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

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

sciences and Techniques of Automatic Control and computer engineering MI-STA, IEEE International Maghreb Meeting of the Conference on

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

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.

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Representation learning with CGAN for causal inference

arXiv

引用

arXiv 2024年

作者： Weng, Zhaotian Hong, Jianbo Wang, Lan School of Software Tsinghua University Beijing China Collage of Computer Science and Technology Jilin University Changchun China College of Engineering Monash University Suzhou China

Conditional Generative Adversarial Nets (CGAN) is often used to improve conditional image generation performance. However, there is little research on Representation learning with CGAN for causal inference. This paper proposes a new method for finding representation learning functions by adopting the adversarial idea. We apply the pattern of CGAN and theoretically demonstrate the feasibility of finding a suitable representation function in the context of two distributions being balanced. The theoretical result shows that when two distributions are balanced, the ideal representation function can be found and thus can be used to further research. © 2024, CC BY.

关键词： Image enhancement

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Deep Learning-Based Probability Model for Traffic Information Estimation

Journal of Network Intelligence

引用

Journal of Network Intelligence 2022年第3期7卷 592-607页

作者： Sun, Zhaoshan Pan, Jeng-Shyang Pan, Tien-Szu Chen, Chi-Hua College of Computer Science and Mathematics Fujian University of Technology Fuzhou350118 China Collage of Computer Science and Engineering Shandong University of Science and Technology China Chaoyang University of Technology Taiwan Fujian University of Technology China Department of Electronic Engineering National Kaohsiung University of Science and Technology 415 Chien-Kung Road Kaohsiung807 Taiwan College of Computer and Data Science College of Software Fuzhou University Fuzhou350116 China

This study proposes a novel traffic information estimation method based on deep learning and cellular floating vehicle data (CFVD). In this paper, a probabilistic analysis model based on deep learning is proposed to consider the relationship between vehicle speed and communication behaviors. Then, a vehicle speed estimation method based on the proposed probabilistic analytical model is proposed to estimate vehicle speed. For traffic flow estimation, normal location update is adopted to estimate traffic flow. The estimated vehicle speed and the estimated traffic flow can be gathered to estimate traffic density. The proposed method is verified by the simulation tool and the experiment results revealed that the accuracies of estimated vehicle speed, estimated traffic flow, and estimated traffic density are 96.36%, 99.80%, and 96.45%, respectively. Thus, this method estimates traffic information accurately and helps improve the performance of the intelligent transportation system (ITS). © 2022, Taiwan Ubiquitous Information CO LTD. All rights reserved.

关键词： Vehicles

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Answer extraction algorithm based on syntax structure feature parsing and classification

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Jisuanji Xuebao/Chinese Journal of computers 2008年第4期31卷 662-676页

作者： Hu, Bao-Shun Wang, Da-Ling Yu, Ge Ma, Ting Department of Computer Science and Technology Software Collage Northeastern University Shenyang 110004 China Institute of Computer Software and Theory School of Information Science and Engineering Northeastern University Shenyang 110004 China

Due to the feature and difficulty of Chinese natural language processing and the lack of related resources, some foreign mature techniques can not be applied in Chinese Question Answering (QA) system. For the Chinese factoid QA system, a new answer extraction method based on syntax structure feature parsing and classification is presented in this paper. With the method, the answer extraction is regarded as candidate answer classification problem, i.e. candidate answers are classified into correct and incorrect answer. According to the part-of-speech information of candidate answers corresponding to question types, the candidate answers and their features (both simple and syntactic) in sentences from snippets are firstly extracted. Then these features are used to train the classifier. Finally, the trained classifier is used to distinguish whether the candidate answer is correct or not. For Chinese factoid questions, comparing to currently typical pattern matching based answer extraction algorithm, the new method improves precision by 6.2% and MRR by 9.7%.

关键词： Information retrieval

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A Framework Design of ML Classifier Algorithm for Retrieve the Information About the Drugs and its Quality

A Framework Design of ML Classifier Algorithm for Retrieve t...

