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A Method based on Multi-agent Systems and Passive Replication Technique for Predicting Failures in Cloud Computing

Recent Advances in computer science and Communications 2023年第1期16卷 18-32页

作者： Hioual, Ouided Hioual, Ouassila Hemam, Sofiane Mounine Maifi, Lyes Mathematics and Computer Science Department Science and Technology Abbes Laghrour University Khenchela Algeria Mathematics and Computer Science Department Science and Technology Abbes Laghrour University Lire laboratory of Constantine 2 Khenchela Algeria Algiers Algeria

Background: Cloud computing refers to the computing capacities of remote computers, where the user has considerable computing power without having to own power units. The probability of failures, which can occur during execution, increases in the number of nodes. Since failures cannot be completely avoided, one solution is to use failure tolerance mechanisms. Predicting failures has become a major task for engineers and software developers, as failure increases resource usage costs. Objectives and Methods: This paper presents a hybrid method of predicting failures in a cloudcomputing environment based on the passive replication technique and multi-agent systems. It detects failures, improves the average response time and minimizes lost time. This method makes it possible to efficiently and transparently guarantee the continuity of cloud computing services in the presence of failures. Results: The results show that the proposed method performs well in the presence of failures, improves the response time and minimizes the additional costs caused by the failures. Conclusion: This paper proposes a hybrid method of predicting failures in cloud-computing based on the passive replication technique and multi-agent systems to detect failures and minimize lost time. The replication technique works by duplicating some system components, which are deployed simultaneously across different resources. This technique aims to make the system robust, increase availability and guarantee the execution of jobs. In addition, it is suitable for long-running tasks. © 2023 Bentham science Publishers.

关键词： Multi agent systems

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Combined deep convolutional neural networks for abnormality classification in wireless capsule endoscopy images

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Multimedia Tools and Applications 2025年 1-29页

作者： Garbaz, Anass Lafraxo, Samira Charfi, Said El Ansari, Mohamed Koutti, Lahcen Salihoun, Mouna LabSIV Department of Computer Science Faculty of Sciences Ibn Zohr University Agadir Morocco Informatics and Applications Laboratory Department of Computer Science Faculty of Sciences My Ismail University Meknes Morocco Faculty of Medicine and Pharmacy of Rabat Mohammed V University Rabat Morocco

A digestive disease is a condition that affects the gastrointestinal (GI) tract, ranging from mild to severe. Common issues include heartburn, cancer, irritable bowel syndrome, and lactose intolerance. Additionally, other intestinal concerns such as bleeding, polyps, celiac disease, Crohn’s disease, ulcers, diverticulitis, small bowel syndrome, and intestinal ischemia represent further digestive illnesses. Wireless capsule endoscopy (WCE) is a device designed for endoscopic exploration that employs a compact camera to capture images of the GI tract as it passes through. WCE significantly aids in the identification of GI tract disorders. However, due to the capsule’s rapid transit and the potential for blurry, ambiguous images, abnormalities in certain regions of the intestine may be overlooked. Moreover, frames can become hazy if obstructed by food or stool waste. In this research, we propose a method for detecting abnormalities in WCE images to address these limitations. To achieve high classification performance through the extraction of both high-level and low-level features, we employ a deep CNN that integrates the DenseNet121 model with a residual block. Experimental studies demonstrate that our proposed method outperforms state-of-the-art techniques on two publicly available WCE datasets, one focused on bleeding and the other on various abnormalities. The accuracy of the proposed architecture is 96.96% for the Kvasir-Capsule Dataset and 99.50% for the MICCAI 2017 dataset. © The Author(s), under exclusive licence to Springer science+Business Media, LLC, part of Springer Nature 2025.

关键词： Endoscopy

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Secure Image Retrieval in an Untrusted Cloud Environment Using Homomorphic and Attribute-Based Encryption 3rd

Secure Image Retrieval in an Untrusted Cloud Environment Usi...

