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Addressing the Libyan Arabic Dialect Identification: A Comparative Study of Ensemble Classification Methods 4

Addressing the Libyan Arabic Dialect Identification: A Compa...

4th IEEE International Maghreb Meeting of the Conference on sciences and Techniques of Automatic Control and computer Engineering, MI-STA 2024

作者： Ghumeid, Najah A. Essgaer, Mansour Sebha University Faculty of Sciences Computer Science Department Sebha Libya Sebha University Faculty of Information Technology Information Systems Department Sebha Libya

ISBN: (纸本)9798350372632

Dialect identification presents a challenging task within the realm of Natural Language Processing. The growing utilization of Arabic dialects in written form, particularly on social media platforms, has created new demands for accurate identification of these dialects. In multilingual datasets, distinguishing the Libyan Arabic dialect from neighboring Arabic dialects has emerged as a crucial goal. To address this, we conducted experiments using different lengths of word n-gram features to determine the most suitable combination of word n-grams and term frequency-inverse document frequency. Our aim was to capture contextual information effectively while mitigating linguistic similarities between Arabic dialects of neighboring countries. The QCRI Arabic Dialects Identification dataset was employed, and we evaluated the performance of three ensemble tree-based classification methods: Random Forest, Gradient Boost Machine, and eXtreme Gradient Boosting. The evaluation, based on the f-measure metric, yielded respective scores of 68%, 37%, and 52% for Random Forest, Gradient Boost Machine, and eXtreme Gradient Boosting. These results underscore the effectiveness of our approach in identifying the Libyan Arabic dialect and highlight the potential of ensemble tree-based methods in Arabic dialect identification tasks. © 2024 IEEE.

关键词： Computational linguistics

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Towards Leveraging Explainable Artificial Intelligent (XAI) in Requirements Engineering (RE) to Identify Aspect (Crosscutting Concern): A Systematic Literature Review (SLR) and Bibliometric Analysis 11

Towards Leveraging Explainable Artificial Intelligent (XAI) ...

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11th International Conference on information Technology, ICIT 2023

作者： Magableh, Aws A. Yarmouk University Faculty of Computer Science and Information Technology Department of Information Systems Irbid Jordan

ISBN: (纸本)9798350320060

The software development life cycle contains many phases, among the most notable ones is requirements engineering (RE), it is considered a very important activity because poorly implemented RE steps can result in poor software quality which might lead to expensive maintenance costs and so many other undesirable issues. Thus, owing to the importance of RE in general and aspect (crosscutting concerns) identification in particular, researchers have proposed many approaches that can help to produce a clear and complete set of requirements document. On the other hand, artificial intelligence methods have a proven record of handling complicated text. Yet there is insufficient understanding of how AI and explainable AI can be employed and incorporated to be utilized in RE. Nevertheless, join forces to produce research for beneficial outcomes in this theme. The present study is a comprehensive SLR that reviewed the current state-of-the-art of AI/XAL in RE and aspect orientation. We reviewed the research from 2007 till 2022, PRISMA methodology is utilized to obtain a final set of (n =23) articles from four major scientific databases (Scopus, and Web of science). Clear and new taxonomies were invented during the review discussion. Additionally, to enhance the contribution of this research, a bibliometric comprehensive analysis was conducted on the basis of annual scientific production, country scientific production, and co-occurrence. As a result, it is deemed a noteworthy topic of extensive exploration especially after we identify critical research gaps with their corresponding solutions and generic thoughts to serve as a guide for future directions. © 2023 IEEE.

关键词： Requirements engineering

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Artificial Intelligence Role in Software Automation Testing

Artificial Intelligence Role in Software Automation Testing

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2024 International Conference on Decision Aid sciences and Applications, DASA 2024

作者： Awad, Abdallah Qutqut, Mahmoud H. Ahmed, Ali Al-Haj, Fatima Almasalha, Fadi Computer Science Department West Mishref Hawalli Kuwait Applied Science Private University Faculty of Information Technology Amman Jordan

ISBN: (纸本)9798350369106

Artificial intelligence (AI) significantly influences the systems and applications that underpin modern life. Large datasets are generated from diverse sources. Hence, there is a need for effective data monitoring, processing, and reporting, which is crucial for making influential decisions. In developing complex software systems, AI has become essential for ensuring rigorous testing that aligns with business requirements within constrained timelines. The main advantage of AI in software testing lies in its ability to enhance accuracy and streamline repetitive tasks, ultimately reducing the time required for testing. To this end, this paper explores the role of AI in automated software testing, critically analyzing the limitations of traditional automated testing methods, which often struggle with inconsistent bug detection and have limited adaptability across different environments. By expanding test coverage and addressing these limitations, AI-driven tools can significantly enhance testing efficiency. Additionally, we investigate various AI-based tools designed to optimize software testing and strengthen quality assurance processes. © 2024 IEEE.

