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International Conference on Telecommunication Systems, Services, and Applications (TSSA)

作者： Afina Anfa Ana Vitradisa Pratama Agus Sukoco Ary Setijadi Prihatmanto Rin Rin Nurmalasari Agus Juhana School of Electrical Engineering and Informatics Institut Teknologi Bandung Bandung Indonesia Departement of Informatics Universitas Mayasari Bakti Tasikmalaya Indonesia Department of Electrical Engineering UIN Sunan Gunung Djati Bandung Indonesia Multimedia Education Study Program Universitas Pendidikan Indonesia Bandung Indonesia

This paper presents an innovative approach to integrating ML-Agents into a microgame-based kart racing environment, showcasing the potential for enhancing gameplay experiences through AI-driven opponents. Kart Unity Game is a racing game that uses small motorized vehicles but still uses the concept of racing in the real world. One of the racing game development platforms is Unity. Unity has developed a platform, Unity Karting Microgame, which users can modify. Unity Karting Microgame applies self-driving to ML-Agents trained and designed to drive their karts with suitable sensors and actuators. The method used to develop the game is to modify the racetrack and modify ML-Agents to make the game more exciting and fun. As a result of this study, successful implementation of ML-Agents in a microgame-based kart racing environment. The study highlights the potential of using Unity ML-Agents to create AI-driven opponents that enrich gameplay experiences. Games can run well and seem varied. ML-Agents can also control their karts on their lanes. Thus, kart racing games can be implemented.

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Integration of Artificial Intelligence in Software Defined Networking Technology through Developing Machine Learning Algorithms 3

Integration of Artificial Intelligence in Software Defined N...

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3rd International Conference on Advance Computing and Innovative Technologies in engineering, ICACITE 2023

作者： Naibaho, Lamhot Saxena, Pulkit Raj Sharma, Kuldeep Alfurhood, Badria Sulaiman Pallavi, L. Pant, Bhasker Faculty of Letters and Languages Christian University of Indonesia English Language Education Study Program Indonesia Teer Thanker Mahaveer University College of Computing Science and Information Technology Uttar Pradesh Moradabad India Lovely Professional University Department of Computer Science and Engineering Punjab Jalandhar Phagwara India College of Computer Information Sciences Princess Nourah Bint Abdulrahman University Department of Computer Sciences Saudi Arabia B v Raju Institute of Technology Cse Telangana Narsapur India Graphic Era Deemed to Be University Computer Science & Engineering Uttarakhand Dehradun India

ISBN: (纸本)9798350399264

A future networking design called "software-defined networking"combines network programmability with centralized administration (SDN). Network administration is currently handled by SDN, which, at the regulator, which acts as the cable network processor and divides the data plane into a control plane and a data aircraft. Recent breakthroughs in artificial intelligence (Al) have shown a stronger inclination for the science community to benefit from their ability to give learning capabilities and enhance Indicator. The author of this research provides a comprehensive analysis of initiatives underway to incorporate Al with SDN. The study concluded that the three primary Al thread where scientific research was centered were computer science, conceptual, and fuzzy reasoning systems. The authors of this paper explore the several fields in which these approaches may be used, their potential future applications, and the innovations made possible by the integration of AI-based methods into the SDN paradigm. By choosing the right SDN controller, any large organization may lower network complexity, implementation expenses, and maintenance costs. The focus of this article is software defined networking's benefits and downsides (SDN). Following is a basic explanation of artificial intelligence and a list of some of its most significant applications in SDN. © 2023 IEEE.

关键词： Software defined networking

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Stunting Classification in Toddlers: Implementation and Evaluation of the Decision Tree Algorithm

Stunting Classification in Toddlers: Implementation and Eval...

