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arXiv 2024年

作者： Liu, Gang Wang, Zhenxiang Xi, Chuanmei He, Ziyang Guo, Shanshan Zhang, Rui Yao, Dezhong School of Electrical and information Engineering Zhengzhou University Zhengzhou450001 China Henan Key Laboratory of Brain Science and Brain Computer Interface Technology Zhengzhou University Zhengzhou450001 China Engineering Department of International College Zhengzhou University 450001 China School of Cyber Science and Engineering Zhengzhou University Zhengzhou450000 China Shanghai Key Laboratory of Brain-Machine Intelligence for Information Behavior Shanghai International Studies University China Clinical Hospital of Chengdu Brain Science Institute MOE Key Laboratory for NeuroInformation University of Electronic Science and Technology of China Chengdu China Research Unit of NeuroInformation Chinese Academy of Medical Sciences Chengdu China

Regression-based sEMG prosthetic hands are widely used for their ability to provide continuous kinetic and kinematic parameters. However, establishing these models requires complex sensors systems to collect corresponding kinetic and kinematic data in synchronization with sEMG, which is cumbersome and user-unfriendly. This paper proposes a kinetic and kinematic sensors-free approach for controlling sEMG prosthetic hands, enabling continuous decoding and execution of three hand movements: individual finger flexion/extension, multiple finger flexion/extension, and fist opening/closing. This approach utilizes only two data points (-1 and 1), representing maximal finger flexion force label and extension force label respectively, and their corresponding sEMG data to establish a near-linear model based on sEMG data and labels. The model's output labels values are used to control the direction and magnitude of fingers forces, enabling the estimation of continuous gestures. To validate this approach, we conducted offline and online experiments using four models: Dendritic Net (DD), Linear Net (LN), Multi-Layer Perceptron (MLP), and Convolutional Neural Network (CNN). The offline analysis assessed each model's ability to classify finger force direction and interpolate intermediate force values, while online experiments evaluated real-time control performance in controlling gestures and accurately adjusting forces. Our results demonstrate that the DD and LN models provide excellent real-time control of finger forces and gestures, highlighting the practical potential of this sensors-free approach for prosthetic applications. This study significantly reduces the complexity of collecting kinetic and kinematic parameters in sEMG-based regression prosthetics, thus enhancing the usability and convenience of prosthetic hands. © 2024, CC BY-NC-SA.

关键词： Multilayer neural networks

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ML-Powered Biomedical Signals Processing using Hybrid Filters

ML-Powered Biomedical Signals Processing using Hybrid Filter...

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Progress in Applied Electrical Engineering (PAEE)

作者： Mariusz Pelc Dariusz Mikołajewski Patryk Mendoń Adrian Luckiewicz Adam Sudoł Edward Jacek Gorzelańczyk Aleksandra Kawala-Sterniuk Institute of Computer Science University of Opole Opole Poland School of Computing and Mathematical Sciences University of Greenwich London UK Institute of Computer Science Kazimierz Wielki University Bydgoszcz Poland Neuropsychological Research Unit 2nd Clinic of the Psychiatry and Psychiatric Rehabilitation Medical University in Lublin Lublin Poland Faculty of Electrical Engineering Automatic Control and Informatics Opole University of Technology Opole Poland Faculty of Natural and Technical Sciences University of Opole Opole Poland Institute of Philosophy Kazimierz Wielki University Bydgoszcz Poland

ISBN: (数字)9798331527563

ISBN: (纸本)9798331527570

This paper deals with the problem of signals filtering using hybrid filters. Such solutions may prove to be especially beneficial in the context of biomedical signals filtering where the signals’ amplitudes are typically not very high and they can be easily affected by different kinds of disturbances. Having a number of different types of filters tuned for some specific purposes we will show how the best filter choice is affected by selecting one ML model over another.

