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Metaheuristic Optimization Through Deep Learning Classification of COVID-19 in Chest X-Ray Images

computers, Materials & Continua 2022年第11期73卷 4193-4210页

作者： Nagwan Abdel Samee El-Sayed M.El-Kenawy Ghada Atteia Mona M.Jamjoom Abdelhameed Ibrahim Abdelaziz A.Abdelhamid Noha E.El-Attar Tarek Gaber Adam Slowik Mahmoud Y.Shams Department of Information Technology College of Computer and Information SciencesPrincess Nourah bint Abdulrahman UniversityRiyadh 11671Saudi Arabia Department of Communications and Electronics Delta Higher Institute of Engineering and TechnologyMansoura 35111Egypt Faculty of Artificial Intelligence Delta University for Science and TechnologyMansoura 35712Egypt Department of Computer Sciences College of Computer and Information SciencesPrincess Nourah bint Abdulrahman UniversityRiyadh 11671Saudi Arabia Computer Engineering and Control Systems Department Faculty of EngineeringMansoura University35516MansouraEgypt Department of Computer Science Faculty of Computer and Information SciencesAin Shams University11566CairoEgypt Department of Computer Science College of Computing and Information TechnologyShaqra University11961Saudi Arabia Faculty of Computers and Artificial Intelligence Benha UniversityEgypt School of Science Engineeringand EnvironmentUniversity of SalfordUK Faculty of Computers and Informatics Suez Canal UniversityIsmailia41522Egypt Koszalin University of Technology Poland Faculty of Artificial Intelligence Kafrelsheikh UniversityKafrelsheikh33511Egypt

As corona virus disease(COVID-19)is still an ongoing global outbreak,countries around the world continue to take precautions and measures to control the spread of the *** of the excessive number of infected patients and the resulting deficiency of testing kits in hospitals,a rapid,reliable,and automatic detection of COVID-19 is in extreme need to curb the number of *** analyzing the COVID-19 chest X-ray images,a novel metaheuristic approach is proposed based on hybrid dipper throated and particle swarm *** lung region was segmented from the original chest X-ray images and augmented using various transformation ***,the augmented images were fed into the VGG19 deep network for feature *** the other hand,a feature selection method is proposed to select the most significant features that can boost the classification ***,the selected features were input into an optimized neural network for *** neural network is optimized using the proposed hybrid *** experimental results showed that the proposed method achieved 99.88%accuracy,outperforming the existing COVID-19 detection *** addition,a deep statistical analysis is performed to study the performance and stability of the proposed *** results confirm the effectiveness and superiority of the proposed approach.

关键词： Covid-19 feature selection dipper throated optimization particle swarm optimization deep learning

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Chaotic American zebra search optimization algorithm for benchmark challenges 15

Chaotic American zebra search optimization algorithm for ben...

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15th International Conference on Electronics, computers and Artificial Intelligence, ECAI 2023

作者： Kamboj, Vikram Kumar Krishna, Ardhala Bala Verma, Chaman Raboaca, Maria Simona Filote, Constantin Stepanova, Maria Schulich School of Engineering University of Calgary AB Canada Malla Reddy Engineering College for Women Dept. of Electrcical and Electronics Engineering Hyderabad India Eötvös Loránd University Faculty of Informatics Dept. of Media and Edu. Informatics Budapest Hungary National Research and Development Institute for Cryogenics and Isotopic Technologies Icsi Energy Department Ramnicu Valcea240050 Romania Stefan Cel Mare University of Suceava Faculty of Electrical Engineering and Computer Science Suceava720229 Romania Bauman Moscow State Technical University Engineering Business and Management Faculty Russia

ISBN: (纸本)9798350321388

In this research, we presented a modification to the recently developed swarm premised American zebra search optimization algorithm (AZOA) using chaos. The particular Singer mapping was proposed by the so-called chaotic AZOA and was already known to perform better in optimization. Twelve uni, multi modal benchmark functions, three-bar truss and ten bar truss optimal structural designs were tested. The chaotic AZOA with Singer map would perform more effectively, more consistently, and quicker than the classical AZOA and other recent metaheuristics in optimization, according to the results, which also supported the viability of the modifications. It was discussed if the chaotic AZAO could be optimized with the original AZOA, and the chaotic AZOA was suggested for use in applications for actual engineering challenges. © 2023 IEEE.