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Information Technology and Computing (ICITCOM), International Conference on

作者： Debby Alita Imam Ahmad Rhaishudin Jafar Rumandan Moh Erkamim Widyasmoro Faculty of Engineering and Computer Science Universitas Teknokrat Indonesia Bandarlampung Indonesia Islamic Education Management Study Program Institut Agama Islam Negeri Ambon Ambon Indonesia Smart City Information Systems Study Program Universitas Tunas Pembangunan Surakarta Indonesia Department of Electrical Engineering Faculty of Engineering Universitas Muhammadiyah Yogyakarta Yogyakarta Indonesia

ISBN: (数字)9798350379839

ISBN: (纸本)9798350379846

Stunting in toddlers is a chronic nutritional issue that affects the physical and cognitive development of children, with serious long-term consequences such as reduced cognitive function and an increased risk of chronic diseases in adulthood. Therefore, early identification and prevention efforts for stunting are crucial. Classifying toddlers into categories of at-risk for stunting or not is essential to provide timely and appropriate interventions. This study employs data mining techniques using the decision tree algorithm to expedite the stunting detection process and improve the accuracy of nutritional status classification in children. The results indicate that the constructed decision tree model can classify children's nutritional status with an accuracy of 83.26%. The decision tree achieves high accuracy in classifying stunting in toddlers due to its ability to handle complex data and identify significant patterns within the data.

关键词： Pediatrics Accuracy Computational modeling Prevention and mitigation Data models Classification algorithms Decision trees Data mining Information technology Diseases

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Multidimensional analysis of sensory perception and neurophysiological responses to marinated beef cooked by diverse methods

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Innovative Food Science & Emerging Technologies 2025年 103卷

作者： Tianyi Yang Wei Jiang Chencheng Luo Jin Hu Wei Xu Peng Zhang Xiaofei Jin Yuxuan Zhou Rui Liu Zhiyu Qian Yamin Yang Department of Biomedical Engineering Key Laboratory of Multi-modal Brain-Computer Precision Drive Ministry of Industry and Information Technology Key Laboratory of Brain-Machine Intelligence Technology Ministry of Education Key Laboratory of Digital Medical Equipment and Technology of Jiangsu Province Nanjing University of Aeronautics and Astronautics Nanjing 210016 China Program in Neuroscience & Behavioral Disorders Duke-NUS Medical School National University of Singapore Singapore 169857 Singapore Department of Neurosurgery Huashan Hospital Shanghai Medical College Fudan University National Center for Neurological Disorders National Key Laboratory of Medical Neurobiology Institutes of Brain Science Shanghai Key Laboratory of Brain Function and Regeneration Institute of Neurosurgery Shanghai 200040 China School of Biomedical Engineering and Informatics Nanjing Medical University Nanjing 211166 China College of Food Science and Engineering Yangzhou University Yangzhou 225127 China

The cooking process significantly influences consumer perception and preference for meat. This study systematically compares the sensory and neurophysiological responses elicited by beef processed via five cooking methods, including boiling (BO), sous-vide (SV), roasting (RO), pan-frying (PF), and deep-frying (DF). Sensory evaluations combining a check-all-that-apply (CATA) questionnaire and hedonic scaling revealed that RO, PF, and DF treatments produced richer flavors and more intense aromas, linked to higher hedonic ratings. In contrast, SV and BO yielded milder sensory profiles with lower flavor intensity and less consumer preference. Electroencephalography (EEG) analysis supported these findings by showing that high-heat methods elicited stronger neural responses, especially in the α, β, and γ frequency bands, which are associated with pleasure, appetite, and cognitive engagement, particularly prominent during olfactory and gustatory tests. Source localization further revealed greater activation in brain regions involved in sensory, reward, memory, emotion, and motivation for high-heat treated beef, demonstrating the connection between enhanced flavor complexity of high-temperature process and consumer appeal. Converging evidence from subjective sensory evaluation and neurophysiological analysis highlights the central role of thermal processing in enhancing food palatability and consumer satisfaction, offering practical insights for improving beef preparation in the food industry.

关键词： Neurons

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Implementation of AI-Based Support Vector Machine Algorithm for Fraud Detection in Digital Payment Systems

Implementation of AI-Based Support Vector Machine Algorithm ...