关键词： Electrical engineering Filters Biological system modeling Biomedical communication

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Hybrid Autoencoder-Multi-Task Lstm Model for Indian Stock Market Ohlc Prediction and Profitable Recommendations

SSRN

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SSRN 2023年

作者： Chakraborty, Debasrita Ghosh, Susmita Ghosh, Ashish IDEAS-Technology Innovation Hub Indian Statistical Institute 203 Barrackpore Trunk Road Kolkata700108 India Department of Computer Science and Engineering Jadavpur University Kolkata700032 India Machine Intelligence Unit Indian Statistical Institute 203 Barrackpore Trunk Road Kolkata700108 India

Stock prices are characterized by high volatility and abrupt changes in trends, posing challenges for traditional forecasting models. Long Short-Term Memory (LSTM) networks are commonly considered state-of-the-art models for such predictions. However, they may struggle to handle sudden and drastic price trend shifts. Moreover, the open, high, low, and close (OHLC) prices of stocks have a few inherent constraints that have not been extensively studied in the literature. We argue that predicting the OHLC prices for the next day is more informative than predicting the overall trends, as trends are usually derived from these OHLC prices. Our focus is on Buy-Today Sell-Tomorrow (BTST) trading, and we propose using Autoencoders (AEs) pre-trained with stock prices to address the problem. We present a novel framework where a pre-trained encoder is integrated into a multi-task predictor network, creating a hybrid network that can handle OHLC constraints and capture sudden price changes effectively. The experiments include recommending the most profitable and overbought stocks for the next day (using William's %R), and our model’s recommendations achieved no losses during a test period of 300 days for multiple Indian companies. © 2023, The Authors. All rights reserved.

关键词： Long short-term memory

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A New DCT-FFT Fusion Based Method for Caption and Scene Text Classification in Action Video Images 1

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2nd International Conference on Pattern Recognition and Artificial Intelligence, ICPRAI 2020

作者： Nandanwar, Lokesh Shivakumara, Palaiahnakote Manna, Suvojit Pal, Umapada Lu, Tong Blumenstein, Michael Faculty of Computer Science and Information Technology University of Malayasia Kuala Lumpur Malaysia Department of Computer Science and Engineering Jalpaiguri Government Engineering College Jalpaiguri India Computer Vision and Pattern Recognition Unit Indian Statistical Institute Kolkata India National Key Lab for Novel Software Technology Nanjing University Nanjing China University of Technology Sydney Ultimo Australia

ISBN: (数字)9783030598303

ISBN: (纸本)9783030598297

Achieving better recognition rate for text in video action images is challenging due to multi-type texts with unpredictable backgrounds. We propose a new method for the classification of captions (which is edited text) and scene texts (which is part of an image in video images of Yoga, Concert, Teleshopping, Craft, and Recipe classes). The proposed method introduces a new fusion criterion-based on DCT and Fourier coefficients to extract features that represent good clarity and visibility of captions to separate them from scene texts. The variances for coefficients of corresponding pixels of DCT and Fourier images are computed to derive the respective weights. The weights and coefficients are further used to generate a fused image. Furthermore, the proposed method estimates sparsity in Canny edge image of each fused image to derive rules for classifying caption and scene texts. Lastly, the proposed method is evaluated on images of five above-mentioned action image classes to validate the derived rules. Comparative studies with the state-of-the-art methods on the standard databases show that the proposed method outperforms the existing methods in terms of classification. The recognition experiments before and after classification show that the recognition performance rate improves significantly after classification. © 2020, Springer Nature Switzerland AG.

关键词： Classification (of information)

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Glioma/Glioblastoma Detection in Brain MRI using Pre-trained Deep-Learning Scheme 3

Glioma/Glioblastoma Detection in Brain MRI using Pre-trained...

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3rd International Conference on Intelligent Computing, Instrumentation and Control Technologies, ICICICT 2022

作者： Rajinikanth, Venkatesan Kadry, Seifedine Damasevicius, Robertas Sujitha, R. Angel Balaji, Gangadharam Mohammed, Mazin Abed Noroff University College Faculty of Applied Computing and Technology Kristiansand94612 Norway Vytautas Magnus University Department of Applied Informatics Kaunas44404 Lithuania St. Joseph's College of Engineering Department of Electronics and Instrumentation Engineering Omr Chennai600119 India M/S Tata Consultancy Services Limited Tcsl Siruseri Sez Unit Sipcot I.T. Park TN Chennai603103 India University of Anbar College of Computer Science and Information Technology Ramadi31001 Iraq

ISBN: (数字)9781665410052

ISBN: (纸本)9781665410052

Convolutional Neural Network (CNN) supported medicinal image examination is extensively accepted due to its reputation and improved accuracy. The investigational outcome obtained with DLS along with a chosen classifier helps to achieve better detection results than the traditional and machine-learning methods. The proposed research examines the performance of pre-trained VGG16 and VGG19 schemes in detecting the brain tumour (Glioma/Glioblastoma) grade using different pooling methods. The classification is performed using SoftMax with five-fold cross-validation, and the products are compared and presented. The brain tumour images considered in this study are collected from The Cancer Imaging Archive (TCIA) dataset. This work considered 2000 images (1000 Glioma and 1000 Glioblastoma) of axial-plane with dimension of 224×224×3 pixels for the assessment, and the attained results are compared. The experimental outcome achieved with Python® confirms that the VGG16 with average-pooling provides a better classification accuracy (>99%) with Decision Tree (DT) compared with other methods considered. © 2022 IEEE.

