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Human-computer Interaction, Optimization and Robotic Applications (HORA), International Congress on

作者： Rasha J.H. Habeeb Kamil Dimililer Department of Computer Engineering Near East University North Cyprus Turkey Department of Electrical and Electronic Engineering Applied Artificial Intelligence Research Center (AAIRC) Center for Technology and Engineering (BILTEM) Near East University North Cyprus Turkey

ISBN: (数字)9798331510886

ISBN: (纸本)9798331510893

In the field of image processing, noise removal and enhancement of resolution are two of the most basic issues for enhancing the quality of an image. In the following paper, the problem of noisy images and low resolution will be solved in a fast and lightweight manner by applying adaptive smoothing and cubic interpolation, respectively. The performance of the proposed method is tested on more than 100 images containing many categories to check the effectiveness and efficiency of the *** squared error was one of the measurement indices used to compare the processed photos in this study with the original dataset in order to assess the findings; mean squared error values ranging from 0.54 to 144.88 were found. The SSIM fluctuated between 0.938 and 0.998, while the PSNR ranged from 26.75 to 46.76. Overall, the findings demonstrate how well the technique works to improve the quality of photos with high PSNR and SSIM *** work also proposes a lightweight way to track outcomes, where most of the steps can be coded to log metrics for each image and a case making the process reproducible. The approach also focuses on how traditional image processing techniques can deliver high-quality results with low computational cost. It is possible that future work investigates the use of these techniques coupled with more complex machine learning algorithms to be more suited for data handling and improving overall result processing.

关键词： Interpolation Smoothing methods Filters Statistical analysis Noise Superresolution Noise reduction Standards Robots Optimization

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Efficient Detection of Knee Osteoporosis Using the Swin Transformer on X-Ray Images

Efficient Detection of Knee Osteoporosis Using the Swin Tran...

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2024 International Conference on computer and Applications, ICCA 2024

作者： Sultana, Rahat Zaman, Muhammad Ivković, Nikola Cengiz, Korhan Rafaqat, Iqra Akhunzada, Adnan The University of Lahore Faculty of Computer Science Sargodha Pakistan Comsats University Islamabad Department of Computer Science Islamabad Pakistan University of Zagreb Faculty of Organization and Informatics Varaždin Croatia Biruni University Department of E&e Engineering Istanbul Turkey University of Doha for Science and Technology Department of Data and Cybersecurity Doha24449 Qatar

ISBN: (纸本)9798350367560

Knee Osteoporosis (KOP) is a skeletal disease that is caused by low bone mineral density and the degradation of bone tissue. It increases the risk of bone fractures in the knee region and is commonly seen in older people but usually ignored due to its silent nature. Osteoporosis is mostly diagnosed via X-ray images and Dual-Energy X-ray Absorptiometry (DXA) scans that can be difficult to read due to their sheer volume the subtle variations. This study aims to develop a Deep Learning (DL) utilizing Swin Transform model that can accurately recognize osteoporosis in knee X-ray images at an early stage. The dataset used for this research comprises knee X-rays labeled as either 'normal' or 'osteoporotic,' with image preprocessing including resizing to standard dimensions, converting the images into tensors, and normalizing pixel values to improve model performance. The model is trained and validated on arranged dataset, characterized by metrics such as accuracy, precision, recall or F1-score that gives an idea how well our model performs for prediction as acknowledged for its performance in image classification tasks. The results demonstrate that the Swin Transformer model has an overall accuracy of 89.38% on the data set for identifying osteoporosis features on knee X-ray images. This approach demonstrates the potential of the Swin Transformer model to serve as an effective tool for early osteoporosis diagnosis, offering improved accuracy and reliability compared to traditional convolutional neural networks (CNNs). © 2024 IEEE.

