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10th International Conference on computer and Communications Management, ICCCM 2022

作者： Chang, Han-Yuan Lin, Yih-Chuan Department of Computer Science and Information Engineering National Formosa University Taiwan Department of Computer Science and Information Engineering Smart Machinery and Intelligent Manufacturing Research Center National Formosa University Taiwan

ISBN: (纸本)9781450396349

The JPEG codec is still the dominant and common image compression format despite the continuous development of new image compression techniques. In order to raise the compression ratio, distortion caused by some quantization operation within the encoding process is unavoidable. Therefore, this paper proposed a deep learning based JPEG decoder to decrease image quality degradation by JPEG encoding quantization. Most of the image processing problems, such as image denoising, image super-resolution, and even style conversion, can be handled well in the spatial domain by deep learning using the fully convolutional networks (FCN). However, because the image is converted from the spatial domain to the frequency domain by the Discrete Cosine Transform (DCT) during JPEG encoding, this paper addresses the issue of FCN might not be suitable for processing frequency-spatial domain directly. Then, this study develops two learning models more suitable for inverse DCT (IDCT) with fully connected layers and transposed convolutional layers than the results with FCN. Finally, we constructed a JPEG decoder model based on deep residual networks. The experimental results showed that the FCN does not handle the frequency domain data well, and our proposed decoder has shown better results in peak signal to noise ratio (PSNR), structural similarity index (SSIM), and multi-scale SSIM (MS-SSIM) than the general JPEG decoder, giving evidence that the learning based JPEG decoder is capable of restoring some of the image quality degradation caused by JPEG quantization. © 2022 ACM.

关键词： Image compression

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Fuzzy Difference Equations in Diagnoses of Glaucoma from Retinal Images Using Deep Learning

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computer Modeling in engineering & sciences 2024年第4期139卷 801-816页

作者： D.Dorathy Prema Kavitha L.Francis Raj Sandeep Kautish Abdulaziz S.Almazyad Karam M.Sallam Ali Wagdy Mohamed Department of Mathematics Voorhees CollegeVelloreTamil NaduIndia Chandigarh Group of Colleges JhanjeriMohaliPunjabIndia Department of Computer Engineering College of Computer and Information SciencesKing Saud UniversityP.O.Box 51178Riyadh11543Saudi Arabia Faculty of Science and Technology School of IT and SystemsUniversity of CanberraCanberraAustralia Operations Research Department Faculty of Graduate Studies for Statistical ResearchCairo UniversityGiza12613Egypt Applied Science Research Center Applied Science Private UniversityAmman11937Jordan

The intuitive fuzzy set has found important application in decision-making and machine *** enrich and utilize the intuitive fuzzy set,this study designed and developed a deep neural network-based glaucoma eye detection using fuzzy difference equations in the domain where the retinal images *** image detections are categorized as normal eye recognition,suspected glaucomatous eye recognition,and glaucomatous eye *** degrees associated with weighted values are calculated to determine the level of concentration between the fuzzy partition and the retinal *** proposed model was used to diagnose glaucoma using retinal images and involved utilizing the Convolutional Neural network(CNN)and deep learning to identify the fuzzy weighted regularization between *** methodology was used to clarify the input images and make them adequate for the process of glaucoma *** objective of this study was to propose a novel approach to the early diagnosis of glaucoma using the Fuzzy Expert System(FES)and Fuzzy differential equation(FDE).The intensities of the different regions in the images and their respective peak levels were *** the peak regions were identified,the recurrence relationships among those peaks were then *** partitioning was done due to varying degrees of similar and dissimilar concentrations in the *** and dissimilar concentration levels and spatial frequency generated a threshold image from the combined fuzzy matrix and *** distinguished between a normal and abnormal eye condition,thus detecting patients with glaucomatous eyes.

关键词： Convolutional Neural network(CNN) glaucomatous eyes fuzzy difference equation intuitive fuzzy sets image segmentation retinal images

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Using Knowledge Graph Embeddings in Embedding Based Recommender Systems 20

Using Knowledge Graph Embeddings in Embedding Based Recommen...

