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2023 International Conference on Sustainable Communication Networks and Application, ICSCNA 2023

作者： Manjula, R. Jagadeesan, S. Ramalingam, Venkadeshan Vetriselvi, T. Johnpeter, T. Srm Institute of Science and Technology College of Engineering and Technology Department of Computing Technologies Chennai India Vellore Institute of Technology School of Computer Science Engineering and Information Systems Department of Software and Systems Engineering Tamilnadu Vellore India Information Technology Department University of Technology and Applied Sciences-Shinas Branch Sultanate of Oman Oman Vellore Institute of Technology School of Computer Science and Engineering Department of Iot Tamilnadu Vellore India K.Ramakrishnan College of Engineering Department of Computer Science and Engineering Trichy India

ISBN: (纸本)9798350313987

Hard hearing and deaf people are communicating with other people using the sign language. This is the only way of communication of such people to convey Since this is the only means by which these individuals can communicate, it is imperative that others be able to understand their language. Sign language recognition by computer begins with the acquisition of sign gestures and ends with recognition and conversion to text or voice. Static and dynamic are the two ways of sign gestures used. This study aims at focusing on the dynamic gesture recognition and the steps for recognizing sign language are discussed. The collected data are preprocessed and feature are extracted for classification. Finally the results are all investigated and developed for single user. © 2023 IEEE.

关键词： Gesture analysis Hand gesture recognition Hand tracking Sign language

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AsPeer: Method to Self and Peer Assessment in Large Online Design Classes

International Journal of Emerging Technologies in Learning

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International Journal of Emerging Technologies in Learning 2023年第10期18卷 19-38页

作者： Sankaran, Hariesh K. Shanmugam, Raju Shah, Bhavesh Ahmad, Sultan Abdeljaber, Hikmat A. M. Nazeer, Jabeen Karnavati University Gujrat Gandhinagar India Department of Computer Science College of Computer Engineering and Sciences Prince Sattam Bin Abdulaziz University Alkharj Saudi Arabia Department of Computer Science Faculty of Information Technology Applied Science Private University Amman Jordan

Peer and self-assessment open opportunities to scale assessments in online classrooms. This article reports our experiences of using AsPeer-peer assessment system, with two iterations of a university online class. We observed that peer grades highly correlated with staff assigned grades. It was recorded that, the peer grade of all student submissions within range of instructor grade averaged to 21.0% and that within the next 2 ranges was 49.0%. We performed three experiments to improve accuracy of peer grading. First, we observed grading bias and introduced a data driven feedback mechanism to inform peers of it. Students aided by feedback were mindful and performed grading with better accuracy. Second, we observed that the rubric lacked efficiency in translating intent to students. Simplified guiding questions improved accuracy in assessment by 89% students. Third, we encouraged peers to provide personalized qualitative feedback along with rating. We provided them with feedback snippets that addressed common issues. 64% students responded that the snippets helped them to critically look at submissions before rating © 2023, International Journal of Emerging Technologies in *** Rights Reserved.

关键词： Students

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Yolo Algorithm to Detect & Clarify All Objectives by Deep Reinforcement Learning Approach 2

Yolo Algorithm to Detect & Clarify All Objectives by Deep Re...

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2nd International Conference Computational and Characterization Techniques in engineering and Sciences, IC3TES 2024

作者： Simhadri, Revathi Sravanthi, Muthyala Nadh Singh, Bhaludra R Hajera Begum, Shaik Saritha, Nandikanti Sandhya, Vankudavath Bhoj Reddy Engineering College for Women Department of Information Technology Telangana Hyderabad India Bhoj Reddy Engineering College for Women Department of Computer Science and Engineering Telangana Hyderabad India

