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Ontological Model for Cohesive Smart Health Services Management

Computers, Materials & Continua 2023年第2期74卷 3679-3695页

作者： Muhammad Raza Naqvi Muhammad Waseem Iqbal Syed Khuram Shahzad M.Usman Ashraf Khalid Alsubhi Hani Moaiteq Aljahdali Le Laboratoire Génie de Production de l’École Nationale d’Ingénieurs de Tarbes(LGP-INP-ENIT) Universitéde ToulouseCEDEXTarbes65016France Department of Software Engineering Superior UniversityLahore53700Pakistan Department of Informatics&Systems University of Management&TechnologyLahore54000Pakistan Department of Computer Science University of Management and TechnologySialkot51310Pakistan Department of Computer Science King Abdul-Aziz UniversityJeddah21577Saudi Arabia Faculty of Computing and Information Technology King Abdulaziz UniversityJeddah21577Saudi Arabia

Health care has become an essential social-economic concern for all stakeholders(e.g.,patients,doctors,hospitals etc.),health needs,private care and the elderly class of *** massive increase in the usage of health care Internet of things(IoT)applications has great technological evolvement in human *** are various smart health care services like remote patient monitoring,diagnostic,disease-specific remote treatments and *** applications are available in a split fashion and provide solutions for variant diseases,medical resources and remote service *** main objective of this research is to provide a management platform where all these services work as a single unit to facilitate the *** ontological model of integrated healthcare services is proposed by getting requirements from various existing healthcare *** were 26 smart health care services and 26 smart health care services to classify the knowledge-based ontological *** proposed ontological model is derived from different classes,relationships,and constraints to integrate health care *** model is developed using Protégébased on each interrelated/correlated health care service having different *** querying SPARQL protocol and RDF query language(SPARQL)were used for knowledge *** Pellet Reasoner is used to check the validity and relations coherency of the proposed ontology *** to other smart health care services integration systems,the proposed ontological model provides more cohesiveness.

关键词： Ontology internet of things smart health services integration

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Agile requirements prioritization in practice: Results of an industrial survey 24

Agile requirements prioritization in practice: Results of an...

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24th KES International Conference on Knowledge-Based and Intelligent Information and engineering Systems, KES 2020

作者： Jarzebowicz, Aleksander Sitko, Natalia Department of Software Engineering Faculty of Electronics Telecommunications and Informatics Gdansk University of Technology Gdansk Poland

Agile software development stresses the importance of providing the customer with a product of a maximized business value. To achieve that, requirements prioritization is used. Agile development methods like Scrum define guidelines for prioritization, however practitioners do not necessarily have to follow them. Our goal was to investigate the industry practice related to requirements prioritization process, including its timing, participants, criteria used and prioritization techniques applied. We designed an on-line questionnaire (based on literature review) and conducted a survey involving practitioners from Polish IT industry. We received 69 valid responses indicating requirements prioritization practices in industrial Agile projects. We found out that despite the fact that business value is the most common criterion used to prioritize requirements, other criteria like complexity, stability and mutual interdependencies are considered as well. Other findings indicate that consideration of such multiple criteria requires different viewpoints, thus making requirements prioritization a process that has to involve many participants of different roles and competencies. © 2020 The Authors. Published by Elsevier B.V.

关键词： Surveys

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Graph Learning: A Survey

IEEE Transactions on Artificial Intelligence

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IEEE Transactions on Artificial Intelligence 2021年第2期2卷 109-127页

作者： Xia, Feng Sun, Ke Yu, Shuo Aziz, Abdul Wan, Liangtian Pan, Shirui Liu, Huan School of Engineering IT and Physical Sciences Federation University Australia BallaratVIC3353 Australia School of Software Dalian University of Technology Dalian116620 China Faculty of Information Technology Monash University MelbourneVIC3800 Australia School of Computing Informatics and Decision Systems Engineering Arizona State University TempeAZ85281 United States

