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TechRxiv 2020年

作者： Ibrahim, Godar J. Rashid, Tarik A. Akinsolu, Mobayode O. Department of Software and Informatics Engineering Salahaddin University-Erbil Erbil Iraq Computer Science and Engineering Department University of Kurdistan Hewlêr Iraq Faculty of Arts Science and Technology Wrexham Glyndwr University Mold Road WrexhamLL11 2AW United Kingdom

By increasing mobile devices in technology and human life, using a runtime and mobile services has gotten more complex along with the composition of a large number of atomic services. Different services are provided by mobile cloud components to represent the nonfunctional properties as Quality of Service (QoS), which is applied by a set of standards. On the other hand, the growth of the energy-source heterogeneity in mobile clouds is an emerging challenge according to the energy-saving problem in mobile nodes. To mobile cloud service composition as an NP-Hard problem, an efficient selection method should be taken by problem using optimal energy-aware methods that can extend the deployment and interoperability of mobile cloud components. Also, an energy-aware service composition mechanism is required to preserve high energy saving scenarios for mobile cloud components. In this paper, an energy-aware mechanism is applied to optimize mobile cloud service composition using a hybrid Shuffled Frog Leaping Algorithm and Genetic Algorithm (SFGA). Experimental results capture that the proposed mechanism improves the feasibility of the service composition with minimum energy consumption, response time, and cost for mobile cloud components against some current algorithms. © 2020, CC BY.

关键词： Energy utilization

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Modeling and analysis of COVID-19 new deaths using tree-based ensemble

TechRxiv

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TechRxiv 2021年

作者： Hashem, Ibrahim Abaker Targio Usmani, Raja Sher Afgun Shah, Asad Ali Almazroi, Abdulwahab Ali Bilal, Muhammad College of Computing and Informatics Department of Computer Science University of Sharjah Sharjah27272 United Arab Emirates Department of Software Engineering Faculty of Computing and Information Technology University of Sialkot Pakistan Department of Computing School of Electrical Engineering & Computer Science National University of Sciences and Technology Pakistan Department of Information Technology College of Computing and Information Technology at Khulais University of Jeddah Jeddah Saudi Arabia Department of Computer Science FAST National University of Computer and Emerging Sciences Islamabad44000 Pakistan

The COVID-19 pandemic has emerged as the world's most serious health crisis, affecting millions of people all over the world. The majority of nations have imposed nationwide curfews and reduced economic activity to combat the spread of this infectious disease. Governments are monitoring the situation and making critical decisions based on the daily number of new cases and deaths reported. Therefore, this study aims to predict the daily new deaths using four tree-based ensemble models i.e., Gradient Tree Boosting (GB), Random Forest (RF), Extreme Gradient Boosting (XGBoost), and Voting Regressor (VR) for the three most affected countries, which are the United States, Brazil, and India. The results showed that VR outperformed other models in predicting daily new deaths for all three countries. The predictions of daily new deaths made using VR for Brazil and India are very close to the actual new deaths, whereas the prediction of daily new deaths for the United States still needs to be improved. © 2021, CC BY.

关键词： COVID-19

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A Brief Review of Explainable Artificial Intelligence in Healthcare

SSRN

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SSRN 2023年

作者： Sadeghi, Zahra Alizadehsani, Roohallah Cifci, Mehmet Akif Kausar, Samina Rehman, Rizwan Mahanta, Priyakshi Bora, Pranjal Kumar Almasri, Ammar Alkhawaldeh, Rami S. Hussain, Sadiq Alatas, Bilal Shoeibi, Afshin Moosaei, Hossein Hladík, Milan Nahavandi, Saeid Pardalo, Panos M. Institute for Big Data Analytics Faculty of Computer Science Dalhousie University Canada Deakin University Geelong Australia The Institute of Computer Technology Tu Wien University Vienna1040 Austria University of Kotli Azad Jammu and Kashmir Azad Kashmir Kotli Pakistan Centre for Computer Science and Applications Dibrugarh University Assam India Department of Management Information Sys Al-Balqa Applied University Salt19117 Jordan Department of Computer Information Systems The University of Jordan Aqaba77110 Jordan Examination Branch Dibrugarh University Assam Dibrugarh India Department of Software Eng. Firat University Elazig23100 Turkey Data Science and Computational Intelligence Institute University of Granada Spain Department of Informatics Faculty of Science Jan Evangelista Purkyně University in Ústí nad Labem Czech Republic Department of Applied Mathematics School of Computer Science Faculty of Mathematics and Physics Charles University Prague Czech Republic Harvard Paulson School of Engineering and Applied Sciences Harvard University AllstonMA02134 United States Swinburne University of Technology HawthornVIC3122 Australia Center for Applied Optimization Department of Industrial and Systems Engineering University of Florida Gainesville32611 United States

