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Achieving Energy Efficiency in Life-Logging applications of Internet of Things Using Data Compression Through Incorporation of machine learning and Edge-Cloud Architecture 1

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3rd international conference on Computing Science, Communication and Security (COMS)

作者： Barot, Vishal Patel, Ritesh LDRP Inst Technol & Res Gandhinagar Gujarat India CSPIT U & PU Patel Dept Comp Engn Anand Gujarat India

ISBN: (数字)9783031105517

ISBN: (纸本)9783031105517;9783031105500

Life logging applications for physiological and behavioral analysis of humans make use of smart wearable devices for capturing the various biosignals of a human. These devices have a combination of sensors that capture the respective biosignals and report the recorded data to the edge device (usually a mobile phone) which in turn acts as a primary communicator delivering the data over cloud for storage and future analysis. As this huge amount of data is transmitted from edge device to the cloud, IoT devices are forced early exhaustion, due to higher energy consumption. We propose a SZ based lossy compression technique on edge devices. To evaluate our proposal we implemented the algorithm over automobile drivers' stress recognition dataset from PhysioNet that resulted in a reduction of the dataset size by 103 times after compression. As information loss is a primary constraint while compressing data, we performed stress prediction over the decompressed data using a fully connected neural network with an accuracy of 98.01% which is more than the accuracy achieved for predictions made on the original - uncompressed dataset.

关键词： Internet of Things machine learning Stress level prediction Data compression Energy consumption optimization

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Optimizing Water Quality Parameters Using machine learning Algorithms 3rd

Optimizing Water Quality Parameters Using Machine Learning A...

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3rd international conference on Mobile Radio Communications and 5G Networks, MRCN 2022

作者： Sharma, Avinash Gupta, Anand Kumar Yadav, Dharminder Barua, Tarkeshwar Ambala Haryana Mullana133207 India Department of Information Technology BlueCrest University Monrovia Liberia Department of Computer Science and Technology Glocal University Uttar Pradesh Saharanpur India Regex Software Services Jaipur India

ISBN: (纸本)9789811979811

Water is the necessity of life;without water, human being not survives, but people are polluting the water. Water pollution is the major problem today and affects the groundwater quality. The main causes of water pollution are industries’ waste product disposal, urbanization, crowded population, wastewater, sewage waste, and harmful chemicals’ released by industries. There is an urgent need to resolve this issue in order for us to have safe drinking water. This article proposes a suitable classification model for classifying water quality that is based on machine learning algorithms and can be used to classify water quality. An evaluation and comparison of the performance of various classification models, visualizations, comparisons, and algorithms were carried out in order to identify the significant features that contributed to classifying the water quality of groundwater in Ambala, Haryana. Three models, each with its own set of algorithms, were tested, and their results were compared to each other as well. According to the results, the random forest algorithm was the best classification model out of the five models tested, with the highest accuracy of 82.67% compared to the other models. Overall, wastewater is hazardous to our health, and using scientific models to solve this problem is an absolute must. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2023.

关键词： Water quality

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A Distributed Framework for Measuring Average Vehicle Speed Using Real-Time Traffic Camera Videos 3rd

A Distributed Framework for Measuring Average Vehicle Speed...

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3rd international conference on Sustainable Expert Systems, ICSES 2022

作者： Sayar, Ahmet Computer Engineering Department Kocaeli University Kocaeli Turkey

ISBN: (纸本)9789811978739

Traffic accidents have dramatically grown in recent years as a result of an increase in the number of vehicles. The driver’s speed is one of the main contributors to these collisions since it is inappropriate for the traffic and road conditions. To stop driver infractions, numerous safety measures have been implemented. Today, radar equipment and camera speed restrictions are widely employed to prevent accidents brought on by speed-related infractions. The average speed of the vehicle is determined in this study utilizing the videos and pictures taken by the Electronic Detection System (EDS) installed at various points along the route of a vehicle. The vehicle is identified by its license plate number, and image processing determines its speed. The data provided by the cameras capturing the same vehicle are used to calculate the average speed. When a vehicle enters the field of vision of a camera, the camera records the vehicle’s time and speed. The distance between any two cameras is the final factor used to compute average speed. With the help of the Bing Maps API, the shortest path distance between any two cameras is determined. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： machine learning

