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Smart E-Waste Management System Utilizing IoT and DL Approaches 11th

Smart E-Waste Management System Utilizing IoT and DL Approac...

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10th international conference on Innovation and New Trends in Information Technology, INTIS 2022 and 11th international conference on Innovation and New Trends in Information Technology, INTIS 2023

作者： Voskergian, Daniel Ishaq, Isam Computer Engineering Department Al-Quds University Jerusalem Palestine

ISBN: (纸本)9783031473654

Electronic waste is presently acknowledged as the rapidly expanding waste stream on a global scale. Consequently, e-waste represents a primary global concern in modern society since electronic equipment contains hazardous substances, and if not managed properly, it will harm human health and the environment. thus, the necessity for more innovative, safer, and greener systems to handle e-waste has never been more urgent. To address this issue, a smart e-waste management system based on the Internet of things (IoT) and TensorFlow-based Deep learning (DL) is designed and developed in this paper. this innovative system offers the potential to manage e-waste more efficiently, supporting green city initiatives and promoting sustainability. By realizing an intelligent green city vision, we can tackle various contamination problems, benefiting both humans and the environment. © 2024, the Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Electronic Waste

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Applying Machine learning Algorithms for Fault Detection and Classification in Transmission Lines 11

Applying Machine Learning Algorithms for Fault Detection and...

引用

11th IEEE international conference on Systems and Control, ICSC 2023

作者： Bouaziz, Fatma Masmoudi, Abdelkarim Abdelkafi, Achraf Krichen, Lotfi Electrical Engineering Department Sfax3038 Tunisia

ISBN: (纸本)9798350304886

Today, it is essential to identify and classify transmission line problems in order to maintain stability and a steady supply of electricity. To ensure safety and reduce power loss owing to the defect, a faulty section must be removed from a healthy section. Nowadays, Machine learning (ML) is widely employed in all aspects of daily life. In this paper, an ML-based system for fault classification and fault detection has been proposed. For that, in order to create normal and fault data (voltage/ current) of four different types of faults, Matlab Simulink was used to simulate the DFIG-WT linked to the grid via the transmission line. the dataset obtained by Matlab is utilized to test and train the models as well as to calculate and analyze the performance of four algorithms by the use of recall, precision, and accuracy metrics. the high-performing classifier is determined by comparing the results of the confusion matrix for machine learning methods. © 2023 IEEE.

关键词： Support vector machines

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Land Use Land Cover Classification using Deep learning Techniques: A Comparative Study 11

Land Use Land Cover Classification using Deep Learning Techn...

引用

11th IEEE international conference on Signal Processing and Integrated Networks, SPIN 2024

作者： Shreehari, A. Chandan, P.R. Vinod, P.V. Hebbar, Ramachandra PES University Department of Electronics and Communication Engineering Bengaluru India Regional Remote Sensing Centre Bengaluru India

ISBN: (纸本)9798350308433

Land Use and Land Cover (LULC) maps serve as an important component for natural resource monitoring. they convey insights into the utilization patterns of the Earth's land surface and the various forms of coverage present therein. In this research, we evaluated the performance of different deep learning architectures in the segmentation of satellite images into six distinct land cover classes such as trees, cropland, water bodies, built-up areas, roads and barren land. A unique dataset was curated utilizing Microsoft Bing Satellite imagery, encompassing the distinct land use classes within a region situated in the Mandya district, Karnataka. the digitization process was conducted using QGIS, facilitating the preparation of training and evaluation data for the development of trained Deep learning (DL) models using popular DL architectures namely, FCN, U-Net, PSP, and DeeplabV3+. the performance of these models was compared with respect to their Fl score and mean Intersection of Union(mloU) metrics, on test datasets. From the comparison, it was observed that U-Net with ResNet-34 as the backbone demonstrated the best performance with a Mean IoU score of 0.803 and an overall Fl score of 0.89 on the test set. the generalization capability of the models was also evaluated by applying them to larger geographical areas, demonstrating their potential for Land Use Land Cover mapping for large areas. © 2024 IEEE.

关键词： Semantic Segmentation

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Impact of blended education system on outcome-based learning and sector skills development 11

Impact of blended education system on outcome-based learning...