XAI refers to the techniques and methods for building AI applications which assist end users to interpret output and predictions of AI models. Black box AI applications in high-stakes decision-making situations, such as medical domain have increased the demand for transparency and explainability since wrong predictions may have severe consequences. Model explainability and interpretability are vital successful deployment of AI models in healthcare practices. AI applications' underlying reasoning needs to be transparent to clinicians in order to gain their trust. This paper presents a systematic review of XAI aspects and challenges in the healthcare domain. The primary goals of this study are to review various XAI methods, their challenges, and related machine learning models in healthcare. The methods are discussed under six categories: Features-oriented methods, global methods, concept models, surrogate models, local pixel-based methods, and human-centric methods. Most importantly, the paper explores XAI role in healthcare problems to clarify its necessity in safety-critical applications. The paper intends to establish a comprehensive understanding of XAI-related applications in the healthcare field by reviewing the related experimental results. To facilitate future research for filling research gaps, the importance of XAI models from different viewpoints and their limitations are investigated. © 2023, The Authors. All rights reserved.

关键词： Decision making

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Corrections to “A Human-in-the-Loop Probabilistic CNN-Fuzzy Logic Framework for Accident Prediction in Vehicular Networks” [Jul 21 15496-15503]

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IEEE Sensors Journal 2022年第10期22卷 10031-10031页

作者： Muhammad Usman Anil Carie Chettupally Bhaskar Marapelli Hayat Dino Bedru Kamanashish Biswas Faculty of Computing Engineering and Science University of South Wales Pontypridd U.K. School of Computer Science VIT-AP Amaravathi India Software Engineering Department College of Computing and Informatics Wolkite University Wolkite Ethiopia School of Software Dalian University of Technology Dalian China Department of Computer Science Engineering Central South University Changsha China Peter Faber Business School Australian Catholic University Brisbane QLD Australia

In the above article [1] , the surname Chettupally of the author Anil Carie Chettupally was inadvertently omitted from the authors list when the article was originally published.

关键词： Probabilistic logic Deep learning Accidents Convolutional neural networks Fuzzy logic Human in the loop Accidents

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2M-SELAR: A Model for Mining Sequential Least Association Rules 2nd

2M-SELAR: A Model for Mining Sequential Least Association Ru...

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2nd International Conference on Advanced Data and Information engineering, DaEng 2015

作者： Abdullah, Zailani Adam, Omer Herawan, Tutut Noraziah, Ahmad Saman, Md Yazid Mohd Hamdan, Abdul Razak School of Informatics and Applied Mathematics Universiti Malaysia Terengganu Kuala Terengganu21030 Malaysia Faculty of Computer Science and Information Technology Universiti Malaya Kuala Lumpur50603 Malaysia Faculty of Computer Systems and Software Engineering Universiti Malaysia Pahang KuantanPahang26300 Malaysia Faculty of Information Science and Technology Universiti Kebangsaan Malaysia BangiSelangor43600 Malaysia

ISBN: (纸本)9789811317972

Recently, mining least association rule from the sequential data becomes more important in certain domain areas such as education, healthcare, text mining, etc. due to its uniqueness and usefulness. However, discovering such rule is a great challenge because it involves with a set of least items which usually holds a very low in term of support. Therefore, in this paper propose a model for mining sequential least association rule (2M-SELAR) that embedded with SELAR algorithm, and Critical Relative Support (CRS) and Definite Factor (DF) measures. The experimental results reveal that 2M-SELAR can successfully generate the desired rule from the given datasets. © Springer Nature Singapore Pte Ltd. 2019.

