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Evaluating oil spill contamination around Qaruh Island, Kuwait: Insights from EOS-04 C-band SAR data

Regional Studies in Marine Science 2025年 89卷

作者： N. Krishnaraj Mohammad Alamgeer Shouki A. Ebad Haya Mesfer Alshahrani Saraswathi A. Sumaiya Begum J. Jayanthi Department of Networking and Communications School of Computing College of Engineering and Technology SRM Institute of Science and Technology Kattankulathur- 603203 Tamil Nadu India Department of Information Systems College of Science & Art at Mahayil King Khalid University Saudi Arabia Department of Computer Science Faculty of Science Northern Border University Arar Saudi Arabia Department of Information Systems College of Computer and Information Sciences Princess Nourah Bint Abdulrahman University P.O. Box 84428 Riyadh Saudi Arabia Department of Computer Science and Engineering Chennai Institute of Technology Chennai Tamil Nadu India Department of Electronics and Communication Engineering R.M.D.Engineering College Tamil Nadu India Department of Computer Science and Engineering Sona College of Technology Salem Tamil Nadu India

Oil spills threaten marine environments, especially in ecologically sensitive regions like Qaruh Island, Kuwait. This study investigates the extent and severity of oil contamination surrounding Qaruh Island by leveraging C-Band Synthetic Aperture Radar (SAR) data acquired from two key satellite sources, Sentinel-1A and EOS-04, on April 23, 2024. Both satellites provided dual-polarized SAR data (HH and VV), which were analysed to enhance oil spill detection through polarization-based discrimination techniques. The analysis compared backscatter values from oil-covered and clean water surfaces to delineate the spill area accurately. Sentinel-1A and EOS-04 SAR data were pre-processed for geometric and radiometric corrections, followed by the application of filtering techniques to mitigate speckle noise and enhance detection clarity. Through this approach, we successfully detected a continuous oil spill extending over an area of approximately 20 square kilometres. The HH polarization was particularly effective in identifying the boundaries of the spill, while VV polarization contributed to confirming the presence of oil by highlighting differences in surface roughness between contaminated and uncontaminated areas. The results demonstrate that dual-polarized SAR data, specifically C-Band data, are crucial in oil spill monitoring, providing timely and accurate contamination assessments in coastal and marine environments. By utilizing Sentinel-1A and EOS-04 SAR datasets, this study underscores the effectiveness of multi-source SAR data for rapid response in environmental monitoring, ultimately supporting resource protection and mitigation efforts around Qaruh Island.

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Kidney Tumor Detection and Classification Based on Deep Learning Approaches: A New Dataset in CT Scans

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Journal of Healthcare engineering 2022年第1期2022卷 3861161页

作者： Alzu'Bi, Dalia Abdullah, Malak Hmeidi, Ismail Alazab, Rami Gharaibeh, Maha El-Heis, Mwaffaq Almotairi, Khaled H. Forestiero, Agostino Hussein, Ahmad Mohdaziz Abualigah, Laith Department of Computer Information Systems Jordan University of Science and Technology Irbid2210 Jordan Department of General Surgery and Urology University of Science and Technology Irbid22110 Jordan Department of Diagnostic and Interventional Radiology Faculty of Medicine Jordan University of Science and Technology Irbid2210 Jordan Computer Engineering Department Computer and Information Systems College Umm Al-Qura University Makkah21955 Saudi Arabia Institute for High Performance Computing and Networking CNR CS Rende Italy Deanship of E-Learning and Distance Education Umm Al-Qura University Makkah21955 Saudi Arabia Hourani Center for Applied Scientific Research Al-Ahliyya Amman University Amman19328 Jordan Faculty of Information Technology Middle East University Amman11831 Jordan School of Computer Sciences Universiti Sains Malaysia Pulau Pinang11800 Malaysia

