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作者： Wang, Yufeng Zhang, Xu Ma, Jianhua Jin, Qun School of Communications and Information Engineering Nanjing University of Posts and Telecommunications Nanjing China Faculty of Computer & Information Sciences Hosei University Tokyo Japan Department of Human Informatics and Cognitive Sciences Waseda University Tokyo Japan

As a distributed learning framework, Federated Learning (FL) allows different local learners/participants to collaboratively train a joint model without exposing their own local data, and offers a feasible solution to legally resolve data islands. However, among them, the data privacy and model security are two challenges. The former means that, if original data are used for trained FL models, various methods can be used to deduce the original data samples, thereby causing the leakage of data. The latter implies that unreliable/malicious participants may affect or destroy the joint FL model, through uploading wrong local model parameters. Therefore, this paper proposes a novel distributed FL training framework, namely LDP-Fed+, which takes into account differential privacy protection and model security defense. Specifically, firstly, a local perturbation module is added at the local learner side, which perturbs the original data of local learners through feature extraction, binary encoding and decoding, and random response. Then, through using the perturbed data, local neural network model is trained to obtain the network parameters that meet local differential protection, to effectively deal with model inversion attacks. Secondly, a security defense module is added on the server side, which uses the auxiliary model and differential index mechanism to select an appropriate number of local disturbance parameters for aggregation to enhance model security defense and deal with membership inference attacks. The experimental results show that, compared with other federated learning models based on differential privacy, LDP-Fed+ has stronger robustness for model security and higher accuracy for model training while ensuring strict privacy protection. © 2022 John Wiley & Sons, Ltd.

关键词： Learning systems

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Creating Own AI Datasets from Different Language Sources Efficiently: Advantages, Disadvantages and Best Pracitces to Make Own Datasets 6

Creating Own AI Datasets from Different Language Sources Eff...

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6th IEEE International Conference and Workshop Obuda on Electrical and Power engineering, CANDO-EPE 2023

作者： Molnar, Gyorgy Cserko, Jazsef Nagy, Eniko Balogh, Zoltan Óbuda University Kandó Kálmán Faculty of Electrical Engineering Budapest Hungary Széchenyi University János Csere Apáczai Faculty Győr Hungary John von Neumann University Gamf Faculty of Engineering and Computer Science Kecskemét Hungary Győr Hungary Institute of Cyberphysical Systems Óbuda University Antal Bejczy Center for Intelligent Robotics John von Neumann Faculty Budapest Hungary Constantine the Philosopher University in Nitra Faculty of Natural Sciences and Informatics Department of Informatics Nitra94901 Slovakia

ISBN: (纸本)9798350328752

Artificial Intelligence (AI) has become a ubiquitous technology that has the potential to revolutionize many industries. However, AI requires access to vast amounts of data to achieve its full potential. By understanding these data sources and the best practices for working with them, we can unlock the full potential of AI and create groundbreaking solutions that benefit society as a whole. Data sources can be utilized to train AI algorithms, including structured and unstructured data, but finding relevant datasets is challenging. The main problem is the international sources. Most of the datasets are available only in English or other common languages. However these are good sources, but translating them is expensive and time-consuming. This paper shows how to generate own datasets from open-source data with translation. © 2023 IEEE.

关键词： Artificial intelligence

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Borneo Smart Forest Information System for Management of Dipterocarp Plants in Kalimantan Rainforest 8

Borneo Smart Forest Information System for Management of Dip...

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8th IEEE Information Technology International Seminar, ITIS 2022

作者： Wati, Masna Puspitasari, Novianti Hairah, Ummul Widians, Joan Angelina Septiarini, Anindita Tjikoa, Ade Fiqri Universitas Mulawarman Faculty of Engineering Dept. of Informatics Samarinda Indonesia Universitas Gadjah Mada Dept. of Computer Science and Electronics Yogyakarta Indonesia

ISBN: (纸本)9798350398199

Kalimantan's tropical rainforest is home to indigenous plants of the Dipterocarpaceae family (tribe), which is renowned for having the most endemic species. Dipterocarp is a family of pantropical plants, many of which are used in the wood industry. This study aims to provide information on the potential of Dipterocarp in Kalimantan forests. Education and conservation efforts for Dipterocarp trees, a prominent component of the forest ecosystem, are crucial in light of the significant harm done to Kalimantan's rainforests. The authors develop the Borneo Smart Forest information system. This system digitally organizes data on the diversity of Dipterocarp in the Kalimantan rainforest based on Web and QR-Code technology. Borneo Smart Forest (BSF) system for monitoring plant inventory and plant markers can be read by smartphones using QR codes. It gives visitors a comprehensive overview of the plants. A QR code is a printed, two-dimensional barcode with limited space for data storage. This technology allows the plant to show earlier administrator-made additions to the inventory information system. The QR code will direct the user to the BSF website, which has data about the plants. The development of this system is one of the efforts to support the achievement of Dipterocarp conservation. © 2022 IEEE.