关键词： Data mining

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Fitting Dense and Sparse Reduced Data 21st

Fitting Dense and Sparse Reduced Data

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21st International Multi-Conference on Advanced Computer Systems, ACS 2018

作者： Kozera, Ryszard Wiliński, Artur Faculty of Applied Informatics and Mathematics Warsaw University of Life Sciences - SGGW ul. Nowoursynowska 159 Warsaw02-776 Poland Department of Computer Science and Software Engineering The University of Western Australia 35 Stirling Highway Crawley PerthWA6009 Australia

ISBN: (纸本)9783030033132

This paper addresses the topic of fitting reduced data represented by the sequence of interpolation points (Formula Presented) in arbitrary Euclidean space Em. The parametric curve γ together with its knots (Formula Presented) (for which (Formula Presented)) are both assumed to be unknown. We look at some recipes to estimate T in the context of dense versus sparse M for various choices of interpolation schemes (Formula Presented). For M dense, the convergence rate to approximate γ with (Formula Presented) is considered as a possible criterion to force a proper choice of new knots (Formula Presented). The latter incorporates the so-called exponential parameterization "retrieving" the missing knots T from the geometrical spread of M. We examine the convergence rate in approximating γ by commonly used interpolants (Formula Presented) based here on M and exponential parameterization. In contrast, for M sparse, a possible optional strategy is to select (Formula Presented) which optimizes a certain cost function depending on the family of admissible knots (Formula Presented). This paper focuses on minimizing "an average acceleration" within the family of natural splines (Formula Presented) fitting M with (Formula Presented) admitted freely in the ascending order. Illustrative examples and some applications listed supplement theoretical component of this work. © 2019, Springer Nature Switzerland AG.

关键词： Interpolation

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Unity3D game engine applied to chemical safety education 3rd

Unity3D game engine applied to chemical safety education

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3rd International Conference on Applied Physics, System Science and Computers, APSAC 2018

作者： Dholakiya, Nishaben S. Kubík, Jan Brozek, Josef Sotek, Karel University of Notingham NotinghamNG7 2RD United Kingdom Faculty of Chemical Engineering and Faculty of Electrical Engineering and Informatics University of Pardubice Pardubice532 10 Czech Republic Laboratory of Application of the Software Technologies - ASOTE Pardubice532 10 Czech Republic Metropolitan University Prague Ucnovska 100/1 Prague190 00 Czech Republic

ISBN: (纸本)9783030215064

The paper is presenting principles of high level safety education in Chemistry taught through the Unity3D Game Engine. Applied paradigms can explain the benefits of using Game Engines instead of other forms of teaching methodology. Especially in some education domains, which are very important, but sometimes boring, very positive results can be achieved. Major rules for developing this kind of software are presented. A case study is presented on using concrete software for chemical safety education. © Springer Nature Switzerland AG 2019.

关键词： E-learning

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Building an agent for factual question generation task 1

Building an agent for factual question generation task

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1st IEEE World Symposium on Digital Intelligence for Systems and Machines, DISA 2018

作者： Blstak, Miroslav Rozinajova, Viera Information Systems and Software Engineering Faculty of Informatics and Information Technologies Slovak University of Technology in Bratislava Institute of Informatics Ilkovicova 2 Bratislava Slovakia

ISBN: (纸本)9781538651025

With the boom of e-learning and online education systems, also question generation systems have become more interesting. Nowadays, it is relatively common and simple to use internet and web technologies for learning. There are many sources of educational materials in various formats like text, audio, video or interactive applications and games. But the automatic evaluation and confirmation of students' knowledge is still difficult. In this paper, we describe our endeavour to design and create an interactive educational agent which to some extent acts as a teacher: it automatically generates factual questions from the educational text and tries to reveal if the student understood the information presented there. We have evaluated our agent in comparison with state-of-the-art question generation systems. In order to assess the applicability of generated questions in educational process we have also compared these questions with those expected by the students. © 2018 IEEE.