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3rd International Conference on Artificial-Business Analytics, Quantum and Machine Learning: Trends, Perspectives, and Prospects, Com-IT-Con 2023

作者： Prakash, Vijay Nanda, Nandini Tiwari, Akriti Dhawan, Budh Singh, Pardeep Garg, Lalit School of Computer Science University of Petroleum and Energy Studies Dehradun India Department of Computer Science Graphics Era Hill University Dehradun India Faculty of ICT University of Malta Msida Malta

ISBN: (纸本)9789819748594

The increasing popularity of cloud computing has led to a growing demand for efficient and secure data storage and retrieval. This is particularly challenging when dealing with image data, which often contains sensitive information that must be protected to ensure privacy and confidentiality. This paper proposes a framework for secure image retrieval in an untrusted cloud environment. A combination of cryptographic techniques and access control policies has been used to address the challenges of secure image retrieval. Specifically, homomorphic encryption enables the secure computation of image features without compromising the privacy of the underlying data. Additionally, we incorporate a novel access control mechanism based on attribute-based encryption (ABE), which allows for fine-grained access control based on user attributes such as job title or department. The proposed framework has a wide range of applications in the fields such as health care, finance, and government, where the need for secure storage and retrieval of image data is critical. By leveraging homomorphic encryption and ABE, the proposed framework provides strong protection against unauthorized access while maintaining high levels of flexibility and scalability. The potential consequences of unauthorized access to sensitive image data can be severe, ranging from personal privacy violations to financial fraud and identity theft. To evaluate the performance of our system, we conduct extensive simulations and achieve high levels of security and efficiency in retrieving image data from an untrusted cloud environment. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Differential privacy

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A survey on text-line segmentation process in historical Arab manuscripts 6

A survey on text-line segmentation process in historical Ara...

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6th International Hybrid Conference on Informatics and Applied Mathematics, IAM 2023

作者： Djaghbellou, Soumia Abdelouahab, Attia Abderraouf, Bouziane Department of Computer Science University of Bordj Bou Arreridj Algeria MSE Laboratory Department of Computer Science University of Bordj Bou Arreridj Algeria

The segmentation process entails dividing or decomposing the entire document image into segments or lines. This technique serves as a fundamental step in developing any writing or optical character recognition system. However, numerous existing segmentation schemes encounter challenges when dealing with specific script styles, like ancient or historical Arabic writing found in ancient manuscripts. which possesses unique characteristics. These characteristics include inclined text lines, overlapping letters, diacritic marks, decorative elements, variable letter forms, and ligatures (combinations of two or more letters merged to form a single connected shape). Thus, in this paper we present a thorough survey of the field. The survey is composed of two segments. The first segment provides a concise overview of the historical Arabic documents. The second, which serves as the primary segment, focuses on the crucial step of handwritten document recognition, specifically segmentation. A detailed and systematic overview of the various approaches to segmentation, including different levels, employed for extracting handwritten Arabic text-lines, is outlined. Subsequently, a literature study is conducted to review and analyze proposed works in this area. © 2023 Copyright for this paper by its authors.

关键词： Image segmentation

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Hybrid Flow Shop with Setup Times Scheduling Problem

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computer Systems science & Engineering 2023年第1期44卷 563-577页

作者： Mahdi Jemmali Lotfi Hidri Department of Computer Science and Information College of Science at ZulfiMajmaah UniversityMajmaah11952Saudi Arabia Mars Laboratory University of SousseSousseTunisia Department of Computer Science Higher Institute of Computer Science and Mathematics of MonastirMonastir UniversityMonastir5000Tunisia Industrial Engineering Department College of EngineeringKing Saud UniversityRiyadh11421Saudi Arabia

The two-stage hybridflow shop problem under setup times is addressed in this *** problem is *** the other hand,the studied problem is modeling different real-life applications especially in manufacturing and high *** this kind of problem requires the development of adapted *** this context,a metaheuristic using the genetic algorithm and three heuristics are proposed in this *** approximate solutions are using the optimal solution of the parallel machines under release and delivery ***,these solutions are iterative procedures focusing each time on a particular stage where a parallel machines problem is called to be *** general solution is then a concatenation of all the solutions in each *** addition,three lower bounds based on the relaxation method are *** lower bounds present a means to evaluate the efficiency of the developed algorithms throughout the measurement of the relative *** experimental result is discussed to evaluate the performance of the developed *** total,8960 instances are implemented and tested to show the results given by the proposed lower bounds and *** indicators are given to compare between *** results illustrated in this paper show the performance of the developed algorithms in terms of gap and running time.