关键词： Mapping

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Unveiling Group-Specific Distributed Concept Drift: A Fairness Imperative in Federated Learning

arXiv

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arXiv 2024年

作者： Salazar, Teresa Gama, João Araujo, Helder Abreu, Pedro Henriques The Centre for Informatics and Systems Department of Informatics Engineering University of Coimbra Coimbra3030-790 Portugal INESC TEC The Faculty of Economy University of Porto Porto4200-465 Portugal The Institute of Systems and Robotics Department of Electrical and Computer Engineering University of Coimbra Coimbra3004-531 Portugal

In the evolving field of machine learning, ensuring fairness has become a critical concern, prompting the development of algorithms designed to mitigate bias in decision-making processes. However, achieving fairness in the presence of group-specific concept drift remains an unexplored frontier, and our research represents pioneering efforts in this regard. Group-specific concept drift refers to situations where one group experiences concept drift over time while another does not, leading to a decrease in fairness even if accuracy remains fairly stable. Within the framework of Federated Learning, where clients collaboratively train models, its distributed nature further amplifies these challenges since each client can experience group-specific concept drift independently while still sharing the same underlying concept, creating a complex and dynamic environment for maintaining fairness. The most significant contribution of our research is the formalization and introduction of the problem of group-specific concept drift and its distributed counterpart, shedding light on its critical importance in the realm of fairness. Additionally, leveraging insights from prior research, we adapt an existing distributed concept drift adaptation algorithm to tackle group-specific distributed concept drift which uses a multi-model approach, a local group-specific drift detection mechanism, and continuous clustering of models over time. The findings from our experiments highlight the importance of addressing group-specific concept drift and its distributed counterpart to advance fairness in machine *** Codes 68T01 © 2024, CC BY.

关键词： Machine learning

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A neural approach for detecting inline mathematical expressions from scientific documents

A neural approach for detecting inline mathematical expressi...

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作者： Madisetty, Sreekanth Maurya, Kaushal Kumar Aizawa, Akiko Desarkar, Maunendra Sankar Department of Computer Science and Engineering Indian Institute of Technology Hyderabad HyderabadTelangana502285 India National Institute of Informatics The University of Tokyo Tokyo Japan

Scientific documents generally contain multiple mathematical expressions in them. Detecting inline mathematical expressions are one of the most important and challenging tasks in scientific text mining. Recent works that detect inline mathematical expressions in scientific documents have looked at the problem from an image processing perspective. There is little work that has targeted the problem from NLP perspective. Towards this, we define a few features and applied Conditional Random Fields (CRF) to detect inline mathematical expressions in scientific documents. Apart from this feature based approach, we also propose a hybrid algorithm that combines Bidirectional Long Short Term Memory networks (Bi-LSTM) and feature-based approach for this task. Experimental results suggest that this proposed hybrid method outperforms several baselines in the literature and also individual methods in the hybrid approach. © 2020 John Wiley & Sons, Ltd

关键词： Natural language processing systems

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Requirement Identification for Blockchain-based Secure Transmission of Oil and Gas

Requirement Identification for Blockchain-based Secure Trans...

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informatics (ICI), International Conference on

作者： Mukta Goyal Nitin Kumar Tyagi Dinesh Kumar Saini Computer Science Engineering & Information Technology Jaypee Institute of Information Technology Noida India National Informatics Centre NIC (MeitY) New Delhi India Computer and Communication Engineering Manipal University Jaipur India

Currently, blockchain is using different areas of life such as finance, education, health, government sector, banking, property chain, etc. As digital technology advances, more and more companies are integrating blockchain to improve security, privacy and data transparency for both small businesses and large enterprises. Hence, there is a need to define a software requirement clearly, and precisely so that developer develops the application efficiently. This paper discusses the requirement analysis of one of the blockchain-based applications for the secure transmission of Oil and Gas.

