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An Ensemble Machine Learning Technique for Stroke Prognosis

computer Systems science & engineering 2023年第10期47卷 413-429页

作者： Mesfer Al Duhayyim Sidra Abbas Abdullah Al Hejaili Natalia Kryvinska Ahmad Almadhor Uzma Ghulam Mohammad Department of Computer Science College of Computer Engineering and SciencesPrince Sattam bin Abdulaziz University Al-Kharj16273Saudi Arabia Department of Computer Science COMSATS UniversityIslamabad53000Pakistan Computer Science Department Faculty of Computers&Information TechnologyUniversity of TabukTabuk71491Saudi Arabia Information Systems Department Faculty of ManagementComenius University in BratislavaOdbojárovBratislava440Slovakia Department of Computer Engineering and Networks College of Computer and Information SciencesJouf UniversitySakaka72388Saudi Arabia Department of Computer Science and Software Engineering International Islamic UniversityIslamabad44000Pakistan

Stroke is a life-threatening disease usually due to blockage of blood or insufficient blood flow to the *** has a tremendous impact on every aspect of life since it is the leading global factor of disability and *** can range from minor to severe(extensive).Thus,early stroke assessment and treatment can enhance survival *** prediction is extremely time and resource *** prediction methods such as Modern Information and Communication Technologies(ICTs),particularly those inMachine Learning(ML)area,are crucial for the early diagnosis and prognosis of ***,this research proposed an ensemble voting model based on three Machine Learning(ML)algorithms:Random Forest(RF),Extreme Gradient Boosting(XGBoost),and Light Gradient Boosting Machine(LGBM).We apply data preprocessing to manage the outliers and useless instances in the ***,to address the problem of imbalanced data,we enhance the minority class’s representation using the Synthetic Minority Over-Sampling Technique(SMOTE),allowing it to engage in the learning process *** reveal that the suggested model outperforms existing studies and other classifiers with 0.96%accuracy,0.97%precision,0.97%recall,and 0.96%*** experiment demonstrates that the proposed ensemble voting model outperforms state-of-the-art and other traditional approaches.

关键词： Stroke prediction machine learning ensemble model data analysis Synthetic Minority Over-Sampling

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Privacy-aware Data Fusion and Prediction for Smart City Services in Edge Computing Environment 15

Privacy-aware Data Fusion and Prediction for Smart City Serv...

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2022 IEEE Congress on Cybermatics: 15th IEEE International Conferences on Internet of Things, iThings 2022, 18th IEEE International Conferences on Green Computing and Communications, GreenCom 2022, 2022 IEEE International Conference on Cyber, Physical and Social Computing, CPSCom 2022 and 8th IEEE International Conference on Smart Data, SmartData 2022

作者： Qi, Lianyong Chi, Xiaoxiao Zhou, Xiaokang Liu, Qi Dai, Fei Xu, Xiaolong Zhang, Xuyun School of Computer Science Qufu Normal University China Faculty of Data Science Shiga University Japan Riken Center for Advanced Intelligence Project Japan School of Computer and Software Nanjing University of Information Science and Technology China School of Big Data and Intelligence Engineering Southwest Forestry University China Department of Computing Macquarie University Australia

ISBN: (数字)9781665454179

ISBN: (纸本)9781665454179

With fast development of Cyber Physical System, the variety and volume of data generated from different edge servers are fairly considerable. Mining and exploiting the data would definitely bring huge advantages. However, there are two major issues exposed in the process. First, utilizing the data with high efficiency is challenging in edge computing environment. Second, data from multiple edge servers often contain sensitive information such as user preferences, which will give rise to privacy breach risks when using the data. Locality-Sensitive Hashing and amplified technology are utilized for dealing with two problems. In this paper, we propose a novel privacy-protection approach named SRchain-LSH in edge computing environment which is based on amplified Locality-Sensitive Hashing technique. In order to verify high accuracy and efficiency of our proposal, large sets of experiments are conducted. According to experimental results, our proposal shows better performance than those of comparative methods in terms of MAE , RMSE and time costs. For our method, the best value of MAE is about 0.41 and time cost is about 0.1 seconds, which are more accurate and efficient than comparative methods. © 2022 IEEE.

