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Peer-to-Peer Networking and Applications 2024年第6期17卷 4396-4396页

作者： Vellingiri, J. Vedhavathy, T.R. Senthil Pandi, S. Bala Subramanian, C. Department of Software and Systems Engineering School of Computer Science Engineering and Information Systems Vellore Institute of Technology Vellore India Department of Networking and Communications School of Computing SRM Institute of Science and Technology Kattankulathur India Department of Computer Science and Engineering Rajalakshmi Engineering College Thandalam Chennai India Department of Computer Science Engineering Kalasalingam Academy of Research and Education Krishnankoil India

Parallel Implementation of Real-Time Object Detection using OpenMP

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2021 International Conference on Information Science and Communications Technologies, ICISCT 2021

作者： Rakhimov, Mekhriddin Elov, Jamshid Khamdamov, Utkir Aminov, Shavkatjon Javliev, Shakhzod Tashkent University of Information Technologies Named after Muhammad Al-Khwarizmi Department of Artificial Intelligence Tashkent Uzbekistan Tashkent University of Information Technologies Named after Muhammad Al-Khwarizmi Department of Hardware and Software of Management Systems in Telecommunication Tashkent Uzbekistan Chonnam National University Department of Computer Engineering Gwangju Korea Republic of Tashkent University of Information Technologies Named after Muhammad Al-Khwarizmi Department of Computer System Tashkent Uzbekistan

ISBN: (纸本)9781665432580

One of the urgent problems of the modern world is the detection and recognition of objects on a video stream in real-time. To work with a video stream, you need to split it into frames that are more convenient for processing. There is a possibility of using multi-core processors for image processing. Typically, the operating system allows only one core of multi-core processors to execute a task. But there are such parallelization tools that allow parallelizing execution when processing data on multi-core processors. And OpenMP is one of them that can be used in real-time object detection processing procedures. But in this case, the main thing is to find that part of the algorithm that is suitable for parallelization. Using OpenMP simply does not give an efficient accelerated result. In this paper, we discuss the implementation of OpenMP technology in real-time object detection algorithms on multi-core processors. Experimental results show that the use of OpenMP technology significantly improves the processing speed for real-time object detection on multi-core processors, when there are no GPUs. © 2021 IEEE.

关键词： Object detection

NER- RoBERTa: Fine-Tuning RoBERTa for Named Entity Recognition (NER) within low-resource languages

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arXiv 2024年

作者： Abdullah, Abdulhady Abas Abdulla, Srwa Hasan Toufiq, Dalia Mohammad Maghdid, Halgurd S. Rashid, Tarik A. Farho, Pakshan F. Sabr, Shadan Sh. Taher, Akar H. Hamad, Darya S. Veisi, Hadi Asaad, Aras T. Artificial Intelligence and Innovation Centre University of Kurdistan Hewler Erbil Iraq Computer Science and Engineering Department University of Kurdistan Hewler KR Erbil Iraq Department of Horticulture College of Agricultural Engineering Sciences University of Sulaimani- Kurdistan Region Iraq Branch of Basic Medical Science College of medicine University of Sulaimani- Kurdistan Region Iraq Department of Engineering Research Center Deanship of R&D Centers Koya University Kurdistan Region Iraq Department of Kurdish Language Faculty of Education Koya University Kurdistan Region Iraq Department of Kurdish Faculty of Education Koya University Kurdistan Region Iraq Department of Software Engineering Faculty of Engineering Koya University Kurdistan Region Iraq School of Intelligent Systems College of Interdisciplinary Science and Technologies University of Tehran Iran

