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QoS Prediction Model of Cloud Services Based on Deep Learning

IEEE/CAA Journal of Automatica Sinica 2022年第3期9卷 564-566页

作者： WenJun Huang PeiYun Zhang YuTong Chen MengChu Zhou Yusuf Al-Turki Abdullah Abusorrah Nanjing University of Information Science&Technology School of Computer and SoftwareNanjingChina IEEE New Jersey Institute of Technology Helen and John C.Hartmann Department of Electrical and Computer EngineeringNewarkUSA Center of Research Excellence in Renewable Energy and Power Systems Department of Electrical and Computer EngineeringFaculty of Engineeringand K.A.CARE Energy Research and Innovation CenterKing Abdulaziz UniversityJeddah 21589Saudi Arabia

Dear editor,This letter presents a deep learning-based prediction model for the quality-of-service(QoS)of cloud ***,to improve the QoS prediction accuracy of cloud services,a new QoS prediction model is proposed,which is based on multi-staged multi-metric feature fusion with individual *** multi-metric features include global,local,and individual *** results show that the proposed model can provide more accurate QoS prediction results of cloud services than several state-of-the-art methods.

关键词： services Services individual

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Security Aspects of Container Orchestration in Kubernetes Environments

Security Aspects of Container Orchestration in Kubernetes En...

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International Symposium on INFOTEH-JAHORINA (INFOTEH)

作者： Sava Stanišić Milan Vesković Olga Ristić Borislav Đorđević Department of Computer and Software Engineering Faculty of Technical Sciences Čačak Republic of Serbia IMP Computer Systems Mihajlo Pupin Institute Belgrade Republic of Serbia

ISBN: (数字)9798331515799

ISBN: (纸本)9798331515805

Security is one of the key challenges in container orchestration, especially in complex environments. This paper explores the security aspects of implementing containerized applications using Docker within a Kubernetes cluster. The first part of the paper describes Docker, Kubernetes, and various ways of applying them within DevOps methodology. It then presents potential vulnerabilities during the implementation of these technologies, as well as vulnerabilities specific to Docker and Kubernetes. Subsequently, some solutions for securing a Kubernetes environment are described.

关键词： DevOps Scalability Organizations Containers Stability analysis Malware Complexity theory Security Object recognition Resilience

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Optimizing Machine Learning Algorithms to Enhance Blood Donation for Liver Patients: A Case Study 7

Optimizing Machine Learning Algorithms to Enhance Blood Dona...

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7th IET Smart Cities Symposium, SCS 2023

作者： Mijwil, Maad M. El-Kenawy, El-Sayed M. Bala, Indu Aljanabi, Mohammad Abotaleb, Mostafa Ibrahim, Abdelhameed Computer Techniques Engineering Department Baghdad College of Economic Sciences University Baghdad Iraq Department of Communications and Electronics Delta Higher Institute of Engineering and Technology Egypt School of Electrical and Electronics Engineering Lovely Professional University Punjab India Department of Computer Aliraqia University Baghdad Iraq Department of System Programming South Ural State University Chelyabinsk Russia Computer Engineering and Control Systems Department Faculty of Engineering Mansoura University Mansoura Egypt

ISBN: (纸本)9781839539831

Blood is vital for transporting oxygen, nutrients, and hormones to all body parts as it circulates through arteries and veins. It removes carbon dioxide, regulates body temperature, and maintains the body's immune system. Individuals constantly need blood and its derivatives to save their lives and improve their health through medical treatments and surgical operations. Liver diseases are one of the diseases that affects the health of individuals and requires blood to continue living. These diseases cause significant damage to people's health, and early diagnosis plays a crucial role in saving lives. In this paper, machine learning algorithms (support vector machine and random forest) are involved in detecting liver diseases and determining whether donors are suitable to donate blood from blood values. This paper is applied research that found that the performance measures of the random forest algorithm achieved excellent performance in identifying suitable people to donate blood. © The Institution of engineering & Technology 2023.