Kidney tumor (KT) is one of the diseases that have affected our society and is the seventh most common tumor in both men and women worldwide. The early detection of KT has significant benefits in reducing death rates, producing preventive measures that reduce effects, and overcoming the tumor. Compared to the tedious and time-consuming traditional diagnosis, automatic detection algorithms of deep learning (DL) can save diagnosis time, improve test accuracy, reduce costs, and reduce the radiologist's workload. In this paper, we present detection models for diagnosing the presence of KTs in computed tomography (CT) scans. Toward detecting and classifying KT, we proposed 2D-CNN models;three models are concerning KT detection such as a 2D convolutional neural network with six layers (CNN-6), a ResNet50 with 50 layers, and a VGG16 with 16 layers. The last model is for KT classification as a 2D convolutional neural network with four layers (CNN-4). In addition, a novel dataset from the King Abdullah University Hospital (KAUH) has been collected that consists of 8,400 images of 120 adult patients who have performed CT scans for suspected kidney masses. The dataset was divided into 80% for the training set and 20% for the testing set. The accuracy results for the detection models of 2D CNN-6 and ResNet50 reached 97%, 96%, and 60%, respectively. At the same time, the accuracy results for the classification model of the 2D CNN-4 reached 92%. Our novel models achieved promising results;they enhance the diagnosis of patient conditions with high accuracy, reducing radiologist's workload and providing them with a tool that can automatically assess the condition of the kidneys, reducing the risk of misdiagnosis. Furthermore, increasing the quality of healthcare service and early detection can change the disease's track and preserve the patient's life. © 2022 Dalia Alzu'bi et al.

关键词： Convolutional neural networks

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DialogueAgents: A Hybrid Agent-Based Speech Synthesis Framework for Multi-Party Dialogue

arXiv

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arXiv 2025年

作者： Li, Xiang Pan, Duyi Xiao, Hongru Han, Jiale Tang, Jing Ma, Jiabao Wang, Wei Cheng, Bo State Key Laboratory of Networking and Switching Technology Beijing University of Posts and Telecommunications China Department of Computer Science and Engineering Hong Kong University of Science and Technology Guangzhou China Hong Kong University of Science and Technology Hong Kong

Speech synthesis is crucial for human-computer interaction, enabling natural and intuitive communication. However, existing datasets involve high construction costs due to manual annotation and suffer from limited character diversity, contextual scenarios, and emotional expressiveness. To address these issues, we propose DialogueAgents, a novel hybrid agent-based speech synthesis framework, which integrates three specialized agents—a script writer, a speech synthesizer, and a dialogue critic—to collaboratively generate dialogues. Grounded in a diverse character pool, the framework iteratively refines dialogue scripts and synthesizes speech based on speech review, boosting emotional expressiveness and paralinguistic features of the synthesized dialogues. Using DialogueAgent, we contribute MultiTalk, a bilingual, multi-party, multi-turn speech dialogue dataset covering diverse topics. Extensive experiments demonstrate the effectiveness of our framework and the high quality of the MultiTalk dataset. We release the dataset and code1 to facilitate future research on advanced speech synthesis models and customized data generation. Copyright © 2025, The Authors. All rights reserved.

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Deep Learning Approach for Enhanced Cyber Threat Indicators in Twitter Stream 1

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7th International Symposium on Security in Computing and Communications, SSCC 2019

作者： Simran, K. Balakrishna, Prathiksha Vinayakumar, R. Soman, K.P. Center for Computational Engineering and Networking Amrita School of Engineering Amrita Vishwa Vidyapeetham Coimbatore India Graduate School Computer Science Department Texas State University San Marcos United States Division of Biomedical Informatics Cincinnati Children’s Hospital Medical Centre CincinnatiOH United States

ISBN: (数字)9789811548253

ISBN: (纸本)9789811548246

In recent days, the amount of Cyber Security text data shared via social media resources mainly Twitter has increased. An accurate analysis of this data can help to develop cyber threat situational awareness framework for a cyber threat. This work proposes a deep learning based approach for tweet data analysis. To convert the tweets into numerical representations, various text representations are employed. These features are feed into deep learning architecture for optimal feature extraction as well as classification. Various hyperparameter tuning approaches are used for identifying optimal text representation method as well as optimal network parameters and network structures for deep learning models. For comparative analysis, the classical text representation method with classical machine learning algorithm is employed. From the detailed analysis of experiments, we found that the deep learning architecture with advanced text representation methods performed better than the classical text representation and classical machine learning algorithms. The primary reason for this is that the advanced text representation methods have the capability to learn sequential properties which exist among the textual data and deep learning architectures learns the optimal features along with decreasing the feature size. © Springer Nature Singapore Pte Ltd. 2020.

关键词： Deep learning

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CholecSeg8k: A semantic segmentation dataset for laparoscopic cholecystectomy based on Cholec80

arXiv

引用

arXiv 2020年

作者： Hong, W.-Y. Kao, C.-L. Kuo, Y.-H. Wang, J.-R. Chang, W.-L. Shih, Chi-Sheng Graduate Institute of Networking and Multimedia Department of Computer Science and Information Engineering National Taiwan University Taipei106 Taiwan

computer-assisted surgery has been developed to enhance surgery correctness and safety. However, the researchers and engineers suffer from limited annotated data to develop and train better algorithms. Consequently, the development of fundamental algorithms such as Simultaneous Localization and Mapping (SLAM) are limited. This article elaborates the efforts of preparing the dataset for semantic segmentation, which is the foundation of many computer-assisted surgery mechanisms. Based on the Cholec80 dataset [3], we extracted 8,080 laparoscopic cholecystectomy image frames from 17 video clips in Cholec80 and annotated the images. The dataset is named CholecSeg8K and its total size is 3GB. Each of these images are annotated at pixel-level for thirteen classes, which are commonly founded in laparoscopic cholecystectomy surgery. CholecSeg8k is released under the license CC BY-NC-SA 4.0. © 2020, CC BY-NC-SA.

