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A fault-tolerant scheduling strategy through proactive and clustering techniques for scientific workflows in cloud computing

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Soft Computing 2025年第2期29卷 755-781页

作者： Farhood, Suha Mubdir Khorsand, Reihaneh Hussein, Nashwan Jasim Ramezanpour, Mohammadreza Branch Islamic Azad University Isfahan Iran Department of Programming Collage of Information Technology University of Babylon Hillah Iraq Department of Computer Engineering Mobarakeh Branch Islamic Azad University Mobarakeh Isfahan Iran

Cloud computing offers solutions for various scientific and business applications. Large-scale scientific applications, which are organized as scientific workflows, are carried out using cloud computing. However, the higher failure rates in cloud computing can be attributed to the numerous servers and components dealing with intense workloads. This study presents a fault-tolerant scheduling approach using proactive and clustering methods for scientific workflows in cloud computing. Initially, the task clustering issue is addressed by consolidating multiple short-duration tasks into a single job to improve the runtime performance of workflow executions. Subsequently, an automated workflow scheduling strategy is outlined with four key stages: monitoring, analysis, planning, and execution. During monitoring, clustered jobs and the capacities of available cloud resources are observed. In the analysis phase, the accuracy of failure prediction is enhanced by employing the Group Method of Data Handling (GMDH) neural network prior to any faults or failures. The planning stage introduces a novel hybrid multi-objective algorithm, MOPSO-aSA, based on MOPSO and adaptive simulated annealing (SA), to streamline workflow scheduling in error-prone execution environments. Moreover, the reliability of application execution is maintained through re-clustering and migration techniques following any faults or failures. Finally, based on the experimental findings, it is evident that the proposed strategy surpasses other methods in terms of makespan, total cost, energy consumption, and failure rate. © The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2025.

关键词： Simulated annealing

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The Problem of Morphological Synthesis and Methods, Algorithms for Handling Errors in Word Forms 3

The Problem of Morphological Synthesis and Methods, Algorith...

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3rd IEEE International Conference on Problems of Informatics, Electronics and Radio Engineering, PIERE 2024

作者： Kochkonbaeva, Buazhar O. Egemberdieva, Julduz S. Satybaev, Abdugany D. Osh State University Department of Information Technology and Programming Osh Kyrgyzstan Kyrgyz-Uzbek International University after named B. Sydykov Department of Information Technology Osh Kyrgyzstan Osh Technological University after named academican M.M. Adyshev Department of Information Technology and Management Osh Kyrgyzstan

ISBN: (纸本)9798331516321

Morphological synthesis is a key aspect of natural language processing that focuses on the generation of word forms from their base components - roots, stems, prefixes, affixes, and inflectional morphemes. It plays a crucial role in understanding and generating various grammatical forms of words in different languages. This process is essential for tasks such as machine translation, speech synthesis, text generation, and information retrieval. In agglutinative languages, words are formed by stringing together affixes that each represent a single grammatical function. Each morpheme retains its distinct meaning and function, making synthesis relatively predictable. In this article, using Kyrgyz as an example, we have examined the rules governing the attachment of affixes to words and assessed whether these affixes are correctly attached or if there are issues with morphological synthesis. This is, of course, a very topical issue. Because the first step in the problems of artificial intelligence is to pay attention to the features and ways of language creation. © 2024 IEEE.

关键词： Natural language processing systems

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Renal Tumor Classification and Detection Based on Artificial Intelligence 3

Renal Tumor Classification and Detection Based on Artificial...

