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3rd International Conference on Automation, computing and Renewable Systems, ICACRS 2024

作者： Majaw, Ezekiel Alaric Sundar, G. Naveen Narmadha, D. Thangavel, Senthil Kumar Venkatesan Ajibesin, Adeyemi Abel Department of Computer Science and Engineering Karunya Institute of Technology and Sciences Coimbatore India Department of Computer Science and Engineering Amrita School of Computing Coimbatore India Department of Information Technology Faculty of Informatics and Design Cape Peninsula University of Technology South Africa

ISBN: (纸本)9798331532420

The most common cause of blindness among diabetics is diabetic retinopathy. This disease must be detected in its early stages as delaying treatment can result in permanent blindness. In today’s modern world with the development of technologies, automated techniques have improved the accuracy and efficiency of detecting and classifying this disease. This study suggests a deep learning model utilizing EfficientNetB0 that focuses on designing a neural network structure that requires fewer parameters and computations compared to traditional architectures. Further to help in increasing the accuracy, the image is being processed with methods like CLAHE which is an image processing algorithm that helps in enhancing the contrast while keeping the noise of the images as low as possible. Also, augmentation is being done on the images and SMOTE is applied to handle class imbalance. We collected the APTOS Blindness Detection dataset consisting of retinal fundus images. The accuracy that is being obtained from the proposed methodology is 98.78% indicating the model's effectiveness in detection and classifying between the various stages of diabetic retinopathy. © 2024 IEEE.

关键词： Contrastive Learning

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Sinhala Character Identification Using Orientation and Support Vector Machine

Sinhala Character Identification Using Orientation and Suppo...

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Advanced Research in computing (ICARC), International Conference on

作者： K.D. Thamarasee R.M.D.B Surendra School of computing Informatics Institute of Technology Colombo Sri Lanka School of computing Universal College Lanka Colombo Sri Lanka

In this paper we proposed a method for printed Sinhala character recognition based on support vector classification and histogram oriented gradient descriptor (HOG descriptor). Converting the text on images into text is a challenging task. It becomes the most difficult task when considering local languages. Having $400+$ characters in the Sinhala alphabet and the curve pattern of the letters causes some difficulties when converting the Sinhala text images into text. A histogram oriented gradient descriptor is used to extract the unique features of each individual character. A linear support vector machine is used to classify each character. Sinhala corpus was created to check the word correctness and permutation method used for the process of creating words.

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Effect of Camera Simulator Application for Basic Photography Subjects at Vocational school: One-Group Pre-test and Post-test Design 9

Effect of Camera Simulator Application for Basic Photography...

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9th International Conference on Education and technology, ICET 2023

作者： Qonita, Qori Suwastika, Novian Anggis Masrom, Maslin 10 Bandung Bandung Indonesia Razak Faculty of Technology and Informatics Universiti Teknologi Malaysia Kuala Lumpur Malaysia Telkom University School of Computing Bandung Indonesia

ISBN: (纸本)9798350358292

This study examines the impact of using the camera simulator application in basic photography subjects for students cognitive skills majoring in broadcasting and film in vocational high schools in Indonesia. Camera simulators have the advantage of providing opportunities for students to practice more freely and independently. In this study, the CamSim Pro application was employed as the camera simulator and was installed on desktop systems. Eighty eligible students from Public Vocational High school (PVHS) 10 Bandung participated in using the camera simulator as an intervention technology in two topics in basic photography subjects: aperture priority mode and shutter priority. This study adopts a quasi-experimental research method with a one-group pre-test and post-test design, examining the impact of camera simulator intervention. This research uses five stages, (1) designing the pre-test and its evaluation, (2) selecting the group/population to be tested, (3) preparing and providing a camera simulator intervention, (4) designing the post-test and evaluating it, and (5) conducting data analysis referring to the results of the pre-test and the post-test. The data normality test uses the Kolgomorov-Smirnov method and the Shapiro-Wilk method. Data analysis to measure the impact of technological interventions uses the Wilcoxon Signed Ranks method and the significance of the technological interventions using the Z statistic and asymptotic significance. Analysis of the collected data unveiled a compelling narrative. The Z statistic exhibited a notable value of -4.897, paired with an asymptotic significance value of © 2023 IEEE.