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Quantum Odyssey: Traversing the NISQ Era's Quantum Terrain 3

Quantum Odyssey: Traversing the NISQ Era's Quantum Terrain

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3rd international conference on Technological Advancements in Computational Sciences, ICTACS 2023

作者： Arya, Swati Ansar, Syed Anas Aggarwal, Shruti Babu Banarsi das University Department of Computer Application Lucknow India Thapar Institute of Engineering and Technology Department of Computer Science and Engineering Punjab India

ISBN: (纸本)9798350342338

The advent of quantum computing has ignited unprecedented anticipation within the realm of IT. This has transcended the limitations of classical computation and revolutionized various facets of technological advancement. At the forefront of this quantum revolution stands the Noisy Intermediate-Scale Quantum (NISQ) era, characterized by a delicate interplay of immense potential and persistent imperfections. This paper delves into the intricacies of NISQ-era quantum computing, unraveling its fundamental principles, challenges, and transformative implications for the IT landscape. Beginning with quantum computing basics-qubits, superposition, entanglement, and gates-the NISQ era's essence addresses error-prone operations and decoherence. Quantum algorithms for NISQ devices, like Grover's search, Shor's factorization, and Variational Quantum Eigensolver (VQE), are scrutinized for their computational potential. Central to NISQ's viability, quantum error correction and mitigation strategies are surveyed, stabilizing quantum computations amidst noise. Examining quantum hardware architectures and performance metrics unveils NISQ devices' potential. applications of NISQ quantum computing in cryptography, optimization, machine learning, and more are explored, showcasing quantum advantage's real-world potential. This paper embarks on a multidimensional exploration of NISQ quantum computing, illuminating its intricate facets, applications, and ethical implications. As quantum computing evolves from a theoretical notion to a tangible paradigm, the insights gleaned herein contribute to a holistic understanding of its role in shaping the next wave of computing. © 2023 IEEE.

关键词： Ethical Implications Noisy Intermediate-Scale Quantum (NISQ) Era Quantum Algorithms Quantum Computing Quantum Error Correction (QEC)

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Design and application of Human resource management system Based on Data Mining Technology 3

Design and Application of Human resource management system B...

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3rd international conference on machine learning and Big Data Analytics for IoT Security and Privacy, SPIoT2023

作者： Liu, Jingjiang School of Business Administration Nanchang Institute of Technology Nanchang330099 China

In order to solve the problem of low efficiency of human resource management in the traditional mode, the author proposes a research on data mining technology. The author applies the technology of data mining to human resource management, and designs the employee performance appraisal algorithm based on the analytic hierarchy process, human resource flow algorithm based on Markov model, human resource management system based on ID3 decision tree employee satisfaction algorithm, and use actual data to test the system. The result shows, in the personnel composition prediction experiment, the system prediction error is less than 0.031, and the prediction accuracy rate is over 95%. The system can predict the future personnel flow of the enterprise;In the actual use experience, the satisfaction with the system is above 75%. Data mining methods are all based on examples, so each enterprise needs to use different methods to analyze the data, and then compare the analysis results, according to the analysis results, find out the method suitable for your enterprise in the follow-up human resource management work. The method adopted by the author integrates data mining into human resource management, it provides a reasonable reference for the changing trend of enterprise personnel structure, but there are systematic and complex problems in actual use, which need further improvement. © 2023 The Authors. Published by Elsevier B.V.

关键词： Decision trees

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Customer Loyalty in the Covid-19 Pandemic: The application of machine learning in Survey Data 3rd

Customer Loyalty in the Covid-19 Pandemic: The Application o...

