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arXiv 2024年

作者： Wang, Yifan Gui, Jie Tang, Yuan Yan Kwok, James Tin-Yau School of Cyber Science and Engineering Southeast University Nanjing210000 China Engineering Research Center of Blockchain Application Supervision and Management Southeast University Ministry of Education Purple Mountain Laboratories Nanjing210000 China The Department of Computer and Information Science University of Macao Macao999078 China The Department of Computer Science and Engineering The Hong Kong University of Science and Technology Hong Kong999077 Hong Kong

Finger vein recognition technology has become one of the primary solutions for high-security identification systems. However, it still has information leakage problems, which seriously jeopardizes user’s privacy and anonymity and cause great security risks. In addition, there is no work to consider a fully integrated secure finger vein recognition system. So, different from the previous systems, we integrate preprocessing and template protection into an integrated deep learning model. We propose an end-to-end cancelable finger vein network (CFVNet), which can be used to design an secure finger vein recognition system. It includes a plug-and-play BWR-ROIAlign unit, which consists of three sub-modules: Localization, Compression and Transformation. The localization module achieves automated localization of stable and unique finger vein ROI. The compression module losslessly removes spatial and channel redundancies. The transformation module uses the proposed BWR method to introduce unlinkability, irreversibility and revocability to the system. BWR-ROIAlign can directly plug into the model to introduce the above features for DCNN-based finger vein recognition systems. We perform extensive experiments on four public datasets to study the performance and cancelable biometric attributes of the CFVNet-based recognition system. The average accuracy, EERs and D↔sys on the four datasets are 99.82%, 0.01% and 0.025, respectively, and achieves competitive performance compared with the state-of-the-arts. © 2024, CC BY-NC-SA.

关键词： Boiling water reactors

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Secured Authentication and Key Sharing Using Encrypted Negative Password in IoT devices 1

Secured Authentication and Key Sharing Using Encrypted Negat...

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1st International Conference on technologies for Smart Green Connected Society 2021, ICTSGS 2021

作者： Sudhakaran, Pradeep Kaliyaperumal, Manikannan Sindhu, V. Department of Computing Technologies School of Computing SRM Institute of Science and Technology Potheri Chengalpattu District Tamilnadu Kattankulathur603203 India Department of Computer Science and Engineering MarriLaxman Reddy Institute of Technology and Management Telangana Dundigal500043 India Department of Information Technology Sri Krishna College of Engineering and Technology Tamilnadu Coimbatore India

ISBN: (纸本)9781607685395

1It's a safe riddle State hoarding is an important consideration in systems that use mystery word request, which is now the most widely used check mechanism for removing security issues. In this study, we present a perplexing word check structure that is expected to provide safe riddle word handling and might potentially be integrated into existing explanation frameworks. They received a clear puzzle enunciation from a client who had astounded out a cryptographic hash work in our sequence of events. The hashed puzzle verbalization is adjusted into a negative problem key at that time. Finally, using a symmetric-key figure, the negative puzzle enigma is combined into an Encoded Negative Enigma state (e.g., AES). In expansion development security, multi-cycle encryption might be used. Avoiding passwords from ENPs is difficult due to cryptographic hash work and symmetric encryption. There are also stores of monitoring ENPs for a particular plain appear up that the ENP might oblige enquire table trap and supplies more solid mystery key security below word reference trap. It's thought that the ENP doesn't appear to have any extra parts (such as salt);apart from that, the ENP may at the very least deny precalculation *** ENP is the most basic key, including symmetrickey figures, cryptographic hash work and negative mystery words all without the requirement for any more information beyond the simple mystery express. © The Electrochemical Society

关键词： Encrypted Negative Password Key sharing negative database Secured-Authentication

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On the potential for human-centered, cognitively inspired AI to bridge the gap between optimism and reality for autonomous robotics in healthcare: a respectful critique

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Proceedings of the International Symposium on Human Factors and Ergonomics in Health Care 2024年第1期13卷 106 - 112页

作者： Stephanie Tulk Jesso Christopher Greene Shiqi Zhang Amy Booth Michael DiFabio Grace Babalola Adeola Adegbemijo Sumantra Sarkar 1Department of Systems Science and Industrial Engineering SUNY Binghamton2Human-Centered Mindful Technologies Lab SUNY Binghamton 1Department of Systems Science and Industrial Engineering SUNY Binghamton 3Department of Computer Science SUNY Binghamton 4New York United Health Services 5School of Management SUNY Binghamton

