propose a model which covers various types of cooperative responses. The paper starts with a classification of cooperative responses and discusses the relation between intentions and responses. Based on the discussion, a user utterance model is introduced and an intention recognition mechanism is developed employing domain-independent rules and knowledge about the normal usage of the topic object. The recognized intentions are then used to generate appropriate cooperative responses. © 1993, Springer Verlag. All rights reserved.

关键词： Intelligent systems

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Enhancing Crop Yield Prediction in India: A Comparative Analysis of machine Learning Models 3

Enhancing Crop Yield Prediction in India: A Comparative Anal...

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3rd international conference on Smart Generation Computing, Communication and Networking, SMART GENCON 2023

作者： Chandhar, Kothakonda Ramineni, Kamalakar Ramakrishna, Erroju Ramana, T. Venkata Sandeep, Achi Kalyan, Karmakonda School of Computer Science & Artificial Intelligence Sr University Telangana Warangal India Department of AIML CVR Engineering College Telangana India Computer Science & Engineering Sri Indu College of Engineering & Technology Rangareddy Ibrahimpatnam India Computer Science & Engineering Jyothismathi Institute of Technology and Science Telangana Karimnagar India

ISBN: (纸本)9798350319125

Farming is very important for India's economy. Lots of people in India depend on farming to make a living. But farming has become harder because the weather, climate, and environment keep changing. These changes make it tough to grow crops well. We proposed a mechanism called crop yield prediction. It helps us predict how much crops will grow and helps farmers decide what to plant. We use things like where the farm is, how much rain it gets, how hot or cold it is, and what kind of soil is there to make these predictions. These things help us figure out how much crop we can expect. In this proposal we tried out some ma-chine learning models like the Gradient Boosting Regressor, Random Forest Regressor, Support Vector Regressor, and Decision Tree Regressor. We wanted to see which one is the best at predicting crop yields. It turns out the Decision Tree Regressor did the best job and was really accurate with the da-ta we had. This method helps farmers because it means we can use this model to make better predictions about how much food we can grow, which helps the farming community in India grow more food in a good way. © 2023 IEEE.

关键词： Decision Tree Regressor Gradient Boosting Regressor Random Forest Regressor Support Vector Regressor

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Random Forests for Predicting Diabetes Progression and Complications 3

Random Forests for Predicting Diabetes Progression and Compl...

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3rd international conference for Advancement in Technology, ICONAT 2024

作者： Ranganathan, Chitra Sabapathy Nandhini, R. Nisha, K. Sumathi, G. Sudhakar, G. Srinivasan, C. Mphasis Corporation Tamil Nadu Chennai India Rajalakshmi Institute of Technology Department of Electronics and Communication Engineering Tamil Nadu Chennai India Chennai Institute of Technology Department of Computer Science and Engineering Tamil Nadu Chennai India Department of Artificial Intelligence & Data Science Tamil Nadu Trichy India Sri Sai Ranganathan Engineering College Department of Computer Science and Engineering Tamil Nadu Coimbatore India Saveetha Institute of Medical and Technical Sciences Saveetha School of Engineering Saveetha University Department of Computer Science and Engineering Tamil Nadu Chennai India

ISBN: (纸本)9798350354171

The main objective of the presented work in this paper is to use Random Forests as an alternative biomarker for predicting the development of diabetes. Identifying high-risk patients is crucial for improving patient outcomes and reducing healthcare expenses. The proposed system improves diabetes treatment via the use of predictive analytics, which promotes preventative and early intervention-focused healthcare practices. It helps manage the risks associated with diabetes by using machine learning, which is part of a larger movement towards precision medicine. This method shows promise to enhance long-term health outcomes, optimize resource allocation, and provide personalized treatment to patients. Findings from the Pima Indians Diabetes Database quantify the risks of diabetes and patient characteristics. Blood pressure, glucose, insulin, and body mass index are all taken from each patient. The dangers of retinopathy, neuropathy, and nephropathy are also numerically encoded, with 0 indicating minimal risk, 1 medium risk, and 2 high risks. HbA1c is also included. The blood pressure ranges from 115/78 to 130/85mmHgA1c levels, the body mass index (BMI) from 26 to 32 mm, and the glucose level from 110 to 140 mm Hg. © 2024 IEEE.

关键词： Blood pressure

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A quick method to obtain 3D surface data

A quick method to obtain 3D surface data

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3rd international conference on Photonics and Imaging in Biology and Medicine

作者： Chen, WJ Su, XY Cao, YP Xiang, LQ Zhang, QC Sichuan Univ Dept Optoelect Chengdu 610064 Peoples R China

ISBN: (纸本)081945138X

Fourier transform profilometry(FTP) based on fringe projection has high speed and high procession advantages, so it is widely used in industrial inspection, solid modeling, biomedicine, machine vision and so on. In this paper, we present a novel method, in which we project a bi-color fringe pattern that is consisted of two interlaced RGB base color fringe patterns with pi phase difference onto the object using digital light projector and the deformed color pattern is captured by color digital camera. Then the deformed color fringe pattern is decoded into two individual fringe patterns with pi phase difference using image-separating technique. After modifying their averages and contrast, we subtract one of the two fringe patterns from the other to eliminate zero spectra. Compared with pi phase-shifting technique, the obvious advantage of this method is that only one fringe pattern is needed to remove the zero spectra and raise the measurable slope of height variation nearly three times while no phase shifting device needed. Computer simulation and experiments verify our method.

关键词： Fourier transform profilometry bi-color fringe pattern projection shape reconstruction

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