Graphs are widely used as a popular representation of the network structure of connected data. Graph data can be found in a broad spectrum of application domains such as social systems, ecosystems, biological networks, knowledge graphs, and information systems. With the continuous penetration of artificial intelligence technologies, graph learning (i.e., machine learning on graphs) is gaining attention from both researchers and practitioners. Graph learning proves effective for many tasks, such as classification, link prediction, and matching. Generally, graph learning methods extract relevant features of graphs by taking advantage of machine learning algorithms. In this survey, we present a comprehensive overview on the state-of-the-art of graph learning. Special attention is paid to four categories of existing graph learning methods, including graph signal processing, matrix factorization, random walk, and deep learning. Major models and algorithms under these categories are reviewed, respectively. We examine graph learning applications in areas such as text, images, science, knowledge graphs, and combinatorial optimization. In addition, we discuss several promising research directions in this field. Impact Statement—Real-world intelligent systems generally rely on machine learning algorithms handling data of various types. Despite their ubiquity, graph data have imposed unprecedented challenges to machine learning due to their inherent complexity. Unlike text, audio and images, graph data are embedded in an irregular domain, making some essential operations of existing machine learning algorithms inapplicable. Many graph learning models and algorithms have been developed to tackle these challenges. This article presents a systematic review of the state-of-the-art graph learning approaches as well as their potential applications. The article serves multiple purposes. First, it acts as a quick reference to graph learning for researchers and practitioners in differe

关键词： Surveys

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Using HLPC for parallelization of autonomous tests of WEB applications from its GUI 34

Using HLPC for parallelization of autonomous tests of WEB ap...

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34th European Modeling and Simulation Symposium, EMSS 2022

作者： Rossainz-López, Mario Islas-Fuentes, Jesús A. Pineda-Torres, Ivo Capel-Tuñón, 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

A proposal for parallelization of autonomous tests of Web applications from its graphical user interface (GUI) is presented to reduce its execution time since these increases exponentially due to the number of combinations that are generated with the different states of the fields, of the forms on the web application pages by creating a tree-like structure. It is proposed to use the model of high-level parallel compositions or HLPC as a suitable model of semi-automatic parallelization that defines a Treelike structure as a communication pattern between processes. The proposed HLPC, which we call HLPC-Tree, uses reinforcement learning that associates each node of the process tree as a slave object using the Q-Learning (QL) algorithm and achieves autonomous recognition of the fields of the forms and valid-invalid options to identify failures and display them with HTTP status codes. In addition, the Mechanize library is used to find the number of possible combinations through the states of the fields of the forms and to know how many nodes are generated at each level of the process tree in the HLPC-Tree when it grows in depth. Finally, the performance analysis of the proposed HLPC is shown with an analysis of the speedup and execution times in an 8-core machine to demonstrate good scalability in its accelerations compared to Amdahl's Law. © 2022 The Authors.

关键词： Reinforcement learning

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Evaluating BERT-based Pre-training Language Models for Detecting Misinformation

arXiv

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arXiv 2022年

作者： Anggrainingsih, Rini Hassan, Ghulam Mubashar Datta, Amitava Informatics Dept Faculty of Mathematics and Natural Sciences Sebelas Maret University Indonesia Department of Computer Science and Software Engineering University of Western Australia Australia

