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23rd International Conference on Cyberworlds, CW 2024

作者： Arfi, Asif Mohammed Toyoura, Masahiro Kashiwagi, Kenji Nishiguchi, Satoshi Go, Kentaro Zhu, Zhenyang Mao, Xiaoyang University of Yamanashi Department of Computer Science and Engineering Yamanashi Kofu Japan University of Yamanashi Faculty of Medicine Department of Ophthalmology Yamanashi Chuo Japan Osaka Institute of Technology Faculty of Information Science and Technology Osaka Hirakata Japan

ISBN: (纸本)9798331527174

The precision of medical diagnoses based on images is inextricably linked to the quality and clarity of the images. Poor image quality can impede accurate diagnosis and pose challenges for both physicians and machine learning algorithms in interpreting images. By automating the process of fundus image quality assessment during capture, we can ensure that only high-quality images are used, improving diagnostic accuracy. Accordingly, we propose a novel approach for automatically assessing the quality of fundus images using deep learning techniques. Our method incorporates retinal vessel segmentation into RGB images to create four-channel images and then trains a deep learning model on these images to identify the image focus and quality of the images. It can evaluate the quality of fundus images and request image recapture with the adjustment of specific parameters that are classified as poor quality. Our proposed method has the potential to improve the diagnostic accuracy and efficiency of retinal disease diagnosis, particularly in telemedicine settings. By automating the process of fundus image quality assessment, we can ensure that only high-quality images are used for diagnosis, thus improving diagnostic precision. It can serve as an efficient screening tool in the initial stage of acquiring high-quality fundus images. © 2024 IEEE.

关键词： Image quality

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Multi-Channel Multi-Frame Integration Detection Method for Weak Target with Coherent MIMO Radar

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IEEE Transactions on Aerospace and Electronic systems 2025年第3期61卷 6518-6536页

作者： Gao, Longji Li, Xiaolong Wang, Mingxing Sun, Zhi Cui, Guolong Yeo, Tat Soon University of Electronic Science and Technology of China School of Information and Communication Engineering Chengdu611731 China National University of Singapore Department of Electrical and Computer Engineering Singapore119077 Singapore

Coherent multiple-input multiple-output (MIMO) radar could significantly improve the weak moving target detection ability by accumulating multi-channel and multi-frame echo signal. However, due to the target motion and the geometric configuration differences of each node relative to the target, the multi-channel multi-frame accumulation of target echoes by coherent MIMO radar faces several difficulties, i.e., range migration (RM) and Doppler frequency migration (DFM) issues within each channel, spatial variations of target signal envelope and phase across different channels, time variations of target signal envelope and phase across different frames. To tackle these problems, two contributions are made in this paper. Firstly, by applying the generalized Radon Fourier transform (GRFT) to correct the RM/DFM and obtain the signal integration within each channel, we propose a multi-channel accumulation algorithm to compensate the spatial variations of target signal and realize the target energy accumulation across different channels, which takes full advantage of the GRFT output characteristics and the radar system topological structure. Secondly, a multi-frame accumulation algorithm is developed to compensate the time variations of target signal and achieve the further integration of target energy distributed in different frames. We also provide explicit expressions and analysis for performance metrics, including the output response, SNR improvement performance. The proposed method's effectiveness is confirmed through simulation experiments. © 1965-2011 IEEE.

关键词： Topology

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Arabic Abstractive Summarization Using the Multilingual T5 Model 6

Arabic Abstractive Summarization Using the Multilingual T5 M...

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6th International Conference on Computing and Informatics, ICCI 2024

作者： Karim, Abdelrhman A. Usama, Mohammed Ibrahim, Manar A. Hatem, Youssef Wael, Waleed Mazrua, Ahmad T. El-Monayer, Ghada K. Elbanhawy, Magy Foad, Khaled Moawad, Ibrahim F. Artificial Intelligence Engineering Department Faculty of Computer Science and Engineering New Mansoura University Dakhlia Egypt Computer Engineering Department Faculty of Computer Science and Engineering New Mansoura University Dakhlia Egypt Artificial Intelligence Science Department Faculty of Computer Science and Engineering New Mansoura University Dakhlia Egypt Faculty of Computer Science and Engineering New Mansoura University Dakhlia Egypt Computer and System Department Faculty of Engineering Portsaid University Egypt Faculty of Computers and Artificial Intelligence Benha University Egypt Faculty of Computer and Information Sciences Ain Shams University Cairo Egypt