关键词： Students

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Sliding Mode Control Strategy for State-of-Charge (SoC) Balancing in Lithium-ion Battery Packs 13

Sliding Mode Control Strategy for State-of-Charge (SoC) Bala...

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13th International Conference on Control, Automation and Information Sciences, ICCAIS 2024

作者： Feng, Yong Han, Fengling Yang, Rebecca Wang, Yanmin Bai, Fan Truong, Nguyen-Vu School of Science Computing and Engineering Technologies Swinburne University of Technology Melbourne Australia School of Science Rmit University Melbourne Australia School of Electrical Engineering and Automation Harbin Institute of Technology Harbin China National Institute of Applied Mechanics and Informatics Vietnam Academy of Science and Technology Ho Chi Minh City Viet Nam

ISBN: (纸本)9798331542047

This paper proposes a sliding mode control strategy for balancing the State of Charge (SoC) in lithium-ion battery packs. By thoroughly analyzing the battery balancing circuit topology, a bidirectional Cuk circuit balancing topology for a series-connected battery pack is established, and its mathematical model is developed. Utilizing sliding mode control technology, a balancing strategy is designed to equalize the SoC of all cells in a battery pack composed of multiple series-connected battery cells. The effectiveness of the proposed method is verified through experiments, demonstrating that the developed battery balancing system can successfully balance the SoC of each cell in the battery pack. © 2024 IEEE.

关键词： State of charge

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GCAT: graph calibration attention transformer for robust object tracking

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Neural computing and Applications 2024年第23期36卷 14151-14172页

作者： Chen, Si Hu, Xinxin Wang, Da-Han Yan, Yan Zhu, Shunzhi Fujian Key Laboratory of Pattern Recognition and Image Understanding School of Computer and Information Engineering Xiamen University of Technology Xiamen361024 China State Key Laboratory of Integrated Services Networks Xidian University Xi’an710071 China Fujian Key Laboratory of Sensing and Computing for Smart City School of Informatics Xiamen University Xiamen361005 China

Recent Siamese trackers have taken advantage of transformers to achieve impressive advancements. However, existing transformer trackers ignore considering the positional and structural information between tokens, and traditional template update strategies easily introduce noises to the dynamic templates during tracking. In order to alleviate this issue, this paper develops a novel end-to-end graph calibration attention transformer network (GCAT) to enhance tracking robustness and accuracy. A graph calibration attention mechanism is first designed to calibrate and aggregate template information, for effectively updating dynamic templates during the tracking process. Specifically, each token is considered as a node in the graph, and then, we calculate the weight relationships between each node and their adjacent nodes. Thus, this mechanism can aggregate the global context information of the template and search nodes and activate feature channels based on weights and biases to obtain more discriminative feature information. Moreover, we leverage a multi-level dropout mechanism to perform the data dropout, the layer dropout, and the feature dropout on the data, network, and attention levels, respectively, to avoid overfitting of local-specific information and improve the generalization ability. Extensive experiments show the proposed method achieves superior performance on seven challenging benchmark datasets, i.e., OTB100, OTB2013, UAV123, LaSOT, GOT10K, VOT2020, and TrackingNet. © The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature 2024.

关键词： Aggregates

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Classification of SEMG Signal Based Hand Movements by Using Support Vector Machine 6

Classification of SEMG Signal Based Hand Movements by Using ...

