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11th IEEE Global Conference on Consumer Electronics, GCCE 2022

作者： Wang, Dao-Jiun Fanjiang, Yong-Yi Hung, Chi-Huang Yeh, Yu-Hsiang Hsiao, Yao-Yun Hsiao, Sheng-En Graduate Institute of Applied Science and Engineering Fu Jen Catholic University New Taipei City Taiwan Graduate Institute of Applied Science and Engineering Dept. of Computer Science and Information Engineering Fu Jen Catholic University New Taipei City Taiwan Dept. of Information Technology Graduate Institute of Applied Science and Engineering Fu Jen Catholic University Lee-Ming Institute of Technology New Taipei City Taiwan Fu Jen Catholic University Department of Computer Science and Information Engineering New Taipei City Taiwan Lee-Ming Institute of Technology Dept. of Digital Multimedia New Taipei City Taiwan Lee-Ming Institute of Technology Dept. of Fashion Business Management New Taipei City Taiwan

ISBN: (纸本)9781665492324

Image recognition based automatic meter reading (AMR) method is getting popular nowadays. As traditional mechanical water meters are usually placed in poor environments, if we install the AMR on the water meter, the power supply of the AMR is an issue that needs to be considered thoughtfully. This study proposes to place a hydroelectric generator in the water pipe to provide auxiliary power in order to improve the power supply of AMR system. The system can also automatically identify the value of the water meter through the cloud service, so as to reduce the personnel cost of the water company, and further manage and control the hydraulic resources more conveniently. © 2022 IEEE.

关键词： Character recognition

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Metrology for Underwater Acoustics: Rethinking Measurement Strategies for the Assessment of the Environmental Status

Metrology for Underwater Acoustics: Rethinking Measurement S...

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IEEE International Workshop on Metrology for the Sea;Learning to Measure Sea Health Parameters (MetroSea)

作者： Pier Francesco Moretti Angela Pomaro Silvano Buogo Department of Earth System Sciences and Environmental Technologies National Research Council of Italy Rome Italy Department of Computer Control and Management Engineering A. Ruberti Sapienza University of Rome Rome Italy Institute of Marine Sciences National Research Council of Italy Venice Italy Institute of Marine Engineering) National Research Council of Italy Rome Italy

ISBN: (数字)9798350379006

ISBN: (纸本)9798350379013

Underwater acoustics is a discipline that is facing an increased political attention due to the impacts of anthropogenic activities on the environment. Metrology for underwater acoustics is not yet linked to an established common frame, introducing uncertainties in the interpretation of signals aiming to provide clues on the status of the marine environmental status. Challenges in adopting technologies and procedures for the calibration and use of hydrophones are reported. An observation strategy that can be considered appropriate for the investigation of physical processes underlying the dynamics of the marine ecosystems is suggested.

关键词： Uncertainty Conferences Sea measurements Metrology Acoustic measurements Marine ecosystems Calibration Underwater acoustics

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Leveraging Machine Learning for Dynamic Resource Allocation in Cloud Automation

Leveraging Machine Learning for Dynamic Resource Allocation ...

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Automation and Computation (AUTOCOM), International Conference on

作者： Gunveen Ahluwalia Vaibhav Goutham Suresh M D Anto Praveena Kalyan Acharjya Trushna Paridaname Centre of Research Impact and Outcome Chitkara University Rajpura Punjab India School of Mangement - PG Management JAIN (Deemed to be University) Bangalore Karnataka Department of Computer Science and Engineering Sathyabama Institute of Science and Technology Chennai Tamil Nadu India Maharishi School of Engineering & Technology Maharishi University of Information Technology Uttar Pradesh India Department of Computer Science and Engineering Siksha ‘O’ Anusandhan (Deemed to be University) Bhubaneswar Odisha India

ISBN: (数字)9798331542375

ISBN: (纸本)9798331542382

Cloud automation enables the dynamic management and allocation of resources in a computing environment and is one of the most common uses available. Yet despite their power when cloud systems were simple and small, traditional approaches to allocating resources have proven far less feasible lately. This scenario has also created the need for new evolution methods that will adapt to dynamic cloud environments and improve the system's overall performance. A solution to this challenge comes from machine learning, which has been increasingly used recently. ML algorithms can process vast amounts of data and identify patterns and trends to predict resource use and user needs. Deep learning helps cloud-enabled automation systems adapt in real-time to changing conditions, distribute resources efficiently and improve user experience. For example, reinforcement learning and deep learning are ML techniques that can be applied to resource allocation algorithms or demand prediction models to increase accuracy. Integrating cloud automation and machine learning brings effectiveness in dynamic resource allocation, boosting the overall performance of a system, which leads to savings, better experience, and greater flexibility in using cloud resources. Reinforcement and deep learning are machine learning techniques to optimize order allocation algorithms and enhance demand estimation models.

