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A Systematic Survey of Human Ear Biometrics Recognition by using 2D images 3

A Systematic Survey of Human Ear Biometrics Recognition by u...

3rd IEEE International Maghreb Meeting of the Conference on Sciences and Techniques of Automatic Control and computer Engineering, MI-STA 2023

作者： Elmarzaki, Howayda Abedallah Gargoum, Bothaina F. Abdo, Amina A University of Benghazi Department of Software Engineering Benghazi Libya University of Benghazi Department of Computer Science Benghazi Libya Collage of Computer Technology Department of Artificial Intelligence Benghazi Libya

ISBN: (纸本)9798350319897

Nowadays, one of the most serious issues is secure verification, especially with the advent of artificial intelligence and machine learning and deep learning algorithms. As a result, the research field of recognizing human biometrics has grown significantly, and various types of algorithms have been proposed to help with security. From the available literature, we noticed that researchers were focusing on various systematic surveys for the recognition of human biometrics, including faces, eyes, finger prints, palms, and etc. Despite the popularity of machine and deep learning methods in the field of human hearing prediction, surveys of such methods are rare. In this work, we present an up-to-date survey of the ear recognition field by using 2D images and display the most relevant papers from the most popular databases by applying different techniques based on several criteria. This review presents an overview of the similarities and differences between different works and provides a statistical analysis view of common techniques commonly used in ear recognition literature. The results presented in this report provide insight on the patterns of this field's research. © 2023 IEEE.

关键词： Biometrics

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Sentiment analysis in social internet of things using contextual representations and dilated convolution neural network

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Neural Computing and Applications 2024年第20期36卷 12357-12370页

作者： Abid, Fazeel Rasheed, Jawad Hamdi, Mohammed Alshahrani, Hani Al Reshan, Mana Saleh Shaikh, Asadullah Department of Information Systems Dr. Hassan Murad School of Management University of Management and Technology Lahore Pakistan Department of Computer Engineering Istanbul Sabahattin Zaim University Istanbul34303 Turkey Department of Software Engineering Istanbul Nisantasi University Istanbul34394 Turkey Department Computer Science College of Computer Science and Information Systems Najran University Najran61441 Saudi Arabia Department Information Systems College of Computer Science and Information Systems Najran University Najran61441 Saudi Arabia

The methodologies based on neural networks are substantial to accomplish sentiment analysis in the Social Internet of Things (SIoT). With social media sentiment analysis, significant insights can produce efficient and intelligent applications. Neural networks such as recurrent neural networks (RNNs) and convolution neural networks (CNNs) have been considered widely in many text classification tasks. However, RNNs are computationally expensive and require complex training to capture contextual information and long-term dependencies. Similarly, traditional CNNs must stack multiple convolutional layers, requiring massive computations and additional parameters. To address these problems, this work initialized the novel architecture, in which contextual representations (CRs) based on the textual framework are proposed at the initial step. In CRs, state-of-the-art word representation models, such as GloVe (global vectors) and FastText (subword information), collectively produce word representations upon the input sequence using a weight mechanism. Secondly, a unique way is introduced: a three-parallel layered dilated convolutional network with global mean pooling. The experimental results show that the proposed methods when compared with baseline methods, the dilation in CNNs following CRs significantly increases the accuracy from 72.45 to 98.98% and reduces computational resources. © The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature 2024.

关键词： Sentiment analysis

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Enhancing Secure Development in Globally Distributed software Product Lines: A Machine Learning-Powered Framework for Cyber-Resilient Ecosystems

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computers, Materials & Continua 2024年第6期79卷 5031-5049页

作者： Marya Iqbal Yaser Hafeez Nabil Almashfi Amjad Alsirhani Faeiz Alserhani Sadia Ali Mamoona Humayun Muhammad Jamal University Institute of Information Technology PMAS-Arid Agriculture UniversityRawalpindiPakistan Department of Software Engineering College of Computer and Information SciencesJouf UniversityAl Jouf72388Saudi Arabia Department of Computer Science College of Computer and Information SciencesJouf UniversityAl Jouf72388Saudi Arabia Department of Computer Engineering&Networks College of Computer and Information SciencesJouf UniversityAl Jouf72388Saudi Arabia Department of Information Systems College of Computer and Information SciencesJouf UniversitySakakaAl Jouf72388Saudi Arabia Department of Mathematics Pir Mehr Ali Shah Arid Agriculture UniversityRawalpindiPakistan