关键词： Adversarial machine learning

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Interactive Chatbot for Improving the Text Classification Data Quality 1

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15th International Conference on Interactive Mobile Communication, Technologies, and Learning, IMCL 2023

作者： Elzanfaly, Doaa S. Mohamed, Nada Amr Othman, Nermin Abdelhakim Department of Information Systems Faculty of Computers and Artificial Intelligence Helwan University Cairo Egypt Department of Information Systems Faculty of Informatics and Computer Science British University in Egypt Cairo Egypt

ISBN: (数字)9783031560750

ISBN: (纸本)9783031560743

The pandemic is affecting the global community in many ways. In most developing countries, there is a limitation in the detection facilities, which affect many suspected cases. This paper proposes a chatbot framework to assist and provide guide for the suspected/infected patients with COVID-19. Conversational software agents activated by natural language processing is known as chatbot, are an excellent example of such machine. Our COVID Bot is based on an integrated model between the rule-based model and the class classification model, having the rule-based model integrated with the MongoDB NoSQL database. Chatbot, using Natural Language Processing (NLP) and data mining techniques to assist patients by providing immediate answers for their questions. It also acts as a novel communication mean for impaired people for sharing knowledge and information, through conversing with them. Based on the literature review, this paper compared our methods with three classical classification algorithms: random forest, gradient boosting, and multi-layer perceptron (MLP). Experimental results show that our proposed chatbot greatly improves the classification performance, with IE-Net as 94.80%, 92.79% as recall, 92.97% as precision and 94.93% as AUC for distinguishing COVID-19 cases from non-COVID-19 cases (with only common clinical diagnose data). © The Author(s), under exclusive license to Springer Nature Switzerland AG 2024.

关键词： Coronavirus

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Enhancing connectivity and coverage in wireless sensor networks: a hybrid comprehensive learning-Fick’s algorithm with particle swarm optimization for router node placement

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Neural Computing and Applications 2024年第34期36卷 21671-21702页

作者： Amer, Dina A. Soliman, Sarah A. Hassan, Asmaa F. Zamel, Amr A. Computer Science Department Higher Technological Institute Tenth of Ramadan44228 Egypt Management Information Systems Department Faculty of Management Economics and Business Technology Egyptian Russian University Badr11829 Egypt Computer and Systems Engineering Department Faculty of Engineering Zagazig University Zagazig44519 Egypt

Wireless Sensor Networks (WSNs) are essential for collecting and transmitting data in modern applications that rely on data, where effective network connectivity and coverage are crucial. The optimal placement of router nodes within WSNs is a fundamental challenge that significantly impacts network performance and reliability. Researchers have explored various approaches using metaheuristic algorithms to address these challenges and optimize WSN performance. This paper introduces a new hybrid algorithm, CFL-PSO, based on combining an enhanced Fick’s Law algorithm with comprehensive learning and Particle Swarm Optimization (PSO). CFL-PSO exploits the strengths of these techniques to strike a balance between network connectivity and coverage, ultimately enhancing the overall performance of WSNs. We evaluate the performance of CFL-PSO by benchmarking it against nine established algorithms, including the conventional Fick’s law algorithm (FLA), Sine Cosine Algorithm (SCA), Multi-Verse Optimizer (MVO), Salp Swarm Optimization (SSO), War Strategy Optimization (WSO), Harris Hawk Optimization (HHO), African Vultures Optimization Algorithm (AVOA), Capuchin Search Algorithm (CapSA), Tunicate Swarm Algorithm (TSA), and PSO. The algorithm’s performance is extensively evaluated using 23 benchmark functions to assess its effectiveness in handling various optimization scenarios. Additionally, its performance on WSN router node placement is compared against the other methods, demonstrating its competitiveness in achieving optimal solutions. These analyses reveal that CFL-PSO outperforms the other algorithms in terms of network connectivity, client coverage, and convergence speed. To further validate CFL-PSO’s effectiveness, experimental studies were conducted using different numbers of clients, routers, deployment areas, and transmission ranges. The findings affirm the effectiveness of CFL-PSO as it consistently delivers favorable optimization results when compared to existing meth

关键词： Particle swarm optimization (PSO)

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Thermal Model Validation of a Power Electronics Module for Digital Twin Application

Thermal Model Validation of a Power Electronics Module for D...