Nowadays natural Language Processing (NLP) is an important tool for most people’s daily life routines, ranging from understating speech, translation, name entity recognition (ENR), and text categorizing, to the generative text models such as ChatGPT. Due to existing big data and consequently big corpora for the most usable languages including English, Spanish, Turkish, Persia, and many more, these applications have been utilized accurately. However, the Kurdish language still needs more corpora and big datasets to be included in the NLP applications. This is because the Kurdish language has a rich linguistic structure, varied dialects, and a poor dataset, which poses unique challenges for Kurdish NLP (KNLP) application development. While several types of research have been conducted in KNLP for various applications, the Kurdish ENR (KNER) remained a challenge for most of the KNLP applications including text analysis and text classification. In this work, we address this limitation by proposing a methodology for fine-tuning the pre-trained RoBERTa model for KNER1. To this end, first, a Kurdish corpus is created, followed by designing a modified model architecture, and the training procedures are implemented. To evaluate the trained model, a set of experiments is conducted to show the performance of the KNER model using different tokenization methods and trained models. The experimental results showed that fine-tuned RoBERTa with sentence-piece tokenization method substantially improves KNER performance by 12.8% (F1-score improvement) in comparison to traditional models and consequently establishes a new benchmark for KNLP. © 2024, CC BY.

关键词： Natural language processing systems

Enhancing Smart Tourism With Chatbots: Focus on the Metamodel of Domain-Specific Language and Emerging Technologies

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Expert systems 2025年第7期42卷

作者： Benaddi, Lamya Souha, Adnane Ouaddi, Charaf Chehri, Abdellah Jakimi, Abdeslam Ouchao, Brahim Software Engineering and Information Systems Engineering Team Faculty of Science and Technology Errachidia Moulay Ismail University Errachidia Morocco Department of Computer Sciences and Mathematics RMC Kingston Canada

The tourism sector is adopting smart solutions to offer visitors more personalised and sustainable experiences. By leveraging urban infrastructure and new technologies, tourist destinations aim to enhance the interaction between travellers and their environment. Artificial intelligence (AI) and natural language processing (NLP) play a key role in this transformation, particularly through chatbots. They are AI-driven applications designed to simulate human-like conversations, enabling users to interact with digital services through text or voice interfaces. In the tourism sector, they facilitate real-time access to information and services, improving the visitors' experience. These applications typically rely on intent recognition APIs, which may be proprietary, requiring access fees and potentially leading to high implementation costs. This study explores the use of a domain-specific language (DSL) dedicated to chatbot development for smart tourism. The first contribution comprises various research topics and emerging technologies used to improve smart tourism experiences and their impact on key tourism components such as attractions, accessibility, amenities, activities, available packages, and ancillary services. Second, this work aims to present the key concepts of model-driven engineering involved in constructing a DSL and to introduce our approach to building a DSL, with a focus on presenting the DSL metamodel. Third, this study identifies the challenges and limitations of using DSLs in chatbot development. © 2025 The Author(s). Expert systems published by John Wiley & Sons Ltd.

关键词： Tourism

An Adaptive Decision-Making Approach for Better Selection of a Blockchain Platform for Health Insurance Frauds Detection with Smart Contracts: Development and Performance Evaluation

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arXiv 2023年

作者： Kaafarani, Rima Ismail, Leila Zahwe, Oussama ICCS-Lab Computer Science Department American University of Culture and Education Beirut1507 Lebanon Laboratory School of Computing and Information Systems The University of Melbourne Melbourne Australia Laboratory Department of Computer Science and Software Engineering College of Information Technology United Arab Emirates University Abu Dhabi United Arab Emirates National Water and Energy Center United Arab Emirates University Abu Dhabi United Arab Emirates

Blockchain technology has piqued the interest of businesses of all types, while consistently improving and adapting to developers and business owners requirements. Therefore, several blockchain platforms have emerged, making it challenging to select a suitable one for a specific type of business. This paper presents a classification of over one hundred blockchain platforms. We develop smart contracts for detecting healthcare insurance frauds using two blockchain platforms selected based on our proposed decision-making map approach for the selection of the top two suitable platforms for healthcare insurance frauds detection application, followed by an evaluation of their performances. Our classification shows that the largest percentage of blockchain platforms could be used for all types of application domains, and the second biggest percentage is to develop financial services only, even though generic platforms can be used, while a small number is for developing in other specific application domains. Our decision-making map revealed that Hyperledger Fabric is the best blockchain platform for detecting healthcare insurance frauds. The performance evaluation of the top two selected platforms indicates that Fabric surpassed Neo in all *** Codes 6804, 68M11, 68U35 © 2023, CC BY-NC-ND.