关键词： Semantics

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Review on Smart Video Streaming Schemes for Cloud Based E-Learning 1

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2nd International Conference on Advanced Data and Information engineering, DaEng 2015

作者： Syamsuddin, Irfan Department of Computer and Networking Engineering CAIR—Center for Applied ICT Research State Polytechnic of Ujung Pandang Makassar Indonesia

ISBN: (数字)9789811317996

ISBN: (纸本)9789811317972

This study is an initial effort to determine the best way to deliver rich educational data such as video materials over cloud computing environment. Cloud computing has been widely applied in various fields of business and private sector to simplify complexity of data management and networking. It also drew attention in educational institutions to improve the existing services of e-learning by adopting cloud technology. However, delivering rich educational content within a mesh network commonly influenced by bandwidth, jitter and loss of data which sometimes significantly reduces its quality at end user side. In dealing with the issues, several smart video schemes have been proposed to improve video streaming quality. The approaches look the issue from different perspectives and use variety of methods to produce the so called smart video streaming scheme as mentioned in literature. This study aims to explore state of the art on smart video streaming schemes and organize them into particular groups. It is expected that the effort will not only beneficial for next steps in the research but will also help other readers better understand advancement in the field as well as indentifying particular gaps where other researchers could contribute in the future. © Springer Nature Singapore Pte Ltd. 2019.

关键词： Video streaming

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Various Deep Learning Methods for Hyperspectral Images

Various Deep Learning Methods for Hyperspectral Images

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2020 International Conference on Computing and Information Technology, ICCIT 2020

作者： Sivaram, M. Shobana, S. Jeya Khan, Mujeebudheen Ramakrishnan, Jayabrabu Goel, Pallavi Murghai Maseleno, Andino Lebanese French University Department of Computer Networking Erbil Kurdistan Region Iraq College of Science Knowledge University Department of Computer Science Erbil Kurdistan Region Iraq Ibra College of Technology Department of Electrical and Electronics Oman Jazan University College of Computer Science and Information Technology Department of Information Technology and Security Saudi Arabia Galgotias University School of Computing Science and Engineering Greater Noida India Universiti Tenaga Nasional Institute of Informatics and Computing Energy Department of Business Administration and Information System Malaysia

ISBN: (纸本)9781728126807

Hyperspectral imagery is widely used in remote sensing applications that take into account thousands of spectral channel compositions over a single scene. Hyperspectral imagery requires accurate models of learning to extract the hyperspectral features in an image. Due to the presence of its spatial and spectral resolution, the image learning model presents a core challenge due to its complicated nature of image frames. In order to assist it during the learning process, several attempts have been made to address its complicated nature. However, these methods failed to provide the hyperspectral imagery with a deeper understanding. Because of the presence of mixed pixels, limited training samples and redundant data, the utilization of deep learning techniques addresses the problems. The deep learning process addresses the complex image data relationship. In this paper, various deep learning methods are studied which are used for the learning of hyperspectral imagery. Initially, we present an overview on various deep learning methods for various image processing techniques. A system review is then carried out on various hyperspectral image learning models based on deep learning. © 2020 IEEE.

关键词： Remote sensing

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Semantic Communications:Theories,Technologies and Applications

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中国通信（英文版） 2024年第7期21卷前插1-前插5页

作者： Zhang Ping Shi Guangming Cui Shuguang Zhang Zhaoyang Niu Kai Xiao Yong Qin Zhijin Dai Jincheng Shao Shuo Deniz Gündüz Eleonora Grassucci School of Information and Communication Engineering at Beijing University of Posts and Telecommunications State Key Laboratory of Networking and Switching Technology IMT-2020(5G)Experts Panel Experts Panel for China's 6G development Peng Cheng Laboratory School of Artifi-cial Intelligence Xidian University electrical and computer engineering with the University of Arizona Texas A&M University UC Davis Chinese Univer-sity of Hong Kong(Shenzhen)(CUHK-Shenzhen) Zhejiang University School of Artificial Intelli-gence BUPT School of Electron-ic Information and Communications at the Huazhong University of Science and Technology(HUST) WuhanChina Peng Cheng Laboratory Guang-zhouChina and the Pazhou Laboratory(Huangpu)Guang-zhouChina Tsinghua University BeijingChina Key Laboratory of Universal Wireless Communications Ministry of EducationBeijing University of Posts and Telecommunications School of Electronics Informa-tion and Electrical EngineeringShanghai Jiao Tong Univer-sityChina Information Processing in the Electrical and Electronic Engineering Department at Imperial College London UK Intelligent Systems and Networks Group Department at Imperial College London UK Department of Information Engineering Electronicsand Telecommunications of the Sapienza University of Rome

