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IAENG International Journal of computer Science 2024年第2期51卷 115-120页

作者： Umilasari, Reni Susilowati, Liliek Slamin Prabhu, Savari Fadekemi, Osaye J. Universitas Muhammadiyah Jember Indonesia Mathematics Department Airlangga University Indonesia Department of Informatics System Jember University Indonesia Mathematics Department Rajalakshmi Engineering College Tamil Nadu Chennai602105 India Department of Mathematics and Computer Science Alabama State University United States

The local resolving dominating set studied in this paper is a notion that combines two concepts in graph theory, the local metric dimension and dominations in graphs. Let G and H be connected graphs of orders n and m, respectively;and x a vertex in H hereafter referred to as a linkage vertex. The comb product of G and H denoted by G.H, is a graph obtained by taking one copy of G and n copies of H and attaching the i-th copy of H at the vertex x to the i-th vertex of G. In this paper, we determine the local resolving dominating set of the comb products G. Sn with two different linkage vertices, G. Kn, G. Km,n and G. Cm graphs. © (2024), (International Association of Engineers). All Rights Reserved.

关键词： Graph theory

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Compact and Efficient Neural Networks for Image Recognition Based on Learned 2D Separable Transform

Compact and Efficient Neural Networks for Image Recognition ...

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Digital Signal Processing and its Applications (DSPA), International Conference on

作者： Maxim Vashkevich Egor Krivalcevich Department of Computer Engineering Belarusian State University of Informatics and Radioelectronics Minsk Belarus

ISBN: (数字)9798331518752

ISBN: (纸本)9798331518769

The paper presents a learned two-dimensional separable transform (LST) that can be considered as a new type of computational layer for constructing neural network (NN) architecture for image recognition tasks. The LST based on the idea of sharing the weights of one fullyconnected (FC) layer to process all rows of an image. After that, a second shared FC layer is used to process all columns of image representation obtained from the first layer. The use of LST layers in a NN architecture significantly reduces the number of model parameters compared to models that use stacked FC layers. We show that a NN-classifier based on a single LST layer followed by an FC layer achieves 98.02% accuracy on the MNIST dataset, while having only 9.5k parameters. We also implemented a LST-based classifier for handwritten digit recognition on the FPGA platform to demonstrate the efficiency of the suggested approach for designing a compact and high-performance implementation of NN models.

关键词： Accuracy Image recognition Quantization (signal) Two-dimensional displays Transforms Artificial neural networks computer architecture Image representation Field programmable gate arrays Image classification

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A Review and Implementation of Object Detection Models and Optimizations for Real-time Medical Mask Detection during the COVID-19 Pandemic

arXiv

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arXiv 2024年

作者： Gogou, Ioanna C. Koutsomitropoulos, Dimitrios A. Dept. of Computer Engineering and Informatics University of Patras Patras Greece

Convolutional Neural Networks (CNN) are commonly used for the problem of object detection thanks to their increased accuracy. Nevertheless, the performance of CNN-based detection models is ambiguous when detection speed is considered. To the best of our knowledge, there has not been sufficient evaluation of the available methods in terms of the speed/accuracy trade-off in related literature. This work assesses the most fundamental object detection models on the Common Objects in Context (COCO) dataset with respect to this trade-off, their memory consumption, and computational and storage cost. Next, we select a highly efficient model called YOLOv5 to train on the topical and unexplored dataset of human faces with medical masks, the Properly-Wearing Masked Faces Dataset (PWMFD), and analyze the benefits of specific optimization techniques for real-time medical mask detection: transfer learning, data augmentations, and a Squeeze-and-Excitation attention mechanism. Using our findings in the context of the COVID-19 pandemic, we propose an optimized model based on YOLOv5s using transfer learning for the detection of correctly and incorrectly worn medical masks that surpassed more than two times in speed (69 frames per second) the state-of-the-art model SE-YOLOv3 on the PWMFD dataset while maintaining the same level of mean Average Precision (67%). © 2024, CC BY.

关键词： Object detection

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Trimmed Non-Local Means Filtering for the Suppression of Mixed Noise in Color Images

Trimmed Non-Local Means Filtering for the Suppression of Mix...

