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7th International Conference on engineering and MIS, ICEMIS 2021

作者： Altabouni, Fathia Farj Huwedi, Ashraf Saad Bozed, Kenz Amhmed Department of Software Engineering Benghazi University Libya Department of Computer System Design Benghazi University Libya

ISBN: (纸本)9781450390446

Optical system for humans has the ability to get to know a very large number of objects or classes of object from two-dimensional visual information or three dimensions. Solving this task of detection or recognition by humans without any major effort is due to the ability of humans for very fast parallel processing and their ability for self development, as well as the ability to learn from experience due to the exceptional structure of the human brain. Computer vision systems exist that are able to detecting an object in image, but usually these systems need to be instructed what the object to detect is. And the main difficulty comes from a very large number of objects, each of these objects may appear in an infinite number of several aspects: dimension, shape, color, position, lighting, and shade. In this paper, we present a robust method attempts to deal with the above challenges by using Local Binary Pattern (LBP) and Random Forest techniques (RF). Results show that using the suggested method 95.39% classification rate in the car detection, as well as 94.9%, for cat detection, and flower detection. © 2021 ACM.

关键词： Optical systems

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Energy Efficiency Improvement System for AC Circuits

Energy Efficiency Improvement System for AC Circuits

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2024 Conference of Young Researchers in Electrical and Electronic engineering, ElCon 2024

作者： Kulagin, Vladimir Selivanov, Kirill Vlasov, Andrey Aminev, Dmitrii Bunina, Lyudmila Bikovsky, Sergey Moscow Technical University of Communications and Informatics Moscow Russia Bauman Moscow State Technical University Bmstu Department of Design and Technology of Electronic Devices Moscow Russia Russian Technological University - Mirea Department of Development of Software Solutions and System Programming Moscow Russia Russian Technological University - Mirea Department of Electrical Engineering Systems Moscow Russia

ISBN: (纸本)9798350360646

The problem of the rational use of energy resources remains constantly relevant and requires the search for new approaches. One of them is power control. In AC circuits, the authors see the most promising method of phase AC power control. Based on it, a power control module was developed. The structural and circuit diagrams of the developed device are presented to implement the proposed solution. The authors produced its experimental prototype and conducted experimental testing at various levels of regulated power. Savings when using the proposed power control module were calculated using the example of energy consumption in various sectors of energy-intensive systems. According to the data obtained, the module allows saving energy consumption without significant discomfort to the energy user. The calculated results allow concluding that the proposed AC power control module is fully operational, and its widespread use will significantly reduce the need for electricity. © 2024 IEEE.

关键词： energy consumption energy efficiency power control

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Extended Collaborative Filtering Approach for Genre-Based Movie Recommender Systems

Extended Collaborative Filtering Approach for Genre-Based Mo...

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International Conference on Web Research (ICWR)

作者： Radmehr Motamedi Behzad Soleimani Neysiani Diploma High School Shahid Ejei School Isfahan Iran Department of Software Engineering Oil Design and Construction Company (ODCC) Isfahan Iran

ISBN: (数字)9798331508913

ISBN: (纸本)9798331508920

Collaborative recommender systems (RSs) play a crucial role in predicting user behaviors and suggesting new items. However, existing RSs face challenges related to accuracy, scalability, and data sparsity. Often, user preferences can be inferred by categorizing their ratings into distinct classes based on specific categorical features. In this study, we propose a novel approach that leverages movie genres to discern users' preferences, classifying them into three categories: “hatred,” “medium,” and “loved” genres. Subsequently, we combine the user's genre preferences with a user similarity method based on Euclidean and Cosine distances to recommend new movies. The proposed method enhances the performance of RSs by incorporating genrebased preferences. The proposed method is evaluated using the MovieLens dataset. Experimental results demonstrate approximately 24%, 51% and 49% improvement in accuracy, precision and recall rates while explores other facets of recommendation performance, such as overall user satisfaction versus traditional kNN.

关键词： Measurement Accuracy Collaborative filtering Scalability Collaboration Nearest neighbor methods Motion pictures Recommender systems Faces Testing

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TexGAN: Textile Pattern Generation Using Deep Convolutional Generative Adversarial Network (DCGAN)

TexGAN: Textile Pattern Generation Using Deep Convolutional ...

