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Deep Learning-Based Robust Morphed Face Authentication Framework for Online Systems

computers, Materials & Continua 2023年第10期77卷 1123-1142页

作者： Harsh Mankodiya Priyal Palkhiwala Rajesh Gupta Nilesh Kumar Jadav Sudeep Tanwar Osama Alfarraj Amr Tolba Maria Simona Raboaca Verdes Marina Department of Computer Science and Engineering Institute of TechnologyNirma UniversityAhmedabad382481India Computer Science Department Community CollegeKing Saud UniversityRiyadh11437Saudi Arabia Doctoral School University Politehnica of BucharestBucharest060042Romania Department of Hydrogen and Fuel Cell National Research and Development Institute for Cryogenics and Isotopic TechnologiesRamnicu Valcea240050Romania Department of Building Services Faculty of Civil Engineering and Building ServicesTechnical University of Gheorghe AsachiIasi700050Romania

The amalgamation of artificial intelligence(AI)with various areas has been in the picture for the past few *** has enhanced the functioning of several services,such as accomplishing better budgets,automating multiple tasks,and data-driven *** hassle-free polling has been one of ***,at the onset of the coronavirus in 2020,almost all worldly affairs occurred online,and many sectors switched to digital *** allows attackers to find security loopholes in digital systems and exploit them for their lucrative *** paper proposes a three-layered deep learning(DL)-based authentication framework to develop a secure online polling *** provides a novel way to overcome security breaches during the face identity(ID)recognition and verification process for online polling *** verification is done by training a pixel-2-pixel Pix2pix generative adversarial network(GAN)for face image reconstruction to remove facial objects present(if any).Furthermore,image-to-image matching is done by implementing the Siamese network and comparing the result of various metrics executed on feature embeddings to obtain the outcome,thus checking the electorate credentials.

关键词： Artificial intelligence discriminator generator Pix2pix GANs Kullback-Leibler(KL)-divergence online voting system Siamese network

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DeepSVDNet:A Deep Learning-Based Approach for Detecting and Classifying Vision-Threatening Diabetic Retinopathy in Retinal Fundus Images

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computer Systems science & engineering 2024年第2期48卷 511-528页

作者： Anas Bilal Azhar Imran Talha Imtiaz Baig Xiaowen Liu Haixia Long Abdulkareem Alzahrani Muhammad Shafiq College of Information Science and Technology Hainan Normal UniversityHaikou571158China Department of Creative Technologies Air UniversityIslamabadPakistan School of Life Science and Technology University of Electronic Science and Technology of China UESTCChengduChina School of Science and Technology University of Management and Technology UMTLahorePunjabPakistan Faculty of Computer Science&Information Technology Al Baha UniversityAl BahaSaudi Arabia School of Information Engineering Qujing Normal UniversityQujingChina

Artificial Intelligence(AI)is being increasingly used for diagnosing Vision-Threatening Diabetic Retinopathy(VTDR),which is a leading cause of visual impairment and blindness ***,previous automated VTDR detection methods have mainly relied on manual feature extraction and classification,leading to *** paper proposes a novel VTDR detection and classification model that combines different models through majority *** proposed methodology involves preprocessing,data augmentation,feature extraction,and classification *** use a hybrid convolutional neural network-singular value decomposition(CNN-SVD)model for feature extraction and selection and an improved SVM-RBF with a Decision Tree(DT)and K-Nearest Neighbor(KNN)for *** tested our model on the IDRiD dataset and achieved an accuracy of 98.06%,a sensitivity of 83.67%,and a specificity of 100%for DR detection and evaluation tests,*** proposed approach outperforms baseline techniques and provides a more robust and accurate method for VTDR detection.

关键词： Diabetic retinopathy(DR) fundus images(FIs) support vector machine(SVM) medical image analysis convolutional neural networks(CNN) singular value decomposition(SVD) classification

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Shared Bodily Fusion: Leveraging Inter-Body Electrical Muscle Stimulation for Social Play

Shared Bodily Fusion: Leveraging Inter-Body Electrical Muscl...