关键词： Support vector machines

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Evaluating Multi-Sensor Placement and Neural Network Architectures for Physical Activity Level Classification

arXiv

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arXiv 2025年

作者： Cui, Bo Song, Xiaowen Monique, Tabak van Beijnum, Bert-Jan Wang, Ying Faculty of Biomedical Signals and Systems Electrical Engineering Mathematics and Computer Science University of Twente Enschede7500 AE Netherlands

Accurate physical activity level (PAL) classification could be beneficial for osteoarthritis (OA) management. This study examines the impact of sensor placement and deep learning models on AL classification using Metabolic Equivalent of Task values. The results show that the addition of an ankle sensor (WA) significantly improves the classification of high intensity activities compared to wrist-only configuration (53% to 86.2%). The CNN-LSTM model achieves the highest accuracy (95.09%). Statistical analysis confirms multi-sensor setups outperform single-sensor configurations (p © 2025, CC BY-NC-ND.

关键词： Deep learning

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Comparative Study of Static Analysis and Machine Learning Approaches for Detecting Android Banking Malware

Comparative Study of Static Analysis and Machine Learning Ap...

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Emerging Smart Technologies and Applications (eSmarTA), International Conference on

作者： Alaa M. Ahmed Mogeeb A. Saeed Amr A. Hamood Abduljabar A. Alazab Kamel A. Ahmed Faculty of Engineering Computer Networks & Distributed Systems Al-saeed IT Taiz University Taiz Yemen Computer Networks & Distributed Systems department Al-saeed Faculty of Engineering & IT Taiz University Taiz Yemen

Android currently dominates the smartphone market, accounting for an impressive market share of over 70%. However, because of its widespread acceptance, mobile operating systems have become a prime target for bad actors looking to profit from them. Particularly Android has been subjected to an increasing barrage of malware assaults, including the infamous Android Banking Trojans. This study investigates the effectiveness of static analysis in locating Android banking malware in order to counter this threat. It does so by utilizing a wide range of features, including permissions, application programming interface (API) calls, opcodes, API packages, system commands, intents, strings, services, receivers, and activities. The study suggests using machine learning techniques to assess the detection of Android malware by utilizing various sets of classifiers in order to achieve this goal. The study also uses a feature selection approach to determine which features are most useful for telling malicious code apart from good code. 500 samples of malicious code and 500 samples of benign code make up the dataset that was used. The XGboost algorithm outperforms others in terms of accuracy, achieving an impressive accuracy value of 99.5% in malware detection after conducting a thorough comparison of various classifier sets. These results demonstrate the potential of static analysis and machine learning as useful tools in fending off the growing threats posed by Android malware.

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Air Particulate Matters Auto-Rule-Based Labeling to Support Long-Distance Run Environment Data Classification 13

Air Particulate Matters Auto-Rule-Based Labeling to Support ...

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13th IEEE Symposium on computer Applications and Industrial Electronics, ISCAIE 2023

作者： Wandy, Wandy Adi, Kusworo Ayu, Media Anugerah Diponegoro University Doctoral Program of Information Systems School of Postgraduate Studies Semarang Indonesia Diponegoro University Faculty of Science and Mathematics Department of Physics Semarang Indonesia Sampoerna University Faculty of Engineering and Technology Department of Computer Science South Jakarta Indonesia

ISBN: (纸本)9798350347319

Sports Science is an interdisciplinary and multidisciplinary science that strives to increase athletic performance and endurance. Sport Science recognizes and prevents injuries. Sensors and statistics formalize Sports Science. Runners need coaches and teams to support them before, during, and after the run race. Coaches generate running training plans to boost performance. Running race performances may be impacted by air pollution exposure while training, so coaches should consider limiting air pollution exposure when training. One of the external factors is Particulate Matter (PM2.5 and PM10). Sensors connecting to the Internet can record external factors and produce csv data. The foundation of supervised machine learning is the labeling process. Labeling a set of data is one of the laborious and time-consuming phases in every machine-learning application because it requires verifying the accuracy of the labels and making any necessary revisions. This research aimed to find a solution to automatically label numerous air particulate matter raw data using a rule based on parameters to reduce manual work, human errors and faster processes. This labeled data will later be used for supervised machine learning classification to support the coach in generating training programs for the runners in a Sports Information System. Based on Indonesia Air Quality index rule-based approach, labeled text data in csv has been generated and tested with PM2.5 and PM10 parameters in three scenarios with a 100% success rate. It was possible to automate the labeling process, and it explained how automation results in fast and accurate results. © 2023 IEEE.