关键词： Deep learning Cloud computing Automation Machine learning algorithms Heuristic algorithms Estimation Dynamic scheduling Prediction algorithms Real-time systems Resource management

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Deciphering User Perspectives and Prejudices: A Comparison of Models to Investigate Sentiments on ChatGPT on Twitter

Deciphering User Perspectives and Prejudices: A Comparison o...

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Trends in Quantum Computing and Emerging Business Technologies (TQCEBT), International Conference on

作者： Anil Tiwari Asif Hasan Meeta Joshi Deepa Pralay Ganguly M. Kalyan Chakravarthi Department of Commerce and Management RNB Global University Bikaner Department of Psychology Aligarh Muslim University Aligarh Faculty of Management Studies Marwadi University M.M. Institute of Computer Technology & Business Management Maharishi Markandeshwar (Deemed to be University) Mullana Ambala Haryana India GNIHM JIS Group School of Electronics Engineering VIT-AP University Amaravathi India

ISBN: (数字)9798350384277

ISBN: (纸本)9798350384284

The capacity of ChatGPT (Chat Generative Pretrained Transformer) to produce responses that resemble those of a human has led to its rise in popularity. It is important to remember that using ChatGPT excessively or blindly can have negative effects, particularly when making important decisions. In a similar vein, a lack of confidence in the technology might result in underutilization and lost possibilities. This study explores a crucial component of this interaction: how user perception affects ChatGPT's efficacy. Although ChatGPT is clearly technologically advanced, its effectiveness depends on how users view its capabilities, limits, and replies. The abstract delves into the complex interplay between user perception and system performance, highlighting the critical roles played by ethical considerations, contextual knowledge, accurate responses, and transparency. The study recognizes the difficulties caused by system constraints and possible misconceptions, but it also highlights the importance of user happiness, trust, and acceptance. Through an analysis of the relationship between user perception and ChatGPT's efficacy, this abstract offers important new perspectives on how to best optimize human-AI interactions for improved user experiences. ChatGPT Has been widely used by the users from its launch. Many users have trust and different types of interaction patterns as well as user experience for enhancing their content creation in different types of careers. This study uses the ai platform for understanding its user perception with the use of tweets for examining their perceptions about this chat GPT. The proposed hybrid model is compared with random forest and svm technique for high accuracy in understanding user perceptions based on text analysis from Twitter.

关键词： Support vector machines Radio frequency Analytical models Social networking (online) Blogs Transforms Chatbots

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Study on Proactive and Reactive Routing Approaches for Flying Ad-hoc Networks

Study on Proactive and Reactive Routing Approaches for Flyin...

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2021 IEEE India Geoscience and Remote Sensing Symposium, InGARSS 2021

作者： Banerjee, Sagnik Basu, Snehasish Halder, Tamesh Das, Amit Kumar Sarkar, Sajal Basak, Arindam Ray, Arundhati Mishra Chakravarty, Debashish Kalinga Institute of Industrial Technology School of Electronics Engineering Bhubaneswar751024 India Iit Kharagpur Dept. of Mining Engineering India Institute of Engineering & Management Dept. of Ece Kolkata India Power Grid Corporation of India Ltd Information Security Department Gurgaon India ISRO Space Application Center Ahmedabad India

ISBN: (纸本)9781665442497

A Flying Ad-hoc Network (FANET) is gaining huge popularity in recent times for its manifold applications in different fields. However, routing in FANETs is very challenging due to wireless shared links, energy constraint, dynamic network topology, etc. In this paper, we study and analyze two main routing approaches such as proactive routing and reactive routing. We study and analyze two main routing protocols viz. Low-energy adaptive clustering hierarchy (LEACH), a type of proactive routing approach, especially used in wireless sensor networks (WSNs) to intensify the residual energy and battery life of sensor nodes and Ad-hoc On-demand Distance Vector (AODV) routing, a type of reactive routing approach, widely used in ad-hoc networks including FANETs. We then propose a diamond-shaped symmetric homogeneous network of four nodes and demonstrate AODV based routing in a FANET. Finally, the Receiver Operating Characteristic-Area Under the ROC Curve (ROC-AUC) curve is plotted for vindication of the routing performance. © 2021 IEEE.

关键词： Routing protocols

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Model predictive control for tracking using artificial references: Fundamentals, recent results and practical implementation

Model predictive control for tracking using artificial refer...

