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Computing and Informatics (ICCI), International Conference on

作者： Mahmoud Nasr Adam Piórkowski Pascal Muam Mah Fathi E. Abd El-Samie Department of Biocybernetics and Biomedical Engineering AGH University of Krakow Cracow Poland Faculty of Engineering and Technology Future University in Egypt Cairo Egypt Department of Technical Informatics and Telecommunications AGH University of Krakow Cracow Poland Department of Electronics and Electrical Communications Engineering Faculty of Electronic Engineering Menoufia University Menouf Egypt Department of Information Technology College of Computer and Information Sciences Princess Nourah Bint Abdulrahman University Riyadh Saudi Arabia

This study introduces a unique cancelable face recognition technique that depends on adaptive filters and quaternion mathematics. The whale optimization is also applied to determine the optimal parameter settings for best performance. The primary concept of the suggested model revolves around the concealment of facial characteristics before their utilization in the face recognition process. The primary aim of this procedure is to guarantee the confidentiality of users' personal information throughout the enrollment and verification stages of the biometric system, particularly when the database is compromised and the stored biometric templates are altered. In this context, the color facial image is utilized to construct a quaternion. After that, a mask image is employed in a separate quaternion. The cancelable templates are obtained by using the input and mask images represented in quaternion format and applying an adaptive filter on both of them. This process involves preserving the weights of the adaptive filter. Investigation of the suggested tehnique performance demonstrates that the obtained templates lead to low (EER) and a large Area under the ROC curve.

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Visualizing Wellness: The Impact of Deep Learning on Healthcare Image Analysis

Visualizing Wellness: The Impact of Deep Learning on Healthc...

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Advances in Computation, Communication and information technology (ICAICCIT), International Conference on

作者： Deepak Kumar Sinha Preeti Naval Samreen Fiza Department of Computer Science Engineering Faculty of Engineering and Technology JAIN (Deemed-to-be University) Bangalore Karnataka India Maharishi School of Engineering & Technology Maharishi University of Information Technology Uttar Pradesh India Department of Electronics and Communication Engineering Presidency University Bangalore Karnataka India

Deep convolution networks have quickly established itself as the main method for deciphering medical pictures due to their widespread use. This extensive study examined almost 300 recent papers, mostly from the preceding year, that summarized the basic artificial intelligence ideas related to medical picture analysis. Applications of deep learning were explored in detail in a number of disciplines, including object recognition, picture grouping, classification, and registering. This book presents a succinct summary of studies conducted in the fields of neurological disorders, retinal cells, asthma, malignant scans, breasts, cardiovascular and cardiac pelvic, and nerve imaging. Study conducted concludes our assessment with a review of the state-of-the-art at this time, a thorough investigation of outstanding problems, and suggestions for further research. All areas, including computerized pathology, musculoskeletal, pulmonary, the heart, breasts, muscles and tendons, retinal, and neurology imaging, are summarized in a summary of current studies. This thorough review ends with an assessment of the field's current state, a critical analysis of ongoing issues, and specific suggestions for further research and development.

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AI Bot to Increase the Accuracy and Efficiency of Hiring Process of Business Organizations

AI Bot to Increase the Accuracy and Efficiency of Hiring Pro...

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Innovative Computing, Intelligent Communication and Smart Electrical Systems (ICSES), International Conference on

作者： Dilusha N.H.A.S Weerasinghe E.J.K.D Chamuditha S.M.A Wijekoon K.W.M Samantha Thelijjagoda Nishantha Giguruwa Faculty of Computing Sri Lanka Institute of Information Technology Malabe Sri Lanka Dept. of Computer System Engineering Sri Lanka Institute of Information Technology Malabe Sri Lanka College of Sustainability and Tourism Ritsumeikan Asia Pacific University Beppu Oita Prefecture Japan

ISBN: (数字)9798331543624

ISBN: (纸本)9798331543631

Recruitment in most organizations is normally a rather inefficient process where it takes quite a long time to match the right candidate to a given job. In this research, AI-based hiring system is recommended to improve the efficiency and accuracy of the hiring process with the help of NLP and machine learning algorithms. Employment enabling processes such as CV filtering, skill evaluation and personality profiling as well as job matching are achieved by the system. An NLP-based tool is used for short listing of CVs, assessment quizzes are used to evaluate different skills and qualifications of the candidates and chatbot enables communication between candidates and HRs, while an AI bot is used to complete different document verifications' processes. Recruitment operation is made easier and efficient, and matching of candidates to the available jobs is done better since it is less time-consuming and energy-demanding, giving a solution that may be implemented on a large scale. Further improvements are to be made in the fine tuning of algorithms, enlargement of comprehensiveness of systems and the implementation of actual sample cases before the introduction into the marketplace.