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20th International Conference on Language engineering, ESOLEC 2022

作者： Ragab, Ahmed Hussein El-Kafrawy, Passant School of Information Technology and Computer Science Computer Science Department Nile University Cairo Egypt School of Information Technology and Computer Science CIS Research Center Nile University Computer Science Department Egypt Effat Collage of Engineering Jedah Saudi Arabia

ISBN: (纸本)9781665453226

This paper proposes using entity2rec [1] which utilizes knowledge graph-based embeddings (node2vec) instead of traditional embedding layers in embedding based recommender systems. This opens the door to increasing the accuracy of some of the most implemented recommender systems running in production in many companies by just replacing the traditional embedding layer with node2vec graph embedding without the risk of completely migrating to newer SOTA systems and risking unexpected performance issues. Also, Graph embeddings will be able to incorporate user and item features which can help in solving the well-known Cold start problem in recommender systems. Both embedding methods are compared on the movie-Lens 100-K dataset in an item-item collaborative filtering recommender and we show that the suggested replacement improves the representation learning of the embedding layer by adding a semantic layer that can increase the overall performance of the normal embedding based recommenders. First, normal Recommender systems are introduced, and a brief explanation of both traditional and graph-based embeddings is presented. Then, the proposed approach is presented along with related work. Finally, results are presented along with future work. © 2022 IEEE.

关键词： Recommender systems

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Lightweight Deep Residual network for Motorcyclist Behavior Recognition Using Inertial Sensors 8

Lightweight Deep Residual Network for Motorcyclist Behavior ...

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8th International Conference on information Technology, InCIT 2024

作者： Hnoohom, Narit Mekruksavanich, Sakorn Jitpattanakul, Anuchit Mahidol University Faculty of Engineering Image Information and Intelligence Laboratory Department of Computer Engineering Nakhon Pathom Thailand University of Phayao Department of Computer Engineering School of Information and Communication Technology Phayao Thailand King Mongkut's University of Technology Faculty of Applied Science Intelligent and Nonlinear Dynamic Innovations Research Center Department of Mathematics North Bangkok Thailand

ISBN: (纸本)9798350366303

Motorcyclist behavior recognition (MBR) is a significant factor in improving road safety and preventing accidents. Inertial sensors, such as accelerometers, gyroscopes, and magnetometers, have shown promising potential for capturing the dynamic behavior of motorcyclists. However, existing approaches often require complex models and significant computational resources, limiting their deployment in real-world scenarios. This study presents a MotoNeXt architecture, which is a light deep residual network for correctly identifying the behavior of motorcyclists using data from inertial sensors. Our approach leverages the power of deep learning while maintaining a compact model size, making it suitable for resource-constrained environments. The proposed network incorporates multi-kernel residual blocks to facilitate the learning of hierarchical features and improve gradient flow during training. We evaluate our method on a publicly available benchmark dataset, MB-IMU, collected from multiple riders under various riding conditions. Experimentation has shown that our lightweight model achieves cutting-edge performance, outperforming a variety of existing techniques. In Scenario I, using accelerometer, gyroscope, and magnetometer data, MotoNeXt achieved an accuracy result of 78.90088% along with an F1-score of 81.56738%. According to Scenario II, which included additional sensor data such as VelInc, OriInc, and Euler angles, MotoNeXt further improved its performance, achieving an accuracy result of 83.29735% along with an F1-score of 84.95352%. The proposed approach offers a practical solution for real-time MBR, enabling the development of intelligent transportation systems and rider assistance technologies. Our experiments highlight the effectiveness of lightweight deep learning models in extracting meaningful patterns from inertial sensor data. This opens up opportunities for further investigation in this field. © 2024 IEEE.

关键词： Accelerometers

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Toolkit for Satellite Visualization using Auxiliary Data 15

Toolkit for Satellite Visualization using Auxiliary Data

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15th International Conference on Advances in Computing, Control, and Telecommunication Technologies, ACT 2024

作者： Veena, O.S. Suhas, G.K. Department of Computer Science & Engineering Akshaya Institute of Technology Affiliated to Visvesvaraya Technological University Tumkur572106 India Research center for Computer & Information Sciences Akshaya Institute of Technology Affiliated to Visvesvaraya Technological University Tumkur572106 India

ISBN: (纸本)9798331300579

This paper provides a complete model for satellite tracking that uses Two Line Elements (TLE) data to accurately predict and track satellite locations over *** three-phase graphical user interface of the model allows for real-time viewing of satellite positions and critical tracking data: input, prediction, and *** work offers a comprehensive method for tracking and visualizing satellites in low Earth orbit (LEO), geostationary orbit, and remote sensing orbit utilizing TLE *** latitude and longitude calculations allow for ongoing trajectory mapping, which makes it easier to track satellite movement *** and clarity are further enhanced by visualizing satellite *** their courses, satellite names are added to make identification *** integrated method has significant benefits for applications including space exploration, telecommunication, and environmental ***, it makes constellation dynamics and satellite interactions more understandable, developing satellite-based technologies for a variety of useful uses. © Grenze Scientific Society, 2024.

关键词： Graphical user interfaces

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Logical Relation Modeling and Mining in Hyperbolic Space for Recommendation 40

Logical Relation Modeling and Mining in Hyperbolic Space for...

