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EXPLORING INFLUENTIAL FACTORS IN KNOWLEDGE SHARING WITHIN INDONESIAN PRISONS 22

EXPLORING INFLUENTIAL FACTORS IN KNOWLEDGE SHARING WITHIN IN...

22nd International Conference on e-Society 2024, ES 2024 and 20th International Conference on Mobile Learning 2024, ML 2024

作者： Imandeka, Ejo Hidayanto, Achmad Nizar Suhartanto, Heru Pidanic, Jan Department of Computer Science Faculty of Computer Science University of Indonesia Depok Indonesia Department of Electrical Engineering Faculty of Electrical Engineering and Informatics Pardubice University Pardubice Czech Republic

The exchange of knowledge is widely recognized as a crucial aspect of effective knowledge management. When it comes to sharing knowledge within Prison settings, things get complicated due to various challenges such as limited resources, social stigma, isolation, communication barriers, and security concerns. Additionally, there's a scarcity of resources dedicated to promoting knowledge sharing in secure environments. This study aims to dig deep into the key factors that influence how knowledge is shared in Indonesian prisons. To navigate this intricate terrain, the study uses questionnaires distributed among Prison officers to understand the complex relationship between the identified critical factors and knowledge sharing in Prison. Leadership support, organizational culture, organizational structure, incentives, and individual beliefs emerge as pivotal elements in knowledge sharing. It's also noted that the connection between these critical factors and the intention to share knowledge is significantly influenced by organizational structure. Recognizing and addressing critical factors in knowledge sharing within prisons not only improves rehabilitation within prison walls but also transforms the broader criminal justice system. This approach fosters evidence-based practices, better communication, and a comprehensive understanding of factors leading to successful rehabilitation and reduced recidivism, ultimately creating a more just and effective system. © 2024 International Conferences e-Society 2024 and Mobile Learning 2024. All rights reserved.

关键词： Critical Factors Indonesia Knowledge Knowledge Sharing Prison

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Improved Accuracy of Decision Tree with XGBoost Technique in Cat Skin Disease Diagnosis 4

Improved Accuracy of Decision Tree with XGBoost Technique in...

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4th International Conference of Science and Information Technology in Smart Administration, ICSINTESA 2024

作者： Safrina, Nanda Lydia, Maya Silvi Nasution, Benny Benyamin Universitas Sumatera Utara Master of Informatics Faculty of Computer Science and Information Technology Medan Indonesia Politeknik Negeri Medan Department of Computer Engineering and Informatics Medan Indonesia

ISBN: (纸本)9798350376111

This research aims to develop an expert system for initial diagnoses of skin diseases in cats using the Decision Tree method. It assists cat owners in identifying skin diseases based on observed symptoms. Data from expert veterinarian drh. Ismi Azima, including various types of cat skin diseases and their symptoms, were used. The research employs 80% training data and 20% testing data to develop the classification model. Testing results indicate the Decision Tree model achieves up to 90% average accuracy in diagnosing cat skin diseases based on 50 datasets. This expert system aids in diagnosis and provides treatment and prevention solutions, beneficial for cat owners, especially in areas with limited veterinary access. In conclusion, this Decision Tree-based expert system effectively provides initial diagnoses of skin diseases in cats with high accuracy. © 2024 IEEE.

关键词： Cat Skin Diseases Decision Tree Expert System XGBoost

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Designing a skilled soccer team for RoboCup: exploring skill-set-primitives through reinforcement learning

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Neural Computing and Applications 2025年第18期37卷 12699-12734页

作者： Abreu, Miguel Reis, Luís Paulo Lau, Nuno LIACC/LASI/FEUP Artificial Intelligence and Computer Science Laboratory Faculty of Engineering University of Porto Porto Portugal IEETA/LASI/DETI Institute of Electronics and Informatics Engineering of Aveiro Department of Electronics Telecommunications and Informatics University of Aveiro Aveiro Portugal

