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international conference on Modern Electronics Devices and Communication Systems, MEDCOM 2021

International Conference on Modern Electronics Devices and C...

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international conference on Modern Electronics Devices and Communication Systems, MEDCOM 2021

ISBN: (纸本)9789811963827

The proceedings contain 46 papers. The special focus in this conference is on Modern Electronics Devices and Communication Systems. The topics include: Medical Image Analysis Using Soft computing Feature Selection and Classification of Skin Cancer;Comparative Study of Fuzzy, PID, and 2DOF-PID Controller for Liquid Level Control System;IOT-Based Self-nourishing System for Plants;Social Distance Alerting Device for COVID-19;BWDS-Based Spatially Dabble Couple Horizontal-Vertical 4-Port PIPO DRA with Poly-Pattern Diversity;Design of U-Shaped MIMO Antenna for Wi-Fi Application;comparative Study of Parametric Disturbances Effect on Cart-Inverted Pendulum System Stabilization;Efficient Design of QCA ADD-SUB Subsystem for Future Microprocessors;predication of Diabetes Through Machine Learning Technique;comparative Analysis for Email Spam Detection Using Machine Learning algorithms;Study of Gate Workfunction Modulated FinFET with Effect of TID;prediction of Accuracy and Performance of Crop Production by Using Machine Learning Algorithm;cognitive Load Classification in Alcoholic and Control Subjects Performing Delayed Matching Task Using Optimizable Classifier Networks;energy Detection in Cognitive Radio Networks to Optimize the Spectrum Utilization Through Cooperative Spectrum Sensing Using Weights;Real-Time Tracking of Health Parameters Using IOT and data Logger: Application to COVID-19;Real-Time Facial Detection Using MATLAB (Utilizing VJ Method);Hardening of EMP in Shielded Enclosure for Radiated Fields;Review on Optimizing Text-Video Retrieval Using CLIP (Contrastive Language-Image Pre-training);argument Mining: A Categorical Review;optimizing Text: Representation of Similarity Using Machine Learning;post-quantum Cryptography: A Solution to the Challenges of Classical Encryption algorithms;experimental Analysis on Aging Evaluation of Transformer Oil via data Collection;designing Adiabatic Techniques for Logic Circuits.

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Robust data-Driven Accelerated Mirror Descent

Robust Data-Driven Accelerated Mirror Descent

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international conference on Acoustics, Speech, and Signal processing (ICASSP)

作者： Hong Ye Tan Subhadip Mukherjee Junqi Tang Andreas Hauptmann Carola-Bibiane Schönlieb Damtp University of Cambridge Cambridge U.K. Department of Computer Science University of Bath U.K. Research Unit of Mathematical Sciences University of Oulu Department of Mathematics University College London London U.K.

Learning-to-optimize is an emerging framework that leverages training data to speed up the solution of certain optimization problems. One such approach is based on the classical mirror descent algorithm, where the mirror map is modelled using input-convex neural networks. In this work, we extend this functional parameterization approach by introducing momentum into the iterations, based on the classical accelerated mirror descent. Our approach combines short-time accelerated convergence with stable long-time behavior. We empirically demonstrate additional robustness with respect to multiple parameters on denoising and deconvolution experiments.

关键词： Deconvolution Neural networks Noise reduction Training data Signal processing algorithms Robustness Mirrors

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Big data Mining And Clustering Using Distributed Bayesian Matrix Decomposition

Big Data Mining And Clustering Using Distributed Bayesian Ma...

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Evolutionary algorithms and Soft computing Techniques (EASCT), international conference on

作者： Bappaditya Das Thejovathi Murari Chandan Das P. Rajeswari Computer Science and Engineering Dr. B.C. Roy Engineering College Durgapur West Bengal India Computer Science and Engineering Acharya Nagarjuna University Guntur Andhra Pradesh India Artificial Intelligence & Data Science Panimalar Engineering College Chennai India

Technological advancement has enabled us to store and process massive amounts of data in a much shorter period of period. The phrase "Big data" merely describes the enormous amounts of data that are now regularly utilized in business and academic settings. The emphasis here is not only on data collection, but also on careful data analysis in order to obtain meaningful results. There are several approaches to dealing with massive data streams. data clustering into compact units is one such method. This not only reduces data size but also allows it to be used more efficiently. Because all of these devices are from different manufacturers, the data generated is heterogeneous in nature and contains noise. A fundamental problem is gaining knowledge from heterogeneous, noisy data. Several strategies for mining knowledge from big data have already been proposed. Because big data is frequently acquired from dispersed locations and stored on multiple machines, handling these extremely large data sets in a single machine is either inefficient or impossible. We provide a distributed Bayesian matrix decomposition model (DBMD) for big data mining and clustering. Statistical inference, alternate direction method of multipliers (ADMM), and accelerated gradient descent (AGD) are the three techniques utilized to implement distributed computing. Investigated are the theoretical convergence tendencies of various approaches. We offer a perfect plug-in weighted average that minimizes estimate variance to get over noise heterogeneity. In real-world experiments, our algorithms outperform or are on par with two popular distributed approaches, Scalable- nonnegative matrix factorization (NMF) and Scalable- k-means++, which are confirmed by our theoretical results from synthetic studies.