关键词： Smart city

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Image Inpainting and Classification Agent Training Based on Reinforcement Learning and Generative Models with Attention Mechanism

Image Inpainting and Classification Agent Training Based on ...

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2021 International Conference on Microelectronics, ICM 2021

作者： Ukwuoma, Chiagoziem C. Belal Bin Heyat, Md Masadeh, Mahmoud Akhtar, Faijan Zhiguang, Qin Bondzie-Selby, Emmanuel Alshorman, Omar Alkahtani, Fahad University of Electronic Science and Technology of China School of Information and Software Engineering Sichuan Chengdu China Shenzhen University IoT Research Center College of Computer Science and Software Engineering Guangdong Shenzhen China Yarmouk University Hijjawi Faculty for Engineering Computer Engineering Deprtment Irbid21163 Jordan University of Electronic Science and Technology of China School of Computer Science and Engineering Sichuan Chengdu China Najran University Faculty of Engineering AlShrouk Trading Company Najran Saudi Arabia Najran University Faculty of Engineering Electrical Engineering Department Najran Saudi Arabia

ISBN: (纸本)9781665408394

What distinguishes the field of artificial intelligence (AI) from others is to develop fully independent agents that learn optimal behavior, change, and evolve solely through the communication of trial and error with the surrounding environment. Reinforcement learning (RL) can be seen in multiple aspects of Machine Learning (ML), provided the environment, reward, actions, the state will be defined. Agent training in previous years is seen to only relate to robotics, games, and self-driving cars. While trying to divert the focus of researchers from the view of self-driving cars, games, robots, etc. Here, we investigated using reinforcement learning in the aspect of task completion. We deployed our architecture in an inpainting task where the agent generates the distorted or missing image content into an eminent fidelity completed the image by using reinforcement learning to influence the generative model utilized. The Generative Adversary Network (GAN) problem of not being steady and challenging to train was overwhelmed by utilizing latent space representation. The dimension is reduced compared to the distorted or corrupted image in training the GAN. Then reinforcement learning was deployed to pick the correct GAN input to get the image's latent space representation that is most suitable for the current input of the missing or distorted image region. In this paper, we also learned that the trained agent enhances the accuracy in a classification task of images with missing data. We successfully examined the classification enhancement on images missing 30%, 50%, and 70%. © 2021 IEEE.

关键词： Reinforcement learning

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Exploring the role of project status information in effective code smell detection

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Cluster Computing 2025年第1期28卷 1-18页

作者： Alkharabsheh, Khalid Alawadi, Sadi Crespo, Yania Taboada, José A. Software Engineering Department Prince Abdullah bin Ghazi Faculty of Information and Communication Technology Al-Balqa Applied University Al-Salt19117 Jordan School of Computer Science Blekinge Tekniska Högskola Blekinge Tekniska Högskola Blekinge Karlskrona37179 Sweden Departamento de Informática Escuela de Ingeniería Informática Universidad de Valladolid Paseo de Belén 15 Valladolid47011 Spain Department of Electronics and Computing Universidad de Santiago de Compostela Santiago15782 Spain