The RoboCup 3D soccer simulation league serves as a competitive platform for showcasing innovation in autonomous humanoid robot agents through simulated soccer matches. Our team, FC Portugal, developed a new codebase from scratch in Python after RoboCup 2021. The team’s performance relies on a set of skills centered around novel unifying primitives and a custom, symmetry-extended version of the proximal policy optimization algorithm. Our methods have been thoroughly tested in official RoboCup matches, where FC Portugal has won the last two main competitions, in 2022 and 2023. This paper presents our training framework, as well as a timeline of skills developed using our skill-set-primitives, which considerably improve the sample efficiency and stability of skills, and motivate seamless transitions. We start with a significantly fast Sprint-Kick developed in 2021 and progress to the most recent skill set, including a multi-purpose omnidirectional walk, a dribble with unprecedented ball control, a solid kick, and a push skill. The push addresses low-level collision scenarios and high-level strategies to increase ball possession. We address the resource-intensive nature of this task through an innovative multi-agent learning approach. Finally, we release the team’s codebase to the RoboCup community, providing other teams with a robust and modern foundation upon which they can build new features. © The Author(s) 2025.

关键词： Reinforcement learning

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Beyond Integration: Advancing Electricity Metering with Secure and Transparent Blockchain-IoT Solutions 47

Beyond Integration: Advancing Electricity Metering with Secu...

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47th International Conference on Telecommunications and Signal Processing, TSP 2024

作者： Koštál, Kristian Bahar, Muhammad Nasim Numyalai, Sultan Ries, Michal Institute of Computer Engineering and Applied Informatics Slovak University of Technology in Bratislava Faculty of Informatics and Information Technologies Bratislava Slovakia

ISBN: (纸本)9798350365597

As the demand for reliable and accurate electricity metering escalates in the digital era, traditional systems increasingly need to improve due to their centralized nature, prone to inaccuracies, privacy breaches, and cybersecurity threats. This paper proposes a novel solution that harnesses the transformative power of Blockchain and Internet of Things (IoT) technologies to address these critical challenges. By integrating the immutable ledger capabilities of blockchain with the real-time data acquisition prowess of IoT, we introduce a decentralized system that ensures the secure and transparent measurement and billing of electricity for domestic consumers. Adopting Hyperledger Fabric within our framework offers a robust, scalable architecture that supports secure transactions and enhances trust through digital identities for all participants. This system mitigates the vulnerabilities associated with centralized authorities and efficiently monitors and manages energy consumption with unprecedented precision. Through this approach, we aim to revolutionize the electricity metering infrastructure, providing a blueprint for future advancements in the energy sector. © 2024 IEEE.

关键词： Smart contract

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Advanced Design Pattern Selection Methodology through Weighted Semantic Ontologies for Microservices

Advanced Design Pattern Selection Methodology through Weight...

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2024 International Conference on computer and Applications, ICCA 2024

作者： Attia, Rabab Atef Ghoniemy, Samy Hamdy, Abeer The British University in Egypt Faculty of Informatics and Computer Science Cairo Egypt Galala University Computer Science and Engineering Program Cairo Egypt

ISBN: (纸本)9798350367560

Seamless communication between authorities, people, and smart devices is crucial in today's globally interconnected world. Unprecedented demands on software design result from the advent of ubiquitous connectivity, which allows connections at any time and from any location. By structuring applications as separate services that can communicate both internally inside a platform and externally across platforms, microservices (MS) architecture satisfies these requirements. One of the most difficult parts, though, is still controlling communication between MS services, which calls for knowledge of the best design patterns. In order to solve this, we suggest a context-aware recommender system that is adapted to MS design patterns. This system not only makes recommendations for appropriate patterns, but it also gives statistical scores that show how relevant each solution is to the particular issue at hand. Other patterns with ratings indicating their possible feasibility are also displayed by our algorithm. On the other hand, sentence transformers handle intricate, human-like problem descriptions, while our method uses MS Domain Ontologies to distinguish pattern applications, constraints, and best practices. Our methodology provides a refined examination of solution suitability and recommendations through the use of weighing and correlation algorithms. Extensively tested with more than 237 developer-style scenarios, our framework offers qualitative and quantitative insights into its performance and efficacy, showing notable gains over current solutions in terms of offering pattern-specific, contextually accurate guidance for MS communication challenges. © 2024 IEEE.