ISBN: (纸本)9798350364699

This research aims to develop a self-learning robotic object-grasping system that is effective for small-volume, high-variation production environments and achieves excellent success rates in object-grabbing and pick-and-place tasks. A neural reinforced learning system that learns on its own and an item recognition algorithm that uses computer vision make up the suggested method. In particular, all items of interest inside the camera's range of view are detected and localized using the YOLO method. The robot manipulator, which serves as the agent of learning, is trained using the Soft Actor-Critic deeper reinforcement learning approach using the localization or detection and classification information obtained from YOLO. This allows the algorithm to determine the desired grasp position for each object. To expedite training and reduce the cost of collecting training data, the Sim-to-Real technique is employed in this work to minimize the risk of damage to the robot manipulator during the training process. A simulation environment built using the V-REP platform is used to train the deeper reinforcement learning neural network. The results of various experiments demonstrate that the 6-DOF industrial manipulator can successfully utilize the proposed approach to grasp even previously unidentified objects. © 2024 IEEE.

关键词： Deep reinforcement learning

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A hybrid personality-aware recommendation system based on personality traits and types models

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Journal of Ambient Intelligence and Humanized Computing 2023年第9期14卷 12775-12788页

作者： Dhelim, Sahraoui Chen, Liming Aung, Nyothiri Zhang, Wenyin Ning, Huansheng School of Computer Science University College Dublin Dublin Ireland School of Computing Ulster University Belfast United Kingdom School of Information Science and Engineering Linyi University Linyi China School of Computer and Communication Engineering University of Science and Technology Beijing Beijing China

Personality-aware recommendation systems have been proven to achieve high accuracy compared to conventional recommendation systems. In addition to that, personality-aware recommendation systems could help alleviate cold start and data sparsity problems by adding the user’s personality traits in the recommendation process. The majority of the literature works used Big-Five personality model to represent the user’s personality, this is due to the popularity of Big-Five model in the literature of psychology. However, from personality computing perspective, the choice of the most suitable personality model that satisfy the requirements of the recommendation application and the recommended content type still needs further investigation. In this paper, we study and compare four personality-aware recommendation systems based on different personality models, namely Big-Five traits model, Eysenck model and HEXACO model from the personality traits theory, and Myers–Briggs Type Indicator (MPTI) from the personality types theory. Furthermore, we propose a hybrid personality model for recommendation that takes advantage of the personality traits models, as well as the personality types models. Through extensive experiments on recommendation dataset, we prove the efficiency of the proposed model, especially in cold start settings. Our proposed hybrid personality-aware recommendation model improves the precision and recall in cold start settings by 21% and 18% respectively compared to the widely used Big-Five traits model. © 2022, The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature.

关键词： Recommender systems

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Applying Machine Learning Techniques to Increase Real-Time Data Analysis Accuracy 1

Applying Machine Learning Techniques to Increase Real-Time D...

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1st IEEE International Conference on Emerging Research in Computational Science, ICERCS 2023

作者： Naval, Preeti Mahesh, T.R. Kumar, Ajay Dhingra, Lovish Janakiraman, V. Banchhor, Chitrakant O. Maharishi University of Information Technology Maharishi School of Engineering and Technology Uttar Pradesh India Faculty of Engineering and Technology Department of Computer Science Engineering Karnataka Bangalore India Vivekananda Global University Department of Computer Science & Engineering Jaipur India Chitkara University Institute of Engineering and Technology Chitkara University Centre of Interdisciplinary Research in Business and Technology Punjab India Prince Shri Venkateshwara Padmavathy Engineering College Department of Mechanical Engineering Chennai127 India Vishwakarma Institute of Information Technology Department of Computer Engineering Pune India

ISBN: (纸本)9798350359756

facts analysis examines, transforms, and models record sets to uncover valuable data and help make higher selections. Machine mastering techniques allow computer systems to automatically learn from records to make predictions primarily based on previously discovered patterns. By using these strategies, businesses can grow actual-time records analysis accuracy and see tendencies faster, allowing them to promptly respond to adjustments inside the surroundings. This paper will speak about using system studying techniques for actual-time statistics evaluation and the benefits of using such strategies. Mainly, techniques that include supervised studying, unsupervised learning, and deep studying will be discussed. Moreover, use instances in which system mastering may be applied, including economic fraud detection software programs, digital advertising and marketing, and predictive preservation, will be explored. Eventually, the outcomes of using system mastering strategies for data analysis, including statistics privateness and safety issues, can be mentioned. © 2023 IEEE.