The rapid growth of internet technology and social media platforms has led to rapid changes in communication by providing many conveniences to users. Despite the comforts provided, it is challenging to control the quality of information due to the lack of supervision over all the information posted on online media. For instance, there are many problems in society because of the numerous rumours and the quick spread of fake news through online media. Manual checking is almost impossible given the vast number of posts made on online media and how quickly they spread. Therefore, there is a need for automated rumour detection technique to limit the adverse effects of spreading misinformation. Previous studies mainly focused on finding and extracting the significant features of text data and used standard supervised learning techniques or deep learning approaches to identify misinformation. However, extracting features is timeconsuming and not a highly effective process due to the nonavailability of some features, affecting the effectiveness and performance of the detection models. Therefore, this study proposes the BERT-based pre-trained language models to encode text data into vectors and utilise neural network models to classify these vectors to detect misinformation. Furthermore, the performance of different language models (LM) containing different number of trainable parameters were compared, including RoBERTa, BERT, and DistillBERT. The proposed technique is tested on different twitter and fake news datasets to represent short and long text data respectively. The study also proposes a standardised distribution ratio for taining, validation and testing of the model. The result of the proposed technique has been compared with the state-of-the-art techniques on the same datasets. The results show that the proposed technique performs better than the state-of-the-art techniques. Moreover, the choice of LM either with significantly large or small number of parameters do

关键词： Social networking (online)

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John Hollerton: a life in industrial analytical spectroscopy, part 2

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Spectroscopy Europe 2023年第1期35卷 21-26页

作者： Davies, Antony N. Cashyap, Mohan Hollerton, John SERC Sustainable Environment Research Centre Faculty of Computing Engineering and Science University of South Wales United Kingdom MASS Informatics Harpenden United Kingdom Hollerton Scientific Software Consultancy St Albans United Kingdom

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Personal Generative Libraries for Smart Computer Science Education 7th

Personal Generative Libraries for Smart Computer Science Edu...

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7th International KES Conference on Smart Education and e-Learning, KES SEEL 2020

作者： Štuikys, Vytautas Burbaitė, Renata Kubiliūnas, Ramūnas Valinčius, Kęstutis Informatics Faculty Department of Software Engineering Kaunas University of Technology Studentų 50 Kaunas51368 Lithuania

ISBN: (纸本)9789811555831

The difficulties of retrieving the educational content from conventional digital libraries for personalised learning (PL) are well known. In this paper, to overcome those issues and enforce learning performance, we propose the concept personal generative library (PGL). We will discuss an experimental system that integrates conventional repositories, the teacher’s PGL, the students’ PGLs, their individual repositories along with the personalised learning processes using the developed framework. The teacher’s individual repository stores the personalised content for all students along with assessment tasks for each type of the content. The teacher’s and students’ PGLs have the identical structure. The student’s content is a direct product of PL obtained during the classroom activities by modifying the teacher’s content due to the needs of personalisation or is a by-product created or searched during outside learner’s activities. We have approved this approach in one high school. We will present experimental results of the PGLs usage and the quality evaluation. Our approach enables enforcing the PL significantly in terms of higher flexibility, efficient search and more efficient procedures to form the personalised learning paths for smart CS education. © 2020, The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Students

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Training Artificial Neural Networks by Coordinate Search Algorithm

arXiv

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arXiv 2024年

作者： Rokhsatyazdi, Ehsan Rahnamayan, Shahryar Miyandoab, Sevil Zanjani Bidgoli, Azam Asilian Tizhoosh, H.R. Lab Department of Electrical Computer and Software Engineering Ontario Tech University OshawaON Canada Department of Engineering Brock University St. CatharinesON Canada Faculty of Science Wilfrid Laurier University WaterlooON Canada Rhazes Lab Department of Artificial Intelligence and Informatics Mayo Clinic RochesterMN United States

