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International Conference on Data Analytics and Management, ICDAM 2023

作者： Parikh, Jolly Antony, Saji M. Jain, Rachna Chawla, Naman Mishra, Ashish de Albuquerque, Victor Hugo C. Neto, Aloísio Vieira Lira Department of Computer Science and Engineering Bharati Vidyapeeth’s College of Engineering New Delhi India Department of Electronics and Communication Engineering Bharati Vidyapeeth’s College of Engineering New Delhi India Department of Information Technology Bhagwan Parshuram Institute of Technology New Delhi India Department of Computer Science and Engineering Bhagwan Parshuram Institute of Technology New Delhi India Department of CSE Gyan Ganga Institute of Technology and Sciences Madhya Pradesh Jabalpur India Federal Institute of Education Science and Technology of Ceara Fortaleza Brazil Federal University of Ceará Fortaleza Ceará Fortaleza Brazil Graduation Program in Telecommunication Engineering Federal University of Ceará Fortaleza Ceará Fortaleza Brazil

ISBN: (纸本)9789819965465

In the contemporary digital era, individuals face an overwhelming challenge of searching for personalized content from a seemingly infinite pool of available options such as books, videos, articles, and movies. Simultaneously, digital content providers strive to attract and retain as many users on their platforms as possible for extended periods. This has led to the evolution of recommender systems, which aim to enhance user engagement and satisfaction by recommending content based on their preferences and interests. These systems employ sophisticated algorithms and data mining techniques to analyze user behavior and feedback, enabling them to make personalized recommendations to users, thereby improving their overall experience. In this paper, an investigation is conducted in which different models in machine learning with their combinations are evaluated to evaluate which model could give the best accuracy with actual ratings given by users. The models used are Lasso regression, XGBoost, K-nearest neighbor, surprise baseline only, SVD, SVDpp, and their combinations. The proposed ensembled model of XGBoost + surprise baseline + surprise KNN baseline + SVD + SVDpp, with the minimum value of RMSE as 1.111410 and MAPE value as 39.261054, is our best model and can serve as a reference for any future recommender system. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd 2024.

关键词： Singular value decomposition

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Selection of Education Assistance Recipients Based on AHP and SAW 23

Selection of Education Assistance Recipients Based on AHP an...

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23rd International Seminar on Intelligent Technology and Its Applications, ISITIA 2022

作者： Pranoto, Gatot Tri Pebrianti, Dwi Darwis, Muhammad Yaddarabullah Krishnasari, Erneza Dewi Pelita Bangsa University Informatics Engineering Study Program Bekasi Indonesia Budi Luhur University Computer Science Jakarta Indonesia Universitas Trilogi Department of Informatics Engineering Jakarta Indonesia Universitas Trilogi Department of Visual Communication Design Jakarta Indonesia

ISBN: (数字)9781665460811

ISBN: (纸本)9781665460811

The final decision of the educational assistance recipient in GNOTA Foundation, Jakarta is still processed manually. They usually only look at the father's occupation criteria without looking at other criteria such as the age of the child, the child's orphan status, the occupation of mother, the age of father, the age of mother and other scholarships received. GNOTA Foundation needs to implement a Decision Support System (DSS) for the selection process, so the results are fast and on target. This research applies the Analytical Hierarchy Process (AHP) and Simple Additive Weighting (SAW) methods for the selection of education assistance recipients so the criteria can be weighted and the prospective recipient students can be ranked. This research determines the priority weight and importance of each criterion, namely the age of child age (0.18), the status of child (0.22), the occupation of father (0.08), the occupation of mother (0.06), the age of father (0.05), the age of mother (0.04) and other scholarship assistance (0.36). The results of the weighting of these criteria have been tested and declared consistent through the Consistency Ratio (CR) value of 0.070. This study also produces a list of the best ranking students who are prospective recipients of educational assistance. The ranking results have been tested based on compliance with the provisions of the GNOTA Foundation of 92.91%. The system in this study was developed using a prototyping system development model and utilizing the Unified Modeling Language (UML) tool. The prototype functionality in this study works well by using the Blackbox Testing (BBT) model based on Equivalence Partitioning. In addition, the level of user acceptance of the prototype in this study was 81.2% using the Technology Acceptance Method (TAM). © 2022 IEEE.

关键词： Decision support systems

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Application of Pose Estimation on Webcam for Fall Risk Assessment 8

Application of Pose Estimation on Webcam for Fall Risk Asses...

