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15th International Conference on Information and Communication Technology Convergence, ICTC 2024

作者： Shin, Mingyu Jung, Soyi Ajou University Dept. Artificial Intelligence Convergence Network Suwon16499 Korea Republic of Ajou University Dept. Electrical and Computer Engineering Suwon16499 Korea Republic of

ISBN: (纸本)9798350364637

Behavior trees (BTs) have gained recognition for their modularity and scalability, establishing them as a robust framework for task automation and planning in various domains, including robotics, game AI, and autonomous systems. This paper presents a comprehensive review of the integration of BTs with reinforcement learning (RL) and learning-from-demonstration (LfD) to enhance decision making and task planning in robotic systems. The review emphasizes the advantages of BTs, such as increased adaptability and efficiency through RL integration and the simplification of robot programming via LfD. Despite these benefits, challenges persist in the areas of computational complexity, scalability in multi-agent systems, and the automatic generation of BTs. This paper concludes by identifying key areas for future research to address these challenges and further advance the development of autonomous robotic systems. © 2024 IEEE.

关键词： Multi agent systems

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Integration between Bioinformatics Algorithms and Neutrosophic Theory

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Neutrosophic Sets and Systems 2024年 66卷 34-54页

作者： Farag, Romany M. Shams, Mahmoud Y. Aldawody, Dalia A. Khalid, Huda E. El-Bakry, Hazem M. Salama, Ahmed A. Dept. of Math and Computer Science Faculty of Science Port Said Univ. Egypt Dept. of Machine Learning Faculty of Artificial Intelligence Kafrelsheikh University Egypt Telafer University Telafer Iraq Dept. of Information Systems Faculty of Computer and Information Sciences Mansoura University Egypt Dept. of Math and Computer Science Faculty of Science Port Said University Egypt

This paper presents a neutrosophic inference model for bioinformatics. The model is used to develop a system for accurate comparisons of human nucleic acids, where the new nucleic acid is compared to a database of old nucleic acids. The comparisons are analyzed in terms of accuracy, certainty, uncertainty, neutrality, and bias. The proposed system achieves good results and provides a reliable standard for future comparisons. It highlights the potential of neutrosophic inference models in bioinformatics applications. Data mining and bioinformatics play a crucial role in computational biology, with applications in scientific research and industrial development. Biological analysts rely on specialized tools and algorithms to collect, store, categorize, and analyze large volumes of unstructured data. Data mining techniques are used to extract valuable information from this data, aiding in the development of new therapies and understanding genetic relationships between organisms. Recent advancements in bioinformatics include gene expression tools, Bio sequencing, and Bio databases, which facilitate the extraction and analysis of vital biological information. These technologies contribute to the analysis of big data, identification of key bioinformatics insights, and generation of new biological knowledge. Data collection, analysis, and interpretation in this field involves the use of modern technologies such as cloud computing, machine learning, and artificial intelligence, enabling more efficient and accurate results. Ultimately, data mining and bioinformatics enhance our understanding of genetic relationships, aid in developing new therapies, and improve healthcare outcomes. © (2024), (Neutrosophic Sets and Systems). All rights reserved.

关键词： Bioinformatics

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Integrating Photonics and Fiber Bragg Grating Sensors with Deep Reinforcement Learning for Advanced Robotic Systems 18

Integrating Photonics and Fiber Bragg Grating Sensors with D...

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18th INDIAcom;11th International Conference on Computing for Sustainable Global Development, INDIACom 2024

作者： Subaranjani, T. Jaideep, R. Manjula, C. Seema, V. The Oxford College of Engineering Dept. of Artificial Intelligence and Machine Learning Bangalore India The Oxford College of Engineering Dept. of Mechatronics Engineering Bangalore India

ISBN: (纸本)9789380544519

This research introduces the Photonics-Enhanced Embedded Robotic intelligence Model (PEERIM), an innovative approach that integrates fiber Bragg grating (FBG) sensors with photonics and deep reinforcement learning (DRL) for advanced robotics applications. Addressing the limitations of current AI models in dynamic and complex environments, PEERIM leverages the rapid data transmission capabilities of photonics to process high-frequency sensor data, enabling real-time decision-making and enhanced automation. The methodology encompasses the development and integration of a variant of the Proximal Policy Optimization (PPO) algorithm, tailored to manage the continuous data stream and execute precise, adaptive control. The proposed model has been empirically evaluated, demonstrating a significant improvement in handling real-time sensor data with an average reward of 99.02 and a low average loss of 0.099, indicating robust performance and learning stability. These findings suggest that PEERIM provides a substantial advantage over existing AI-driven robotic systems, offering a scalable solution for a variety of challenging applications. The study's contributions lay the groundwork for future advancements in autonomous systems, promising a new era of precision and reliability in robotics. © 2024 Bharati Vidyapeeth, New Delhi.

关键词： Autonomous Systems Deep Reinforcement Learning Fiber Bragg Grating Sensors Photonics-Enhanced Robotics Proximal Policy Optimization Real-Time Decision Making

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Symptoms-based Classification of Disease Using Various ML Algorithms and Interpretation using LIME and SHAP KERNELS 4

Symptoms-based Classification of Disease Using Various ML Al...

