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Smart Generation Computing, Communication and Networking (SMART GENCON), International Conference on

作者： Girija Shankar Sahoo Ramesh Sekaran G. Renuka Dharmesh Dhabliya Pramod Kumar Faujdar Deepak Minhas Maharishi School of Engineering and Technology Maharishi University of Information Technology Uttar Pradesh India Department of Computer Science and Engineering Faculty of Engineering and Technology JAIN (Deemed-to-be University) Ramnagar Karnataka India Department of Electronics and Communication Engineering Prince Shri Venkateshwara Padmavathy Engineering College Chennai Department of Information Technology Vishwakarma Institute of Information Technology Pune Maharashtra India Department of Mechanical Engineering Vivekananda Global University Jaipur India Centre of Interdisciplinary Research in Business and Technology Chitkara University Institute of Engineering and technology Chitkara University Punjab India

This studies focuses on leveraging self-sustaining deep getting to know techniques for real-time records analysis in device getting to know. We propose a machine this is enabled by means of artificial intelligence to streamline the technique of extracting significant insights from sensor-based records. Our approach considers two middle components: interpreting data with recurrent neural networks and deploying a mastering agent to routinely whole the statistics analysis process. The getting to know agent's training is based on supervised mastering, enabling it to discover and traverse styles inside the information. The recurrent neural community (RNN) offers the functionality to seize temporal styles within the information, with its capacity to make predictions based on previous input. As a result, our system can produce fairly correct insights with minimal human intervention. This makes it suitable for time-sensitive information analysis situations in the industrial area in which decisions have to be taken in actual-time.

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Fault-tolerant Mobile Service Offloading in Mobile Edge Computing

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IEEE Transactions on Automation Science and engineering 2025年

作者： Long, Tingyan Xia, Yunni Zhou, Mengchu Li, Jianqi Ma, Yong Ai-Turki, Yusuf Guizhou University School of Computer Science and Technology Guiyang550025 China Chongqing University College of Computer Science Chongqing400030 China New Jersey Institute of Technology Department of Electrical and Computer Engineering NewarkNJ07102 United States Global energy interconnection research institute Co. Ltd Beijing102209 China Jiangxi Normal University School of Computer and Information Engineering Nanchang China King Abdulaziz University K. A. Care Energy Research and Innovation Center The Center of Research Excellence in Renewable Energy and Power Systems Department of Electrical and Computer Engineering Faculty of Engineering Jeddah21589 Saudi Arabia

Mobile edge computing (MEC) is an evolving paradigm for rendering services through network-accessible resources deployed over Internet of Things (IoT) nodes at the edge. Nevertheless, an MEC environment usually employs thousands of physical machines connected via hundreds of switches/routers that communicate and coordinate to deliver computing service. In such complicated systems, faults caused by software, human errors, and hardware are often unavoidable. The edge of network presents a dynamic environment with great quantities of terminals, high mobility of mobile devices, heterogeneous applications, and intermittent traffic. In such an environment, MEC can suffer from unbalanced resource provisioning and interruptions of faults occurring at different levels, which further causes task faults and affects service quality. To address this challenge, this work proposes a novel fault-tolerant offloading method for handling faults by leveraging a reinforcement-learning-based service offloading decision model. The model synthesizes a Dueling Deep Q Network (DQN)-based algorithm for deciding user offloading behaviors and an adaptive checkpointing method for improving task execution reliability. For the purpose of model validation and comparison, extensive simulations are conducted. Numerical results clearly demonstrate that the proposed method is highly effective and outperforms existing methods. © 2004-2012 IEEE.

关键词： Mobile applications

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Ameliorating Semantic Search Through Advanced AI Techniques

Ameliorating Semantic Search Through Advanced AI Techniques

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Smart Generation Computing, Communication and Networking (SMART GENCON), International Conference on

作者： Ravikumar V Dharmesh Dhabliya Mala Mathur Sidhant Das Ritesh Kumar Srikrishna B Rao Department of Computer Science and Engineering Prince Shri Venkateshwara Padmavathy Engineering College Chennai Department of Information Technology Vishwakarma Institute of Information Technology Pune Maharashtra India Department of Civil Engineering Vivekananda Global University Jaipur India 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 Department of Mechanical Engineering Faculty of Engineering and Technology JAIN (Deemed-to-be University) Ramnagar Karnataka India