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2024 ACM Designing Interactive Systems Conference, DIS 2024

作者： Patibanda, Rakesh Overdevest, Nathalie Nisal, Shreyas Saini, Aryan Elvitigala, Don Samitha Knibbe, Jarrod van den Hoven, Elise Mueller, Florian Exertion Games Lab Department of Human-Centred Computing Monash University Melbourne Australia School of Electrical Engineering and Computer Science Faculty of Engineering The University of Queensland St LuciaQLD Australia Materialising Memories & Visualisation Institute University of Technology Sydney Sydney Australia Eindhoven University of Technology Eindhoven Netherlands

ISBN: (纸本)9798400705830

Traditional games like "Tag" rely on shared control via inter-body interactions (IBIs) – touching, pushing, and pulling – that foster emotional and social connection. Digital games largely limit IBIs, with players using their bodies as input to control virtual avatars instead. Our "Shared Bodily Fusion" approach addresses this by fusing players’ bodies through a mediating computer, creating a shared input and output system. We demonstrate this approach with "Hidden Touch", a game where a novel social electrical muscle stimulation system transforms touch (input) into muscle actuations (output), facilitating IBIs. Through a study (n=27), we identifed three player experience themes. Informed by these fndings and our design process, we mapped their trajectories across our three experiential spaces – threshold, tolerance, and precision – which collectively form our design framework. This framework facilitates the creation of future digital games where IBIs are intrinsic, ultimately promoting the many benefts of social play. © 2024 Copyright held by the owner/author(s).

关键词： Muscle

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A high-order predictor–corrector method for initial value problems with fractional derivative involving Mittag–Leffler kernel

A high-order predictor–corrector method for initial value p...

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作者： Aljhani, Sami Noorani, Mohd Salmi Md Douaifia, Redouane Abdelmalek, Salem Department of Mathematical Sciences Faculty of Science and Technology Universiti Kebangsaan Malaysia Selangor Bangi Malaysia Process Engineering Department Faculty of Technology University of Saad Dahlab Blida 1 Blida Algeria Echahid Cheikh Larbi Tebessi University Tebessa Algeria Department of Mathematics and Computer Science Echahid Cheikh Larbi Tebessi University Tebessa Algeria

In this paper, we propose a numerical scheme of the predictor–corrector type for solving nonlinear fractional initial value problems;the chosen fractional derivative is called the Atangana–Baleanu derivative defined in Caputo sense (ABC). This proposed method is based on Lagrangian quadratic polynomials to approximate the nonlinearity implied in the Volterra integral, which is obtained by reducing the given fractional differential equation via the properties of the ABC-fractional derivative. Through this technique, we get corrector formula with high accuracy, which is implicit, as well as predictor formula, which is explicit and has the same precision order as the corrective formula. On the other hand, the so-called memory term is computed only once for both prediction and correction phases, which indicates the low cost of the proposed method. Also, the error bound of the proposed numerical scheme is offered. Furthermore, numerical experiments are presented in order to assess the accuracy of the new method on two differential equations. Moreover, a case study is considered where the proposed predictor–corrector scheme is used to obtain approximate solutions of a coupled nonautonomous ABC-fractional differential system. © 2023 John Wiley & Sons, Ltd.

关键词： Initial value problems

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Corrosion Resistance Strategies for Biomedical Implants: A Comprehensive Review

Corrosion Resistance Strategies for Biomedical Implants: A C...

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Emerging Trends in engineering and medical sciences (ICETEMS), International Conference on

作者： Prajwal Bandre Subodh Dharonde Trishul Dhale Department of Computer Science and Medical Engineering Faculty of Engineering and Technology Datta Meghe Institute of Higher Education and Research Wardha Maharashtra India Department of Biomedical Engineering Faculty of Engineering and Technology Datta Meghe Institute of Higher Education and Research Wardha Maharashtra India

ISBN: (数字)9798331529352

ISBN: (纸本)9798331529390

Since biomaterials are essential to the life of patients with acute heart disease, osteoporosis, arthritis, and other joint illnesses, their corrosion is a crucial issue in the field of materials research. This article discusses a variety of issues pertaining to the biological degradation of various implant kinds, such as cardiac, dental, and orthopedic stents. These implants often fail and eventually shatter because of the variety of materials used to produce them, which include metallic materials like cobalt chromium, titanium and its alloys, bio ceramics, composites, and polymers that are constantly in touch with physiological fluids that can be corrosive. The influence of surface oxide film and corrosion products on implant failure are discussed, along with the various corrosion behaviors of implants. The greatest technique to stop corrosion, prolong the life of implants, and increase human lifespan is believed to be surface modification of implants, which is the subject of this article. The text delves into the latest advancements in coating methodologies that leverage the exceptional characteristics of nanomaterials to provide enhanced mechanical and biocompatible attributes.

