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International Conference on Electronics, information and Communications (ICEIC)

作者： Sherin Sheela May Phu Piang Sakda Sakorntanant Suvit Poomrittigul Treesukon Treebupachatsakul Department of Biomedical Engineering School of Engineering King Mongkut's Institute of Technology Ladkrabang Thailand Department of Software Engineering and Information System Faculty of Science and Technology Pathumwan Institute of Technology Bangkok Thailand School of Information Technology King Mongkut's Institute of Technology Ladkrabang Thailand

Bacterial strains in the same genus share highly similar morphology, gram-staining characteristics, colony sizes, and spatial arrangements. Therefore, identifying them by deep learning can be quite challenging. This study aimed to assess the classification of 7 species of bacteria from 2 genera of Bacillus and Vibrio by using 8 Convolutional Neural Network (CNN) models. We implemented Python programming along with Keras API within the Jupyter Notebook. The models were constructed and evaluated under unbalanced and balanced datasets by augmentation (rotation, flip, etc.). Transfer learning with fine-tuning, and pre-processing of mixup and label smoothing were also applied to reduce overfitting and enhance generalization. Based on the experimental results on private dataset, the results of InceptionResNetV2 emerged as the top-performing model with a notable accuracy of 82.8%, 88.6% precision, 78.4% recall, and 78.0% F1-score when label smoothing was applied at 0.5 on balanced dataset.

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Deep Learning based Multilabel Hateful Speech Text Comments Recognition and Classification Model for Resource Scarce Ethiopian Language: The case of Afaan Oromo

Deep Learning based Multilabel Hateful Speech Text Comments ...

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2022 IEEE International Conference on Current Development in engineering and technology, CCET 2022

作者： Defersha, Naol Bakala Abawajy, Jemal Kekeba, Kula College of Electrical and Mechanical Engineering Centre of Excellence for Hpc & Big Data Analytics Addis Ababa Science and Technology Software Engineering Department 16417 Ethiopia School of Information Technology Deakin University Faculty of Science Engineering and Built Environment GeelongVIC3220 Australia Centre of Excellence College of Electrical and Mechanical Engineering Addis Ababa Science and Technology Department of Software Engineering Technology 16417 Ethiopia

ISBN: (纸本)9781665454155

In response to the phenomenon of hate speech on popular social media such as Facebook, a number of researchers have investigated and developed different automated techniques in order to detect and moderate hate speech. Reliable hate speech detection tools ranging from traditional machine learning models to deep neural network models have been developed and deployed for resource rich languages such as English, French and Arabic. On the Other hand, the topic of building automated techniques for hate speech detection in Afaan Oromo is still the subject of very limited studies. In this study, we have used pretrained word embedding approaches such as word2vec and fast text, and assessed their effectiveness for hateful speech recognition and classification in Afaan Oromo text comments on Facebook. By using the proposed model, the effectiveness of deep learning algorithms like CNN and LSTM was also assessed. Moreover, the significance of one of the recent topic modeling approaches, i.e., BERTopic, has been investigated to in extracting topics that can help the categorization of Afaan Oromo hate speech. Afaan Oromo Multilabel Hateful Speech Detection and Dataset (AOMLHSDD), which contains 30728 text comments, was created after we employed the BERTopic to extract relevant topics. Accordingly, Then, we have conducted different experiments to test the effectiveness of CNN and LSTM models by using word2vec and fast text. The result of our experiments has shown showed that LSTM has outperformed the CNN approach by achieving the accuracy of 98.32%. © 2022 IEEE.

关键词： Speech recognition

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An Enriched e-Learning Model to Teach Kids in Arab Countries How to Write Code

An Enriched e-Learning Model to Teach Kids in Arab Countries...

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information technology (ACIT)

作者： Hamzeh Aljawawdeh Department of Software Engineering Faculty of Information Technology Zarqa Jordan

ISBN: (数字)9798350320244

ISBN: (纸本)9798350320251

Even for children, e-learning is essential today. Future generations will need to learn how to code. Because codes are complex, teaching them can be difficult. In this research, we present a model for teaching children programming. This study is an attempt to prove that teaching students to write code in Arab schools is feasible. The paper offers several insights and suggestions that would improve the mechanism of teaching young children to create code segments. Additionally, it highlights the significance of utilizing HCI to promote online education for children.

关键词： Computer languages Codes Electronic learning Education Tutorials Writing software

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Patterns of Remote Work Environments 27

Patterns of Remote Work Environments

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2022 27th European Conference on Pattern Languages of Programs, EuroPLoP 2022

作者： Toldy, Kristian Sulaiman Khail, Waheedullah Vranic, Valentino Institute of Informatics Faculty of Informatics and Information Technologies Information Systems and Software Engineering Slovak University of Technology in Bratislava Bratislava Slovakia Helmholtz-Zentrum Berlin fur Materialien und Energie Gmbh Berlin Germany

ISBN: (纸本)9781450395946

People are more and more in a position to work remotely. They face the same problems of establishing and running remote work environments over and over again. In order to help them a bit, six organizational patterns of remote work environments are presented in this paper: Unofficial Meetings, Remote Mentor, Unified Communication Channels, Standard Operating Procedures, information Hub, and Cut Meetings. The patterns come from a firsthand remote work experience obtained in a company focusing on quality assurance through a crowd testing methodology where software companies get real users to test their newest products. © 2022 ACM.