关键词： Diseases

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Arabic Sentiment Analysis of Food Delivery Services Reviews

Arabic Sentiment Analysis of Food Delivery Services Reviews

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2023 International Symposium on Networks, computers and Communications, ISNCC 2023

作者： Mustafa, Dheya Khabour, Safaa M. Shatnawi, Ahmed S. Taqieddin, Eyad The Hashemite University Faculty of Engineering Dept. of Computer Engineering Zarqa13133 Jordan Yarmouk University Faculty of Information Technology and Computer Sciences Dept. of Information Systems Irbid21163 Jordan Jordan University of Science & Technology Dept. of Software Engineering Irbid21110 Jordan School of Computing and Informatics Dept. of Cyber Security Al-Hussein Technical University Amman Jordan

ISBN: (纸本)9798350335590

Customer reviews on online platforms have grown to become an important source of insight into a company's performance. Food delivery services (FDS) companies aim to effectively use customers' feedback to identify areas for improvement of customer satisfaction. Although Arabic is becoming one of the most widely used languages on the Internet, only a few studies have focused on Arabic sentiment analysis to date. The present study conducts an extensive emotion mining and sentiment analysis on FDS-related reviews in Arabic, exploiting natural language processing, and machine learning techniques to extract information that reflects customers' subjective viewpoints, recognize their feelings, and determine polarity in the FDS domain. This work begins with collecting the FDS dataset (Talabat), and then extracting and creating a dialects lexicon for Arabic dialects, followed by walking the reader through detailed steps of cleaning and pre-processing a manually annotated dataset. Finally, we examined classification algorithms including Decision Tree (DT), and Support Vector Machine (SVM). We achieved a maximum accuracy of about 82% using the SVM classifier. © 2023 IEEE.

关键词： Sentiment analysis

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Performance loss rate estimation for systems affected by potential induced degradation 49

Performance loss rate estimation for systems affected by pot...

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49th IEEE Photovoltaics Specialists Conference, PVSC 2022

作者： Goumenos, Panagiotis Livera, Andreas Florides, Michalis Georghiou, George E. University of Cyprus Photovoltaic Technology Laboratory Foss Research Centre for Sustainable Energy Department of Electrical and Computer Engineering Panepistimiou 1 Avenue P.O. Box 20537 Nicosia1678 Cyprus

ISBN: (数字)9781728161174

ISBN: (纸本)9781728161174

The impact of potential induced degradation (PID) on the performance degradation is an area which has not been fully explored yet. The scope of this work is to investigate the impact of PID on the performance loss rate (PLR) estimation of fielded photovoltaic (PV) systems. To extract the PLR, three common statistical methods were used;linear regression (LR) with ordinary least squares, locally estimated scatterplot smoothing (LOESS) and robust principal component analysis (RPCA). The results demonstrated PLR values higher than-1 %/year for PID affected systems, while values lower than-0.5 %/year were obtained for non-affected PID systems. Additionally, PID affected PV systems exhibited higher PLR in the early years of operation in contrast to the obtained PLR value in the subsequent years. © 2022 IEEE.

关键词： Least squares approximations

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Mobile Vision Transformer for Surface Defect Classification from A Tiny Dataset 14

Mobile Vision Transformer for Surface Defect Classification ...

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14th International Conference on Information and Communication technology and System, ICTS 2023

作者： Permatasari, Ghaluh Indah Pao, Hsing-Kuo Pribadi, Rudy Cahyadi Hario Iqbal, Mohammad National Taiwan University of Science and Technology Department of Computer Science and Information Engineering Taipei106 Taiwan University of Bhayangkara Department of Informatics Engineering Surabaya Indonesia Institut Teknologi Sepuluh Nopember Department of Mathematics Surabaya60111 Indonesia

ISBN: (纸本)9798350312164

Surface defect classification is one pivotal step during the last product inspection before selling it to the market. Indeed, we want to serve our product to the customer in good condition. Speaking of smart manufacturing, existing studies on hot-rolled steel strip surface defect classification used deep learning models, which are very costly in terms of hardware, parameters, and dataset size. In this work, we propose a finetuned lightweight model from the Mobile Vision Transformer (MobileViT) to efficiently classify the surface defect of hot-rolled steel strips from a tiny data set. In this study, we experimented on a public benchmark dataset for surface defects of hot-rolled steel strip classification. The results showed that the proposed model can predict the defect accurately with fewer parameters and a smaller dataset than the SOTA ones. © 2023 IEEE.