关键词： Marketing

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Personalized Privacy-Preserving Data Utilization Approach Powered by Distributed-GAN

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Big Data Mining and Analytics 2024年第4期7卷 1098-1113页

作者： Shuo Wang Chao Wang Tianshuo Dong Yunhua He Ke Xiao Department of Information Science and Technology North China University of TechnologyBeijing 100144Chinaand also with Department of Computer Science and EngineeringSouthern University of Science and TechnologyShenzhen 518055China Department of Information Science and Technology North China University of TechnologyBeijing 100144China

Recently,Machine Learning(ML)has made great achievements in a wide range of fields with the increasing data ***,the development of ML faces the challenges of data silos due to privacy *** with distributed ML architecture appearing,the training tasks can be completed without sharing *** particular,distributed Generative Adversarial Network(GAN),as a generative model,can rebuild dataset for downstream tasks and does not need to re-access users’private datasets even if the target task ***,the training of distributed-GAN still faces serious privacy *** work usually adopts a uniform level of privacy protection,which does not meet the requirements about personalized privacy protection for each *** this paper,we propose a privacy-preserving training framework of distributed-GAN,which combines differential privacy to provide users with personalized privacy ***,a privacy-driven incentive strategy is proposed,which designs a series of smart contracts to provide customized payments for data owners with different privacy preferences as compensation for their privacy *** last,we conduct sufficient privacy analysis and experimental validation,which demonstrate that our approach can optimize generative model with lower privacy cost and generate higher quality data for downstream tasks.

关键词： Generative Adversarial Network(GAN) differential privacy distributed training

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A Neuro-Fuzzy Predictor Trained by an Elitism Artificial Electric Field Algorithm for Estimation of Compressive Strength of Concrete Structures

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Informatica (Slovenia) 2023年第5期47卷 41-48页

作者： Kumar Dash, Ch Sanjeev Nayak, Sarat Chandra Behera, Ajit Kumar Dehuri, Satchidananda Department of Computer Science and Engineering Silicon Institute of Technology Silicon Hills Patia Bhubaneswar751024 India Department of Artificial Intelligence Engineering and Machine Learning CMR College of Engineering and Technology Hyderabad501401 India Department of Information and Communication Technology Fakir Mohan University Vyasa Vihar Odisha Balasore756019 India

Meta-heuristic learning-based forecasting is widely acceptable vis-à-vis manual and statistical methods in estimating the compressive strength of concrete structures. However, there is always a scope for exploring an effective, automated, and accurate predictor for this domain. This article proposes an elitism artificial electric field algorithm-based neuro-fuzzy network (eAEFA+NFN) for the prediction of compressive strength of concrete structures. The elitism method helps AEFA preserve the best individuals from iteration to iteration, by directly placing best fit particles into the population for the next generation and thus strengthening the optimization capacity of AEFA. A single hidden layer neural network (SHNN) is used as the base model and its inputs are fuzzified using the Gaussian triangular membership function with a degree of membership to different classes. The optimal number of input data, hidden neurons, bias, and weights for the hidden layer are decided by eAEFA. The model is evaluated on samples from a publicly available dataset with curing ages at 3, 7, 14, and 28 days. Considering four sample series, the eAEFA+NFN produced an average MAPE of 0.092073 and ARV of 0.139731 which are better compared to others. The experimental outcomes and analysis are in favor of the eAEFA+NFN-based forecasting. © 2023 Slovene Society Informatika. All rights reserved.

关键词： Electric fields

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Establishment of Speaker Recognition Corpus for Intelligent Attendance System 2022

Establishment of Speaker Recognition Corpus for Intelligent ...