Embracing software product lines(SPLs)is pivotal in the dynamic landscape of contemporary software ***,the flexibility and global distribution inherent in modern systems pose significant challenges to managing SPL variability,underscoring the critical importance of robust cybersecurity *** paper advocates for leveraging machine learning(ML)to address variability management issues and fortify the security of *** the context of the broader special issue theme on innovative cybersecurity approaches,our proposed ML-based framework offers an interdisciplinary perspective,blending insights from computing,social sciences,and ***,it employs ML for demand analysis,dynamic feature extraction,and enhanced feature selection in distributed settings,contributing to cyber-resilient *** experiments demonstrate the framework’s superiority,emphasizing its potential to boost productivity and security in *** digital threats evolve,this research catalyzes interdisciplinary collaborations,aligning with the special issue’s goal of breaking down academic barriers to strengthen digital ecosystems against sophisticated attacks while upholding ethics,privacy,and human values.

关键词： Machine Learning variability management cybersecurity digital ecosystems cyber-resilience

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Innovating Project Management: AI Applications for Success Prediction and Resource Optimization 2nd

Innovating Project Management: AI Applications for Success P...

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2nd International Conference on Advances in Computing Research, ACR 2024

作者： Tarawneh, Monther AbdAlwahed, Huda AlZyoud, Faisal Computer Science Department Isra University Amman Jordan Software Engineering Department Isra University Amman Jordan Information Technology Department Tafilah Technical University Tafila Jordan

ISBN: (纸本)9783031569494

The revolution of Artificial Intelligence (AI) has made transformative changes in all industries. The field of software engineering has great benefit from AI by Automat all process. Applying Artificial intelligence (AI) technologies, particularly Deep Neural Network (DNN) models, to project management will transform traditional methodologies to new ways. This work investigates how AI can be used within project management to better allocate resources, estimate time, and predict costs. Data from many recourses used to improve accuracy in project planning, refine how risks are managed, and develop new methods. Data includes real projects portfolio, open-source data, and simulated data. The integration of AI in project management examines project success and client satisfaction. This work shows 99% accuracy for learning and 78% for testing of predicting project success and highlights the need to constantly update methods to effectively use the big potential changes AI can bring to project management. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2024.

关键词： Deep neural networks

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Streamlining plant disease diagnosis with convolutional neural networks and edge devices

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Neural Computing and Applications 2024年第29期36卷 18445-18477页

作者： Ahamed, Md. Faysal Salam, Abdus Nahiduzzaman, Md. Abdullah-Al-Wadud, M. Islam, S. M. Riazul Department of Computer Science and Engineering Rajshahi University of Engineering & ampTechnology Rajshahi6204 Bangladesh Department of Electrical and Computer Engineering Rajshahi University of Engineering & ampTechnology Rajshahi6204 Bangladesh Department of Electrical Engineering Qatar University Doha Qatar Department of Software Engineering College of Computer and Information Sciences King Saud University Riyadh11543 Saudi Arabia School of Natural and Computing Sciences University of Aberdeen AberdeenAB24 3FX United Kingdom

In addressing labor-intensive process of manual plant disease detection, this article introduces an innovative solution—the lightweight parallel depthwise separable convolutional neural network (PDSCNN) coupled with a hybrid ridge regression extreme learning machine classifier. This combined approach automates plant disease classification, significantly expediting the learning process and reducing computational complexity. The PDSCNN model excels in feature capture, boasting a mere 9 layers and 0.053 million parameters, resulting in exceptional computational efficiency. On top of that, the classification performances of traditional ELM increased by introducing a hybrid ridge ELM model. Its accelerated operations, reduced memory usage, and optimized parallel processing render it lightweight and ideal for edge devices. The proposed framework exhibits remarkable efficacy, showcasing a precision of 95.76%, a recall of 89.29%, an F1-score of 91.25%, an accuracy of 94.22%, and an AUC of 99.72%. Moreover, training and testing times clock in at just 0.2314 and 0.0259 s, respectively. A comparative analysis against state-of-the-art transfer learning methods underscores its real-time analytical prowess. To demonstrate real-world applicability, we successfully validated our model on the Jetson Nano hardware, making it suitable for resource-constrained settings. © The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature 2024.

关键词： Computational efficiency

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Enhancing Deep Convolutional Neural Networks to Detect Pedestrians Effectively in Various Urban Environments 6

Enhancing Deep Convolutional Neural Networks to Detect Pedes...