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11th international conference on Internet of Everything, Microwave engineering, Communication and Networks, IEMECON 2023

作者： Podder, Suplab Kanti Samanta, Debabrata thomas, Benny Dutta, Soumi Bhattacharya, Abhishek Dayananda Sagar College of Arts Science and Commerce Dept. of Commerce and Management Karnataka Bangalore India Dept. of Computing and Information Technologies Kosovo Europe Prishtina10000 Serbia Christ University Dept. of Cse Karnataka Bangalore India Institute of Engineering & Management Dept. of Computer Science & Applications Kolkata India

ISBN: (纸本)9781665475129

An effective education system transforms the teaching and learning process into innovative idea generation and independent working ability. A blended education system is the representation of effective education that connects the teachers, students, and educational institutions for content development, delivery of effective teaching methods, and choice-based learning. the motive for initiating the research work was to address the demand for outcome-based learning in society that can fulfill the sector-wise human resource requirements and sector skill development. A blended education system helps to design effective courses and degrees with the capacity of choosing subjects, lectures, and teachers either in online or offline mode of education. the system may also assist in preparing the learning pattern like classroom-based learning, internship-based learning, or learning through project works. the researchers identified the dependent and independent variables with the help of expert opinion. the questionnaire was designed with all relevant questions based on the variables and refined through a pilot study. the research outcomes are described by understanding the nature of quantitative data using statistical tools like frequency distribution, t-test, and ANOVA test with the connectivity of qualitative data and the reality of social issues. © 2023 IEEE.

关键词： learning systems

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EFFICIENT MODEL UPDATES FOR APPROXIMATE UNlearning OF GRAPH-STRUCTURED data 11

EFFICIENT MODEL UPDATES FOR APPROXIMATE UNLEARNING OF GRAPH-...

引用

11th international conference on learning Representations, ICLR 2023

作者： Chien, Eli Pan, Chao Milenkovic, Olgica Department of Electrical and Computer Engineering University of Illinois Urbana-Champaign United States

With the adoption of recent laws ensuring the "right to be forgotten", the problem of machine unlearning has become of significant importance. this is particularly the case for graph-structured data, and learning tools specialized for such data, including graph neural networks (GNNs). this work introduces the first known approach for approximate graph unlearning with provable theoretical guarantees. the challenges in addressing the problem are two-fold. First, there exist multiple different types of unlearning requests that need to be considered, including node feature, edge and node unlearning. Second, to establish provable performance guarantees, one needs to carefully evaluate the process of feature mixing during propagation. We focus on analyzing Simple Graph Convolutions (SGC) and their generalized PageRank (GPR) extensions, thereby laying the theoretical foundations for unlearning GNNs. Empirical evaluations of six benchmark datasets demonstrate excellent performance/complexity/privacy trade-offs of our approach compared to complete retraining and general methods that do not leverage graph information. For example, unlearning 200 out of 1208 training nodes of the Cora dataset only leads to a 0.1% loss in test accuracy, but offers a 4-fold speed-up compared to complete retraining with a (ϵ, δ) = (1, 10−4) "privacy cost". We also exhibit a 12% increase in test accuracy for the same dataset when compared to unlearning methods that do not leverage graph information, with comparable time complexity and the same privacy guarantee. Our code is available online. © 2023 11th international conference on learning Representations, ICLR 2023. All rights reserved.

关键词： Graph neural networks

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the Effect of Running LED on the Formation of Basic Logic, Basic Understanding of Algorithms, Creativity, and Aptitude of Children 11

The Effect of Running LED on the Formation of Basic Logic, B...

引用

11th international conference on ICT for Smart Society, ICISS 2024

作者： Purba, Yehezkiel Bona Sasmoko Saputra, Marcel Indrianti, Yasinta Binus UNiversity Faculty of Humanities Primary Teacher Education Dept Jakarta Indonesia Binus University Faculty of Engineering Computer Engineering Dept Jakarta Indonesia Podomoro University Research Interest Group in Education Technology Entrepreneurship Department Jakarta Indonesia

ISBN: (纸本)9798350389654

this study focuses on the application of Running LED media to investigate the formation of basic logic, algorithm, understanding, creativity, and aptitude in children. this research uses quantitative methods with quasi experimental approach. the target population for data collection technique was carried out through a non-test instrument in the form of a questionnaire with a total of five indicators and eleven question items to be assessed through a likert scale of points 1 - 5. the results shows that Running LED media has a positive influence on basic logic, basic algorithms, creativity and aptitude in elementary school children. this is evidenced by the results of data analysis which shows that the majority of students have good scores in all four aspects. the implications of these findings suggest that integrating Running LED media into educational practices can significantly enhance children's cognitive and creative abilities, providing a robust foundation for their future learning and problem soving skills. © 2024 IEEE.

关键词： Computer circuits

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Fairness-Aware Mutual Information for Multimodal Recommendation 11

Fairness-Aware Mutual Information for Multimodal Recommendat...