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2023 IEEE International Conference on Emerging Trends in engineering, Sciences and Technology, ICES and T 2023

作者： Yar, Ghulam Nabi Ahmad Hassan Taha, Muhammad Afzal, Zeshan Zafar, Farhan Shahid, Inam-Ur-Rehman Noor-Ul-Hassan, Abubakar AI Developer Maanz-AI Pakistan Team Lead RandD Technosense21 Pvt Ltd The Islamia University Department of Software Engineering Islamabad Pakistan ZR-Tech Greater Manchester United Kingdom Design Engineer Embedded System AKSA-SDS Pakistan Software Developer Nakisa Inc. Pakistan

ISBN: (纸本)9781665455602

Generative Adversarial Networks (GANs) are used for image, video, and 3D scene generation along with their ability to enhance images. In image generation, GANs generate new images by learning features from the existing images. Features are extracted by a convolutional network, and images are generated through a de-convolutional network. GANs have been recently applied to the field of fashion where they have been used to generate new kinds of shoes and clothes. In the field of textile design, designs are printed onto clothes. These designs are mostly made of single repetitive patterns. These patterns need to be created by the designers and then these patterns are photoshopped to make the whole design. Generating textile patterns through GANs can help designers to take inspiration for new ideas. To achieve this goal GANs have been proposed to generate new patterns. Different variants of GANs are available in fields and they helped in many other fields along with the field of fashion. At first, we formulated a dataset of 17k+ images collected via google images. The dataset is available at the GitHub repository of the project (https://***/Gnahy/***). We provided an idea of generating textile patterns in general and one can also specify the patterns they want to generate i.e., cheetah, geometric, and abstract. DCGAN has been trained on all the patterns collectively up to a specific point and the weights from the trained model have been saved. After that point model is trained on specific kinds of patterns i.e., cheetah in order to generate that specific kind of new patterns. This technique helps even when the data for the new patterns are small and resources to train the model are fewer. © 2023 IEEE.

关键词： Generative adversarial networks

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Machine Learning Stroke Prediction in Smart Healthcare:Integrating Fuzzy K-Nearest Neighbor and Artificial Neural Networks with Feature Selection Techniques

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Computers, Materials & Continua 2025年第3期82卷 5115-5134页

作者： Abdul Ahad Ira Puspitasari Jiangbin Zheng Shamsher Ullah Farhan Ullah Sheikh Tahir Bakhsh Ivan Miguel Pires Department Information System Study Program Faculty of Science and TechnologyUniversitas AirlanggaSurabaya60286Indonesia School of Software Northwestern Polytechnical UniversityXi’an710072China Research Center for Quantum Engineering Design Faculty of Science and TechnologyUniversitas AirlanggaSurabaya60286Indonesia School of Computer Science and Software Engineering Shenzhen UniversityShenzhen518061China Cybersecurity Center Prince Mohammad Bin Fahd University617Al JawharahKhobarDhahran34754Saudi Arabia Cardiff School of Technologies Cardiff Metropolitan UniversityWestern AvenueCardiffCF52YBUK Instituto de Telecomunicacoes Escola Superior de Tecnologia e Gestao de AguedaUniversidade de Aveiroágueda3750-127Portugal

This research explores the use of Fuzzy K-Nearest Neighbor(F-KNN)and Artificial Neural Networks(ANN)for predicting heart stroke incidents,focusing on the impact of feature selection methods,specifically Chi-Square and Best First Search(BFS).The study demonstrates that BFS significantly enhances the performance of both *** BFS preprocessing,the ANN model achieved an impressive accuracy of 97.5%,precision and recall of 97.5%,and an Receiver Operating Characteristics(ROC)area of 97.9%,outperforming the Chi-Square-based ANN,which recorded an accuracy of 91.4%.Similarly,the F-KNN model with BFS achieved an accuracy of 96.3%,precision and recall of 96.3%,and a Receiver Operating Characteristics(ROC)area of 96.2%,surpassing the performance of the Chi-Square F-KNN model,which showed an accuracy of 95%.These results highlight that BFS improves the ability to select the most relevant features,contributing to more reliable and accurate stroke *** findings underscore the importance of using advanced feature selection methods like BFS to enhance the performance of machine learning models in healthcare applications,leading to better stroke risk management and improved patient outcomes.

