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Study of Low-Cost-Based Smart Home Control Using IoT Powered by Photovoltaic Cells 1

16th International Conference on Intelligent Information Hiding and Multimedia Signal Processing, IIH-MSP 2020 in conjunction with the 13th International Conference on Frontiers of Information technology, Applications and Tools, FITAT 2020

作者： Ahmad, Shoaib Chang, Kuo-Chi Sung, Tien-Wen Chu, Kai-Chun Zhou, Yu-Wen Omer, Abdalaziz Altayeb Ibrahim Amesimenu, Governor David Kwabena Chang, Fu-Hsiang Fujian Provincial Key Laboratory of Big Data Mining and Applications Fujian University of Technology Fuzhou China School of Information Science and Engineering Fujian University of Technology Fuzhou China Department of Business Management Fujian University of Technology Fuzhou China College of Mechanical and Electrical Engineering National Taipei University of Technology Taipei Taiwan Department of Business Administration North Borneo University College Kota Kinabalu Sabah Malaysia Department of Tourism Shih-Hsin University Taipei Taiwan

ISBN: (数字)9789813364202

ISBN: (纸本)9789813364196

This research mainly uses the Wi-Fi module (ESP8266) microcontroller to implement the IoT-based smart home system. In this study of smart home system is able to power the circuit by the photovoltaic cell itself and then wirelessly switch to the required load. As technology is expanding every day, mobile, robotics, machines learning are advancing their technology then why our house is an exception. Today’s house moving slowly based on common/human input controlling to smart/IoT-enabled devices to be controlled remotely. Currently, use the existing smart home system technology that is limited to this device only. Because of this, IoT devices are now being applied in various fields, but unfortunately, low cost and simple IoT applications are still insufficient in smart home. However, this study is concerned with the Node MCU (ESP8266) microcontroller used to control the power switches remotely. The users can control witches using web application after authenticating based on low cost and simple IoT application systems;this study has important contributions to the development of smart homes with high price–performance ratio in future. © 2021, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Microcontrollers

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Which Classifier to Deploy? A Hybrid Sampling Approach for Evaluating Classifiers with Unbalanced data in Medical Expert systems

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SSRN 2023年

作者： Yu, Haiyan Qiu, Hang Piri, Saeed Miao, Hongxia Center for Data and Decision Sciences School of Economics and Management Chongqing University of Posts and Telecommunications Chongqing400065 China Health Big Data Research Institute School of Computing and Engineering University of Electronic Science and Technology of China Chengdu611731 China Department of Operations and Business Analytics Lundquist College of Business University of Oregon EugeneOR97403 United States

To develop medical expert systems (MES), researchers and practitioners usually apply a machine learning (ML) classifier they expect to be the best through ML effect estimation. A standard classifier can perform well on balanced datasets, but its performance often dramatically decreases when applied to unbalanced datasets. To address these challenges, this paper proposes a hybrid sampling approach for evaluating classifiers with unbalanced data in MES. In this framework, the mean responses of the classification experiments are employed to estimate the ML effect. Meanwhile, uniform designs are used in hybrid sampling (HSUD) to retrieve the optimal parameters. The efficacy of HSUD is validated through applications on six real-world clinical datasets (unbalanced) using six ML algorithms (including zero rule). The ratio of the minority to the majority ranges from 0.09 to 0.80 in the six datasets, and the sample size ranges from 187 to 15151. The results suggest that our approach is more effective than existing unbalanced data learning methods. In particular, the AUC with HSUD is 2.48% and 8.60% higher than those with proportional and balanced sampling methods in the testing, respectively. Moreover, HSUD is efficient in choosing classifiers with unbalanced data in MES. © 2023, The Authors. All rights reserved.

关键词： Machine learning

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Cybersecurity Enhancement in IoT-Enabled Public Health Information systems Using Deep Learning Techniques

Cybersecurity Enhancement in IoT-Enabled Public Health Infor...

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AI-Driven Smart Healthcare for Society 5.0

作者： Mahesh Kumar Mishra Lalit Mohan Trivedi Mansoor Farooq Divya Nimma Malik Bader Alazzam B Kiran Bala Sr SAP Consultant Delta System and Software Inc Texas USA Department of ASH Moradabad Institute of Technology Moradabad UP India Department of Cybersecurity American University of Bahrain Bahrain PhD in Computational Science University of Southern Mississippi Data Analyst in UMMC USA Faculty of Information Technology Jadara University Irbid Jordan Department of AI & DS K.Ramakrishnan College of Engineering Trichy India

