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4th International Conference on Technological Advancements in Computational sciences, ICTACS 2024

作者： Rajinikanth, Venkatesan Damaševičius, Robertas Taniar, David Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Division of Research and Innovation Department of Computer Science and Engineering TN Chennai602105 India Kaunas University of Technology Centre of Real Time Computer Systems Kaunas44249 Lithuania Monash University Faculty of Information Technolog MelbourneVIC3800 Australia

ISBN: (纸本)9798350387490

computer algorithm supported medical image analysis is a prevalent method in clinics, which effectively alleviates the diagnostic burden associated with traditional image assessment methods. This study aims to create a method that utilizes a Convolutional-Neural-Network (CNN) to accurately segment the Femoropopliteal Artery Stent (FAS) in Digital Fluoroscopy Images (DFI). In order to improve the accuracy of segmentation, this study used the combination of Otsu's tri-level thresholding and the Lévy-Flight Distribution Algorithm (LFDA). The suggested technique consists of many stages: collecting and resizing images and masks, boosting the DFI using Otsu and LFDA, using CNN-segmentation to extract the stent region, comparing segmented stent with mask, and calculating the required performance metrics. The efficacy of the suggested system is validated by analyzing the raw and thresholded DFI. The investigational results of this work affirm that this method achieves a higher level of accuracy (>96%) in segmenting images when the Otsu's preprocessed image is utilized. The experimental study employs the UNet-variants, and in the future, alternative CNN-segmentation methodologies can be utilized to validate the effectiveness of the presented scheme. © 2024 IEEE.

关键词： Convolutional neural networks

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Design of Scatterer Configuration for Spectral Optimization of Random Lasers

Design of Scatterer Configuration for Spectral Optimization ...

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2022 Conference on Lasers and Electro-Optics Pacific Rim, CLEO-PR 2022

作者： Okamoto, Takashi Kajisa, Kouki Ohshige, Ryo Faculty of Computer Science and Systems Engineering Kyushu Institute of Technology 680-4 Kawazu Fukuoka Iizuka820-8502 Japan

ISBN: (纸本)9798350350012

The two-dimensional structure of a random gain medium was designed to obtain the specified emission spectrum from a random laser. Structural optimization was performed using a direct binary search method. The simulation results showed that the emitted light can be concentrated within one or more specific wavelength ranges. The effect of the fabrication errors on the optimized laser emission spectrum was also examined. © IEEE 2022.

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Robust Tweets Classification Using Arithmetic Optimization with Deep Learning for Sustainable Urban Living

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SN computer science 2024年第5期5卷 549页

作者： Hamza, Manar Ahmed Hashim, Aisha Hassan Abdalla Motwakel, Abdelwahed Elhameed, Elmouez Samir Abd Osman, Mohammed Kumar, Arun Singla, Chinu Munjal, Muskaan Department of Electrical and Computer Engineering International Islamic University Malaysia Kuala Lumpur 53100 Malaysia Department of Computer and Self Development Preparatory Year Deanship Prince Sattam bin Abdulaziz University AlKharj Saudi Arabia Department of Management Information Systems College of Business Administration in Hawtat bani Tamim Prince Sattam bin Abdulaziz University AlKharj Saudi Arabia Department of Computer Science College of Post-Graduated Studies University of Science and Technology Omdurman Sudan Department of Information System Faculty of Computer Science and Information Technology Omdurman Islamic University Omdurman Sudan Department of Electronics and Communication Engineering New Horizon College of engineering Bengaluru India Computer Engineering Thapar Institute of Engineering and Technology Punjab Patiala India

Natural Language Processing (NLP) with Deep Learning (DL) for Tweets Classification includes use of advanced neural network designs to analyse and classify Twitter messages. DL techniques like recurrent neural network (RNN) or transformer-based frameworks like BERT are used to mechanically learn difficult linguistic patterns and contextual info from tweet data. These techniques able to capture subtleties of language with sarcasm, sentiment, and context-specific meanings and making them suitable for tasks like sentiment analysis or topic classification in realm of social media. Leveraging deep symbols learned from great amounts of textual data, these NLP techniques permit precise and nuanced classification of tweets, donating to enhanced information retrieval, sentiment tracking, and trend analysis in dynamic and fast-paced world of social media communication. In this view, this research develops an arithmetic optimization algorithm with deep learning based tweets classification (AOADL-TC) approach for sustainable living. The goal of the AOADL-TC technique is to identify and discriminate different kinds of sentiments that exist in the tweet data. At the initial stage, the AOADL-TC model pre-processes tweet data to convert uniform data into a useful format. For sentiment detection, the AOADL-TC technique applies a parallel bidirectional gated recurrent unit (BiGRU) model. At last, tuning of parameters related to parallel BiGRU model performed by AOA. An wide set of tests carried out to illustrate better performance of AOADL-TC model. The experimental outcomes portrayed that AOADL-TC technique demonstrates the supremacy of the AOADL-TC technique in terms of different evaluation metrics. © The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd. 2024.