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IEEE Conference on Decision and Control

作者： P. Krupa J. Köhler A. Ferramosca I. Alvarado M.N. Zeilinger T. Alamo D. Limon Computer Science Group Gran Sasso Science Institute (GSSI) L’Aquila Italy ETH Zürich Institute for Dynamic Systems and Control Zürich Switzerland Department of Management Information and Production Engineering University of Bergamo Via Marconi 5 Dalmine (BG) Italy Department of Systems Engineering and Automation Universidad de Sevilla Seville Spain

ISBN: (数字)9798350316339

ISBN: (纸本)9798350316346

This paper provides a comprehensive tutorial on a family of Model Predictive Control (MPC) formulations, known as MPC for tracking, which are characterized by including an artificial reference as part of the decision variables in the optimization problem. These formulations have several benefits with respect to the classical MPC formulations, including guaranteed recursive feasibility under online reference changes, as well as asymptotic stability and an increased domain of attraction. This tutorial paper introduces the concept of using an artificial reference in MPC, presenting the benefits and theoretical guarantees obtained by its use. We then provide a survey of the main advances and extensions of the original linear MPC for tracking, including its non-linear extension. Additionally, we discuss its application to learning-based MPC, and discuss optimization aspects related to its implementation.

关键词： Surveys Asymptotic stability Costs Tutorials Optimization Predictive control

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An Incentive Mechanism Considering Privacy Metric for Personalized application

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IEEE Transactions on Consumer Electronics 2025年

作者： Xie, Yun Li, Peng Zhang, Jindan Attar, Razaz Waheeb Arya, Varsha Gupta, Brij B. Nanjing Vocational College of Information Technology Jiangsu Province Service Customization Network Application Engineering Research Center Jiangsu Province Service Customization Network Application Engineering Research Center Nanjing210023 China Nanjing University of Posts and Telecommunications School of Computer Science Nanjing210023 China Jiangsu High Technology Research Key Laboratory for Wireless Sensor Networks Nanjing210023 China Xianyang Polytechnic Institute China Princess Nourah bint Abdulrahman University College of Business Administration Management Department Riyadh11671 Saudi Arabia Hong Kong Metropolitan University Hong Kong Hong Kong Center for Interdisciplinary Research Dehradun India Asia University Department of Computer Science and Information Engineering Taichung413 Taiwan China Medical University Hospital China Medical University Department of Medical Research Taichung Taiwan Pune India Korea University School of Cybersecurity Seoul Korea Republic of Kyung Hee University Kyungheedae-ro Seoul26 Korea Republic of

Edge-based federated learning (EFL) is a solution for personalized applications. It addresses the limitations of cloud-based consumer service systems, such as data privacy and network latency. To further enhance the EFL system’s ability to protect data privacy, differential privacy (DP) is introduced. It is crucial to motivate high-quality collaboration among participants for optimal performance of the DP-EFL system. Researchers primarily focus on efficient learning algorithms, optimized resource scheduling, and user-oriented incentive mechanisms. Existing research exhibits notable limitations in modeling and analyzing the heterogeneity of resource costs (e.g., communication costs), particularly failing to comprehensively account for the additional costs imposed by participants’ privacy protection mechanisms‌. To address this gap, we propose a novel market transaction model based on Stackelberg game theory. This framework integrates incentive-driven reward allocation mechanisms to rigorously investigate the collaborative behaviors and strategic interactions between edge nodes (ENs) and the cloud server (CS)‌. We incorporate privacy costs into the multidimensional costs of participants with privacy metric, and based on this, derived collaboration strategies between ENs and CS, thus solving the Stackelberg game problem by theoretical analysis. Furthermore, numerical experiments validated the rationality of our proposed model, the impact of strategies on the performance of collaborators, and the advantages over other incentive mechanisms. © 2025 IEEE. All rights reserved.

关键词： Federated learning

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Using the Heterogeneous Network's Self-Adaptive Topological Structure for Drug-Protein Interaction Prediction

Using the Heterogeneous Network's Self-Adaptive Topological ...

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Communications and Information Technologies (GCCIT), Global Conference on

作者： Vikrant Sharma Niranjani B Baishakhi Debnath V. A. Mishra K. Yuvaraj Ansh Kataria Computer Science and Engineering Graphic Era Hill University Dehradun Uttarakhand India Graphic Era Deemed to be University Dehradun Uttarakhand India Department of Business Administration Prince Shri Venkateshwara Padmavathy Engineering College Chennai School of Management - UG Management JAIN (Deemed to be University) Bangalore Karnataka Computer Engg. Vishwakarma Institute of Technology Pune India Department of Computer Science Karpagam Academy of Higher Education Coimbatore Centre of Research Impact and Outcome Chitkara University Rajpura Punjab India