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40th IEEE International Conference on Data engineering, ICDE 2024

作者： Tan, Yanchao Lv, Hang Zhou, Zihao Guo, Wenzhong Xiong, Bo Liu, Weiming Chen, Chaochao Wang, Shiping Yang, Carl College of Computer and Data Science Fuzhou University Fuzhou China Engineering Research Center of Big Data Intelligence Ministry of Education Fuzhou China Fujian Key Laboratory of Network Computing and Intelligent Information Processing Fuzhou University Fuzhou China Institute for Artificial Intelligence University of Stuttgart Stuttgart Germany College of Computer Science Zhejiang University Hangzhou China Emory University Department of Computer Science Atlanta United States

ISBN: (纸本)9798350317152

The sparse interactions between users and items have aggravated the difficulty of their representations in recommender systems. Existing methods leverage tags to alleviate the sparsity problem but ignore prevalent logical relations among items and tags (e.g., membership, hierarchy, and exclusion), which can be leveraged to enhance the accuracy of modeling user preferences and conducting recommendations. To this end, we propose to extract logical relations among item tags from existing tag taxonomies and exploit the individual strengths of the Poincaré and the Lorentz models in hyperbolic space for logical relation modeling towards enhanced recommendations. Moreover, we find that the logical relations directly extracted from existing tag taxonomies can be inaccurate and coarse. Therefore, we further devise innovative consistency-based and granularity-based weighting mechanisms based on user behavior patterns for data-driven logical relation mining that can be jointly optimized along with recommendations in an end-to-end fashion. Extensive experiments on four real-world benchmark datasets show drastic performance gains brought by our proposed framework, which constantly achieves an average of 8.25% improvement over state-of-the-art competitors regarding both Recall and NDCG metrics. Insightful case studies further demonstrate that our automatically refined logical relations are highly accurate and interpretable. © 2024 IEEE.

关键词： Recommender systems

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IoT-Enabled Automated Smart Irrigation System Incorporating Sensor Parameters 5th

IoT-Enabled Automated Smart Irrigation System Incorporating ...

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5th Doctoral Symposium on Computational Intelligence, DoSCI 2024

作者： Singh, Jagendra Neeraj Srinivas Reddy, P. Hasan, Dler Salih Alsahlanee, Abbas Thajeel Rhaif Shaik, Nazeer Alliance School of Advanced Computing Alliance University Bangalore India School of Computer Science Engineering and Technology Bennett University Greater Noida India Department of Mechanical Engineering CVR College of Engineering Hyderabad India Department of Computer Science and Information Technology University of Salahaddin-Erbil Erbil Iraq Development and Continuous Education Center University of Thi-Qar Thi-Qar Iraq Department of Computer Science and Engineering Srinivasa Ramanujan Institute of Technology—Autonomous Anantapur India

ISBN: (纸本)9789819763177

This article presents the experimental findings of a smart irrigation system evaluated in two separate agricultural settings—small-scale farming and large-scale agriculture. The system combines sensors, microcontrollers, and actuators to precisely monitor and regulate the irrigation process in order to optimum water use and crop output. Water savings range from 20 to 45% when using smart irrigation technology in small-scale agriculture. By only turning on the irrigation pump when necessary, the technology optimizes water use and fosters sustainable farming methods. Furthermore, by sustaining perfect growing conditions, the approach boosts crop output, with yield benefits ranging from 10 to 22%. These improvements assist small-scale farmers in becoming more profitable and raising their level of living. In large-scale agriculture, smart irrigation systems allow excellent water management and resource optimization across huge agricultural fields. The technology consistently saves a large quantity of water, often between 25 and 50%, addressing water scarcity issues. Additionally, this method has led to a 12–25% increase in aggregate crop yields, thereby raising farm income and increasing agricultural firm profitability. Experiments shows that this smart irrigation system could bring cost-effective, sustainable techniques to extensive and intensive agriculture alike. Intelligence-guided decision-making technology is applied here, and cutting-edge technologies are brought to bear on the water use problem. The system conserves precious resources and increases agricultural output at the same time. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Smart agriculture

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Lightweight Authenticated Key Agreement Protocol for Smart Power Grid Systems Using PUF

IEEE Open Journal of the Communications Society

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IEEE Open Journal of the Communications Society 2024年 5卷 3568-3580页

作者： Zahoor, Amina Mahmood, Khalid Saleem, Muhammad Asad Badar, Hafiz Muhammad Sanaullah Le, Tuan-Vinh Das, Ashok Kumar Department of Computer Science Sahiwal57000 Pakistan National Yunlin University of Science and Technology Graduate School of Intelligent Data Science Douliu64002 Taiwan University of Electronic Science and Technology of China School of Computer Science and Engineering Chengdu611731 China Emerson University Multan Department of Emerging Computing Technologies Multan61000 Pakistan Fu Jen Catholic University Bachelor's Program of Artificial Intelligence and Information Security New Taipei City24206 Taiwan International Institute of Information Technology Hyderabad Center for Security Theory and Algorithmic Research Hyderabad500032 India