关键词： Deep learning Machine learning Natural language processing Sentiment analysis Social media

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Exploring the Role of Humanoid Robots in Adaptive Learning systems: A Case Study on Quizzing

Exploring the Role of Humanoid Robots in Adaptive Learning S...

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Mobile, Intelligent, and Ubiquitous Computing Conference (MIUCC), International

作者： Eman A. Noh Haya k. Elsonbaty Noure A. Elnakeib Ghada K. El-Monayer Nada M. Enan Omar M Elzeki Aya M. Al-Zoghby Khaled M. Fouad Artificial Intelligence Science Faculty of Computer Science and Engineering New Mansoura University Dakhlia Egypt Faculty of Computer Science and Engineering New Mansoura University Faculty of Computer Science & Engineering Faculty of Computers and Information Mansoura University New Mansoura University Mansoura Egypt Computer Science Department Faculty of Computers and Artificial Intelligence Damietta University Damietta Egypt Faculty of Computer Science and Engineering New Mansoura University Dakhlia Egypt Information Systems Faculty of Computers and Artificial Intelligence Banha University Egypt

ISBN: (数字)9798350367775

ISBN: (纸本)9798350367782

This paper analyses how Quizmaster can be incorporated into the humanoid robot to improve learning processes in educational environments. The humanoid robot incorporates artificial intelligence in its function as an educator and adds a new dimension to the way an interactive learning environment can be achieved in the form of quiz-based learning modules. The current system of Quizmaster has been designed as a separate application but has been modified to work in harmony with the robot and all its hardware and software components. The idea of this integration is to develop an effective learning tool that will help teachers present personalized educational material and evaluate learners' performance immediately. The paper covers the technical changes needed for this integration and the obstacles met while implementing it, as well as possible educational benefits of using robots in classrooms. The effectiveness of the system is established according to the degrees of usability and learners' interest and knowledge gains data gathered from the pilot implementations in differentiated learning contexts. Thus, the results state the positive progress of student's engagement and their knowledge, and it proves how robotics is useful in current education. Thus, this paper provides practical recommendations for expanding research and mass application of robotics-based learning aids in educational organizations.

关键词： Humanoid robots Learning (artificial intelligence) Organizations Educational technology Ubiquitous computing Software Real-time systems Hardware Usability Testing

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VHF Band III Spectrum Availability and Utilization for Cognitive Radio Implementation in Malaysia 22

VHF Band III Spectrum Availability and Utilization for Cogni...

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22nd International Learning and Technology Conference, L and T 2025

作者： Elshafie, Hashim Mohamad, Hafizal Saeed, Rashid A. Hamdan, Mosab Khattab, Mahmoud M. Ahmed, Abdelmoty M. King Khalid University College of Computer Science 61421 Saudi Arabia Universiti Sains Islam Malaysia Faculty of Engineering and Built Environment Negeri Sembilan Nilai71800 Malaysia Taif21974 Saudi Arabia South East Technological University Walton Institute for Information and Communication Systems Science WaterfordX91 HE36 Ireland Al Yamamah University College of Engineering and Architecture Riyadh13541 Saudi Arabia Nahda University Faculty of Computer Science Beni Suef Egypt

ISBN: (纸本)9798331532215

The VHF Band III spectrum (174-230 MHz), recognized for its low utilization, was opened by the FCC in 2002 for opportunistic use by communication technologies. This paper presents a quantitative analysis of the availability of VHF Band III for cognitive radio CR access in Malaysia. Measurements were conducted over a week in 18 Malaysian cities to analyze spectrum utilization and received signal strength, alongside the impact of broadcasts from neighboring countries. The findings highlight actual utilization patterns and reveal the spectrum's potential for opportunistic access, especially post-digital switchover (DSO). © 2025 IEEE.

关键词： Cognitive radio

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Transformative effects of ChatGPT on modern education: Emerging Era of AI Chatbots

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Internet of Things and Cyber-Physical systems 2024年第1期4卷 19-23页