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Joint 8th International Conference on Digital Arts, Media and Technology and 6th ECTI Northern Section Conference on Electrical, Electronics, computer and Telecommunications engineering, ECTI DAMT and NCON 2023

作者： Uttama, Surapong Chumpoo, Patsakorn Rueangsirarak, Worasak Center of Excellence in Artificial Intelligence and Emerging Technologies School of Information Technology Mae Fah Luang University Chiang Rai Thailand School of Information Computer Science Program Technology Mae Fah Luang University Chiang Rai Thailand School of Information Technology Computer and Communication Engineering for Capacity Building Mae Fah Luang University Chiang Rai Thailand

ISBN: (纸本)9798350396546

Fall risk assessment is an effective and simple measure to evaluate the balance of people especially the elderly who are likely to have balance disorder. Lose of body balance increases a fall risk which could lead to severe damage to aging persons. In this paper, we propose to apply pose estimation technique based on PoseNet method to detect body joints from webcam's video for two types of fall risk assessment: Five Times Sit to Stand Test (FTSTS) and 30-Second Chair Stand Test (30CST). The experiments were performed with 17 volunteers concurrently with the measures of a healthcare expert. The results revealed that our proposed technique corresponds well with the measure of the expert evaluating by a Pearson correlation coefficient which equals to 0.903 and 0.980 for FTSTS and 30CST respectively. © 2023 IEEE.

关键词： Risk assessment

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The importance of mobile applications for companies' brand image: A study using structural equations 9th

The importance of mobile applications for companies' brand i...

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9th International Conference on Information Technology and Quantitative Management, ITQM 2022

作者： Mariano, Ari Melo Silva, Marcel Carneiro Mello, Tarcilla Mariano Santos, Maíra Rocha Department of Production Engineering University of Brasília Brazil Department of Computer Science University of Brasilia Brazil Postgraduate Program in Communication University of Brasília Brazil Center for Advanced Multidisciplinary Studies University of Brasilia Brazil

Because of the advancement of new technologies and the popularity of mobile devices, this study was designed to identify whether apps have a representative influence on companies' brand image. To fulfill this objective, descriptive research based on the structural equation model was carried out with the help of the statistical software Adanco. The study was conducted in Brasilia and applied in a virtual environment in the Google forms platform. The link was made available and disseminated to students of one university and through the WhatsApp and Facebook social networks, having 350 mobile device users as participants. The questionnaire was validated with α=0.81. The results of this research suggest that applications can influence 30.5% of a brand's image, thus raising the discussion that mobile applications will soon have equal or even greater importance than a website and social networks, tools that were once trends and are now essential requirements for organizations. Thus, for a company to be successful, it must not only be seen as a company with high performance and quality standards, but it must also represent a solid brand with a good reputation in the market and create and cultivate an image that connects emotionally with its consumer public. © 2022 The Authors. Published by Elsevier B.V.

关键词： Social networking (online)

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High-Efficiency Power Generation for Floating Photovoltaic Systems: Economic Viability and Simulation Analysis

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Physica Status Solidi (A) Applications and Materials Science 2025年第10期222卷

作者： Wang, Xiaobo Yousuf, Hasnain Chu, Mengmeng Polgampola, Chamani Madara Mohammed, Shurouq Abdulqadir Khokhar, Muhammad Quddamah Park, Sangheon Yi, Junsin Department of Electrical and Computer Engineering Sungkyunkwan University Gyeonggi-Do Suwon16419 Korea Republic of Interdisciplinary Program in Photovoltaic System Engineering Sungkyunkwan University Gyeonggi-Do Suwon16419 Korea Republic of College of Information and Communication Engineering Sungkyunkwan University Gyeonggi-Do Suwon16419 Korea Republic of

As global demand for renewable energy continues to rise and available land resources become increasingly scarce, floating photovoltaic (FPV) systems have emerged as a cost-effective solution to meet energy needs while minimizing environmental impacts. In 2023, the global installed capacity of FPV reached 5.9 GW and is projected to grow to 10 GW by 2030. This review systematically examines the current status and historical development of floating solar technology, focusing on key challenges and solutions in structural design and material selection. It also explores methods to enhance FPV efficiency, including optimal tilt angles, cooling systems, bifacial modules, and tracking systems, while addressing the economic feasibility of FPV. Furthermore, a simulated 2 MW FPV system based on meteorological data from Suwon, South Korea, is analyzed. The system demonstrates a high-performance ratio of 90.5% and a maximum system production output of 1835 MWh when the tilt angle is optimized between 32° and 34° and the azimuth angle is set to 0°. © 2025 Wiley-VCH GmbH.

关键词： floating solar high efficiency photovoltaic modules renewable energies simulations

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A Smote-Based Churn Prediction System Using Machine Learning Techniques

A Smote-Based Churn Prediction System Using Machine Learning...