Recent synthetic intelligence (AI) advances have enabled some thrilling semantic search applications. In particular, the development of advanced AI strategies, which include natural language processing (NLP) and deep learning, has enabled the creation of search engines like Google and Yahoo that leverage semantic data to enhance the accuracy of the quest outcomes. No longer simplest does this enable customers to find applicable statistics speedy. However, it additionally facilitates ameliorating any semantic mistakes resulting from typos, incorrect syntax, or wrong interpretations of the question. In this paper, we present a survey of new advances in semantic seek powered by AI and techniques for query expansion, generating question-precise training statistics, and semi-supervised gaining knowledge. We additionally talk about the results for the future of semantic search and gift some case research on the successful implementation of semantic seek with AI structures. Our assessment concludes with a dialogue of the open challenges and potential research avenues.

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Long Short Term Memory Networks for Stroke Activity Recognition base on Smartphone 5

Long Short Term Memory Networks for Stroke Activity Recognit...

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5th IEEE International Conference on information technology, information Systems and Electrical engineering, ICITISEE 2021

作者： Siagian, Pandapotan Fernando, Erick Del Institute Of Technology Faculty Of Informatics And Electrical Engineering Computer Engineering Study Program North Sumatera 22381 Indonesia Bina Nusantara University School Of Information Systems Information Systems Department Jakarta11480 Indonesia

ISBN: (纸本)9781665401968

Aims this study to analyze rehabilitation exercises using sensor data embedded in smartphones is widely used to recognize human activities regularly to gain a better understanding of human behavior. However, it is rarely used to measure accelerators of patient movement in hospital wards. This paper proposes an optimized neural network-based feature extraction approach based on the Long Short Term Memory technique to recognize human activity using data from a tri-axial accelerometer. The activity to be measured is the Range of Motion for patient rehabilitation, and rehabilitation exercises are one of the most important steps for recovery. The results of the softmax regression trained on the dataset set have 1, 076, 105 records and 12 class attributes (class distribution). The experimental results on the SAR dataset show that our Long Short Term Memory-based approach is practical and successful, with an accuracy of 89.74%. © 2021 IEEE.

关键词： Smartphones

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Delayed Coding Scheme for Channels with Insertion, Deletion, and Substitution Errors

arXiv

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arXiv 2022年

作者： Shibata, Ryo Yashima, Hiroyuki Dept. of Information and Computer Technology Faculty of Engineering Tokyo University of Science Tokyo125-8585 Japan

We propose a new coding scheme, called the delayed coding (DC) scheme, for channels with insertion, deletion, and substitution (IDS) errors. The proposed scheme employs delayed encoding and non-iterative detection and decoding strategies to manage the transmission of multiple codewords in a linear code. In the DC scheme, a channel input sequence consists of subblocks of multiple codewords from the previous to current time instances. At the receiver side, the maximum a posteriori detection applies to the received sequences that contain information of the codeword at the current time instance, where priorly decoded codewords aid the detection. The channel code decoding is then performed, and extrinsic messages are exploited for the codeword estimations of the following time instances. We show that the rate achievable with the DC scheme over the IDS channel approaches the symmetric information rate of the channel. Moreover, we show the excellent asymptotic and finite-length performances of the DC scheme in conjunction with low-density parity-check codes. Copyright © 2022, The Authors. All rights reserved.

关键词： Channel coding

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CaPAN: Class-aware Prototypical Adversarial Networks for Unsupervised Domain Adaptation

CaPAN: Class-aware Prototypical Adversarial Networks for Uns...

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IEEE International Conference on Multimedia and Expo (ICME)

作者： Zhenyu Yu Pei Wang Guangzhou University Guangzhou China CISiP Faculty of Computer Science and Information Technology Universiti Malaya Kuala Lumpur Malaysia National Pilot School of Software Yunnan University Kunming China School of Information Science and Engineering Yunnan University Kunming China

ISBN: (数字)9798350390155

ISBN: (纸本)9798350390162

Adversarial domain adaptation has achieved impressive performances for unsupervised domain adaptation (UDA). However, existing adversarial UDA methods often rely on multiple domain discriminators to capture diverse patterns, which limit their scalability and resulting in dispersed features. To address these issues, we propose a novel method called Class-aware Prototypical Adversarial Network (CaPAN), which efficiently extracts transferable and discriminative features. Specifically, our class-aware adversarial learning employs a single multi-class discriminator to capture various patterns, aligning class-level features. Furthermore, to enhance the discriminative ability of our model, we introduce a prototypical domain discriminator to enhance the discriminatively of the learned features by aligning target sample towards prototypes (centers of each class), resulting in a more compact feature space. Extensive experiments validate the effectiveness of CaPAN, which can also be integrated as a regularization technique for existing methods to further improve their performance. Code is available at https://***/YuZhenyuLindy/CaPAN.