ISBN: (数字)9798331536336

ISBN: (纸本)9798331536343

The rising use of IoT devices in Public Health Information systems (PHIS) has revolutionized the way patient surveillance and record keeping is being done in real-time, but this has exposed systems to serious cybersecurity threats. Generated by using traditional approaches, like rule-based detection and basic artificial neural network-based models, is not capable of dealing with the dynamic and diverse nature of threats in IoT networks. These approaches are often inadequate in providing a detailed analysis of the attack especially because they do not have the ability to learn from sequences of features. This work presents an improved predictive analytics model using CNN with BiLSTM to improve cybersecurity in IoT connected PHIS. The CNN component performs feature extraction on spatial elements in the data received while the BiLSTM uncovers temporal dependencies and sequence of attacks, which enhance threats identification accuracy. The process of the proposed methodology includes dataset acquisition and cleansing of IoT healthcare datasets and finally, the training and testing of the model with the help of relevant parameter such as accuracy, precision, and recall values. Qualitative comparison with the baseline models also shows that the proposed hybrid CNN-BiLSTM model performs better, and it has fewer false alarms to detect a range of cyber threats. Accordingly, the results point to a feasible solution for enhancing security and reliability of IoT-based healthcare applications to protect patients’ record from cyber threats.

关键词： Training Accuracy Surveillance Bidirectional long short term memory Threat assessment Internet of Things Computer security Public healthcare Information systems Testing

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Evaluating Classifiers of Unbalanced data with Parameter Retrieval in Hybrid Sampling for Clinical Decision Support systems

SSRN

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SSRN 2023年

作者： Yu, Haiyan Piri, Saeed Qiu, Hang Miao, Hongxia Center for Data and Decision Sciences School of Economics and Management Chongqing University of Posts and Telecommunications Chongqing400065 China Department of Operations and Business Analytics Lundquist College of Business University of Oregon EugeneOR97403 United States Health Big Data Research Institute School of Computing and Engineering University of Electronic Science and Technology of China Chengdu611731 China

ContextTo develop clinical decision support systems (CDSS), researchers and practitioners usually apply a machine learning (ML) classifier they expect to be the best through ML effect estimation. A standard classifier can perform well on balanced datasets, but its performance often dramatically decreases when applied to unbalanced *** objective was to conduct an experiment study for choosing the best-suited ML algorithms in CDSS and improving the classifying performance of unbalanced data with parameter retrieval in hybrid *** paper proposes a hybrid sampling approach for evaluating classifiers with unbalanced data in CDSS. In this framework, the mean responses of the classification experiments are employed to estimate the ML effect. Meanwhile, uniform designs are used in hybrid sampling (HSUD) to retrieve the optimal parameters. The efficacy of HSUD is validated through applications on six real-world clinical datasets (unbalanced) using six ML algorithms (including zero rule). The ratio of the minority to the majority ranges from 0.09 to 0.80 in the six datasets, and the sample size ranges from 187 to *** results suggest that our approach is more effective than existing unbalanced data learning methods. In particular, the AUC with HSUD is 2.48% and 8.60% higher than those with proportional and balanced sampling methods in the testing, respectively. Moreover, HSUD is efficient in choosing classifiers with unbalanced data in *** study compared multiple ML algorithms and chose the best-suited ML algorithms with unbalanced data sets in CDSS, improved their classifying performance, and provided guidelines for parameter retrieval in developing CDSS. © 2023, The Authors. All rights reserved.

关键词： Machine learning

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The Smart Optimization and Estimating the Performance of Encapsulating Security Payload in Wireless Network Security

The Smart Optimization and Estimating the Performance of Enc...

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

作者： Sripriya. T Monali R. Borade Neeta Deepaware Jambi Ratna Raja Kumar Md. Saifullah Khalid K. Ranjith Singh Department of Electronics and Communication Engineering Prince Shri Venkateshwara Padmavathy Engineering College Chennai Department of Artificial Intelligence & Data Science Vishwakarma Institute of Information Technology Pune India Department of Management Mangalayatan University Jabalpur India Department of Computer Engineering Genba Sopanrao Moze College of Engineering Balewadi Pune Maharashtra India Faculty of Business Mangagement & Commerce Usha Martin University Ranchi India Department of Computer Science Karpagam Academy of Higher Education Coimbatore

The Encapsulating Security Payload (ESP) is the primary issue of safety structure for wireless networks. It inspires to make specific cozy switches of records across a wireless network by offering encrypted messages, information integrity guarantee, and authentication of communications. ESP can also provide excellent selective filtering of community site visitors and provides levels of authentication required for ease of network access. Due to the advances in the wireless era, the overall performance of ESP in Wi-Fi networks is becoming increasingly essential. Consequently, intelligent optimization and estimating the overall performance of ESP in wireless networks is an important task. Clever optimization of ESP in Wi-Fi networks determines the most reliable parameters for ESP to maximize its performance and reduce the network overhead that may arise while looking to take care of records transmission and information protection. Traditional optimization techniques, including Genetic Algorithms, can be used for this project, as they give more accurate outcomes and might effortlessly be implemented in the community infrastructure. Clever optimization can't only decorate the performance of ESP but can also reduce the variety of false alarms in the machine. Moreover, developing an accurate estimation for the overall performance of ESP in Wi-Fi networks is essential to get the most out of its talents. The usage of mathematical models, including a Markov Chain modeling technique, to symbolize various.

