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5th International Conference on Water Resources, ICWR 2021

作者： Bong, C.H.J. Liew, S.C. Neging, M. Matthew Ruji, E. Gabda, D. Faculty of Engineering Universiti Malaysia Sarawak Kota Samarahan Malaysia Faculty of Engineering Computing and Science Swinburne University of Technology Sarawak Campus Kuching Malaysia School of Civil Engineering College of Engineering Universiti Teknologi MARA Cawangan Sarawak Kota Samarahan Malaysia Department of Irrigation and Drainage Sarawak Kuching Malaysia Faculty of Science and Natural Resources Universiti Malaysia Sabah Kota Kinabalu Malaysia

ISBN: (纸本)9789811959462

Kuching city and its surrounding urban areas frequently experience extreme high annual maximum daily rainfall (AMDR), resulting in flash floods. This study aims to carry out statistical and trend analysis of extreme AMDR events for Kuching Airport rainfall station from 1975 to 2017. From the analysis, the AMDR records a high variability with a value of 36.9% while January has the highest occurrence of AMDR with 53.5% of the total data. Findings from the linear regression plot have shown that the AMDR has a slight decreasing trend over the past four decades though the trend was insignificant. Based on the drainage design capacity of Kuching city, AMDR of magnitude 180 mm was identified as a threshold. The frequency analysis results showed that the return period of flooding events with daily rainfall exceeding 180 mm was 2.69 years. The occurrence probability of the flood event at least once in 1, 2, 3, 4 and 5 years was 0.37, 0.60, 0.75, 0.84 and 0.90, respectively. This study contributed to understanding the magnitude and frequency of extreme high AMDR which could lead to flooding events in Kuching city. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Climate change

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A Secured and Optimized Broadcast Authentication Scheme for the Internet of Medical Things 4

A Secured and Optimized Broadcast Authentication Scheme for ...

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4th IEEE International Conference on Electrical, Computer, and Energy Technologies, ICECET 2024

作者： Riaz, Syed Waleed Khan, Fazlullah Ullah, Insaf Alshawi, Bandar Alturki, Ryan Wedyan, Mohammad Abdul Wali Khan University Mardan Computer Science Department KPK Pakistan School of Computer Science University of Nottingham Ningbo China Faculty of Science and Engineering Zhejiang Ningbo315104 China Institute for Analytics and Data Science University of Essex ColchesterCO4 3SQ United Kingdom College of Computing Umm Al-Qura University Department of Computer and Network Engineering Makkah Saudi Arabia College of Computing Umm Al-Qura University Department of Software Engineering Makkah Saudi Arabia Faculty of Information Technology and Computer Sciences Department of Computer Sciences Irbid21163 Jordan

ISBN: (纸本)9798350395914

This Internet of Medical Things (IoMT), facilitates the medical stop regarding real-time monitoring of patients, medical emergency management, remote surgery, patient information management, medical equipment, drug monitoring, etc. However, IoMT devices communicate with each other in an open environment that makes them vulnerable to a wide range of malicious threats from malicious entities. To protect the devices and their associated data, we designed a broadcast authentication scheme for IoMT devices using identity-based public key cryptography that exploits the lightweight features of the Hyper-Elliptic Curve (HEC). We then performed the security analysis based on the Random Oracle Model (ROM), in which we have proved that the proposed scheme is unforgeable under the hardiness of the hyperelliptic curve discrete logarithm problem. The proposed scheme is analyzed in terms of computational and communication overhead and the experimental results justify the superiority of the proposed work in comparison to the existing schemes. © 2024 IEEE.

关键词： Authentication

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ISO X3D 4.0 Audio Graph for web3D applications. Latest trends in declarative 3d sound for the web

ISO X3D 4.0 Audio Graph for web3D applications. Latest trend...

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2023 Immersive and 3D Audio: from Architecture to Automotive, I3DA 2023

作者： Papadaki, Maria Brutzman, Don Lakka, Eftychia Puk, Richard Daskalakis, Georgios Malamos, Athanasios G. Hellenic Mediterranean University Dept of Electrical and Computer Engineering Heraklion Greece MontereyCA United States University of South Wales Faculty of Computing Engineering and Science United Kingdom Intelligraphics Inc.