关键词： Python

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Elephants Herding Optimization to Solve the Multiple Travelling Salesman Problem 2nd

Elephants Herding Optimization to Solve the Multiple Travell...

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2nd International Conference on Advanced Intelligent Systems for Sustainable Development, AI2SD 2019

作者： Hossam, Anass Bouzidi, Abdelhamid Riffi, Mohammed Essaid Laboratory LAROSERI Department of Computer Science Faculty of Science Chouaib Doukkali University EI Jadida Morocco Information Technology and Modeling Systems Research Unit Science Faculty Abdelmalek Essaadi University Tetouan Morocco

ISBN: (纸本)9783030366735

The Multiple travelling salesman problem (MTSP) is a typical computationally complex combinatorial optimization problem, which is an extension of the famous travelling salesman problem (TSP) classified as NP-Hard. The MTSP can be generalized to a wide variety of routing and scheduling problems. The paper proposes a novel metaheuristic called Elephant Herding Optimization (EHO) to solve the MTSP. The EHO algorithm is bio-inspired from the natural herding behavior of elephant groups, which has proved its efficiency in solving the classic TSP and continued optimization problems. So, to extend the application of this algorithm, The paper makes the attempt to show how the EHO algorithm can be applied to the MTSP by testing some random benchmark instances of TSPLIB. The obtained results shows the performance of the proposed method. © 2020, Springer Nature Switzerland AG.

关键词： Combinatorial optimization

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Ice-Breakers, Turn-Takers and Fun-Makers: Exploring Robots for Groups with Teenagers

Ice-Breakers, Turn-Takers and Fun-Makers: Exploring Robots f...

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IEEE International Workshop on Robot and Human Communication (ROMAN)

作者： Sarah Gillet Katie Winkle Giulia Belgiovine Iolanda Leite Division of Robotics Perception and Learning School of Electrical Engineering and Computer Science KTH Royal Institute of Technology Sweden DIBRIS Department CONTACT Unit at Istituto Italiano di Tecnologia Università degli Studi di Genova

ISBN: (数字)9781728188591

ISBN: (纸本)9781665406802

Successful, enjoyable group interactions are important in public and personal contexts, especially for teenagers whose peer groups are important for self-identity and self-esteem. Social robots seemingly have the potential to positively shape group interactions, but it seems difficult to effect such impact by designing robot behaviors solely based on related (human interaction) literature. In this article, we take a user-centered approach to explore how teenagers envisage a social robot "group assistant". We engaged 16 teenagers in focus groups, interviews, and robot testing to capture their views and reflections about robots for groups. Over the course of a two-week summer school, participants co-designed the action space for such a robot and experienced working with/wizarding it for 10+ hours. This experience further altered and deepened their insights into using robots as group assistants. We report results regarding teenagers’ views on the applicability and use of a robot group assistant, how these expectations evolved throughout the study, and their repeat interactions with the robot. Our results indicate that each group moves on a spectrum of need for the robot, reflected in use of the robot more (or less) for ice-breaking, turn-taking, and fun-making as the situation demanded.

关键词： Shape Social robots Training data Reflection Behavioral sciences Interviews Robots

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OPTIMIZING FOREST FIRE PREVENTION: INTELLIGENT SCHEDULING ALGORITHMS FOR DRONE-BASED SURVEILLANCE SYSTEM

arXiv

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arXiv 2023年

作者： Jemmali, Mahdi Melhim, Loai Kayed B. Boulila, Wadii Amdouni, Hajer Alharbi, Mafawez T. Department of Computer Science and Information College of Science at Zulfi Majmaah University Al-Majmaah11952 Saudi Arabia MARS Laboratory University of Sousse Sousse Tunisia Department of Computer Science Higher Institute of Computer Science and Mathematics Monastir University Monastir5000 Tunisia Department of Health Information Management and Technology College of Applied Medical Sciences University of Hafr Al Batin Hafr Al Batin39524 Saudi Arabia Robotics and Internet-of-Things Laboratory Prince Sultan University Riyadh12435 Saudi Arabia RIADI Laboratory National School of Computer Sciences University of Manouba Manouba Tunisia Higher School of Economic and Commercial Sciences Tunis University of Tunis Tunisia Unit of Scientific Research Applied College Qassim University Buraydah51452 Saudi Arabia