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7th International Conference on Multimedia and Image Processing, ICMIP 2022

作者： Chen, Shuxi Sun, Yiyang School of Computer and Information Engineering Nantong Institute of Technology China

ISBN: (纸本)9781450387408

With the rapid development of information technology, student attendance has changed from paper attendance to machine attendance, such as taking photos, scanning QR codes, positioning, etc. These attendance needs to turn on the camera to take photos, which is slightly inefficient, or turn on the positioning service. However, many people think that turning on the positioning service will infringe on personal privacy. Therefore, we need to consider a more efficient Attendance method that does not infringe on personal privacy. Voice, as a signal that can quickly obtain and contain a variety of information, can be used for class students' attendance. Speaker recognition corpus is the basis of speech speaker recognition research. Diversified, large-scale and high-quality speaker recognition corpus plays an important role in improving the performance of speaker recognition system. At present, although there are many standardized corpora, there are few corpora for student attendance scenes. Therefore, this topic studies the speaker's speech feature parameters, and selects the appropriate Chinese phrases to establish the speaker's corpus. © 2022 ACM.

关键词： Speech recognition

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A Novel Smartphone-Based Human Activity Recognition Using Deep Learning in Health care 3rd

A Novel Smartphone-Based Human Activity Recognition Using De...

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3rd International Conference on Machine Learning, Image Processing, Network Security and Data Sciences, MIND 2021

作者： Soni, Vaibhav Yadav, Himanshu Semwal, Vijay Bhaskar Roy, Bholanath Choubey, Dilip Kumar Mallick, Dheeresh K. Department of Computer Science and Engineering Maulana Azad National Institute of Technology Madhya Pradesh Bhopal India Department of Computer Science and Engineering Indian Institute of Information Technology Bihar Bhagalpur India Department of Computer Science and Engineering Birla Institute of Technology Mesra Jharkhand Ranchi India

ISBN: (纸本)9789811958670

Nowadays, most smartphones feature a number of strong sensors, such as direction, network, location, and motion sensors. Motion and inertial sensors (i.e., accelerometer, gyroscope, etc.) are particularly popular in human activity recognition (HAR) for detecting human physical activity which helps in many research areas of the Internet of healthcare things (IoHT) including several patient’s diseases such as Parkinson, obesity, cardiovascular, and diabetes. Deep learning (DL) techniques have been increasingly popular over the years as a result of their significant outcomes. The use of deep learning techniques to recognize human physical activity in wearable and mobile sensor situations has also gotten a lot of attention from around the world. In this paper, a deep neural network (DNN) combining bidirectional long short-term memory (Bi-LSTM) and convolutional neural network (CNN) is proposed. The performance of the model has been evaluated on two publicly available datasets: WISDM and UCI-HAR. The model has achieved 97.96 and 97.15% accuracy for the WISDM and UCI-HAR, respectively. Moreover, the simulation results show the effectiveness of the proposed work compared to other state-of-the-art methods. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Long short-term memory

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Investigating the Benefits of Interoperability in 5G-Enabled Health Care Management Models 5th

Investigating the Benefits of Interoperability in 5G-Enabled...

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5th International Conference on Data Science, Machine Learning and Applications, ICDSMLA 2023

作者： Devrani, Rekha Batra, Raman Gobi, N. Chaudhary, Chetan Department of Computer Science and Engineering Vivekananda Global University Jaipur India Department of Mechanical Engineering Noida Institute of Engineering and Technology Uttar Pradesh Greater Noida India Karnataka Bengaluru India Maharishi School of Engineering and Technology Maharishi University of Information Technology Uttar Pradesh Lucknow India

ISBN: (纸本)9789819780426

Interoperability, or correspondence between at least two programming applications, is a vital improvement in 5G innovation that can have critical ramifications for medical services the executive’s models. The efficiency with which health care professionals can access various patient data points, manage their workflow, and provide higher-quality care is enhanced by software applications’ ability to interact with one another. Furthermore, interoperability can assist with lessening medical services costs, work on understanding results, and work with better correspondence between medical cares suppliers. This paper investigates existing examinations on interoperability and the potential advantages it could bring to 5G Based medical services the executives models. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： 5G mobile communication systems

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