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Optimization Task Scheduling Using Cooperation Search Algorithm for Heterogeneous Cloud Computing Systems

computers, Materials & Continua 2023年第1期74卷 2133-2148页

作者： Ahmed Y.Hamed M.Kh.Elnahary Faisal S.Alsubaei Hamdy H.El-Sayed Department of Computer Science Faculty of Computers and Artificial IntelligenceSohag UniversitySohag82524Egypt Department of Cybersecurity College of Computer Science and EngineeringUniversity of JeddahJeddah21959Saudi Arabia

Cloud computing has taken over the high-performance distributed computing area,and it currently provides on-demand services and resource polling over the *** a result of constantly changing user service demand,the task scheduling problem has emerged as a critical analytical topic in cloud *** primary goal of scheduling tasks is to distribute tasks to available processors to construct the shortest possible schedule without breaching precedence *** and schedules of tasks substantially influence system operation in a heterogeneous multiprocessor *** diverse processes inside the heuristic-based task scheduling method will result in varying makespan in the heterogeneous computing *** a result,an intelligent scheduling algorithm should efficiently determine the priority of every subtask based on the resources necessary to lower the *** research introduced a novel efficient scheduling task method in cloud computing systems based on the cooperation search algorithm to tackle an essential task and schedule a heterogeneous cloud computing *** basic idea of thismethod is to use the advantages of meta-heuristic algorithms to get the optimal *** assess our algorithm’s performance by running it through three scenarios with varying numbers of *** findings demonstrate that the suggested technique beats existingmethods NewGenetic Algorithm(NGA),Genetic Algorithm(GA),Whale Optimization Algorithm(WOA),Gravitational Search Algorithm(GSA),and Hybrid Heuristic and Genetic(HHG)by 7.9%,2.1%,8.8%,7.7%,3.4%respectively according to makespan.

关键词： Heterogeneous processors cooperation search algorithm task scheduling cloud computing

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Arabic Abstractive Summarization Using the Multilingual T5 Model 6

Arabic Abstractive Summarization Using the Multilingual T5 M...

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6th International Conference on Computing and Informatics, ICCI 2024

作者： Karim, Abdelrhman A. Usama, Mohammed Ibrahim, Manar A. Hatem, Youssef Wael, Waleed Mazrua, Ahmad T. El-Monayer, Ghada K. Elbanhawy, Magy Foad, Khaled Moawad, Ibrahim F. Artificial Intelligence Engineering Department Faculty of Computer Science and Engineering New Mansoura University Dakhlia Egypt Computer Engineering Department Faculty of Computer Science and Engineering New Mansoura University Dakhlia Egypt Artificial Intelligence Science Department Faculty of Computer Science and Engineering New Mansoura University Dakhlia Egypt Faculty of Computer Science and Engineering New Mansoura University Dakhlia Egypt Computer and System Department Faculty of Engineering Portsaid University Egypt Faculty of Computers and Artificial Intelligence Benha University Egypt Faculty of Computer and Information Sciences Ain Shams University Cairo Egypt

ISBN: (纸本)9798350373875

Natural language processing (NLP) is a branch of artificial intelligence (Al) that enables computers to comprehend, generate, and manipulate human language. Natural language processing can interrogate the data with natural language text or voice. Abstractive Text Summarization is based on the Natural Language Processing technique that tries to provide new and more concise textual summaries for huge texts. artificial intelligence and deep learning techniques are used in abstractive summarization to examine the text's essential information and create a new summary that conveys the content more concisely and accurately. This type of summary differs from normal extractive summarizing in that it can generate new summaries beyond simply extracting key lines from the source text. This study presents an abstractive Arabic summarization based on the Multilingual TS (MTS) model AASMTS to address these concerns. This technique is based on deep neural networks and models like transformers, which have changed the ability to summarize text. This research has explored the various types of summarizations and highlighted the significance of two prominent techniques: abstractive and extractive summarization. This research describes a complete process for developing an Arabic abstractive summarization model using the MTS architecture. Experiments on different datasets show that this model achieves state-of-the-art results across MTS. © 2024 IEEE.

