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

作者： Kasno, Mohammad Afif Yatim, Norhidayah Mohamad Kang, Tae-Won Hee, Jo Min Nam, JunWoo Jung, Jin-Woo Dongguk University Dept. of Computer Science and Egineering Seoul Korea Republic of Faculty of Electronics and Computer Technology and Engineering Malacca Malaysia Dongguk University Dept. of Artificial Intelligence Seoul Korea Republic of

ISBN: (纸本)9798350364637

This study presents a unique approach for operating the TurtleBot3 Waffle-Pi robot with voice instructions using the ROS Noetic framework. The system uses the SpeechRecognition library to translate voice inputs into movement commands, with the goal of improving user engagement and providing an intuitive control mechanism that can be applied to a variety of domains including assistive robotics. The system naturally interfaces with ROS Noetic, which uses Python for scripting and control logic. The method entails real-time recording audio with a microphone, filtering it to remove noise, then interpreting commands with Google's speech recognition API. For the TurtleBot3, commands like 'forward,' 'back,' 'left,' 'right,' and 'stop' are assigned to matching movements. Noise filtering and command disambiguation problems are solved by modifying the recognizer's energy threshold and using a dynamic energy threshold. A complete test was carried out in controlled situations to determine command recognition accuracy, response time, and navigation precision. The results showed good reliability and effectiveness, with accurate command recognition and minimum reaction delay. © 2024 IEEE.

关键词： Speech recognition

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Revolutionizing Obstetric Care: IoT, AI-Enabled, and Data-Driven Partograph System

Revolutionizing Obstetric Care: IoT, AI-Enabled, and Data-Dr...

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2023 International Conference on Emerging Trends in Networks and computer Communications, ETNCC 2023

作者： Zaveri, Dishant Jagtap, Vividha Gill, Jasleen Jain, Krishi Sheth, Shivam Shekokar, Narendra Djsce Dept. of Computer Engineering Mumbai India Djsce Dept. of Artificial Intelligence and Machine Learning Mumbai India Mumbai India

ISBN: (纸本)9798350314878

This research paper introduces a comprehensive real-time labor and delivery monitoring system that uses various hardware devices like ultrasound transducers, cardiotocographs, sphygmomanometers, and IoT devices. It creates a database accessible via a dedicated mobile app for doctors, nurses, and receptionists. The system includes features to identify and notify medical staff of potential risks such as preterm labor or fetal distress, using SMS alerts and LED lights for immediate attention. In emergencies, it notifies relevant medical personnel and receptionists for urgent interventions like cesarean sections. If needed facilities aren't available nearby, the system searches for hospitals within an 8-kilometer radius and sends alerts to check availability, arranging for an ambulance if necessary. This system allows real-time access to patient data, leading to faster responses and improved outcomes, enhancing decision-making and reducing maternal and child mortality through timely interventions and improved maternal care quality. © 2023 IEEE.

关键词： Ultrasonic transducers

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CNN-LSTM Based Prediction of LoS Duration in Satellite Communication Network 15

CNN-LSTM Based Prediction of LoS Duration in Satellite Commu...

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

作者： Jang, Huiyeon 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

Satellite communication system is crucial to the advancement of sixth-generation (6G) networks, providing global connectivity and supporting applications that demand ultraspeed high and low latency. However, the high mobility of low Earth orbit (LEO) satellites poses significant challenges to maintaining consistent communication links, primarily due to the frequent handover required by user equipment. Traditional signal strength-based handover technique, which is effective in terrestrial networks, struggles in the satellite network due to the minimal signal strength variation across overlapping cells. This leads to frequent and unnecessary handovers, resulting in reduced network performance. To address these challenges, we propose an CNN-LSTM based predictive line-of-sight (LoS) duration estimation method that allows user terminals to anticipate satellite connection stability, minimizing unnecessary handovers and improving network efficiency in dynamic satellite environments. © 2024 IEEE.

关键词： Satellite communication systems

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Deep Learning Approaches for Stress Detection: A Survey

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IEEE Transactions on Affective Computing 2024年第2期16卷 499-517页

作者： Kyrou, Maria Kompatsiaris, IoannisSenior Petrantonakis, Panagiotis C. Aristotle University of Thessaloniki Artificial Intelligence and Information Analysis Laboratory Dept. of Informatics GR 54124 Greece Information Technologies Institute Centre for Research and Technology Hellas GR 57001 Greece Aristotle University of Thessaloniki Dept. of Electrical and Computer Engineering GR 54124 Greece

Stress has a severe impact on individuals irrespective of age, sex, work, or background. The reliable development of stress detection techniques enhances the social, educational, physical, economic, and professional quality of life, preventing chronic stress and proposing alleviation strategies. Research studies examine psychological, cognitive, behavioral, and physiological reactions to identify stress adequately. Deep Learning (DL) has received significant attention in recent years as it deals with high-dimensional, heterogeneous data and automatically learns representative features. This paper presents a survey on stress detection with recent DL approaches, leveraging data from all possible sources (physiological, speech, facial expressions, gestures, and social media content). The methodological outlines, the best results, and the main contributions of each study are discussed. We also describe publicly available datasets used by several of the presented works. Finally, we emphasize various open issues within the field of research and highlight key directions for future work © 2024 IEEE.