关键词： Accuracy Machine learning algorithms Filtering Organizations Machine learning Chatbots Organizational aspects Tuning Recruitment Qualifications

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Application of Image Processing and Machine Learning Technologies for Home Care Bed

Application of Image Processing and Machine Learning Technol...

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2022 International Conference on Electrical, computer, Communications and Mechatronics engineering, ICECCME 2022

作者： Suponenkovs, Artjoms Lacis, Matiss Markovics, Zigurds Kovalovs, Mihails Platkajis, Ardis Kalnins, Andrejs Process Engineering and Equipment Manufacturing Company PERUZA Riga Latvia Riga Technical University Department of Computer Control and Computer Networks Riga Latvia Riga Technical University Faculty of Computed Science and Information Technology Riga Latvia Riga Stradins University Department of Radiology Riga Latvia

ISBN: (数字)9781665470957

ISBN: (纸本)9781665470957

The problem of analysing a bed bound patient (that is, a patient who is confined to their bed) is a relevant factor in whether or not they will receive timely medical care. This paper aims to investigate the challenges and possible solutions to such problems. It describes an approach and methods for such analysis, as well as for home care bed control. This paper also addresses the challenges of Image Processing and Machine Learning. The experimental part describes important aspects regarding analysing brain activity, facial expression, blood flow, and human body pose. These experimental results and the proposed approach based upon them can be helpful for ensuring timely medical care. In addition, the results of this work can be useful for home care bed development. © 2022 IEEE.

关键词： Machine learning

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Correction to: A Contemporary Systematic Review on Meta-heuristic Optimization Algorithms with Their MATLAB and Python Code Reference (Archives of Computational Methods in engineering, (2024), 31, 3, (1749-1822), 10.1007/s11831-023-10030-1)

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Archives of Computational Methods in engineering 2024年 1-1页

作者： Salgotra, Rohit Sharma, Pankaj Raju, Saravanakumar gandomi, Amir H. Faculty of Physics and Applied Computer Science AGH University of Science and Technology Kraków Poland MEU Research Unit Middle East University Amman Jordan School of Electrical Engineering Vellore Institute of Technology Vellore India Faculty of Engineering and Information Technology University of Technology Sydney SydneyNSW 2007 Australia Óbuda University Budapest1034 Hungary

https://***/10.1007/s11831-023-10030-1. In this article the optimization algorithm ‘Geometric Mean Optimizer’ was abbreviated as ‘GeMO’, but should instead have been abbreviated as ‘GMO’. The original article ha...

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Reassessing Deep Learning in Time Series: Predicting Time with Foresight

Reassessing Deep Learning in Time Series: Predicting Time wi...

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Advances in Computation, Communication and information technology (ICAICCIT), International Conference on

作者： Vinutha C B Vivek V Megha Pandeya Department of Electronics and Communication Engineering Presidency University Bangalore Karnataka India Department of Computer Science Engineering Faculty of Engineering and Technology JAIN (Deemed-to-be University) Bangalore Karnataka India Maharishi School of Engineering & Technology Maharishi University of Information Technology Uttar Pradesh India

Machine learning relies heavily on time series forecasting, which has a wide range of uses, including although not limited to forecasting air quality, traffic patterns, and power use. Recent developments in deep training and matrix factorization models, however, have shown competitive performance and are emerging as alternatives. However, a fundamental disadvantage of these contemporary models over more established ones is their complexity. The Gradient Boosted Regression Tree, a well-known machine learning baseline, and popular deep learning models are compared in this work. We convert the time series forecast challenge into a window-based prediction issue, which is akin to (DNN) models. In addition, we design the GBRT model's input and output structure. In order to construct a single input instance to simulate a multi-output GBRT model, we concatenate the desired values with additional characteristics for each training window before flattening them. Using nine datasets, we undertake a thorough investigation and evaluate eight cutting-edge deep learning models recently presented at prestigious conferences. The findings show that a simple GBRT model performs much better than the state-of-the-art DNN models evaluated in this research thanks to the window-based input transformation.

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Attack Detection in IoT using RF and ANN

Attack Detection in IoT using RF and ANN

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Smart information Systems and Technologies (SIST), IEEE International Conference on

作者： Aigul Adamova Tamara Zhukabayeva Nurgalym Adamov Department of Computer Engineering Astana IT University Astana Kazakhstan Faculty of Information Technology L.N. Gumilyov Eurasian National University Astana Kazakhstan Astana International University Astana Kazakhstan

ISBN: (数字)9798350374865

ISBN: (纸本)9798350374872

Internet of Things (IoT) devices are the weak link in organizing a Wireless Sensor Network. Various Attacks on IoT devices can lead to different complex consequences. Real applications of the IoT generate a large amount of data every second, the confidentiality of which is of very high value. Therefore, detecting attacks in IoT interactions is of paramount interest from both science and industry. Among various attack detection approaches, machine learning methods show great potential due to their early detection ability. The paper presents a methodology for detecting attacks based on two machine learning methods: Random Forest (RF) and Artificial Neural Network (ANN). To conduct the experiment, various data sets were considered, the descriptions of which are given in the work. The experiment was carried out using open data sets obtained from real IoT devices. As a result, RF demonstrated a high accuracy of 98.6%.

