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23rd International Arab Conference on Information Technology, ACIT 2022

作者： Odeh, Yousra Tbaishat, Dina Shamieh, Omar Odeh, Mohammad Philadelphia University Software Engineering Department Amman Jordan College of Technological Innovation Zayed University Dubai United Arab Emirates University of Jordan Jordan King Hussain Cancer Department of Hospice Palliative Care Centre Jordan University of Jordan Faculty of Medicine Jordan King Hussain Cancer Cancer Care Informatics Directorship Centre Jordan University of West of England United Kingdom

ISBN: (纸本)9798350320244

Palliative care services are rapidly evolving in cancer care organisations. In palliative care processes, administratives are involved without acknowledging that they may hinder rather than facilitate the path of palliative care in respect to cancer patients. Comprehending a palliative care organisation, without looking at current running processes is groundless. Therefore, a critical understanding of palliative care processes is necessary for improving respective cancer care systems. In this research palliative care Business Process Models (BPMs) have been developed to empower palliative care domain experts not only attaining a critical understanding of the currently running palliative care operations, but also in informing further required improvements with associated implications on cancer patients. Amongst the key outcomes of developing and analysing palliative BPMs in a cancer care organisation revealed identifying gaps, limitations, challenges, and opportunities to reflectively improve palliative care processes in cancer care centres. Finally, this research suggests further re-engineering of palliative care processes as reference models that may be instantiated in specific socio-cultural, person-centered, and other contextual settings aimed at improved qualitative palliative care for cancer patients. © 2022 IEEE.

关键词： Diseases

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Improving Estimation Accuracy Prediction of software Development Effort: A Proposed Ensemble Model 2

Improving Estimation Accuracy Prediction of Software Develop...

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2nd International Conference on Electrical, Communication and Computer engineering, ICECCE 2020

作者： Mahmood, Yasir Kama, Nazri Azmi, Azri Ali, Mazlan Faculty of Information Technology University of Lahore Department of Software Engineering Lahore Pakistan Razak Faculty of Technology and Informatics Universiti Teknologi Malaysia Advanced Informatics Department Kuala Lumpur Malaysia Perdana Centre Universiti Teknologi Malaysia Razak Faculty of Technology and Informatics Kuala Lumpur Malaysia

ISBN: (纸本)9781728171166

software effort estimation is an essential feature of software engineering for effective planning, controlling and delivering successful software projects. The overestimation and underestimation both are the key challenges for future software development. The failure to acknowledge the effort estimation accuracy may lead to customer disappointment, inaccurate estimation and hence, contribute to either poor software development process or project failure. The main aim of this research is to optimize the estimation accuracy prediction of software development effort to support software development firms and practitioners. In this paper, we propose an ensemble software effort estimation model based on Use Case Points (UCP), expert judgment and Case-Based Reasoning (CBR) techniques. This research is conducted through primary (a multi-case involving software companies) study to make an ensemble model. The estimation accuracy prediction of the proposed model will be evaluated by selecting projects from primary studies as case selections in applying a quantitative approach through industrial experts, archival data about estimates and evaluation metrics. The proposed model produced at the end of this research will be used by software development firms and practitioners as an instrument to estimate the effort required to develop new software projects at an earlier stage. © 2020 IEEE.

关键词： Case based reasoning

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A note on the affective computing systems and machines: a classification and appraisal

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Procedia Computer Science 2022年 207卷 3798-3807页

作者： Paweł Weichbroth Wiktor Sroka Gdańsk University of Technology Faculty of Electronics Telecommunications and Informatics Department of Software Engineering Gabriela Narutowicza 11/12 Gdańsk 80-233 Poland SAP (Schweiz/Suisse) AG Zürich The Circle 66 8058 Zürich Switzerland

Affective computing (AfC) is a continuously growing multidisciplinary field, spanning areas from artificial intelligence, throughout engineering, psychology, education, cognitive science, to sociology. Therefore, many studies have been devoted to the aim of addressing numerous issues, regarding different facets of AfC solutions. However, there is a lack of classification of the AfC systems. This study aims to fill this gap by reviewing and evaluating the state-of-the-art studies in a qualitative manner. In this line of thinking, we put forward a threefold classification that breaks down to desktop and mobile AfC systems, and AfC machines. Moreover, we identified four types of AfC systems, based on the features extracted. In our opinion, the results of this study can serve as a guide for future affect-related research and design, on the one hand, and provide a better understanding on the role of emotions and affect in human-computer interaction, on the other hand.

关键词： Affective Computing Artificial Emotional Intelligence Classification System Machine

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Multidisciplinary Hackathons: Towards Developing Practical software engineering Skills

Multidisciplinary Hackathons: Towards Developing Practical S...