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6th International Conference on Electrical Engineering and Information and Communication technology, ICEEICT 2024

作者： Hasan, Md. Mehedi Aman, Nakib Jahan, Md. Wadud Software Engineer i Developer EXperience Hub Rajshahi Bangladesh Pabna University of Science and Technology Department of Computer Science and Engineering Pabna Bangladesh

ISBN: (纸本)9798350385779

In computer vision, accurately identifying pedestrians is a crucial difficulty, especially in applications where safety is of the highest priority, such autonomous driving and urban planning. Deep learning has made progress, but challenges such changing conditions, barriers, and processing power requirements still exist. In this paper, we present an improved deep convolutional neural network (DCNN) designed with pedestrian identification in mind. Improving accuracy in various settings while maximizing processing efficiency is our goal. We carefully prepare the data and create a novel DCNN architecture that can handle a variety of pedestrian appearances and urban surroundings by utilizing well-known datasets such as Penn-Fudan and PnPLO. Our model performs exceptionally well in terms of accuracy, speed, and durability, according to a thorough evaluation that compares it to recognized benchmarks. In addition to making important advances, our study has shown novel approaches to enhancing performance in challenging environments, such as the analysis of transfer learning and the deployment of real-time systems. This work paves the way for future developments in intelligent, self-governing systems and is a maj or leap forward in pedestrian detection technology. © 2024 IEEE.

关键词： Convolutional neural networks

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A Neural Computing-Based Access Control Protocol for AI-Driven Intelligent Flying Vehicles in Industry 5.0-Assisted Consumer Electronics

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IEEE Transactions on Consumer Electronics 2024年第1期70卷 3573-3581页

作者： Mahmood, Khalid Tariq, Tayyaba Sangaiah, Arun Kumar Ghaffar, Zahid Saleem, Muhammad Asad Shamshad, Salman National Yunlin University of Science and Technology Graduate School of Intelligent Data Science Douliu64002 Taiwan National Yunlin University of Science and Technology Department of Computer Science and Information Engineering Douliu64002 Taiwan National Yunlin University of Science and Technology International Graduate School of AI Douliu64002 Taiwan Lebanese American University Department of Electrical and Computer Engineering Byblos1102 2801 Lebanon University of Electronic Science and Technology of China School of Computer Science and Engineering Sichuan Chengdu611731 China The University of Lahore Department of Software Engineering Lahore54590 Pakistan

The continuous revolution in Artificial Intelligence (AI) has played a significant role in the development of key consumer applications, including Industry 5.0, autonomous decision-making, fault diagnosis, etc. In practice, autonomous and intelligent robots and flying vehicles (i.e., drones) are also becoming the integral part of Industry 5.0. These vehicles are attaining significant importance nowadays because of their applications in consumer electronics. However, there may be numerous privacy issues in such an infrastructure. Nevertheless, neural computing-driven security solutions have played a significant role in overcoming the security and privacy issues of AI-driven intelligent flying vehicles. Motivated by these facts, this article presents a neural computing-based access control protocol for AI-driven flying vehicles to prevent all possible security threats. A detailed security evaluation of the devised protocol is done formally using the extensively recognized Real-Or-Random (ROR) oracle model. Similarly, the informal security analysis ensures the resistance of our scheme against various potential attacks under the assumptions of CK and DY adversarial models. Moreover, detailed performance scrutiny of the devised protocol with various existing schemes shows the robustness and supremacy of our scheme in terms of computation, communication overhead, and security attributes. © 1975-2011 IEEE.

关键词： Authentication

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Context Query Generation using Scene Graph approach 24

Context Query Generation using Scene Graph approach

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24th IEEE International Conference on Mobile Data Management, MDM 2023

作者： De Silva, Ravindi Zaslavsky, Arkady Loke, Seng Jayaraman, Prem Prakash Deakin University School of Information Technology Melbourne Australia Swinburne University of Technology Department of Computer Sceince and Software Engineering Melbourne Australia

ISBN: (纸本)9798350341010

Context-awareness (CA) has become an evolving trend, especially in the domain of Internet of Things (IoT). With the progress of IoT, the necessity for accessing real-time contextual information has become a critical factor for the advancement of IoT applications. Context management platforms (CMPs) have been proposed in the literature to support the needs of such Context-aware IoT applications. However, there are still significant gaps in terms of supporting the increasing needs of Context-aware applications, including the performance analysis of CMPs. In this paper, we propose a scene-graph based approach to generate context queries which primarily intends to support the performance analysis of CMPs and its ability to support plethora of Context-aware IoT application needs. Given the situation driven nature of IoT applications, the ability to generate relevant queries needs to be very realistic. Hence, we propose a novel Situation State Machine based approach to capture and model real-world situations. To demonstrate the potential to generate relevant context queries based on dynamic situations, a bicycle dooring use case is considered. We then present a template-based query generation approach to create realistic queries that represent real-world IoT application environment. The dooring use case is considered to validate the ability to represent complex queries, and the ability to generate complex queries in linear time. © 2023 IEEE.