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2023 International Conference on Science, engineering and Business for Sustainable Development Goals, SEB-SDG 2023

作者： Akinrotimi, Akinyemi Omololu Ogundokun, Roseline Oluwaseun Mabayoje, Modinat Abolore Oyekunle, Rafiat Ajibade Adebiyi, Marion Olubunmi Augustine University Department of Mathematics Sciences Computer Science Program Ilara-Epe Epe Nigeria Kaunas University of Technology Department of Multimedia Engineering Lithuania Landmark University Omu Aran Sdg 4. Quality Education Nigeria University of Ilorin Department of Computer Science Ilorin Nigeria University of Ilorin Department of Information and Communication Ilorin Nigeria Landmark University Omu-Aran Department of Computer Science Omu Aran Nigeria

ISBN: (纸本)9798350324785

The financial backbone of every telecommunications company is strictly made up of the number of customers patronizing the organization. Due to the high level of competition amongst existing telecommunication companies, customers sometimes leave, dropping the services of a particular one for the other. Predicting which subscribers may want to leave a telecommunications company and providing solutions to keep them from doing so, are the main objectives of customer churn prediction. Churn prediction helps classify consumers who are likely to move from one business to another. The ability to foresee churners before they leave has been increasingly useful in recent years, especially in light of increased competition among communications carriers. To compute churners for telecommunications providers, the study described in this paper used oversampling techniques to balance churn data and applied the dimensionality reduction technique to discover optimal features with a strong predictive ability for detecting would-be churners and non-churners. The model used Logistic Regression and the Naïve Bayes Classification Algorithm to implement comparative classification strategies. The performance levels of these strategies were compared using a Telecommunications Customer Churn data set, as well as performance metrics such as accuracy, sensitivity, and specificity. The Naïve Bayes Algorithm proves more efficient than its counterpart giving higher positive rate detection and a lower negative rate detection. © 2023 IEEE.

关键词： Forecasting

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Low Power Consumption of Non-Volatile Memory Device by Tunneling Process engineering

SSRN

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SSRN 2024年

作者： Wang, Fucheng Chu, Mengmeng Chen, Jingwen Pan, Zhong Kim, Yongsang Song, Jang Kun Yi, Junsin Khokhar, Muhammad Quddamah Department of Electrical and Computer Engineering Sungkyunkwan University Suwon16419 Korea Republic of Interdisciplinary Program in Photovoltaic System Engineering Sungkyunkwan University Suwon16419 Korea Republic of College of Information and Communication Engineering Sungkyunkwan University Suwon16419 Korea Republic of

Compared with Si3N4 and Al2O3, SiO2 grown using thermal oxidation process as tunneling layer has the advantages of high bandgap and well interface contact with the surface of silicon wafer, which can be a great solution to the leakage current problem of metal-insulator-semiconductor (MIS) devices. This study investigates the effect of improving the SiO2 tunnel layer on the operating voltage of MIS devices with a SiO2/HfAlOx/Al2O3 structure. The result shows the operating voltage changes as the tunneling layer thickness decreases, with a minimum of only 12 V for a 1.5 nm tunneling layer thickness. In addition, we found that pinholes are generated on the film surface when annealing a 1.5 nm SiO2 tunnel layer at 850 °C N2, in which case the operating voltage of the device is reduced to only 10 V, though it was also accompanied by the deterioration of the retention characteristics. © 2024, The Authors. All rights reserved.

关键词： Silicon wafers

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Enhanced Plant Health Recognition via Humanoid Robot Advanced Sensing and AI

Enhanced Plant Health Recognition via Humanoid Robot Advance...

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Mobile, Intelligent, and Ubiquitous Computing Conference (MIUCC), International

作者： Manar A. Ibrahim Haneen I. Fayez Salma M. Abdo Toqa E. Ghaly Ingy A. Ghanem Ibrahim Attiya Aya M. Ai-Zoghby Khaled M. Foad Faculty of Computer Science and Engineering Artificial Intelligence Science New Mansoura University Dakhlia Egypt Computer Science Faculty of Computer Science and Engineering New Mansoura University Dakhlia Egypt Faculty of Engineering Computer and Communication program Faculty of Computer Science and Engineering Mansoura University New Mansoura University Dakhlia Egypt Department of Mathematics Faculty of Science Zagazig University Zagazig Egypt Faculty of Computer Science and Engineering New Mansoura University Dakhlia Egypt Computer Science Department Faculty of Computers and Artificial lntelegence Damietta University Damietta Egypt

ISBN: (数字)9798350367775

ISBN: (纸本)9798350367782

Accurate and timely detection of plant diseases is crucial for protecting crop yields and promoting sustainable agriculture. This study introduces a deep learning-based approach for plant health detection by integrating a Convolutional Neural Network (CNN) with a Humanoid robot for real-time monitoring. The approach leverages advanced tools such as TensorFlow for model development, OpenCV for image processing, and YOLOv5 for object detection. A dataset comprising 1,530 images, labeled as “Healthy,” “Powdery,” and “Rust,” was used to train, validate, and test the model. Through pre-processing techniques like rescaling, data augmentation, and feature extraction, the model achieved impressive results, with a training accuracy of 98.4%, validation accuracy of 98.2%, and testing accuracy of 99.3%. This approach marks a significant improvement in precision agriculture, offering a scalable and highly accurate solution for early plant health detection.

