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Job failure prediction in Hadoop based on log file analysis

International Journal of computers and Applications 2022年第3期44卷 260-269页

作者： Shirzad, Ehsan Saadatfar, Hamid Faculty of Electrical and Computer Engineering University of Birjand Birjand Iran Department of Computer Engineering Faculty of Electrical and Computer Engineering University of Birjand Birjand Iran

Hadoop is a popular framework based on MapReduce programming model to allow for distributed processing of large datasets across clusters with various number of computer nodes. Just like any dynamic computational environment, Hadoop has some problems and one of which is unsuccessful execution of MapReduce jobs. Job failures can cause significant resource wasting, performance deterioration, and user dissatisfaction. Therefore, a proactive and predictive management approach could be very useful in Hadoop systems. In this paper, we try to predict the futurity of MapReduce jobs in OpenCloud Hadoop cluster by using its log files. OpenCloud is a research cluster managed by CMU’s Parallel Data Lab which uses Hadoop to process big data. We first tried to study the log files and analyze the relationship between the jobs, resources, and workload characteristics and the failures in order to discover the effective features for the prediction process. After recognizing the job failure patterns, some popular machine learning algorithms are deployed to predict the success/failure status of the jobs before they start to execute. Eventually, we compared the learning methods and showed that the C5.0 algorithm had the best results with an accuracy of 91.37%, a recall of 74.43%, and a precision of 80.31%. © 2020 Informa UK Limited, trading as Taylor & Francis Group.

关键词： MapReduce

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Optimization of Cooperative RelayingMolecular Communications for Nanomedical Applications

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computer Modeling in engineering & Sciences 2024年第2期138卷 1259-1275页

作者： Eman S.Attia Ashraf A.M.Khalaf Fathi E.Abd El-Samie Saied M.Abd El-atty Konstantinos A.Lizos Osama Alfarraj Heba M.El-Hoseny Department of Electrical Engineering Faculty of EngineeringMinia UniversityMinia61111Egypt Department of Electronics and Electrical Communications Engineering Faculty of Electronic EngineeringMenoufia UniversityMenouf32952Egypt Department of Informatics Faculty of Mathematics and Natural SciencesUniversity of Oslo(UiO)OsloNorway Computer Science Department Community CollegeKing Saud UniversityRiyadh11437Saudi Arabia Department of Computer science The Higher Future Institute for Specialized Technological StudiesEl ShoroukEgypt

Recently,nano-systems based on molecular communications via diffusion(MCvD)have been implemented in a variety of nanomedical applications,most notably in targeted drug delivery system(TDDS)***,because the MCvD is unreliable and there exists molecular noise and inter symbol interference(ISI),cooperative nano-relays can acquire the reliability for drug delivery to targeted diseased cells,especially if the separation distance between the nano transmitter and nano receiver is *** this work,we propose an approach for optimizing the performance of the nano system using cooperative molecular communications with a nano relay scheme,while accounting for blood flow effects in terms of drift *** fractions of the molecular drug that should be allocated to the nano transmitter and nano relay positioning are computed using a collaborative optimization problem solved by theModified Central Force Optimization(MCFO)*** the previous work,the probability of bit error is expressed in a closed-form *** is used as an objective function to determine the optimal velocity of the drug molecules and the detection threshold at the nano *** simulation results show that the probability of bit error can be dramatically reduced by optimizing the drift velocity,detection threshold,location of the nano-relay in the proposed nano system,and molecular drug budget.

关键词： Nanomedical system molecular communication cooperative relay optimization

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Reward Shaping in Reinforcement Learning of Multi-Objective Safety Critical Systems 20

Reward Shaping in Reinforcement Learning of Multi-Objective ...