ISBN: (纸本)9798350373875

Natural language processing (NLP) is a branch of artificial intelligence (Al) that enables computers to comprehend, generate, and manipulate human language. Natural language processing can interrogate the data with natural language text or voice. Abstractive Text Summarization is based on the Natural Language Processing technique that tries to provide new and more concise textual summaries for huge texts. Artificial intelligence and deep learning techniques are used in abstractive summarization to examine the text's essential information and create a new summary that conveys the content more concisely and accurately. This type of summary differs from normal extractive summarizing in that it can generate new summaries beyond simply extracting key lines from the source text. This study presents an abstractive Arabic summarization based on the Multilingual TS (MTS) model AASMTS to address these concerns. This technique is based on deep neural networks and models like transformers, which have changed the ability to summarize text. This research has explored the various types of summarizations and highlighted the significance of two prominent techniques: abstractive and extractive summarization. This research describes a complete process for developing an Arabic abstractive summarization model using the MTS architecture. Experiments on different datasets show that this model achieves state-of-the-art results across MTS. © 2024 IEEE.

关键词： Deep neural networks

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Enhancing Phishing URL Detection: A Comparative Study of Machine Learning Algorithms 23

Enhancing Phishing URL Detection: A Comparative Study of Mac...

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2023 Asia Conference on Artificial Intelligence, Machine Learning and Robotics, AIMLR 2023

作者： Alsarhan, Ayoub Igried, Bashar Bani Saleem, Raad Mohammad Alauthman, Mohammad Aljaidi, Mohammad Department of Information Technology Faculty of Prince Al-Hussein Bin Abdallah II for Information Technology The Hashemite University Zarqa Jordan Department of Computer Science and Applications Faculty of Prince Al-Hussein Bin Abdallah II for Information Technology TheHashemite University Zarqa Jordan Department of Information Security University of Petra Amman Jordan Department of Computer Science Faculty of Information Technology Zarqa University Zarqa Jordan

ISBN: (纸本)9798400708312

Phishing constitutes a significant threat in the digital world, often exploiting human vulnerabilities to illicitly obtain sensitive data such as personal credentials, financial details, and private information. Misusing this information results in substantial financial loss and personal harm to victims. This study introduces an innovative approach to mitigate the risk of phishing attacks by employing machine learning algorithms to detect phishing URLs. The proposed method applies a suite of algorithms, including j48, Naïve Bayes, JRip, and Decision Table, to a robust dataset of 11,430 URLs, each with 87 extracted features. The results reveal the considerable potential of machine learning in identifying phishing threats. Furthermore, the study explores a novel server-side analysis concept where the server scrutinizes links transmitted via emails or social communication platforms such as WhatsApp, Messenger, and Instagram. The application of phishing detection algorithms filters and prevents the delivery of phishing links, thus reducing the potential harm to users. This research is poised to significantly contribute to cybersecurity by enhancing phishing detection mechanisms' accuracy and efficiency. © 2023 ACM.

关键词： Sensitive data

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A Comprehensive Evaluation of State-of-the-Art Deep Learning Models for Road Surface Type Classification

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Intelligent Automation & Soft Computing 2023年第8期37卷 1275-1291页

作者： Narit Hnoohom Sakorn Mekruksavanich Anuchit Jitpattanakul Image Information and Intelligence Laboratory Department of Computer EngineeringFaculty of EngineeringMahidol UniversityNakhon Pathom73170Thailand Department of Computer Engineering School of Information and Communication TechnologyUniversity of PhayaoPhayao56000Thailand Department of Mathematics Faculty of Applied ScienceKing Mongkut’s University of Technology North BangkokBangkok10800Thailand Intelligent and Nonlinear Dynamic Innovations Research Center Science and Technology Research InstituteKing Mongkut’s University of Technology North BangkokBangkok10800Thailand

In recent years,as intelligent transportation systems(ITS)such as autonomous driving and advanced driver-assistance systems have become more popular,there has been a rise in the need for different sources of traffic situation *** classification of the road surface type,also known as the RST,is among the most essential of these situational data and can be utilized across the entirety of the ITS ***,the benefits of deep learning(DL)approaches for sensor-based RST classification have been demonstrated by automatic feature extraction without manual *** ability to extract important features is vital in making RST classification more *** work investigates the most recent advances in DL algorithms for sensor-based RST classification and explores appropriate feature extraction *** used different convolutional neural networks to understand the functional architecture better;we constructed an enhanced DL model called SE-ResNet,which uses residual connections and squeeze-and-excitation mod-ules to improve the classification *** experiments with a publicly available benchmark dataset,the passive vehicular sensors dataset,have shown that SE-ResNet outperforms other state-of-the-art *** proposed model achieved the highest accuracy of 98.41%and the highest F1-score of 98.19%when classifying surfaces into segments of dirt,cobblestone,or asphalt ***,the proposed model significantly outperforms DL networks(CNN,LSTM,and CNN-LSTM).The proposed RE-ResNet achieved the classification accuracies of asphalt roads at 98.98,cobblestone roads at 97.02,and dirt roads at 99.56%,respectively.