ISBN: (数字)9798350388916

ISBN: (纸本)9798350388923

The prediction of drug-protein interactions (DPIs) has been the subject of many robust computer approaches using graph neural networks (GNNs). The burden in laboratories and the costs associated with drug discovery and repurposing may be significantly decreased with its help. The unreported indications of many proteins and medications render their clinical activities unclear, nonetheless. Therefore, using the data that is currently available, it is challenging to construct a trustworthy drug-protein heterogeneous network that can characterize the interactions between medicines and proteins. We provide TAG, a DPI prediction approach that may self-adaptively alter the topological framework of the heterogeneous networks, as a solution to this challenge. Graph Neural Networks (GNNs) have recently gained a lot of interest due to their exceptional compatibility with viral proteins and pharmacological substances. Building such a model that accurately depicts the molecular structures of living molecules is, however, no easy feat. Also, newly created GNNs have been overfitting on Coronavirus datasets as they are still not widely used. Next, we propose a new model called Topology-enhanced Graph Neural Network using Multi-hop Gating Mechanism to fix those problems. Firstly, our model is able to learn more specific properties of proteins and chemicals. Conversely, we provide a novel gating approach for improving atom representation using non-neighbor data. In order to implement the drug-protein heterogeneous network, TAG sets up a representation learning module that is based on graph attention network. The network's topology may be fine-tuned in response to the training loss, and it can learn the connections between nodes using their properties. Lastly, TAG uses the embeddings of medicines and proteins to forecast the likelihood of their interactions. The experimental findings demonstrate that TAG is capable of significantly enhancing the network's topological structure. Across

关键词： Proteins Bridges Training Support vector machines Representation learning Network topology Spread spectrum communication Graph neural networks Heterogeneous networks Overfitting

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Turiyam Set a Fourth Dimension Data Representation

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Journal of Applied Mathematics and Physics 2021年第7期9卷 1821-1828页

作者： Prem Kumar Singh Department of Computer Science and Engineering Gandhi Institute of Technology and Management-Visakhapatnam Andhra Pradesh India

Recently most of the authors focused on dealing with uncertainty. To deal with that algebra of fuzzy sets is introduced in three-way fuzzy space. It gave way to characterize the attributes in true, false or uncertain regions. It is found in chemistry also as an electron, proton and neutron. The problem arises when the attributes are beyond the three-dimensions. One of suitable examples is winning, draw or loss of a match cannot define the win draw or loss of series. It is measured in international relationships also like USA-China, USA-Russia, India-Pakistan or Israel-Philistine. Some countries support the USA, some countries support China, some countries are neutral in hidden consciousness to fulfill their agenda. It is also measured in democratic countries like India where some people vote in favor of a party, some people vote against the given party, some people may be absent or walkout whereas some people vote NOTA. This last dimension is called as Turiyam or fourth dimension. This last dimension is influenced from Yin-Yang theory of Chinese Taoism as well as Sanskrit Yamma-Yammi and its complement. The Turiya means measuring each dimension in silent mode. There is less attention paid towards the necessity of fourth dimensions and its algebra. This paper will discuss gimbal lock of three-way fuzzy space using the Tuiyam set and its algebra.

关键词： Fourth Dimensions Plithogenic Set Three-Way Decision Space Turiyam Set Granulation

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A Comprehensive Review Analysis of Supervised Machine Learning Techniques

A Comprehensive Review Analysis of Supervised Machine Learni...

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Smart Electronics and Communication (ICOSEC), International Conference on

作者： Shivangi Ghildiyal Raghav Garg Saurabh Dhanik Argya Sarkar Gayatry Sharma Pushpendra Singh Kharayat Deptt. of Computer Application Invertis University Bareilly Uttar Pradesh India Deptt. of Computer Sci. & Engg Tula’s Institute Dehradun U.K. India School of Computing Graphic Era Hill University Bhimtal India Graduate School of Business Tula’s Institute Dehradun U.K. India Department of Computer Science MIET Kumaun Haldwani Uttarakhand India Department of Electrical Engineering Graphic Era Deemed to be university Dehradun India

ISBN: (数字)9798331504403

ISBN: (纸本)9798331504410

This study presents an exhaustive review analysis of the supervised machine learning techniques. The different types of supervised machine learning methods which are discussed in this study are linear Regression, Logistic Regression, Decision Trees, Random Forest, Support Vector Machines (SVM), k-Nearest Neighbors (k-NN), Naive Bayes, Gradient Boosting Machines (GBM) and Neural Networks. All these methods are showing its relevance and utility in the various domain of engineering and bio medical engineering. Using supervised machine learning methods, review evaluation involves assessing and deriving insight from evaluations, frequently in order to categorize them, identify themes, or forecast scores.

关键词： Support vector machines Logistic regression Machine learning algorithms Reviews Neural networks Nearest neighbor methods Boosting Bayes methods Decision trees Random forests

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