Historically, the fields of computer science, cognitive science, and neuroscience have been tightly linked. To date, this collaboration has yielded major advances in how the brain and mind are understood, as well as the ways in which artificial minds can be constructed to serve as new collaborators to humans. Yet there are still significant gaps between the capabilities of state-of-the-art autonomous robots and the expectations developed by real users who are now encountering autonomous robots on the job. We present our views as well as a case study of our evaluation of two autonomous robots intended to aid nurses within hospital settings: Moxi and TUG. Both cobots were originally considered for procurement by our collaborating healthcare system,UHS, at which time our team began the process of trying to systematically vet each option to aid in the decision making process. What we found was a lack of evidence on either platform in academic literature, which led us to analyze user comments on social media. In order to improve the fit of autonomous robots into human environments, research must be conducted and evidence must be shared, and the Human Factors community can aid in this effort

关键词： Cognitively Plausible AI Autonomous Robots Cobots Healthcare Nursing 4E Cognition

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Adaptive Security management Model for Networks

Adaptive Security Management Model for Networks

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Smart Electronics and Communication (ICOSEC), International Conference on

作者： Lakshmana Phaneendra Maguluri Jemi Gold P S. Rahini Sudha K.P. Aishwarya Jayanthi L S Narendra Singh Bohra Department of Computer Science and Engineering Koneru Lakshmaiah Education Foundation Vaddeswaram India Department of Computer Science and Engineering RMK College of Engineering and Technology Tiruvallur India Department of Computer Science Engineering Velammal Institute of Technology Thiruvallur India Department of Information Technology Panimalar Engineering College Chennai India Department of Mathematics Vidya Academy of Science and Technology Kilimanoor India Department of Management Studies Graphic Era Deemed to be University Dehradun India

Adaptive security is considered as an approach in cybersecurity that analyzes events and against events and behaviors to protect a network. This study will provide details about the different algorithms being used to secure networks. These approaches are driven by a small quantity of labeled data and a massive amount of unlabeled data. In this context, contemporary semi-supervised learning strategies base their operations on the assumption that the distributions of labeled and unlabeled data are comparable. This assumption has a substantial influence on how well these strategies perform overall. If unlabeled data contain information that does not belong to a particular category, the efficiency of the system will deteriorate.

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A Dual-Level Cancelable Framework for Palmprint Verification and Hack-Proof Data Storage

arXiv

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arXiv 2024年

作者： Yang, Ziyuan Kang, Ming Teoh, Andrew Beng Jin Gao, Chengrui Chen, Wen Zhang, Bob Zhang, Yi The College of Computer Science Key Laboratory of Data Protection and Intelligent Management Ministry of Education Sichuan University Chengdu610065 China The School of Cyber Science and Engineering Sichuan University Chengdu610065 China The School of Electrical and Electronic Engineering College of Engineering Yonsei University Seoul Korea Republic of The College of Computer Science Sichuan University Chengdu610065 China The Pattern Analysis and Machine Intelligence Group Department of Computer and Information Science University of Macau Taipa China The School of Cyber Science and Engineering Key Laboratory of Data Protection and Intelligent Management Ministry of Education Sichuan University Chengdu610065 China

In recent years, palmprints have been widely used for individual verification. The rich privacy information in palmprint data necessitates its protection to ensure security and privacy without sacrificing system performance. Existing systems often use cancelable technologies to protect templates, but these technologies ignore the potential risk of data leakage. Upon breaching the system and gaining access to the stored database, a hacker could easily manipulate the stored templates, compromising the security of the verification system. To address this issue, we propose a dual-level cancelable palmprint verification framework in this paper. Specifically, the raw template is initially encrypted using a competition hashing network with a first-level token, facilitating the end-to-end generation of cancelable templates. Different from previous works, the protected template undergoes further encryption to differentiate the second-level protected template from the first-level one. The system specifically creates a negative database (NDB) with the second-level token for dual-level protection during the enrollment stage. Reversing the NDB is NP-hard and a fine-grained algorithm for NDB generation is introduced to manage the noise and specified bits. During the verification stage, we propose an NDB matching algorithm based on matrix operation to accelerate the matching process of previous NDB methods caused by dictionary-based matching rules. This approach circumvents the need to store templates identical to those utilized for verification, reducing the risk of potential data leakage. Extensive experiments conducted on public palmprint datasets have confirmed the effectiveness and generality of the proposed framework. Upon acceptance of the paper, the code will be accessible at https://***/Deep-Imaging-Group/NPR. Copyright © 2024, The Authors. All rights reserved.