关键词： Fuzzy K-nearest neighbor artificial neural network accuracy precision recall F-measure Chi-Square best search first heart stroke

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Unusually high corrosion resistance in Mo_(x)CrNiCo medium entropy alloy enhanced by acidity in aqueous solution

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Journal of Materials Science & Technology 2023年第8期139卷 59-68页

作者： S.Shuang G.J.Lyu D.Chung X.Z.Wang X.Gao H.H.Mao W.P.Li Q.F.He B.S.Guo X.Y.Zhong Y.J.Wang Y.Yang Department of Mechanical Engineering College of EngineeringCity University of Hong KongKowloon TongKowloonHong KongChina State Key Laboratory of Nonlinear Mechanics Institute of MechanicsChinese Academy of SciencesBeijing 100190China School of Engineering Science University of Chinese Academy of SciencesBeijing 101408China State Key Laboratory of Solidification Processing Center of Advanced Lubrication and Seal MaterialsNorthwestern Polytechnical UniversityXi’an 710072China Unit of Structures Department of Materials Science and EngineeringKTHStockholm SE-10044Sweden Thermo-Calc Software Råsundav.18SolnaSE-16767Sweden Department of Materials Science and Engineering College of EngineeringCity University of Hong KongKowloon TongKowloonHong KongChina Institute of Advanced Wear&Corrosion Resistant and Functional Materials Jinan UniversityGuangzhou 510632China Department of Advanced Design and System Engineering College of EngineeringCity University of Hong KongKowloon TongKowloonHong KongChina

High corrosion resistance of alloys is essential for their structural applications;however,most alloys suffer from degradation of their corrosion resistance with the increasing acidity of their ***,we developed a series of medium-entropy alloys(MEAs)in this work,which ex-hibit high strength,superior fracture toughness and ultra-high corrosion resistance,outperforming the variety of corrosion resistant alloys hitherto *** interestingly,our MEAs exhibit an unusual anti-corrosion behavior and their corrosion resistance increases with acidity in Cl−containing *** extensive thermodynamic calculations,density functional theory(DFT)simulations and experi-ments,we reveal that the unusual anti-corrosion behavior of our MEAs can be attributed to their surface chemical complexity,which facilitates the physio-chemical-absorption of H_(2)O and O_(2)and thus the rapid formation of metastable medium entropy passive films that contain the lowest amount of defects,as compared to the passive films on conventional alloys reported in the literature.

关键词： Corrosion Passivation Alloy design Multi-principal element alloys Medium-entropy alloys

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Towards Automated Support for the Co-Evolution of Meta-Models and Grammars

arXiv

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arXiv 2023年

作者： Zhang, Weixing Department of Computer Science & Engineering Division of Interaction Design & Software Engineering Chalmers University of Technology University of Gothenburg Gothenburg Sweden

Blended modeling is an emerging paradigm involving seamless interaction between multiple notations for the same underlying modeling language. We focus on a model-driven engineering (MDE) approach based on meta-models to develop textual languages to improve the blended modeling capabilities of modeling tools. In this thesis, we propose an approach that can support the co-evolution of meta-models and grammars as language engineers develop textual languages in a meta-model-based MDE setting. Firstly, we comprehensively report on the challenges and limitations of modeling tools that support blended modeling, as well as opportunities to improve them. Second, we demonstrate how language engineers can extend Xtext’s generator capabilities according to their needs. Third, we propose a semi-automatic method to transform a language with a generated grammar into a Python-style language. Finally, we provide a solution (i.e., GrammarOptimizer) that can support rapid prototyping of languages in different styles and the co-evolution of meta-models and grammars of evolving languages. © 2023, CC BY-NC-ND.

关键词： Modeling languages

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Computational Model of Swarm Algorithm for Optimizing Process of Keywords Extraction from Text Information Presented as Graph

Computational Model of Swarm Algorithm for Optimizing Proces...