作者： Gill, Sukhpal Singh Xu, Minxian Patros, Panos Wu, Huaming Kaur, Rupinder Kaur, Kamalpreet Fuller, Stephanie Singh, Manmeet Arora, Priyansh Parlikad, Ajith Kumar Stankovski, Vlado Abraham, Ajith Ghosh, Soumya K. Lutfiyya, Hanan Kanhere, Salil S. Bahsoon, Rami Rana, Omer Dustdar, Schahram Sakellariou, Rizos Uhlig, Steve Buyya, Rajkumar School of Electronic Engineering and Computer Science Queen Mary University of London London United Kingdom Shenzhen Institute of Advanced Technology Chinese Academy of Sciences Shenzhen China Raygun Performance Monitoring Wellington New Zealand Center for Applied Mathematics Tianjin University Tianjin China Department of Science Kings Education London United Kingdom Cymax Group Technologies BC Canada QM Academy Queen Mary University of London London United Kingdom Jackson School of Geosciences University of Texas at Austin AustinTX United States Centre for Climate Change Research Indian Institute of Tropical Meteorology Pune India Microsoft Hyderabad India Institute for Manufacturing Department of Engineering University of Cambridge Cambridge United Kingdom Faculty of Computer and Information Science University of Ljubljana Ljubljana Slovenia Machine Intelligence Research Labs AuburnWA United States Faculty of Computing and Data Science FLAME University Maharashtra Pune India Department of Computer Science and Engineering Indian Institute of Technology Kharagpur India Department of Computer Science University of Western Ontario London Canada Sydney Australia School of Computer Science University of Birmingham United Kingdom School of Computer Science and Informatics Cardiff University Cardiff United Kingdom Distributed Systems Group Vienna University of Technology Vienna Austria Department of Computer Science University of Manchester Oxford Road Manchester United Kingdom Laboratory School of Computing and Information Systems The University of Melbourne Australia

ChatGPT, an AI-based chatbot, offers coherent and useful replies based on analysis of large volumes of data. In this article, leading academics, scientists, distinguish researchers and engineers discuss the transformative effects of ChatGPT on modern education. This research discusses ChatGPT capabilities and its use in the education sector, identifies potential concerns and challenges. Our preliminary evaluation shows that ChatGPT perform differently in different subject areas including finance, coding, maths, and general public queries. While ChatGPT has the ability to help educators by creating instructional content, offering suggestions and acting as an online educator to learners by answering questions, transforming education through smartphones and IoT gadgets, and promoting group work, there are clear drawbacks in its use, such as the possibility of producing inaccurate or false data and circumventing duplicate content (plagiarism) detectors where originality is essential. The often reported "hallucinations" within GenerativeAI in general, and also relevant for ChatGPT, can render its use of limited benefit where accuracy is essential. What ChatGPT lacks is a stochastic measure to help provide sincere and sensitive communication with its users. Academic regulations and evaluation practices used in educational institutions need to be updated, should ChatGPT be used as a tool in education. To address the transformative effects of ChatGPT on the learning environment, educating teachers and students alike about its capabilities and limitations will be crucial. © 2023 The Authors

关键词： Machine learning

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Multimodal Fusion for Enhanced Parkinson's Disease Screening: Integrating Brain MRI, Hand Drawing, Facial Expressions, and Voice Analysis 5

Multimodal Fusion for Enhanced Parkinson's Disease Screening...

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5th International Conference on Advancements in Computing, ICAC 2023

作者： Warnakulasuriya, Naveen De Silva, Shalinika Madushika, Janani Gamage, Sadali Jayawardena, Sanath Karunasena, Anuradha Sri Lanka Institute of Information Technology Faculty of Computing Department of Information Technology Malabe Sri Lanka Sri Lanka Institute of Information Technology Faculty of Computing Department of Computer Science and Software Engineering Malabe Sri Lanka Sri Lanka Institute of Information Technology Faculty of Computing Department of Computer Systems Engineering Malabe Sri Lanka

ISBN: (纸本)9798350358131

Parkinson's disease (PD) profoundly impacts millions in Sri Lanka, emphasizing the importance of early detection for better patient outcomes. We introduce 'NeuraTrace PD,' an innovative application for early PD screening, combining brain MRI, hand-drawn image analysis, facial expression assessment, and voice analysis. For brain MRI analysis, we employed a Graph Convolutional Network (GCN), achieving an impressive accuracy of 91% and identifying distinct graph connectivity patterns using graph theory. We also use a deep Convolutional Neural Network (CNN) to reach 90% accuracy in analyzing hand-drawn images, while optimizing image resolution for efficiency and information retention. Facial expression analysis is a vital component, leveraging CNN and Constrained Local Models (CLM) to accurately extract Facial Action Units (FAUs), which contribute to a remarkable 95% accuracy using the XGBoost machine learning model. We introduce a Machine Learning (ML) algorithm and advanced signal processing to enhance voice feature analysis, offering a thorough understanding and classification of PD patients. It reaches 99% accuracy. In summary, our approach holds significant promise for advancing early PD diagnosis, ultimately improving the quality of life for those affected by this challenging condition. © 2023 IEEE.

关键词： Image resolution

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A Bibliometric Analysis and Research Landscape of Artificial Intelligence in Education 14

A Bibliometric Analysis and Research Landscape of Artificial...