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Modeling and Simulation for Battery Energy Storage system Participating in Power system Frequency Modulation Based on Power system Analysis Software Package 1

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作者： Cao, Pengcheng Li, Peiqiang Sun, Peidong Zhang, Zhongkai Chang, Kuo-Chi School of Information Science and Engineering Fujian University of Technology Fuzhou China Fujian Provincial Key Laboratory of Big Data Mining and Applications Fujian University of Technology Fuzhou China College of Mechanical and Electrical Engineering National Taipei University of Technology Taipei Taiwan Department of Business Administration North Borneo University College Kota Kinabalu Sabah Malaysia

ISBN: (数字)9789813364202

ISBN: (纸本)9789813364196

Battery energy storage technology, with its fast and accurate power response, has become the focus of the auxiliary means of power system frequency modulation. However, the traditional simulation software lacks an accurate battery energy storage system component model, which affects the accuracy of analyzing the response characteristics of the energy storage system to a certain extent. This paper presents an electromechanical transient model of battery energy storage system without time delay, which considers the participation of energy storage system in frequency modulation dead zone and battery charging and discharging power. The control model is built based on the node current injection method by using the power system analysis integrated program, and the EPRI-7 standard system is selected for simulation analysis. The simulation results show that the model has good implant ability. And when the load is cut off in the power system, the energy storage system participating in frequency modulation can effectively maintain the frequency stability. © 2021, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Computer software

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An Improved Whale Optimization Algorithm and Its Application to Power Generation in Cascade Reservoir 16th

An Improved Whale Optimization Algorithm and Its Application...

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作者： Lü, Ji-Xiang Yan, Li-Jun Pan, Tien-Szu Chu, Shu-Chuan Pan, Jeng-Shyang He, Xian-Kang Chang, Kuo-Chi School of Information Science and Engineering Fujian University of Technology Fuzhou350108 China Shenzhen Institute of Information Technology Shenzhen518172 China Department of Electronic Engineering National Kaohsiung University of Science and Technology Kaohsiung82445 Taiwan College of Computer Science and Engineering Shandong University of Science and Technology Qingdao266590 China College of Science and Engineering Flinders University Sturt Rd Bedford ParkSA5042 Australia Fujian Provincial Key Laboratory of Big Data Mining and Applications Fujian University of Technology Fuzhou350108 China College of Mechanical & Electrical Engineering National Taipei University of Technology Taipei10608 Taiwan Department of Business Administration North Borneo University College Sabah Malaysia

ISBN: (纸本)9789813364196

Nowadays, there is a very popular artificial intelligence algorithm called whale optimization algorithm (WOA). WOA is obtained through the special bubble net foraging process of humpback whales. It has a special search mechanism, and is very helpful to solve some complex optimization problems and large scale. An improved WOA is implemented to optimize the cascade reservoir power generation. Firstly, by introducing nonlinear time-varying adaptive weights, the WOA performance in the local optimization and global exploration stages is improved;secondly, the differential mutation perturbation factor is introduced in the shrinking and surrounding stage of the whale algorithm to prevent premature convergence. In addition, the logarithmic spiral search method of whale individuals has been improved so that the ability to solve the algorithm traversal can be found. Experimental results show that it has a great improvement in accuracy and convergence compared with the original WOA in the optimal dispatch model of cascade reservoir power generation. © 2021, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Optimization

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Fuzhou PM2.5 Prediction and Related Factors Analysis 16th

Fuzhou PM2.5 Prediction and Related Factors Analysis

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作者： Zhang, Wen-Ji Chen, Li-Wen Zhou, Yao Zheng, Ri-Jing Chang, Kuo-Chi School of Information Science and Engineering Fujian University of Technology Fujian350118 China Fujian Provincial Key Laboratory of Big Data Mining and Applications Fujian University of Technology Fujian350118 China College of Mechanical & Electrical Engineering National Taipei University of Technology Taipei Taiwan Department of Business Administration North Borneo University College Sabah Malaysia

ISBN: (纸本)9789813364196

The prediction of urban air pollutant concentration requires processing of large amounts of meteorological data and complex changes of air pollutants. PM2.5 is affected by many factors, including meteorological factors and pollutant factors. However, most existing methods either do not consider the corresponding relationship between the surrounding detection sites or ignore the strength of the correlation between them. Without considering the spatial correlation or having too much weak correlation factors, the input will affect the accuracy of the prediction results. To solve this limitation, we use the Pearson correlation coefficient (PCC) to make a correlation analysis of PM2.5 influencing factors. According to PCC, we can extract highly relevant data and drop weaker data. Then, we use the long short-term memory (LSTM) model to predict the PM2.5 of the monitoring station. The experimental results show that selecting input factors based on PCC can improve the prediction performance of the model. © 2021, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Long short-term memory

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Utilizing Autoencoders to Reveal Hidden Insights for Effective Prediction of Employee Turnover in Organizations

Utilizing Autoencoders to Reveal Hidden Insights for Effecti...