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Analysis Techniques for Pharmaceutical Drugs Biomedicine Big data Analytics and Machine Learning

Analysis Techniques for Pharmaceutical Drugs Biomedicine Big...

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international conference on ent computing and Control Systems (ICICCS)

作者： P A Padmaavathy A. Celina R. Prameela Devi B N Arathi G. Sureshkumar G. Ramachandran Department of Management Faculty of Arts Science commerce and Management Karpagam Academy of Higher Education Coimbatore College of Management SRMIST Kattankulathur Department of ECE CVR College of Engineering Hyderabad Telangana Dept of Computer science and Engineering PES University Bengaluru Karnataka ECE Vinayaka Mission’s Kirupananda Variyar Engineering College Vinayaka Mission’s Research Foundation (Deemed to be University) Salem

The phases of drug discovery and development, starting with drug design and moving through crucial clinical trials to clinical practice, are being revolutionized by machine learning techniques. A more sophisticated kind of learning is machine learning (ML), which may be used to identify patterns in the solutions to solve current problems and learn to solve unsolved problems in future. Following the pre-processing of the necessary data, the objective is to learn from the data and identify patterns. The results can then be obtained by applying the patterns found to a different dataset. In general, when using larger, more agile data samples on high-dimensional datasets, ML algorithms can make predictions that are more accurate. Along with the rapid development of bioinformatics, there has been a significant global evolution in drug discovery and design technologies over the past ten years. Various artificial intelligence and machine learning (ML) methodologies have been used in drug discovery and design, leading to changes in all of their stages, including the time-consuming drug-target interaction (DTI) prediction and medicines and compounds discrimination.

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Application of Big data Fusion Model in the Architecture Design of Smart Departure Service Platform

Application of Big Data Fusion Model in the Architecture Des...

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Artificial Intelligence and Autonomous Robot Systems (AIARS), international conference on

作者： Li Dong Xiao Huang Yi Zhuang Gang Yu Guowei Cai State Grid Zhejiang Electric Power Co. Ltd. Hangzhou China Zhejiang Huayun Information Technology Co. Ltd. Hangzhou China

The data volume of the smart retreat service platform is large, and the slow data processing rate will affect the service quality of the smart retreat service platform. Based on this, the big data fusion model is applied to the architecture design of the intelligent retreat service platform. The service layer is designed by various hardware and service ports; the functional layer is designed by user management modules, elderly care service management modules and building modules of big data fusion model; the platform layer is designed by big data preprocessing modules, data storage and management modules and big data processing module, among which, in the big data processing module, based on the big data fusion model, the matrix decomposition algorithm is used to process the big data. Through the experiment, the smart retreat service platform is tested first, and the data processing rate of different data processing algorithms is compared with two traditional algorithms. The test results show that the data processing rate of the proposed algorithm is 300kb/s higher than that of traditional algorithm 1, and 200 kb/s than traditional algorithm 2. The matrix factorization algorithm based on big data fusion model has better data processing effect.

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Issues and Challenges of KDD Model for Distributed data Mining Techniques and Architecture

Issues and Challenges of KDD Model for Distributed Data Mini...

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international conference on computing for Sustainable Global Development (INDIACom)

作者： Mohammad Waseem Shafiqul Abidin Department of Computer Science Aligarh Muslim University Aligarh India

We are currently seeing a data explosion and witnessing a meteoric rise in database sizes; therefore, with the widespread availability of such large volumes of data, there has arisen an urgent need to transform it into actionable insights and knowledge. So, data mining is a very important tool. The knowledge that is produced is put to use in a wide variety of contexts, containing but not limited to the following: science, technology, engineering, commerce, study, social, health, education, entertainment, etc. Market analysis, client retention, and production control are just some of the many uses for the knowledge gained from data. In this study, we explore the systems, methodologies, techniques, and algorithms for Distributed data Mining that may be used to effectively and efficiently deal with distributed data and extract useful insights. Moreover, some selected KDD models for Distributed data Mining have been systematically reviewed.

关键词： Knowledge engineering Distributed databases Entertainment industry Computer architecture Transforms Production control data models

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Blockchain and Edge computing Integration Boosts Scalability and Reduces Latency

Blockchain and Edge Computing Integration Boosts Scalability...