ISBN: (纸本)9798350311044

Our work, as 3D Audio Working Group of Web3D Consortium, aims to integrate acoustic properties associated with geometric shapes together with 3D spatial sound and insert this new technology into the X3D v4.0 ISO standard. In this paper we present X3D 4.0 Audio Graph API as it is embedded with all the latest improvements and examples. Moreover, we introduce novel ideas and use cases on how X3D v4.0 may improve immersion experience in Metaverse games, theatre or music performances and concerts. In this paper we introduce new audio components of the X3D standard and we present a case study of a platform that enhances users' experience in scenarios of live online performances, mainly theater or music concerts, by enriching the realism of the streamed audio through auralization and virtual specialization. Our strong point is that we manage to include all the necessary information inside the scene nodes and combine three technologies that serve different purposes to work harmonically together, allowing multiple people to stream and enjoy the performance as they would in a real theater. We achieve this goal by joining state of the art technologies that involves web3D, teleconferencing and sound. Thus we may encourage community to create new real time applications like virtual theatre or tele-orchestras. © 2023 IEEE.

关键词： ISO Standards

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The Role of Deep Learning in Parking Space Identification and Prediction Systems

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Computers, Materials & Continua 2023年第4期75卷 761-784页

作者： Faizan Rasheed Yasir Saleem Kok-Lim Alvin Yau Yung-Wey Chong Sye Loong Keoh School of Physics Engineering and Computer ScienceUniversity of HertfordshireHatfieldAL109ABUK Department of Computer Science Aberystwyth UniversityAberystwyth SY233FLUK Lee Kong Chian Faculty of Engineering and Science Universiti Tunku Abdul RahmanKajang43200SelangorMalaysia National Advanced IPv6 Centre(NAv6) Universiti Sains MalaysiaPenang11800Malaysia School of Computing Science University of Glasgow Singapore737729Singapore

In today’s smart city transportation,traffic congestion is a vexing issue,and vehicles seeking parking spaces have been identified as one of the causes leading to approximately 40%of traffic *** parking spaces alone is insufficient because an identified available parking space may have been taken by another vehicle when it arrives,resulting in the driver’s frustration and aggravating traffic jams while searching for another parking *** explains the need to predict the availability of parking ***,deep learning(DL)has been shown to facilitate drivers to find parking spaces efficiently,leading to a promising performance enhancement in parking identification and prediction ***,no work reviews DL approaches applied to solve parking identification and prediction *** by this gap,the purpose of this work is to investigate,highlight,and report on recent advances inDLapproaches applied to predict and identify the availability of parking *** of DL-based parking identification and prediction systems is established as a methodology by classifying and categorizing existing literature,and by doing so,the salient and supportive features of different DL techniques for providing parking solutions are ***,several open research challenges are *** work identifies that there are various DL architectures,datasets,and performance measures used to address parking identification and prediction ***,there are some open-source implementations available that can be used directly either to extend existing works or explore a new *** is the first short survey article that focuses on the use of DL-based techniques in parking identification and prediction systems for smart *** study concludes that although the deployment of DL in parking identification and prediction systems provides various benefits,the convergence of these two types of systems and DL brings about new i

关键词： Convolutional neural network deep learning neural networks parking identification parking prediction smart city

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Hybrid Dipper Throated and Grey Wolf Optimization for Feature Selection Applied to Life Benchmark Datasets

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Computers, Materials & Continua 2023年第2期74卷 4531-4545页

作者： Doaa Sami Khafaga El-Sayed M.El-kenawy Faten Khalid Karim Mostafa Abotaleb Abdelhameed Ibrahim Abdelaziz A.Abdelhamid D.L.Elsheweikh Department of Computer Sciences College of Computer and Information SciencesPrincess Nourah bint Abdulrahman UniversityP.O.Box 84428Riyadh11671Saudi Arabia Department of Communications and Electronics Delta Higher Institute of Engineering and TechnologyMansoura35111Egypt Faculty of Artificial Intelligence Delta University for Science and TechnologyMansoura35712Egypt Department of System Programming South Ural State UniversityChelyabinsk454080Russia Computer Engineering and Control Systems Department Faculty of EngineeringMansoura UniversityMansoura35516Egypt Department of Computer Science Faculty of Computer and Information SciencesAin Shams UniversityCairo11566Egypt Department of Computer Science College of Computing and Information TechnologyShaqra University11961Saudi Arabia Department of Computer Science Faculty of Specific EducationMansoura UniversityEgypt

Selecting the most relevant subset of features from a dataset is a vital step in data mining and machine *** feature in a dataset has 2n possible subsets,making it challenging to select the optimum collection of features using typical *** a result,a new metaheuristicsbased feature selection method based on the dipper-throated and grey-wolf optimization(DTO-GW)algorithms has been developed in this *** can result when the selection of features is subject to metaheuristics,which can lead to a wide range of ***,we adopted hybrid optimization in our method of optimizing,which allowed us to better balance exploration and harvesting chores more *** propose utilizing the binary DTO-GW search approach we previously devised for selecting the optimal subset of *** the proposed method,the number of features selected is minimized,while classification accuracy is *** test the proposed method’s performance against eleven other state-of-theart approaches,eight datasets from the UCI repository were used,such as binary grey wolf search(bGWO),binary hybrid grey wolf,and particle swarm optimization(bGWO-PSO),bPSO,binary stochastic fractal search(bSFS),binary whale optimization algorithm(bWOA),binary modified grey wolf optimization(bMGWO),binary multiverse optimization(bMVO),binary bowerbird optimization(bSBO),binary hysteresis optimization(bHy),and binary hysteresis optimization(bHWO).The suggested method is superior 4532 CMC,2023,vol.74,no.2 and successful in handling the problem of feature selection,according to the results of the experiments.