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2023 International Conference on Electrical, computer, Communications and Mechatronics engineering, ICECCME 2023

作者： Kusnik, Damian Smolka, Bogdan Smolka, Milena Silesian University of Technology Faculty of Automatics Electronics and Informatics Gliwice Poland Agh University of Science and Technology Faculty of Electrical Engineering Automatics Computer Science and Biomedical Engineering Krakow Poland

ISBN: (纸本)9798350322972

In this paper, a novel approach to the problem of mixed Gaussian and impulsive noise reduction in color images is proposed. The elaborated denoising framework is based on the Non-Local Means technique, which has been shown to effectively suppress Gaussian noise. To overcome the inability of this filter to deal with impulsive distortions, a robust similarity measure between image patches, that is insensitive to the impact of impulsive distortions, was developed. First, the distorted pixels of the image are detected using the concept of a peer group. If the number of neighbors of a pixel at the center of a filtering window, whose Euclidean distance in the RGB color space does not exceed a threshold value, is smaller than a predefined value, then the considered pixel is treated as corrupted. Afterwards, the similarity between image patches are calculated in a standard way omitting the noisy pixels. In this way, only the average of distances between pairs of unaffected pixels are calculated. Thus, the closeness measure of the patches, which serves as the coefficient in weighted averaging of pixels in the processing block, is not affected by impulsive noise and is still able to cope with the Gaussian noise component. The proposed filtering design is capable of reducing even strong mixed noise while preserving image details and edges. Moreover, it is not computationally intensive, so it can be used in real-time image processing scenarios. © 2023 IEEE.

关键词： Pixels

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Lightweight Privacy Architecture for Homomorphic Operations in e-Health Systems

Lightweight Privacy Architecture for Homomorphic Operations ...

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IEEE International Conference on Internet of Things and Intelligence System (IOTAIS)

作者： K. Tsantikidou N. Sklavos Computer Engineering and Informatics Department SCYTALE Group University of Patras Hellas

ISBN: (数字)9798331521219

ISBN: (纸本)9798331521226

In recent years, traditional hospital facilities are adopting new technologies that provide novel services. Nevertheless, these services may be performed by third parties, that pose a threat to data privacy. As health data is sensitive, it must be constantly encrypted from the moment it is collected. Thus, an appropriate privacy scheme that is suitable for the resource constrained e-Health environment is required. In this paper, a lightweight privacy architecture, applied for homomorphic operations in IoT systems for e-Health is proposed. The focus is its easy implementation in devices with resource limitations, while also enhancing privacy. Two security versions of the same architecture are presented, with different parameters. Each proposed version achieves a minimum of 70.27% and 49.2% resource reduction compared to published related works. The proposed compact version achieves a minimum of 19.7% area-delay reduction compared to published designs. The main reasons for these results are the employment of synthesizable operations, the fusion of functions and the pipeline architecture of operations.

关键词： Privacy Data privacy Hospitals Pipelines Employment Internet of Things Cryptography Homomorphic encryption

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Book Recommender System Using Content-Based Filtering for PNJ Press Website

Book Recommender System Using Content-Based Filtering for PN...

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International Conference on Education and Technology (ICET)

作者： Dewi Yanti Liliana Rizki Elisa Nalawati Ratna Widya Iswara Alifiah Putri Aisyah Hana Octavia Trinida Malo Department of Computer and Informatics Engineering Politeknik Negeri Jakarta Depok Indonesia

ISBN: (数字)9798331522483

ISBN: (纸本)9798331522490

The vast collections in book publishers and bookstores make it challenging for users to find books on specific topics without knowing the exact titles. An information system with a book recommendation feature can significantly enhance the efficiency of a book search. Recommender systems, which predict or suggest items of interest based on user interactions and queries, are widely used across various platforms to personalize user experiences. This study focuses on developing a Content-Based Filtering recommender system for PNJ Press, a book publisher of the academic community of Jakarta State Polytechnic. The system compares user search queries with the content of book titles and abstracts to provide relevant recommendations. Previous research has demonstrated the effectiveness of Content-Based Filtering in various domains, highlighting its potential for improving book search efficiency on the PNJ Press website. The system achieved a precision of 91.84% and a recall of 97.83%, resulting in an overall accuracy rate of 90%. Additionally, the study found that the combined attributes of book titles and abstracts significantly influence recommendation results.

