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检索条件"任意字段=4th International Conference on Smart Learning Ecosystems and Regional Development"

共 901 条记录，以下是121-130 订阅

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2023 4th international conference on Computing and Communication Systems, I3CS 2023

2023 4th International Conference on Computing and Communica...

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4th international conference on Computing and Communication Systems, I3CS 2023

ISBN: (纸本)9798350323771

the proceedings contain 83 papers. the topics discussed include: random forest regression based water quality prediction for smart aquaculture;image representation of numerical data-points for classification using convolutional neural network;homomorphic encryption based federated learning for financial data security;a transfer learning based approach for human foot image classification;design and development of a wearable wristband for measuring pulse transit time (PTT) using pressure sensors;landslide susceptibility mapping using support vector machine for Meghalaya, India;a lightweight semantic model for IoT architecture: smart water meter usecase;validating user-centric business process based service composition;identification and analysis of Assamese vowel speech signal using formant feature-fusion and feed-forward neural network model;and real-time classification of haze and non-haze images on Arduino nano BLE using edge impulse.

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Research on Optimizing Demand Response Programs using LSTM Networks 4

Research on Optimizing Demand Response Programs using LSTM N...

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4th international conference on Robotics, Electrical and Signal Processing Techniques, ICREST 2025

作者： Faiz, Rethwan Alam, Nuzat Nuary Md Shatil, Abu Hena Islam, Rakibul Haque, Ahsanul Hasan Imam, Mohammad American International University Bangladesh Department of Electrical and Electronics Engineering Dhaka Bangladesh

ISBN: (纸本)9798331530938

this paper presents a novel approach to optimizing Demand Response (DR) programs using Long Short-Term Mem-ory (LSTM) networks. We propose a methodology that leverages LSTM's capability to handle sequential data for accurate load forecasting and DR strategy optimization. Our approach integrates smart meter data, weather information, and socio-economic factors to enhance prediction accuracy. We implement a Deep Reinforcement learning-based Price Control (DRL-PC) algorithm for DR optimization. the results demonstrate significant improvements in forecasting accuracy, with a Mean Absolute Percentage Error (MAPE) of 4.02%. the proposed method achieved energy savings of up to 3.06% per month, with cumulative savings fluctuating throughout the year. this research contributes to the development of more efficient and reliable demand response strategies, promoting grid stability and sustainability in smart energy systems. © 2025 IEEE.

关键词： Demand response

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Low-cost Digital Twin Design for Power Electronics using Deep Neural Networks 4

Low-cost Digital Twin Design for Power Electronics using Dee...

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4th international conference on smart Grid and Renewable Energy, SGRE 2024

作者： Kamal, Naheel Faisal Sharida, Ali Bayhan, Sertac Alnuweiri, Hussein Abu-Rub, Haitham Texas A&M University at Qatar Qatar Foundation Department of Electrical and Computer Engineering Doha Qatar Texas A&M University Electrical and Computer Engineering Department College Station United States HBKU Qatar Foundation Doha Qatar

ISBN: (纸本)9798350306262

Deep learning-based digital twin (DT) models have been used in many real-time applications in the literature showing a superior behavior to traditional models. In particular, power electronics (PE) industrial applications demand a very fast response with little room for error. the superiority of such deep learning models comes at the cost of increased complexity in time and space;thus, higher hardware requirements and higher cost. this paper shows detailed guideline on the methodology of building DT models using Deep neural networks (DNNs) for PE applications (PEDTD) using low-cost microcontrollers. PEDTD models are analyzed in this paper with respect to their space and time empirical complexity while showing the limitations of the models based on the available hardware specifications. A use case scenario of switch fault localization and detection in T-type three level rectifiers is presented showing that the proposed technique is applicable in practical applications. © 2024 IEEE.

关键词： E-learning

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Improving Energy Efficiency and Sustainability through smart Energy Management 4

Improving Energy Efficiency and Sustainability through Smart...

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4th international conference on Power, Energy, Control and Transmission Systems, ICPECTS 2024

作者： Shali, A. Vishal, N. Dhanush, S. Sri Sairam Engineering College Chennai India

ISBN: (纸本)9798331508845

this research paper presents a smart Energy Management System (SEMS) designed to tackle energy consumption challenges in rural areas. the project's main objective is to create an innovative solution that not only monitors energy use but also offers actionable insights for optimizing it [1]. the methodology combines hardware components like Arduino microcontrollers, ESP8266 Wi-Fi modules, and current sensors with software algorithms and machine learning techniques [3]. these are integrated into an energy monitoring system deployed in rural households [4]. the system collects energy consumption data, transmits it via Wi-Fi [2], and analyzes it in real-time, enabling users to make informed decisions and reduce energy consumption [5]. Key findings from the implementation include a noticeable reduction in energy usage [6], identification of energy-intensive appliances and peak demand periods [7], and the feasibility of integrating renewable energy sources like solar panels [8]. the implications are significant, as this system empowers residents to lower energy bills, reduce grid strain, and promote environmental sustainability [9]. It also has the potential for broader global applications in addressing energy challenges in remote regions [10]. this research contributes to the field of smart energy management, offering insights for policymakers, utility providers, and researchers working towards United Nations Sustainable development Goal 7: Affordable and Clean Energy [11]. © 2024 IEEE.