The last seventy years have witnessed the transi-tion of communication from Shannon's theoretical concept to current high-efficiency practical *** respect to Shannon information theory,to fulfill the high-bandwidth utilization,any further increase in the data rate requires a significant augmentation in the received signal power unless the bandwidth is extended in proportion to the incre-mental data rate.

关键词： Special issues and sections Semantics Communication systems Data processing Information theory Data augmentation

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A survey on security attacks and defense techniques for Connected and Autonomous Vehicles

arXiv

引用

arXiv 2020年

作者： Pham, Minh Xiong, Kaiqi Department of Computer Science and Engineering University of South Florida United States Intelligent Computer Networking and Security Laboratory University of South Florida United States

Autonomous Vehicle has been transforming intelligent transportation systems. As telecommunication technology improves, autonomous vehicles are getting connected to each other and to infrastructures, forming Connected and Autonomous Vehicles (CAVs). CAVs will help humans achieve safe, efficient, and autonomous transportation systems. However, CAVs will face significant security challenges because many of their components are vulnerable to attacks, and a successful attack on a CAV may have significant impacts on other CAVs and infrastructures due to their communications. In this paper, we conduct a survey on 184 papers from 2000 to 2020 to understand state-of-the-art CAV attacks and defense techniques. This survey first presents a comprehensive overview of security attacks and their corresponding countermeasures on CAVs. We then discuss the details of attack models based on the targeted CAV components of attacks, access requirements, and attack motives. Finally, we identify some current research challenges and trends from the perspectives of both academic research and industrial development. Based on our studies of academic literature and industrial publications, we have not found any strong connection between academic research and industry’s implementation on CAV-related security issues. While efforts from CAV manufacturers to secure CAVs have been reported, there is no evidence to show that CAVs on the market have the ability to defend against some novel attack models that the research community has recently found. This survey may give researchers and engineers a better understanding of the current status and trend of CAV security for CAV future improvement. Copyright © 2020, The Authors. All rights reserved.

关键词： Intelligent systems

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Lower Bounds for the Minimum Spanning Tree Cycle Intersection Problem

arXiv

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arXiv 2024年

作者： Dubinsky, Manuel Chao, Kun-Mao Massri, César Taubin, Gabriel Ingeniería en Informática Departamento de Tecnología y Administración Universidad Nacional de Avellaneda Argentina Departamento de Computación Facultad de Ciencias Exactas y Naturales Universidad de Buenos Aires Argentina Department of Computer Science and Information Engineering National Taiwan University Taipei Taiwan Graduate Institute of Biomedical Electronics and Bioinformatics National Taiwan University Taipei Taiwan Graduate Institute of Networking and Multimedia National Taiwan University Taipei Taiwan Departamento de Matemática Universidad de CAECE CABA Argentina School of Engineering Brown University ProvidenceRI United States

Minimum spanning trees are important tools in the analysis and design of networks. Many practical applications require their computation, ranging from biology and linguistics to economy and telecommunications. The set of cycles of a network has a vector space structure. Given a spanning tree, the set of non-tree edges defines cycles that determine a basis. The intersection of two such cycles is the number of edges they have in common and the intersection number -denoted ∩(G)-, is the number of non-empty pairwise intersections of the cycles of the basis. The Minimum Spanning Tree Cycle Intersection problem consists in finding a spanning tree such that the intersection number is minimum. This problem is relevant in order to integrate discrete differential forms. In this paper, we present two lower bounds of the intersection number of an arbitrary connected graph G = (V, E). In the first part, we prove the following statement: (Equation presented) where n = |V | and ν is the cyclomatic number of G. In the second part, based on some experimental results and a new observation, we conjecture the following improved tight lower bound: (Equation presented) where 2ν = q(n − 1) + r is the integer division of 2ν and n − 1. This is the first result in a general context, that is for an arbitrary connected graph. © 2024, CC BY.

关键词： Vector spaces

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