关键词： Deep neural networks

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Deep learning framework for automatic detection and classification of sleep apnea severity from polysomnography signals

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Neural Computing and Applications 2024年第28期36卷 17483-17493页

作者： Raja Brundha, A. Lakshmi Sangeetha, A. Balajiganesh, A. Department of Artificial Intelligence and Data Science Sri Sai Ram Engineering College Chennai India Department of Computer Science Engineering Velammal Engineering College Chennai India

Sleep apnea (SA) is a sleep-related breathing disorder characterized by breathing pauses during sleep. A person’s sleep schedule is significantly influenced by that person’s hectic lifestyle, which may include unhealthy eating habits and their line of work. Polysomnographic (PSG) sleep studies examine sleep-related disorders by recording various biosignals from the human body. However, SA classification methods could be more robust in terms of performance because they rely on feature-engineering strategies or employ a particular signal from PSG recording for diagnosis. This study aims to classify the severity of SA according to the apnea–hypopnea index (AHI) into normal, mild, and moderate-to-severe groups using oxygen saturation (SpO2), electroencephalogram (EEG), and electrocardiogram (ECG) signals. The proposed deep neural network (DNN)-bidirectional long short-term memory (DNN-BiLSTM) framework addresses the issue of low detection accuracy in analysis. The DNN-BiLSTM approach employs features extracted from a multiscale dilation attention 1D convolutional neural network (MSDA-1DCNN) as input for detection and classification purposes. The MSDA-1DCNN network extracts deep features from processed SpO2, EEG, and ECG signals. The developed firefly combined electric fish optimization (FCEFO) algorithm improves performance by optimizing the hidden neuron count of the DNN and the learning rate of the BiLSTM framework. The performance measures proved the effectiveness of the model over conventional machine and deep learning approaches. With the integration of deep features, the proposed DNN-BiLSTM model provides enhanced performance in terms of accuracy and precision. Thus, the proposed approach is progressive and can be used for medical diagnosis. © The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature 2024.

关键词： Electrocardiograms

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Survey on Video Security:Examining Threats,Challenges,and Future Trends

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computers, Materials & Continua 2024年第9期80卷 3591-3635页

作者： Ali Asghar Amna Shifa Mamoona Naveed Asghar Department of Artificial Intelligence Faculty of ComputingThe Islamia University of BahawalpurBahawalpur63100Pakistan School of Computer Science College of Science and EngineeringUniversity of GalwayGalwayH91 TK33Ireland

Videos represent the most prevailing form of digital media for communication,information dissemination,and ***,theirwidespread use has increased the risks of unauthorised access andmanipulation,posing significant *** response,various protection approaches have been developed to secure,authenticate,and ensure the integrity of digital *** study provides a comprehensive survey of the challenges associated with maintaining the confidentiality,integrity,and availability of video content,and examining how it can be *** then investigates current developments in the field of video security by exploring two critical research ***,it examine the techniques used by adversaries to compromise video data and evaluate their *** these attack methodologies is crucial for developing effective defense ***,it explores the various security approaches that can be employed to protect video data,enhancing its transparency,integrity,and *** compares the effectiveness of these approaches across different use cases,including surveillance,video on demand(VoD),and medical videos related to disease ***,it identifies potential research opportunities to enhance video data protection in response to the evolving threat *** this investigation,this study aims to contribute to the ongoing efforts in securing video data,providing insights that are vital for researchers,practitioners,and policymakers dedicated to enhancing the safety and reliability of video content in our digital world.

关键词： Attacks threats security services video manipulation video security

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Influence of the Control Points Position on the Accuracy of Heat Transfer Coefficient Selection

Acta Physica Polonica A

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Acta Physica Polonica A 2024年第6期146卷 794-797页

作者： Dyja, R. Gawronska, E. Zych, M. Czestochowa University of Technology Faculty of Computer Science and Artificial Intelligence Dqbrowskiego 71 Czestochowa42-201 Poland

The presented paper focuses on investigating the influence of the control points position on the accuracy of the heat transfer coefficient selection. In the presented study, the main focus is on determining the heat transfer coefficient in a layer separating two bodies. Such a situation occurs, for example, in solidification problems where a cast is held inside a mold and heat transfer takes place from the cast to the mold. The authors use swarm intelligence algorithms to the task of determining the heat transfer coefficient. In the presented study, two different swarm intelligence algorithms are employed, i.e., artificial bee colony and ant colony optimization. The numerical model is based on the authors' own implementation of the transient heat transfer solver that uses the finite element method to solve the appropriate differential equation. The study presents the results of the selection of the heat transfer coefficient in the computational domain of one quarter of a square casting inside a square mold. Both swarm intelligence algorithms were run for sets of 10, 15, and 20 individuals with 2 and 6 iterations. The study also takes into account possible inaccuracies in reference temperatures in the form of 1%, 2%, and 5% noise. For both algorithms, three different sets of control points were used: one with points directly on the contact boundaries and two sets with increasing distance from the boundaries. The results obtained in this work show that the location of control points has an impact on the quality of the results obtained in the coefficient selection. © 2024 Polska Akademia Nauk. All rights reserved.

