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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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Product Information Visualization Systems in the Era of intelligence

Product Information Visualization Systems in the Era of Inte...

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international conference on Artificial intelligence and Advanced Manufacturing (AIAM)

作者： Xinwei Li Muzi Qu Luxun Academy of Fine Arts ShenyangChina

ISBN: (纸本)9781665417334

In the era of artificial intelligence and big data, visualization is an inevitable trend for product information expression. To transmit comprehensive, accurate, efficient, and clear product information, designers need to leverage information technologies such as big data analytics, image recognition and natural language processing to enrich product information visualization. This paper discussed the key practices when utilizing big data and artificial intelligence in product information design and proposed a methodology of product information visualization. Through qualitative analysis, interdisciplinary and comparative research methods, intelligent data information is implemented in human-computer interaction experience. It is an urgent development problem of the integration of design and technology in the AI's times.

关键词： Industries Visualization Image recognition Data visualization Organizations Big Data Market research

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Deep Learning Approaches for Enhancing Content Retrieval 3

Deep Learning Approaches for Enhancing Content Retrieval

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

作者： Khurana, Saniya Singh, Shweta Ranganathaswamy, M.K. Renuka, G. Diwakar, Mandar P. Jain, Sandeep Kumar Centre of Interdisciplinary Research in Business and Technology Chitkara University Institute of Engineering and Technology Chitkara University Punjab India Maharishi School of Engineering and Technology Maharishi University of Information Technology Uttar Pradesh India Faculty of Engineering and Technology Department of Mechanical Engineering Karnataka Ramnagar562112 India Prince Shri Venkateshwara Padmavathy Engineering College Department of Electronics and Communication Engineering Chennai127 India Vishwakarma Institute of Information Technology Department of Artificial Intelligence & Data Science Pune India Vivekananda Global University Department of Electrical Engineering Jaipur India

ISBN: (纸本)9798350319125

Deep learning strategies for content material retrieval have been explored and updated to cope with the want for accurate search effects. This machine up to date is used to date apprehend and examine text-up-to-date content material from sources, up-to-date webpages, or virtual up-todate motion pictures. As the quantity and complexity of online content will increase, so up-to-date have the want for higher up-to-date up to date those resources. Recent advancements in deep gaining knowledge have enabled better content retrieval structures that are up to date, updated correctly, and speedy to understand statistics from one-of-a-kind sources. Such structures include natural language processing (NLP), sentiment analysis, entity reputation, and up-to-date updated reputation. Those deep studying technologies have been used to decorate content material retrieval by enhancing search effects' accuracy, robotically updated extracting applicable statistics, categorizing information, and identifying patterns. Additionally, deep-getting, updated systems are up-to-date and quick to examine large volumes of data, generating and relevant consequences than up-to-date methods. In summary, deep up-to-date techniques can beautify the retrieval system of content material efficiently, enhancing the accuracy and pace of seek effects. © 2023 IEEE.

关键词： categorizing comprehend effectively learning techniques

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Applied machine Learning and Data Analytics 1

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丛书名： Communications in Computer and Information Science

1000年

作者： M. A. Jabbar Fernando Ortiz-Rodríguez Sanju Tiwari Patrick Siarry

ISBN: (数字)9783031342226

ISBN: (纸本)9783031342219

This book constitutes the refereed proceedings of the 5th international conference on Applied machine Learning and Data Analytics, AMLDA 2022, held in Reynosa, Tamaulipas, Mexico, during December 22–23, 2022. The 16 full papers and 4 short papers included in this book were carefully reviewed and selected from 89 submissions. They were organized in topical sections as follows: machine learning, Healthcare and medical imaging informatics; biometrics; forensics; precision agriculture; risk management; robotics and satellite imaging.

关键词： Artificial intelligence Computer Applications Computer Imaging, Vision, pattern recognition and Graphics Database Management

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Inspired Nature Meta-Heuristics Minimizing Total Tardiness for Manufacturing Flow Shop Scheduling under Setup Time Constraint 2nd

Inspired Nature Meta-Heuristics Minimizing Total Tardiness f...

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3rd international conference on Digital Technologies and Applications

作者： Nejjarou, Omar Aqil, Said Lahby, Mohamed Hassan II Univ Casablanca Lab Appl Math & Comp Sci Higher Normal Sch Casablanca 50069 Morocco Univ Hassan II Casablanca Lab Artificial Intelligence & Complex Syst Engn Natl & Iigher Sch Arts & Crafts Casablanca Morocco

ISBN: (纸本)9783031298592;9783031298608

The study of the variant under machine unavailability and setup time constraints is of great industrial importance. In this paper, we present an optimization of manufacturing flow shop scheduling under setup time constraints. The objective is to minimize the total tardiness for a set of jobs with deadline times to respect. This intention is aimed at satisfying customer requirements and gaining their trust. To investigate this problem, we applied three inspired-nature meta-heuristics: the artificial bee colony algorithm, the genetic technique algorithm and finally the migratory bird optimization algorithm. A set of numerical simulations on a variety of instances is suggested to check the efficiency and strength of our algorithms. The results obtained show the strong success of the migratory bird optimization algorithm compared to other algorithms.

