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2nd InternationalWorkshop on New Approaches for Multidimensional Signal Processing, NAMSP 2021

作者： Ahmed, Mona A. Roushdy, Mohamed Salem, Abdel-Badeeh M. Faculty of Computer and Information Sciences Computer Science Department Ain Shams University Cairo Egypt Faculty of Computer and Information Technology Computer Science Department Future University New Cairo Egypt

ISBN: (纸本)9789811685576

Multimodal biometric systems have been widely used to achieve high recognition accuracy. This paper presents a new multimodal biometric system using an intelligent technique to authenticate human by fusion of palm and dorsal hand veins pattern. We developed an image analysis technique to extract region of interest (ROI) from palm and dorsal hand veins image. After extracting ROI we design a sequence of preprocessing steps to improve palm and dorsal hand veins images using Homomorphic, Median filter, Wiener filter and Contrast Limited Adaptive Histogram Equalization (CLAHE) to enhance vein image. Our smart technique is based on the following intelligent algorithms, namely;principal component analysis (PCA) algorithm for feature extraction and k-Nearest Neighbors (K-NN) classifier for matching operation. The database chosen was the CASIA Multi-Spectral Palmprint Image Database V1.0 and Bosphorus Hand Vein Database. The achieved result for the fusion of both biometric traits was Correct Recognition Rate (CRR) is 97.6% with FAlse Reject Rate (FRR) 2.4%. © 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Median filters

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Performance Comparison of Convolutional Neural Network Deep Learning Architectures for Remote Sensing Image Segmentation 4th

Performance Comparison of Convolutional Neural Network Deep ...

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4th International Conference on Advances in Computational science and Engineering, ICACSE 2023

作者： Shoaib, Asim Vadiveloo, Mogana Lim, Seng Poh Department of Computer Science Faculty of Information and Communication Technology Universiti Tunku Abdul Rahman Jalan Universiti Bandar Barat Perak Kampar31900 Malaysia

ISBN: (纸本)9789819729760

In this paper, the performance of five convolutional neural network (CNN) deep learning architectures were evaluated for delineating high complexity building regions in remote sensing (RS) images. They are the U-Net, SegNet, ResU-Net, AttentionU-Net, and V-Net architectures that are commonly used for image segmentation. In the existing works, the robustness of these deep learning architectures was evaluated in delineating the regions of interest (ROIs) in various RS image as well as segmenting anatomical structures as ROIs in the medical images, individually or in pairs. It is crucial to evaluate the performance efficiency of these architectures on the same image dataset that has high complexity of ROIs for delineation. Therefore, the performances of the five mentioned deep learning architectures were evaluated for comparison in segmenting buildings regions as ROIs in the WHU RS images dataset, which consist of various colors, shapes, sizes, and textures features. The experimental results showed that the AttentionU-Net deep learning architecture accurately segmented the buildings regions achieving an average Intersection over Union (IoU) of 0.9780, 0.9436, 0.9469 and average F-measure of 0.9690, 0.9230, 0.9301 for training, validation, and test images, respectively with 62 epochs in comparison to the other four architectures. This is mainly because of the incorporation of the attention gates (AGs) in the AttentionU-Net architecture that are able to extract the fine feature details of the complex building regions in the WHU RS images dataset. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Convolutional neural networks

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When LoRa meets distributed machine learning to optimize the network connectivity for green and intelligent transportation system

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Green Energy and Intelligent Transportation 2024年第3期3卷 24-41页

作者： Malak Abid Ali Khan Hongbin Ma Arshad Farhad Asad Mujeeb Imran Khan Mirani Muhammad Hamza National Key Lab of Autonomous Intelligent Unmanned Systems School of AutomationBeijing Institute of TechnologyBeijing 100081China Department of Computer Science Namal UniversityPakistan Department of Electrical Engineering Tsinghua UniversityBeijing 100084China Faculty of Information Technology Beijing University of TechnologyBeijing 100124China School of Mechanical Engineering Beijing Institute of TechnologyBeijing 100081China