The Smart Power Grid (SPG) is pivotal in orchestrating and managing demand response in contemporary smart cities, leveraging the prowess of information and Communication Technologies (ICTs). Within the immersive SPG environment, the ubiquitous deployment of smart meters stands as a testament to their paramount importance in the realm of vigilant monitoring and oversight. These smart meters are installed on high-tension electricity lines and transmit information about electricity outages and other issues to the utility center. To access services from utility centers, smart meters need to communicate securely over a public channel, even though the network itself is insecure. However, potential attacks from adversaries ( Ad) can exploit this communication. Therefore, protecting this communication is of utmost importance. Several privacy-preserving authentication protocols designed for SPG have been introduced in the literature. Nevertheless, a significant number of these protocols exhibit vulnerabilities to diverse security attacks. This article introduces a lightweight and anonymous authentication protocol specifically designed to address these concerns in the SPG environment. Our protocol ensures both security and efficiency, surpassing other comparable protocols in terms of its lightweight nature. By employing both formal and informal analysis, we showcase the robustness of our protocol against significant attacks while remaining lightweight. The proposed protocol provides added security features and incurs 23.8879% lower computation cost than related protocols. Consequently, our protocol is highly suitable for implementation in the SPG system. © 2020 IEEE.

关键词： Hash functions

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SMOTE Oversampling and Near Miss Undersampling Based Diabetes Diagnosis from Imbalanced Dataset with XAI Visualization 28

SMOTE Oversampling and Near Miss Undersampling Based Diabete...

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28th IEEE Symposium on computers and Communications, ISCC 2023

作者： Nayan, Nasim Mahmud Islam, Ashraful Islam, Muhammad Usama Ahmed, Eshtiak Hossain, Mohammad Mobarak Alam, Md Zahangir University of Information Technology and Sciences Department of Computer Science and Engineering Bangladesh Independent University Center for Computational and Data Sciences Bangladesh School of Computing and Informatics University of Louisiana at Lafayette United States Independent University Department of Computer Science and Engineering Bangladesh

ISBN: (纸本)9798350300482

This study investigated the predictive ability of ten different machine learning (ML) models for diabetes using a dataset that was not evenly distributed. Additionally, the study evaluated the effectiveness of two oversampling and undersampling methods, namely the Synthetic Minority Oversampling Technique (SMOTE) and the Near-Miss algorithm. Explainable Artificial Intelligence (XAI) techniques were employed to enhance the interpretability of the model's predictions. The results indicate that the extreme gradient boosting (XGB) model combined with SMOTE oversampling technique exhibited the highest accuracy and an F1-score of 99% and 1.00 respectively. Furthermore, the utilization of XAI methods increased the dependability of the model's decision-making process, rendering it more appropriate for clinical use. These results imply that integrating XAI with ML and oversampling techniques can enhance the early detection and management of diabetes, leading to better diagnosis and intervention. © 2023 IEEE.

关键词： Machine learning

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Outage Performance Analysis in Cognitive RIS-NOMA System under Imperfect CSI 3

Outage Performance Analysis in Cognitive RIS-NOMA System und...

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3rd IEEE International Mediterranean Conference on Communications and networking, MeditCom 2023

作者： Xue, K. Li, M. Chen, W. Han, Z. Yuan, H. Taiyuan University of Science and Technology School of Electronics Information Engineering China Tsinghua University Beijing National Research Center for Information Science and Technology Department of Electronic Engineering Beijing China University of Houston Department of Electrical and Computer Engineering United States Kyung Hee University Department of Computer Science and Engineering Korea Republic of Kingston University School of Computer Science and Mathematics KT1 2EE United Kingdom

ISBN: (纸本)9798350333732

Cognitive non-orthogonal multiple access (NOMA) and reconfigurable intelligent surface (RIS) have been envisioned as two promising technologies for vehicle to everything (V2X) networks. This paper considers an RIS-aided V2X network with cognitive NOMA while considering the imperfect channel state information (CSI) due to the mobility of the vehicles. The new approximate outage probability (OP) expressions of the supposed system are derived over double Rayleigh fading channels. Numerical and Monte Carlo simulation results are presented to corroborate the analysis. The results demonstrated that imperfect CSI has a significant but different impact on the outage performance of the far and near users, which are also different under different numbers of reflecting units. © 2023 IEEE.

关键词： Monte Carlo methods

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