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2023 International Conference in Advances in Power, Signal, and Information Technology, APSIT 2023

作者： Dhala, Rasmi Rani Pavan Kumar, A.V.S. Panda, Soumya Priyadarsini Giet University Dept. of Computer Science and Engineering Gunupur India Sit Dept. of Computer Science and Engineering Bhubaneswar India

ISBN: (纸本)9798350339369

Natural Language Processing (NLP) is an emerging field that aims to enable machines to understand and interpret human language. Keyword extraction and synonym generation are essential tasks in natural language processing. They play a significant role in information retrieval, text classification, and sentiment analysis. In this paper, we explore three different approaches to keyword extraction and synonym generation: rule-based model, statistical model, and extreme learning machine (ELM) model. We compare the performance of each method on a corpus of text and analyze the strengths and weaknesses of each approach. Our results show that the ELM model outperforms the other two methods in terms of accuracy and efficiency. © 2023 IEEE.

关键词： Neural network models

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Analysis on the Contribution and Economic Feasibility of Pumped Storage Hydropower in Korea Power System Considering the Penetration of Renewable Energy Generation

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Transactions of the Korean Institute of electrical Engineers 2023年第6期72卷 709-716页

作者： Kim, Hyun Joong Oh, Jae Young Yoon, Yong Tae Yoon, Yong Beum Dept. of Electrical and Computer Engineering Seoul National University Korea Republic of Dept. KEPCO International Nuclear Graduate School Korea Republic of

The government of South Korea proclaimed Energy Transition 3020 and the details thereupon with the generation mix. According to the Basic Plan for Long-term Electricity, additional Pump Storage Hydropower (PSH) were planned to be installed as measure for the variability from the Renewables. However, despite of the astronomical hard cost required for the PSHs, there has been a lack of assessments on the economic feasibility and effectiveness of the PSHs. According to the simulation, there were total three scenarios with high load variability due to the Renewables. The monitoring factors in each scenario were changes, in the generation cost, generation by fuel type, reserve capability by the PSHs, and emission of product gas. Furthermore, the economic feasibility was assessed from the perspectives of the PSHs. Based on the simulation, it was concluded that the additional PSHs decrease the daily generation cost and the ramping of the thermal generation, the greater reserve capabilities procured. On the other hand, emission of the product gas increases due to greater pumping load on the thermal generation. Moreover, it is economically infeasible for the PSHs to salvage their initial investment cost. Thus, this paper implicates the regulatory improvements for the formulation of the just market environment. Copyright © The Korean Institute of electrical Engineers.

关键词： Power markets

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Eye-Tracking Based Control of a Robotic Arm and Wheelchair for People with Severe Speech and Motor Impairment (SSMI) 11

Eye-Tracking Based Control of a Robotic Arm and Wheelchair f...

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11th RSI International Conference on Robotics and Mechatronics, ICRoM 2023

作者： Samani, Maryam Asad Hooshanfar, Kiana Jey, Helia Shams Esmailzadeh, Seyed Majid Iran University of Science and Technology Dept. Electrical Engineering Tehran Iran University of Tehran Dept. Electrical and Computer Engineering Tehran Iran

ISBN: (纸本)9798350308105

Eye-tracking technology has rapidly gained popularity as a revolutionary solution for individuals with severe physical disabilities, empowering them to engage in their daily activities with new found independence and efficiency. By utilizing advanced eye-tracking systems, individuals with limited mobility are able to control various devices and interfaces simply by moving their eyes. This paper utilizes deep learning techniques to create a low-cost real-time eye-tracking interface for controlling systems. A smart wheelchair and a robotic arm have been developed to design an eye-tracker, aiming to address the challenges faced by paralyzed people with severe physical limitations. The results demonstrate that eye-tracking is both fast and accurate, making it an effective tool for improving the interactions and accessibility for disabled individuals. © 2023 IEEE.

关键词： Robotic arms

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Exploring the Impact of Robotics in STEM Education Activities and Competitive Challenges 2

Exploring the Impact of Robotics in STEM Education Activitie...

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2nd International Conference on Cybernetics and Innovations, ICCI 2023

作者： Farooq, Adil Zukhraf, Syeda Zillay Nain Maryam, Hafsa Attard, Conrad Kamal, Mohsin University of Cyprus Dept. of Electrical and Computer Engineering Cyprus National University of Computer and Emerging Sciences Dept. of Electrical Engineering Pakistan University of Malta Dept. of Computer Information Systems Msida Malta

ISBN: (纸本)9798350336573

As robotics and technology become increasingly integrated into education and daily life, it is important to recognize their essential role. Even young children have access to electronic toys and gadgets, which can facilitate their learning and interaction. This study highlights the significance of STEM (science, technology, engineering, and mathematics) education at the elementary school level, emphasizing how university students from a variety of disciplines can design and construct robots capable of carrying out challenging tasks for humanitarian purposes. The study also emphasizes the need to encourage women's participation in robotics to showcase their skills and innovative ideas. Furthermore, the study demonstrates how robotics can effectively be used in both STEM education and competitive challenges, showcasing its effectiveness in teaching and promoting technological advancement. By encouraging early engagement in STEM education and promoting diversity in the field of robotics, we can empower future generations to lead the way in creating innovative solutions to global challenges. © 2023 IEEE.