关键词： Road surface type classification deep learning inertial sensor deep pyramidal residual network squeeze-and-excitation module

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A comprehensive examination of load balancing algorithms in cloud environments: a systematic literature review, comparative analysis, taxonomy, open challenges, and future trends

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Iran Journal of computer science 2024年第3期7卷 663-698页

作者： Prity, Farida Siddiqi Hossain, Md. Maruf Department of Information and Communication Engineering Noakhali Science and Technology University Noakhali 3814 Bangladesh Department of Information and Communication Engineering Pabna University of Science and Technology Pabna 6600 Bangladesh Department of Computer Science and Engineering Shanto-Mariam University of Creative Technology Dhaka 1230 Bangladesh

Cloud computing is a robust paradigm that empowers users and organizations to procure services tailored to their needs. This model encompasses many offerings, including storage solutions, platforms for seamless deployment, and convenient access to web services. Load balancing, a fundamental pillar in cloud computing, is crucial in distributing requests across multiple servers to optimize resource utilization and reduce response times. However, load balancing presents a common challenge in the cloud environment, as it hampers the ability to maintain optimal application performance while adhering to the stringent requirements of Quality of Service (QoS) measurements and Service Level Agreement (SLA) compliance mandated by cloud providers to enterprises. The equitable workload distribution across servers poses a significant challenge for cloud providers. Hence, an efficient load-balancing technique should optimize resource utilization in Virtual Machines (VMs) to ensure maximum user satisfaction and overall system efficiency. However, existing review papers on load balancing in cloud environments often exhibit limitations, lacking in-depth analyses, graphical representations, and comprehensive evaluations of performance metrics. This review paper aims to fill these gaps by providing a novel taxonomy of load balancing algorithms divided into four categories (types of algorithms, nature of problem, metrics, and simulation tools) and thoroughly examining their objectives, parameters, and operational flows. It evaluates the strengths and weaknesses of these algorithms, considering their nature and type, and employs qualitative QoS parameter-based criteria for effectiveness evaluation. The paper also includes a comparative analysis of simulation tools, visual representations, and experimental results. By offering valuable insights, open issues, recommendations, and future directions, this review paper equips researchers, practitioners, and cloud service providers with the k

关键词： Algorithms Cloud computing Load balancing Taxonomy

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A Secure Vehicle Registration and Authentication system Using Distributed Blockchain technology 3

A Secure Vehicle Registration and Authentication System Usin...

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3rd International Conference on Advancement in Electrical and Electronic Engineering, ICAEEE 2024

作者： Rakibul Hasan, M.M. Kashem, Mohammod Abul Tahsin, Akil Mohammad Ronju, Md Raziur Rahman Bangladesh University of Professionals Department of Information and Communication Technology Dhaka Bangladesh International University of Business Agriculture and Technology Department of Computer Science and Engineering Dhaka Bangladesh Dhaka University of Engineering and Technology Department of Computer Science and Engineering Gazipur Bangladesh

ISBN: (纸本)9798350388282

Vehicle fraud is a significant problem as the vehicle registration data can easily be fabricated and duplicated. The reason for the forgery is that in the usual registration process, car data is maintained in a centralized database, which is readily compromised. Again, the law enforcement agencies waste a large amount of time authorizing the license and registration papers. We used blockchain in our work to solve the vulnerability issues with traditional database. In this blockchain network, the data is encrypted using keccak256 hashing algorithm and the documents are secured in an IPFS network to provide topmost security. Our research also implements a secure authentication process to verify the registration and license of a vehicle owner and keep track of crime history. The use of this system will eliminate the deception of vehicle registration and traffic will be much safer with easier knowledge of vehicle fitness and crime history. © 2024 IEEE.

关键词： Blockchain

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Multi-Label Text Classification for Judicial Texts via Dual Graph and Label Feature Fusion 10

Multi-Label Text Classification for Judicial Texts via Dual ...