关键词： ant colony optimization artificial bee colony heat transfer inverse problems

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Impact of asymptomatic infected individuals on epidemic transmission dynamics in multiplex networks with partial coupling

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Chinese Physics B 2024年第10期33卷 80-87页

作者： Xin Hu Jiaxing Chen Chengyi Xia School of Control Science and Engineering Tiangong UniversityTianjin 300387China School of Computer Science and Engineering Tianjin University of TechnologyTianjin 300384China School of Artificial Intelligence Tiangong UniversityTianjin 300387China

The theory of network science has attracted great interest of many researchers in the realm of biomathematics and public health,and numerous valuable epidemic models have been *** previous studies,it is common to set up a one-to-one correspondence between the nodes of a multi-layer network,ignoring the more complex situations in *** the present work,we explore this situation by setting up a partially coupled model of a two-layer network and investigating the impact of asymptomatic infected individuals on *** propose a self-discovery mechanism for asymptomatic infected individuals,taking into account situations such as nucleic acid testing in the community and individuals performing self-antigen testing during the *** these factors together,through the microscopic Markov chain approach(MMCA)and extensive Monte Carlo(MC)numerical simulations,we find that the greater the coupling between the networks,the more information dissemination is *** order to control the epidemics,more asymptomatic infected individuals should be made aware of their *** adoption of nucleic acid testing and individual adoption of antigenic self-testing can help to contain epidemic ***,the epidemic threshold of the proposed model is derived,and then miscellaneous factors affecting the epidemic threshold are also *** results are conducive to devising the prevention and control policies of pandemics.

关键词： asymptomatic infected individuals multi-layer networks partial interdependence

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Novel Steganalysis Method for Stego-Images Directly Constructed from Color Images Based on Their Quantum Noise

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Journal of Shanghai Jiaotong University (science) 2024年 1-11页

作者： Abdeltawwab, Mohammed R. Taie, Shereen A. El-Bahnasawy, Hany H. Eissa, Amir Department of Basic Science Faculty of Computers and Artificial Intelligence Fayoum University El Fayoum63514 Egypt Department of Computer Science Faculty of Computers and Artificial Intelligence Fayoum University El Fayoum63514 Egypt Department of Physics Faculty of Science Al-Azhar University Nasr City Cairo11884 Egypt

Steganography and steganalysis are two different sides of the same coin. Both are just as important as the other. Image steganography is considered one of the most promising secure data transmission methods because it hides the data in an image file. In contrast, steganalysis tries to attack steganography and retrieve the hidden data that can be a secret message. Many robust and powerful image steganography methods have been presented in the literature. One of these methods is referred to as steganography without embedding (SWEM). It is considered more secure because it adopts the concept of data transmission without embedding or concealing it in the file. Like any new technology, image steganography may have a negative impact and can be misused. There is no steganalysis method for distinguishing and attacking the image file that has been constructed by the SWEM method. To overcome the aforementioned issues, the main contribution of this paper is to propose a new image steganalysis method based on the fast Fourier transform (FFT). The proposed method starts by examining a suspected image using FFT. Then, by calculating the ratio of different parts of the resulting FFT spectrum and comparing the result with a threshold value, we can decide whether the suspected image has been constructed by the SWEM or not. The proposed method overcomes the other existing methods that fail to attack this strong image steganography method. To check the generalizability of the proposed method as a method for distinguishing and attacking the image file that is created by SWEM, we constructed a data set of images, used the SWEM algorithm to construct images with concealed secret messages without embedding, and applied the proposed method and other two common existing steganalysis methods. The results show that the proposed method is able to distinguish these images with a high recognition rate compared to the results of the two methods. (Figure presented.) © Shanghai Jiao Tong University

关键词： Quantum noise

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Advanced Machine Learning Approaches for Breast Cancer Detection with Neutrosophic Sets

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Neutrosophic Sets and Systems 2025年 81卷 273-284页