关键词： Social psychology

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Enhancing Roadside 3D Object Detection Using Dynamic Pseudo HD Map 15

Enhancing Roadside 3D Object Detection Using Dynamic Pseudo ...

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

作者： Park, Byungwoo Kim, Yecheol Kim, Jisong Choi, Jun Won Hanyang University Dept. of Artificial Intelligence Seoul Korea Republic of Hanyang University Dept. of Electrical Engineering Seoul Korea Republic of Seoul National University Dept. of Electrical and Computer Engineering Seoul Korea Republic of

ISBN: (纸本)9798350364637

Roadside 3D object detection plays a pivotal role in enhancing autonomous driving systems. Elevated sensors, such as those mounted on utility poles, offer distinct advantages in traffic safety by expanding the visual field and reducing occlusions. Despite these benefits, such configurations still face challenges related to occlusion and limited detection range. To address these limitations, we propose a Multi-LiDAR Stitching technique designed to mitigate occlusion and enhance detection coverage. Furthermore, we introduce a dynamic pseudo HD map, which continuously updates via a memory bank, effectively reducing the high costs and maintenance complexities associated with traditional HD maps while adapting to dynamic environmental changes. Evaluations on our newly constructed Multi-LiDAR Stitching dataset show a 3.3% improvement in mAP over the baseline, underscoring the effectiveness of our approach in improving roadside object detection and advancing traffic safety in autonomous driving systems. © 2024 IEEE.

关键词： Digital storage

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Enhancing Hospital Mortality Prediction for Heart Failure Patients through Hyperparameter Optimization and Interpretability Analysis with LIME and SHAP 1

Enhancing Hospital Mortality Prediction for Heart Failure Pa...

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1st International Conference for Women in Computing, InCoWoCo 2024

作者： Meena, S. Murthy, Monisha Kodipalli, Ashwini Global Academy of Technology Dept. of Computer Science and Engineering Karnataka Bengaluru India Global Academy of Technology Dept. of Artificial Intelligence & Data Science Karnataka Bengaluru India

ISBN: (纸本)9798331518943

Heart failure is one of the primary causes for deaths caused in the hospital. Predicting mortality rate of such patients is extremely important for the efficient use of health care resources. This research aims to estimate the hospital mortality rate of Heart Failure(HF) patients using machine learning(ML). Using computational technique, the obtained model has given the most efficient accuracy. This paper uses the classification model to detect two outcomes (0 and 1, i.e., if the patient will survive or not). The classification models used for the hospital mortality prediction are, single classifiers such as Logistic regression, SVM, KNN, Decision tree (Ginni and Entropy);with Logistic regression obtaining highest accuracy of 88.9%, Bagging and boosting models such as Randomforest, Adaptive boosting, Gradient boosting, XG boosting, Categorical boosting;with CatBoost obtaining highest accuracy of 90.2%. To achieve the best accuracy, ensemble models were used out of which ensemble classifier ensemble gave 91.1% accuracy. To understand the features playing important role in decision making, explainable AI(XAI) is used. LIME and SHAP are considered. LIME generated local explanations, it explained the decision of a complex model for a specific instance or observation. In contrast, SHAP provided global explanations, it explained the overall behavior of the model across all instances. © 2024 IEEE.

关键词： Adaptive boosting

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Detection of PCOS Leveraging Machine Learning and Interpretation Using Explainable AI 1

Detection of PCOS Leveraging Machine Learning and Interpreta...