关键词： Information systems

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A Bibliometric Analysis and Research Landscape of Artificial Intelligence in Education 14

A Bibliometric Analysis and Research Landscape of Artificial...

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14th IEEE International Conference on Control System, Computing and engineering, ICCSCE 2024

作者： Ajibade, Samuel-Soma M. Issa, Bayan Jasser, Muhammed Basheer Hassan, Farrukh Aldharhani, Ghassan Saleh Al-Hadi, Ismail Ahmed Al-Qasem Akintoye, Kayode Akinlekan Tabuena, Almighty Cortezo School of Engineering and Technology Sunway University Department of Computing and Information Systems No. 5 Jalan Universiti Selangor Darul Ehsan Bandar Sunway47500 Malaysia Istanbul Ticaret University Department of Computer Engineering Istanbul Turkey University of Aleppo Faculty of Informatics Engineering Syria Institute of Computer Science and Digital Innovation UCSI University Kuala Lumpur Malaysia UKM University Faculty of Information Science and Technology Selangor Bangi Malaysia The Federal Polytechnic Department of Computer Science Ado Ekiti Ekiti State Nigeria University of the City of Valenzuela College of Arts and Sciences Philippines

ISBN: (纸本)9798350364507

This study examines the academic field of Artificial Intelligence in Education (AI-ED) by examining the publications and indexed documents in Scopus from 2010 to 2023. Consequently, we examined the publishing trends, exemplary articles, and the most engaged participants and funding institutions in the domain of AI-ED research. A bibliometric analysis was performed to examine the network of co-authorships, keywords, and citations in the domain of AIED research. The Scopus search resulted in the discovery of 10,474 published documents on AI-ED. Additionally, trends analysis indicated a significant increase of over 3,000% in publications between 2010 and 2023. Conference papers are the most often utilized document type, including 6757 publications or 64.5% of the overall amount. The ACM International Conference Proceeding Series is the primary source for published materials, accounting for 675 publications or 6.44% of the total publications on the topic. The most prolific researchers in the field of AI-ED are Breazeal, C and Chai C.S, each having published 18 documents. Additionally, the two institutions that have produced the highest number of published documents are Carnegie Mellon University (CMU) and the University of NC State University (NCSU). The output of the affiliations active in the topic is primarily attributed to the funders, specifically the National Science Foundation, which is the leading funding institution based in the United States and has contributed to 354 publications. An analysis of keyword co-occurrence revealed 6 main study areas that cover the essential tools, theories, methodologies, and socioeconomic and financial aspects of artificial intelligence in education (AIED). Future research in the field of artificial intelligence in education will focus on employing sophisticated deep learning, machine learning, and neural network algorithms to precisely predict students' learning patterns. © 2024 IEEE.

关键词： Adversarial machine learning

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E-Learning: A Successful Learning Environment 1

E-Learning: A Successful Learning Environment

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1st International Conference on computer Science and Emerging Technologies, CSET 2023

作者： Momani, Fayiz Hatamleh, Amer Ahmad Amir Fazamin Wan Hamzah, Wan Mohd Jusoh, Julaily Aida Zakaria, Muhammad D. Das, Sunanda Faculty of Management and Financial Sciences E-business & Commerce Department Jordan MIS Bi and Data Analysis Irbid National University Faculty of Business and Financial Science Irbid Jordan Faculty of Informatics and Computing UniSZA Malaysia School of Computer Science and Engineering Jain University Cyber Security Bangalore India