关键词： Codes Scalability Prototypes Feature extraction Adversarial machine learning Periodic structures

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Systematic Advancements in IoT: Integrating Edge Computing for Enhanced Architectures in Next-Generation Devices 1st

Systematic Advancements in IoT: Integrating Edge Computing f...

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1st International Conference on Advanced Network Technologies and Computational Intelligence, ICANTCI 2024

作者： Rajawat, Anand Singh Goyal, S.B. Islam, Sardar M. N. Malik, Varun School of Computer Sciences and Engineering Sandeep University Nashik India Faculty of Information Technology City University Petaling Jaya46100 Malaysia Institute for Sustainable Industries and Liveable Cities Victoria University Melbourne Australia Chitkara University Institute of Engineering and Technology Chitkara University Punjab Rajpura India

ISBN: (纸本)9783031860683

This paper introduces a methodical strategy for creating and executing IoT designs that are enhanced by edge computing. The goal is to meet the changing requirements of future devices. With the rise of the Internet of Things (IoT), there is a significant increase in the amount of data being generated due to the large number of interconnected devices. Conventional cloud-based processing models frequently encounter difficulties related to latency, bandwidth, and privacy problems. In order to address these difficulties, we provide a pioneering framework that incorporates edge computing into Internet of Things (IoT) systems. Our methodology disperses data processing, relocating computing in proximity to the data origin at the periphery of the network. By implementing this, the latency is reduced, the bandwidth utilization is minimized, and the data privacy is enhanced. In this document, we delineate the fundamental concepts that govern the design of this architecture, with a specific emphasis on modularity, scalability, and security. The architecture is intentionally built to be flexible, capable of accommodating a diverse array of IoT devices and applications, spanning from smart home systems to industrial automation. We showcase a preliminary version of our framework, illustrating its efficacy in several settings. Performance assessments demonstrate substantial enhancements in reaction time and bandwidth economy when compared to conventional cloud-based models. Our architecture additionally integrates sophisticated security protocols to protect against potential risks in IoT contexts. We analyze the consequences of our methodology on the future of IoT, emphasizing how architectures improved by edge computing can enable innovative opportunities for intelligent, effective, and protected IoT systems. This study establishes the foundation for future research and development in this potential subject, providing a detailed plan for the creation of the next generation of IoT

关键词： Cloud computing architecture

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A Comprehensive analysis of the Efficiency of Dynamic Spectrum Allocation Methods in Optical Fiber Communications

A Comprehensive analysis of the Efficiency of Dynamic Spectr...

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Emerging Research in Computational Science (ICERCS), International Conference on

作者： Beemkumar N Aditya Kumar Prateek Garg D Little Femilin Jana Dattatray G. Takale Neeraj Das Department of Mechanical Engineering Faculty of Engineering and Technology JAIN (Deemed-to-be University) Bangalore Karnataka India Department of Computer Science & Engineering Vivekananda Global University Jaipur India Centre of Interdisciplinary Research in Business and Technology Chitkara University Institute of Engineering and Technology Chitkara University Punjab India Department of Science and Humanities Prince Shri Venkateshwara Padmavathy Engineering College Chennai Department of Computer Engineering Vishwakarma Institute of Information Technology Pune India Maharishi School of Engineering and Technology Maharishi University of Information Technology Uttar Pradesh India

The efficient utilization of optical fiber communications dramatically affects the community's overall performance. As the ability demand for data communication increased through the years, various dynamic spectrum allocation (DSA) methods evolved to optimize the aid usage of optical fiber networks. This paper discusses a complete evaluation of the performance of various DSA methods, consisting of a detailed comparison of various methodologies and implementation techniques for spectrum allocation. The overall performance metrics considered in the evaluation consist of the variety of failing connections, latency, records fee, quantity of spectrum used, quantity of break up-spectrum assignments, overall latency, and throughput of the simulated network topologies. Experimental consequences reveal that single-track, multi-tune, and dynamic venture DSA techniques have higher efficiency than constant spectrum allocation strategies. Moreover, the effects screen shows that the end-to-give-up overall performance of the dynamic spectrum allocation scheme is akin to that of fixed spectrum allocation. Moreover, power efficiency in phrases of the overall wide variety of failed connections for spectrum admission to optical fiber networks is tested and discussed. The evaluation of the efficiency of numerous DSA methods presented in this paper is helpful for the layout and optimization of verbal exchange structures utilizing optical fiber networks.