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20th CSI International Symposium on Artificial Intelligence and Signal Processing, AISP 2024

作者： Ravari, Fatemeh Yousefinejad Jalili, Saeed Tarbiat Modares University Electrical and Computer Engineering Faculty Computer Engineering Department Tehran Iran

ISBN: (纸本)9798350383942

Reinforcement learning (RL) depends critically on how well the reward function formulates the aim of the application. In safety-critical systems, several safety properties should be met during the RL process in addition to achieving the primary target. In this paper, we utilize the reward shaping technique to guide the RL algorithm in a way that satisfies the target and safety properties and fulfills other properties, such as efficiency, as much as possible. Reward shaping is a technique to enhance the performance of RL by adding more information to the reward function. Reward shaping functions utilize the system's main target for the base reward signal and add some other reward signals based on other system properties. Suppose each property has its own reward function. In this case, the reward is a vector rather than a scalar value, and the original reinforcement learning approach converts to multi-objective reinforcement learning (MORL). In MORL, one scalarized objective can be defined by fusing several objectives. This paper proposes an aggregation method based on prioritized aggregation operators for scalarizing multiple reward functions. We illustrate our method with two case studies: cart-pole with an obstacle and lunar-lander. The simulation results show that our method guides the RL algorithm to converge to optimal policies faster than existing approaches. © 2024 IEEE.

关键词： Reinforcement learning

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Practical autoencoder based anomaly detection by using vector reconstruction error

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Cybersecurity 2023年第2期6卷 1-13页

作者： Hasan Torabi Seyedeh Leili Mirtaheri Sergio Greco Department of Electrical and Computer Engineering Faculty of EngineeringKharazmi UniversityTehranIran Department of Informatics ModelingElectronics and System EngineeringUniversity of CalabriaArcavacataItaly.

Nowadays,cloud computing provides easy access to a set of variable and configurable computing resources based on user demand through the *** computing services are available through common internet protocols and network standards.n addition to the unique benefits of cloud computing,insecure communication and attacks on cloud networks cannot be *** are several techniques for dealing with network *** this end,network anomaly detection systems are widely used as an effective countermeasure against network *** anomaly-based approach generally learns normal traffic patterns in various ways and identifies patterns of *** anomaly detection systems have gained much attention in intelligently monitoring network traffic using machine learning *** paper presents an efficient model based on autoencoders for anomaly detection in cloud computing *** autoencoder learns a basic representation of the normal data and its reconstruction with minimum ***,the reconstruction error is used as an anomaly or classification *** addition,to detecting anomaly data from normal data,the classification of anomaly types has also been *** have proposed a new approach by examining an autoencoder's anomaly detection method based on data reconstruction *** the existing autoencoder-based anomaly detection techniques that consider the reconstruction error of all input features as a single value,we assume that the reconstruction error is a *** enables our model to use the reconstruction error of every input feature as an anomaly or classification *** further propose a multi-class classification structure to classify the *** use the CIDDS-001 dataset as a commonly accepted dataset in the *** evaluations show that the performance of the proposed method has improved considerably compared to the existing ones in terms of accuracy,recall,false-positive rate,and F1-score

关键词： Cloud Practical Anomaly detection Autoencoder Reconstruction error Machine learning

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Resilient Distributed Optimization for Multi-Agent Cyberphysical Systems

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IEEE Transactions on Automatic control 2025年第6期70卷 3952-3967页

作者： Yemini, Michal Nedic, Angelia Goldsmith, Andrea J. Gil, Stephanie Bar-Ilan University Faculty of Engineering Israel Arizona State University School of Electrical Computer and Energy Engineering TempeAZ85281 United States Princeton University Department of Electrical and Computer Engineering PrincetonNJ08544 United States Harvard University Computer Science Department School of Engineering and Applied Sciences CambridgeMA02139 United States

This work focuses on the problem of distributed optimization in multi-agent cyberphysical systems, where a legitimate agent's iterates are influenced both by the values it receives from potentially malicious neighboring agents, and by its own self-serving target function. We develop a new algorithmic and analytical framework to achieve resilience for the class of problems where stochastic values of trust between agents exist and can be exploited. In this case, we show that convergence to the true global optimal point can be recovered, both in mean and almost surely, even in the presence of malicious agents. Furthermore, we provide expected convergence rate guarantees in the form of upper bounds on the expected squared distance to the optimal value. Finally, numerical results are presented that validate our analytical convergence guarantees even when the malicious agents compose the majority of agents in the network and where existing methods fail to converge to the optimal nominal points. © 1963-2012 IEEE.

关键词： Multi agent systems

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BBTM-IoT: New IoT Systems Trust Management Model Using Blockchain 19

BBTM-IoT: New IoT Systems Trust Management Model Using Block...