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International Seminar on Research of Information Technology and Intelligent Systems (ISRITI)

作者： Edy Prayitno Rikie Kartadie Uning Lestari Munengsih Sari Bunga Accounting Information Systems Faculty of Information Technology Universitas Teknologi Digital Indonesia Yogyakarta Indonesia Computer Engineering Study Program Faculty of Information Technology Universitas Teknologi Digital Indonesia Yogyakarta Indonesia Informatics Study Program Faculty of Science and Information Technology Universitas Akprind Indonesia Yogyakarta Indonesia Department of Computer Engineering Smart City Information Systems Politeknik Negeri Indramayu Indramayu Indonesia

ISBN: (数字)9798331519643

ISBN: (纸本)9798331519650

The increasing use of digital payment systems has led to a rise in fraudulent activities, presenting a significant challenge in ensuring secure transactions. This research focuses on implementing the Support Vector Machine (SVM) algorithm with a Radial Basis Function (RBF) kernel to detect fraud in digital payment systems. One of the main challenges addressed in this study is the severe class imbalance in the dataset, where fraudulent transactions account for only 0.17% of total transactions. To overcome this, the SMOTE (Synthetic Minority Over-sampling Technique) method was applied to balance the dataset, allowing the model to better recognize fraudulent patterns. The results indicate that the SVM model achieved an accuracy of 99.93%, with a precision of 86.23% and a recall of 75.51%. These results demonstrate that SVM, combined with SMOTE and RBF kernel, is highly effective in detecting fraudulent transactions while minimizing false positives. This research provides a strong foundation for improving fraud detection models in the context of digital payment systems, offering enhanced security and trust for users. Further research could explore hybrid models and real-time data analysis to improve performance.

关键词： Support vector machines Seminars Adaptation models Accuracy Data models Real-time systems Fraud Security Kernel Intelligent systems

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Analysis and Comparative Between Profile Matching and SAW Method in Decision Support

Analysis and Comparative Between Profile Matching and SAW Me...

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International Conference on Cyber and IT Service Management (CITSM)

作者： Helmi Kurniawan Ashari P Swondo Eka Purnama Sari Khairul Ummi Muhammad Rusdi Tanjung Yusfrizal Faculty of Engineering and Computer Science Universitas Potensi Utama Medan Indonesia English Education Study Program Universitas Potensi Utama Medan Indonesia Faculty of Economy and Business Universitas Potensi Utama Medan Indonesia Visual Communication Design Study Program Universitas Potensi Utama Medan Indonesia

ISBN: (数字)9781728177014

ISBN: (纸本)9781728177021

A decision support system is a solution to help provide decision recommendations in determining which options to choose as a solution, in this decision support system research, namely analyzing and comparing the Matching Profile method and the SAW (Simple Additive Weighting) method in providing the best choice solution in the case of determining teaching staff acceptance. Determination of Admission, The selected teaching staff, are teaching staff who are in accordance with the criteria and quality. In an educational institution, namely TPQ Anbata, it is a very important issue to get elected teaching staff. One way to get teaching staff who is in accordance with the cafeteria is determined by selecting or determining prospective teaching staff by paying attention to various assessment criteria so that the selected teaching staff is feasible. From the tests that have been carried out, the results obtained include all test cases, and the testing is appropriate. The results of the system calculation accuracy test show that the calculation of Matching Profile and SAW (Simple Additive Weighting Method carried out by the system with manual calculations is correct and the feasibility test. Appropriate to use in making decisions determining the recruitment of teachers than the profile matching method in Anbata TPQ. The difference is in the highest value in each method. In the profile matching method, the highest value is Salman Al Fariz with a value of 4.70 while in the SAW method the highest value is Ali Gufron with a value of 8,778., it can be concluded that the most appropriate method in determining the recruitment of teaching staff at TPQ Anbata is the Profile Matching Method.

关键词： education Surface acoustic waves Decision support systems Additives Recruitment Decision making Writing

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Maternal Health Risk Classification - A Comparison between Algorithm Decision Tree and k-Nearest Neighbor (kNN)

Maternal Health Risk Classification - A Comparison between A...