关键词： Intelligent systems

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Designing a Secure Audio / Text Based Captcha Using Neural Network 13

Designing a Secure Audio / Text Based Captcha Using Neural N...

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13th International Conference on Cloud Computing, Data science and engineering, Confluence 2023

作者： Pai, H. Aditya Anandkumar, M. Prasad, M.S. Guru Agarwal, Jyoti Christa, Sharon Dept. of Computer Science and Engineering Dehradun India Dept. of Computer Application Dehradun India

ISBN: (纸本)9781665462631

A CAPTCHA is used to reduce the misuse in the case of rising automated bots or software systems and also to avoid any corruption of the web services. The Captcha is used as solving a turing test problem by the users, before they get access to the software system. The paper discusses about the model that extracts the alphanumeric characters from either of audio or text-based captchas. Finally, the model's performance is found based on different parameters like accuracy level, specificity, precision etc. © 2023 IEEE.

关键词： Web services

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Early Detection of Infant Cerebral Palsy Risk based on Pose Estimation using OpenPose and Advanced Algorithms from Limited and Imbalance Dataset

Early Detection of Infant Cerebral Palsy Risk based on Pose ...

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2023 IEEE International Symposium on Medical Measurements and Applications, MeMeA 2023

作者： Ningrum, Endah Suryawati Yuniarno, Eko Mulyanto Purnomo, Mauridhi Hery Institut Teknologi Sepuluh Nopember Dept. of Electrical Engineering Dept. of Computer Engineering Surabaya Indonesia Institut Teknologi Sepuluh Nopember Dept. of Electrical Engineering Dept. of Computer Engineering Surabaya Indonesia

ISBN: (纸本)9781665493840

Detection of the risk of cerebral palsy existance in infant phase is critical during human development. The fidgety movements of infant during this phase plays an important role in indication of normal or abnormality of balanced and coordination. Previous researches have shown the possibility of abnormality detection using infant pose estimation. However, in particular for predicting the risk of cerebral palsy (CP) based on the estimation of the infant's movement poses, it is not optimal in its classification due to the rarity of dataset sources. This research aimed to develop a classifier based on OpenPose and advanced algorithms, including a Long Short-Term Memory (LSTM) network, 1-dimensional Convolutional Neural Network (CNN) combined with LSTM, and Gated Recurrent Unit (GRU), to predict the likelihood of cerebral palsy in infants, where amount of data is limited and there is an imbalance in categories. Such dataset was obtained from Chambers et al. and divided into 'at-risk' and 'healthy' categories. This research evaluates the performance of different algorithms in classifying infants with cerebral palsy and those without. After perfecting the model, ID CNN combined with LSTM outperformed other models with an accuracy of 0.96. Meanwhile, GRU achieved an accuracy of 0.83, and LSTM achieved an accuracy of 0.77. This research also highlights the potential of using OpenPose and advanced algorithms to accurately predict and prevent cerebral palsy in infants, providing valuable insights for future research in this area. © 2023 IEEE.

关键词： Long short-term memory

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Framework for Cloud-Based Data Management in Autonomous Vehicles

Framework for Cloud-Based Data Management in Autonomous Vehi...

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2025 IEEE International Students' Conference on electrical, Electronics and computer science, SCEECS 2025

作者： Pallapothu, Harshith Gopi Krishna, G. Sunil, Digvijay Teja Yadav, S. Nagasundari, S. Pes University Dept. of Computer Science & Engineering Bengaluru India Research Center for Information Security Forensics and Cyber Resilience Pes University Dept. of Computer Science & Engineering Bengaluru India

ISBN: (纸本)9798331529833

The increasing adoption of autonomous vehicles has driven the need for robust data management solutions that support real-time operations and ensure vehicle safety and efficiency. This work introduces a cloud-based framework for management of sensor data from autonomous vehicles, focusing on optimizing payload transmission rates. The framework leverages AWS IoT Core and AWS IoT Analytics to ensure efficient data flow from vehicle sensors to cloud storage. This framework demonstrates its potential for scalable deployment in real-world autonomous vehicle networks, contributing to the evolution of connected vehicle technologies and intelligent transport systems. This framework ensures reliable data transmission up to 250 payloads per second, beyond which data loss occurs. © 2025 IEEE.

关键词： Cloud platforms

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Psychological Wellbeingness Prediction Using Machine Learning with Hybrid Hyparameter Tuning Techniques

Psychological Wellbeingness Prediction Using Machine Learnin...

