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5th International Conference on Information and Communication Technology for Development for Africa, ICT4DA 2023

作者： Wubineh, Betelhem Zewdu Metekiya, Biniyam Yirsaw Wroclaw University of Science and Technology Faculty of Information and Communication Technology Wroclaw Poland School of Computing and Informatics Wachemo University Department of Information Technology Hossana Ethiopia College of Informatics Department of Information System University of Gondar Gondar Ethiopia

ISBN: (纸本)9798350328486

Crime is a complex social phenomenon, and its cost continues to rise over time. Police have collected a large amount of data on criminals every day for different crimes. However, extracting valuable information from these data takes time and effort for the law enforcement organization at hand. Therefore, effective crime prevention strategies and policies are necessary to prevent crime. Criminology is one of the most important areas for data mining. The purpose of this study is to extract useful crime patterns from past data and develop a knowledge-based system to prevent crime. Different criminals commit different crimes, but we do not know which criminal commits which crime. For this purpose, the 910 criminal records are used to train and test the model. To predict a crime, the classification technique and the J48 decision tree, random forest (RF), PART, and ensemble algorithms are used. As a result, the algorithm scored an accuracy of 75.38%, 80.4%,73.7%, and 81.3% in the J48, RF, PART, and ensemble methods, respectively, in a 10-fold cross-validation. Additionally, the offender's job is the determining factor that causes and predicts crimes. Finally, we integrate the rules extracted from a data mining task into a knowledge base system using PROLOG, which is used to make decisions. © 2023 IEEE.

关键词： Forecasting

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Development and Implementation of a Machine Learning-Based Flood Forecasting System in Kasese District, Uganda

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Journal of Flood Risk Management 2025年第2期18卷

作者： Miiro, Edward Kato, Ismael Nantege, Zuhra Ssendi, Samuel Bassajjalaba, Khasim Faculty of Computing and Informatics Makerere University Business School Kampala Uganda

This study aimed to develop a proof-of-concept prototype of a machine learning system to forecast and mitigate the effect of floods in Kasese District. The researchers used a participatory design science approach. The researchers conducted document reviews and brainstorming to obtain past climate data from the representatives of affected communities, the Makerere University Department of Meteorology, and the Uganda National Meteorological Authority. Qualitative data were transcribed from recordings of the brainstorming sessions and notes from literature. The data were then summarized in tables and analyzed using Visual Network Analysis (VNA) with Word Clouds and Gephi Open Source Software. We employed a combination of C++ programming, sensors wired to Arduino 2 and 3 Integrated Development Environment System to build the prototype. Two machine learning algorithms, including linear regression and K-nearest neighbours (KNN) were used to learn from collected hydrological data and make necessary predictions. Using sensors, we were able to read water levels, temperature, and humidity. The prototype successfully demonstrated the ability to send early-warning alerts to users, contributing to both theoretical advancements in disaster risk reduction and practical tools for mitigating flood-related losses in Uganda. The researchers recommend further study to validate the use of this system and evaluate its efficacy and predictive accuracy in averting floods in affected areas. © 2025 The Author(s). Journal of Flood Risk Management published by Chartered Institution of Water and Environmental Management and John Wiley & Sons Ltd.

关键词： early warning systems forecasting and flood mitigation Kasese District machine learning

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Toward a Cognitive Data Model: Exploring a Mind-Inspired Approach to Database Design

arXiv

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arXiv 2025年

作者： Pieris, Dhammika Department of Information Systems Engineering and Informatics Faculty of Computing University of Sri Jayawardenepura Sri Lanka

The Cognitive Data Model (CDM) is proposed. A novel approach to database design, inspired by the belief that the human brain operates with a logical data model independent of its anatomical structure. The study aims to identify and replicate this CDM to enhance database design, resolving limitations of the existing models in handling complex relationships, scalability, and adaptability. The methodology involves empirical observation, cognitive experiments, iterative modelling and critical thinking. Findings suggest that the information processing in the brain occurs sequentially with forming associations on atomic static data in a pairwise, time-dependent manner. This insight led to the development of a meta-framework for the brain’s information processing to design CDM based on it. The CDM offers improved data modelling and database design, efficient querying, and potential applications in AI, machine learning, and big data analytics. Future research will focus on formalizing the model, implementing it in 3D environments, and developing a query language. Copyright © 2025, The Authors. All rights reserved.