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Sustainable Emerging Innovations in engineering and Technology (ICSEIET), International Conference on

作者： Maylia Fatmawati Mega Novita Nugroho Dwi Saputro Alok Singh Chauhan Noora Qotrun Nada Amit Sinha Bambang Agus Herlambang Shruti Gupta Department of Informatics Faculty of Engineering and Informatics Universitas PGRI Semarang Semarang Indonesia Postgraduate Program of Science Education Universitas PGRI Semarang Semarang Indonesia School of Computing Science & Engineering Galgotias University Greater Noida India Department of Computer Science & Engineering ABES Engineering College Ghaziabad Uttar Pradesh India

Maternal health is a critical concern, particularly for individuals who are pregnant and will shape the future generations. However, not all expectant mothers receive tailored attention and care for their unique health needs. Consequently, many mothers face high levels of health risks during pregnancy, highlighting the importance of classifying maternal health risks. Classification can help identify influential factors affecting maternal health and predict risk levels for individual mothers. In the field of data mining, various classification methods exist for analyzing complex datasets. This study focuses on two widely used algorithms: decision tree and k-Nearest Neighbor (kNN). By employing these algorithms, we aim to classify a dataset comprising 1,014 records of maternal health risk levels. Through cross-validation and T-tests, we rigorously evaluate the performance of the decision tree and kNN algorithms in this context. Our findings indicate that the kNN algorithm is more suitable for classifying maternal health risks, achieving a significantly higher accuracy of 71.50% compared to the decision tree algorithm's accuracy of 70.71%. This result establishes the superiority of the kNN algorithm in accurately classifying maternal health risks. These findings have implications for developing targeted interventions and tailored healthcare strategies to mitigate identified risks.

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Smart Home Gas Sensor Optimization Using Kalman Filter for Data Processing

Smart Home Gas Sensor Optimization Using Kalman Filter for D...

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International Conference on informatics and Computing (ICIC)

作者： Somantri Muhamad Ikhsan Thohir Alun Sujjada Deni Setiawan Ilham Komara Fikri Ardiansyah Efendi Muhamad Muslih Zubaile Bin Abdullah Mohd Zainuri Saringat Department of Software Engineering University Tun Hussein Onn Malaysia Informatics Engineering Study Program Nusa Putra University Sukabumi Indonesia Informatics Engineering Study Program Nusa Putra University Sukabumi Indonesia Department of Software Engineering University Tun Hussein Onn Malaysia Sukabumi Indonesia Department of Information System Nusa Putra University Sukabumi Indonesia Department of Software Engineering Faculty of Computer Science and Information Technology Universiti Tun Hussein Onn Malaysia Johor Bahru Malaysia

ISBN: (数字)9798331517601

ISBN: (纸本)9798331517618

The integration of Internet of Things (IoT) technologies into modern homes has enhanced safety and comfort, particularly in detecting gas leaks, which pose serious fire hazards. Gas leaks can often be detected by smell, but this method fails when no one is present. Previous research using microcontrollers and sensors to detect gas leaks faced challenges with accuracy due to noise interference. This paper proposes the use of a Kalman filter, developed by Rudolf Emil Kalman in 1960, to improve gas leak detection accuracy by filtering out noise. The system comprises an Arduino Nano, ESP8266 WiFi module, MQ-2 gas sensor, buzzer, and ThingSpeak cloud platform. By applying the Kalman filter, noise and data oscillations are reduced, enhancing detection accuracy. Experimental results show the system effectively detects gas leaks, provides real-time data, and triggers alarms. Future improvements could include additional sensors and features to further increase the system’s reliability and functionality.

关键词： Accuracy Noise Sensor phenomena and characterization Sensor systems Real-time systems Kalman filters Internet of Things Reliability Leak detection Gas detectors

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Digital Forensics Investigation and Incident Response in Internet of Things (IoT-DFIR): Challenges and Models 9

Digital Forensics Investigation and Incident Response in Int...