关键词： Automation Cloud Computing Data Analysis Demand Response Energy Conservation Energy Consumption Patterns Energy Efficiency Energy Optimization Energy Usage Visualization Environmental Sustainability HVAC Control IoT (Internet of things) Predictive Analytics Real-time Monitoring Remote Control Renewable Energy Sensor Networks smart Building Technology smart Grid smart Meters

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A Machine learning Framework for Bearing Fault Detection in three-Phase Induction Motors 4

A Machine Learning Framework for Bearing Fault Detection in ...

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4th international conference on smart Grid and Renewable Energy, SGRE 2024

作者： Rohouma, Wesam Zaitouny, Ayham Wahid, Md Ferdous Ali, Hassan Refaat, Shady S. University of Doha for Science and Technology College of Engineering Technology P.O. Box 24449 Doha Qatar University UAEU College of Science Department of Mathematical Sciences United Arab Emirates University at Qatar Mechanical Engineering Texas A&M Doha Qatar University of Hertfordshire School of Physics Engineering and Computer Science Department of Engineering United Kingdom

ISBN: (纸本)9798350306262

three-phase induction motors are widely employed in industry due to their rugged performance and easy maintenance. Bearing faults in three phase induction motors are responsible for 40%-50% of unplanned shutdowns in industrial settings. therefore, early detection of bearing faults is essential to implement preventive measures and enhance planning of maintenance strategies. this paper thus proposes a machine learning (ML) framework that consistently monitors acceleration and temperature of bearing to detect bearing faults. the results show that the ML framework using k-nearest neighbor (k-NN) and support vector machine (SVM) approaches is better than the variation-based thresholding approach, where the former method is able to detect faulty conditions with more than 99% accuracy. © 2024 IEEE.

关键词： Condition monitoring

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CNN-RNN Hybrid Model For Dangerous Sound Detection in Urban Area 4

CNN-RNN Hybrid Model For Dangerous Sound Detection in Urban ...

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4th IEEE international conference on smart Information Systems and Technologies, SIST 2024

作者： Momynkulov, Zeinel Omarov, Nurzhan Altayeva, Aigerim Department of Mathematical and Computer Modeling International Inforamtion Technology University Almaty Kazakhstan Department of Information Systems Al-Farabi Kazakh National University Almaty Kazakhstan

ISBN: (纸本)9798350374865

In the realm of audio signal processing, detecting impulsive urban sounds within continuous audio streams remains a complex endeavor. this paper presents an innovative solution via a hybrid Convolutional Neural Network - Recurrent Neural Network (CNN-RNN) framework. the CNN segments of the model target the extraction of spatial hierarchies from the audio, while the RNN components leverage their temporal memory capabilities to recognize and sequence sporadic urban auditory events. Drawing data from a rich collection of urban audio signals sourced from multiple global metropolises, the hybrid model was rigorously trained and evaluated. Findings underscore its superior detection accuracy in comparison to conventional methods, with a marked decrease in false positives. Moreover, the adaptability of the CNN-RNN architecture was evidenced by its consistent performance across diverse acoustic environments. this advancement in impulsive urban sound detection not only accentuates the efficacy of combined CNN-RNN systems but also has broader implications for urban planning, environmental assessments, and public safety initiatives. © 2024 IEEE.

关键词： Recurrent neural networks

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Machine learning-Based Cardiovascular Disease Prediction 1

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4th international conference on smart Systems: Innovations in Computing, SSIC 2023

作者： Kumar, Sudhanshu Yadav, Himanshu Kaur, Mandeep Goyal, Mayank Kumar Department of Computer Science and Engineering Sharda University Greater Noida India

ISBN: (数字)9789819736904

ISBN: (纸本)9789819736898

In contemporary times, cardiovascular diseases have emerged as a significant and critical cause of mortality for individuals. therefore, it would be a boon for human existence that there exists a technology with high accuracy capable of detecting these diseases at an earlier phase but it has emerged as a challenge for data processing techniques. It has been surveyed that machine learning algorithms are capable of analysing, predicting and generating judgements. Due to advancements in IoT a variety of sensors had been developed that had contributed towards the development of better ML methods. In few articles it is observed that cardiovascular diseases are anticipated by examining the symptoms. therefore, using machine learning techniques, a new and better solution in this domain is proposed that can predict cardiovascular diseases with better accuracy. the proposed article examines the probability of a cardiovascular disease in humans and classifies patient risk levels using various data mining techniques. this article brings out the analysis of various machine learning-based algorithms that are implemented to predict cardiovascular diseases. © the Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Adversarial machine learning

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Model development in Recognizing Affect through Facial Expressions of Students' Engagement in Synchronous Class 4

Model Development in Recognizing Affect through Facial Expre...