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Frontiers in Education (FIE) Conference

作者： Mojgan Moshirpour Tariq Al Shoura Linda Duffett-Leger Mohammad Moshirpour Werklund School of Education University of Calgary Calgary Alberta Canada Dept. Electrical and Software Engineering University of Calgary Calgary Alberta Canada Faculty of Nursing University of Calgary Calgary Alberta Canada Dept. of Informatics University of California Irvine Irvine California USA

software engineers often work on multidisciplinary projects as they collaborate with domain experts in a variety of different disciplines to effectively develop software systems. While the focus of software engineering curricula is generally on teaching technical skills, it is highly desirable to provide students with hands-on experience in working on multidisciplinary projects. This research attempts to facilitate the experience of working on projects with stakeholders of diverse expertise through the use of multidisciplinary hackathons. In recent years, hackathons have been considered effective teaching and evaluation tools that can contribute to the development of practical skills for students. Hackathons can be used as an instrument to improve the educational experience through learning by doing in which learners can test their problem-solving, project management, and prioritization skills in a limited time. This research presents a case study of hackathons in the field of health informatics involving students from software engineering and nursing. To facilitate a consistent and supportive multidisciplinary learning experience, the hackathon was introduced as a course component in two different courses in nursing and software engineering. The data presented in this research is from two consecutive executions of the hackathon involving two different cohorts of students in 2022 and 2023. The results of this study show that students believe a multidisciplinary course would be a welcomed addition as it facilitated an exciting collaborative environment to work on real-world problems; providing a unique opportunity for interactions between different disciplines. The addition of hackathons into the courses enhanced the students' understanding of how to work with requirements from gathering to analysis to development, and allowed them the unique opportunity to examine the different challenges, technologies, and tools involved with other disciplines.

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Mindset-Shaping Solutions with Innovative IT and PM Technologies in Higher Education

Mindset-Shaping Solutions with Innovative IT and PM Technolo...

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International Symposium on Applied Computational Intelligence and informatics ( SACI)

作者： Mariann Vàraljai Enikő Nagy Bàlint Nagy Zoltàn Balogh György Molnàr Department of Software Development and Application University of Dunaújváros Dunaújváros Hungary John von Neumann Faculty Óbuda University Antal Bejczy Center for Intelligent Robotics Budapest Hungary Department of Mathematics Institute of Computer Engineering University of Dunaújváros Dunaújváros Hungary Department of Applied Quantitative Methods Faculty of Finance and Accountancy Budapest Business University Budapest Hungary Department of Natural Sciences Faculty of Electrical Engineering Institute of Electrophysics Kandó Kálmán Óbuda University Budapest Hungary Department of Informatics Faculty of Natural Sciences and Informatics Constantine the Philosopher University in Nitra Nitra Slovakia Kandó Kálmán Faculty of Electrical Engineering Óbuda University Budapest Hungary Apáczai Csere János Faculty of Humanities Education and Social Sciences Széchenyi University Győr Hungary

ISBN: (数字)9798350329520

ISBN: (纸本)9798350329537

Every society has its own characteristics, shaped from time to time by the various innovative, disruptive, but in any case, dynamically changing mechanisms that affect it most. In today's society, technological innovation plays a major role in the development of individuals and their abilities. Education is the most typical and obvious arena for shaping, developing, and preparing for the challenges of the future. As in the leading sectors, in the world of education, rapid responses and immediate reactions to change significantly increase the ability to achieve and maintain success and thus marketability. As a sector responsible for developing the competences of individuals, building knowledge and training professionals, education should seek to keep pace with trends and break with traditional approaches and methods, so that, in addition to incorporating technological developments, a renewed approach to organizational development and methodology is an important means of achieving the desired success and effectiveness. In the course of our work, we provide a summary insight into the project work of student groups who require up-to-date IT skills, use advanced IT tools, plan, and implement their web development workflows supported by database management, front-end planning and design, and back-end programming with a project management approach according to the latest trends, interpreting the advantages and disadvantages of working methods for success.

关键词： Training Knowledge engineering Technological innovation Shape Project management Market research Planning

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ARIMA vs LSTM on NASDAQ stock exchange data

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Procedia Computer Science 2022年 207卷 3836-3845页

作者： Dariusz Kobiela Dawid Krefta Weronika Król Paweł Weichbroth Gdańsk University of Technology Gabriela Narutowicza 11/12 Gdańsk 80-233 Poland Gdańsk University of Technology Faculty of Electronics Telecommunications and Informatics Department of Software Engineering Gabriela Narutowicza 11/12 Gdańsk 80-233 Poland