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3rd international conference on Smart Systems - Innovations in Computing (SSIC)

作者： Bui Thanh Khoa Nguyen Thi Trang Oanh Vo Thi Thao Uyen Dang Cuu Hanh Dung Ind Univ Ho Chi Minh City Ho Chi Minh City Vietnam

ISBN: (纸本)9789811628771;9789811628764

E-commerce has brought many benefits to businesses and customers. However, the online market's fierce competition is also pressure for businesses, especially when the electronic marketplace (e-marketplace) is launched. The competition takes place in e-marketplace not only between brands but also between foreign and domestic businesses. This study is aimed to explore the effects of trust, customer ethnocentrism, service quality, and perceived risk on customer loyalty. The mixed-method research was applied to archive the research objective. Based on the machine learning computation, which became more popular and gained significant traction in the research world, the result showed that trust, ethnocentrism, and service quality positively impacted customer loyalty;perceived risk negatively affected customer loyalty toward domestic products on the e-marketplace in the Covid-19 pandemic. Some managerial implications were also proposed based on the research result.

关键词： Customer ethnocentrism Customer loyalty E-marketplace machine learning Perceived risk Service quality Trust

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3rd EAI international conference on Data and Information in Online Environments, DIONE 2022

3rd EAI International Conference on Data and Information in ...

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3rd EAI international conference on Data and Information in Online Environments, DIONE 2022

ISBN: (纸本)9783031223235

The proceedings contain 20 papers. The special focus in this conference is on Data and Information in Online Environments. The topics include: Contributions and Limitations About the Use of Deep learning for Skin Diagnosis: A Review;an Expanded Study of the application of Deep learning Models in Energy Consumption Prediction;application and Evaluation of a Taxonomy in the Context of Software Requirements Management;assessment of Heart Patients Dataset Through Regression and Classification Algorithms;classification Model Based on Chatbot and Unsupervised Algorithms to Determine Psychological Intervention Programs in Peruvian University Students;comparison of Algorithms for Classification of Financial Intelligence Reports;natural Language Processing of Messages from a Social Network for Inflation Analysis;Mining Association Rules in Commuter Feedback Comments from Facebook of Swiss National Railways (SBB) Using Apriori Algorithm;enhancement of Voting Scores with Multiple Attributes Based on VoteRank++ to Identify Influential Nodes in Social Networks;overview of Citizen Science Projects Contemplated in the Civis Platform;identification of the Relationships Between Data Provenance and Blockchain as a Contributing Factor for Health Information Systems;National Registry of Scientific Data Management Plans by IBICT;collection and Integration of Patent Data for Analysis and Validation of Brazilian Technical Production;publications in Scientific Events as a Data Source for Scientometric Analysis;publications in Open Access Journals: A Bibliometric Analysis;scientific Divulgation Before the Post-truth and the Crisis of Credibility of Science in the Context of Digital Humanities.

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Analysis of Mobile Accelerometer Sensor Movement Using machine learning Algorithm

Analysis of Mobile Accelerometer Sensor Movement Using Machi...

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Sciences and Techniques of Automatic Control and computer Engineering MI-STA, IEEE international Maghreb Meeting of the conference on

作者： Ahmed J. Abougarair Majdeddin Ellafi Alfian Ma'Arif Omran Salih Electrical and Electronics Engineering University of Tripoli Tripoli Libya Electrical and Electronics Engineering Universitas Ahmad Dahlan Yogyakarta Indonesia Math and Comp Science Dep Alzaiem Alazhari University Khartoum Sudan

Currently, many mobile phones come equipped with sensors that enable the delivery of advanced features and services to users. One type of sensor that is commonly embedded in mobile phones is the accelerometer. By applying machine learning algorithms to analyze the movement patterns of a mobile device through its accelerometer data, valuable insights can be gained. This analysis can classify different types of movement, such as walking, running, or driving. These insights can be applied in various settings, such as providing better fitness tracking apps and developing new methods for user authentication. The purpose of this research is to explore and examine the movement patterns of mobile devices through the application of machine learning techniques to accelerometer sensor data.

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Supervised learning Algorithm for Water Leakage Detection through the Pipelines 3

Supervised Learning Algorithm for Water Leakage Detection th...