Given the importance of forests and their role in maintaining the ecological balance, which directly affects the planet, the climate, and the life on this planet, this research presents the problem of forest fire monitoring using drones. The forest monitoring process is performed continuously to track any changes in the monitored region within the forest. During fires, drones’ capture data is used to increase the follow-up speed and enhance the control process of these fires to prevent their spread. The time factor in such problems determines the success rate of the fire extinguishing process, as appropriate data at the right time may be the decisive factor in controlling fires, preventing their spread, extinguishing them, and limiting their losses. Therefore, this research presented the problem of monitoring task scheduling for drones in the forest monitoring system. This problem is solved by developing several algorithms with the aim of minimizing the total completion time required to carry out all the drones’ assigned tasks. System performance is measured by using 990 instances of three different classes. The performed experimental results indicated the effectiveness of the proposed algorithms and their ability to act efficiently to achieve the desired goal. The algorithm RID achieved the best performance with a percentage rate of up to 90.3% with a time of 0.088 seconds. © 2023, CC BY.

关键词： Fires

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A new parameter optimization strategy to improve students' performances 10

A new parameter optimization strategy to improve students' p...

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10th International Conference on Virtual Campus, JICV 2020

作者： Fouki, Mohammed El Aknin, Noura Kadiri, K. Ed El. Abdelmalek Essaadi University Information Technology and Modeling Systems Research Unit Computer Science Operational Research and Applied Statistics Laboratory Tetouan Morocco Abdelmalek Essaadi University Computer Science Operational Research and Applied Statistics Laboratory Tetouan Morocco

ISBN: (纸本)9780738124728

In recent years, artificial intelligence has been involved in the field of education in order to discover new knowledge about students and to improve educational environments. Machine learning, and more specifically data mining, is one of the most successful areas of artificial intelligence for educational data mining. In this study, we propose an advanced parameter optimization strategy based on deep learning model to predict students' performances for an e-learning platform operated by Abdelmalk Essaadi University. © 2020 IEEE.

关键词： Data mining

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Enhancement of Channel Capacity in 5G Ultra Dense Network-UDN

Enhancement of Channel Capacity in 5G Ultra Dense Network-UD...

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International Conference on Edge Computing and Applications (ICECAA)

作者： Ritesh Rastogi T. Shanthi Pramod Kumar Naik M. Sahaya Sheela A.K Mishra Nandhini T J Department of IT and CS M.Tech(Int) Noida Institute of Engineering and Technology Delhi India Department of ECE Paavai Engineering College Chennai India Departmeny of Computer Science Engineering Dayananda Sagar University Bangalore India Department of ECE Vel Tech Rangarajan Dr.Sagunthala R&D Institute of Science and Technology Chennai India Math Unit Foundation Program Dhofar University Salalah Oman Saveetha School of Engineering Saveetha Institute of Medical and Technical Science - (SIMATS) Chennai India

A 5G ultra dense network architecture makes use of a high density of micro cell base stations to provide increased coverage, capacity, and performance for 5G communication systems. This is accomplished by the utilization of a very densely packed network. A 5G ultradense network is another name for this particular category of network. Ultradense networks are built to overcome the limitations of ordinary cellular networks. This is accomplished by increasing the number of base stations that are located inside a given area. This is performed by reducing the coverage area provided by each base station, which ultimately leads to an increase in the capacity of the entire network. The maximum data rate or capacity that may be obtained through a certain wireless channel is referred to as the “channel capacity” in a 5G ultradense network, when the phrase “channel capacity” is used. One possibility is that this represents the maximum quantity of data that can be transmitted across the channel. These criteria, which represent the capabilities of the channel, can be used to evaluate the performance of channels and equipment on 5G networks. This article presents for further research a model for improving the channel capacity of the 5G ultradense network. The model can be found at the end of the article. This change will result in an increase in both the usefulness of the channel and the amount of bandwidth that it consumes; both of these aspects will improve. The fact that it uses a very small quantity of energy and power in compared to other methods is the major trait that sets it apart from other methods. This technology is superior to others in terms of both the amount of data it can communicate and the amount of electricity it consumes in the process.

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