Repairing code smells detected in the code or design of the system is one of the activities contributing to increasing the software quality. In this study, we investigate the impact of non-numerical information of software, such as project status information combined with machine learning techniques, on improving code smell detection. For this purpose, we constructed a dataset consisting of 22 systems with various project statuses, 12,040 classes, and 18 features that included 1935 large classes. A set of experiments was conducted with ten different machine learning techniques by dividing the dataset into training, validation, and testing sets to detect the large class code smell. Feature selection and data balancing techniques have been applied. The classifier’s performance was evaluated using six indicators: precision, recall, F-measure, MCC, ROC area, and Kappa tests. The preliminary experimental results reveal that feature selection and data balancing have poor influence on the accuracy of machine learning classifiers. Moreover, they vary their behavior when utilized in sets with different values for the selected project status information of their classes. The average value of classifiers performance when fed with status information is better than without. The Random Forest achieved the best behavior according to all performance indicators (100%) with status information, while AdaBoostM1 and SMO achieved the worst in most of them (> 86%). According to the findings of this study, providing machine learning techniques with project status information about the classes to be analyzed can improve the results of large class detection. © The Author(s) 2024.

关键词： software quality

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cybersecurity Threats in the Era of AI: Detection of Phishing Domains Through Classification Rules 2

Cybersecurity Threats in the Era of AI: Detection of Phishin...

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2nd International engineering Conference on Electrical, Energy, and Artificial Intelligence, EICEEAI 2023

作者： Aljaidi, Mohammad Alsarhan, Ayoub Al-Fraihat, Dimah Al-Arjan, Ahmed Igried, Bashar El-Salhi, Subhieh M. Khalid, Muhammad Al-Na'amneh, Qais Zarqa University Faculty of Information Technology Department of Computer Science Zarqa13110 Jordan The Hashemite University Department of Information Technology Jordan Isra University Faculty of Information Technology Department of Software Engineering Amman11622 Jordan The Hashemite University Faculty of Prince AL-Hussein Bin Abdullah Ii of Information Technology Department of Information Technology Jordan The Hashemite University Department of Computer Science and Applications Zarqa Jordan The Hashemite University P. O Box 330127 Zarqa13133 Jordan University of Hull Department of Computer Sciences and Technology Kingston upon Hull United Kingdom Applied Science Private University Dep. of Cyber Security Amman Jordan

ISBN: (纸本)9798350373363

Phishing is a cyber threat where attackers create deceptive websites or emails to exploit individuals for fraudulent purposes such as providing sensitive information like usernames, passwords, or financial details. Hence, detecting phishing emails is of paramount importance to protect sensitive information, preventing financial losses, protecting against identity theft, and maintaining trust. This study employs machine learning techniques to classify and categorize websites that are used for phishing attacks. To achieve this, we proposed a classification model for the detection of phishing domains based on classification rules. These rules were formulated based on analyzing and distributing them into three classifiers: domain name features, search engine opinion (SEO) information of the domain, and security scans by security vendors. A total of eleven classification rules were formulated which were distributed as follows: six rules for the first classifier, four rules for the second classifier, and one rule for the third classifier. Each classifier feeds its results to the main classifier, which then evaluates the received results based on its rules. Domain names from 222 confirmed phishing URLs were used to test the suggested model. The model demonstrated exceptional performance, achieving an impressive accuracy rate of 99.1% during testing. © 2023 IEEE.

关键词： Websites

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Integrating Design Thinking into software Process Improvement 4

Integrating Design Thinking into Software Process Improvemen...

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4th International Conference on Emerging Trends in Networks and computer Communications, ETNCC 2024

作者： Khalid, Anum Hashmi, Amber Sarwar Kiani, Azaz Ahmed Muzammal, Syeda Mariam Jhanjhi, N.Z. Bibi, Ruqia Kumar, Sandeep Foundation University Department of Software Engineering Islamabad Pakistan Rawalpindi Women University Department of Software Engineering Rawalpindi Pakistan National University of Modern Languages Rawalpindi Pakistan School of Computer Science Taylor's University Faculty of Innovation and Technology Subang Jaya Malaysia University Institute of Information Technology Pir Mehr Ali Shah Arid Agriculture University Rawalpindi Pakistan Lovely Professional University Division of Research and Development Phagwara144411 India KKR and KSR institute of technology and sciences Andhra Pradesh Guntur India