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2024 International Conference on Artificial Intelligence and Emerging Technology, Global AI Summit 2024

作者： Rajah, Puvanenthira Habib, Masruk Ravikumar, R.N. Nadarajan, Sivakumar Chougule, Bhagyashree Amit Kirubhakaran, M. Marwadi University Dept. of Computer Engineering Rajkot India Alliance University Dept. of Computer Science and Engineering Bangalore India Tatyasaheb Kore Institute of Engineering and Technology Dept. of Computer Engineering Maharashtra India Suriya Engineering College Dept. of Ai & Ds Erode India

ISBN: (纸本)9798350379716

This study investigates the application of machine learning (ML) techniques in predicting Psychological Well-being outcomes, emphasizing the use of ensemble methods like AdaBoost and Random Forest for enhanced accuracy. With Psychological well-being issues affecting a significant portion of the global population, traditional assessment methods are challenged by issues of accessibility, stigma, and subjectivity. Leveraging data from various surveys, this research compares different ML algorithms, including Logistic Regression and Support Vector Machines, in predicting psychological well-being conditions. The results highlight the superior performance of AdaBoost and Random Forest, particularly when hyperparameters are finely tuned, achieving accuracies up to 0.996. The study underscores the potential of ML in improving psychological well-being interventions by offering precise, unbiased, and accessible diagnostic tools. Future research directions include the integration of multimodal data and the development of models that can suggest personalized treatment plans while ensuring data privacy. This paper advocates for the integration of advanced computational techniques with clinical insights to revolutionize psychological well-being diagnostics and care. © 2024 IEEE.

关键词： Clinical research

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Enhancing 3D Modeling Quality Through Orthorectified Image Accumulation

Enhancing 3D Modeling Quality Through Orthorectified Image A...

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2025 International Conference on Electronics, Information, and Communication, ICEIC 2025

作者： Kwag, Cheolsu Kim, Euntaek Lee, Yongsuk Jung, Seungmin Hwang, Sung Soo Ryu, Hwangryol Moon, Youngmin Handong Global University Dept. of Computer Science and Electrical Engineering Pohang Korea Republic of School of Computer Science and Electrical Engineering Handong Global University Pohang Korea Republic of Research Institute of Industrial Science and Technology Smart Engineering Research Laboratory Pohang Korea Republic of

ISBN: (纸本)9798331510756

This study introduces a novel methodology designed to facilitate the capture of comprehensive image datasets, crucial for accurate 3D modeling of expansive indoor spaces. Leveraging orthophotos generated from panoramic camera data and visualizing image coverage density through heatmaps, our approach effectively identifies under-captured and unvisited areas. This enables targeted image acquisition, minimizes redundant reshoots, and enhances the overall quality and fidelity of 3D models. © 2025 IEEE.

关键词： Digital elevation model

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Deep Learning-Driven Cloud Intrusion Detection: Internet of Things and Cloud Networks 1

Deep Learning-Driven Cloud Intrusion Detection: Internet of ...

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1st International Conference on Advances in computer science, electrical, Electronics, and Communication Technologies, CE2CT 2025

作者： Moon, Iffath Tanjim Islam Mondal, Md. Nazrul Rajshahi University of Engineering & Technology Dept. of Computer Science & Engineering Rajshahi6204 Bangladesh

ISBN: (纸本)9798331518578

In this paper, we have utilized deep learning approaches to detect cloud intrusion for the Internet of Things (IoT). The emerging growth of IoT and cloud environments has revolutionized many industries by allowing real-time data sharing and storage. Cloud platforms offer the essential infrastructure for managing the massive volumes of data produced by IoT devices. However, due to several flaws, including insufficient encryption, insecure APIs, and weak authentication, intruders can exploit the vulnerabilities to interrupt services and obtain unauthorized access. Therefore, deep learning can be an effective solution to detect cloud intrusion in IoT. In this research, we have developed individual convolutional neural network (CNN) and long short-term memory (LSTM) models, along with a hybrid CNN-LSTM model for multiclass classification and binary classification to detect cloud intrusion. In addition, we have examined which models perform the best for cloud intrusion detection for IoT among the individual and hybrid models. The proposed models are effective in detecting cloud intrusion, as CNN can extract spatial features, and LSTM can capture temporal dependencies from network traffic data. Furthermore, we have utilized one of the most recent and comprehensive datasets for IoT, the ToN-IoT dataset, containing real-world data. In multiclass classification, the hybrid CNN-LSTM has given the best performance, achieving an accuracy of 97.65%, precision of 97.72%, recall of 97.65%, f1 score of 97.67%, and AUC of 99.93%. In binary classification, all CNN, LSTM, and hybrid CNN-LSTM have demonstrated high performance, where LSTM has achieved the highest accuracy of 99.97%. Moreover, we have evaluated the performance of multiclass classification for detecting nine distinct kinds of attacks. In this instance, CNN has shown better performance in detecting backdoor attacks. Thus, this research can make the IoT platform more secure by efficiently detecting cloud intrusion. © 2025

关键词： Network intrusion

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