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3rd international conference on Smart Electronics and Communication, ICOSEC 2022

作者： Aravind Britto, K.R. Prasad, Dvsssv Ragavendiran, S D Prabu Shreepad, Sarange Singh, Nishant Kumar Bhowmick, Avijit Siva Ramkumar, M. Psna College of Engineering and Technology Department of Electronics and Communication Engineering Tamil Nadu Dindigul624622 India Aditya College of Engineering Department of Mechanical Engineering Andhra Pradesh Surampalem533437 India Rvs Technical Campus-Coimbatore Department of Computer Science and Engineering Tamil Nadu Coimbatore641402 India Dr. D.Y. Patil School of Engineering and Technology Department of Mechanical Maharashtra Pune412105 India SRMIST Ncr Campus Modinagar Department of Computer Science and Engineering Uttar Pradesh Ghaziabad201204 India Budge Budge Institute of Technology Department of Computer Science & Engineering West Bengal Kolkata700137 India Karpagam Academy of Higher Education Department of Eee India

ISBN: (数字)9781665497640

ISBN: (纸本)9781665497640

The water distribution system is an efficient, quick, cost-effective, and ecologically friendly method of providing *** pipeline networks frequently lack planning as the population *** is often provided to end customers through a water supply network, which consists of subterranean and above-ground pipelines. The water distribution sector is increasingly concerned about leakage in water pipeline networks. This necessitates substantial development in leakage sensing technologies to both avoid and mitigate the potential of leakage. As a solution to this, in this study,a supervised learning algorithm-based water leakage detection through the pipeline idea is proposed. The dataset required for this study is collected in real-time using a hardware set. The hardware setup consists of a water storage tank that is connected with pipelines thatare directed toward the consumers. The pipeline is fitted with microphones that measure the sound of the water flow through the pipeline and the values are stored in a Data Acquisition (DAQ) module. The values in DAQ are further analyzed by the computer system that is integrated with the DAQ. The dataset is further pre-processed and dimensionally reduced to make them compatible with machine learning (ML) models. The ML models used in this study further evaluate and classify the data values and helps in detecting the water leakage in pipelines. The ML model Naïve Bayes used in this study shows an accuracy of 97.5% and has the highest accuracy among the three ML models used in this study and is concluded as the most efficient model among the other ML models. © 2022 IEEE.

关键词： Data acquisition

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Alzheimer's Detection And Categorization using a Deep-learning Approach 3

Alzheimer's Detection And Categorization using a Deep-Learni...

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3rd international conference on Intelligent Computing, Instrumentation and Control Technologies, ICICICT 2022

作者： Singh, Pooja Mishra, Sushil Kumar University Institute of Engineering Chandigarh University Department of Computer Science & Engineering Mohali India

ISBN: (数字)9781665410052

ISBN: (纸本)9781665410052

Alzheimer's disease (AZM) is a deadly neurology syndrome caused by memory loss. It is the most common kind of dementia. It is a syndrome that begins with modest memory loss and gradually deteriorates your thinking abilities, occasions, and ability to execute day-to-day routine tasks. It affects the areas of the brain that govern cognition, memory, and language. It has the potential to have a significant influence on a person's ability to carry out everyday responsibility. Because no drug or cure has yet been discovered, it is critical to forecast it at an early stage. The prediction of Alzheimer's disease is done manually by evaluating MRI scans;nevertheless, the risks of human mistake are quite high with this method. The purpose of this research is to improve prediction accuracy in order to detect AZM at an early stage and prevent the patient from progressing to a severe state of the disease. In this research, a Convolutional Neural Network (CNN) with Softmax classifier (i.e., CNN+Softmax) has been used to identify and categories the AZM MRI into four distinct classes, namely mild demented (MD), moderate demented (MOD), non-demented (ND), very mild demented (VMD). The proposed CNN+Softmax model is then compared by replacing the dense layer of CNN to Support Vector machine (SVM) classifier, i.e., CNN+SVM and the work is calculated on the various grids which includes accuracy, precision, recall and f1-score. On test data, the presented approach surmounted the CNN+SVM model with a classification accuracy of 98.59 percent. © 2022 IEEE.

关键词： Support vector machines

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