This study compares the results of two completely different models: statistical one (ARIMA) and deep learning one (LSTM) based on a chosen set of NASDAQ data. Both models are used to predict daily or monthly average prices of chosen companies listed on the NASDAQ stock exchange. Research shows which model performs better in terms of the chosen input data, parameters and number of features. The chosen models were compared using the relative metric mean square error (MSE) and mean absolute percentage error (MAPE). Selected metrics are typically used in regression problems. The performed analysis shows which model achieves better results by comparing the chosen metrics in different models. It is concluded that the ARIMA model performs better than the LSTM model in terms of using just one feature – historical price values – and predicting more than one time period, using the p, q parameters in the range from 0 to 2, Adam optimizer, tanh activation function, and 2xLSTM layer architecture. The longer the data window period, the better ARIMA performs, and the worse LSTM performs. The comparison of the models was made by comparing the values of the MAPE error. When predicting 30 days, ARIMA is about 3.4 times better than LSTM. When predicting an averaged 3 months, ARIMA is about 1.8 times better than LSTM. When predicting an averaged 9 months, ARIMA is about 2.1 times better than LSTM.

关键词： Time Series Analysis Forecasting Nasdaq Autoregressive Integrated Moving Average ARIMA Long-Short Term Memory LSTM

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Artificial Intelligence Technologies in Education: Benefits, Challenges and Strategies of Implementation 1

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7th IFIP WG 12.6 International Workshop on Artiﬁcial Intelligence for Knowledge Management, AI4KM 2019 held in conjunction with 28th International Joint Conference on Artiﬁcial Intelligence, IJCAI 2019

作者： Owoc, Mieczyslaw L. Sawicka, Agnieszka Weichbroth, Pawel Wroclaw University of Economics and Business Komandorska 118/120 street Wroclaw53-345 Poland Faculty of Electronics Telecommunications and Informatics Department of Software Engineering Gdańsk University of Technology 11/12 Gabriela Narutowicza Street Gdańsk80-233 Poland

ISBN: (数字)9783030850012

ISBN: (纸本)9783030850005

Since the education sector is associated with highly dynamic business environments which are controlled and maintained by information systems, recent technological advancements and the increasing pace of adopting artificial intelligence (AI) technologies constitute a need to identify and analyze the issues regarding their implementation in education sector. However, a study of the contemporary literature reveled that relatively little research has been undertaken in this area. To fill this void, we have identified the benefits and challenges of implementing artificial intelligence in the education sector, preceded by a short discussion on the concepts of AI and its evolution over time. Moreover, we have also reviewed modern AI technologies for learners and educators, currently available on the software market, evaluating their usefulness. Last but not least, we have developed a strategy implementation model, described by a five-stage, generic process, along with the corresponding configuration guide. To verify and validate their design, we separately developed three implementation strategies for three different higher education organizations. We believe that the obtained results will contribute to better understanding the specificities of AI systems, services and tools, and afterwards pave a smooth way in their implementation. © 2021, IFIP International Federation for Information Processing.

关键词： Artificial intelligence

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Investigation of the Impact of Height and Bandwidth on Path Loss Exponent of 5G FR1 Wireless Networks at 800, 3500, and 5900 MHz 2

Investigation of the Impact of Height and Bandwidth on Path ...

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2nd International Conference on Multidisciplinary engineering and Applied Science, ICMEAS 2023

作者： Imam-Fulani, Yusuf Olayinka Faruk, Nasir Usman, Aliyu D. Abdulkarim, Abubakar Tekanyi, Abdoulie M.S Yaro, Abdulmalik S. Alozie, Emmanuel Garba, Salisu Adewole, Kayode S. Oloyede, Abdulkarim A. Sowande, Olugbenga A. Abdullahi Baba, Bashir Musa, Abdulwaheed Imoize, Agbotiname Lucky Adediran, Yinusa A. Olagunju, Hawau I. Taura, Lawan S. University of Ilorin Department of Telecommunication Science Ilorin Nigeria Ahmadu Bello University Department of Electronics and Telecommunication Engineering Zaria Nigeria Sule Lamido University Department of Information Technology Kafin Hausa Nigeria Sule Lamido University Directorate of Information and Communication Technology Kafin Hausa Nigeria Ahmadu Bello University Department of Electrical Engineering Zaria Nigeria Kampala International University Department of Electrical and Telecommunication Engineering Kampala Uganda University of Hradec Králové Faculty of Informatics and Management Department of Informatics and Qualitative Methods Hradec Králové Czech Republic Sule Lamido University Department of Software Engineering Kafin Hausa Nigeria University of Ilorin Department of Computer Science Ilorin Nigeria Kwara State University Department of Electrical and Computer Engineering Malete Nigeria University of Johannesburg Institute for Intelligent Systems Johannesburg South Africa University of Lagos Department of Electrical and Electronics Engineering Lagos Nigeria Ruhr University Institute of Digital Communication Department of Electrical Engineering and Information Technology Bochum Germany University of Ilorin Department of Electrical and Electronics Engineering Ilorin Nigeria Sule Lamido University Department of Physics Kafin Hausa Nigeria