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3rd International Conference on Communications, information System, and Data Science, CISDS 2024

作者： Al-Taie, Raflaa Hilmi Hussein, Nashwan J. Physiology and Organ Functions Department College of Medicine University of Babylon Iraq Department of Electrical Power Engineering AL-Hussain University College Department of Programming Collage of Information Technology University of Babylon Iraq

ISBN: (数字)9781510688322

ISBN: (纸本)9781510688315

The kidney is a crucial organ in the human body, co-operating billions of pipelines to cleanse the body's water. Kidney failure, renal tumor happens when cells divide uncontrollably and form an aberrant collection of cells surrounding or within the kidney. This cell type has the ability to disrupt regular kidney activity and destroy healthy cells. The prompt diagnosis of renal tumor is critical since they can be fatal if left untreated. Because it is dependent on the skill of the person analyzing the images, the traditional approach of manually checking the MR image may not be very accurate. The study concentrated on the diagnosis of normal and normal renal tumor. To enhance accuracy and expedite diagnosis, using publicly accessible individual records, the present research used methodologies for machine learning, involving support vector machine learning (SVM), adaptive optimization (AO), as well as gradient enhancement (GE). It uses an approach for making the dataset reduced multidimensional. Image feature extraction is a data preprocessing method that minimizes the time required to train the proposed algorithm. The accuracy rates of the algorithms for diagnosing normal and up normal are reported to be 88.8 % for GB, 83.8 % for ADA, 86.1 % for SVM and 93.3% for KNN and for up normal diagnosis tumor are reported to be 55.3 % for GB, 55.4 % for SVM, 55.3 % for ADA and 51.0% for KNN. © 2025 SPIE.

关键词： Image enhancement

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A zero-watermarking algorithm for use in RGB and monochrome images 5

A zero-watermarking algorithm for use in RGB and monochrome ...

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5th IEEE KhPI Week on Advanced technology, KhPIWeek 2024

作者： Poddubnyi, Vadym Gvozdov, Roman Sievierinov, Oleksandr Sukhoteplyi, Vladyslav Bulba, Serhii Lysytsia, Dmytro Department of of information technology security Kharkiv National University of Radio Electronic Kharkiv Ukraine Department of Computer Engineering and Programming Ivan Kozhedub Kharkiv National Air Force University Kharkiv Ukraine Department of Computer Engineering and Programming National Technical University "Kharkiv Polytechnic Institute" Kharkiv Ukraine

ISBN: (纸本)9798350384499

the article proposes a method of zero-watermarking for colored RGB bitmap images. This algorithm can be used to protect property rights to multimedia resources in which it is impossible to use ordinary watermarks due to the impossibility of changing the image (such as medical data where high accuracy is required and the appearance of artifacts is unacceptable) or due to the presence of a higher distortion of the container. This algorithm is based on DWT, SVD and operations in the field by modulo 256. The software implementation of the algorithm was developed (using the Accord and *** libraries to implement DWT, SVD). The software implementation of the algorithm was tested. This algorithm has advantages in the form of normalization of the average error, but it needs further research to improve its performance. ©2024 IEEE.

关键词： Image reconstruction

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Classification Algorithms with a Complex Structure in Pattern Recognition 25

Classification Algorithms with a Complex Structure in Patter...

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25th IEEE International Conference of Young Professionals in Electron Devices and Materials, EDM 2024

作者： Nishanov, Akram Kh. Allamov, Oybek Rakhmanov, Askar T. Yusupova, Janar Ruzibaev, Ortik B. Department of Systematic and Practical Programming Tashkent University of Information Technologies named after Muhammad al-Khwarizmi Tashkent Uzbekistan Department of Software Engineering Urgench Branch of Tashkent University of Information Technology named after Muhammad al Khwarizmi Urgench Uzbekistan Department of Systematic and Practical Programming Tashkent University of Information Technologies named after Muhammad al-Khwarizmi Tashkent Uzbekistan Faculty of Software Engineering Tashkent University of Information Technologies named after Muhammad al-Khwarizmi Tashkent Uzbekistan