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International Conference on Information Technologies in engineering Education (Inforino)

作者： Yury Kravchenko Elmar Kuliev Daniil Kravchenko Sergey Rodzin Nikolay Pavlov Lada Rodzina Dept. of Computer Aided Design Southern Federal University Taganrog Russia Department of Software Engineering Southern Federal University Taganrog Russia

ISBN: (数字)9798350371369

ISBN: (纸本)9798350371376

The article describes the scientific problem of optimizing the processes of acquiring knowledge when processing textual information presented in the form of a graph, based on the development of a modified bioinspired algorithm for extracting keywords. The solution proposed by the authors differs from the known ones by introducing new attractive operators and behavioral patterns of bacterial agents into the computational model of the bacterial swarm algorithm. This approach allows you to balance the ratio of the speed of convergence of the algorithm with the diversification of the search space for solutions. The article also presents an analytical study of the features of two types of parsing: constituency parsing and dependency parsing. As part of the study, algorithms for syntactic parsing of dependencies between elements of text information and a graph-based dependency parsing algorithm are described. The formulation of the problem of keyword extraction is given. The task of achieving a balance between accuracy and speed of a linguistic parser is an open topic that requires further research due to the importance of this problem for improving the efficiency of text analysis, especially in applications that require real-time accuracy. To increase the efficiency of solving the problem, the authors propose a modified bioinspired algorithm for extracting keywords. The computational experiment carried out allows us to conclude that the modified bacterial optimization method proposed by the authors is in a leading position, which confirms the need to continue research into the applicability of bioinspired methods for solving problems of acquiring knowledge when processing text information presented in the form of a graph. This article is focused on the direction of study in the master’s program “Informatics and Computer Science”, as well as the scientific specialty of postgraduate training “Artificial Intelligence and Machine Learning”.

关键词： Microorganisms Accuracy Text analysis Machine learning algorithms Computational modeling Optimization methods Syntactics

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Malware Detection for Android TV

SSRN

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SSRN 2024年

作者： Ozogur, Gokhan Erturk, Mehmet Ali Aydin, Muhammed Ali R&D Software Design Department Arcelik A.S. Istanbul Turkey Department of Computer Engineering Istanbul University Istanbul Turkey Department of Computer Engineering Istanbul University-Cerrahpasa Istanbul Turkey

Security of Android applications is a well-studied area in the literature but it is very limited for other types of devices like Android TVs. Android TVs are growing in popularity with advances in integrated services and gaming features, and are also attracting the attention of attackers. Although there is extensive literature on Android security for mobile devices, the literature on malware detection for Android TV devices is quite limited. In this study, we collected Android TV applications from markets and injected malicious payload into some of the benign applications to have Android TV malware which is hard to find in the market. We extracted n-grams from *** and binary source (***) files in Android TV application packages, and obtained 1000 features using the TF-IDF method. We publicly shared these features and class (benign or malicious) labels of the Android TV applications in spreadsheet format as a dataset for future studies. Also, we implemented 3 separate classification models using Extreme Gradient Boosting (XGBoost), Neural Network (NN) and Support Vector Machine (SVM) methods to compare their performance on this dataset for malware detection. We observed that XGBoost model perfomed better than NN and SVM models. XGBoost model classifies a TV application as malicious or benign in average of 0.0102 milliseconds with 97.31% F1-score and 94.62% MCC scores. © 2024, The Authors. All rights reserved.

关键词： Android (operating system)

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A car-following model based on the optimized velocity and its security analysis

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Transportation Safety and Environment 2023年第4期5卷 127-134页

作者： Rong Fei Lu Yang Xinhong Hei Bo Hu Aimin Li School of Computer Science and Engineering Xi’an University of TechnologyNo.5 Jinhua South RoadXi’an 710048ShaanxiP.R.China Software Design Department Hangzhou Holly Sys Automation Co.Ltd.No.29Jinye 1st RoadXi’an 710065ShaanxiP.R.China

An enhanced optimal velocity model(EOVM)that considers driving safety is established to alleviate traffic congestion and ensure driving *** headway is introduced as a criterion for determining whether the car is *** the time headway is less discussed to ensure the model's safety and maintain the following state.A stability analysis of the model was carried out to determine than the minimum time headway(TH_(min))or more than the most comfortable time headway(TH_(com)),the acceleration constraints are the stability conditions of the *** EOVM is compared with the optimal velocity model(OVM)and fuzzy car-following model using the real *** show that the EOVM model has the smallest error in average,maximum and median with the real *** confirm the model's safety,design fleet simulation experiments were conducted for three actual scenarios of starting,stopping and uniform process.

关键词： optimized velocity constraint optimization security analysis

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