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4th international conference of Science and Information Technology in smart Administration, ICSINTESA 2024

作者： Agatep, John Lenon E. Ramon Magsaysay State University Zambales Iba Philippines

ISBN: (纸本)9798350376111

the occurrence of unprecedented disaster risks brought changes to educational institutions the way in innovating delivery of learning, resulted to flexible modalities as observed in the new normal of education. Flexible modalities considered applicability of delivery mode either synchronous, asynchronous or face-to-face to fit with the situation available. In such premise, ensuring engagement of students in the virtual classroom remains a challenge. this pilot study developed a model that recognize the engagement of students through facial features in a synchronous session set-up. Facial Action Units, Head Pose and Eye Gaze of student-participants were extracted for every 5-second video segment. In forming the data sets, the video segments were then labeled either engaged or disengaged. the 80-20 ratio of training-testing data sets were utilized in splitting the dataset. the study findings revealed that the developed Decision Tree classifier had the best model performance manifested on its accuracy (97.0%) and kappa statistic (89.1%). the accuracy findings imply a high correct prediction based from the observations, while the kappa statistic calculations manifest a very strong agreement between the machine labeling and annotators in recognizing the engagement of student-participants in a synchronous class set-up. © 2024 IEEE.

关键词： Students

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Applying Machine learning Approaches with Integrated Internet of things for Water Management System 4th

Applying Machine Learning Approaches with Integrated Interne...

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4th international conference on Intelligent Systems and Pattern Recognition-ISPR

作者： Belmir, Meroua Difallah, Wafa Ghazli, Abdelkader Tahri Mohammed Univ Bechar Fac Exact Sci Dept Math & Comp Sci Bechar Algeria Coding & Informat Secur Lab LACOSI Oran Algeria Lab Energet Arid Zones ENERGARID Bechar Algeria

ISBN: (纸本)9783031821523;9783031821530

the challenges of water loss and inadequate scheduling in traditional irrigation methods hinder the ability to meet the increasing demand for food production while also conserving valuable water resources. In order to overcome these issues, this study introduces an advanced smart irrigation system that harnesses the capabilities of the Internet of things (IoT), data analysis, and machine learning. the primary objective is to determine the most effective method for applying and scheduling irrigation, thus optimizing resource usage. this innovative system relies on a meticulously curated dataset, "INTELLIGENT IRRIGATION SYSTEM" obtained from Kaggle. the dataset encompasses four essential attributes, extensively detailed in the metadata, providing foundational information crucial for precise irrigation management. this paper proposes an intelligent irrigation approach that can be deployed in different contexts. this approach is based on machine learning algorithms. Multiple models, including K-Nearest Neighbors (KNN), Logistic Regression (LR), and Gaussian process (GP) classifier, were employed in the development of this approach. the results showed that LR and GP are better with an accuracy of 0.9833, precision of 0.9782, F1 score of 0.9890, specificity of 0.9333, and sensitivity of 1.0 compared to KNN model.

关键词： smart irrigation system machine learning smart agriculture Internet of things

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the Influence of Perceived Authenticity and EWOM on Price Sensitivity, Perceived Value, Perceived Risk, and Repurchase Intentions in E-commerce Sites 4

The Influence of Perceived Authenticity and EWOM on Price Se...

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4th international conference of Science and Information Technology in smart Administration, ICSINTESA 2024

作者： Fatin, Ainiyyah Saviera, Amadea Rizky Medikawati, Mita Yuniarty Hartono, Ferry Bina Nusantara University Management Department Binus Online Learning Jakarta11480 Indonesia E-Commerce Industry Jakarta11840 Indonesia

ISBN: (纸本)9798350376111

the exponential growth of online shopping and ecommerce has magnified concerns regarding product assessment, security, and privacy in consumer transactions. this study delves into these challenges, investigating how various factors perceived by consumers impact their intention to repurchase from e-commerce platforms. Employing a quantitative approach, we distributed online questionnaires to 190 respondents and conducted data analysis using SEM-PLS. Our findings reveal that perceived value, perceived risk, and electronic word-of-mouth significantly influence consumers' repurchase intentions on e-commerce sites. However, perceived authenticity and price sensitivity do not exhibit a significant impact on repurchase intentions. Furthermore, our research unveils that perceived risk and authenticity significantly influence consumers' perceived value when shopping online. Electronic word-of-mouth significantly shapes consumers' perceived risk, while perceived authenticity and price sensitivity do not have a significant effect on perceived risk. this study contributes to the understanding of consumer behavior in ecommerce contexts, shedding light on the intricate relationships between perceived factors and repurchase intentions, thus aiding in the development of strategies to enhance consumer trust and loyalty in online shopping environments. © 2024 IEEE.

关键词： Marketplaces

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