关键词： Metaheuristics dipper throated optimization grey wolf optimization binary optimizer feature selection

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Chicken Swarm Algorithm with Deep Learning for Plant Leaf Disease Detection and Classification 23th

Chicken Swarm Algorithm with Deep Learning for Plant Leaf Di...

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23rd International Conference on Intelligent Systems Design and Applications, ISDA 2023

作者： Subbiah, Priyanga Tyagi, Amit Kumar Krishnaraj, N. Department of Networking and Communications School of Computing Faculty of Engineering and Technology SRM Institute of Science and Technology Kattankulathur Chengalpattu603203 India Department of Fashion Technology National Institute of Fashion Technology New Delhi India

ISBN: (纸本)9783031648359

Diseases that affect plants have a major influence on crop yields around the globe. Crop yields and plant development have been severely limited in many parts of the world due to plant diseases for quite some time. The food supply has been negatively affected as a result of this. In certain cases, depending on the specifics of the issue, it may be possible to make out the plant’s leaves. Photo documentation of the plant’s leaves revealed the presence of many illnesses. It is crucial to determine the type of infestation before initiating any action to eradicate it. Among the many factors considered in this case study are the rapid spread of illnesses and the farmers’ lack of diagnostic expertise. Fortunately, by merging deep learning (DL) techniques into computer vision (CV), we might be able to simplify machine learning (ML) and so lessen this issue. The purpose of this study is to identify plant leaf diseases using a Deep Learning system in conjunction with a Chicken Swarm Algorithm. Helping farmers increase agricultural output while decreasing crop losses is one of the aims of the proposed method. Visual descriptions of images are used to achieve this. Accurately classifying the occurrence of diseases that commonly impact leaves is the goal of the proposed technique. The goal of this comprehensive experimental study was to compare the proposed method to a reference database and find out if it had any unique features. Based on the testing findings, the proposed algorithm outperformed the previous systems on multiple performance metrics. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2024.

关键词： Diseases

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Improved Wild Horse Optimizer with Deep Learning Model for Skin Lesion Detection and Classification on Dermoscopic Images 23th

Improved Wild Horse Optimizer with Deep Learning Model for S...

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23rd International Conference on Intelligent Systems Design and Applications, ISDA 2023

作者： Maranco, M. Tyagi, Amit Kumar Sivakumar, M. Department of Networking and Communications School of Computing Faculty of Engineering and Technology SRM Institute of Science and Technology Kattankulathur Chengalpattu 603203 India Department of Fashion Technology National Institute of Fashion Technology New Delhi India

ISBN: (纸本)9783031648120

Melanoma is a deadly kind of skin cancer which can spread to other parts of the body. Therefore, it is necessary to identify melanoma at the beginning level. Visual examinationat the time of medical examination of skin lesion is a challenging process as there exist high resemblance among the lesions. Besides, dermoscopy is a non-invasive imaging tool which enables visualizationof the skin surface by light magnifying device and immersion fluid. Traditional image processing models such as histogram thresholding, clustering, or active contours are employed to segment skin lesions. This motivates the study of how to best employ AI, and deep learning (DL) algorithms in particular, to identify skin lesions in dermoscopic pictures. By using these images, current research refines the existing wild horse optimizer (IWHO) by using a deep learning driven skin lesion detection and classification (IWHODL-SLDC) *** proposed model utilizes optimal Tsallis entropy based image segmentation. Besides, Squeeze Netapproach can be employed to derive feature vectors. Also, IWHO with deep wavelet neural network (DWNN) technique is used to classify the dermoscopic images. Numerous tests are carried out to guarantee the enhancement of the IWHODL-SLDC method on the reference dataset. It was found via experimentation that the IWHODL-SLDC model outperformed the other approaches in a wide range of ways. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2024.