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4th IEEE Asian Conference on Innovation in Technology, ASIANCON 2024

作者： Soujanya, K.J. Kodipalli, Ashwini Gosai, Sanjeev Sneha, B.J. Rao, Trupthi Global Academy of Technology Dept. of Artificial Intelligence & Data Science Karnataka Bangalore India Global Academy of Technology Dept. of Computer Science & Engineering Karnataka Bangalore India

ISBN: (纸本)9798350354218

Using a variety of machine learning techniques, this research study suggests a unique method for classifying diseases using symptom-based analysis. To improve model transparency and comprehension, the study makes use of the interpretability tools, SHAP kernels and LIME. The research attempts to close the gap between interpretability and prediction accuracy in medical diagnosis by combining two approaches. A thorough assessment of symptom-based categorization is ensured by the use of many machine learning methods. The application of LIME and SHAP kernels aids in the clarification of model predictions and provides information on the primary symptoms that impact illness categorization. This study advances the creation of accurate and comprehensible illness categorization models, promoting confidence in the use of machine learning methods in healthcare. © 2024 IEEE.

关键词： Adversarial machine learning

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Enhancing Medical Image Quality using Neutrosophic Fuzzy Domain and Multi-Level Enhancement Transforms: A Comparative Study for Leukemia Detection and Classification

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Neutrosophic Sets and Systems 2024年 65卷 32-56页

作者： Salama, A.A. Shams, Mahmoud Y. Khalid, Huda E. Mousa, Doaa E. Dept. of Math and Computer Sci. Faculty of Science Port Said Univ. Egypt Dept. of Machine Learning Faculty of Artificial Intelligence Kafrelsheikh University Egypt Telafer University Telafer University Telafer Iraq

Medical image processing has become a critical research area due to the vast amounts of digital image data available. However, medical images often suffer from poor illumination and low visibility of significant structures, requiring image enhancement to improve image quality before processing. In this paper, we propose a technique for enhancing medical images by removing noise and improving contrast based on three different enhancing transforms. The proposed technique embeds the image into a neutrosophic fuzzy domain, where it is mapped into three different levels of trueness, falseness, and indeterminacy, and each level is processed individually using the enhancement transforms. We compare the proposed technique with four other systems for leukemia detection and classification using accuracy and T, I, and F values. The proposed system performs the best with an accuracy of 98%, outperforming the other systems in terms of accuracy, degree of indeterminacy, and falsity. The proposed system uses different algorithms and filters to process images and extract features like color and texture. The system’s classification uses k-means for segmentation and SVM for classification. The paper highlights the importance of considering T, I, and F values in evaluating the performance of different systems for leukemia detection and classification, providing a more accurate representation of the uncertainty and ambiguity involved in the evaluation process. © (2024), (Neutrosophic Sets and Systems). All Rights Reserved.

关键词： Image enhancement

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Runtime WCET Estimation Using Machine Learning 23

Runtime WCET Estimation Using Machine Learning

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29th Annual International Conference on Mobile Computing and Networking, MobiCom 2023

作者： Yun, Sangwoon Kang, Kyungtae Dept. of Computer Science and Engineering Hanyang University Ansan60211585 Korea Republic of Dept. of Artificial Intelligence Hanyang Univeristy Ansan60211585 Korea Republic of Major in Bio Artificial Intelligence Korea Republic of

ISBN: (纸本)9781450399906

Accurate task execution time estimation is vital for efficient and dependable operation of safety-critical systems. However, modern automotive functions' complexity challenges conventional estimation methods. To address this, we propose a novel technique that combines execution time and job sequence data using a multi-layer perceptron (MLP) neural network. Leveraging MLP's capabilities, our approach achieves impressive 99.7% prediction accuracy with a mere 38.33 μs latency. Integrating our technique into safety-critical systems optimizes resource allocation and scheduling, enhancing performance and reliability. Importantly, our method extends beyond automotive systems, finding potential in diverse safety-critical domains. By precisely estimating task execution time, we enhance operational efficiency and decision-making in complex systems. © 2023 ACM.

关键词： Real time systems

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Edge Computing Devices Authentication using Quantum Computing 15

Edge Computing Devices Authentication using Quantum Computin...

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15th International Conference on Computing Communication and Networking Technologies, ICCCNT 2024

作者： Telsang, Vinayak A. Kakkasageri, Mahabaleshwar S. Devangavi, Anil D. Dept. of Computer Science and Engineering Biluru Gurubasava Mahaswamiji Institute of Technology Mudhol India Dept. Electronics and Communication Engineering Basaveshwar Engineering College Bagalkote India Dept. of Artificial Intelligence and Machine Learning Basaveshwar Engineering College Bagalkote India

ISBN: (纸本)9798350370249

Edge computing allows different technologies such as Internet of Things (IoT), vehicle-to-vehicle communications(VV), Industrial IOT (IIOT) to connect to the cloud computing to facilitate end users. The edge networks uses low bandwidth, and enhances the processing speed. With more number of IoT devices connected in the network the privacy of data stored at the edge server also under threat. Authentication is a major challenge which is be addressed to provide security and privacy to IoT devices connected in the network. The existing works uses cryptographic algorithms to solve the authentication problems, but with increase in growth of quantum computers and quantum computing, breaking the complex cryptographic algorithm is not an issue. Hence one should adopt the quantum approach to provide authentication in edge network. The proposed work uses quantum key and context information of IoT devices to store in the edge server using blockchain, which proves to be more secure. The performance analysis shows that it is better with respect to registration, encryption, decryption and authentication of IoT devices. ©2024 IEEE.