关键词： Inspired nature metaheuristics Manufacturing flow shop Optimization Sequence dependent setup time Total tardiness

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Face recognition Using Local Ternary pattern and Booth's Algorithm

Face Recognition Using Local Ternary Pattern and Booth's Alg...

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international conference on Eco-friendly Computing and Communication Systems (ICECCS)

作者： Abdullah Gubbi Mohammad Fazle Azeem Nishatbanu Z.H. Nayakwadi Dept of E & C P.A. College of Engnineering Mangalore Karnataka India EE Dept. AMU Aligarh India Dept of Computer science Muscat Sultanate of Oman

In the literature of face recognition many methods have been proposed which extract local texture features for robust pattern classification. But for final computation of the feature the information about central pixel is not taken into account. In this paper, we propose a novel method which utilizes Local Ternary pattern and Booth's Algorithm techniques to capture the local face features, which utilize central pixel for computation of the feature. Face images are spatially varied and classification works better with local descriptors, a Non-overlapping block wise processing is done on image to limit the features. The Support Vector machine (SVM) and KNN classifier with proposed similarity measure is used for face classification. Finally, ROC and CMC are plotted for analysis of the system. Experiments are conducted on ORL and faces94 datasets demonstrates that the proposed method has better classification accuracy than previously proposed methods.

关键词： Face recognition Databases Support vector machines Feature extraction Face Principal component analysis

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Intelligent Robotics and Applications 1

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丛书名： Lecture Notes in Computer Science

1000年

作者： Honghai Liu Naoyuki Kubota Xiangyang Zhu Rüdiger Dillmann

ISBN: (数字)9783319228730

ISBN: (纸本)9783319228723

This three volume set LNAI 9244, 9245, and 9246 constitutes the refereed proceedings of the 8th international conference on Intelligent Robotics and Applications, ICIRA 2015, held in Portsmouth, UK, in August 2015. The 46 papers included in the third volume are organized in topical sections on mobile robots and intelligent autonomous systems; intelligent system and cybernetics; robot mechanism and design; robotic vision; recognition and reconstruction; and active control in tunneling boring machine.

关键词： Artificial intelligence Computer Imaging, Vision, pattern recognition and Graphics Control, Robotics, Mechatronics Computers and Society Computer Applications Special Purpose and Application-Based 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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Ensemble Nonlinear machine Learning Model for Chronic Kidney Diseases Prediction 3

Ensemble Nonlinear Machine Learning Model for Chronic Kidney...

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3rd IEEE Mysore Sub Section international conference, MysuruCon 2023

作者： Sampath, S. Prasad, Mudarakola Lakshmi Hussain, Mohammad Manzoor Parameswari, R. Anil Kumar, D. Reddy, Pundru Chandra Shaker Adichunchanagiri Institute of Technology Department of Information Science and Engineering Karnataka Chikkamagaluru India Institute of Aeronautical Engineering Computer Science and Engineering Hyderabad Telangana Dundigal India B v Raju Institute of Technology Computer Science and Engineering Telangana Narsapur India Sathyabama Institute of Science and Technology Department of Mathematics TamilNadu India PACE Institute of Technology and Sciences Department of Electronics and Communication Engineering AP Ongole India SR University School of Computer Science and Artificial Intelligence Telangana Warangal506371 India

ISBN: (纸本)9798350340358

Living in a major metropolitan area has been linked to an increased risk of developing multiple forms of chronic-kidney-disease(CKD). In developed nations, predicting CKDs is a top priority. Predictive analytics for the purpose of predicting CKDs are the primary focus of this work. However, it is getting harder and harder to forecast outcomes for massive samples. While doing so, the MapReduce architecture makes it possible to write predictive algorithms by combining map and reduce operations. Problems with the scalability and effectiveness of anticipative learning approaches are alleviated by the comparatively straightforward programming interface. To efficiently handle small subsets of massive datasets, the authors propose using an iterative weighted mapreduce approach. Ensemble-nonlinear support-vector-machines(ENSVM) and random-forests(RF) are used to design a binary classification issue. As a result, the suggested approach generates nonlinear blends of kernel activations in example prototypes, as opposed to the conventional linear combination of activations. In addition, an ensemble of deep-SVM is utilized to integrate the descriptors, with the product rule being employed to merge the classifiers' likelihood estimates. Prediction accuracy and results interpretability are used to gauge performance. © 2023 IEEE.

关键词： MapReduce

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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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