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11th IEEE international conference on Behavioural and Social Computing, BESC 2024

作者： Lu, Dan Chen, Xu Chen, Rui Wu, Shiqing Xu, Guandong Harbin Engineering University Harbin China University of Technology Sydney Sydney Australia The Education University of Hong Kong Hong Kong Hong Kong

ISBN: (纸本)9798331531904

In recent years, multimodal recommendations have attracted considerable attention due to the increasing presence of multimedia information. Leveraging multimodal information helps alleviate the data sparsity issue in conventional recom-mender systems, thereby enhancing recommendation accuracy. However, integrating multimodal information introduces the additional challenge of managing sensitive information, as these data may implicitly or explicitly convey sensitive attributes about users, potentially exacerbating fairness issues. While existing methods have addressed fairness issues in recommender systems, most neglect the potentially sensitive information within modalities. In this light, we propose a modality-guided representation learning framework using fairness-aware mutual information to disentangle sensitive and non-sensitive information from modal embeddings. Specifically, we adopt a dual mutual information objective to decompose modal embeddings, capturing sensitive information while encouraging embeddings to contain as much non-sensitive information as possible. these disentangled embeddings are then used to enhance user representations. Extensive experiments on two public datasets have demonstrated the effectiveness of our approach. © 2024 IEEE.

关键词： Sensitive data

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Photovoltaic Power Nowcasting Using Decision-Trees Based Algorithms and Neural Networks 11

Photovoltaic Power Nowcasting Using Decision-Trees Based Alg...

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11th international conference on Electrical, Electronic and Computing engineering, IcETRAN 2024

作者： Amin, M. Asim La Fata, Alice Procopio, Renato Invernizzi, Marco Petronijevic, Milutin Mitic, Ivana Radonjic DITEN University of Genoa Italy University of Niš Serbia

ISBN: (纸本)9798350386998

Precise Photovoltaic (PV) power forecasting tools are needed to integrate PV into the new framework of the energy sector. Also, the related intermittent and random nature needs to be appropriately considered. In this context, this paper examines various machine learning algorithms used for one hour ahead forecasting of PV power production. Specifically, the performances of two Long Short-Term Memory (LSTM) recurrent neural networks, a Gradient Boost Machine (GBM) model and an Extreme Gradient Boosting (XGB) model, are compared. Six years of data are retrieved from the 81 kW PV power plant in the Savona campus of the University of Genoa and are used to train and test the algorithms. the performances of all the algorithms are compared over the original dataset, composed of meteorological variables linked with the PV production, a dataset using seasonal and trend decomposition (STL) of the meteorological variables, and some reduced datasets that mimic the situation in which some of the features are not available at least for some time steps. By comparing the results through different case studies, it can be justified that the XGB outperforms the other algorithms and the STL decomposition is helpful in increasing the performance of the model. © 2024 IEEE.

关键词： day-ahead forecasting machine learning Photovoltaic production recurrent neural networks

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Cardiovascular Disease Prediction Using Machine learning 8th

Cardiovascular Disease Prediction Using Machine Learning

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8th international conference on Information System Design and intelligent Applications, ISDIA 2024

作者： Sesha Talpa Sai, P.H.V. Chaitanya Lahari, M.L.R. Shagin, C.M. Vineeth, C.A. Vijayakumar, P. Naveen Kumar, G.S. Bhaumik, Amiya Department of Mechanical Engineering Malla Reddy College of Engineering and Technology Hyderabad India School of Electrical and Electronics Engineering Lincoln University College Petaling Jaya Malaysia Malla Reddy University Hyderabad India Lincoln University College Petaling Jaya Malaysia

ISBN: (纸本)9789819765805

Cardiovascular Diseases (CVDs) have become the leading cause of death worldwide in recent decades and are now the most dangerous illness, not just in India but throughout the world. therefore, a system that is dependable, accurate, and workable is required to identify these illnesses in time for appropriate treatment. Large-scale and sophisticated data processing has been automated by using machine learning techniques and algorithms to a variety of medical datasets. the enormous number of deaths from heart disease must be decreased, hence a rapid and effective diagnosis method must be found. Machine learning algorithms and data mining techniques are essential in this industry. the primary goal of this effort is to use machine learning algorithms to predict a patient's cardiac problems. © the Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Adversarial machine learning

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Grape leaf disease prediction using various machine learning techniques: - A technical review 11

Grape leaf disease prediction using various machine learning...

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11th international conference on Internet of Everything, Microwave engineering, Communication and Networks, IEMECON 2023

作者： Patil, Rasika More, Ajit Bharati Vidyapeeth Deemed to Be University Pune India Bharati Vidyapeeth's Institute of Management and Technology Navi- Mumbai Mumbai University India

ISBN: (纸本)9781665475129

Grape leaf infection analysis has become important part due to its variety of nature to catch disease. there are various diseases like black rot esca black measles, blight isariopsis, etc which need high end data analytics and predictive analysis for carrying out disease prediction. Convolution neural network integrated with data augmentation has brought a greater degree of verification for leaf diseases prediction. Proper confusion matrix was generated for support vector machine powered with CNN for disease predictive analytics. Results compared with k-mean clustering, fuzzy logics with proper feature extraction and color moment definition. the results show a higher efficiency of up to 95% in accurate grapes leaf disease prediction. © 2023 IEEE.

关键词： Fuzzy logic

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