关键词： Hybridﬂow shop genetic algorithm setup times heuristics lower bound

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A Novel Ensemble Learning Approach for Diabetes Prediction in Imbalanced Datasets 13

A Novel Ensemble Learning Approach for Diabetes Prediction i...

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13th International Conference on Research in ComputIng at Feminine, RIF 2024

作者： Boughareb, Djalila Bouteldja, Said Seridi, Hamid Department of Computer Science LabStic Laboratory University 8 May 1945 Guelma Algeria Department of Computer Science University of 8 May 1945 Guelma Algeria

The incapacity of the body to effectively make or use insulin results in diabetes, a chronic illness. Over time, this illness may cause harm to the kidneys, blood vessels, heart, eyes, nerves, and kidneys. Timely treatment is essential to stop the progression of diabetes and requires early detection. We provide a hybrid machine learning strategy in this work that predicts diabetes by combining two strong algorithms. XGBoost (eXtreme Gradient Boosting)-based voting classifier and bagging classifier are the two main components of our system. We evaluated our model using three distinct datasets: the Pima Indian diabetes dataset (PIDD), its extended version, and the Frankfurt Hospital Germany Diabetes Dataset (FHGDD). In comparison to individual algorithms (XGBoost, Bagging with Decision Tree) and other ensemble methods (Voting Classifier, HM-Bag Moov Voting Classifier, XGBoost+ Data feature stitching, and Soft), our experimental results show that our proposed approach achieved higher accuracy of 92.7%, precision of 97.1%, recall of 81.7%, and an F1 score of 88.7%%. Therefore, our results imply that the hybrid machine learning approach that has been suggested can be a dependable tool for the early diagnosis of diabetes, resulting in better patient outcomes and more prompt and efficient therapies. © 2024 Copyright for this paper by its authors.

关键词： Bagging Decision Tree Diabetes Prediction Ensemble Learning FHGDD Machine Learning Pima XGBoost

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Building Automatic Speech Recognition Systems for Moroccan Dialect: A Phoneme-Based Approach

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SN computer science 2024年第6期5卷 721页

作者： Ezzine, Abderrahim Laaidi, Naouar Zealouk, Ouissam Satori, Hassan Department of Computer Science and Mathematics Faculty of Sciences Dhar Mahraz Sidi Mohamed Ben Abbdallah University Laboratory of Computer Science Signals Automation and Cognition (LISAC)

Building efficient acoustic models for dialects is a major challenge in Automatic Speech Recognition (ASR) systems. In this paper, we investigate the Moroccan Fessi dialect speech recognition system based on phoneme modeling. We employed a combined approach, including the Hidden Markov Model (HMM) and the Gaussian Mixture Model (GMM). Also, the ASR dialect specificity was analysed, including phonemes nature and phonetic inventory. Our results show the best performance was found by using 3 HMM and 4 GMM configurations, achieving an accuracy of 97.33%. Additionally, we observed that the digits containing voiced pharyngeal phonemes, particularly the phoneme /ʕ/, achieved the highest recognition rate, while words containing the phoneme /s/ exhibited multiple substitutions. © The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd. 2024.