ISBN: (纸本)9798350389234

The article considers in the intellectual processing of information, moving from a large unit of measurement to a smaller, more important unit of measurement, selecting sets of informative symbols and classifying symbols, developing modern recognition systems that help specialists in the field, as well as evaluating the states of objects to be classified, developing a classification algorithm for multi-class cases of educational samples. The development of a multi-class algorithm for symbol classification serves to develop the theory of symbol recognition. It deals with the construction of a decision rule construction algorithm. Its main purpose is to determine which of the given classes the recognized object belongs to. The complexity or simplicity of the proposed algorithm depends on the number of classes participating in the study sample. If the number of classes is two, then the formulation of decision rules is simple. That is why the classification algorithms proposed by most authors are solved for cases where the number of classes is three or more, the decisive rule in the matter of classification of objects is solved by making two classes. The construction of a multi-class decision rule for classification, which takes into account all classes at the same time, is a complex and urgent issue. In the article, in the classification of objects, a multi-level complex structured decision rule was developed based on special inequalities, and two-, three- and multi-class classification algorithms were developed. © 2024 IEEE.

关键词： Consensus algorithm

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Hybrid Grey Wolf and Dipper Throated Optimization in Network Intrusion Detection Systems

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Computers, Materials & Continua 2023年第2期74卷 2695-2709页

作者： Reem Alkanhel Doaa Sami Khafaga El-Sayed M.El-kenawy Abdelaziz A.Abdelhamid Abdelhameed Ibrahim Rashid Amin Mostafa Abotaleb B.M.El-den Department of Information Technology College of Computer and Information SciencesPrincess Nourah bint Abdulrahman UniversityP.O.Box 84428Riyadh11671Saudi Arabia Department of Computer Sciences College of Computer and Information SciencesPrincess Nourah bint Abdulrahman UniversityP.O.Box 84428Riyadh11671Saudi Arabia Department of Communications and Electronics Delta Higher Institute of Engineering and TechnologyMansoura35111Egypt Department of Computer Science Faculty of Computer and Information SciencesAin Shams UniversityCairo11566Egypt Department of Computer Science College of Computing and Information TechnologyShaqra University11961Saudi Arabia Computer Engineering and Control Systems Department Faculty of EngineeringMansoura UniversityMansoura35516Egypt Department of Computer Science University of Engineering and TechnologyTaxilaPakistan Department of System Programming South Ural State UniversityChelyabinsk454080Russia

The Internet of Things(IoT)is a modern approach that enables connection with a wide variety of devices *** to the resource constraints and open nature of IoT nodes,the routing protocol for low power and lossy(RPL)networks may be vulnerable to several routing ***’s why a network intrusion detection system(NIDS)is needed to guard against routing assaults on RPL-based IoT *** imbalance between the false and valid attacks in the training set degrades the performance of machine learning employed to detect network ***,we propose in this paper a novel approach to balance the dataset classes based on metaheuristic optimization applied to locality-sensitive hashing and synthetic minority oversampling technique(LSH-SMOTE).The proposed optimization approach is based on a new hybrid between the grey wolf and dipper throated optimization *** prove the effectiveness of the proposed approach,a set of experiments were conducted to evaluate the performance of NIDS for three cases,namely,detection without dataset balancing,detection with SMOTE balancing,and detection with the proposed optimized LSHSOMTE *** results showed that the proposed approach outperforms the other approaches and could boost the detection *** addition,a statistical analysis is performed to study the significance and stability of the proposed *** conducted experiments include seven different types of attack cases in the RPL-NIDS17 *** on the 2696 CMC,2023,vol.74,no.2 proposed approach,the achieved accuracy is(98.1%),sensitivity is(97.8%),and specificity is(98.8%).

关键词： Metaheuristics grey wolf optimization dipper throated optimization dataset balancing locality sensitive hashing SMOTE

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Dipper Throated Optimization for Detecting Black-Hole Attacks inMANETs

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Computers, Materials & Continua 2023年第1期74卷 1905-1921页