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14th IEEE International Conference on Control System, Computing and engineering, ICCSCE 2024

作者： Ajibade, Samuel-Soma M. Issa, Bayan Jasser, Muhammed Basheer Hassan, Farrukh Aldharhani, Ghassan Saleh Al-Hadi, Ismail Ahmed Al-Qasem Akintoye, Kayode Akinlekan Tabuena, Almighty Cortezo School of Engineering and Technology Sunway University Department of Computing and Information Systems No. 5 Jalan Universiti Selangor Darul Ehsan Bandar Sunway47500 Malaysia Istanbul Ticaret University Department of Computer Engineering Istanbul Turkey University of Aleppo Faculty of Informatics Engineering Syria Institute of Computer Science and Digital Innovation UCSI University Kuala Lumpur Malaysia UKM University Faculty of Information Science and Technology Selangor Bangi Malaysia The Federal Polytechnic Department of Computer Science Ado Ekiti Ekiti State Nigeria University of the City of Valenzuela College of Arts and Sciences Philippines

ISBN: (纸本)9798350364507

This study examines the academic field of Artificial Intelligence in Education (AI-ED) by examining the publications and indexed documents in Scopus from 2010 to 2023. Consequently, we examined the publishing trends, exemplary articles, and the most engaged participants and funding institutions in the domain of AI-ED research. A bibliometric analysis was performed to examine the network of co-authorships, keywords, and citations in the domain of AIED research. The Scopus search resulted in the discovery of 10,474 published documents on AI-ED. Additionally, trends analysis indicated a significant increase of over 3,000% in publications between 2010 and 2023. Conference papers are the most often utilized document type, including 6757 publications or 64.5% of the overall amount. The ACM International Conference Proceeding Series is the primary source for published materials, accounting for 675 publications or 6.44% of the total publications on the topic. The most prolific researchers in the field of AI-ED are Breazeal, C and Chai C.S, each having published 18 documents. Additionally, the two institutions that have produced the highest number of published documents are Carnegie Mellon University (CMU) and the University of NC State University (NCSU). The output of the affiliations active in the topic is primarily attributed to the funders, specifically the National science Foundation, which is the leading funding institution based in the United States and has contributed to 354 publications. An analysis of keyword co-occurrence revealed 6 main study areas that cover the essential tools, theories, methodologies, and socioeconomic and financial aspects of artificial intelligence in education (AIED). Future research in the field of artificial intelligence in education will focus on employing sophisticated deep learning, machine learning, and neural network algorithms to precisely predict students' learning patterns. © 2024 IEEE.

关键词： Adversarial machine learning

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Monkeypox Detection with Deep-Learning and Ensemble of Features: A Study 4

Monkeypox Detection with Deep-Learning and Ensemble of Featu...

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4th International Conference on Technological Advancements in Computational sciences, ICTACS 2024

作者： Rajinikanth, Venkatesan Taniar, David Damaševičius, Robertas Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Division of Research and Innovation Department of Computer Science and Engineering TN Chennai602105 India Monash University Faculty of Information Technology MelbourneVIC3800 Australia Centre of Real Time Computer Systems Kaunas University of Technology Kaunas44249 Lithuania

ISBN: (纸本)9798350387490

There is a growing global prevalence of infectious diseases in the human population and it is imperative to have precise diagnosis and therapy in order to effectively cure these diseases. Monkeypox (Mpox) is an infectious disease that can cause varying degrees of skin infection, ranging from minor to severe. The recent spread of Mpox has been confirmed as Public Health Emergency of International Concern (PHEIC) due to its large infection rate. This study aims to develop a deep-learning (DL) scheme for Mpox identification. To enhance the detection accuracy, this study considered preprocessed skin images for the analysis and employed the binary classification with Ensemble of Deep-Features (EDF) using 5-fold cross validation. The outcome of this investigation validate that the suggested deep learning tool enables the attainment of a detection accuracy of 100% on the augmented images from the Monkeypox Skin Images Dataset (MSID). Compared to the existing results in the literature, this research provides a significant result on the chosen MSID database. © 2024 IEEE.

关键词： Classification Ensemble of deep-features Healthcare Illness Monkeypox

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Design of Scatterer Configuration for Spectral Optimization of Random Lasers

Design of Scatterer Configuration for Spectral Optimization ...

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2022 Conference on Lasers and Electro-Optics Pacific Rim, CLEO/PR 2022

作者： Okamoto, Takashi Kajisa, Kouki Ohshige, Ryo Faculty of Computer Science and Systems Engineering Kyushu Institute of Technology 680-4 Kawazu Fukuoka Iizuka820-8502 Japan

关键词： Structural optimization

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