引用

Advance Computing and Innovative Technologies in engineering (ICACITE), International Conference on

作者： M Meenakshi S. Ruban T J Nandhini S Loganayagi Raad Muhammed Sayed Enas Hassan Imad Al-Din Hussein Al-Saidi Saveetha School of Engineering Saveetha University Thandalam Chennai department of software technology St aloysius deemed to be university Department of computer science and engineering saveetha school of engineering simats saveetha university chennai india department of computer science and engineering saveetha school of engineering(simats) thandalam chennai tamil nadu india collage of nursing national university of science and tqechnology dhi qar iraq Al-Farahidi University Baghdad Iraq Computer Engineer Department al-kunooze university college basra iraq

ISBN: (数字)9798350360165

ISBN: (纸本)9798350360172

Every day, pharmaceutical firms have to do extensive product analyses. The same product is frequently registered more than once in several systems, each using unique properties. For these businesses, precise and high-quality information is crucial, especially considering the nature of the pharmaceuticals these goods contain. The hypothesis of this research project is that machine learning provides a workable way to effectively combine different data sources and match records pertaining to the same product. It is no longer feasible to match records by hand because of the extraordinarily large volume that has to be processed. In a large data context, this paper presents a framework for matching pharmacological records using machine learning approaches. The framework trains machine learning by utilising specified criteria for record matching. These models are then tested by forecasting matches between records that deviate from these preset guidelines. Lastly, by creating a huge variety of record combinations and forecasting matches based on them, the system mimics the production environment. The findings show that although the training datasets produce good results—the top model has an average accuracy of about $85 \%$—the production environment has its own set of difficulties. In spite of this, matches that depart from established norms are effectively predicted by the framework. These results highlight how machine learning can be used to perform record matching tasks, as manual processing at this size is impossible.

关键词： Training Machine learning algorithms Soft sensors Machine learning Production Manuals Predictive models

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Test Generation for Mutation Testing by Symbolic Execution

Test Generation for Mutation Testing by Symbolic Execution

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IEEE International Conference on software Quality, Reliability and Security Companion (QRS-C)

作者： Rui Su Zhiyi Zhang Yuqian Zhou Yongming Yao Collage of Computer Science and Technology Nanjing University of Aeronautics and Astronautics Nanjing China Collaborative Innovation Center of Novel Software Technology and Industrialization Nanjing China Ministry Key Laboratory for Safety-Critical Software Development and Verification Nanjing University of Aeronautics and Astronautics Nanjing China Army Engineering of PLA Nanjing China

Mutation testing is a crucial technique to assess the effectiveness of test suites, but it can be computationally expensive. Symbolic execution is a formal technique that generates high-coverage test cases, but its scalability is limited. In this paper, we present a novel approach to mutation testing that utilizes symbolic execution, which explores only program regions impacted by mutations, prioritizes paths containing mutant branches, and generates test cases aimed at mutations. We implemented our approach in MutSym and evaluated it on real-world programs, demonstrating its effectiveness and efficiency. Our approach enhances the quality and reliability of software systems by detecting and eliminating faults early in the development process.

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EATS: Efficient Adaptive Test Case Selection for Deep Neural Networks

EATS: Efficient Adaptive Test Case Selection for Deep Neural...

引用

IEEE International Conference on software Quality, Reliability and Security (QRS)

作者： Huanze Meng Zhiyi Zhang Yuchen Ding Shuxian Chen Yongming Yao Collage of Computer Science and Technology Nanjing University of Aeronautics and Astronautics Nanjing China Collaborative Innovation Center of Novel Software Technology and Industrialization Nanjing China School of Mathematical Science Yangzhou University Yangzhou China Ministry Key Laboratory for Safety-Critical Software Development and Verification Nanjing University of Aeronautics and Astronautics Nanjing China Army Engineering of PLA Nanjing China