关键词： Classification (of information)

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SLAH: A Siamese Network Vision Tracking Algorithm Using LSTM Adaptive Head 13

SLAH: A Siamese Network Vision Tracking Algorithm Using LSTM...

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13th International Conference on Information Science and technology, ICIST 2023

作者： Liu, Zhitao Wang, Dong Luo, Xiaonan Li, Fang School of Computer Science and Information Security Guilin University of Electronic Technology Guilin China College of Mathematics and Informatics College of Software Engineering South China Agricultural Univerrsity Guangzhou China

ISBN: (纸本)9798350313925

In the field of single object tracking, conventional methods often rely on correlation filters or visual image processing. However, these approaches typically focus solely on extracting target object features and consider only the position information in the current frame. They lack integration of contextual and spatial-temporal information, which can limit tracking *** method takes inspiration from single object detection and tracking techniques. It combines efficient model-based object detection with the spatial position of the final target in the image. This approach offers significant advantages in reducing tracking drift (deviation from the target object) and improving tracking accuracy (predicting bounding boxes that closely follow the target).To extract spatial information, we utilize a three-layer ResNet and Convolutional Block Attention Module (CBAM) in conjunction with a siamese network. This combination effectively captures the spatial characteristics of the target object. Additionally, we employ an adaptive processing head with an internal structure of Long Short Term Memory (LSTM) to capture temporal information. By integrating spatial and temporal cues, our method achieves more robust and accurate *** comprehensive comparison and ablation experiments across multiple datasets, we have demonstrated notable improvements with our method. It tightly connects the predicted bounding box with the tracking target and effectively combines spatial and temporal information for precise object tracking. © 2023 IEEE.

关键词： Long short-term memory

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Defect Recognition based on Radon Transform in Pentacene organic thin-film Archimedean interdigitated spirals transistor

Defect Recognition based on Radon Transform in Pentacene org...

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2023 International Conference on Clean Electrical Power, ICCEP 2023

作者： Sciuto, Grazia Lo Linde, Sivan Shikler, Rafi Kowol, Pawel Coco, Salvo Capizzi, Giacomo Silesian University of Technology Department of Mechatronics Gliwice Poland Ben-Gurion University of the Negev School of Electrical and Computer Engineering Optoelectronic Organic Semiconductor Devices Laboratory Department of Electrical and Computer Engineering Beer-Sheva Israel Ben-Gurion University of the Negev Ilse Katz Institute for Nanoscale Science and Technology Beer-Sheva Israel University of Catania Department of Electrical Electronics and Informatics Engineering Catania Italy

ISBN: (纸本)9798350348378

In this paper the problem of automatic defects detection in thin film transistor is considered in order to increase the efficiency of electronic devices. An effective approach based on defect pattern recognition in organic transistor is developed using the Radon transform. The adopted design and technology in Pentacene organic thin-film transistor exhibits two interdigitated Archimedean spirals pattern. In the conducted experiments, the images acquisition is performed with a microscope camera to capture the various defects of surface morphology on the top of the examined transistors. The defects detection is obtained by using the Radon transform on each channel (R,G,B) of these images. © 2023 IEEE.

关键词： Thin film transistors

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Generic Drone Simulator: Design, Development and User Testing in a Virtual Search and Rescue Mission

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IEEE computer Graphics and Applications 2025年 PP卷 PP页

作者： Pappas, Georgios Lappas, Dimitrios Karampelas, Panagiotis Siegel, Joshua E. Templalexis, Ioannis Stavrou, Stavros Krishnan, Sriram Open University of Cyprus Lab of Educational Material and Methodology Latsia Nicosia Cyprus University of the Aegean Learning Technology and Educational Engineering Laboratory Rhodes Greece Hellenic Air Force Academy Department of Informatics & Computers Dekelia Attiki Greece Michigan State University Department of Computer Science and Engineering East LansingMI United States Hellenic Air Force Academy Department of Thermodynamics Propulsion and Power Systems Dekelia Attiki Greece Open University of Cyprus Cybersecurity and Telecommunications Research Lab Latsia Nicosia Cyprus Indian Institute of Technology The Energy Consortium Madras India

