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IEEE International Conference on Bioinformatics and Biomedicine (BIBM)

作者： Henry Musto Daniel Stamate Doina Logofatu Lahcen Ouarbya Department of Computing Goldsmiths College University of London United Kingdom Department of Computing Goldsmiths College University of London and School of Health Sciences The University of Manchester United Kingdom Faculty of Computer Science and Engineering Frankfurt University of Applied Sciences Germany

ISBN: (数字)9798350337488

ISBN: (纸本)9798350337495

In recent years, attention within the clinical prediction community has turned to the use of survival machine learning as a tool for predicting the risk of developing a disease as a function of time. The current work seeks to contribute to existing literature which demonstrates the utility of these methods when applied to a dementia prediction context. We use the Alzheimer's Disease Neuroimaging Initiative ADNI dataset and model deterioration within two distinct groups, those deemed cognitively normal and those with a formal diagnosis of Mild Cognitive Impairment. In agreement with existing literature we find that survival machine learning outperforms standard survival analysis methods such as Cox PH model, and has very good predictive ability. We propose an innovative approach to predicting dementia diagnosis risk on ADNI, which explores the use of survival neural network and survival extreme gradient boosting techniques that have hitherto seldom been applied to this context. The stability of our models was investigated within a Monte Carlo simulation framework.

关键词： Analytical models Biological system modeling Neural networks Predictive models Boosting Stability analysis Alzheimer's disease

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User Interaction Development for Radiotherapy Virtual Reality Training Application

User Interaction Development for Radiotherapy Virtual Realit...

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2023 IEEE International Conference on computing, ICOCO 2023

作者： Chan, Vei Siang Iglesias, Andres Mohamed, Farhan Salleh, Faezah M. Haron, Habibollah Jamaludin, Najwa Ayuni Galvez, Akemi Prada, Pedro J. Ruiz, Samuel You, Lihua Universiti Teknologi Malaysia Faculty of Computing Johor Bahru Malaysia Universidad de Cantabria Dept. of Applied Mathematics and Computational Sciences Santander Spain Toho University Faculty of Pharmaceutical Sciences Funabashi Japan Media and Game Innovation Center of Excellence Universiti Teknologi Malaysia Johor Bahru Malaysia Universiti Teknologi Malaysia Faculty of Science Johor Bahru Malaysia Hospital Universitario Marqués de Valdecilla Santander Spain National Center for Computer Animation Bournemouth University Poole United Kingdom

ISBN: (纸本)9798350302684

The advancement of virtual reality (VR) technology can enrich pedagogical approaches and information communication. In the oncology radiotherapy treatment, the medical practitioners revealed two main issues: the lack of training infrastructure and patient comprehension of the treatment process. Virtual simulation can assist in solving these complexities by offering an economical virtual embodiment of the medical facility. This study aims to improve the user interface and experience of VR training applications from previous work by conducting a domain expert intervention methodology. This paper discusses the main challenges and features of user interaction design for effective VR training systems and optimises the system based on feedback from domain experts. The authors anticipate that the key factors and design considerations proposed for this VR training application can benefit the development of related training systems in many other domains. © 2023 IEEE.

关键词： Virtual reality

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Integrating Artificial Intelligence in Improving Educational System: The Mediating role of Smart Learning 2

Integrating Artificial Intelligence in Improving Educational...