关键词： Quality assurance

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E-Learning Migration Model for Cloud-based Education

E-Learning Migration Model for Cloud-based Education

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information technology (ACIT)

作者： Omar Dahham Hamed Fawareh Software Engineering Dept Faculty of Information Technology Zarqa University

ISBN: (数字)9798350320244

ISBN: (纸本)9798350320251

Cloud computing is one of the technological advancements that have led to rising prospects in the field of e-learning and has become necessary. E-learning is mostly dependent on technology; it necessitates the use of learning management systems that can be hosted on cloud computing platforms to provide alternative solutions to higher education institutions' inadequate infrastructure. In this paper, we proposed a new migration education model that can be used by educational institutions to migrate from the traditional e-learning system to the cloud-based e-learning system. The proposed model takes into account the education and e-learning education requirements to complete and plan for migration, the model divides the migration process into three phases namely, Pre-migration, migration, and post-migration. Finally, we consider in the education migration model several points that will be taken in the proposed model. The proposed model focus on the importance of cloud computing as a solution for e-learning systems in higher education institutions.

关键词： Cloud computing Learning management systems Electronic learning Computational modeling Education Companies Developing countries

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OPTIMAL SCHEDULE OF A SPORT SHOOTING COMPETITION 17

OPTIMAL SCHEDULE OF A SPORT SHOOTING COMPETITION

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17th International Symposium on Operational Research in Slovenia, SOR 2023

作者： Ercsey, Zsolt Kovács, Zoltán Storcz, Tamás Department of Systems and Software Technologies Faculty of Engineering and Information Technology University of Pécs Boszorkány Street 2 H-7624 Hungary Optin Ltd. Oroszlán u. 4 SzegedH-6720 Hungary

ISBN: (纸本)9789616165617

The paper explains the problem of amateur sport shooting competition scheduling with a manual and an automated solution. The automated solution provides an optimal schedule to minimize the infrastructure and human resource occupation. The proposed programming model also minimizes the attendance of participating sport clubs, because considers that the amateur competitors want to spend minimum time necessary (with required overhead) for the competition and the team of a sport club usually travel together. © Proceedings of the 17th International Symposium on Operational Research in Slovenia, SOR 2023. All rights reserved.

关键词： Sports

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Adaptive Deep Learning Model to Enhance Smart Greenhouse Agriculture

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Computers, Materials & Continua 2023年第11期77卷 2545-2564页

作者： Medhat A.Tawfeek Nacim Yanes Leila Jamel Ghadah Aldehim Mahmood A.Mahmood Department of Computer Science College of Computer and Information SciencesJouf UniversityP.O.Box 72314SkakaSaudi Arabia Department of Computer Science Faculty of Computers and InformationMenoufia UniversityP.O.Box 6121890MenoufiaEgypt Department of Software Engineering College of Computer and Information SciencesJouf UniversityP.O.Box 72314SkakaSaudi Arabia RIADI Laboratory La Manouba UniversityP.O.Box2010ManoubaTunisia Department of Information Systems College of Computer and Information SciencesPrincess Nourah bint Abdulrahman UniversityP.O.Box 84428Riyadh11671Saudi Arabia Department of Information Systems and Technology Faculty of Graduate Studies and ResearchCairo UniversityP.O.Box 3753450GizaEgypt

The trend towards smart greenhouses stems from various factors,including a lack of agricultural land area owing to population concentration and housing construction on agricultural land,as well as water *** study proposes building a full farming adaptation model that depends on current sensor readings and available datasets from different agricultural research *** proposed model uses a one-dimensional convolutional neural network(CNN)deep learning model to control the growth of strategic crops,including cucumber,pepper,tomato,and *** proposed model uses the Internet of Things(IoT)to collect data on agricultural operations and then uses this data to control and monitor these operations in real *** helps to ensure that crops are getting the right amount of fertilizer,water,light,and temperature,which can lead to improved yields and a reduced risk of crop *** dataset is based on data collected from expert farmers,the photovoltaic construction process,agricultural engineers,and research *** experimental results showed that the precision,recall,F1-measures,and accuracy of the one-dimensional CNN for the tested dataset were approximately 97.3%,98.2%,97.25%,and 97.56%,*** new smart greenhouse automation system was also evaluated on four crops with a high turnover *** system has been found to be highly effective in terms of crop productivity,temperature management and water conservation.

关键词： Greenhouse wireless sensor network deep learning Internet of Things strategic crops monitoring smart irrigation

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Intelligent-Based Extraction of Business Capabilities from Company Descriptions

Intelligent-Based Extraction of Business Capabilities from C...