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Efficient online shopping platforms in Southeast Asia 2

Efficient online shopping platforms in Southeast Asia

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2nd International Conference on Smart Computing and Electronic Enterprise, ICSCEE 2021

作者： Bamansoor, Samer Pande, Bishwajeet Al Moaiad, Yazeed Pathmanathan, P. Ravindran El-Ebiary, Yousef A. Baker Latiff, Nurul Adilah Abdul Aseh, Khairi Aledinat, Lowai Saleh Yusoff, Fakhrul Hazman MEDIU Faculty of Computer and Information Technology Malaysia Faculty of Informatics and Computing UniSZA Malaysia Gyancity Research Consultancy Pvt Ltd Motihari India MEDIU Faculty of Computer and Information Technology Malaysia International Institute of Applied Science Swiss School of Management Netherlands UniSZA Faculty of Informatics and Computing Malaysia Universiti Malaysia Terengganu Faculty of Ocean Engineering Technology and Informatics Malaysia UniSZA Faculty of Informatics and Computing Malaysia Universiti Teknologi MARA Malaysia

ISBN: (纸本)9781665432221

With the emergence of current developments and their devices that allow correspondence between organizations with each other, just as it is between organizations and service providers, just as with their customers, for some reasons, including advertising and a return to what is acceptable to them with the organization. Items, deals, etc., therefore, this examination provides an internal and external investigation of the current web-based business phase with a focus on promising conditions, risks, qualities, and shortcomings of FashionValet's electronic stage, whose administrations offer more than the Internet. Also, FashionValet is a stage organizer for online shopping and style in Southeast Asia other than Poplook, Zalora, and others, and the bulk of them are important competitors to FashionValet and they implement their own systems. The FashionValet experience, for example, is the history of the creator just as the other company's foundation has been pointed out to characterize how organizations, especially FashionValet, have moved from their primary stage to where they are today as the most aggressive design project. In addition, the technologies and innovations used by FashionValet are shown to provide a comprehensive detailed blueprint. To understand the improvement in style organizations in Malaysia, it was examined further. The paper outlines the critical elements to FashionValet's thriving as an end. © 2021 IEEE.

关键词： Online systems

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A hybrid-Lp-Norms-Oriented Robot Kinematic Calibration Model Optimized by a Novel Newton Interpolated Differential Evolution Algorithm

A hybrid-Lp-Norms-Oriented Robot Kinematic Calibration Model...

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IEEE International Conference on Data Mining Workshops (ICDM Workshops)

作者： Tinghui Chen Shuai Li School of Computer Science and Technology Chongqing University of Posts and Telecommunications Chongqing China Faculty of Information Technology and Electrical Engineering University of Oulu VTT-Technical Research Center of Finland Oulu Finland

Kinematic calibration can obtain real kinematic parameters by the establishment and the solution of the kinematic error models, achieving high-precision control of an industrial robot. However, the data collection points frequently fail to cover the entire robot workspace in data collection due to human error, thereby yielding incomplete robot kinematic calibration (RKC) data. Moreover, existing RKC models all adopt a single distance-oriented Loss scheme, resulting in the ignoration of the RKC data’s characteristics demonstrated by other metrics. Motivated by these discoveries, this work first fills in the RKC data as a preprocessing step for robot calibration. Then, we propose a hybrid-L p -norms-oriented RKC (HLRKC) model to address the RKC data’s characteristics issue. It adopts twofold ideas: a) aggregating multiple L p norm effects to form a hybrid Loss; b) adaptively adjusting the weights of all L p norms in the hybrid Loss. Subsequently, based on the HLRKC model, we present a new Newton Interpolated Differential Evolution (NIDE) algorithm to form a NIDE-HLRKC calibrator. Experimental results demonstrate that the HLRKC-NIDE calibrator substantially exceeds the state-of-the-art calibrators in calibration accuracy and convergence rate. Hence, it strongly supports its excellent potential in robot calibration issues.

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