关键词： Presses Accuracy Filtering Collaborative filtering Education Market research Information filters User experience Recommender systems Information systems

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TCIP: Transformed Colour Intensity Profiles analysis for fault detection in power line insulators

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Multimedia Tools and Applications 2025年第8期84卷 4503-4534页

作者： Tomaszewski, Michal Gasz, Rafal Kasana, Singara Singh Osuchowski, Jakub Singh, Simranjit Zator, Slawomir Department of Computer Science Faculty of Electrical Engineering Automatic Control and Informatics Opole University of Technology Prószkowska 76 Opole45-758 Poland Department of Computer Science and Information Technology Central University of Haryana Haryana Mahendergarh123031 India Department of Information Technology Dr B. R. Ambedkar National Institute of Technology Punjab Jalandhar India Department of Safety Engineering and Technical Systems Faculty of Production Engineering and Logistics Opole University of Technology Prószkowska 76 Opole45-758 Poland

In response to the escalating demand for electricity, the aging process and inherent failures in power lines have become unavoidable challenges in their operational integrity. This research addresses the imperative need for a robust method to detect damage in power insulator disc images, introducing a novel feature extraction technique grounded in color intensity profile transformation and ensemble learning. For the purposes of the research, a specific dataset was developed based on publicly available images of power insulators. The collection consists of 9232 images of insulator discs which included 2184 images of damaged discs. The discernible damages include breakages and burnouts. Two distinct analysis variants were employed. In the first variant (V1), two classification groups were selected: disc with damages and disc without damages. In the second variant (V2), three classification groups were selected: discs without damage, broken discs, and burnout discs. The described research was divided into two successive stages. In the first of them (S1), the transformed color intensity profiles for the prepared data set were built and various machine learning models for their classification were trained and evaluated. In the next stage (S2), previously developed, highest efficient machine learning models, were used to make the detection of various damaged types of overhead line insulator discs. For the first stage of research high-performance results and a high value of the F1 score parameters were obtained (F1 score = 0.999 for the first variant (S1V1) and F1 score = 0.992 for the second variant (S1V2). In the second stage of research and the first variant (S2V1), the highest result of the F1 score was 0.932 and for the second variant (S2V2) F1 score was 0.930. © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2024.

关键词： Feature extraction

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Adaptive Technique of Impulsive Noise Removal in Color Digital Images 10

Adaptive Technique of Impulsive Noise Removal in Color Digit...

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10th International Conference on Electrical and Electronics engineering, ICEEE 2023

作者： Smolka, Bogdan Mendrela, Monika Kusnik, Damian Smolka, Milena Silesian University of Technology Faculty of Automatics Electronics and Informatics Gliwice Poland Agh University of Science and Technology Faculty of Electrical Engineering Automatics Computer Science and Biomedical Engineering Krakow Poland

ISBN: (纸本)9798350304299

This paper describes a new technique for removing impulsive noise in digital color images. The proposed method uses a measure of the pixel distortion caused by the impulsive noise, expressed as the sum of the Euclidean distances between the central pixel and its closest neighbors in the filtering window. This corruption measure is used to determine a weighted average of the pixels from the local neighborhood that is not affected by the distorted pixels. The filtering output is obtained using a soft switching scheme, where the denoising result is determined as a weighted average of the central pixel of a processing window and its robust estimate. The weighting coefficient is calculated based on the amount of distortion of the central pixel. Thus, if the central pixel is highly distorted, it is replaced by its robust estimate, otherwise it is not significantly changed. The parameters that determine the image enhancement result are adaptively adjusted by using a noise-resistant image contrast measure. The proposed filter design is able to effectively suppress impulsive noise while preserving fine image details and edges. Comparison with existing algorithms shows that the proposed method provides better denoising results than the most efficient filters designed for impulsive noise suppression in color images. Moreover, the new method is very fast and can be used in real-time denoising scenarios. © 2023 IEEE.