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2024 Energy Conversion Congress and Expo Europe, ECCE Europe 2024

作者： Zoller, Svenja Cabre-Piqueras, Claudia Schilder, Boris Filba-Martinez, Alber Acker, Lukas Frankfurt University of Applied Science Faculty of Computer Science and Engineering Frankfurt Germany Power Systems Dept. Barcelona Spain

ISBN: (纸本)9798350364446

Thermal stress is a critical failure factor for power electronics components. Hence, when developing a precise digital twin for a power system, it is imperative that thermal models provide a reliable portrayal of the component physics. In that regard, manufacturers offer simplified thermal models that enable designers and users to estimate its behaviour. Nevertheless, the accuracy of these models remains unknown, thus compromising the overall certainty of the digital twin. Therefore, it is of interest to discern whether these simplified models are sufficient for the precision required by the digital twin, and to assess the need of developing a more accurate model. Hence, this paper focuses on validating the theoretical thermal model of a power electronics switch module setup by using the deviation of the module mean time to failure (MTTF) as the acceptance parameter. To achieve this, a novel experimental validation methodology is proposed to retrieve an experimental thermal model and compare it to the theoretical one. This validation is performed considering the discrete thermal impedances and introducing the study of the coupling effects within the module. © 2024 IEEE.

关键词： Insulated gate bipolar transistors (IGBT)

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Enhancement of High-Definition Map for Autonomous Vehicle Application by Light Intensity Equalization Technique

Enhancement of High-Definition Map for Autonomous Vehicle Ap...

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2024 IEEE International Conference on Industry 4.0, Artificial Intelligence, and Communications Technology, IAICT 2024

作者： Nokkaew, Pattarapoom Sutthisangiam, Nikorn Department of Computer and Information Science Faculty of Applied Science King Mongkut's University of Technology North Bangkok Bangkok Thailand

ISBN: (纸本)9798350353464

This research aims to enhance point cloud data and simulate the operations of autonomous vehicles following data refinement. The study utilizes high-resolution point cloud data generated by a mobile mapping system and employs histogram equalization techniques to broaden the range of intensity levels, enhance contrast, and overall improve intensity values. The findings demonstrate a heightened density of dispersed intensity values and an expanded range of intensity levels compared to previous states. The point cloud data file size contribute to a 21% increase from additional shade values recording format, allows for clearer displays of road details and surrounding areas. while simultaneously increasing the accuracy of coordinates on these maps by 15%, as evaluated through simulations. Additionally, this process can aid in the creation of vector maps, a component of high-resolution maps for autonomous vehicles, thereby enhancing performance in various scenarios such as decision-making at intersections and obstacle detection. ©2024 IEEE.

关键词： Obstacle detectors

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White Leg Shrimp Larvae Counting System 16

White Leg Shrimp Larvae Counting System

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16th International Conference on Knowledge and Smart Technology, KST 2024

作者： Kamthang, Vutichai Anekboon, Khantharat King Mongkut's University of Technology North Bangkok Faculty of Applied Science Department of Computer and Information Science Bangkok Thailand

ISBN: (纸本)9798350370737

White leg shrimp is one of the animals that is popularly consumed and exported in Thailand. A lot of farmers cultivate white leg shrimp in many areas. To raise white leg shrimp larvae into mature white leg shrimp that can be sold in the market for further consumption or export. Before a farm sells white leg shrimp larvae to a farmer, the farm staff needs to count the number of them. The traditional counting method is naked-eye estimation which requires experience and gives a rough estimation. A traditional technique causes time- consuming and human error. Moreover, a farm usually adds some scoops to make sure that the number of white leg shrimp larvae is not less than an order. This situation affects the farm's costs. This study proposes a new system for automated counting white leg shrimp larvae by applying the simple blob detector technique with opening and closing of morphological operations. The experimental result of the proposed system shows that this proposed system gives the best accuracy (94.20%), precision (97.68%), recall (96.35%), and F1 score (96.98%) compared with Canny Edge Detection and Blob analysis, morphological operations (opening, dilatation and gamma correction), and the blue sense system. © 2024 IEEE.