LoRa technology contributes to green energy by enabling efficient,long-range communication for the Internet of Things(IoT).This paper addresses the challenges related to coverage range in outdoor monitoring systems utilizing LoRa,where the network performance is affected by the density of gateways(GWs)and end devices(EDs),as well as environmental *** mitigate interference,data throughput losses,and high-power consumption,the proposed spreading factor(SF)and hybrid(data rate|SF)models dynamically adjust the transmission *** orchestration of concurrent data modifications within the network server(NS)is crucial for uninterrupted communication between GWs and EDs,especially in monitoring electric vehicle(EV)stations to reduce traffic congestion and *** K-means and density-based spatial clustering of applications with noise(DBSCAN)algorithms optimizes ED allocation,averts data congestion,and improves the signal-tointerference noise ratio(SINR).These methods ensure seamless information reception by meticulously allocated EDs across various GW *** estimate the free-space losses(FSL),a log-distance path loss model(log-PL)is *** various bandwidths(BWs),bidirectional communications,and duty cycles(DCs)helps to prevent saturation,thus prolonging the operational lifespan of *** findings reveal a notable packet rejection rate(PRR)of 0%for the DBSCAN(hybrid model).In contrast,the K-means exhibits a PRR ranging from 5%(hybrid model)to 35.29%(SF model)for the ten GWs ***,the network saturation is reduced to 10.185%and 9.503%,respectively,highlighting an improvement in the average efficiency of slotted ALOHA(91.1%)and pure ALOHA(90.7%).These enhancements increase the lifespan of EDs to 15,465.27 days.

关键词： DBSCAN Green transportation Hybrid model Intelligent transportation K-means

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A Comprehensive Study on Mechanism Knowledge Techniques for Intrusion Detection in Network Security

A Comprehensive Study on Mechanism Knowledge Techniques for ...

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2024 International Conference on Optimization Computing and Wireless Communication, ICOCWC 2024

作者： Manivasagam Agarwal, Ankita Bishnoi, Amit Kumar Department of Computer Science and Information Technology Bangalore India Maharishi School of Engineering & Technology Maharishi University of Information Technology Uttar Pradesh India Teerthanker Mahaveer University Department of Computing Sciences and Information Technology Uttar Pradesh Moradabad India

ISBN: (纸本)9798350383348

This technical abstract provides a comprehensive observation of the system studying strategies for intrusion detection in network protection. Various tactics are discussed, along with supervised getting-to-know, unsupervised studying, and deep Knowledge. Facts units, KDD Cup '99 and NSL-KDD, are used to research the overall performance of Mechanism Knowledge algorithms. Experiments were carried out on the datasets to validate the performance of the algorithms in terms of accuracy, precision, consideration, and F-degree. The outcomes show that Mechanism-getting-to-know algorithms can efficaciously achieve better overall performance compared to standard techniques. Primarily based on the effects, this paper offers contributions in phrases of (1) discovering newer algorithms, (2) knowledge of the overall performance of present algorithms on distinctive intrusion detection duties, and (three) locating relationships among the features. © 2024 IEEE.

关键词： Algorithms Comprehensive Datasets Experiments Features Knowledge Mechanism Knowledge

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An Edge-Computing-Based Machine Learning Method for Detecting Goggle-Lens Defects 13

An Edge-Computing-Based Machine Learning Method for Detectin...

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13th IEEE Global Conference on Consumer Electronic, GCCE 2024

作者： Lin, You-Sheng Jiang, Rui-Jie Fajar, Muhammad Wang, Jing-Wein Wang, Chih-Chiang National Kaohsiung University of Science and Technology Department of Computer Science and Information Engineering Kaohsiung Taiwan National Kaohsiung University of Science and Technology Institute of Photonics Kaohsiung Taiwan

ISBN: (纸本)9798350355079

Defects in ski-goggle lenses are difficult to detect through human's naked-eye inspection because the lenses are highly reflective and the defects are tiny, measured in micrometers. To address these issues, this paper presents an edge-computing-based machine learning method for Automated Optical Inspection system, aiming to achieve fast and accurate detection of defects in ski-goggle lenses. The experimental results show that comparing CNN and Random-Forest methods, the former achieves an average accuracy rate of approximately 98% at a cost of longer training time, while the latter achieves an average accuracy around 91%. In addition, further enhancing the proposed method with edge-computing paradigm can lead to a substantial improvement on the detection time. © 2024 IEEE.