ISBN: (纸本)9798350353266

The need for software improvement is growing widely due to the demand for technology development. The issues that arise by expanding the software processes in the workplace are expense, lack of understanding, time constrained, and lack user experience. This research paper focuses on proposing a framework for integrating design thinking into software process improvement. The secret to project success may lie in fusing methodological and reusable approaches with human-centric techniques. A mixed-methods research strategy is proposed to compile thorough information and reach conclusions. In addition, it incorporates components of both qualitative and quantitative research (secondary research and descriptive analysis). The assumption is that the existing model significantly affects the user satisfaction variable. This indicates a statistically significant association between the individual's experience (number of years) and design thinking integration into the software process improvement. The variables in the proposed model and the dependent variable user satisfaction accounts for 6.1 © 2024 IEEE.

关键词： software design

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SYNFER: TOWARDS BOOSTING FACIAL EXPRESSION RECOGNITION WITH SYNTHETIC DATA

arXiv

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arXiv 2024年

作者： He, Xilin Luo, Cheng Xian, Xiaole Li, Bing Song, Siyang Khan, Muhammad Haris Xie, Weicheng Shen, Linlin Ge, Zongyuan Computer Vision Institute School of Computer Science & Software Engineering Shenzhen University China AIM Lab Faculty of IT Monash University Australia KAUST Saudi Arabia University of Exter United Kingdom MBZUAI United Arab Emirates

Facial expression datasets remain limited in scale due to privacy concerns, the subjectivity of annotations, and the labor-intensive nature of data collection. This limitation poses a significant challenge for developing modern deep learning-based facial expression analysis models, particularly foundation models, that rely on large-scale data for optimal performance. To tackle the overarching and complex challenge, we introduce SynFER (Synthesis of Facial Expressions with Refined Control), a novel framework for synthesizing facial expression image data based on high-level textual descriptions as well as more fine-grained and precise control through facial action units. To ensure the quality and reliability of the synthetic data, we propose a semantic guidance technique to steer the generation process and a pseudo-label generator to help rectify the facial expression labels for the synthetic images. To demonstrate the generation fidelity and the effectiveness of the synthetic data from SynFER, we conduct extensive experiments on representation learning using both synthetic data and real-world data. Experiment results validate the efficacy of the proposed approach and the synthetic data. Notably, our approach achieves a 67.23% classification accuracy on AffectNet when training solely with synthetic data equivalent to the AffectNet training set size, which increases to 69.84% when scaling up to five times the original size. Our code will be made publicly available. © 2024, CC BY.

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A parallel proposal with message passing for the implementation of a Pipeline in the development of the video game SIMON 35

A parallel proposal with message passing for the implementat...

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35th European Modeling and Simulation Symposium, EMSS 2023

作者： Rossainz-López, Mario Cabrera-Hernández, Liosbel Sánchez-Rinza, Bárbara Capel-Tuñon, Manuel Faculty of Computer Science Autonomous University of Puebla Av. San Claudio and 14 Sur Street San Manuel PueblaC.P. 72570 Mexico Software Engineering Department College of Informatics and Telecommunications ETSIIT University of Granada Daniel Saucedo Aranda s/n Granada18071 Spain

ISBN: (纸本)9788885741881

The design and development of the inter-process communication pattern called Pipeline is presented as a proposal of Parallel Object Composition to solve simple way problems that can be solved with this same parallel control structure. A particular class library called JPMI (Java Passing Message Interface) is used for parallel programming with message passing and to implement an original and particular version of the well-known video game called SIMON with the objective, on the one hand, to show the usefulness of this design within Structured Parallel Programming and, on the other hand, that this proposal serves to guarantee good performance in the execution of real time applications. An example of this type of applications is precisely video games. The parallel algorithm implemented as a Composition of Parallel Objects is based on the development and use of a methodology where the algorithmic design represents the parallel control structure common to a given algorithmic technique that can use the pipeline communication pattern, generating a generic and abstract parallel program from which programs that solve specific problems using the same communication pattern can be derived. The implementation of this proposal within structured parallel programming tries to facilitate to the novice programmer in parallelism the reusability, genericity, and uniformity of code abstract enough to be suitable for any problem that can be solved with a pipeline offered implemented with a parallel message passing structure. This particularized proposal for the implementation of the SIMON video game is compared with another using a thread library called boost and ZeroC Ice for remote invocation of distributed objects. The execution times and speedups of both proposals are compared to identify how similar or different they are in their respective performances with training tests using AI modules with sequences of 500000 colors in a cluster of 2 Intel Xeon CPUs of 8 cores each and 2 nodes,