关键词： Structured Query Language

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TeFaHub: Dashboard Applications for Supporting the Teaching Factory Program in Indonesian Vocational High School 2

TeFaHub: Dashboard Applications for Supporting the Teaching ...

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2nd International Conference on Software Engineering and Information Technology, ICoSEIT 2024

作者： Suwastika, Novian Anggis Qonita, Qori Masrom, Maslin Telkom University School of Computing Bandung Indonesia Public Vocational High School 10 Bandung Broadcasting and Film Bandung Indonesia Universiti Teknologi Malaysia Razak Faculty of Technology and Informatics Kuala Lumpur Malaysia

ISBN: (纸本)9798350317503

The aim of this research is to develop a dashboard application to support the Teaching Factory (TeFa) program in Vocational High Schools in Indonesia, referred to as Teaching Factory Hub (TeFaHub). The study employs the Rapid Application Development (RAD) method to build TeFaHub, with a specific focus on the user requirements and user design phases within the RAD methodology. User requirements for this research are identified through a rapid literature review to gather feedback on the issues and challenges in implementing the TeFa program. A literature review query targeting the implementation of TeFa in vocational high schools in Indonesia, with a focus on information management, was conducted. Through screening and synthesis processes, 21 relevant articles were identified. Analysis of the synthesis results revealed that TeFa program stakeholders consist of four categories: TeFa program managers, industry representatives, teachers, and students. The identified issues were categorized into three stages: preparation, implementation, and evaluation. The analysis results served as a reference for constructing a sitemap, outlining functionalities to address the challenges and issues in implementing TeFa. The findings of this research can serve as a foundation for the development of TeFaHub in the development, construction, and cutover phases. © 2024 IEEE.

关键词： Application programs

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Exploring Lightweight YOLOv8 Architectures for Circuit Board Defect Detection

Exploring Lightweight YOLOv8 Architectures for Circuit Board...

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Intelligent computing and Emerging Communication Technologies (ICEC), International Conference on

作者： Luka Jovanovic Aleksandar Jokic Marina Milovanovic Milos Antonijevic Miodrag Zivkovic Nebojsa Bacanin Faculty of Technical Sciences Singidunum University Belgrade Serbia Faculty of Informatics and Computing Singidunum University Belgrade Serbia

ISBN: (数字)9798331508432

ISBN: (纸本)9798331508449

Modern electronics relies heavily on circuit boards, which must be designed and produced using a variety of procedures in order to be filled with electrical components. This procedure includes drilling and plating holes, applying masks, painting and etching fiberglass-reinforced epoxy resin boards, and adding components to the boards. Because of variations in the materials and processes, mistakes might occur at any stage. Traditionally, conductivity, impedance, and visual testing are used in a multi-stage approach to identify and resolve these problems. With modest hardware requirements, computer vision, especially using convolutional networks, provides considerable promise to improve defect detection. Once implemented, these models may significantly increase error detection and lower board failure rates, despite the significant hardware requirements for training. Using a publicly accessible dataset, this study investigates the use of the YOLO model for fault identification in circuit board fabrication. The YOLOv8 model’s lightweight design is examined; the medium-sized model achieves a mAP@50 score of 0.990, indicating that it considerably improves fault detection.