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19th Iranian Conference on Intelligent Systems, ICIS 2024

作者： Sharifpour, Mohammadjavad Tochae, Seyyed Amir Asghari Bohlooli, Shabnam Department of Computer Engineering Tehran Iran Kharazmi University Faculty of Engineering Department of Electrical and Computer Engineering Tehran Iran

ISBN: (纸本)9798331507565

The Internet of Things (IoT) seeks to establish a vast network comprising billions of interconnected devices to enable seamless data exchange and intelligent interactions between people and objects. This network is characterized by openness, diversity, and dynamicity, leading to trust issues that impede the widespread adoption of IoT. Trust management plays a crucial role in identifying malicious nodes, fostering trust relationships, and bolstering system security. Conventional trust management systems face significant challenges that hinder their effectiveness in supporting IoT development. Blockchain, as a disruptive technology, shows promise in addressing these trust management challenges by leveraging its decentralized, consistent, and tamper-proof features. This study aims to enhance trust in the Internet of Things by leveraging blockchain technology. The article introduces key concepts such as blockchain technology, CTL (Certificate Trust List), IoT, and trust management, emphasizing critical research and development in trust management models. To highlight the importance of blockchain in maintaining trust, a novel trust management model is proposed, incorporating CTL to validate IoT Devices' legitimacy in blockchain transactions. Finally, the article assesses the efficiency of the proposed model, with the outcomes providing strong proof of its effectiveness. Our simulation results indicate that utilizing the PoW (Proof of Work) consensus model in an IoT system may not be the most optimal approach. The model we have presented displays a reduced memory growth rate of only 0.39% per block. This enhancement is projected to greatly improve the process of block verification. Specifically, the PoW algorithm took 4.2 seconds to incorporate 10 blocks, whereas our model completed the same task in just 0.61 seconds. © 2024 IEEE.

关键词： Blockchain

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Ultra-High Step-Up Quadratic DC-DC Converter with Soft Switching, High Efficiency, and Cost Effectiveness for DC Microgrid Applications 16

Ultra-High Step-Up Quadratic DC-DC Converter with Soft Switc...

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16th Annual Power Electronics, Drive Systems, and Technologies Conference, PEDSTC 2025

作者： Nadermohammadi, Ali Hayati, Mohammad Mohsen Oshnoei, Arman Hosseini, Seyed Hossein Babaei, Ebrahim Abapour, Mehdi University of Tabriz Faculty of Electrical and Computer Engineering Tabriz Iran University Faculty of Electrical and Computer Engineering Tabriz Iran Aalborg University Aalborg Department of Energy Denmark

ISBN: (纸本)9798331533946

This study presents a novel ultra-high step-up (UHSU) DC-DC topology tailored for applications in DC microgrids. The proposed configuration utilizes a quadraticbased topology, achieving a remarkably high voltage gain by elevating the turn ratio of the coupled inductor (CI). Notable advantages of this design include its capability to achieve ultrahigh voltage amplification, reduced diode reverse recovery losses, minimized voltage stress on switching components, continuous input current flow, and a shared ground connection linking the input and output. Additionally, the converter is equipped with soft-switching functionality for some of semiconductor elements, which significantly boosts efficiency and minimizes energy dissipation. By maintaining low voltage stress on the switches, the system ensures enhanced operational reliability and overall performance. The operational principles of the converter are analyzed in detail, and its effectiveness is highlighted through a comparison with other existing topologies. Finally, the proposed design's performance is confirmed through the fabrication and experimental validation of a 400-W prototype, performing at a switching frequency of 50 k H z, with an input voltage of 30 V and an output voltage of 360 v. © 2025 IEEE.

关键词： coupled inductor DC-DC converter quadratic converter softswitching ultra-high step-up

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Insertion of Additional Image Charges in Method of Images for Small Ground Planes to Improve Prediction in Two-Dimensional Electrostatic Problems

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Journal of The Institution of Engineers (India): Series B 2023年第1期104卷 193-200页

作者： Vetharatnam, Gobi Ghanbarisabagh, Mohammad Senjuti, Sifat Ara Department of Electrical and Electronic Engineering Lee Kong Chian Faculty of Engineering and Science University Tunku Abdul Rahman Cheras Selangor Kajang Malaysia Department of Electrical Engineering Faculty of Electrical Engineering and Computer Sciences Islamic Azad University North Tehran Branch Hakimieh Tehran Iran

Traditional method of images is routinely applied in electrostatic problems with infinite ground planes. However, practical situations almost always deal with finite ground planes. This causes the prediction to be inexact. For the first time, the differences between them are analyzed and a new model for the image is proposed, for small ground planes in two-dimension. In this model, two additional charges are introduced at either side of the ground plane to improve the prediction. This model predicts the potential with less error compared with the traditional method of images. © 2022, The Institution of Engineers (India).