关键词： Digital storage

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On Multi-objective Fuzzy Shortest Path Problem

On Multi-objective Fuzzy Shortest Path Problem

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International Conference on Advances in Data science and Computing technologies, ASDC 2022

作者： Halder, Swapna Majumder, Saibal Biswas, Arindam Mandal, Bijoy Kumar Peng, Sheng-Lung Centre for IOT and AI Integration with Education–Industry–Agriculture Kazi Nazrul University West Bengal Asansol India Department of Computer Science and Engineering St. Thomas’ College of Engineering and Technology West Bengal Kolkata India Dr. B.C. Roy Engineering College West Bengal Durgapur India School of Mines and Metallurgy Kazi Nazrul University West Bengal Asansol India Department of Computer Science and Engineering NSHM Knowledge Campus West Bengal Durgapur India Department of Creative Technologies and Product Design National Taipei University of Business Taoyuan32462 Taiwan

ISBN: (纸本)9789819936557

Being a network optimization problem, the shortest path problem (SPP) is considered as one of the popular and frequently encountered optimization problems to address real-world problems. In this paper, we investigate a multi-objective SPP under the paradigm of fuzzy set theory. The proposed problem minimizes both cost and time of a given network, where every edges are associated with cost and time parameters represented as fuzzy triangular numbers to incorporate uncertainty involved with real-world phenomenon. Here, we have modeled a fuzzy chance-constrained model of multi-objective SPP and eventually solved the deterministic transformation of the model using weighted sum method. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Fuzzy set theory

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Covid Analysis using Recurrent Neural Network

Covid Analysis using Recurrent Neural Network

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Integrated Circuits and Communication Systems (ICICACS), IEEE International Conference on

作者： Manoj Kumar Padhi Sahana D S Bindu Madhavi K Bechoo Lal Karunakara Rai B Department of Computer Science and Engineering Koneru Lakshmaiah Education Foundation Vaadeswaram India Department of Computer Science & Engineering GITAM School of Technology GITAM University India Department of Electronics and Communication Engineering Hyderabad Institute of Technology and Management Telangana India Department of Computer Science & Engineering K L University Vadeeshwaram Vijayawada India Department of Electronics and Communication Engineering Nitte Meenakshi Institute of Technology Bengaluru India

The examination of medical images has benefited greatly from the use of artificial intelligence. In contrast to deep learning systems, which do feature extraction automatically and without human interaction, traditional computer vision methods rely on manually produced features that are particular to a certain domain. Having access to medical information for automated analysis is another major factor driving the trend towards deep learning. Chest x-ray pictures are processed in order to segment the lungs and identify diseases in this thesis. Due to its cheap cost, ease of capture, and non-invasive nature, chest x-ray is the most often used medical imaging technology. However, automatic diagnosis in chest x-rays is difficult due to (1) the presence of the rib-cage and clavicle bones, which can obscure abnormalities that are located beneath them, and (2) the fuzzy intensity transitions near the lung and heart, dense abnormalities, rib-cages, and clavicle bones, which make the identification of lung contours subtle. In x-ray image processing, the Convolutional Neural Network (CNN) is the most often used deep learning architecture. Because to the enormous number of parameters in deep CNN architectures, intensive computing resources are required to train these models. Additionally, chest x-ray datasets are often rather tiny, and there is always the risk of overfitting when developing a model. In this dissertation, we propose five convolutional neural networks (CNNs) to identify illness and segment the lungs in chest x-rays. New Line, New Line In the first research paper, an adaptive lightweight convolutional neural network (ALCNN) is created to detect pneumothorax with few parameters. The model readjusts the feature calibration channel-wise using the convolutional layer and attention mechanism. The suggested model outperformed state-of-the-art deep models trained using three different transfer learning methods. One notable aspect of the suggested model is that it require

关键词： Deep learning Solid modeling Adaptation models Lung Medical services Feature extraction Convolutional neural networks

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The Performance Evaluation of Federated Identity management in Securing Wireless Networks 3

The Performance Evaluation of Federated Identity Management ...

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3rd International Conference on Smart Generation Computing, Communication and Networking, SMART GENCON 2023

作者： Lavanya, R. Pund, Sachin S. Jhansirani, Paritala Kaushal, Ashish Agnes, J Sandra Chaturvedi, Sudhir Kumar Vel Tech Rangarajan Dr. Sagunthala R&d Institute of Science and Technology Department of Ece India Shri Ramdeobaba College of Engineering and Management Department of Industrial Engineering Maharashtra Nagpur India Hcl Technologies Hcl Tss Hyderabad India Jindal Global Business School O P Jindal Global University Haryana Sonipat India St Philomenas College Department of Computer Science Mysore India Upes Department of School of Engineering Dehradun India