关键词： Classification (of information)

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Technologies Applied to Solve Facility Layout Problems with Resilience – A Systematic Review 1

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3rd International Conference on Applied Technologies, ICAT 2021

作者： Vázquez, Jorge Llivisaca, Juan Carlos Ortiz, Daisy Naranjo, Israel Siguenza-Guzman, Lorena Department of Computer Sciences Faculty of Engineering University of Cuenca Cuenca Ecuador Department of Applied Chemistry and Production Systems Faculty of Chemical Sciences University of Cuenca Cuenca Ecuador Faculty of Systems Electronics and Industrial Engineering Technical University of Ambato Ambato Ecuador Research Centre Accountancy Faculty of Economics and Business KU Leuven Leuven Belgium

ISBN: (数字)9783031038846

ISBN: (纸本)9783031038839

In the industry, the need to optimize daily tasks became mainly a requirement to stay competitive. The Facility Layout Problem (FLP) arises from this need and is the most used technique to improved manufacturing processes. Over the years, the distribution of the different facilities became more complex and more susceptible to interventions in their usual workflow. Hence, FLPs emerged to solve these interventions. This capacity to solve various problems is known as resilience. By searching the literature for information on technologies used to solve FLP, many software solutions can be obtained. Still, none of them manage resilience as a factor in the solution. They have also not been compiled in an analysis to determine the most modern options for existing solutions. The current study reviews the most widely used software tools that solve FLP and how these studies view resilience. A systematic literature review was applied to select relevant studies from 2015–2021. This work allowed analyzing a final sample of 133 articles obtained in two different search strings. As a result, this study identified two manners to solve FLP with software tools: using already developed tools and developing an own tool. In the former, three proprietary software were identified: MATLAB, CPLEX, and LINGO. When developing their software tool, the technology used varies significantly between studies. They use very different language programming, depending on the researcher’s domain. Furthermore, the results do not find any case in which some resilience factor was part of the solution obtained. © 2022, Springer Nature Switzerland AG.

关键词： MATLAB

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Maize Crop Yield Prediction Using Machine Learning Regression Approach 5th

Maize Crop Yield Prediction Using Machine Learning Regressio...

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5th International Conference on Advances in Distributed Computing and Machine Learning, ICADCML 2024

作者： Bedane, Tarikwa Tesfa Kebede, Kidistie Bizuneh Mohapatra, Sudhir Kumar Das, Tapan Kumar Tripathy, Asis Kumar Department of Software Engineering Addis Ababa Science and Technology University Addis Ababa Ethiopia Agricultural Transformation Institute Addis Ababa Ethiopia Faculty of Emerging Technologies Sri Sri University Cuttack India School of Computer Science Engineering and Information Systems Vellore Institute of Technology Vellore India

ISBN: (纸本)9789819735228

Given maize’s significant role as a staple crop, it becomes imperative to carry out precise crop yield prediction to ensure food security. This research employs machine learning algorithms to analyze historical data related to maize crop yields, with the aim of developing a predictive model. The outcome of this study can serve as a valuable tool for pre-harvest planning and measuring crop production per unit of land area. The dataset utilized in this research spans from 2010 to 2021 and was sourced from Ethiopia’s Central Statistical Agency. Three machine learning regression algorithms—Logistic Regression, XGBoost, and Random Forest—were employed to design the predictive model. After data preparation and preprocessing, a total of 436,358 records were used for model development. The models were further trained with reduced features using principal component analysis (PCA). The experimental results demonstrate that the Random Forest algorithm, trained with a reduced number of features through PCA, yielded superior performance, with a lower error rate and a high R2 score of 0.98. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Machine learning

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Alzheimer's Disease Classification Approach Using MRI Imaging

Alzheimer's Disease Classification Approach Using MRI Imagin...