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6th IEEE Symposium on Computers and informatics, ISCI 2024

作者： Zahra, Syeda Rabail Ismail, Sadia Ali, Muhammad Danish Khan, Muhammad Amir Darus, Mohamad Yusof Mazhar, Tahseen Gomal University Institute of Computing & Information Technology Dera Ismail Khan Pakistan University Islamabad Computer Science Comsats Abbottabad Pakistan Universiti Teknologi Mara School of Computing Sciences College of Computing Informatics and Mathematics Selangor Shah Alam40450 Malaysia Virtual University of Pakistan Department of Computer Science Pakistan

ISBN: (纸本)9798350353853

This paper explores the application of Surface Electromyography (SEMG) for hand movement classification using machine learning techniques. SEMG signals play a crucial role in sports medicine, rehabilitation, prosthesis control, and medical diagnosis. The study addresses a multi-class problem, extending the dataset with limited prior exploration. Nine features from SEMG signal's time domain are extracted, including Maximum Amplitude, Root Mean Square, Slope Sign Change, Variance, Simple Square Integral, Zero Crossing, Waveform Length, and Willison Amplitude. Linear Discriminant Analysis is employed for dimensionality reduction as a feature selection technique. The proposed algorithm achieves an overall classification accuracy of 72% for the classification of six different hand movements performed by five healthy subjects using Support Vector Machines. This research contributes to the advancement of SEMG-based hand movement classification in various practical applications. © 2024 IEEE.

关键词： Dimensionality reduction

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A Comparison Study on Generative Adversarial Networks (GANs) for Satellite Image Upscaling 12

A Comparison Study on Generative Adversarial Networks (GANs)...

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12th IEEE Conference on Systems, Process and Control, ICSPC 2024

作者： Li, Brandon Kah Wei Jasser, Muhammed Basheer Issa, Bayan Ajibade, Samuel-Soma M. Chua, Hui Na Wong, Richard T.K. Hamzah, Muzaffar Sunway University No. 5 Jalan Universiti School of Engineering and Technology Department of Computing and Information Systems Selangor Darul Ehsan Bandar Sunway47500 Malaysia School of Engineering and Technology Selangor Darul Ehsan Bandar Sunway47500 Malaysia University of Aleppo Faculty of Informatics Engineering Syria Istanbul Ticaret University Department of Computer Engineering Istanbul Turkey Universiti Malaysia Sabah Faculty of Computing and Informatics Sabah Kota Kinabalu88450 Malaysia

ISBN: (纸本)9798350391398

Satellite images that are publicly available on the internet such as Google Maps are mostly low-quality and blurry which is not very useful for research or decision-making due to the lack of details. Recently image upscaling has been increasing in popularity to address several real-world problems where one approach is using Convolutional Neural Networks (CNNs). However, CNNs have difficulty in restoring fine details and this has led to the use of Generative adversarial networks (GANs) for image scaling. In this paper, three GAN models are trained which are Enhanced Super-Resolution Generative Adversarial Networks (Real-ESRGAN), Image Restoration Using Swin Transformer (SwinIR-Medium), and Dual Aggregation Transformer (DAT-2). The models are evaluated with well-known image metrics to determine each model's effectiveness in upscaling low-resolution satellite images. The results show that all models can reconstruct the image to some degree where the best out of the three is DAT-2. Further fine-tuning of the best model by changing the loss functions has shown improvements in structural integrity for the upscaled image. © 2024 IEEE.

关键词： Generative adversarial networks

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Augmented Reality in Education: Transformative Innovations and Immersive Learning Experiences 4th

Augmented Reality in Education: Transformative Innovations a...