关键词： Mathematical morphology

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Project Assessment in Offshore Software Maintenance Outsourcing Using Deep Extreme Learning Machines

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computers, Materials & Continua 2023年第1期74卷 1871-1886页

作者： Atif Ikram Masita Abdul Jalil Amir Bin Ngah Saqib Raza Ahmad Salman Khan Yasir Mahmood Nazri Kama Azri Azmi Assad Alzayed Faculty of Ocean Engineering Technology and Informatics University Malaysia TerengganuKuala NerusMalaysia Department of Computer Science&Information Technology Faculty of Information TechnologyThe University of LahoreLahore54000Pakistan Department of Software Engineering Faculty of Information TechnologyThe University of LahoreLahore54000Pakistan Advanced Informatics Department Razak Faculty of Technology and InformaticsUniversiti Teknologi Malaysia54100Kuala LumpurMalaysia Computer Science and Information Systems Department College of Business Studies Public Authority for Applied Education and Training(PAAET)AdailiyahKuwait Department of Computer Science Sharif College of Engineering&TechnologyLahore54000Pakistan

Software maintenance is the process of fixing,modifying,and improving software deliverables after they are delivered to the *** can benefit from offshore software maintenance outsourcing(OSMO)in different ways,including time savings,cost savings,and improving the software quality and *** of the hardest challenges for the OSMO vendor is to choose a suitable project among several clients’*** goal of the current study is to recommend a machine learning-based decision support system that OSMO vendors can utilize to forecast or assess the project of OSMO *** projects belong to OSMO vendors,having offices in developing countries while providing services to developed *** the current study,Extreme Learning Machine’s(ELM’s)variant called Deep Extreme Learning Machines(DELMs)is used.A novel dataset consisting of 195 projects data is proposed to train the model and to evaluate the overall efficiency of the proposed *** proposed DELM’s based model evaluations achieved 90.017%training accuracy having a value with 1.412×10^(-3) Root Mean Square Error(RMSE)and 85.772%testing accuracy with 1.569×10^(-3) RMSE with five DELMs hidden *** results express that the suggested model has gained a notable recognition rate in comparison to any previous *** current study also concludes DELMs as the most applicable and useful technique for OSMO client’s project assessment.

关键词： Software outsourcing deep extreme learning machine(DELM) machine learning(ML) extreme learning machine assessment

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Toward Fine-grained Image Retrieval with Adaptive Deep Learning for Cultural Heritage Image

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computer systems science & Engineering 2023年第2期44卷 1295-1307页

作者： Sathit Prasomphan Department of Computer and Information Science Faculty of Applied ScienceKing Mongkut’s University of Technology North Bangkok1518 Pracharat 1 Rd.WongsawangBangsueBangkok10800Thailand

Fine-grained image classiﬁcation is a challenging research topic because of the high degree of similarity among categories and the high degree of dissimilarity for a speciﬁc category caused by different poses and scales.A cul-tural heritage image is one of theﬁne-grained images because each image has the same similarity in most *** the classiﬁcation technique,distinguishing cultural heritage architecture may be difﬁ*** study proposes a cultural heri-tage content retrieval method using adaptive deep learning forﬁne-grained image *** key contribution of this research was the creation of a retrieval mod-el that could handle incremental streams of new categories while maintaining its past performance in old categories and not losing the old categorization of a cul-tural heritage *** goal of the proposed method is to perform a retrieval task for *** learning for new classes was conducted to reduce the re-training *** this step,the original class is not necessary for re-train-ing which we call an adaptive deep learning *** heritage in the case of Thai archaeological site architecture was retrieved through machine learn-ing and image *** analyze the experimental results of incremental learning forﬁne-grained images with images of Thai archaeological site architec-ture from world heritage provinces in Thailand,which have a similar *** aﬁne-grained image retrieval technique for this group of cultural heritage images in a database can solve the problem of a high degree of similarity among categories and a high degree of dissimilarity for a speciﬁc *** proposed method for retrieving the correct image from a database can deliver an average accuracy of 85 *** deep learning forﬁne-grained image retrieval was used to retrieve cultural heritage content,and it outperformed state-of-the-art methods inﬁne-grained image retrieval.

关键词： Fine-grained image adaptive deep learning cultural heritage image retrieval

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