作者： Elbehiery, Hussam Fathi, Hanaa Eassa, Mohamed Abdelhafeez, Ahmed Abdellah, Mohamed Refaat Mahmoud, Hadeer Computer Science Department Faculty of Information System and Computer Science October 6 University Giza12585 Egypt College of Information Technology Amman Arab University Amman Jordan Applied Science Research Center Applied Science Private University Amman Jordan The Department of Computer Science College of Information Technology Misr University for Science and Technology Cairo Egypt Department of Artificial Intelligence Faculty of Computers and Artificial Intelligence Modern University for Technology & Information Cairo Egypt

Breast Cancer (BC) remains a significant health challenge for women and is one of the leading causes of mortality worldwide. Accurate diagnosis is critical for successful therapy and increased survival rates. Recent advances in medical imaging and computational technologies have enabled more precise methods of detecting and evaluating breast cancer. Accurate analysis and diagnosis utilizing medical imaging have developed as essential research topics, providing important help in clinical decision-making for various illnesses, including breast cancer. Machine learning (ML) can accurately predict breast cancer. But the breast cancer data has vague and uncertainty information. So, the neutrosophic sets (NSs) are used in this study to deal with uncertainty data. We convert the original dataset into neutrosophic data with three components such as truth, indeterminacy, and falsity values. Then we applied four ML models with N-data such as logistic regression, gradient boosting (GB), k-nearest neighbor (KNN), and support vector machines (SVM), to improve diagnostic accuracy. Then we compared the ML models with and without using N-data. The results show the logistic regression has higher accuracy with 98.6% with the N-data and 95.80% without N-data. So, the NSs can improve the accuracy of ML models. © 2025, University of New Mexico. All rights reserved.

关键词： Lung cancer

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Analysis and Classification of Fake News Using Sequential Pattern Mining

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Big Data Mining and Analytics 2024年第3期7卷 942-963页

作者： M.Zohaib Nawaz M.Saqib Nawaz Philippe Fournier-Viger Yulin He College of Computer Science and Software Engineering Shenzhen UniversityShenzhen 518060 Guangdong Laboratory of Artificial Intelligence and Digital Economy(SZ) Shenzhen 518107China Department of Computer Science Faculty of Computing and Information TechnologyUnivesity of SargodhaSargodha 40100Pakistan

Disinformation,often known as fake news,is a major issue that has received a lot of attention *** researchers have proposed effective means of detecting and addressing *** machine and deep learning based methodologies for classification/detection of fake news are content-based,network(propagation)based,or multimodal methods that combine both textual and visual *** introduce here a framework,called FNACSPM,based on sequential pattern mining(SPM),for fake news analysis and *** this framework,six publicly available datasets,containing a diverse range of fake and real news,and their combination,are first transformed into a proper ***,algorithms for SPM are applied to the transformed datasets to extract frequent patterns(and rules)of words,phrases,or linguistic *** obtained patterns capture distinctive characteristics associated with fake or real news content,providing valuable insights into the underlying structures and commonalities of ***,the discovered frequent patterns are used as features for fake news *** framework is evaluated with eight classifiers,and their performance is assessed with various *** experiments were performed and obtained results show that FNACSPM outperformed other state-of-the-art approaches for fake news classification,and that it expedites the classification task with high accuracy.

关键词： disinformation fake news sequential pattern mining(SPM) frequent patterns classification

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A Lightweight Multidendritic Pyramidal Neuron Model With Neural Plasticity on Image Recognition

IEEE Transactions on Artificial Intelligence

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IEEE Transactions on artificial intelligence 2024年第9期5卷 4415-4427页

作者： Zhang, Yu Cai, Pengxing Sun, Yanan Zhang, Zhiming Lei, Zhenyu Gao, Shangce University of Toyama Faculty of Engineering Toyama-shi930-8555 Japan Sichuan University Department of Artificial Intelligence College of Computer Science Chengdu610065 China

Simulating the method of neurons in the human brain that process signals is crucial for constructing a neural network with biological interpretability. However, existing deep neural networks simplify the function of a single neuron without considering dendritic plasticity. In this article, we present a multidendrite pyramidal neuron model (MDPN) for image classification, which mimics the multilevel dendritic structure of a nerve cell. Unlike the traditional feedforward network model, MDPN discards premature linear summation integration and employs a nonlinear dendritic computation such that improving the neuroplasticity. To model a lightweight and effective classification system, we emphasized the importance of single neuron and redefined the function of each subcomponent. Experimental results verify the effectiveness and robustness of our proposed MDPN in classifying 16 standardized image datasets with different characteristics. Compared to other state-of-the-art and well-known networks, MDPN is superior in terms of classifica-tion accuracy. © 2020 IEEE.

关键词： computer architecture

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