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Lectures on Parallel Computing

arXiv

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arXiv 2024年

作者： Träff, Jesper Larsson TU Wien Faculty of Informatics Institute of Computer Engineering Research Group Parallel Computing 191-4 Treitlstrasse 3 5th Floor Vienna1040 Austria

These lecture notes are designed to accompany an imaginary, virtual, undergraduate, one or two semester course on fundamentals of Parallel Computing as well as to serve as background and reference for graduate courses on High-Performance Computing, parallel algorithms and shared-memory multiprocessor programming. They introduce theoretical concepts and tools for expressing, analyzing and judging parallel algorithms and, in detail, cover the two most widely used concrete frameworks OpenMP and MPI as well as the threading interface pthreads for writing parallel programs for either shared or distributed memory parallel computers with emphasis on general concepts and principles. Code examples are given in a C-like style and many are actual, correct C code. The lecture notes deliberately do not cover GPU architectures and GPU programming, but the general concerns, guidelines and principles (time, work, cost, efficiency, scalability, memory structure and bandwidth) will be just as relevant for efficiently utilizing various GPU architectures. Likewise, the lecture notes focus on deterministic algorithms only and do not use randomization. Slides or blackboard drawings are imagined to be worked out for the actual lectures by the lecturer, so the lecture notes deliberately do not provide such important visual aid: some is available from the author on request. Also the student of this material will find it instructive to take the time to understand concepts and algorithms visually. The exercises can be used for self-study and as inspiration for small implementation projects in OpenMP and MPI that can and should accompany any serious course on Parallel Computing. The student will benefit from actually implementing and carefully benchmarking the suggested algorithms on the parallel computing system that may or should be made available as part of such a Parallel Computing course. In class, the exercises can be used as basis for hand-ins and small programming projects for which su

关键词： Parallel algorithms

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Automated File Labeling for Heterogeneous Files Organization Using Machine Learning

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computers, Materials & Continua 2023年第2期74卷 3263-3278页

作者： Sagheer Abbas Syed Ali Raza MAKhan Muhammad Adnan Khan Atta-ur-Rahman Kiran Sultan Amir Mosavi School of Computer Science National College of Business Administration&EconomicsLahore54000Pakistan Department of Computer Science GC University LahorePakistan Riphah School of Computing&Innovation Faculty of ComputingRiphah International UniversityLahore CampusLahore54000Pakistan Department of Software Pattern Recognition and Machine Learning LabGachon UniversitySeongnam13120Korea Department of Computer Science College of Computer Science and Information Technology(CCSIT)Imam Abdulrahman Bin Faisal University(IAU)P.O.Box 1982Dammam31441Saudi Arabia Department of CIT The Applied CollegeKing Abdulaziz UniversityJeddah31261Saudi Arabia John von Neumann Faculty of Informatics Obuda UniversityBudapest1034Hungary Institute of Information Engineering Automation and MathematicsSlovak University of Technology in BratislavaBratislava81107Slovakia Faculty of Civil Engineering TU-DresdenDresden01062Germany

File labeling techniques have a long history in analyzing the anthological trends in computational *** situation becomes worse in the case of files downloaded into systems from the ***,most users either have to change file names manually or leave a meaningless name of the files,which increases the time to search required files and results in redundancy and duplications of user ***,no significant work is done on automated file labeling during the organization of heterogeneous user files.A few attempts have been made in topic ***,one major drawback of current topic modeling approaches is better *** rely on specific language types and domain similarity of the *** this research,machine learning approaches have been employed to analyze and extract the information from heterogeneous corpus.A different file labeling technique has also been used to get the meaningful and`cohesive topic of the *** results show that the proposed methodology can generate relevant and context-sensitive names for heterogeneous data files and provide additional insight into automated file labeling in operating systems.

关键词： Automated file labeling file organization machine learning topic modeling

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Development of a Calibration-Free Brain-computer Interface Utilizing Common Spatial Patterns and Artificial Neural Networks for EEG Signal Analysis 6

Development of a Calibration-Free Brain-Computer Interface U...

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6th International Congress on Human-computer Interaction, Optimization and Robotic Applications, HORA 2024

作者： Cengiz, Korhan Shreyas, J. Udayaprasad, P.K. Gururaj, H.L. Kumar, Dilip S.M. College of Information Technology University of Fujairah United Arab Emirates Faculty of Organization and Informatics University of Zagreb Croatia Croatia Department of Information Technology Manipal Institute of Technology Bengaluru Manipal Academy of Higher Education Karnataka Manipal576104 India Department of Artificial Intelligence & Machine Learning Global Academy of Technology Bengaluru India Dept. of Computer Science and Engineering University Visvesvaraya College of Engineering Bangalore India