关键词： Semantics

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A machine learning approach for skin lesion classification on iOS: implementing and optimizing a convolutional transfer learning model with Create ML

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International Journal of computers and Applications 2024年第8期46卷 666-685页

作者： Benedek Szabo, Aron Katona, Jozsef Department of Software Development and Application Institute of Computer Engineering University of Dunaujvaros Dunaujvaros Hungary Department of Instrumentation and Automation Institute of Electronics and Communication Systems Kandó Kálmán Faculty of Electrical Engineering Obuda University Budapest Hungary Department of Informatics GAMF Faculty of Engineering and Computer Science Kecskemét Hungary

The integration of machine learning (ML) into mobile applications presents unique challenges, particularly in resource-constrained environments such as iOS devices. Skin lesion classification is a critical task in dermatology, where accurate and efficient diagnostic tools can significantly aid in early detection of malignant lesions. This study aims to implement a machine learning-based iOS mobile application and develop a binary classification model for skin lesion images to determine whether a lesion is malignant. The research utilized Create ML to develop a convolutional neural network optimized for iOS, employing a transfer learning approach. A logistic regression model was cascaded with the convolutional neural network N to enhance classification accuracy. The model's performance was assessed through various validation metrics, ensuring its robustness and efficiency within the constraints of mobile hardware. A curated dataset from the International Skin Imaging Collaboration archive was used for training and testing. The model achieved an accuracy of 92%, a precision of 90%, a recall of 93%, and an F1-score of 91% in classifying skin lesions. These metrics validate the model's efficacy in identifying malignant lesions. Data curation involved collecting, labeling, and preparing a dataset from publicly available sources, ensuring the inclusion of diagnostically relevant features. The final model was integrated into an iOS application using Core ML and Vision frameworks. The developed application demonstrates reliable performance in classifying skin lesions with high accuracy on iOS devices. The inclusion of comprehensive performance metrics justifies the efficacy of the proposed approach. Future work will explore enhancements in model architecture and object detection capabilities to further improve diagnostic precision and application usability. © 2024 Informa UK Limited, trading as Taylor & Francis Group.

关键词： Image recognition

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Lightweight Deep Learning for Human Activity Recognition in Ambient Assisted Living

Lightweight Deep Learning for Human Activity Recognition in ...

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2024 Multimedia University engineering Conference, MECON 2024

作者： Bari, Ahsanul Farid, Fahmid Al Sarker, Md. Tanjil Mansor, Sarina Karim, Hezerul Abdul Bhuiyan, Md Roman Bannah, Hasanul Multimedia University Faculty of Engineering Cyberjaya63100 Malaysia Berlin School of Business and Informatics Faculty of Computer Science and Informatics Berlin12043 Germany

ISBN: (纸本)9798331530747

This Lightweight Deep Learning (LDL) for Multi-View Human Activity Recognition in Ambient Assisted Living Systems can significantly improve the conditions of daily activities for people living with the elderly, disabled, or simply needing assistance. Human Activity Recognition (HAR) is essential for these systems;currently, the task of identifying the wide array of human activities remains complicated. This study introduces an improved deep learning solution for recognizing human activity from multiple viewpoints in AAL environments. By utilizing skeleton data from various perspectives, such as the RHM-HAR-SK dataset, we demonstrate useful improvements in accuracy for activity recognition when compared with single-view methods. Our LeNet and Vision Transformer models have been carefully designed to excel with skeletal data, delivering outstanding performance while keeping computational requirements to a minimum. In this research study also highlights the benefits of multi-view human activity recognition in ambient assisted living and emphasizes the effectiveness of efficient deep learning models in enabling assistive technologies that are real-time and resource friendly. These advancements support more independence and safety by supporting the development of AAL systems that are more sensitive and intelligent. © 2024 IEEE.

关键词： Active learning

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Comparative Study of Facial Expression Recognition Using Facial Landmark in e-Learning System 11

Comparative Study of Facial Expression Recognition Using Fac...