ISBN: (纸本)9798350341737

Education is developing with the economy. This led to the creation of e-Iearning tools for instructors and students. In e-learning, electronic resources are used to teach. E-learning can be taught in or out of school, but computers and the Internet are essential. That is, e-learning involves delivering education to a big group of people at once or over time. So, overall, in this research is about E-learning that make a successful environment. We chose this term since it is widely used in E-learning research to improve student academic achievement. Due to the difficulties of accessing adequate study materials during the Covid-19 epidemic, distance learning has grown more popular among students. This study proposes a unique learning technique that involves choosing and organizing important learning items using a recommender system. We also studied our method's efficacy. Using a recommended system to help online learning activities was proven to be effective. Software development methodology is a collection of principles and processes used to build software. Systematic software development is the goal. The research approach utilized for this topic is Lean Development Methodology. We also examine the advantages and disadvantages of e-learning. This research also introduces various new strategies and methodologies that may be employed in the future to improve system-learning. It also presents system that we choose and why we choose it. So, it aims to eliminate waste and increase production. Following the principles can help developers avoid nonproductive chores while still producing high-quality results. The ultimate objective is to develop an efficient and error-free system. Finally, E-learning is a social revolution. It's part of a bigger rethinking of how we teach future employees and students. The eLearning platform enabled the learning data visualization dashboard beautifully. User-centered design lets the dashboard adapt to its users' demands. © 2023 IEEE.

关键词： Learning systems

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Revolutionizing Recruitment Management: Optimizing Advanced Digital Solutions for Manpower Planning in Higher Education

Revolutionizing Recruitment Management: Optimizing Advanced ...

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IEEE Conference on Systems, Process & Control (ICSPC)

作者： Ahmad Mukafi Andrian Nor Kamaliah Khamis Ahgalya Subbiah Halimatuz Zuhriyah Safira Faizah Faculty of Information Sciences and Engineering Management and Science University Section 13 Shah Alam Selangor Department Informatics Engineering Faculty Engineering and Computer Science Jakarta Global University Depok Indonesia

ISBN: (数字)9798350391398

ISBN: (纸本)9798350391404

Effective manpower planning is crucial for any organization, including universities, as it directly influences performance, productivity, and overall success. Inefficient recruitment processes can lead to increased costs, higher turnover rates, and suboptimal utilization of human resources, negatively impacting the institution's ability to achieve its goals and maintain a competitive advantage. The objective of this study is to develop a comprehensive digital solution to streamline and enhance recruitment and manpower planning processes, replacing traditional manual methods. This paper presents the Recruitment and Manpower Planning System, an innovative web-based platform designed using Extreme Programming (XP) methodologies. The system integrates modern programming tools such as Visual Studio Code, Laragon, Laravel 9, and MySQL, and utilizes Unified Modelling Language (UML) for precise system design. A mixed-methods approach, incorporating quantitative data from structured questionnaires administered to 500 users, informed the system's design. Data analysis indicates a 40% reduction in recruitment time, a 35% increase in successful job placements, and a 90% user satisfaction rate. Rigorous testing phases ensured system accuracy and quality. This digital solution not only optimizes recruitment processes but also provides a robust prototype for future advancements in manpower planning systems.

关键词： Productivity Visualization Unified modeling language Prototypes Organizations Labor resources System analysis and design Programming profession Recruitment Testing

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A Semi-automated Method to Measure the Height of Lumbar Intervertebral Discs 2

A Semi-automated Method to Measure the Height of Lumbar Inte...

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2nd International Conference on computers and Automation, CompAuto 2022

作者： Natalia, Friska Sudirman, Sud Faculty of Engineering and Informatics Universitas Multimedia Nusantara Tangerang Indonesia School of Computer Science and Mathematics Liverpool John Moores University Liverpool United Kingdom

ISBN: (纸本)9781665481946

Measuring the height of a lumbar intervertebral disc is an important step in categorizing the degenerative level of the disc. Popular disc categorization techniques, such as the Pfirrmann grading system, use several visual features that physicians can observe directly from the medical images - including the disc height. In this paper, we propose a semiautomated method for measuring the average height of a lumbar intervertebral disc from the sagittal Magnetic Resonance Images of lumbar vertebrae. The technique uses a combination of approaches starting with selecting a mid-sagittal slice from each patient study using a deep learning model, manual image segmentation, linear regression to find a mid-height line, and geometry to split the disc region into its upper and lower regions and calculate its height. We show that our results generally compare well with others and identify an area that warrants further investigation. © 2022 IEEE.

关键词： Magnetic resonance imaging

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Comparative Analysis of Edge Detection Algorithms with Particle Swarm Optimization for Enhanced Image Processing

Comparative Analysis of Edge Detection Algorithms with Parti...