ISBN: (纸本)9798350319125

Federated identification management (FIM) is a sort of protection architecture designed to relaxed get admission to to wireless networks. FIM provides a way to authenticate users, furnish get entry to to a disbursed network, and guard the overall gadget against malicious attacks. This look at evaluates the overall performance of FIM in securing a distributed Wi-Fi community. The evaluation makes a specialty of the effectiveness of Edam in authentication and authorization, presenting comfortable get admission to control, and resisting malicious attacks. The evaluation system entails testing the performance of the Edam in terms of response time, user throughput, packet loss, authentication fulfillment price, access manage degrees, and assault resistance. Results of the evaluation method imply that FIM is effective in providing at ease Wi-Fi network access, with nicely-described authentication and authorization procedures. The have a look at also found that FIM is capable of face up to malicious assaults and provide effective get entry to manipulate levels. The evaluation also indicates that Edam has a low rate of packet loss and a particularly excessive user throughput. Based at the results of the assessment, it could be concluded that FIM is an effective approach of securing an allotted wireless network. © 2023 IEEE.

关键词： Assessment Authentication management Particularly Procedures

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Effectiveness of improved bootstrap aggregation (IBA) technique in mapping hydropower to climate variables

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International Journal of Energy and Water Resources 2021年第3期5卷 299-310页

作者： Aiyelokun, O. Ogunsanwo, G. Oyelakin, J. Agbede, O. Department of Civil Engineering University of Ibadan Ibadan Nigeria Department of Computer and Information Science Tai Solarin University of Education Ogun Ijebu-Ode Nigeria Department of Geography and Environmental Management Tai Solarin University of Education Ogun Ijebu-Ode Nigeria

There is a growing interest in the application of computational intelligent models in different fields due to their robustness and capability to approximate complex non-linear systems. The motivation of this research is to evaluate the effectiveness of the improved bootstrap aggregation (IBA) technique in predicting hydropower generation in Nigeria using climate variables as predictors. The most important climate variables for the optimum approximation of hydropower as well as the parameters of the model were assessed. The developed model has an accuracy of up to 99% at the calibration phase and more than 95% at the validation phase. The insight mined from this study could serve as an actionable decision-support tool in water resources management for the optimization of hydropower generation. © Islamic Azad University (IAU) 2021.

关键词： Bootstrap aggregation Computational intelligent models Ensemble methods

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Jellyfish Search Optimizer with Deep Learning Driven Cervical Cancer Recognition and Classification Model on Biomedical Images 2

Jellyfish Search Optimizer with Deep Learning Driven Cervica...

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2nd IEEE International Conference on Advances in Information Technology, ICAIT 2024

作者： Anand, M. Suresh Venkatramulu, S. Vidhya, Sesha S. Chembian, W.T. Yashaswini, B.M. Kumar, Magesh B. Department of Computing Technologies School of Computing SRM Institute of Science & Technology Kattankulathur603203 India Department of CSE Kakatiya Institute of Technology and Science Warangal India Department of ECE RMK College of Engineering and Technology India Department of CSE Vel Tech High Tech Dr Rangarajan Dr Sakuntala Engineering College Avadi Morai India Electronics & Communication Engineering JSS Academy of Technical Education JSSATE-B Campus Dr. Vishnuvardhan Rd Uttarahalli-Kengeri Main Road JSS Campus Rd Srinivaspura Karnataka Bengaluru560060 India Department of Computer Science and Engineering Bannari Amman Institute of Technology Erode Sathyamangalam India

ISBN: (纸本)9798350383867

The emergence of Cervical Cancer (CC) Recognition and Classification Model for Biomedical Images is a main advancement in fields of medical imaging and diagnostics. Leveraging state-of-the-art image processing and machine learning algorithms, this model can accurately detect and classify CC-related abnormalities in medical images, namely colposcopy or Pap smear images. By providing automated and precise assessments, the model empowers medical experts to make timely and informed decisions, which leads to earlier interference and potential life-saving treatment. With its potential to improve diagnostic accuracy and reduce human error, the model holds great promise in the fight against CC, especially in resource-constrained healthcare settings where medical practitioners may be limited. This study presents a Jellyfish Search Optimizer with Deep Learning Driven CC Recognition and Classification (JSODL-CCRC) model on Biomedical Images. The JSODL-CCRC technique is mainly developed to investigate the medical images for detection and classification of CC. The JSODL-CCRC approach comprises median filtering (MF) for noise reduction, followed by feature extraction utilizing Inceptionv3 to derive complex image patterns. Besides, the JSO based hyperparameter selection process is performed for the Inception v3 model. At last, Extreme Learning Machine (ELM) algorithm is applied to categorize the images which validates efficacy in handling complex biomedical image data. The experimental results stated that the JSODL-CCRC model enables effective detection results over other models. © 2024 IEEE.

关键词： Median filters

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