Training Artificial Neural Networks (ANNs) poses a challenging and critical problem in machine learning. Despite the effectiveness of gradient-based learning methods, such as Stochastic Gradient Descent (SGD), in training neural networks, they do have several limitations. For instance, they require differentiable activation functions, and cannot optimize a model based on several independent non-differentiable loss functions simultaneously;for example, the F1-score, which is used during testing, can be used during training when a gradient-free optimization algorithm is utilized. Furthermore, the training (i.e., optimization of weights) in any DNN can be possible with a small size of the training dataset. To address these concerns, we propose an efficient version of the gradient-free Coordinate Search (CS) algorithm, an instance of General Pattern Search (GPS) methods, for training (i.e., optimizing) neural networks. The proposed algorithm can be used with non-differentiable activation functions and tailored to multi-objective/multi-loss problems. Finding the optimal values for weights of ANNs is a large-scale optimization problem. Therefore, instead of finding the optimal value for each variable, which is the common technique in classical CS, we accelerate optimization and convergence by bundling the variables (i.e., weights). In fact, this strategy is a form of dimension reduction for optimization problems. Based on the experimental results, the proposed method is comparable with the SGD algorithm, and in some cases, it outperforms the gradient-based approach. Particularly, in situations with insufficient labeled training data, the proposed CS method performs better. The performance plots demonstrate a high convergence rate, highlighting the capability of our suggested method to find a reasonable solution with fewer function calls. As of now, the only practical and efficient way of training ANNs with hundreds of thousands of weights is gradient-based algorithms such

关键词： Optimization

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Absolute Pose from One or Two Scaled and Oriented Features

Absolute Pose from One or Two Scaled and Oriented Features

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Conference on Computer Vision and Pattern Recognition (CVPR)

作者： Jonathan Ventura Zuzana Kukelova Torsten Sattler Dániel Baráth Department of Computer Science & Software Engineering Cal Poly San Luis Obispo Faculty of Electrical Engineering Visual Recognition Group Czech Technical University in Prague Czech Institute of Informatics Robotics and Cybernetics Czech Technical University in Prague Department of Computer Science Computer Vision and Geometry Group ETH Zürich

ISBN: (数字)9798350353006

ISBN: (纸本)9798350353013

Keypoints used for image matching often include an estimate of the feature scale and orientation. While recent work has demonstrated the advantages of using feature scales and orientations for relative pose estimation, relatively little work has considered their use for absolute pose estimation. We introduce minimal solutions for absolute pose from two oriented feature correspondences in the general case, or one scaled and oriented correspondence given a known vertical direction. Nowadays, assuming a known direction is not particularly restrictive as modern consumer devices, such as smartphones or drones, are equipped with Inertial Measurement Units (IMU) that provide the gravity direction by default. Compared to traditional absolute pose methods requiring three point correspondences, our solvers need a smaller minimal sample, reducing the cost and complexity of robust estimation. Evaluations on large-scale and public real datasets demonstrate the advantage of our methods for fast and accurate localization in challenging conditions. Code is available at https://***/danini/absolute-pose-from-oriented-and-sealed-features.

关键词： Location awareness Accuracy Simultaneous localization and mapping Measurement units Pose estimation Noise Inertial navigation

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Probabilistic Graph Modeling based Safety Classifier Algorithm for Smart Transportation

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Procedia Computer Science 2024年 236卷 502-507页

作者： Najib El Karkouri Smail Tigani Rachid Saadane Abdelah Chehri Samuel Pierre Noureddine Neya Mathematics and Informatics Lab Faculty of Sciences University Ibno Toufial Kenitra Morocco Research and Development Unit Accsellium LLC Fez Morocco SIRC-LaGeS Hassania School of Public Labors 20250 Casablanca Morocco Department of Mathematics and Computer Science Royal Military College of Canada Kingston Canada Department of Computer and Software Engineering Polytechnique Montreal Canada

This research introduces a novel Probabilistic Graph Modeling-based Safety Classifier Algorithm designed for the purpose of classifying road safety in smart transportation systems. Leveraging a combination of numerical integration methods and Gaussian kernel density estimation models, the proposed algorithm offers an effective approach to assess and categorizing the safety levels of roads. The integration of these techniques enables a comprehensive analysis, allowing for a more nuanced understanding of the complex interactions within transportation networks. The algorithm's efficacy in road safety classification holds promising implications for enhancing traffic management and promoting safer urban environments.

关键词： Probabilistic Graph Numerical Computation Smart Transportation Classification Road Safety

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