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9th IEEE/ACIS International Conference on Big Data, Cloud Computing, and Data Science, BCD 2024

作者： Perikos, Isidoros University of Patras Computer Engineering and Informatics Department Patras Greece University of Peloponnese Computer Technology Institute and Press 'Diophantus' Electrical and Computer Engineering Dept. Patras Greece

ISBN: (纸本)9798350394191

In the dynamic landscape of online social networks, recognizing sensitive content is essential for safeguarding user privacy, fostering inclusivity, and enhancing diversity awareness. Building on prior research, this study explores new dimensions and methodologies for detecting sensitive content. We examine the temporal evolution of sensitive content, revealing how patterns shift over time, and address cross-linguistic challenges, emphasizing cultural and contextual nuances in detection. We employ advanced machine learning techniques, including deep learning models and BERT that improve the accuracy and robustness of the detection procedure. In the experimental study, BERT transformer reported the best performance in detecting sensitive content in text. Additionally, we incorporate explainability techniques such as LIME and SHAP to provide deeper insights into the model's decision-making processes, ensuring predictions are interpretable and reliable. Our work enhances the theoretical framework of sensitive content detection in social networks and provide methods that are accurate and scalable and can facilitate the creation of user-centric interaction that prioritize privacy and user experience. © 2024 IEEE.

关键词： Adversarial machine learning

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The SNS based E mentoring and Development of Computational Thinking for Undergraduate Students in an Online Course

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educational Technology and Society 2023年第2期26卷 147-164页

作者： Jang, Yeonju Choi, Seongyune Kim, Seonghun Kim, Hyeoncheol Dept. of Computer Science and Engineering The Graduate School of Korea University South Korea Dept. of Education Gachon University South Korea Dept. of Computer Science and Engineering Korea University South Korea

Given the importance of digital technology in daily life, computational thinking (CT) has become a necessary skill for everyone, not just for computer scientists. For CT development, students need to receive appropriate social learning support. However, instructors find it difficult to provide such support to many students in online courses. This study aimed to examine the effectiveness of e-mentoring via social network services (SNS) in developing students’ CT during large-scale online courses. A total of 327 undergraduate students volunteered to participate in this study, which included 16 weeks of lectures aimed at developing CT. The effects of SNS-based e-mentoring on CT development, the influences of each e-mentoring activity on CT development, and gender differences were analyzed using data on participants’ CT assistance scores and their utilization of e-mentoring activities. The findings indicated that SNS-based e-mentoring was effective in developing the CT of undergraduate students’ engagement in a large-scale online course. The most influential e-mentoring activities for students’ CT development were informational and technical support in a group and informational support in a private environment. Female students benefited more from SNS-based e-mentoring than male students, and they also engaged in more types of e-mentoring activities than male students. Participation in SNS-based e-mentoring was found to lower the gap in CT between students with and without prior learning experience. Our study findings can be used by educational institutions and instructors when designing courses for students’ CT development in large-scale online courses or when developing strategies to close the gender gap in CT ability. © 2023,educational Technology and Society. All Rights Reserved.

关键词： Computational thinking Computational thinking and prior learning experience e-Mentoring Gender difference Social Network Service (SNS)

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Detection and Classification of Cardiac Arrhythmia from Photoplethysmography Signal

Detection and Classification of Cardiac Arrhythmia from Phot...

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2023 IEEE International Conference on Internet of Things and Intelligence Systems, IoTaIS 2023

作者： Dewantara, Bima Sena Bayu Sari, Dewi Mutiara Fahmi, Ahmad Nur Politeknik Elektronika Negeri Surabaya Dept. of Informatics and Computer Engineering Surabaya Indonesia

ISBN: (纸本)9798350319040

This paper presents the development of an application for classifying cardiac arrhythmia by utilizing photoplethys-mography sensor signals. The Photoplethysmography (PPG) sensor is used as a reader for the cardiovascular system by measuring changes in blood volume in the skin tissue. The PPG sensor captures electrical signals originating from the reflected light source due to changes in blood flow during heart activity. After going through the preprocessing stage, the signals are then extracted into several features that will be classified using the Random Forest algorithm. The results from the Random Forest algorithm will provide a diagnosis based on the detection of arrhythmia. Based on the experimental results, the accuracy achieved using the method and dataset is 72%. The accuracy of the system testing using the smartwatch under normal heart conditions is 88.88%. © 2023 IEEE.