关键词： Image classification

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Quantifying mangrove export of dissolved organic carbon on large scales and at fine resolution: a review of current technologies and the path forward

Discover Oceans

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Discover Oceans 2025年第1期2卷 1-17页

作者： Sullivan, Chance W. Hartstein, Neil David Müller, Moritz Faculty of Engineering Computing and Science Swinburne University of Technology Sarawak Campus Kuching Malaysia ADS Environmental Services Kota Kinabalu Malaysia

Mangroves are carbon dense forests that are threatened by human driven factors. Their ability to sequester carbon can play a key role in meeting climate targets like the Paris Climate Agreement and the United Nations Sustainable Development Goals. In addition to sequestration, mangroves are significant sources of carbon to the ocean sink, which has implications for global carbon budgets. Mangrove carbon export is estimated to be equivalent to about 4% of yearly anthropogenic CO2 emissions. Currently, most estimates of global mangrove dissolved organic carbon (DOC) export are scaled from local analyses. However, mangrove ecosystems are dynamic and local analyses alone are not sufficient for accurate quantification of DOC export. Studies of mangrove DOC export need to expand spatial and temporal scales and refine temporal resolution. Technologies like advanced in-situ sampling, laboratory methods, remote sensing, modelling and machine learning can help to achieve this, but they are underutilised in mangrove DOC export research. In this review we explore the current state, existing and emerging technologies, and future needs for the study of mangrove DOC export.

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A Unified Decision-Making Technique for Analysing Treatments in Pandemic Context

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Computers, Materials & Continua 2022年第11期73卷 2591-2618页

作者： Fawaz Alsolami Abdullah Saad Al-Malaise Alghamdi Asif Irshad Khan Yoosef B.Abushark Abdulmohsen Almalawi Farrukh Saleem Alka Agrawal Rajeev Kumar Raees Ahmad Khan Department of Computer Science Faculty of Computing and Information TechnologyKing Abdulaziz UniversityJeddah21589Saudi Arabia Department of Information Systems Faculty of Computing and Information TechnologyKing Abdulaziz UniversityJeddah21589Saudi Arabia Department of Information Technology Babasaheb Bhimrao Ambedkar UniversityLucknow226025Uttar PradeshIndia Department of Computer Science and Engineering Babu Banarasi Das UniversityLucknow226028India

The COVID-19 pandemic has triggered a global humanitarian disaster that has never been seen *** experts,on the other hand,are undecided on the most valuable treatments of therapy because people ill with this infection exhibit a wide range of illness indications at different phases of ***,this project aims to undertake an experimental investigation to determine which treatments for COVID-19 disease is the most effective and *** research analysis is based on vast data gathered from professionals and research journals,making this study a comprehensive *** solve this challenging task,the researchers used the HF AHPTOPSIS Methodology,which is a well-known and highly effective MultiCriteria Decision Making(MCDM)*** technique assesses the many treatment options identified through various research papers and guidelines proposed by various countries,based on the recommendations of medical practitioners and *** review process begins with a ranking of different treatments based on their effectiveness using the HF-AHP approach and then evaluates the results in five different hospitals chosen by the authors as *** also perform robustness analysis to validate the conclusions of our *** a result,we obtained highly corroborative results that can be used as a *** results suggest that convalescent plasma has the greatest rank and priority in terms of effectiveness and demand,implying that convalescent plasma is the most effective treatment for SARS-CoV-2 in our *** also has the lowest priority in the estimation.

关键词： AHP-TOPSIS hesitant fuzzy sets SARS-CoV-2 healthcare sector preventive drugs

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Guarding Voices, Protecting Homes: A Comprehensive Case Study on Voice Assistant Security in Smart Living 12

Guarding Voices, Protecting Homes: A Comprehensive Case Stud...

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12th International Symposium on Digital Forensics and Security, ISDFS 2024

作者： Rehman, Fazalur Hashmi, Joudat Abdullah, Moiz Zaman, Hafsah School of Electrical Engineering and Computer Science National University of Sciences and Technology Islamabad Pakistan College of Engineering Georgia Tech University Department of Analytics United States Air University Faculty of Computing and Artificial Intelligence Department of Cyber Security Islamabad Pakistan

ISBN: (纸本)9798350330366

The evolution of technologies in the last decades has transformed the whole human interaction experience with the digital world. Integrated into users' home voice assistant devices has rationalized personal interaction with the technology however convenience of intelligent voice assistant presents security and privacy risks. This paper investigates the security vulnerabilities of smart voice assistant devices specifically focusing on Google Home Mini. Through a series of simulated attacks and comprehensive analysis, this study uncovers potential risks ranging from unauthorized access to user data privacy compromise within home networks. The objective is to raise awareness about these vulnerabilities and their implication urging the need for robust security measures and providing valuable observation for future research. As voice devices are becoming an integral part of daily life addressing these challenges is paramount to ensuring user privacy data integrity and overall security of the smart home ecosystem. © 2024 IEEE.

关键词： Risk assessment

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