关键词： Smart contract

CASL-W60: A Word-Level Dataset for Central African Sign Language Recognition

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Data in Brief 2025年

作者： Mwaka Lucky Njayou Youssouf Hasan Mahmud Md Kamrul Hasan Systems and Software Lab (SSL) Department of Computer Science Engineering (CSE) Islamic University of Technology (IUT) Board Bazar Gazipur 1704 Dhaka Bangladesh

Sign language is a non-verbal discourse system used by people who are hard of hearing. It also carries cultural context and regional constructs, enabling meaningful communication and often preserving unique traditions. In the Central African region, local sign languages have distinct linguistic constructs but remain underrepresented in the literature, creating a significant gap in regional word-level datasets for machine learning practitioners. In this research, we present a dataset (CASL-W60) comprising 60 word-level Central African sign language (CASL), collected from 19 volunteers. Each word contains 10–12 video samples per signer, captured following standard African sign language video references. The dataset comprises MP4 video files that are systematically organized and made available through an online repository. We demonstrate its applicability through word-level classification of the 60 sign words. This dataset serves as a valuable resource for developing various applications, including sign language translation, sentence recognition or generation from word-level signs, and sign gloss detection.

关键词： MediaPipe Hand Landmarks Regional Sign Language Recognition Video frames Machine Learning

Practitioner's Perspective on Motivators of Agile in Global software Development

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arXiv 2022年

作者： Akbar, Muhammad Azeem Al-Sanad, Abeer Rafi, Saima Wang, Yuqing Alzahrani, Musaad Department of Software Engineering LUT University Lappeenranta Finland Imam Mohammad Ibn Saud Islamic University Information Systems Department Riyadh11432 Saudi Arabia University of Murcia Department of Informatics and Systems Murcia30100 Spain M3S Univeristy of Oulu Finland Department of Computer Science Albaha University Albaha65799 Saudi Arabia

In modern software development world, experts are trying to provide the best solutions to their clients. To achieve this, the organizations opt for the agile software development process as it enables them to develop and deliver the product in-time and as per client's expectations. Consequently, in software engineering industry, the Global software Development (GSD) is the most widely considering software development paradigm as it offers significant strategic and business gains. Seeking the benefits of GSD, the European software engineering organizations are outsourcing their development activities in developing countries. Considering the criticalities of agile adoption in GSD, this work empirically studies the motivators that could positively influence the execution of agile-based GSD in European software industry. A quantitative survey was conducted and data from 139 practitioners working in agile and GSD based projects was collected. The collected observations were further analyzed using Smart-PLS (3.0). The results show that the identified motivators are important to consider by industry experts to successfully apply the agile practices in GSD context. © 2022, CC BY.

关键词： Developing countries

Designing Semi-Automated Decision-Making Expert systems for Healthcare Tasks

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Designing Semi-Automated Decision-Making Expert Systems for ...

International computer Sciences and Information Technologies (CSIT)

作者： Oksana Mulesa Olga Kachmar Ivan Myronyuk Petro Horvat Tamara Radivilova Yevhenii Kykyna Department of Software Systems Uzhhorod National University Uzhhorod Ukraine Department of Applied Linguistics Uzhhorod National University Uzhhorod Ukraine Department of Health Sciences Uzhhorod National University Uzhhorod Ukraine Department of Computer Systems and Networks Uzhhorod National University Uzhhorod Ukraine V.V.Popovskyi department of Infocommunication engineering Kharkiv National University of Radio Electronics Kharkiv Ukraine

ISBN: (纸本)9798350334326

The problem of designing expert systems of semi-automated decision-making in health care has been considered. A verbal and mathematical formulation of the problem of forming a target group of persons from the initial sample has been constructed. The decomposition of the decision-making process on the assignment of persons to the target group has been executed. Productive rules for the structural elements of the expert system have been developed. The logical scheme of the expert system for one health care task has been developed.