作者： Reem Alkanhel El-Sayed M.El-kenawy Abdelaziz A.Abdelhamid Abdelhameed Ibrahim Mostafa Abotaleb Doaa Sami Khafaga Department of Information Technology College of Computer and Information SciencesPrincess Nourah bint Abdulrahman UniversityP.O.Box 84428Riyadh11671Saudi Arabia Department of Communications and Electronics Delta Higher Institute of Engineering and TechnologyMansoura35111Egypt Faculty of Artificial Intelligence Delta University for Science and TechnologyMansoura35712Egypt Department of Computer Science Faculty of Computer and Information SciencesAin Shams UniversityCairo11566Egypt Department of Computer Science College of Computing and Information TechnologyShaqra University11961Saudi Arabia Computer Engineering and Control Systems Department Faculty of EngineeringMansoura UniversityMansoura35516Egypt Department of System Programming South Ural State UniversityChelyabinsk454080Russia Department of Computer Sciences College of Computer and Information SciencesPrincess Nourah Bint Abdulrahman UniversityP.O.Box 84428Riyadh11671Saudi Arabia

In terms of security and privacy,mobile ad-hoc network(MANET)continues to be in demand for additional debate and *** more MANET applications become data-oriented,implementing a secure and reliable data transfer protocol becomes a major concern in the ***,MANET’s lack of infrastructure,unpredictable topology,and restricted resources,as well as the lack of a previously permitted trust relationship among connected nodes,contribute to the attack detection burden.A novel detection approach is presented in this paper to classify passive and active black-hole *** proposed approach is based on the dipper throated optimization(DTO)algorithm,which presents a plausible path out of multiple paths for statistics transmission to boost MANETs’quality of service.A group of selected packet features will then be weighed by the DTO-based multi-layer perceptron(DTO-MLP),and these features are collected from nodes using the Low Energy Adaptive Clustering Hierarchical(LEACH)clustering *** is a powerful classifier and the DTO weight optimization method has a significant impact on improving the classification process by strengthening the weights of key features while suppressing the weights ofminor *** hybridmethod is primarily designed to combat active black-hole *** the LEACH clustering phase,however,can also detect passive black-hole *** effect of mobility variation on detection error and routing overhead is explored and evaluated using the suggested *** diverse mobility situations,the results demonstrate up to 97%detection accuracy and faster execution ***,the suggested approach uses an adjustable threshold value to make a correct conclusion regarding whether a node is malicious or benign.

关键词： Black-hole attack mobile ad-hoc network optimization dipper throated optimization

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Evolutionary Algorithm for Calculating the Optimal Temperature Regime of the Catalytic Process 5

Evolutionary Algorithm for Calculating the Optimal Temperatu...

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5th International Conference on Control Systems, Mathematical Modeling, Automation and Energy Efficiency, SUMMA 2023

作者： Antipina, Evgenia Mustafina, Svetlana Antipin, Andrey Ufa University of Science and Technology Department of Scientific Research Ufa Russia Ufa University of Science and Technology Faculty of Mathematics and Information Technologies Ufa Russia Sterlitamak Branch of Ufa University of Science and Technology Department of Applied Informatics and Programming Sterlitamak Russia

ISBN: (纸本)9798350309126

The paper formulates an algorithm for searching the optimal temperature regime of the catalytic process in an ideal mixing reactor. The problem of optimal control of the catalytic process is formulated in a general form. To find its numerical solution, an algorithm based on the method of differential evolution is described. The algorithm allows to obtain an approximate solution of the optimization problem in a time acceptable from the practical point of view. A computational experiment for the dimerization process of alphamethylstyrene is carried out. The optimal temperature profile for achieving the maximum yield of reaction products - linear dimers - is obtained. © 2023 IEEE.

关键词： Evolutionary algorithms

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Network Intrusion Detection Based on Feature Selection and Hybrid Metaheuristic Optimization

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Computers, Materials & Continua 2023年第2期74卷 2677-2693页