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Mobile, Intelligent, and Ubiquitous Computing Conference (MIUCC), International

作者： Eman Khaled Farah Amr Farah Essam Maryam Nouh Youssef Sherif Zyad Yakan Ziad El Gayar Ashraf AbdelRaouf Department of Computer Engineering and Software Systems Faculty of Engineering Ain Shams University Cairo Egypt Faculty of Computer Scinece Misr International University Cairo Egypt

Alzheimer's disease (AD) is a slowly progressing, irreversible brain condition that weakens memory and negatively affects the patient's quality of life. Alzheimer's disease (AD) can be identified using Magnetic Resonance Imaging (MRI) data. For an early diagnosis of the disease, various medical and diagnostic approaches are being investigated. Even while MRI is a useful tool for locating AD-related brain symptoms, the acquisition process is time-consuming, largely because workflow bottlenecks must be manually evaluated. In order to find the best effective method for detecting the disease, this research examines the basic technique for analyzing MRI images. To carry out our study to slow progression of the disease by the use of Alzheimer's disease (AD) prognosis, a dataset from The Alzheimer's Disease Neuroimaging Initiative (ADNI) will be imported and fitted. The outcomes highlight the tremendous potential of integrating imaging data for automated categorization of Alzheimer's disease (AD) using multidisciplinary AI techniques. With a deep three-dimensional convolutional network (3D CNN) being used to handle the three-dimensional MRI input and a Transformer encoder being applied to manage the genetic sequence input, the suggested solution merges machine learning, bioinformatics, and other image processing techniques. After various experiments by checking the results accuracy, it is stated that the CNN model is never enough to provide us with the desired accuracy either by training on both skull stripped data or the GM tissue segmented data. Although, it is relatively better at the skull stripped dataset training, but the results accuracy and predicted classes show that inferring some classifiers after extracting the features from the CNN would increase the accuracy and results. After applying Support Vector Machine SVM-RBF, SVM-POLY, and XGBoost, it is concluded that the training of the Skull Stripped Dataset with features extracted from the CNN model we provided an

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Corpus of Human-Labeled Indirect Harassment

Corpus of Human-Labeled Indirect Harassment

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International Conference on Emerging Technologies, ICET

作者： Ebtihaj Abdullah Waseem Ullah Khan Safdar Nawaz Khan Marwat Salman Ahmed Dept. of Computer Systems Engg University of Engineering & Technology Peshawar Pakistan Faculty of Computer Science and Engg GIK Institute of Engineering Sciences & Technology Swabi Pakistan

ISBN: (数字)9798350379587

ISBN: (纸本)9798350379594

In an age of information and digital communication, social media platforms are becoming essential spaces where individuals can voice their thoughts, exchange ideas, and take part in discussions on a variety of subjects. High quality and high value datasets of such ideas and discussions in textual and formatted form are fundamental to the research aspects of Artificial Intelligence and data-science. In this paper, we present a dataset of indirect harassment. There are approximately 10,700 tweets with binary labels. The labels were assigned by a team of researchers collectively. The dataset contains approximately 19 percent positive indirect harassment labels and 81 percent negative indirect harassment labels. The data is useful for training and running machine and deep learning models to detect indirect and direct harassment. The ease in understanding the data for researchers and other peers is also very crucial. The corpus mentioned in this paper is easy to understand via the use of a binary label. The data is simplistic in nature because there are only two columns with one being the text and the other being the indirect label. This work was necessary because it was a component of a larger project that also needed this kind of dataset.

关键词： Deep learning Training Social networking (online) Oral communication Data science Digital communication Information age Data models

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