关键词： Image enhancement

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IoT Educational Management and Consultancy System for Egyptian Public Schools 16

IoT Educational Management and Consultancy System for Egypti...

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16th IEEE AFRICON, AFRICON 2023

作者： Saleh, Omar Ashraf Elsherif, Salma Sherif, Dina Mohamed Sharaf, Nada Zaghloul, Yasmine The German International University Faculty of Informatics and Computer Science Cairo Egypt The German International University Faculty of Engineering Cairo Egypt German International University Faculty of Engineering Cairo Egypt

ISBN: (纸本)9798350336214

Educational systems in Egypt face a shortage in the number of teachers as there is an annual increment in the number of students who are enrolled in public schools. Therefore, voluntary teaching is proposed to assist the hierarchy of educational objectives and to compensate for this shortage. With the help of technologies such as QR code scanners, Blue-tooth, Wifi, RFID, and computer Vision, a technological system guarantees a high quality and high performance of monitoring teaching activities. Nevertheless, an implementation of a mobile application guides the volunteer, the student, and the school administrators to monitor and access the course content easily along with a rewarding system that credits the volunteers for their effort. © 2023 IEEE.

关键词： Students

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CNN-Based Deep Learning Model for Early Identification and Categorization of Melanoma Skin Cancer Using Medical Imaging

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SN computer Science 2024年第7期5卷 911页

作者： Pandimurugan, V. Ahmad, Sultan Prabu, A.V. Rahmani, Mohammad Khalid Imam Abdeljaber, Hikmat A. M. Eswaran, Malathi Nazeer, Jabeen Computer Science and Engineering Acharya Institute of Technology Bangalore India Department of Computer Science College of Computer Engineering and Sciences Prince Sattam Bin Abdulaziz University Alkharj 11942 Saudi Arabia University Center for Research and Development (UCRD) Department of Computer Science and Engineering Chandigarh University Gharuan Punjab Mohali 140413 India Department of Electronics and Communication Engineering Koneru Lakshmaiah Education Foundation Vaddeswaram AP Guntur 522502 India College of Computing and Informatics Saudi Electronic University Riyadh Saudi Arabia Department of Computer Science Faculty of Information Technology Applied Science Private University Amman Jordan MEU Research Unit Middle East University Amman 11831 Jordan Department of Computer Technology Kongu Engineering College Perundurai Tamil Nadu Erode India

In human life, skin cancer is a curse. If not appropriately diagnosed, it spreads around all body parts in the earlier stage. The melanoma skin cancer death rate is 75% all over the world. There is an urgent need for a cure to be in place. Swarm and evolutionary computation for healthcare deals with real-world healthcare applications. One of the best ways to do this is to find the genes that need to be turned off such that only cancer cells can die. The current therapies used to cure cancer focus on turning off cancer cells but, at times, affect healthy tissues. Using the power of Artificial Intelligence, one can easily mark out the cancer-affected area and provide treatment accordingly. Segmentation techniques can be used to mark cancer lesions, and advanced deep learning algorithms in computer vision can help classify which class of cancer lesions. Automatic detection of melanoma by using a dermoscopic sample is tricky to find the stage and percentage of lesions affected by using deep convolutional neural networks with the help of machine vision tools. To predict skin lesions, we developed a model with three layers, each having output channels of 16, 32, and 64, respectively. For this research, different samples were collected from the international skin imaging collaboration database (ISIC2019, ISIC2020, ISIC2021). The study computed the most important parameters for classifying and identifying the lesion with accuracy, precision, recall, and specificity values. The proposed DCNN model classifier attained an accuracy of 88.82%, 93.45%, and 95.15% with the respective datasets of ISIC2019, ISIC2020, and ISIC2021, indicating high performance compared with the other state-of-the-art networks. The proposed model is a better framework for automating early detection and classification of melanoma skin cancer to protect many lives and avoid mishaps. © The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd. 2024.

关键词： Classification Convolution neural network Deep learning Medical imaging Melanoma Skin cancer Swarm and evolutionary computation

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