关键词： Heart Reviews Corrosion Implants Titanium Physiology Ceramics Coatings Surface treatment Biological materials

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Unleashing the Potential of IoT in Tackling Ocean Pollution: A Data-Driven Approach for Marine Ecosystem Conservation 2

Unleashing the Potential of IoT in Tackling Ocean Pollution:...

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2nd International Conference on Smart Technologies for Smart Nation, SmartTechCon 2023

作者： Srinivasan, S. Vimala Ithayan, J. Devi, A. Sangeerani Saravanan, T.R. Ganesh, E.N. Department of Biomedical Engineering Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Saveetha University Tamil Nadu Chennai India Department of Computer Science and Engineering Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology Tamil Nadu Chennai India Department of Computer Science and Engineering Sri Sairam Engineering College Tamil Nadu Chennai India Department of Computational Intelligence SRM Institute of Science and Technology Kattankulathur Tamil Nadu Chennai India Avadi Tamil Nadu Chennai India

ISBN: (纸本)9798350305418

This study investigates the potentially revolutionary role that the Internet of Things (IoT) may play in combating the urgent ocean pollution problem and promoting the growth of ecologically sound marine ecosystems. An approach that is data-driven has become essential in light of the fact that pollution poses a risk to the delicate balance of marine life and puts the future of the seas in jeopardy. The user can get real-time data on water quality, pollutant levels, and plastic trash dispersion if the user installs sensors and devices that the IoT enables. This will allow us to gain a greater knowledge of the patterns and causes of pollution. Using the plethora of information at people’s disposal, the system can develop innovative intervention tactics that successfully and efficiently combat ocean pollution. In addition, early warning systems powered by the Internet of Things might make it easier to respond quickly to oil spills, reducing the negative effects on the environment. This research highlights the importance of cooperation among stakeholders and underlines the need for public awareness and education to create changes in behavioral patterns. The adoption of technologies related to the Internet of Things is the most important step that can be taken to protect marine biodiversity and to create a brighter, more sustainable future for seas. © 2023 IEEE.

关键词： Critical Ocean Pollution Internet of Things (IoT) Marine Conservation Initiative Marine Ecosystems Real-Time Data

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Human Activity Recognition in Logistics Using Wearable Sensors and Deep Residual Network 38

Human Activity Recognition in Logistics Using Wearable Senso...

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38th IEEE Region 10 Conference, TENCON 2023

作者： Mekruksavanich, Sakorn Tancharoen, Datchakorn Jitpattanakul, Anuchit School of Information and Communication Technology University of Phayao Department of Computer Engineering Phayao Thailand Panyapiwat Institute of Management Faculty of Engineering and Technology Nonthaburi Thailand King Mongkut's University of Technology North Bangkok Intelligent and Nonlinear Dynamic Innovations Research Center Faculty of Applied Science Department of Mathematics Bangkok Thailand

ISBN: (纸本)9798350302196

Human action identification is a practical area of study with broad applicability in various domains, such as medical care, sport science, and manufacturing management. In logistics, it is essential to identify and examine individual actions, enabling machines to perceive and comprehend human motions for non-verbal interaction. This study specifically fo-cuses on efficiently classifying working activities in the logistics industry using wearable sensors, particularly in the context of human activity recognition. To achieve the research objective, a deep residual neural network was introduced, integrating convolutional layers, shortcut connections, and aggregated transformation for human activity recognition in logistics. The authors evaluated the effectiveness of their proposed deep learning model using the publicly accessible LARa dataset. The LARa dataset comprises a diverse range of human actions in the logistics domain, including standing, walking, cart handling, and synchronization. The details of activity were captured using wearable sensors affixed to different anatomical sites of the study participants. The experimental findings indicate that the model achieved a maximum F-measure of 85.30%. © 2023 IEEE.