ISBN: (数字)9798350365634

ISBN: (纸本)9798350365641

As deep neural network (DNN) has made significant advancements across various fields, systematically testing DNN has become increasingly crucial. To uncover potential faults within DNN, a vast number of test cases and their correct labels are required, but the process of labeling is time-consuming and labor-intensive. To alleviate the burden on developers, test case selection techniques for DNN models have been proposed, assisting in the selection of test cases from large datasets that are more likely to reveal model faults. In this study, we introduce an efficient adaptive test case selection method based on the principle of uniform distribution of test cases, named EATS. In addition, we propose a test case optimization method and an image similarity calculation method. The optimization method can save time during the test case selection process, while the image similarity calculation method computes the degree of difference between images based on model uncertainty. EATS leverages the model’s uncertainty to achieve a more uniform distribution of selected test cases, aiming to select test cases that can induce a diversity of model fault predictions, thereby optimizing model performance. We conducted comparative experiments of EATS and other test case selection strategies on four common datasets and their corresponding DNN models. The experimental results show that EATS outperforms other methods in terms of the uniformity of test case distribution, diversity of errors discovered, and model optimization. It also demonstrates excellent time efficiency.

关键词： Adaptation models Uncertainty Costs Computational modeling Optimization methods Artificial neural networks Predictive models

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A Way to Protect the Surveillance Data Stored in the Cloud Using FC and SVM Methods

A Way to Protect the Surveillance Data Stored in the Cloud U...

引用

Advance Computing and Innovative Technologies in engineering (ICACITE), International Conference on

作者： D. Suganthi M Raju N.B. Mahesh Kumar M. Vasavi Oday Faris Washeel Tareq Hafdhi Abdtawfeeq Department of Computer Science Saveetha College of Liberal Arts and Sciences SIMATS Thandalam Chennai Tamilnadu India Department of Software Systems Sri Krishna Arts and Science College Coimbatore Department: Computer Science and Engineering Institute/university: Hindusthan Institute of Technology Valley Campus Pollachi Main Road Othakkalmandapam (Post) Coimbatore Tamil Nadu INDIA ET department CVR College of Engineering Hyderabad Collage of Nursing National University of Science and Technology Dhi Qar Iraq

ISBN: (数字)9798350360165

ISBN: (纸本)9798350360172

Building trust between consumers and service providers is critical to the functioning of cloud computing, a technology that offers a range of services over the Internet. When consumers choose which cloud services to utilise for processing and storing sensitive data, trust values are a major factor. In addition, service providers can employ trust values as a means of granting access to resources, protecting against malevolent users. The subjective trust model category, which evaluates trust based on user and service provider behaviour, is the one that this research suggests for trust models. Because trust is intrinsically ambiguous in our method, trust levels for cloud consumers and service providers are calculated using fuzzy logic and support vector machines. Using triangle membership functions for defuzzification and Gauss membership functions for fuzzification, we use the Mamdani fuzzy approach. In order to determine how trustworthy a resource is, our paradigm for evaluating trust takes performance and flexibility into account. Performance evaluation includes measurements related to workload and reaction time, whereas elasticity evaluation takes into account elements like scalability, availability, security, and usability. We apply fuzzy C-means clustering on pertinent criteria to establish trust levels for users, including faulty requests, fake requests, unauthorised requests, and total requests.

关键词： Support vector machines Performance evaluation Fuzzy logic Cloud computing Surveillance Scalability Vectors

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Delay-Aware Service Caching in Edge Cloud: An Adversarial Semi-Bandits Learning-Based Approach

Delay-Aware Service Caching in Edge Cloud: An Adversarial Se...