关键词： Students

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Multi-Scale Generative Transformer-Based Primal-Dual PPO Framework for AAV-Aided Intelligent Transportation Networks

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IEEE Transactions on Intelligent Transportation Systems 2025年

作者： Adam, Abuzar B. M. Kamal, Tahir Elhassan, Mohammed A. M. Alshahrani, Abdullah Alsamhi, Saeed Hamood Aziz, Ahmed Luxembourg City1855 Luxembourg University of Electronic Science and Technology of China School of Information and Software Engineering Chengdu611731 China Zhejiang Normal University School of Mathematics and Computer Science Jinhua321017 China University of Jeddah College of Computer Science and Engineering Department of Computer Science and Artificial Intelligence Jeddah21493 Saudi Arabia University of Galway Insight Centre for Data Analytics GalwayH91 TK33 Ireland Korea University College of Informatics Department of Computer Science and Engineering Seoul02841 Korea Republic of Bahrain Polytechnic School of ICT Isa Town00973 Bahrain IBB University Faculty of Engineering Electrical Engineering Department Ibb Yemen Benha University Faculty of Computer and Artificial Intelligence Department of Computer Science Banha13511 Egypt Central Asian University Engineering School Tashkent111221 Uzbekistan

Intelligent transportation networks are increasingly integrating autonomous aerial vehicles (AAVs) to enable services essential for modernizing industries like transportation, logistics, and search and rescue. A significant beneficiary is the Internet of Connected Vehicles (IoCVs), where AAVs play a transformative role in supporting next-generation cellular networks. This paper addresses the challenge of minimizing the cost of delivering content to vehicles on road segments with congested traffic or insufficient infrastructure. Vehicles entering these segments request content from a AAV-hosted library that updates dynamically based on item popularity. Each vehicle submits a request, requiring the AAV to compute an optimal trajectory to maximize operational utility. Given the AAV’s limited energy, we aim to develop an energy-efficient solution. The problem is framed as a joint optimization of caching decisions, AAV trajectory planning, and radio resource allocation, formulated as a mixed integer non-linear programming (MINLP) problem. The environment’s complexity, with random vehicle arrivals and fluctuating content, makes traditional optimization techniques inadequate. To address this, we reformulate the problem as a constrained Markov decision process (CMDP) and employ a primal-dual proximal policy optimization (PPO) algorithm, enhanced by a multi-scale generative transformer (MGFormer) for improved speed and accuracy over traditional deep neural networks (DNNs). Simulation results demonstrate that the proposed framework reduces service costs by up to 30% and achieves robust performance even under high-density traffic and Doppler shifts, validating its superiority over traditional Primal-dual PPO. © 2000-2011 IEEE.

关键词： Markov processes

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Novel approaches to evaluate the ability of vehicles for secured transportation

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Periodica Polytechnica Transportation engineering 2020年第1期49卷 80-88页

作者： Kocsis, Bence Vida, Gábor Szalay, Zsolt Ágoston, György Department of Automotive Technologies Faculty of Transportation Engineering and Vehicle Engineering Budapest University of Technology and Economics P. O. B. 91 BudapestH-1521 Hungary Department of Software Development and Application Institute of Informatics University of Dunaújváros P. O. B. 152 DunaújvárosH-2401 Hungary

The assurance of process safety plays an important role in the field of information technology. Securing the information has become one of the biggest challenges in the present day. Whenever we think about the protected systems the first thing that comes to our mind can be malicious interventions which are increasing immensely day by day. Nowadays we live the world of huge automotive developments with the appearance of the demand for autonomous vehicles. On the other hand, technological developments also provide a lot of advantages for the society. The benefits of autonomous cars include reduced mobility costs, increased safety, increased mobility, significant reduction of traffic collisions. However, it cannot be forgotten that the extension of cyberspace affects the transportation increasingly. Accordingly, cars are produced with high level of connectivity and automation. Therefore, the risks arriving from the cyberspace can now endanger the safe and secured transportation. These tendencies shall motivate manufacturers and developers to permanently improve the ability of vehicles to protect themselves and their passengers. © 2020 Budapest University of Technology and Economics. All rights reserved.

关键词： Autonomous vehicles

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