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9th International Conference on Wireless and Telematics, ICWT 2023

作者： Ahdan, Syaiful Zaki Hamidi, Eki Ahmad Riskiono, Sampurna Dadi Negara, Ridha Muldina Mayasari, Ratna Ramadona, Suci Nurhayati, Ade Sucipto, Adi Jupriyadi Nurkahfi, Galih Nugraha Farahiyah, Dzata Naseer, Muchammad Universitas Teknokrat Indonesia Faculty of Engineering & Computer Science Lampung35132 Indonesia Uin Sunan Gunung Djati Bandung Electrical Engineering Department Bandung40614 Indonesia School of Electrical Engineering Telkom University Bandung40257 Indonesia Elearical Engineering Study Program Politeknik Caltex Riau Riau28265 Indonesia Institut Teknologi Telkom Jakarta Faculty of Engineering Jakarta11710 Indonesia Bandung40135 Indonesia Universitas Islam Indonesia Yogyakarta Faculty of Industrial Technology Yogyakarta55584 Indonesia Sekolah Tinggi Teknologi Bandung Informatics Department Bandung40235 Indonesia

ISBN: (纸本)9798350305029

Many different industries are currently making substantial use of the Internet of Things (IoT). IoT is the process through which electronic devices communicate with their surrounding virtual environment by continuously exchanging data via sensors. Due to increased IoT security concerns, digital forensic investigators now possess greater knowledge and abilities to investigate IoT devices. IoT-DigFor are required in terms of cybercrime due to the rapid growth in the number of electronic devices and the collection and consumption of enormous amounts of data. IoT systems with billions of devices have produced a significant amount of evidence, which presents a significant challenge to digital investigators and practitioners who must connect with IoT devices to conduct fast and thorough investigations into cybercrimes. This article presents detailed information about forensics in the IoT environment. The IoT forensic paradigm and the challenges that design-based security requirements and IoT system security offer for IoT-DigFor are covered in great detail at the beginning of the article. © 2023 IEEE.

关键词： Internet of things

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Enhancing the sustainability and effectiveness of non-governmental organizations: An integrated neutrosophic decision-making model for determining the most effective corporate governance strategies

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engineering Applications of Artificial Intelligence 2025年 156卷

作者： Emrah Ayhan Hakan Gökhan Gündoğdu Ahmet Aytekin Ahmet Furkan Karakaya Vladimir Simic Dragan Pamucar Department of Political Science and Public Administration Faculty of Economics and Administrative Sciences Anadolu University Tepebaşı 26470 Eskişehir Türkiye Department of Business Administration Hopa Faculty of Economics and Administrative Sciences Artvin Çoruh University Hopa 08100 Artvin Türkiye Public Administration Master Program Graduate Education Institute Anadolu University Tepebaşı 26470 Eskişehir Türkiye University of Belgrade Faculty of Transport and Traffic Engineering Vojvode Stepe 305 11010 Belgrade Serbia Yuan Ze University College of Engineering Department of Industrial Engineering and Management Taoyuan City 320315 Taiwan Department of Computer Science and Engineering College of Informatics Korea University 145 Anam-ro Seongbuk-gu Seoul 02841 Republic of Korea Department of Operations Research and Statistics Faculty of Organizational Sciences University of Belgrade Belgrade Serbia Department of Mechanics and Mathematics Western Caspian University Baku Azerbaijan İstinye University Faculty of Engineering and Natural Sciences Industrial Engineering Department Sarıyer 34396 İstanbul Türkiye

This research explores the development of effective corporate governance strategies for non-governmental organizations (NGOs) by evaluating both critical decision criteria and innovative organizational models. Based on an extensive literature review, seven governance criteria were identified: credibility and non-profit orientation, regular information flow and transparency, level of institutionalization, frequency of leadership turnover, internal and external stakeholder relations, annual budget, and ideological or religious motivation. In addition, five innovative organizational models were selected as strategic alternatives: continuous learning organization, technology-oriented policy development, income diversification, pluralistic governance, and transformational leadership. The empirical data were gathered through interviews with 16 experts from academia, civil society, and public institutions in Türkiye. To address the complexity and uncertainty of decision-making processes, a hybrid type-2 neutrosophic number (T2NN) multi-criteria decision-making methodology is introduced. The T2NN-ranking comparison and T2NN-criteria importance through intercriteria correlation methods are used to determine the weight of criteria, while T2NN-stable preference ordering towards ideal solution is utilized to rank the alternative models. Results show that the level of institutionalization is the most significant criterion, and income diversification is the most effective organizational model for improving NGO sustainability. The study not only provides practical guidance for NGO leaders but also introduces a robust methodological approach applicable to governance challenges under uncertain conditions.

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