关键词： YOLO Computer vision Computational modeling Fault detection Printed circuits Predictive models Hardware Data models Integrated circuit modeling Defect detection

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Multi-criteria rating conversion without relation loss for recommender systems

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International Journal of Computers and Applications 2022年第2期44卷 130-138页

作者： Sitkrongwong, Padipat Maneeroj, Saranya Takasu, Atsuhiro Department of Informatics School of Multidisciplinary Sciences The Graduate University for Advanced Studies SOKENDAI Tokyo Japan Advanced Virtual and Intelligent Computing Center Department of Mathematics and Computer Science Faculty of Science Chulalongkorn University Bangkok Thailand National Institute of Informatics Tokyo Japan

Traditional recommender systems let users provide a single rating indicating their overall preferences toward items. Beside overall rating, multi-criteria recommender systems let users rate on multiple aspects of items with multi-criteria ratings. Most methods in recommender systems are based on collaborative filtering, which makes recommendations by exploiting ratings from neighbor users. However, most of them ignore the fact that rating behaviors of users vary due to their personal preference biases. Thus, exploiting ratings from neighbors directly might result in a poor recommendation. To solve this, the rating conversion techniques have been applied in some single criterion recommendations. For multi-criteria ratings, converting each criterion rating independently might result in loss of relation among criteria ratings. We propose a novel method that simultaneously converts all criteria ratings between users to maintain their implicit relations. The multi-criteria ratings are first normalized by variances of users and principle component analysis is applied to extract user preference patterns. Such patterns are then used for multi-criteria rating conversion. The experiment results show that our method outperforms both current single and multi-criteria rating conversion techniques with RMSEs of 1.1082 and 3.5574 on TripAdvisor and Yahoo datasets respectively, while maintaining considerably high level of prediction coverages. © 2019 Informa UK Limited, trading as Taylor & Francis Group.

关键词： Recommender systems

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Digital Twin System for Smart Campuses

Digital Twin System for Smart Campuses

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2024 IEEE International Conference on Control Science and Systems Engineering, ICCSSE 2024

作者： Wang, Huijie Cai, Yuanyuan Yue, Fabu Luo, Hao Abdipoor, Sina Goh, Say Leng School of Big Data BaoShan University Yunnan BaoShan678000 China Universiti Putra Malaysia Department of Computer Science 43400 Malaysia Universiti Malaysia Sabah Faculty of Computing and Informatics Labuan87000 Malaysia

ISBN: (纸本)9798331517199

In an era where educational institutions are increasingly embracing technology to improve efficiency and enhance learning environments, the concept of smart campuses has emerged as a crucial development. The traditional approach of monitoring campus environments through video footage and manual inspections is becoming outdated as technology advances. To empower smarter campus management and enhance the educational experience, this study presents a systematic approach for smart campuses built on the foundation of digital twins. This technology creates a virtual replica of the physical campus, enabling real-time monitoring of various aspects like security, teaching, maintenance, environment, assets, and energy usage. We simulate real-world campus behavior by constructing this digital counterpart. The resulting digital twin image can be visualized through various user-friendly interfaces, including virtual reality and 3D models, empowering managers to make informed decisions for a more efficient and secure campus environment. Implementing a digital twin system in a smart campus setting demonstrates significant improvements in operational efficiency, security management, and environmental sustainability. By leveraging the power of digital twins, educational institutions can transition towards smarter, more responsive, and sustainable campus environments, ultimately enriching the educational experience for students and staff. © 2024 IEEE.

关键词： 3d modeling campus management digital twin real-time monitoring

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DeepDect: A Facial Deepfake Video Detection Application using Ensemble Learning 12

DeepDect: A Facial Deepfake Video Detection Application usin...

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12th IEEE Conference on Systems, Process and Control, ICSPC 2024

作者： Tay, Wan Xin Chua, Hui Na Jasser, Muhammed Basheer Issa, Bayan Wong, Richard T.K. Sunway University No. 5 Jalan Universiti School of Engineering and Technology Department of Computing and Information Systems Selangor Darul Ehsan Bandar Sunway47500 Malaysia University of Aleppo Faculty of Informatics Engineering Syria