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International Conference on ICT For Smart Society (ICISS)

作者： Sri Rahayu Pulung Nurtantio Andono Pujiono Ahmad Zainul Fanani Department of Informatics Engineering Faculty of Computer Science Dian Nuswantoro University Semarang Indonesia Computer Science Department Institut Teknologi Garut Garut Indonesia

ISBN: (数字)9798350389654

ISBN: (纸本)9798350389661

Edge detection plays an important role in various fields by identifying object boundaries and supporting advanced image analysis, such as segmentation, recognition, and tracking. Many edge detection algorithms, such as Canny, Laplacian, and Prewitt, have been developed to address issues such as noise sensitivity, computational complexity, and detection accuracy. This research compares the optimized algorithms using Particle Swarm Optimization (PSO). The results of this study show that the optimized algorithm provides better performance based on the evaluation conducted using entropy, Mean Squared Error (MSE), and computation time, and has practical implications. This research also determines the most effective edge detection technique in various image processing scenarios, thus contributing to the optimization of image analysis workflows in real-world contexts.

关键词： Accuracy Laplace equations Image analysis Image edge detection Noise Prediction algorithms Entropy Computational efficiency Particle swarm optimization Optimization

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Missing Data Imputation: A Comprehensive Review

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Journal of computer and Communications 2024年第11期12卷 53-75页

作者： Majed Alwateer El-Sayed Atlam Mahmoud Mohammed Abd El-Raouf Osama A. Ghoneim Ibrahim Gad Department of Computer Science College of Computer Science and Engineering Taibah University Yanbu Saudi Arabia Computer Science Department Faculty of Science Tanta University Tanta Egypt Basic and Applied Science Institute College of Engineering and Technology Arab Academy for Science and Technology (AAST) Alexandria Egypt Department of Computer Science Faculty of Computers and informatics Tanta University Tanta Egypt

Missing data presents a significant challenge in statistical analysis and machine learning, often resulting in biased outcomes and diminished efficiency. This comprehensive review investigates various imputation techniques, categorizing them into three primary approaches: deterministic methods, probabilistic models, and machine learning algorithms. Traditional techniques, including mean or mode imputation, regression imputation, and last observation carried forward, are evaluated alongside more contemporary methods such as multiple imputation, expectation-maximization, and deep learning strategies. The strengths and limitations of each approach are outlined. Key considerations for selecting appropriate methods, based on data characteristics and research objectives, are discussed. The importance of evaluating imputation’s impact on subsequent analyses is emphasized. This synthesis of recent advancements and best practices provides researchers with a robust framework for effectively handling missing data, thereby improving the reliability of empirical findings across diverse disciplines.

关键词： Missing Data Machine Learning Prediction Deep Learning Imputation

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A Comparative Analysis of Machine Learning Techniques for Heart Disease Prediction 21

A Comparative Analysis of Machine Learning Techniques for He...

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21st IEEE Student Conference on Research and Development, SCOReD 2023

作者： Alqushaibi, Alawi Ragab, Mohammed Gamal Sumiea, Ebrahim Hamid Al-Selwi, Safwan Mahmood Hasan, Mohd Hilmi Abdulkadir, Said Jadid Alhussian, Hitham Al-Asbahi, Mahmoud Mustafa Universiti Teknologi PETRONAS Computer and Information Sciences Perak Malaysia Universiti Teknologi PETRONAS Centre for Research in Data Science Perak Malaysia Universiti Malaysia Terengganu Faculty of Ocean Engineering Technology and Informatics Terengganu Malaysia

ISBN: (纸本)9798350318821

Detection of heart disease is critical, as it is a common and serious health condition. Predicting its occurrence is vital in the healthcare domain, and machine learning techniques are increasingly being utilized to accurately identify patients at risk. To evaluate how well machine learning models can effectively predict heart disease, we used a pre-existing dataset from the UCI repository's Cleveland database. Though the dataset originally contained 303 records and 76 attributes, we followed the literature by narrowing the focus to 14 relevant attributes in order to improve the precision of various algorithmic approaches. Our study employed five machine learning classifiers, namely KNearest Neighbors (KNN), CatBoost Classifier (CBC), Decision Tree Classifier (DT), XGBClassifier (XGBC), and Support Vector Classifier (SVM). We evaluated their performance using different metrics like accuracy, precision, AUC, and F1 score on the heart disease dataset. We found that the CatBoost Classifier had the best performance, achieving high accuracy and AUC with 0.88% and 0.94% values, respectively. This suggests that machine learning algorithms could aid in identifying heart disease at an early stage and have significant implications for improving patient outcomes in healthcare. © 2023 IEEE.

关键词： Forecasting

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