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2nd International Conference on Business Analytics for Technology and Security, ICBATS 2023

作者： Akour, Iman A. Muhammad Alshurideh, Hevron Alzoubi, Haitham M. Alshurideh, Muhammad Turki Ahmad Antouz, Yasin University of Sharjah College of Computing and Informatics Department of Information Systems Sharjah27272 United Arab Emirates The University of Jordan Faculty of English Language and Literature Department of Foreign Languages Amman11942 Jordan Skyline University College School of Business Sharjah United Arab Emirates Applied Science Private University Applied Science Research Center Amman Jordan University of Sharjah College of Business Department of Management Sharjah27272 United Arab Emirates The University of Jordan School of Business Department of Marketing Amman11942 Jordan

ISBN: (纸本)9798350335644

One of the contemporary developments in educational technology is artificial intelligence (AI) in the education. The use of AI will lead to a significant change in governance across the board for UAE institutes of learning. The potential use of AI in education includes an examination of the educational consequences, including how teachers would be enriched, how students would learn, and how fast and accurate judgements could be made in education institutions. Given this situation, AI assistance is absolutely necessary. Despite the fact that it has been around for close to 30 years, educators are still confused of how to use it pedagogically on a broader scale and how it may actually have a big impact on teaching and learning in higher education. Through an empirical review, this research aims to present an empirical analysis of the application of artificial intelligence in educational system with mediating effect of smart learning. This research carried out empirical evidence from 28 educational institutions based in UAE. The managerial and professors were approached in order to answer the online questionnaire. 195 valid responses were considered for data analysis using SmartPLS. Model assessment done on the basis of various tests, for instance, reliability, discriminant validity, PLS-SEM. The findings suggest a significant relationship between artificial intelligence and educational system, and mediating role of smart learning is predicted to be significant. Hence, the intention to use artificial intelligence with smart learning can enhance the academic learning outcomes. © 2023 IEEE.

关键词： Artificial intelligence

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Deep Learning Enabled Microarray Gene Expression Classification for Data science Applications

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Computers, Materials & Continua 2022年第11期73卷 4277-4290页

作者： Areej A.Malibari Reem M.Alshehri Fahd N.Al-Wesabi Noha Negm Mesfer Al Duhayyim Anwer Mustafa Hilal Ishfaq Yaseen Abdelwahed Motwakel Department of Computer Science Faculty of Computing and Information TechnologyKing Abdulaziz UniversityJeddah21589Saudi Arabia Department of Information Technology College of Computers and Information TechnologyTaif UniversityTaif21944Saudi Arabia Department of Computer Science College of Science&Art at MahayilKing Khalid UniversitySaudi Arabia Department of Natural and Applied Sciences College of Community-AflajPrince Sattam bin Abdulaziz UniversitySaudi Arabia Department of Computer and Self Development Preparatory Year DeanshipPrince Sattam bin Abdulaziz UniversityAlKharjSaudi Arabia

In bioinformatics applications,examination of microarray data has received significant interest to diagnose *** gene expression data can be defined by a massive searching space that poses a primary challenge in the appropriate selection of *** data classification incorporates multiple disciplines such as bioinformatics,machine learning(ML),data science,and pattern *** paper designs an optimal deep neural network based microarray gene expression classification(ODNN-MGEC)model for bioinformatics *** proposed ODNN-MGEC technique performs data normalization process to normalize the data into a uniform ***,improved fruit fly optimization(IFFO)based feature selection technique is used to reduce the high dimensionality in the biomedical ***,deep neural network(DNN)model is applied for the classification of microarray gene expression data and the hyperparameter tuning of the DNN model is carried out using the Symbiotic Organisms Search(SOS)*** utilization of IFFO and SOS algorithms pave the way for accomplishing maximum gene expression classification *** examining the improved outcomes of the ODNN-MGEC technique,a wide ranging experimental analysis is made against benchmark *** extensive comparison study with recent approaches demonstrates the enhanced outcomes of the ODNN-MGEC technique in terms of different measures.

关键词： Bioinformatics data science microarray gene expression data classification deep learning metaheuristics

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A Comparative Study of the Coulomb’s and Franklin’s Laws Inspired Algorithm (CFA) with Modern Evolutionary Algorithms for Numerical Optimization 1st

A Comparative Study of the Coulomb’s and Franklin’s Laws I...