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1st International Conference for Women in Computing, InCoWoCo 2024

作者： Madhavi, Bindu V. Firdouse, Asfiya Kodipalli, Ashwini Global Academy of Technology Dept. of Computer Science and Engineering Karnataka Bengaluru India Global Academy of Technology Dept. of Artificial Intelligence & Data Science Karnataka Bengaluru India

ISBN: (纸本)9798331518943

Polycystic Ovary Syndrome (PCOS) is a widespread endocrine disorder impacting women globally. This research aims to early predict and detect PCOS which is needed to reduce long-term complications. Since it is considered to be a hard-to-diagnose disorder, machine learning is utilized for this purpose. This study leveraged the power of computational algorithms trained on patient data for model prediction where Decision tree with criterion Gini index outperformed with 88.07% accuracy, 85.29% precision, 78.37% recall and 81.69% F1 Score. In addition to this, Bagging and boosting algorithms were used to monitor their performance metrics where Gradient Boost stood out with a remarkable accuracy of 91.74%, 91.00% precision, 97.00% recall and 94.00% F1 Score. By optimizing the chosen parameters through Hyperparameter tuning, a notable increase in the model's accuracy was observed. Besides, three ensemble models were proposed out of which the ensemble classifier ensemble model involving bagging, boosting and single classifiers brought a significant difference of 95.41% accuracy. Additionally, Explainable (XAI) methodologies like LIME (Local Interpretable Model-Agnostic Explanations) and SHAP (SHapley Additive exPlanations) were used for model interpretability. © 2024 IEEE.

关键词： Adversarial machine learning

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Reinforcement Learning-Based Motion Planning for Robotic Manipulators in Smart Industry 15

Reinforcement Learning-Based Motion Planning for Robotic Man...

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

作者： Cho, Junhyung 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

The advent of smart industry, propelled by the inte-gration of digital technologies and automation, has revolutionized manufacturing and industrial processes. Robotics and artificial intelligence (AI) are at the forefront of this transformation, driving extensive research into robotic automation and motion planning. Traditional motion planning algorithms, such as artificial potential fields, bio-inspired heuristics, and sampling-based methods, often falter in complex environments due to their high computational demands and tendency to produce non-optimal solutions. Reinforcement learning (RL) has emerged as a powerful alternative, offering real-time adaptation and optimal decision-making in dynamic settings. This paper reviews the inherent limitations of classical motion planning approaches and explores contemporary trends in RL-based methods, with a focus on their application in smart industry. It highlights the advantages of RL in enhancing adaptability, efficiency, and robustness, particularly in high-dimensional and dynamic environments. Key discussions include the integration of RL with traditional techniques, the extension of RL applications across various domains, and the role of sensor-based approaches in improving motion control. © 2024 IEEE.

关键词： Smart manufacturing

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Low Earth Orbit Satellite Scheduling Optimization Based on Deep Reinforcement Learning 15

Low Earth Orbit Satellite Scheduling Optimization Based on D...

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

作者： Kim, Junyoung 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

In next-generation 6G scenarios, non-terrestrial net-works employing low Earth orbit (LEO) satellites will be pivotal in achieving ultra-wide coverage, ultra-connectivity, and ultra-precision. Although LEO satellites provide comprehensive global coverage, their rapid mobility introduces frequent handovers, requiring sophisticated scheduling to maintain uninterrupted service. This paper proposes a deep reinforcement learning-based scheduling algorithm in order to improve service rate and continuity for terrestrial users in multi-LEO) environments. © 2024 IEEE.

关键词： Deep reinforcement learning

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Enhancing Detection of Breast Cancer Through a Novel Framework: A Machine Learning Approach with Explainable AI Interpretation 1

Enhancing Detection of Breast Cancer Through a Novel Framewo...

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1st International Conference for Women in Computing, InCoWoCo 2024

作者： Vidyashree, R. Kodipalli, Ashwini Rao, Trupthi Global Academy of Technology Dept. of Computer Science & Engineering Karnataka Bengaluru India Global Academy of Technology Dept. of Artificial Intelligence & Data Science Karnataka Bangalore India

ISBN: (纸本)9798331518943

Breast cancer, the most prevalent cancer among women, significantly contributes to increased mortality rates and ranks as the second leading cause of cancer-related deaths in women. Early detection is crucial for effective treatment, yet traditional diagnostic approaches often involve multiple laboratory tests supervised by medical experts. To mitigate human error and expedite breast cancer detection, there is a pressing need for automated systems capable of accurate and timely diagnosis. This study aims to investigate the application of machine learning (ML) techniques in classifying breast cancer using features from digitized images of fine-needle aspiration (FNA) breast mass samples. Machine learning methods are instrumental in addressing this imperative. The study conducts a comparative analysis of various ML classification methods, including Logistic Regression, Support Vector Machine (SVM), K Nearest Neighbor (KNN), Decision Trees, Bagging-Boosting, and ensemble models, using a breast cancer dataset to establish a novel framework. The performance of each algorithm is assessed based on metrics such as accuracy, precision, recall, and F1 score. The findings underscore the effectiveness of ensemble classifier models, achieving a notable accuracy rate of 98.87%. Additionally, the integration of explainable artificial intelligence enhances the interpretability of the models by providing valuable insights into their decision-making processes. © 2024 IEEE.

关键词： Contrastive Learning

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