关键词： YOLO Training Accuracy Humanoid robots Robot sensing systems Ubiquitous computing Real-time systems Sensors Convolutional neural networks Testing

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Double-SP Method to Enhance Privacy and Security of Smart City Applications 10

Double-SP Method to Enhance Privacy and Security of Smart Ci...

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17th INDIACom;2023 10th International Conference on Computing for Sustainable Global Development, INDIACom 2023

作者： Bahbouh, Nour Mahmoud Hakami, Abdulelah Alhussain Hassan Aljohani, Yaser Mohammed Sen, Adnan Ahmed Abi Alkhodre, Ahmad Alsughayyir, Aeshah Granada University Department of Information and Communication Sciences Granada Spain Saudi Electronic University Al-Madinah Saudi Arabia YasRef Program Yanbu Saudi Arabia Computing and Information Technology Islamic University Al-Madinah Saudi Arabia Islamic University Faculty of Computer and Information Systems Al-Madinah Saudi Arabia College of Computer Science and Engineering Taibah University Al-Madinah Saudi Arabia

ISBN: (纸本)9789380544472

Smart cities promise a lot of well-being to their users in all areas of life through millions of applications and services. Smart services rely heavily on collecting data and the preferences of users. But on the dark side, the users' information is posed to threat and penetration during transmission or stored far in the clouds. Depending on encryption only is not sufficient if the attacker has strong resources or if the attacker is the service provider (SP) itself. In addition, changing data before sending is not a practical solution in many systems because of the adverse impact on the quality of a main service. This research presented a new idea to address the issue of protection. The proposed method enhanced the privacy and security of users' data without affecting the accuracy of service. The core of the suggested solution relies on a knowledge base of services that are managed by experts. Also, the solution depends on fog nodes to measure the level of security and privacy of users' queries without delay. Moreover, the fog nodes manage contact with SPs. Finally, the proposed method divided the SP into two, one for user queries and the other for user data. The simulation and analytical discussion on a practical case in smart cities demonstrated the superiority of the proposed approach over previous methods in the level of protection by maintaining the quality of services, and the resistance to attacks. © 2023 Bharati Vidyapeeth, New Delhi.

关键词： Fog

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AI-Powered Precision: Revolutionizing Skin Cancer Detection with Advanced Machine Learning

AI-Powered Precision: Revolutionizing Skin Cancer Detection ...

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Novel Intelligent and Leading Emerging Sciences Conference (NILES)

作者： Mariam M. Abd-elaziz Basma M. Elshafie Hagar Abdullah Karen Y. Gerges Ingy A. Ghanem Khaled M. Fouad Amir El-Ghamry Ibrahim F. Moawad Sara El-Metwally Faculty of computer science and engineering New Mansoura university Dakahlia Egypt Faculty of Engineering Computer and Communication program Faculty of Computer Science and Engineering Mansoura University New Mansoura University Information Systems Faculty of Computers and Artificial Intelligence Benha University Egypt Faculty of Computer and Information Sciences Faculty of Computer Science and Engineering Mansoura University New Mansoura University Egypt School of Engineering and Computer Science University of Hertfordshire GAF Egypt Faculty of Computer and Information Sciences Ain Shams University Cairo Egypt Department of Artificial Intelligence College of Computer Science and Engineering Taibah University Medina Saudi Arabia

ISBN: (数字)9798350378511

ISBN: (纸本)9798350378528

Skin cancer's increasing incidence rates necessitate advanced diagnostic tools. This research uses MobileNet architecture to develop an enhanced system for skin cancer detection. MobileNet's efficient CNN architecture, combined with Keras for model lifecycle management and TensorFlow for real-time inference, forms the foundation of this study. Using 10,015 dermatoscopic pictures from seven different classifications of skin cancer in the HAM10000 dataset, the methodology includes several key stages: image preprocessing to correct illumination and adjust resolution, data augmentation to balance the dataset, and model training using Transfer Learning. The MobileNet model was trained over 50 epochs with a comprehensive architecture incorporating multiple specialized layers. By combining large-scale data analysis and adaptive learning, this methodology demonstrates The revolutionary possibilities of AI and ML in improving skin cancer diagnostics and public health outcomes. Keywords Skin Cancer Detection, Dermatoscopic Images, Machine Learning, Deep Learning

关键词： Training Deep learning Adaptation models Image resolution Transfer learning Lighting Real-time systems Image preprocessing Public healthcare Skin cancer

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