关键词： Wearable sensors

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QoS Prediction Model of Cloud Services Based on Deep Learning

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IEEE/CAA Journal of Automatica Sinica 2022年第3期9卷 564-566页

作者： WenJun Huang PeiYun Zhang YuTong Chen MengChu Zhou Yusuf Al-Turki Abdullah Abusorrah Nanjing University of Information Science&Technology School of Computer and SoftwareNanjingChina IEEE New Jersey Institute of Technology Helen and John C.Hartmann Department of Electrical and Computer EngineeringNewarkUSA Center of Research Excellence in Renewable Energy and Power Systems Department of Electrical and Computer EngineeringFaculty of Engineeringand K.A.CARE Energy Research and Innovation CenterKing Abdulaziz UniversityJeddah 21589Saudi Arabia

Dear editor,This letter presents a deep learning-based prediction model for the quality-of-service(QoS)of cloud ***,to improve the QoS prediction accuracy of cloud services,a new QoS prediction model is proposed,which is based on multi-staged multi-metric feature fusion with individual *** multi-metric features include global,local,and individual *** results show that the proposed model can provide more accurate QoS prediction results of cloud services than several state-of-the-art methods.

关键词： services Services individual

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Hierarchical Image Segmentation Based on Bi-Directional Cascade Network

Hierarchical Image Segmentation Based on Bi-Directional Casc...

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Software engineering and computer science (CSECS), International Conference on

作者： Yunping Zheng ShiQiang Shu Jinzhao Huang Mudar Sarem School of Computer Science and Engineering South China University of Technology Guangzhou China Faculty of Engineering Manara University Latakia Syria

ISBN: (数字)9798331522216

ISBN: (纸本)9798331522223

Image segmentation is the way of splitting digital data into subgroups known as image segments, which reduces the entire image's complexity and allows for further processing or image analysis of each segment. The general segmentation techniques of images are divided into traditional methods and deep learning methods. In this paper, we introduce a deep-learning approach to a traditional image segmentation method (i.e., edge detection) and propose a new framework for hierarchical image segmentation. In the new framework, we first use the Bi-Directional Cascade Network (BDCN) model as our edge detector to preprocess the input image in order to form a feature map with coarse edges. Then, we refine the edges of the image by using the watershed algorithm. Finally, we form a segmentation dendrogram by using the Ultrametric Contour Map (UCM) algorithm. Extensive experiments have demonstrated that our proposed framework has significant advantages in segmentation performance compared to the state-of-the-art algorithms.

关键词： Image segmentation Image analysis Image edge detection Software algorithms Bidirectional control Watersheds Detectors Feature extraction Software engineering Digital information

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A Hybrid Deep Neural Network with Attention Mechanism for Human Activity Recognition Based on Smartphone Sensors 7

A Hybrid Deep Neural Network with Attention Mechanism for Hu...

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7th International Conference on Information technology, InCIT 2023

作者： Mekruksavanich, Sakorn Tancharoen, Datchakorn Jitpattanakul, Anuchit University of Phayao School of Information and Communication Technology Department of Computer Engineering Phayao Thailand Panyapiwat Institute of Management Faculty of Engineering and Technology Nonthaburi Thailand Faculty of Applied Science King Mongkut's University of Technology North Bangkok Intelligent and Nonlinear Dynamic Innovations Research Center Department of Mathematics Bangkok Thailand

ISBN: (纸本)9798350358698

Human activity recognition (HAR) is an area of study that seeks to automatically and precisely detect an individual's behavior by analyzing bio-signal data. Bio-signal data can be acquired using sensing technology integrated into smartphones and other worn intelligent gadgets. Nevertheless, collecting sensor data tagged with activity information employing individual smartphones can result in data being analyzed in varying contexts, potentially compromising the accuracy of machine learning prediction methodologies. This paper introduces a novel HAR approach via smartphone sensors. The proposed method incorporates a hybrid deep neural network architecture enhanced with an attention mechanism. The deep learning model under consideration is referred to as the Att-CNN-BLSTM network. This particular network can autonomously extract significant features from smartphone sensor data. This feature extraction aims to effectively classify different human actions with a high degree of accuracy. In order to assess the efficacy of the hybrid model, a series of investigations were undertaken utilizing a publically accessible HAR dataset. This dataset was employed for training and testing purposes, employing a 5-fold cross-validation approach. We additionally carried out an analytical comparison utilizing state-of-the-art deep learning models from prior research. The findings from our experimentation demonstrate superior performance of the Att-CNN-BLSTM model over other advanced AI algorithms, with the highest accuracy rate of 90.54% and top F1-score of 86.88% attained. © 2023 IEEE.

关键词： Smartphones

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