关键词： Decision making Sociology Buildings Medical services software Expert systems Task analysis

Improving Gamma Imaging in Proton Therapy by Sanitizing Compton Camera Simulated Patient Data using Neural Networks through the BRIDE Pipeline

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Improving Gamma Imaging in Proton Therapy by Sanitizing Comp...

2024 IEEE International Conference on Big Data, BigData 2024

作者： Chen, Michael O. Hodge, Julian Jin, Peter L. Protz, Ella Wong, Elizabeth Obe, Ruth Shakeri, Ehsan Cham, Mostafa Gobbert, Matthias K. Barajas, Carlos A. Sharma, Vijay R. Mossahebi, Sina Ren, Lei Peterson, Stephen W. Polf, Jerimy C. Dartmouth College Departments of Mathematics United States U. of Maryland Dept. of Mathematics and Statistics United States James M. Bennett High School SalisburyMD United States Florida Atlantic University Department of Mathematics and Sciences United States Brookdale Community College Department of Mathematics United States U. of Houston Dept. of Computer Science and of Software Engineering Clear Lake United States U. of Maryland Dept. of Information Systems United States U. of Maryland School of Medicine Dept. of Radiation Oncology United States U. of Cape Town Dept. of Physics South Africa M3D Inc. United States

ISBN: (纸本)9798350362480

Precision medicine in cancer treatment increasingly relies on advanced radiotherapies, such as proton beam radiotherapy, to enhance efficacy of the treatment. When the proton beam in this treatment interacts with patient matter, the excited nuclei may emit prompt gamma ray interactions that can be captured by a Compton camera. The image reconstruction from this captured data faces the issue of mischaracterizing the sequences of incoming scattering events, leading to excessive background noise. To address this problem, several machine learning models such as Feedfoward Neural Networks (FNN) and Recurrent Neural Networks (RNN) were developed in PyTorch to properly characterize the scattering sequences on simulated datasets, including newly-created patient medium data, which were generated by using a pipeline comprised of the GEANT4 and Monte-Carlo Detector Effects (MCDE) softwares. These models were implemented using the novel 'Big-data REU Integrated Development and Experimentation' (BRIDE) platform, a modular pipeline that streamlines preprocessing, feature engineering, and model development and evaluation on parallelized GPU processors. Hyperparameter studies were done on the novel patient data as well as on water phantom datasets used during previous research. Patient data was more difficult than water phantom data to classify for both FNN and RNN models. FNN models had higher accuracy on patient medium data but lower accuracy on water phantom data when compared to RNN models. Previous results on several different datasets were reproduced on BRIDE and multiple new models achieved greater performance than in previous research. © 2024 IEEE.

关键词： Phantoms

Automated Computation of Therapies Using Failure Mode and Effects Analysis in the Medical Domain

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arXiv 2024年

作者： Luttermann, Malte Baake, Edgar Bouchagiar, Juljan Gebel, Benjamin Grüning, Philipp Manikwadura, Dilini Schollemann, Franziska Teifke, Elisa Rostalski, Philipp Möller, Ralf Institute of Information Systems University of Lübeck Germany Institute of Telematics University of Lübeck Germany Institute for Software Engineering and Programming Languages University of Lübeck Germany Department of Infectious Diseases and Microbiology University Hospital Schleswig-Holstein Campus Lübeck Germany Institute for Neuro- and Bioinformatics University of Lübeck Germany Institute for Molecular Medicine University of Lübeck Germany Institute for Electrical Engineering in Medicine University of Lübeck Germany franziska.schollemann Department of Anesthesiology and Intensive Care University Hospital Schleswig-Holstein Campus Lübeck Germany

Failure mode and effects analysis (FMEA) is a systematic approach to identify and analyse potential failures and their effects in a system or process. The FMEA approach, however, requires domain experts to manually analyse the FMEA model to derive risk-reducing actions that should be applied. In this paper, we provide a formal framework to allow for automatic planning and acting in FMEA models. More specifically, we cast the FMEA model into a Markov decision process which can then be solved by existing solvers. We show that the FMEA approach can not only be used to support medical experts during the modelling process but also to automatically derive optimal therapies for the treatment of patients. © 2024, CC BY.

关键词： Patient treatment