关键词： Training Performance evaluation Wireless sensor networks Accuracy Training data Artificial neural networks Internet of Things

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Data-efficient Large Vision Models through Sequential Autoregression

arXiv

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arXiv 2024年

作者： Guo, Jianyuan Hao, Zhiwei Wang, Chengcheng Tang, Yehui Wu, Han Hu, Han Han, Kai Xu, Chang School of Computer Science Faculty of Engineering University of Sydney Sydney Australia School of information and Electronics Beijing Institute of Technology Beijing China Huawei Noah's Ark Lab Beijing China

Training general-purpose vision models on purely sequential visual data, eschewing linguistic inputs, has heralded a new frontier in visual understanding. These models are intended to not only comprehend but also seamlessly transit to out-of-domain tasks. However, current endeavors are hamstrung by an over-reliance on colossal models, exemplified by models with upwards of 3B parameters, and the necessity for an extensive corpus of visual data, often comprising a staggering 400B tokens (Bai et al., 2023). In this paper, we delve into the development of an efficient, autoregression-based vision model, in-novatively architected to operate on a limited dataset. We meticulously demonstrate how this model achieves proficiency in a spectrum of visual tasks spanning both high-level and low-level semantic understanding during the testing phase. Our empirical evaluations underscore the model's agility in adapting to various tasks, heralding a significant reduction in the parameter footprint, and a marked decrease in training data requirements, thereby paving the way for more sustainable and accessible advancements in the field of generalist vision models. The code is available at https://***/ggjy/DeLVM. Copyright © 2024, The Authors. All rights reserved.

关键词： Semantics

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Vehicle orientation detection based on synthetic data with image-to-image translation and domain adaptation

Vehicle orientation detection based on synthetic data with i...

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IEEE International Conference on Big Data

作者： Nguyen Thanh Thien Faculty of Computer Engineering University of Information Technology VNU-HCM Ho Chi Minh City Vietnam

ISBN: (纸本)9781665480468

In this paper, the author describes the solution for the IEEE BigData Cup 2022 Challenge: Vehicle class and orientation detection in the real-world using synthetic images from driving simulators. This challenge’s task is to enhance the performance of the vehicle detection models, which are trained solely on photo-realistic images, in real-world scenarios. By applying image-to-image translation and domain adaptation technique, the proposed solution can improve the detection results on real-world images using models trained on synthetic data. Furthermore, the proposed method ranked 2nd in the leaderboard with final score of 0.4456.

关键词： Adaptation models Vehicle detection Big Data Data models Task analysis Synthetic data

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Service-Centric Architectures in Cloud Security: An In-Depth Analysis

Service-Centric Architectures in Cloud Security: An In-Depth...

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Advances in Computation, Communication and information technology (ICAICCIT), International Conference on

作者： Natya S Vivek V Dhananjay Kumar Yadav Department of Electronics and Communication Engineering Presidency University Bangalore Karnataka India Department of Computer Science Engineering Faculty of Engineering and Technology JAIN (Deemed-to-be University) Bangalore Karnataka India Maharishi School of Engineering and Technology Maharishi University of Information Technology Uttar Pradesh India

Due to its affordable and high-quality services, cloud computing has lately attracted a lot of interest. Due to the on- demand, pay-per-use characteristics that cloud services provide, which encourage companies to outsource parts of their operations with better service delivery and more value, they have become an essential component of both businesses et people's everyday lives during the last ten years. In the next years, a continued migration towards cloud-based services is predicted by the current market trend, which started in 2019. Despite the many benefits that the cloud computing paradigm offers to both enterprises and consumers, security issues are expected to be the biggest cloud computing challenges in 2020. Security problems are a result of a number of variables, including the streaming nature of these solutions and the technology that support cloud computing, which includes hybridization and multitenancy. This research examines security concerns in service-driven cloud computing in order to shed light on the situation of the industry at the moment. This paper's main contribution is an analysis of cloud security over the last ten years and the presentation of a thorough taxonomy of security concerns throughout the three-layer architecture, which includes Infrastructure-as-a-Service (IaaS), Platform-as- (PaaS), and Software-as-a-Service (SaaS).

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