关键词： Decision trees

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A novel medical image data protection scheme for smart healthcare system

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CAAI Transactions on Intelligence technology 2024年第4期9卷 821-836页

作者： Mujeeb Ur Rehman Arslan Shafique Muhammad Shahbaz Khan Maha Driss Wadii Boulila Yazeed Yasin Ghadi Suresh Babu Changalasetty Majed Alhaisoni Jawad Ahmad School of Computer Science and Informatics Cyber Technology InstituteDe Montfort UniversityLeicesterUK Department of Biomedical Engineering University of GlasgowGlasgowUK School of Computing Engineering and the Built EnvironmentEdinburgh Napier UniversityEdinburghUK Robotics and Internet‐of‐Things Laboratory Prince Sultan UniversityRiyadhSaudi Arabia Department of Computer Science Al Ain UniversityAl AinUnited Arab Emirates Department of Computer Engineering College of Computer Science King Khalid UniversityAbhaSaudi Arabia Computer Sciences Department College of Computer and Information SciencesPrincess Nourah bint Abdulrahman UniversityRiyadhSaudi Arabia

The Internet of Multimedia Things(IoMT)refers to a network of interconnected multimedia devices that communicate with each other over the ***,smart healthcare has emerged as a significant application of the IoMT,particularly in the context of knowledge‐based learning *** healthcare systems leverage knowledge‐based learning to become more context‐aware,adaptable,and auditable while maintain-ing the ability to learn from historical *** smart healthcare systems,devices capture images,such as X‐rays,Magnetic Resonance *** security and integrity of these images are crucial for the databases used in knowledge‐based learning systems to foster structured decision‐making and enhance the learning abilities of ***,in knowledge‐driven systems,the storage and transmission of HD medical images exert a burden on the limited bandwidth of the communication channel,leading to data trans-mission *** address the security and latency concerns,this paper presents a lightweight medical image encryption scheme utilising bit‐plane decomposition and chaos *** results of the experiment yield entropy,energy,and correlation values of 7.999,0.0156,and 0.0001,*** validates the effectiveness of the encryption system proposed in this paper,which offers high‐quality encryption,a large key space,key sensitivity,and resistance to statistical attacks.

关键词： data analysis medical image processing security

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Evolutionary privacy-preserving learning strategies for edge-based IoT data sharing schemes

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Digital Communications and Networks 2023年第4期9卷 906-919页

作者： Yizhou Shen Shigen Shen Qi Li Haiping Zhou Zongda Wu Youyang Qu Department of Computer Science and Engineering Shaoxing UniversityShaoxing312000China School of Computer Science and Informatics Cardiff UniversityCardiffCF243AAUnited Kingdom School of Information Engineering Huzhou UniversityHuzhou 313000ZhejiangChina School of Information Technology Deakin UniversityBurwoodVIC3125Australia

The fast proliferation of edge devices for the Internet of Things(IoT)has led to massive volumes of data *** generated data is collected and shared using edge-based IoT structures at a considerably high ***,the data-sharing privacy exposure issue is increasingly intimidating when IoT devices make malicious requests for filching sensitive information from a cloud storage system through edge *** address the identified issue,we present evolutionary privacy preservation learning strategies for an edge computing-based IoT data sharing *** particular,we introduce evolutionary game theory and construct a payoff matrix to symbolize intercommunication between IoT devices and edge nodes,where IoT devices and edge nodes are two parties of the *** devices may make malicious requests to achieve their goals of stealing ***,edge nodes should deny malicious IoT device requests to prevent IoT data from being *** dynamically adjust their own strategies according to the opponent's strategy and finally maximize the *** upon a developed application framework to illustrate the concrete data sharing architecture,a novel algorithm is proposed that can derive the optimal evolutionary learning ***,we numerically simulate evolutionarily stable strategies,and the final results experimentally verify the correctness of the IoT data sharing privacy preservation ***,the proposed model can effectively defeat malicious invasion and protect sensitive information from leaking when IoT data is shared.

关键词： Privacy preservation Internet of things Evolutionary game Data sharing Edge computing

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A Survey on Usability Evaluation in Digital Health and Potential Efficiency Issues

A Survey on Usability Evaluation in Digital Health and Poten...

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17th International Joint Conference on Biomedical Engineering systems and Technologies, BIOSTEC 2024

作者： Maqbool, Bilal Karegar, Farzaneh Herold, Sebastian Department of Mathematics and Computer Science Faculty of Health Science and Technology Karlstad University Karlstad Sweden Department of Information Systems Karlstad University Karlstad Sweden

Context: Usability is a major factor in the acceptance of digital health (DH) solutions. Problem: Despite its importance, usability experts have expressed concerns about the insufficient attention given to usability evaluation in practice, indicating potential efficiency problems of common evaluation methods in the health care domain. Objectives: This research paper aimed to analyse industrial usability evaluation practices in digital health to identify potential threats to the efficiency of their application. Method: To this end, we conducted an online survey of 144 usability experts experienced in usability evaluations for digital health applications. The survey questions aimed to explore the prevalence of techniques applied, and the participants’familiarity and perceptions regarding tools and techniques. Results: The prevalently applied techniques might impose efficiency problems in common scenarios in digital health. Participant recruitment is considered time consuming and selecting the most appropriate evaluation method for a given context is perceived difficult. The results highlight a lack of utilisation of tools automating aspects of usability evaluation. Conclusions: A more widespread adoption of tools for automating usability evaluation activities seems desirable as well as guidelines for selecting evaluation techniques in a given context. We furthermore recommend to explore AI-based solutions to address the problem of involving targeted user groups that are difficult to access for usability evaluations. © 2024 by SCITEPRESS – science and technology Publications, Lda.