关键词： Parallel programming

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A Chatbot Assistant for Reducing Risk in Machinery Design

A Chatbot Assistant for Reducing Risk in Machinery Design

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IEEE International Conference on Industrial Informatics (INDIN)

作者： Padma Iyenghar Michael Kieviet Elke Pulvermueller Juergen Wuebbelmann innotec GmbH Erlenweg 12 Melle Germany Faculty of Engineering and Computer Science Osnabrueck University of Applied Sciences Germany Software Engineering Research Group University of Osnabrueck Germany

In this paper, a novel chatbot for risk reduction as an aid during machinery design is presented. The general workflow of the chatbot involves the identification of the hazard described by the user using a neural network model followed by an interactive dialog based conversation, in which the risk reduction measures are outlined. A prototype implementation of the chatbot presents the steps to generate and pre-process the training data for Artificial Intelligence (AI) based models. Different neural network models are trained and evaluated for the proposed risk reduction chatbot. A comparative study is presented by employing an in-depth qualitative and quantitative evaluation. The work presented in this paper shows significant promise in ensuring safety awareness, thereby aiding in implementing functional safety in the early stages of machinery design and development.

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A Machine-Learning-based Surrogate Modeling Methodology for Submodel Integration in the Holistic Railway Digital Twin Platform 33

A Machine-Learning-based Surrogate Modeling Methodology for ...

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33rd CIRP Design Conference

作者： Zhou, Shiyang Meierhofer, Alexander Kugu, Ozan Xia, Yuxi Grafinger, Manfred TU Wien Institute of Engineering Design and Product Development Lehargasse 6/307 Vienna1060 Austria Virtual Vehicle Research GmbH Co-Simulation and Software Group Inffeldgasse 21a Graz8010 Austria University of Vienna Faculty of Computer Science Kolingasse 14-16 Vienna1090 Austria

A holistic railway infrastructure digital twin (DT) platform is sophisticated and consists of a series of submodels (e.g., turnouts, tracks, vehicles, etc.) that are built through various methodologies and software. However, integrating these submodels into the DT platform is tremendously challenging due to considerable computational complexity, software and interface restrictions. To this end, we designed a machine learning (ML) based surrogate modeling methodology for the submodel integration in the holistic railway infrastructure DT platform and illustrated the methodology through a case study. In this case study, an ML-based surrogate model for multibody simulation of railway vehicle-track dynamics is created, which can replace the railway vehicle-track simulation executed with the Multibody Dynamics (MBD) Simulation commercial software SimPACK. The well-built ML model can accurately and quickly predict the vehicle-track system's dynamic responses to different track irregularities. Besides, the integration process of the ML-based surrogate model into the DT platform through a standardized open-source Functional Mock-up Interface (FMI) is also proposed. The developed surrogate modeling methodology shows great promise owing to its high fidelity, which is verified by the measurement data collected from the Austrian national railway track system. The main contribution of our work lies in the well-built ML-based surrogate modeling methodology for reducing the computation complexity and time of different submodels, which facilitates the unification and integration of different submodels. Furthermore, this approach can also be applied to other submodels and help to build the holistic railway DT platform collaboratively. © 2023 The Authors. Published by Elsevier B.V. This is an open access article under the CC BY-NC-ND license (https://***/licenses/by-nc-nd/4.0)

关键词： Machine learning

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