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Innovations in High Speed Communication and Signal Processing (IHCSP), International Conference on

作者： Rosy Kumar Loganathan Chenniappan Divya Nimma Anuradha Duvvuri C. Madhuri S Muthuperumal Saveetha School of Law Saveetha Institute of Medical and Technical Science SIMATS Chennai India College of Economics & Business Administration University of Technology and Applied Sciences Ibri Sultanate of Oman PhD in Computational Science University of Southern Mississippi Data Analyst in UMMC USA St Mary’s College Hyderabad Nava Bharathi College of PG Studies Bolarum Secunderabad Department of Mathematics K.Ramakrishnan College of Engineering Trichy India

ISBN: (数字)9798350368949

ISBN: (纸本)9798350368956

Employee turnover remains a important assignment for organizations, impacting productivity, morale, and operational continuity. Traditional strategies for predicting turnover regularly depend on demographic and task-related variables, yet fail to seize nuanced insights from unstructured statistics sources together with worker sentiment and engagement. This takes a look at proposes a singular technique utilizing autoencoders, a type of artificial neural network capable of mastering efficient representations of enter statistics, to uncover hidden styles inside various datasets associated with worker turnover. By leveraging anonymized textual statistics from employee surveys, overall performance reviews, and social media sentiments, the autoencoder model extracts latent features that reflect underlying elements contributing to turnover. The encoded representations are then utilized as inputs to predictive models, enhancing the accuracy and interpretability of turnover predictions. Through a comprehensive empirical analysis, the use of actual-global organizational information, we show the effectiveness of our technique in outperforming traditional methods, reaching better prediction accuracy and supplying actionable insights for HR practitioners. Furthermore, interpretability strategies inclusive of feature importance evaluation and visualization of latent spaces permit stakeholders to understand the driving forces behind turnover, facilitating proactive interventions and strategic choice-making. This research contributes to the sphere through bridging the space among superior machine gaining knowledge of strategies and human useful resource management, offering a robust framework for groups to preemptively cope with turnover challenges and foster a extra stable and engaged staff.

关键词： Surveys Technological innovation Accuracy Social networking (online) Autoencoders Organizations Signal processing Feature extraction Stability analysis Stakeholders

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Predictive Analytics for Ear Health Management Using IoT and Ensemble Learning

Predictive Analytics for Ear Health Management Using IoT and...

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2025 International Conference on Intelligent Control, Computing and Communications, IC3 2025

作者： Saravanan, S.K. Parvathy, Palaniraj Rajidurai Meenakshi, R. Prabu, K. Ganeshbabu, T.R. Meenakshi, B. Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Saveetha University Department of Big Data and Network Security Tamil Nadu Chennai India Mphasis Corporation ChandlerAZ United States Chennai Institute of Technology Department of Computer Science and Engineering Tamil Nadu Chennai India Gojan School of Business and Technology Department of Electronics and Communication Engineering Redhills Tamil Nadu Chennai India Department of Electronics and Communication Tamil Nadu Namakkal India Sri Sairam Engineering College Department of Electrical and Electronics Engineering Tamil Nadu Chennai India

ISBN: (纸本)9798331527495

Proactive health monitoring solutions are becoming more important due to a growing population of older people. To better manage the ear health of the elderly, this research investigates ensemble learning methods within the domain of predictive analytics using the Internet of Things (IoT). It provides an ensemble learning approach that uses IoT capabilities to predict potential ear health problems in this population. It evaluates the efficacy of many ensembles learning approaches, including bagging, boosting, and stacking, using real-world sensor data acquired from IoT devices. Our results show that ensemble learning methods considerably improve prediction accuracy compared to standalone models. Ensemble learning overcomes the limitations of individual algorithms and maximizes their combined prediction potential by efficiently combining varied models. Our findings have important implications for the management of healthcare for the elderly, providing new opportunities to enhance early diagnosis and intervention techniques for ear health concerns via the use of predictive analytics provided by the IoT. It highlights the potential of ensemble learning approaches to improve predictive analytics in the senior healthcare sector, especially with IoT-driven solutions. © 2025 IEEE.

关键词： mHealth

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