作者： Reem Alkanhel El-Sayed M.El-kenawy Abdelaziz A.Abdelhamid Abdelhameed Ibrahim Manal Abdullah Alohali Mostafa Abotaleb Doaa Sami Khafaga Department of Information Technology College of Computer and Information SciencesPrincess Nourah bint Abdulrahman UniversityP.O.Box 84428Riyadh11671Saudi Arabia Department of Communications and Electronics Delta Higher Institute of Engineering and TechnologyMansoura35111Egypt Department of Computer Science Faculty of Computer and Information SciencesAin Shams UniversityCairo11566Egypt Department of Computer Science College of Computing and Information TechnologyShaqra University11961Saudi Arabia Computer Engineering and Control Systems Department Faculty of EngineeringMansoura UniversityMansoura35516Egypt Information Systems Department College of Computer and Information SciencesPrincess Nourah bint Abdulrahman UniversityP.O.Box 84428Riyadh11671Saudi Arabia Department of System Programming South Ural State UniversityChelyabinsk454080Russia Department of Computer Sciences College of Computer and Information SciencesPrincess Nourah bint Abdulrahman UniversityP.O.Box 84428Riyadh11671Saudi Arabia

Applications of internet-of-things(IoT)are increasingly being used in many facets of our daily life,which results in an enormous volume of *** computing and fog computing,two of the most common technologies used in IoT applications,have led to major security *** are on the rise as a result of the usage of these technologies since present security measures are *** artificial intelligence(AI)based security solutions,such as intrusion detection systems(IDS),have been proposed in recent *** technologies that require data preprocessing and machine learning algorithm-performance augmentation require the use of feature selection(FS)techniques to increase classification accuracy by minimizing the number of features *** the other hand,metaheuristic optimization algorithms have been widely used in feature selection in recent *** this paper,we proposed a hybrid optimization algorithm for feature selection in *** proposed algorithm is based on grey wolf(GW),and dipper throated optimization(DTO)algorithms and is referred to as *** proposed algorithm has a better balance between the exploration and exploitation steps of the optimization process and thus could achieve better *** the employed IoT-IDS dataset,the performance of the proposed GWDTO algorithm was assessed using a set of evaluation metrics and compared to other optimization approaches in 2678 CMC,2023,vol.74,no.2 the literature to validate its *** addition,a statistical analysis is performed to assess the stability and effectiveness of the proposed *** results confirmed the superiority of the proposed approach in boosting the classification accuracy of the intrusion in IoT-based networks.

关键词： Feature selection grey wolf optimization dipper throated optimization intrusion detection internet-of-things(IoT)

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A Case Study on Data Protection for a Cloud- and AI-Based Homecare Medical Device 43rd

A Case Study on Data Protection for a Cloud- and AI-Based...

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43rd International Conference on Application and Theory of Petri Nets and Concurrency , Petri Nets 2022

作者： Bende, Philipp Vovk, Olga Caraveo, David Pechmann, Ludwig Leucker, Martin Institute for Software Engineering and Programming Languages University of Lübeck Lübeck Germany School of Information Technologies Department of Health Technologies Tallinn University of Technology Tallinn Estonia UniTransferKlinik Lübeck GmbH Lübeck Germany

ISBN: (纸本)9783662681909

To improve the treatment of many diseases, continuous monitoring of the patient at home with the ability of doctors to interact with individual cases demands an increasing number of medical devices connected to the cloud. To support the doctor’s duties, such devices may benefit from AI-based diagnosis routines. In order for such devices to be approved and placed on the market, they need to comply with various legal, regulatory, economic, and social requirements. An integral part of these requirements is the protection of the patients’ data. In this paper, based on a current use case, we describe a workflow on how to identify risks and address their mitigations. To this end, we recall the relevant legal, regulatory, economic, and social data protection requirements. We pursue our findings on a Homecare OCT device that is intended to be used by elderly patients on a daily basis, by taking images of their eyes and sending them for further analysis to a cloud- and AI-based system. The patient’s ophthalmologist gets notified for further dedicated treatment depending on the result. We then compare the Homecare OCT device with a clinical OCT System in regard to various risks to patient data which arise when a medical system is used outside of a secure hospital environment. To perform the risk management, we describe (i) the architecture of both systems, (ii) analyze their data flow, (iii) discuss several vectors of attack, (iv) propose ways to mitigate the risks, and (v) discuss the handling of potential data breaches. © 2024, The Author(s), under exclusive license to Springer-Verlag GmbH, DE, part of Springer Nature.

关键词： Decision support systems

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