关键词： Electronic health record

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A Review of IoT Security: Machine Learning and Deep Learning Perspective 2

A Review of IoT Security: Machine Learning and Deep Learning...

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2nd International Conference on Machine Learning and Data Engineering, ICMLDE 2023

作者： Dubey, Krati Dubey, Rishav Panedy, Sudhakar Kumar, Sanjay Department of Information Technology National Institute of Technology Raipur 492010 India Department of Computer Science and Engineering Manipal University Jaipur Jaipur303007 India

The Internet of Things (IoT) gives the ability to people and machines to interact and learn from billions of items, including sensors, actuators, services, and other things that are connected to the Internet. The services provided by IoT are becoming an indispensable part of human life, further, the growth in the number of IoT devices is predicted to increase exponentially in the next few years. However, the IoT network consists of a variety of different types of devices being placed in heterogeneous environments. The data these devices generate contains noise in significant proportion. Also, these devices are vulnerable to various different kinds of attacks, identification of which is a challenging task in itself as they are in large numbers. The detection of faults is necessary for the early stages. Generic methods of authentication and authorization are not sufficient for these devices. Further, we need methods that can detect the attack at an early stage based on previous patterns and alert the system at the right time. In recent years machine learning and deep learning (ML/DL) algorithms have advanced considerably from being an experimental project in the research community to being applied in industry-standard applications. These algorithms have the potential to be better than simple security methods in providing intelligence to the system. In this article, we have reviewed various ML/DL algorithms with their advantages, and disadvantages, and how they can be applied in securing IoTs. Initially, a detailed overview of IoT, components, and applications are presented. After that, we present an overview of various threats, and attack methods are discussed with the probable ML/DL solutions. © 2024 Elsevier B.V.. All rights reserved.

关键词： Internet of things

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Deep Learning-Based Mask Identification system Using ResNet Transfer Learning Architecture

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computer systems science & Engineering 2024年第2期48卷 341-362页

作者： Arpit Jain Nageswara Rao Moparthi A.Swathi Yogesh Kumar Sharma Nitin Mittal Ahmed Alhussen Zamil S.Alzamil MohdAnul Haq Department of Computer Science and Engineering KoneruLakshmaiah Education FoundationVaddeswaram522302India Department of Computer Science and Engineering Sreyas Institute of Engineering and TechnologyHyderabad500068India University Centre for Research and Development Chandigarh UniversityMohali140413India Department of Computer Engineering College of Computer and Information SciencesMajmaah UniversityAl-Majmaah11952Saudi Arabia Department of Computer Science College of Computer and Information SciencesMajmaah UniversityAl-Majmaah11952Saudi Arabia

Recently,the coronavirus disease 2019 has shown excellent attention in the global community regarding health and the *** Health Organization(WHO)and many others advised controlling Corona Virus Disease in *** limited treatment resources,medical resources,and unawareness of immunity is an essential horizon to *** all resources,wearing a mask is the primary non-pharmaceutical intervention to stop the spreading of the virus caused by Severe Acute Respiratory Syndrome Coronavirus 2(SARS-CoV-2)*** countries made masks mandatory to prevent *** such enforcement,automatic and effective face detection systems are *** study presents a face mask identification approach for static photos and real-time movies that distinguishes between images with and without *** contribute to society,we worked on mask detection of an individual to adhere to the rule and provide awareness to the public or *** paper aims to get detection accuracy using transfer learning from Residual Neural Network 50(ResNet-50)architecture and works on detection *** experiment is tested with other popular pre-trained models such as Deep Convolutional Neural Networks(AlexNet),Residual Neural Networks(ResNet),and Visual Geometry Group Networks(VGG-Net)advanced *** proposed system generates an accuracy of 98.4%when modeled using Residual Neural Network 50(ResNet-50).Also,the precision and recall values are proved as better when compared to the existing *** outstanding work also can be used in video surveillance applications.

关键词： Transfer learning depth analysis convolutional neural networks(CNN) COVID-19

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