ISBN: (纸本)9798350394634

A brain computer interface (BCI) system uses a technique known calibration, that takes 20 to 30 minutes to accomplish. For the objective of creating a reliable decoder, the calibration process is challenging and expensive. In order to address the drawbacks of the current system, a spectral-spatial technique has been suggested. The motor imagery (MI) data set, comprising 15 electroencephalography (EEG) signals and fourteen test subjects, is taken into consideration. The two modules are designed to extract characteristics and process data. An artificial neural network (ANN) is used to independently train and test the suggested spectral-spatial algorithm. Based on it, a variety of machine learning techniques, including random forest (RF), neural networks (NN), and XGboost, are used to classify the data, that is then sent to the hidden layer (Lth layer). The obtained results indicates 2% of improvement in comparison with existing methodology. © 2024 IEEE.

关键词： Electroencephalography

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Probabilistic Health Monitoring and Maintenance Scheduling for Nuclear Power Systems

Probabilistic Health Monitoring and Maintenance Scheduling f...

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AIAA Science and Technology Forum and Exposition, AIAA SciTech Forum 2025

作者： Amer, Ahmad Tsilifis, Panagiotis Saxena, Abhinav Baglietto, Emilio Khan, Genghis Probabilistic Design & Material Informatics GE Aerospace Research Niskayuna NY12309 United States Robotics AI & Computer Vision GE Vernova Advanced Research Niskayuna NY12309 United States AI & Computer Vision GE Aerospace Research San RamonCA94583 United States Department of Nuclear Science & Engineering Massachusetts Institute of Technology CambridgeMA02139 United States Sustainable Systems GE Aerospace Research Niskayuna NY12309 United States

ISBN: (数字)9781624107238

ISBN: (纸本)9781624107238

Operations and Maintenance (O&M) cost optimization in the nuclear energy industry is an imperative task for developing sustainable systems and efficient renewable technologies. We present a modular probabilistic framework for addressing maintenance scheduling challenges for small modular nuclear reactors using a Dynamic Bayesian Network (DBN) approach that monitors fatigue and performance in multiple subsystems of the reactors and provides a probabilistic assessment for potential failure events. The DBN framework fuses information from potential digital twins that assimilate real time data from several sensors with different time granularities in order to monitor and update thecurrentstate of knowledge aboutthestatus of several fatigue or performance criteria. Comprised of multi-modal digital twins (empirical, data- and physics-based twins), this DBN serves as a predictive framework forecasting the future state of specific reactor subsystems and enhancing decision-making capabilities associated with preventing system failure and minimizing the time and cost of maintenance events. The work presented in this paper is a holistic approach for monitoring and safeguarding complex, multi-component engineering systems where maintenance scheduling is critical and does not restrict itself to nuclear environments. However, our goal is to illustrate how such a framework can be adjusted to small modular water reactors. © 2025, American institute of Aeronautics and Astronautics Inc, AIAA. All rights reserved.

关键词： Nuclear energy

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Optimal, Non-pipelined Reduce-scatter and Allreduce Algorithms

arXiv

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arXiv 2024年

作者： Träff, Jesper Larsson TU Wien Faculty of Informatics Institute of Computer Engineering Research Group Parallel Computing 191-4 Treitlstrasse 3 5th Floor Vienna1040 Austria

The reduce-scatter collective operation in which p processors in a network of processors collectively reduce p input vectors into a result vector that is partitioned over the processors is important both in its own right and as building block for other collective operations. We present a surprisingly simple, but non-trivial algorithm for solving this problem optimally in ⌈log2 p⌉ communication rounds with each processor sending, receiving and reducing exactly p−1 blocks of vector elements. We combine this with a similarly simple, well-known allgather algorithm to get a volume optimal algorithm for the allreduce collective operation where the result vector is replicated on all processors. The communication pattern is a simple, ⌈log2 p⌉-regular, circulant graph also used elsewhere. The algorithms assume the binary reduction operator to be commutative and we discuss this assumption. The algorithms can readily be implemented and used for the collective operations MPI_Reduce_scatter_block, MPI_Reduce_scatter and MPI_Allreduce as specified in the MPI standard. We also observe that the reduce-scatter algorithm can be used as a template for round-optimal all-to-all communication and the collective MPI_Alltoall operation. Copyright © 2024, The Authors. All rights reserved.

关键词： Vectors

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