关键词： HMM-GMM In-house corpus Machine learning Moroccan dialect Phoneme modeling Speech recognition

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Neural Network-Based State Estimation for Nonlinear Systems With Denial-of-Service Attack Under Try-Once-Discard Protocol

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IEEE/CAA Journal of Automatica Sinica 2024年第10期11卷 2182-2184页

作者： Xueli Wang Shangwei Zhao Ming Yang Xin Wang Xiaoming Wu the Key Laboratory of Computing Power Network and Information Security Ministry of EducationShandong Computer Science CenterQilu University of Technology(Shandong Academy of Sciences)Jinan 250014 Shandong Provincial Key Laboratory of Computer Networks Shandong Fundamental Research Center for Computer ScienceJinan 250014China Department of Automation Shanghai Jiao Tong UniversityShanghai 200240China

Dear Editor,This letter deals with state estimation issues of discrete-time nonlinear systems subject to denial-of-service(DoS)attacks under the try-once-discard(TOD)*** specifically,to reduce the communication burden,a TOD protocol with novel update rules on protocol weights is designed for scheduling measurement *** addition,unknown nonlinear functions vulnerable to DoS attacks are considered due to the openness and vulnerability of the network.

关键词： Service DoS service

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3D Facial Reconstruction Based on a Single Image Using CNN 3rd

3D Facial Reconstruction Based on a Single Image Using CNN

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3rd International Conference on Intelligent Systems and Pattern Recognition, ISPR 2023

作者： Agaba, Ramzi Malah, Mehdi Abbas, Fayçal Babahenini, Mohamed Chaouki ReLaCS2 Laboratory Computer Science Department University Larbi Ben Mhidi Oum El Bouaghi Algeria ICOSI Laboratory Computer Science Department University of Abbes Laghrour Khenchela Khenchela Algeria LESIA Laboratory Computer Science Department University of Abbes Laghrour Khenchela Khenchela Algeria LESIA Laboratory Computer Science Department University of Mohamed Khider Biskra Biskra Algeria

ISBN: (纸本)9783031463341

In recent years, 3D facial reconstruction marked its presence in several areas, such as biometric security, computer vision, image synthesis, and video games. Many facial recognition applications require accurate 3D reconstruction;however, this task is complex and requires many calculations. This paper presents a new method based on CNN, which offers an automatic solution for the 3D reconstruction of the face depending on a single 2D image. Our method operates in three steps: the first step is to train a convolutional neural network with a self-made dataset to predict and detect the landmarks of the face and estimate the positions accurately from a single facial image in the image space. The second step involves producing the face’s geometric shape (mesh). Finally, the third step is to do an automatic translation between the 3D space of the object and the 2D image space to determine the texture referrals that correspond to each face polygon;thus, our method produces superior results in terms of accuracy and visual quality, considering all the parameters of the model (shape, expression, reflectance, and lighting) as inputs. Our method is simple, easy to implement, and offers a real-time 3D reconstruction of the face. © 2024, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Convolution

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Mitigating Jamming Attacks in IoT RF-Devices through Dynamic Channel Hopping: A Novel Petri-nets Formulation 5

Mitigating Jamming Attacks in IoT RF-Devices through Dynamic...

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5th International Conference on computer and Applications, ICCA 2023

作者： Oukas, Nourredine Boulif, Menouar Abbas, Akli Bahmed, Kahina Mosteghanemi, Yasmine University of Bouira LIM Laboratory Department of Computer Science Algeria University of Boumerdes LIMOSE Laboratory Department of Computer Science Algeria

ISBN: (纸本)9798350303254

This paper introduces a new modeling leveraging Generalized Stochastic Petri Nets (GSPN) to study smart devices behaviour within the Internet of Things (IoT). These devices are equipped with energy harvesting systems that utilize radio frequency (RF) signals for remote battery recharging. Nonetheless, RF-based energy sources are vulnerable to disruptive jamming attacks that can severely impact device functionality. To tackle this challenge, our study offers a modeling solution that integrates channel hopping strategy, aiming to strike a balance between energy consumption and responsiveness of services. Through comprehensive analysis, the proposed approach helps to identify optimal configuration parameters for practical implementation, enhancing the resilience of these devices against jamming attacks. © 2023 IEEE.

关键词： Internet of things

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