关键词： Photoplethysmography

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Navigation System Development on ATRACBOT using F-SFM 25

Navigation System Development on ATRACBOT using F-SFM

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25th International Electronics Symposium, IES 2023

作者： Widarto, Mochamad Farhan Eka Marta, Bayu Sandi Dewantara, Bima Sena Bayu Setiawardhana Politeknik Elektronika Negeri Surabaya Dept. of Informatics and Computer Engineering Surabaya Indonesia

ISBN: (纸本)9798350314731

Navigation systems are very important for social robots, especially autonomous robots, because they are required to be adaptive to their surrounding environment. This paper proposes a new navigation system for the Autonomous Trash Can Robot (ATRACBOT) using the Fuzzy-based Social Force Model (F-SFM). The navigation system employed in earlier stages of development depended on the Braitenberg approach, leading to the robot's motion appearing rigid. On the other hand, the Social Force Model (SFM) employs the forces surrounding objects to engage with and evade other entities, presenting a different approach. The magnitude of the force to avoid objects is determined by the SFM parameters and the stimulus received by the robot. The Fuzzy Inference System (FIS) is used to obtain the optimal value of the gain factor, k, which is one of the SFM parameters, based on the relative distance, d, and relative angle, \gamma, between the obstacle and robot. The FIS will process both inputs and produce a suitable gain factor, k, so that the stimulus received can match the robot's reactivity and responsiveness. Based on the results from a realistic 3D simulator V-Rep, the implementation of the F-SFM method can deliver the robot to the goal point without hitting obstacles and make the robot's movements more natural compared to the Braitenberg method. © 2023 IEEE.

关键词： Navigation systems

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Quantum-Enhanced Machine Learning for Precision Breast Cancer Diagnosis 17

Quantum-Enhanced Machine Learning for Precision Breast Cance...

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17th International Conference on COMmunication Systems and NETworkS, COMSNETS 2025

作者： Banerjee, Agniva Indian Institute of Science Education and Research Bhopal Dept. Electrical Engineering and Computer Science Bhopal India

ISBN: (纸本)9798331531195

Cancer is the leading cause of premature mortality. Globally, the prevalence of cancer is on the rise, and early-stage diagnosis is crucial for cancer recovery and survival. However, the presence of circulating tumor cells (CTCs) and heterogeneity within the tumor makes it hard to track how cancer changes over time and how resistance to treatment develops. Unfortunately, the cancer diagnosis precision remains inadequate, endangering precious human lives and necessitating costly procedures for accurate treatment. Machine learning (ML) has proven capable of discovering meaningful information and making predictions, particularly with large datasets. Moreover, quantum computers (QCs) inherently possess superposition and parallel computation, features not found in classical computing. Researchers have demonstrated that quantum neural networks (QNNs) achieve a significantly better effective dimension than classical NNs. In this context, this study investigates the application of classical and quantum machine learning (QML) algorithms to classify breast cancer images. Region-based segmentation is used to identify regions of interest and image processing algorithms are performed on segmented breast cancer images to extract meaningful features (e.g., perimeter, area, compactness). An user interface (UI) based software is created to swiftly distinguish benign and malignant instances during image analysis. Although QNNs perform poorly than the classical NNs, there is still a long way to go in developing feature maps and techniques for better performance. In contrast, the classical NNs have gone a long way with well-established algorithms and packages available to us. This research shows that with further development, QML has the potential to enhance cancer diagnosis accuracy and speed, ultimately improving early detection and treatment outcomes. © 2025 IEEE.

关键词： Diseases

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A Game Theoretic Approach to Enhance DCF Performance in Full Duplex Ad-hoc WLANs 12

A Game Theoretic Approach to Enhance DCF Performance in Full...

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12th International Conference on Modern Circuits and Systems Technologies, MOCAST 2023

作者： Politis, Anastasios C. Anastassiu, Hristos T. Hilas, Constantinos S. International Hellenic University Informatics and Telecommunications Engineering Dept. of Computer Serres Greece

ISBN: (纸本)9798350321074

Physical Full Duplex (FD) operation in WLANs is a promising technology aiming at substituting the traditional Half Duplex (HD) functionality. An increased performance is expected which, theoretically, can be expressed as a double increase in system throughput. However, due to the initial design of the basic access method in WLANs, known as Distributed Coordination Function (DCF), this performance boost is poorly exhibited. Game theory is an invaluable tool to model the performance of Medium Access Control (MAC) protocols used in WLANs. In this paper, we exploit game theory to propose a simple enhancement to the DCF access method, in order to increase the benefit received when the FD capability is enabled in ad-hoc WLANs. © 2023 IEEE.

关键词： Game theory

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A Framework Leveraging Robotics and Machine Learning Technologies for Early Disease and Pest Detection in Greenhouse Tomato Crops 4

A Framework Leveraging Robotics and Machine Learning Technol...

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4th International Conference on Control, Artificial Intelligence, Robotics and Optimization, ICCAIRO 2023

作者： Kounalakis, Nikolaos Kalykakis, Emmanouil Kosmopoulos, Dimitrios Fasoulas, John Sfakiotakis, Michael Heraklion Greece University of Patras Dept. of Computer Engineering and Informatics Patras Greece

ISBN: (纸本)9798350300918

We present an integrated framework for intelligent automated plant monitoring, towards early disease and pest detection in greenhouse tomato crops. The framework combines the use of a robotic mobile platform to autonomously collect multi-spectral images of the plants, with a tool that utilizes Faster R-CNN to detect regions that signify the presence of a disease or pest. The robot is based on a modified mobile vertical mast lift platform, and integrates a 6-dof robotic arm that is used to position the plant imaging multi-spectral camera. The robot can navigate autonomously inside the greenhouse via a magnetic guidance sensor. Results from a series of experiments demonstrate the validity and effectiveness of the implemented framework. © 2023 IEEE.