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Business Analytics for Technology and Security (ICBATS), international conference on

作者： Mahmoud Mahfuri Ahmad Enad Alnawaiseh Umber Asad Muhammad Yousif Shoukat Aslam Zakariya Chabani Muhammad Fiaz Faculty of Engineering - Computer and Information Technolgy Department International University for Science and Technolgy Orange-Jordan Telecom Company Jordan School of Computer Computer NCBA&E Lahore Pakistan Faculty of Management Canadian University Dubai Dubai UAE School of Computer Science NCBA&E Lahore Pakistan Applied Science Research Center Applied Science Private University Amman Jordan

Nowadays, the integration of blockchain and edge computing has become important in computer science due to the significant advantages it can bring to a range of real-world applications. By combining the strengths of both technologies, it is Combining the strengths of both technologies makes it possible to create a highly secure and reliable network with reduced latency and efficient transaction processing. This article explores the challenges and opportunities of combining these two technologies, including the scalability barrier of blockchain and the management and security challenges of edge computing. This paper discusses some of the theories and research challenges associated with this integration, including developing efficient consensus algorithms and enabling data sharing and provenance. this paper concludes by highlighting the potential benefits of this technology in the field of medicine and healthcare, finance, supply chain management, and Internet of Things.

关键词： Supply chain management Scalability Process control Organizations Reliability theory Blockchains Security

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Application of data Mining Technology in the Construction of Cultural Confidence System

Application of Data Mining Technology in the Construction of...

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Smart Generation computing, Communication and Networking (SMART GENCON), international conference on

作者： Lili Xu Xi'an Fanyi University Xi'an Shaanxi Province China

Red culture is the precious spiritual wealth left by China's revolutionary predecessors and one of the sources of strength to promote social progress and development. Therefore, building cultural self-confidence plays an important role in the dissemination and promotion of advanced culture by contemporary people. Therefore, this paper proposes a data mining technique to evaluate and analyze the innovative cultural confidence system. With the rapid development of information technology and explosive growth of data scale, the era of big data has arrived. In this era, data has become an important resource and plays a vital role in the development of all walks of life. Among them, data mining technology, as one of the core technologies of big data processing, has attracted wide attention and application

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BreathIE: Estimating Breathing Inhale Exhale Ratio Using Motion Sensor data from Consumer Earbuds

BreathIE: Estimating Breathing Inhale Exhale Ratio Using Mot...

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international conference on Acoustics, Speech, and Signal processing (ICASSP)

作者： Nafiul Rashid Md Mahbubur Rahman Tousif Ahmed Jilong Kuang Jun Alex Gao Digital Health Lab Samsung Research America Mountain View CA USA

Breathing Inhale/Exhale (IE) ratio is one of the critical breathing biomarkers for pulmonary patients and healthy individuals. It can indicate the severity of lung obstruction for chronic lung patients and help detect psycho-social stress for healthy individuals. With the advancement of wearable technologies, common consumer wearables such as smartwatches offer breathing rates. However, IE ratio measurement is not available in consumer wearable devices till today. In this paper, we present a novel algorithm, BreathIE, to estimate breathing rate and IE ratio using a low-power motion sensor embedded in consumer-grade earbuds. Moreover, our algorithm is adaptive and dynamically adjusts to the user’s breathing habit by accommodating varying breathing durations at run time. We conducted a study with 30 participants, where both earbuds and a reference chestband device were used simultaneously. Experimental evaluation against the annotated reference data shows that our algorithm can estimate breathing rate with a mean absolute error (MAE) of 2.37 breaths per minute (BPM) and breathing IE ratio of 0.27 MAE while outperforming the state-of-the-art algorithms.

关键词： Heuristic algorithms Wearable computers Signal processing algorithms Lung Signal processing Biomarkers Motion detection

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Resource-Coupling-Oriented Business Process Modeling and Optimal Sequencing

Resource-Coupling-Oriented Business Process Modeling and Opt...

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international conference on Wireless Communications, Networking and Applications (WCNA 2022)

作者： Han, Ke Li, Kunlin Cao, Zhiyong Wu, Wendou Cai, Xiaobo College of Electrical and Mechanical Kunming Metallurgy College Kunming China Laboratory of Intelligent Organic Tea Garden Construction in Universities of Yunnan Province Kunming China Big Data College of Yunnan Agricultural University Kunming China

ISBN: (纸本)9789819939503

In this paper, key theoretical approaches such as modeling and identification of business process-oriented coupling structure problems are studied from the perspective of resource allocation efficiency of computation, network and storage that can be provided by terminal sensing devices and edge servers of edge computing. For the DSM-based business process general matrix model, the business process coupling structure is identified and studied by applying relevant algorithms, and the activity ordering method of the multidimensional asynchronous random perturbation particle swarm optimization algorithm (MSPSO) is proposed in combination with the inertial weight concave function decreasing strategy to study how to efficiently allocate the resources of the edge server to dynamically perform the near-optimal process ordering for all applications and to make all The average completion time of business activities and the average energy consumption of both mobile terminals and edge servers are approximated to be optimal. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Edge computing

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