关键词： Forecasting

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PV Grid Forecasting Analysis using Artificial Neural Network 25

PV Grid Forecasting Analysis using Artificial Neural Network

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25th International Middle East Power System Conference, MEPCON 2024

作者： Aboelmagd, Mohamed R. Selim, Ali Abdel-Akher, Mamdouh Aswan University Faculty of Engineering Department of Electrical Engineering Aswan Egypt Almaaqal University Department of Control and Computer Engineering Dep. Basra Iraq

ISBN: (纸本)9798350379648

In addition to its use in building and agriculture, global solar irradiance is one of the most critical aspects in designing and considering any solar station's volume. Because the Iraqi metrological organization and seismology lack exact information regarding irradiance, this project aims to use historical global data, develop numerical analysis using artificial neural networks, and forecast hourly irradiance. The test is run over Basra to determine references to their nearest places. The learning technique in this study is a forward neural network (FNN) and time series neural network. Their comparison depends on seven input variables: temperature, wind speed, wind direction, sunshine duration, and date. One of the most essential integrated and impactful events for managing the system of new and renewable energy sources in the power grid is accurate solar energy forecasting. The study offers a valuable method for forecasting solar energy generation using neural networks. It is difficult to apply neural networks to anticipate PV power generation because of the unpredictability of variations in the weather;unlike traditional mathematical models, which struggle to handle complicated nonlinear interactions and provide poor predictions, neural network approaches achieve superior prediction performance. The suggested forecasting model based on neural networks provides a practical means of raising the accuracy of PV generation estimation. © 2024 IEEE.

关键词： Solar irradiance

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Review of dehazing techniques: challenges and future trends

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Multimedia Tools and Applications 2025年第3期84卷 1103-1131页

作者： Ayoub, Abeer El-Shafai, Walid El-Samie, Fathi E. Abd Hamad, Ehab K. I. EL-Rabaie, El-Sayed M. Department of Electronics and Electrical Communication Engineering Faculty of Electronic Engineering Menoufia University Menouf32952 Egypt Security Engineering Lab Computer Science Department Prince Sultan University Riyadh11586 Saudi Arabia Department of Information Technology College of Computer and Information Sciences Princess Nourah Bint Abdulrahman University Riyadh11671 Saudi Arabia Electrical Engineering Department Faculty of Engineering Aswan University Aswan81542 Egypt

The phenomenon of atmospheric haze arises due to the scattering of light by minute particles suspended in the atmosphere. This optical effect gives rise to visual degradation in images and videos. The degradation is primarily influenced by two key factors: atmospheric attenuation and scattered light. Scattered light causes an image to be veiled in a whitish veil, while attenuation diminishes the image inherent contrast. Efforts to enhance image and video quality necessitate the development of dehazing techniques capable of mitigating the adverse impact of haze. This scholarly endeavor presents a comprehensive survey of recent advancements in the domain of dehazing techniques, encompassing both conventional methodologies and those founded on machine learning principles. Traditional dehazing techniques leverage a haze model to deduce a dehazed rendition of an image or frame. In contrast, learning-based techniques employ sophisticated mechanisms such as Convolutional Neural Networks (CNNs) and different deep Generative Adversarial Networks (GANs) to create models that can discern dehazed representations by learning intricate parameters like transmission maps, atmospheric light conditions, or their combined effects. Furthermore, some learning-based approaches facilitate the direct generation of dehazed outputs from hazy inputs by assimilating the non-linear mapping between the two. This review study delves into a comprehensive examination of datasets utilized within learning-based dehazing methodologies, elucidating their characteristics and relevance. Furthermore, a systematic exposition of the merits and demerits inherent in distinct dehazing techniques is presented. The discourse culminates in the synthesis of the primary quandaries and challenges confronted by prevailing dehazing techniques. The assessment of dehazed image and frame quality is facilitated through the application of rigorous evaluation metrics, a discussion of which is incorporated. To provide empiri

关键词： Generative adversarial networks

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