ISBN: (纸本)9798350391398

The emerging technology of deepfake video poses significant threats to information integrity and public trust. Deepfake videos come in various forms, including face swaps, lip-syncing, and full-body simulations. Detection algorithms may struggle to generalize across these different types of deepfakes, leading to lower effectiveness in real-world applications. An advanced deepfake detection system is developed using an ensemble approach, combining the strengths of three pre-trained Convolutional Neural Networks (CNNs)ResNeXt50, Xception, and EfficientNet-v2-with Long ShortTerm Memory (LSTM) layers to capture both spatial and temporal features from videos to improve detection accuracy. The ensemble model is further enhanced by incorporating additional features such as lip movements, eye blinks, head poses, and color contrasts to identify subtle inconsistencies in deepfake videos that are often ignored by traditional detection methods. The ensemble model achieves an accuracy of 84.06%, with a good balance between precision and recall, indicating its effectiveness in real-world applications. We developed DeepDect, a web application embedded with the ensemble model, to provide a user-friendly interface for deepfake detection by uploading videos or submitting URL links. The application includes educational content about deepfake techniques to raise user awareness about the potential dangers of deepfakes. © 2024 IEEE.

关键词： Convolutional neural networks

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Extreme Learning Machine Optimization for Employee Satisfaction With Modified Metaheuristic

Extreme Learning Machine Optimization for Employee Satisfact...

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Expert Clouds and Applications (ICOECA), International Conference on

作者： Tasnim Abdulla Luka Jovanovic Branislav Radomirovic Angelina Njegus Miodrag Zivkovic Nebojsa Bacanin Faculty of Informatics and Computing Singidunum University Belgrade Serbia Faculty of Technical Sciences Singidunum University Belgrade Serbia

ISBN: (数字)9798350385793

ISBN: (纸本)9798350385809

In the contemporary business landscape, the success of a company is intricately linked to the engagement and satisfaction of its workforce. This study analyzes the signifi-cance of developing a contented and engaged employee base, emphasizing the direct impact of workplace satisfaction on productivity, turnover rates, and overall organizational dynamics. Organizational culture emerges as a pivotal factor influencing the recruitment, retention, and satisfaction of talented employees. To address the complexities of identifying and mitigating employee dissatisfaction, this research work proposes a comprehensive solution harnessing the capabilities of cloud-based technologies, specifically text mining, Natural Language Processing (NLP), and modified metaheuristic techniques. The study explores the application of an extreme learning machine as a classifier for assessing employee satisfaction within a cloud computing framework. Acknowledging the critical role of hyperparameter selection in model performance, metaheuristic optimizers and cloud platforms implementation are employed to enhance accu-racy and effectiveness. Furthermore, a novel modification to a metaheuristic algorithm for satisfying the unique requirements of this research is introduced. This research study demonstrates the efficiency of the optimized models, achieving an accuracy rate surpassing 84%. By integrating cloud computing technologies into the proposed framework, organizations gain a powerful and scalable tool for proactively identifying and addressing employee dissatisfaction, ultimately contributing to the improved employee well-being and organizational success in the cloud era.

关键词： Text mining Productivity Cloud computing Accuracy Extreme learning machines Computational modeling Metaheuristics

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Forecasting Ethereum Price by Tuned Long Short-Term Memory Model 30

Forecasting Ethereum Price by Tuned Long Short-Term Memory M...

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30th Telecommunications Forum, TELFOR 2022

作者： Stankovic, Marko Bacanin, Nebojsa Zivkovic, Miodrag Jovanovic, Luka Mani, Joseph Antonijevic, Milos Singidunum University Technical Faculty Belgrade Serbia Singidunum University Faculty of Informatics and Computing Belgrade Serbia Computer Science Department Muscat Oman

ISBN: (数字)9781665472739

ISBN: (纸本)9781665472739

Cryptocurrencies have established a firm position in the economic world in the past decade, with thousands of distinctive currencies available for electronic payments. The majority of cryptocurrencies, however, experience extremely volatile price perturbations, drastically affecting investors and traders. To address this problem, this paper proposes long short-term memory approach tuned by salp swarm metaheuristics. This hybrid model has been validated on a benchmark financial dataset, and the outcomes have been compared to other cutting-edge methods. The results suggest that the proposed method outperformed the competitors, showing significant potential in time-series prediction tasks. © 2022 IEEE.

关键词： Brain

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