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11th International Conference on Electrical engineering, computer Science and informatics, EECSI 2024

作者： Bachtiar, Fitra Abdurrachman Nainggolan, Cesilia Natasya Soetedjo, Riza Setiawan Rokhmawati, Retno Indah Muflikhah, Lailil Brawijaya University Department of Informatics Engineering Faculty of Computer Science Malang Indonesia Brawijaya University Faculty of Computer Science Department of Information Systems Malang Indonesia

ISBN: (纸本)9798350355314

The e-Iearning system or distance learning system has become important, especially during the COVID-19 pan-demic. Several tertiary institutions have made the e-Iearning system an alternative teaching and learning activity. Research on the use of e-Iearning needs to be explored to obtain innovative, creative, and fun learning. One aspect that can be examined in e-learning is students' emotional response to the learning activities. The emotional state of students directly or indirectly affects the learning process. The student's emotional responses can be measured by their facial expressions. In this study, an exploration was carried out on the facial expressions shown by students in HSS Learning, an e-Iearning for learning web programming. The student's facial expressions were extracted using 68 pairs of facial landmarks and additional 6 facial feature vectors. The total number of features used is 142. Machine learning methods for classification are applied to determine the performance of several classification models, including the k-Nearest Neighbor (k- NN) algorithm, Logistic Regression, and Support Vector Machine (SVM). Machine learning performance evaluation is done using the Fl-score and confusion matrix. The results show that the accuracy of the k-NN, Logistic Regression, and SVM are 91.4%, 92.2%, and 92.1 %, respectively. However, the Fl-score macro values are relatively low due to data imbalance with the k- NN, Logistic Regression, and SVM models are 0.250, 0.272, and 0.243, respectively. While the study provides insight into the student learning experience in e-Iearning settings, this study needs to be explored more. © 2024 IEEE.

关键词： Contrastive Learning

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Advancements in Multi-View Human Activity Recognition for Ambient Assisted Living

Advancements in Multi-View Human Activity Recognition for Am...

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2024 Multimedia University engineering Conference, MECON 2024

ISBN: (纸本)9798331530747

This Ambient Assisted Living (AAL) uses technology to improve the well-being, autonomy, and security of seniors and disabled individuals. AAL services depend on Human Activity Recognition to detect human behaviors from sensor data. Compared to single-view models, multi-view HAR, which aggregates data from multiple sensors, provides improved accuracy and reliable detection. In this review, we assess recent progress in multi-view HAR for AAL, focusing on the value of lightweight deep learning models. We cover the development of HAR systems, as well as the emergence of multi-view datasets and highly efficient deep learning models that are explicitly designed for AAL scenarios. Furthermore, we address problems such as data synchronization, data protection, and adaptability, as well as possible remedies like sensor fusion and transfer learning. As a result, we also demonstrate the potential of multi-view HAR to increase AAL services and outlining future research trends in providing adaptive, efficient, and privacy-aware systems. © 2024 IEEE.

关键词： Adversarial machine learning

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Predictive Analysis of Sarajevo's AQI using Machine Learning Models for Varied Data Granularity and Prediction Windows 47

Predictive Analysis of Sarajevo's AQI using Machine Learning...

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47th ICT and Electronics Convention, MIPRO 2024

作者： Zolota, Emina Hasić, Vahidin Mević, Amina Delić, Amra Krivić, Senka University Of Sarajevo Faculty Of Electrical Engineering Department For Computer Science And Informatics Sarajevo Bosnia and Herzegovina

ISBN: (纸本)9798350382495

This study scrutinizes five years of Sarajevo's Air Quality Index (AQI) data using diverse machine learning models - Fourier autoregressive integrated moving average (Fourier ARIMA), Prophet, and Long short-term memory (LSTM) - to forecast AQI levels. Focusing on various prediction frames, we evaluate model performances and identify optimal strategies for different temporal granularities. Our research unveils subtle insights into each model's efficacy, shedding light on their strengths and limitations in predicting AQI across varied timeframes. This research presents a robust framework for automatic optimization of AQI predictions, emphasizing the influence of temporal granularity on prediction accuracy, automatically selecting the most efficient models and parameters. These insights hold significant implications for data-driven decision-making in urban air quality control, paving the way for proactive and targeted interventions to improve air quality in Sarajevo and similar urban environments. © 2024 IEEE.

关键词： Air quality

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