关键词： Authentication Blockchain Cryptography Edge computing Quantum key

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Phoneme Classification in Imagined Speech Using Explainable Machine Learning 13

Phoneme Classification in Imagined Speech Using Explainable ...

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13th International Winter Conference on Brain-computer Interface, BCI 2025

作者： Kim, Sejin Kang, Hyunwook Jeong, Ji-Hoon Lee, Minji Dept. Artificial Intelligence The Catholic University of Korea Bucheon Korea Republic of Dept. Electrical and Computer Engineering Inha University Incheon Korea Republic of Dept. Computer Science Chungbuk National University Cheongju Korea Republic of Dept. Biomedical Software Engineering The Catholic University of Korea Bucheon Korea Republic of

ISBN: (纸本)9798331521929

Imagined speech is gaining attention as a next-generation paradigm for brain-computer interfaces in terms of its intuitiveness in communication. Many studies have focused on classifying imagined words as the basis of communication, but some studies on phoneme classification based on imagined speech have begun to be conducted in terms of the fact that combinations of phonemes can further expand sound images. In addition, explainability is a major issue in classification results in machine learning, but there have been few studies that analyzed the explainability of imagined speech classification. In this study, we examined explainability using SHAP for phoneme classification based on imagined speech. FEIS dataset was used, and 16 phonemes were classified using the XGBoost classifier. In 21 subjects, the average accuracy was 40.36%, the average precision was 41.7%, the average recall was 40.34%, and the average F1-score was 40.7%. In addition, when looking at Sub 04, which had the highest performance, the SHAP values were high in AF3 and F7 channels, which correspond to Wernicke’s area related to speech. In this regard, we could observe the feature importance of this area. These results prove that Wernicke’s area actually plays an important role in classifying phoneme units in imagined speech, and can provide important insights to improve the performance of imagined speech for intuitive communication based on brain-computer interface in the future. © 2025 IEEE.

关键词： Adversarial machine learning

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Group-based Random Access with Age of Information Minimization in 6G umMTC 14

Group-based Random Access with Age of Information Minimizati...

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14th International Conference on Information and Communication Technology Convergence, ICTC 2023

作者： Youn, Jiseung Park, Joohan Ahn, Seyoung Kim, Soohyeong Cho, Sunghyun Major in Bio-Artificial Intelligence Hanyang University Dept. of Computer Science & Engineering Ansan Korea Republic of Hanyang University Dept. of Computer Science & Engineering Ansan Korea Republic of

ISBN: (纸本)9798350313277

6G has defined ultra-massive machine type communication (umMTC) as a key scenario in response to the advancement of IoT technologies. The presence of a large number of machine-type communication devices (MTCDs) in umMTC calls for the research of efficient random access (RA) schemes. To address the problem of high collision rates in dense networks, a group-based RA approach has been introduced. In the group-based RA approach, we design an optimal congestion control method to minimize the Age of Information (AoI). Since AoI and collision rate are in a trade-off relationship, we aim to maximize the successful RA rate by iteratively searching for the optimal congestion control factor. Simulation results represent that the proposed scheme achieves a 10% collision rate and an average AoI of less than 100 age, enabling the 90% successful RA rate in 100,000 MTCDs deployment scenario. © 2023 IEEE.

关键词： AoI Group-based random access umMTC

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Voice AI-intelligence Based Voice Assistant 3

Voice AI-Intelligence Based Voice Assistant

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3rd International Conference on Intelligent and Innovative Technologies in Computing, Electrical and Electronics, IITCEE 2025

作者： Garai, Pooja Nikam, Aditya Kerkar, Suyash Deshmukh, Samruddhi Mane, Atharva Bhise, Suvarna Vasantdada Patil College Of Engineering & Visual Arts Dept. of Artificial Intelligence & Data Science Mumbai India

ISBN: (纸本)9798331515911

Voice assistant applications have become integral parts of modern technology ecosystems, offering users convenient and efficient ways to interact with their devices. This paper introduces Voice Ai, a versatile voice assistant application developed using Android Studio and Google's Machine Learning Kit Machine learning(MLKit) for Optical Character Recognition (OCR) functionalities. The app includes features like setting alarms, making calls, opening applications, and conducting question-answer sessions powered by G4F. A comparative analysis between G4F and Google's backend is performed, assessing their performance and user satisfaction during question-answer interactions. Voice Ai simplifies daily tasks and boosts productivity, providing insights for developers, researchers, and end-users interested in AI-based voice assistant applications. © 2025 IEEE.

关键词： Adversarial machine learning

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