引用

IEEE International Conference on Cloud Computing, CLOUD

作者： Jinpeng Li Yunni Xia Xiaoning Sun Peng Chen Xiaobo Li Jiafeng Feng Collage of Computer Science Chongqing University Chongqing China School of Computer and Information Science Chongqing Normal University Chongqing China Yunnan Key Laboratory of Service Computing Yunnan University of Finance and Economics Kunming China School of Computer and Software Engineering Xihua University Chengdu China Chongqing Animal Husbandry Techniques Extension Center Chongqing China China Huadian Corporation Guangdong Branch Guangdong China

ISBN: (数字)9798350368536

ISBN: (纸本)9798350368543

Mobile Edge Computing (MEC) is an emerging computing paradigm that offloads cloud center functions to the edge server. In a MEC environment, edge servers' limited storage and processing capacity require selective service caching, where only a part of required content can be placed directly upon the destination edge server and the remaining at remote cloud end. A primary challenge in this context is the creation of an effective and responsive service caching algorithm that improves the Quality of Service (QoS) perceived by users while reducing operational costs. This study applies an $M$ / G /1 queuing model as the foundational framework and transforms the service caching problem as an adversarial semi-bandit problem. We propose a delay-aware Genetic-Follow-the-Regularized-Leader (GFRL) algorithm, which is capable of guiding decentralized caching decisions. Experimental results indicate that GFRL outperforms traditional methods across various performance metrics.

关键词： Measurement Cloud computing Upper bound Multi-access edge computing Costs Quality of service Transforms

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COVID-CheXNet: hybrid deep learning framework for identifying COVID-19 virus in chest X-rays images

引用

Soft computing 2023年第5期27卷 2657-2672页

作者： Alaa S Al-Waisy Shumoos Al-Fahdawi Mazin Abed Mohammed Karrar Hameed Abdulkareem Salama A Mostafa Mashael S Maashi Muhammad Arif Begonya Garcia-Zapirain Communications Engineering Techniques Department Information Technology Collage Imam Ja'afar Al-Sadiq University Baghdad Iraq. Computer Science Department Al-Ma'aref University College Anbar Iraq. College of Computer Science and Information Technology University of Anbar 11 Ramadi Anbar Iraq. College of Agriculture Al-Muthanna University Samawah 66001 Iraq. Faculty of Computer Science and Information Technology University Tun Hussein Onn Malaysia 86400 Batu Pahat Johor Malaysia. Software Engineering Department College of Computer and Information Sciences King Saud University Riyadh 11451 Saudi Arabia. School of Computer Science Guangzhou University Guangzhou China. eVIDA Lab The University of Deusto Avda/Universidades 24 48007 Bilbao Spain.

The outbreaks of Coronavirus (COVID-19) epidemic have increased the pressure on healthcare and medical systems worldwide. The timely diagnosis of infected patients is a critical step to limit the spread of the COVID-19 epidemic. The chest radiography imaging has shown to be an effective screening technique in diagnosing the COVID-19 epidemic. To reduce the pressure on radiologists and control of the epidemic, fast and accurate a hybrid deep learning framework for diagnosing COVID-19 virus in chest X-ray images is developed and termed as the COVID-CheXNet system. First, the contrast of the X-ray image was enhanced and the noise level was reduced using the contrast-limited adaptive histogram equalization and Butterworth bandpass filter, respectively. This was followed by fusing the results obtained from two different pre-trained deep learning models based on the incorporation of a ResNet34 and high-resolution network model trained using a large-scale dataset. Herein, the parallel architecture was considered, which provides radiologists with a high degree of confidence to discriminate between the healthy and COVID-19 infected people. The proposed COVID-CheXNet system has managed to correctly and accurately diagnose the COVID-19 patients with a detection accuracy rate of 99.99%, sensitivity of 99.98%, specificity of 100%, precision of 100%, F1-score of 99.99%, MSE of 0.011%, and RMSE of 0.012% using the weighted sum rule at the score-level. The efficiency and usefulness of the proposed COVID-CheXNet system are established along with the possibility of using it in real clinical centers for fast diagnosis and treatment supplement, with less than 2 s per image to get the prediction result.

关键词： Chest X-ray images Chest radiography imaging Coronavirus COVID-19 epidemic Deep learning ResNet34 model Transfer learning

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