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International Conference on information technology, information Systems and Electrical engineering (ICITISEE)

作者： Faisal Fahmi Siti Hafizah Ab Hamid Imam Yuadi Department of Library and Information Science Faculty of Political and Social Science Airlangga University Department of Software Engineering Faculty of Computer Science & Information Technology Universiti Malaya

In the era of Big Data, information can be generated, extracted, and utilized in diverse ways. In business, information about business capabilities can be a crucial aspect in understanding the strengths and competencies of organizations. This information can empower stakeholders to make informed decisions regarding partnerships, acquisitions, and market positioning. Additionally, by comparing capabilities across companies and industries, organizations can identify their competitive advantages, industry trends, potential disruptions, and effective resource allocations, leading to improved strategies and better market opportunities. Traditional methods for extracting business capabilities primarily rely on keyword matching or rule-based techniques, which often fail to capture the complex relationships between words in a sentence. This paper presents a comprehensive study on extracting business capabilities using dependency parsing techniques in natural language processing (NLP) to overcome limitations of the traditional methods and improve the accuracy of business capability extraction. Dependency parsing utilizes pre-trained language models to analyze the grammatical structure of sentences, identifying the dependencies between words and capturing their relationships. The feasibility and effectiveness of the proposed approach is demonstrated through experiments conducted on more than 40K real-world company descriptions crawled from Wikipedia.

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Expert Phishing Detection System

Expert Phishing Detection System

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IEEE International Conference on Industry 4.0, Artificial Intelligence, and Communications technology (IAICT)

作者： Ayoub Alsarhan Issa Al-Aiash Dimah Al-Fraihat Mohammad Aljaidi Dena Abd Al Hakem Abu Laila Department of Information Technology Faculty of Prince Al-Hussein Bin Abdallah II for Information Technology The Hashemite University Zarqa Jordan Department of Cybersecurity Faculty of Prince Al-Hussein Bin Abdallah II for Information Technology The Hashemite University Zarqa Jordan Department of Software Engineering Faculty of Information Technology Isra University Amman Jordan Department of Computer Science Faculty of Information Technology Zarqa University Zarqa Jordan Department of Cyber Security Faculty of Information Technology Zarqa Technical Intermediate College Zarqa Jordan

ISBN: (数字)9798350353464

ISBN: (纸本)9798350353471

Phishing attacks are a threat that continuously emphasizes the need for effective detection methods. This paper presents a machine learning detection method that employs a random forest classifier, which is an adaptable algorithm that can handle a variety of feature sets. The dataset used in this work offers a range of features extracted from various URLs, addressing elements such as URL length, domain structure, and keyword presence. We deployed a computational approach for feature engineering. The random forest classifier is employed for both prediction and to assess feature importance, guiding an automated feature selection process. By doing this, the model's performance is optimized, and the most important features are utilized. The findings of this study show the effectiveness of our method with an impressive accuracy of 98.45% on a test set. The classifier exhibits balanced precision and recall for both phishing and legitimate instances. Moreover, the automated feature selection process improves the interpretation of the model, emphasizing key indicators of phishing attacks. The proposed approach not only produces outstanding outcomes but also offers a flexible and scalable approach to address the dynamically evolving threat landscape of phishing attempts.

关键词： Uniform resource locators Visualization Accuracy Machine learning algorithms Phishing Predictive models Feature extraction

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MQTT based monitoring and control system for remote stations in automotive industry

MQTT based monitoring and control system for remote stations...

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Zooming Innovation in Consumer Electronics International Conference (ZINC)

作者： Denis Ivanović Josip Job Marko Puntarić Marijan Herceg CX TTTech-Auto Osijek Croatia Department of Software Engineering Faculty of Electrical Engineering Computer Science and Information Technology Osijek Croatia Department of Communications Faculty of Electrical Engineering Computer Science and Information Technology Osijek Croatia

In this paper is presented implementation of a lightweight supervisory control and data acquisition system for remote test stations as a part of a larger test house in the automotive industry. It is necessary to allow remote set up and monitoring of the test stations, which means defining the structure of a system with numerous test stations as endpoints. The test stations are meant to be virtual machines but can be both virtual and physical machines. A communication protocol must be used that enables this within the organization’s network, regardless of actual distance and location. The parameters of the test stations to be monitored can be defined according to the needs and purpose. Even when creating your own tool, this function is not unique, i.e. on one test station parameters of operating system resources such as memory usage can be monitored, while on another test station logged-in users or running programs, reported system errors and others must be monitored. Data storage is one of the mandatory functions of this system. This process allows viewing data throughout history and more detailed analysis of events. There are numerous benefits that can be drawn and used from the data stored in the database. It is necessary to pay attention to the data stored as well as the scope and retention throughout history. The correct and secure setup of a database is one of the prerequisites for any serious system that processes data. Appropriate views should be created for users involved in the data monitoring process using all the elements mentioned so far. Creating a visually acceptable and easy-to-use interface that serves the main purpose is the final presentation of the entire system to users. A complete system leads to higher productivity, profitability and better organization of the company. It also allows project managers to better plan the use and occupancy of test stations. At any time, they have insight into the status of individual test stations, their activitie

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