关键词： Machine learning

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To Split or not to Split? A Simulation Study on the Network Convergence Duration of Multi-Area OSPF 46

To Split or not to Split? A Simulation Study on the Network ...

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46th International Conference on Telecommunications and Signal Processing, TSP 2023

作者： Sachinidis, Theofilos Politis, Anastasios C. Hilas, Constantinos S. International Hellenic University Dept. of Computer Informatics and Telecommunications Engineering Serres Greece

ISBN: (纸本)9798350303964

Internetworks with a large number of routing devices that utilise the Open Shortest Path First (OSPF) routing protocol, benefit greatly if they are designed in a hierarchical manner. The multi-area feature of OSPF leads to reduced network overhead, low processing and memory requirements and smaller routing tables. This, however, comes at the cost of increased configuration complexity, especially at the Area Border Routers. The threshold after which splitting the OSPF domain into multiple areas, is an ongoing debate among network administrators. A well known rule-of-thumb is recommended by large vendors of networking devices setting that threshold to 50 routing devices. The objective of this preliminary work is to investigate that threshold by conducting a simulation-based study using the OPNET network simulation tool. To assess the network performance, we select the network convergence duration as the quantitative Key Performance Indicator (KPI) of our study. Results indicate that the above mentioned threshold does not appear to be appropriate and a revision of that empirical rule should be considered. © 2023 IEEE.

关键词： Routing protocols

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Enhancing Autism Spectrum Disorder Identification: A Machine Learning Approach using CatBoost

Enhancing Autism Spectrum Disorder Identification: A Machine...

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2024 IEEE International Conference on Innovation and Novelty in engineering and Technology, INNOVA 2024

作者： Rathod, Vijayalaxmi N. Goudar, R.H. Dhananjaya, G.M. Patil, Minal Hukkeri, Geetabai S. Kaliwal, Rohit B. Dept. of Computer Science and Engineering Visvesvaraya Technological University Karnataka Belagavi India Dept. of Computer Science and Engineering Manipal Institute of Technology Manipal Academy of Higher Education Bengaluru India

ISBN: (纸本)9798331505134

Autism is a complex neurodevelopment condition that affects an individual's behavior, communication, and social interaction. Identification is a critical endeavor in healthcare, necessitating accurate and efficient diagnostic methodologies, early identification is pivotal for timely intervention and improved outcomes in affected individuals. This paper investigates the use of machine learning algorithms, specifically CatBoost, for Autism trait identification using heterogeneous datasets from toddlers, children, adolescents, and adults. The research investigates the performance of CatBoost in handling mixed data types, including categorical features and missing values, without extensive preprocessing. Utilizing gradient boosting on decision trees, CatBoost demonstrates its efficacy in capturing complex relationships between features, facilitating high predictive accuracy in autism identification. Through rigorous evaluation metrics such as accuracy, precision, recall, and F1 score, the designed system achieves a precise accuracy of 92% for adult datasets and 88% for child and adolescent datasets. This study delineates CatBoost's robustness across diverse age groups, providing insightful information on its applicability for Autism Spectrum Disorder diagnosis in the healthcare domain. © 2024 IEEE.

关键词： Diseases

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Diabetes Mellitus Diagnosis using Optical Ring Resonators 18

Diabetes Mellitus Diagnosis using Optical Ring Resonators

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

作者： Prajwal, Manush Jesy Janet Kumari, J. Uppu, Maanas Mitrahass Chirchi, Vanajaroselin Vishalatchi, S. Darshan, D.N. The Oxford College of Engineering Dept. Computer Science and Engineering Bangalore India The Oxford College of Engineering Dept. of Isc Bangalore India Kalasalingam Academy of Research and Education Dept. of Ece Krishnankoil India

ISBN: (纸本)9789380544519

The research shows the use of optical ring resonators (ORRs) as a promising non-invasive tool for diagnosing diabetes mellitus, potentially replacing invasive procedures. By accurately measuring glucose levels in biological samples through resonance frequency shifts closely linked to varying glucose concentrations, ORRs point to innovative and precise glucose measurement for early diabetes detection. This advancement could transform disease monitoring by reducing patient discomfort from current invasive techniques. The study develops a highly sensitive, non-invasive method for detecting diabetes by analyzing subtle optical signal changes related to glucose levels in urine is around 165-180 mg/dl (normal) and resonant frequency output is around 36.026459 but in case of blood resonant frequency output is around0.348478amusing advanced optical ring resonator technology. The methodology involves designing optical ring resonators capable of detecting minute refractive index variations connected to glucose concentrations. Major findings demonstrate the precision of diagnosing diabetes, showcasing the potential for a rapid and reliable diagnostic tool. Interpretations emphasize the innovative approach's ability to significantly improve early detection, enabling timely intervention and enhanced diabetes management for better healthcare outcomes and quality of life for individuals with diabetes. © 2024 Bharati Vidyapeeth, New Delhi.

关键词： diabetes diagnosis diabetes mellitus non-invasive method optical signal ring resonator

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