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4th International Conference on Advances in Computational Science and Engineering, ICACSE 2023

作者： Abdul Rahim, Rini Hafzah Mohd Nizam, Dinna Nina Mohd Naim, Nur Faraha Baharum, Aslina User Experience Group Faculty of Computing and Informatics Universiti Malaysia Sabah Jalan UMS Sabah Kota Kinabalu88400 Malaysia Department of Computing and Information System School of Engineering and Technology Sunway University No 5 Jalan University Selangor Bandar Sunway57500 Malaysia

ISBN: (纸本)9789819729760

The use of technology, such as Augmented Reality Immersive Learning (ARIL), in education to improve student learning by fusing the actual world with the virtual world is thoroughly examined. This research aims to ascertain how Augmented Reality (AR) technology might improve immersive learning for practical skills. AR has exciting opportunities for immersive learning since it can blend digital content with the real world. Using Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) and three databases—Scopus, the Institute of Electrical and Electronics Engineers (IEEE), and the Education Resources Information Center (ERIC)—206 publications have been carefully selected for the study. Three main themes emerged from the 34 articles that met the qualifying requirements: Enhancing Learning Experiences with AR in Education, Innovative AR Applications in Education, and Immersive Technologies in Education: Challenges and Innovations. The three themes’ findings point to a common theme: the effective use of technology in the classroom to improve students’ immersive learning, confidence, memory retention abilities, positive concern, creative thinking, and self-awareness. Furthermore, the usage of AR may be coupled with other technologies such as chatbots, Artificial Intelligence (AI), Internet of Things (IoT), Mixed Reality (MR), Extended Reality (XR), and others. One significant finding from the study was that certain frameworks, such as the AR Immersive Learning Design (AILeaD), the Game-Based Assessment Framework (GBAF), and AR immersion (ARI), might serve as useful recommendations for implementing AR technology for educational purposes. Further studies should be conducted and considering that every student has a particular learning style and preference, more research should investigate using this framework and approach to help students learn theory or practical in various academic topics. © The Author(s), under exclusive license to Springer Nature Singa

关键词： Contrastive Learning

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Password Manager with Multi-Factor Authentication 1

Password Manager with Multi-Factor Authentication

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1st IEEE International Conference on Networking and Communications, ICNWC 2023

作者： Dhanalakshmi, R. Vijayaraghavan, N. Narasimhan, S. Basha, Saleem Vellore Institute of Technology School of Computer Science and Engineering Chennai India KCG College of Technology Department of Mathematics Chennai India Mazoon University College Computing and Informatics Department muscat Oman

ISBN: (纸本)9798350336009

Data breach is a serious issue as it leaks the personal information of more than billions of users and their privacy is compromised. More than 77% of organizations do not have a Cyber Security Incident Response plan. So, it is necessary to be informed of network security and ways to store and generate secured passwords. Having different and random passwords for ones digital accounts can exponentially increase the security of user's data. The goal of this project is to build a password manager which can securely store and encrypt passwords and other data. The multifactor authentication will provide increased security to validate the user into password management system. Multifactor authentication system includes physical security key and graphical password authentication. © 2023 IEEE.

关键词： Cryptography

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A Logical Investigation of Stock Market Prediction and Analysis using Supervised Machine Learning Algorithm 1

A Logical Investigation of Stock Market Prediction and Analy...

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1st IEEE International Conference on Networking and Communications, ICNWC 2023

作者： Dhanalakshmi, R. Kumar, V. Vasantha Basha, Saleem Vijayaraghavan, N. Vellore Institute of Technology School of Computer Science and Engineering Chennai India Mazoon University College Computing and Informatics Department Muscat Oman KCG College of Technology Department of CSE Chennai India

ISBN: (纸本)9798350336009

In the Field of computer science, artificial intelligence (AI) is a broad field, which is concerned with structuring smart products and machines able to perform tasks which require the intellectual capability of humans. Artificial Intelligence is in need for Stock trading's future. Robotic advisers examine millions of pieces of data using artificial intelligence and carry out the analysis at the best possible price. Market forecasts are more precise and effective thanks to the analysts. For providing higher returns, the trading firms efficiently mitigate the risk using Artificial Intelligence Techniques. Our proposed Stock Market system is a Web Application which uses the Random Forest Algorithm. The Random Forest Algorithm comes under Supervised Learning. With the system, one can forecast the stock prices for the subsequent days. © 2023 IEEE.

关键词： Forecasting

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