This study addresses the need for scenario-specific drone simulators to enhance research, education, pilot training, and performance evaluation across diverse mission contexts. A gamified simulator was developed for the joint master's degree program "Security and Defence" between the Open university of cyprus (OUC) and the Hellenic Air Force Academy (HAFA), with a focus on Search and Rescue mission training and planning. The simulator balances interface design, physics accuracy, and environmental realism to support specialized professional training and research. A survey-based evaluation involving 57 participants –pilots, engineers, and air traffic controllers –demonstrated the tool's ability to provide realistic experiences for trained pilots and meaningful learning opportunities for novices. Statistical analysis revealed significant correlations between gaming familiarity, task completion times, and participant expertise. © 1981-2012 IEEE.

关键词： Drones

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The Integration of 6G and Blockchain into an Efficient AIoT-Based Smart Education Model 6

The Integration of 6G and Blockchain into an Efficient AIoT-...

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6th World Symposium on Communication engineering, WSCE 2023

作者： Lioupa, Anna Memos, Vasileios A. Stergiou, Christos L. Ishibashi, Yutaka Psannis, Konstantinos E. School of Information Sciences University of Macedonia Dept. of Applied Informatics Thessaloniki Greece Graduate School of Engineering Nagoya Institute of Technology Dept. of Computer Science Nagoya Japan

ISBN: (纸本)9798350339505

The technological advances impact the future classrooms and the whole education system. The evolution of 6G promises faster speeds based on terahertz, wireless communication, the leverage of artificial intelligence (AL), virtually reality (VR) and generally a fully connected world. The use of applications of IoT and AIoT improves the education environment, but the intelligent network services undoubtedly gather, store and analyze big data which are vulnerable to attackers. As a solution we may consider the technology of blockchain. The trust and security of next generation intelligent networks are essential part of their design. In this article we suggest a data security system based on blockchain for AL applications in 6G networks. First, we present two Al-enabled applications, indoor positioning and mobile payment within the framework of 6G in the university area. We present the efficiency of blockchain in data security for Al-enabled systems using two case studies of indoor positioning system and mobile payment. The 6G system which is fully supported by Al and the integration of this in technology of blockchain, is ensured the security in future wireless communication. © 2023 IEEE.

关键词： Blockchain

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WSports-50: An Image Dataset for Women’s Sport Action Classification 4th

WSports-50: An Image Dataset for Women’s Sport Action Class...

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4th International Conference on Frontiers in Computing and Systems, COMSYS 2023

作者： Ray, Palash Bera, Asish Krejcar, Ondrej Bhattacharjee, Debotosh Nasipuri, Mita Department of Computer Science and Engineering Haldia Institute of Technology West Bengal Haldia India Department of Computer Science and Information Systems BITS Pilani Rajasthan Pilani India Center for Basic and Applied Science Faculty of Informatics and Management University of Hradec Kralove Rokitanskeho 62 Hradec Kralove500 03 Czech Republic Department of Computer Science and Engineering Jadavpur University West Bengal Kolkata India

ISBN: (纸本)9789819726134

Sport action recognition is an interesting area in computer vision. Categorization of sport actions, representing difficult and complex body postures, is regarded as a fine-grained visual classification problem. The Convolutional Neural Networks (CNNs) have attained enhanced performance over conventional feature descriptors in recognizing various sport activities. In general, though decent improvement has been gained using deep learning for sport action recognition, however, recognition of women’s sport activities is not widely explored. Even, no benchmark dataset depicting women’s sport action with sufficient variations is available yet for study. Hence, fine-grained image classification of diverse sport categories involving female/women athletics requires immediate research attention. To overcome this limitation, this paper proposes an image dataset comprising worldwide popular 50 sport categories of women players only. A simple deep learning model is proposed that extracts the high-level deep features using a backbone CNN. Then, these features are pooled from a collection of regular regions representing local discriminative information. The spatial pyramid pooling is applied for mining semantic information and enhancing feature aggregation for classification. The proposed method has achieved satisfactory performance on the Women Sports dataset using four standard backbone CNNs. Moreover, our method has achieved better accuracy on the Yoga-82 pose recognition dataset with a significant margin, e.g., 11.6% gain using ResNet-50 base CNN. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Classification (of information)

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