关键词： Internet of things

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The Impact of Blockchain technology for Evaluation of Electronic Medical Certification

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SN computer Science 2025年第5期6卷 1-14页

作者： Azam Zia, M. Anwar, Munazza Mehmood, Faisal Razzaq, Abdul Asif, Muhammad Department of Computer Science University of Agriculture Faisalabad Punjab Faisalabad 38000 Pakistan Department of Civil and Transportation Engineering Shenzhen University Shenzhen 518060 China Computer Science Muhammad Nawaz Shareef University of Agriculture Punjab Multan 59300 Pakistan Department of Computer and Software Engineering Information Technology University (ITU) Punjab Lahore Pakistan

Evaluation of Electronic Medical Certification (EMC) is integral to today's healthcare systems since they are a consolidated database of individual patients' medical histories. Security measures and privacy issues are standard in electronic medical certification. Incompatible systems make sharing data across healthcare providers difficult, which may lead to data breaches and a lack of patient authority over their medical data. Blockchain has become essential for real-world corporate applications where confidentiality of information is paramount. The handling of security in medical records through a decentralized peer-to-peer network might be revolutionized by blockchain technology and improving the protection of the interchange of healthcare records. This study proposes a lightweight flask framework-based blockchain strategy to store and display patient status and transaction log information about their disease diagnosis. In a potential Blockchain system, the diseased patients act as assets, and their records contain any prior illnesses as blocks. The report analyzes a lot of national and international EHR standards. It identifies which of these interoperability needs to be addressed, discusses the interoperability challenges in but also outlines the standards. Current blockchain-based EHR frameworks. Without the suggested has the requirement for centralized systems. By using this framework, the healthcare industry can be provided safer ways to while also providing the exchange of health information. It offers features of immutability, security, and user control over stored records. This work contributes to an understanding of what potential Blockchain technology has offered an interoperable application in the EHR frameworks. Meet the blockchain-based EHR framework. Multiple national and international EHR standards. © The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd. 2025.

关键词： Blockchain Cryptography Decentralized Electronic medical certification (EMC) Transaction

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Optimal Resource Allocation in Fog Computing for Healthcare Applications

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computers, Materials & Continua 2022年第6期71卷 6147-6163页

作者： Salman Khan Ibrar Ali Shah Nasser Tairan Habib Shah Muhammad Faisal Nadeem Department of Computer Software Engineering University of Engineering and TechnologyMardan23200Pakistan Department of Computer Science College of Computer ScienceKing Khalid UniversityAbhaK.S.A Informatics Complex H-8Islamabad44000Pakistan

In recent years,the significant growth in the Internet of Things(IoT)technology has brought a lot of attention to information and communication *** IoT paradigms like the Internet of Vehicle Things(IoVT)and the Internet of Health Things(IoHT)create massive volumes of data every day which consume a lot of bandwidth and ***,to process such large volumes of data,the existing cloud computing platforms offer limited resources due to their distance from IoT ***,cloudcomputing systems produce intolerable latency problems for latency-sensitive real-time ***,a newparadigm called fog computingmakes use of computing nodes in the form of mobile devices,which utilize and process the real-time IoT devices data in orders of *** paper proposes workload-aware efficient resource allocation and load balancing in the fog-computing environment for the *** proposed algorithmic framework consists of the following components:task sequencing,dynamic resource allocation,and load *** consider electrocardiography(ECG)sensors for patient’s critical tasks to achieve maximum load balancing among fog nodes and to measure the performance of end-to-end delay,energy,network consumption and average *** proposed algorithm has been evaluated using the iFogSim tool,and results with the existing approach have been *** experimental results exhibit that the proposed technique achieves a 45%decrease in delay,37%reduction in energy consumption,and 25%decrease in network bandwidth consumption compared to the existing studies.

关键词： Fog computing internet of health things resource management

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