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10th IEEE Smart World Congress, SWC 2024

作者： Gu, Qiliang Lu, Qin Shandong Engineering Research Center of Big Data Applied Technology Faculty of Computer Science and Technology Jinan China Key Laboratory of Computing Power Network and Information Security Ministry of Education Shandong Computer Science Center Jinan China Shandong Fundamental Research Center for Computer Science Shandong Provincial Key Laboratory of Industrial Network and Information System Security Jinan China

ISBN: (纸本)9798331520861

The legal judgement prediction (LJP) of judicial texts represents a multi-label text classification (MLTC) problem, which in turn involves three distinct tasks: the prediction of charges, legal articles, and terms of penalty. Nevertheless, extant multi-label text classification models tend to eschew the consideration of multiple correlations and semantic information between labels when predicting judicial texts, which may result in the loss of pertinent information. Furthermore, these models do not take full advantage of the local and overall information of the labels in the process of selecting appropriate labels for the text using the multi-head attention mechanism. To address these issues, we propose a new model, BGFLFF, which explores label correlation and semantics among the three tasks by employing Graph Convolutional Network (GCN) and multi-head attention mechanisms. In particular, we propose a Bi-Graph Fusion GCN (BGF-GCN), which fuses the co-occurrence matrix of labels with the cosine similarity matrix, thereby fully exploiting the multiple correlations between labels. Furthermore, prior to embedding the tags, the interpretations of the labels are obtained via Google in order to enhance the semantic information between the labels. To further reinforce the interconnections between labels and text and more effectively comprehend the interrelationships between the three tasks, we propose a Multi-Head Label Feature Fusion Attentional Mechanism (MH-LFFAtt), which assigns distinct weights to the pertinent label information in the text by fusing the local and overall features of the labels. The experimental results demonstrate that the F1 score exhibits an improvement of up to 2. 2 8 % and a minimum of 1. 2 9 % across the various tasks of the two datasets. This evidence substantiates the assertion that BGFLFF outperforms the existing baseline model. © 2024 IEEE.

关键词： Semantics

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EVASS: Emotional Variational End-to-End Speech Synthesis with Semi-Supervised and Adverserial Learning 2

EVASS: Emotional Variational End-to-End Speech Synthesis wit...

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2nd International Mobile, Intelligent, and Ubiquitous Computing Conference, MIUCC 2022

作者： Osman, Mohamed Ahram Canadian University Faculty of Computer Science and Information Technology Computer Science Department Egypt

ISBN: (纸本)9781665466776

Communicating one's inner state-their emotions and feelings-forms one of the core principles of social communication and behavior in humans. Emotion is an important component of speech, and its inclusion in synthetic speech will allow for breakthroughs in applications like human-machine interfacing, e-book reading, and voice acting. However, modelling emotions in speech in an end-to-end manner has so far remained an under-explored topic of research. To address this, we experiment with novel methods in global emotional modelling in unsupervised, semi-supervised and adverserial contexts using an end-to-end text-to-speech (TTS) architecture. We condition the latent space, duration prediction and audio generation on novel hybrid labels based on ground truth data-14 emotion labels, 64 sentiment analysis labels, and speaker labels-which may be inferred from input text during inference. Experiments on conditional discriminators were also performed. The final proposed model produces high quality expressive results comparable to the state of the art. © 2022 IEEE.

关键词： Training Sentiment analysis Pipelines computer architecture Ubiquitous computing Hybrid power systems Speech synthesis

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Detection and Security in Falls with IoT Server 3

Detection and Security in Falls with IoT Server

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3rd International Conference on Artificial Intelligence and Smart Energy, ICAIS 2023

作者： Kavitha, C. Sridevi, N. Dhivagar, D. Department of Information Technology Villupuram India Sathyabama Institution of Science and Technology Department of Computer Science and Engineering Chennai India Department of It Department of Information Technology Villupuram India

ISBN: (纸本)9781665462167

Heart rate (HR) and Heart rate variability (HRV) have received a great deal of attention that promises to change the dimension of awareness of health and fitness while swimming. HRV is very useful to understand physiological and psychological status of an individual. The variation in HR, provides a reliable information about the role of Autonomic Nervous system (ANS). HRV is very convenient to understand the overall physiological status of an individual. Due to individuality of the HRV, regular monitoring HRV is useful to understand training adaptation, load, recovery, overtraining. The study provides a brief concept on HR and HRV in swimming individual. Although RR intervals are highly individual centric but due to same practice pattern or same type of physical activity, the swimmer group has very small quartile range. A significance difference in RR intervals between control group and swimmer group may come from two different effects of the nervous system. Either it indicates a significant increase in parasympathetic tone due to normal training adaptation or a sign of overtraining that has caused increase in parasympathetic tone. High HRV denotes good indication of positive adaptation, good cardiovascular efficiency. Low HRV score indicates deterioration in VO2max. © 2023 IEEE.

关键词： Internet of things

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