ISBN: (纸本)9798350358834

5G communication systems provide an end-to-end wireless connection to billions of users and devices across the globe. The quality of signals received during radio communication is notably influenced by the behavior of the radio propagation channel. A major parameter used in characterizing this channel is the Path Loss Exponent (PLE). Several works that have estimated and analyzed the PLE mostly considered the effect of distance and carrier frequency. However, the effect of base station antenna height and channel bandwidth on the PLE for 5G networks have not been adequately considered. To address this, the impact of antenna height of base station and channel bandwidth on the PLE within the 5G Frequency Range 1 (FR1) frequencies was investigated in this study, specifically at 800, 3500, and 5900 MHz. The licensed EDX Signal Pro software® with Cirrus high resolution global terrain and clutter data base was utilized to model, simulate and analyze the PLE for Kano City, Nigeria. Results showed that for the tested frequencies, an increase in either base station height or channel bandwidth leads to a significant reduction in the PLE. This study can be utilized by network planning engineers and the wireless research community to further improve network implementation and optimization toward understanding the behavior of signal propagation in 5G networks and beyond. © 2023 IEEE.

关键词： 5G mobile communication systems

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The OPS-SAT benchmark for detecting anomalies in satellite telemetry

arXiv

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arXiv 2024年

作者： Ruszczak, Bogdan Kotowski, Krzysztof Evans, David Nalepa, Jakub Faculty of Electrical Engineering Automatic Control and Informatics Department of Informatics Opole University of Technology Prószkowska Str. 76 Opole45-758 Poland Faculty of Automatic Control Electronics and Computer Science Department of Algorithmics and Software Silesian University of Technology Akademicka Str. 16 Gliwice44-100 Poland KP Labs Bojkowska Str. 37J Gliwice44-100 Poland European Space Agency ESOC Robert-Bosch-Str. 5 Darmstadt64293 Germany

Detecting anomalous events in satellite telemetry is a critical task in space operations. This task, however, is extremely time-consuming, error-prone and human dependent, thus automated data-driven anomaly detection algorithms have been emerging at a steady pace. However, there are no publicly available datasets of real satellite telemetry accompanied with the ground-truth annotations that could be used to train and verify anomaly detection supervised models. In this article, we address this research gap and introduce the AI-ready benchmark dataset (OPSSAT-AD) containing the telemetry data acquired on board OPS-SAT—a CubeSat mission which has been operated by the European Space Agency which has come to an end during the night of 22–23 May 2024 (CEST). The dataset is accompanied with the baseline results obtained using 30 supervised and unsupervised classic and deep machine learning algorithms for anomaly detection. They were trained and validated using the training-test dataset split introduced in this work, and we present a suggested set of quality metrics which should be always calculated to confront the new algorithms for anomaly detection while exploiting OPSSAT-AD. We believe that this work may become an important step toward building a fair, reproducible and objective validation procedure that can be used to quantify the capabilities of the emerging anomaly detection techniques in an unbiased and fully transparent way. © 2024, CC BY.

关键词： Anomaly detection

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A Workflow Management Framework for the Dynamic Generation of Workflows that is Independent of the Application Environment

A Workflow Management Framework for the Dynamic Generation o...

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IFIP/IEEE International Symposium on Integrated Network Management

作者： Andrzej Jasinski Yuansong Qiao Enda Fallon Ronan Flynn Software Research Institute Athlone Institute of Technology Athlone Ireland Faculty of Engineering & Informatics Athlone Institute of Technology Athlone Ireland

Workflow is a well-known and widely used technology in business management. Traditional workflow solutions are designed for humans and generally use a graphical representation of workflow elements that reflect the involvement of human factors. Additionally, in a situation where workflow execution is not possible, human intervention is necessary. This means that current workflow design is limited in flexibility, in terms of tasks supported, and that it cannot be easily scaled or adopted. Furthermore, current workflow design is limited in efficiency and efficacy, especially in modern environments (e.g. 5G and IoT) where problems can be complex and solutions unpredictable. This paper proposes a workflow management framework that uses dynamically generated workflows to control a managed environment. Exception detection and handling in workflow generation produce recommendations for mitigating incidents that might occur. The key characteristics of the proposed framework are its ease of implementation, flexibility and scalability. These characteristics allow for the quick definition of new tasks, known and unknown, and to assess the quality of the generated recommendation through feedback from the managed environment. Experiments performed in two different environments, robotics and networking, demonstrate the elasticity and functionality of the proposed method to dynamically generated workflows.

关键词： 5G mobile communication Scalability Human factors Elasticity Task analysis Robots Business

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