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1st International Conference on Pervasive Knowledge and Collective Intelligence on Web and Social Media, PerSOM 2022

作者： Ghasemi, Mojtaba Zare, Mohsen Zahedi, Amir Hemmati, Rasul Abualigah, Laith Forestiero, Agostino Department of Electronics and Electrical Engineering Shiraz University of Technology Shiraz Iran Department of Electrical Engineering Faculty of Engineering Jahrom University Fras Jahrom Iran Department of Electrical and Computer Engineering Tarbiat Modares University Tehran Iran Department of Electrical and Computer Engineering Marquette University MilwaukeeWI United States Hourani Center for Applied Scientific Research Al-Ahliyya Amman University Amman Jordan Faculty of Information Technology Middle East University Amman11831 Jordan Applied Science Research Center Applied Science Private University Amman11931 Jordan School of Computer Sciences Universiti Sains Malaysia Pulau Pinang George Town11800 Malaysia Computer Science Department Prince Hussein Bin Abdullah Faculty for Information Technology Al Al-Bayt University Mafraq25113 Jordan Institute for High Performance Computing and Networking National Research Council of Italy Rende Italy

ISBN: (纸本)9783031314681

Coulomb and Franklin’s electricity laws are used in this paper to model an efficient optimization algorithm based on electric particle searches, which has been named CFA. For the CFA optimizer, the influence of electrically charged particles on each other in charged things has been predicated on the forces of attraction and repulsion. Evolutionary algorithms (EA) such as hybrid real coded genetic algorithm (RCGA) which combines the global and local search (GL-25), differential evolution (DE) with strategy adaptation (SaDE), composite DE (CoDE), the improved standard particle swarm optimization 2011 (SPSO2013) and the grouped comprehensive learning PSO (GCLPSO) are compared to the CFA optimizer for finding global solutions of seven basic benchmark functions of high dimension D = 50. (GCLPSO). Experiments have shown that the suggested CFA optimizer is quite effective and competitive for the benchmark functions. Note that the source code of the CFA algorithm is publicly available at https://***/projects/cfa, https://***/matlabcentral/fileexchange/127727-franklin-s-laws-inspired-algorithm-cfa. © 2023, ICST Institute for Computer sciences, Social Informatics and Telecommunications Engineering.

关键词： Charged particles

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Efficient online shopping platforms in Southeast Asia 2

Efficient online shopping platforms in Southeast Asia

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2nd International Conference on Smart computing and Electronic Enterprise, ICSCEE 2021

作者： Bamansoor, Samer Pande, Bishwajeet Al Moaiad, Yazeed Pathmanathan, P. Ravindran El-Ebiary, Yousef A. Baker Latiff, Nurul Adilah Abdul Aseh, Khairi Aledinat, Lowai Saleh Yusoff, Fakhrul Hazman MEDIU Faculty of Computer and Information Technology Malaysia Faculty of Informatics and Computing UniSZA Malaysia Gyancity Research Consultancy Pvt Ltd Motihari India MEDIU Faculty of Computer and Information Technology Malaysia International Institute of Applied Science Swiss School of Management Netherlands UniSZA Faculty of Informatics and Computing Malaysia Universiti Malaysia Terengganu Faculty of Ocean Engineering Technology and Informatics Malaysia UniSZA Faculty of Informatics and Computing Malaysia Universiti Teknologi MARA Malaysia

ISBN: (纸本)9781665432221

With the emergence of current developments and their devices that allow correspondence between organizations with each other, just as it is between organizations and service providers, just as with their customers, for some reasons, including advertising and a return to what is acceptable to them with the organization. Items, deals, etc., therefore, this examination provides an internal and external investigation of the current web-based business phase with a focus on promising conditions, risks, qualities, and shortcomings of FashionValet's electronic stage, whose administrations offer more than the Internet. Also, FashionValet is a stage organizer for online shopping and style in Southeast Asia other than Poplook, Zalora, and others, and the bulk of them are important competitors to FashionValet and they implement their own systems. The FashionValet experience, for example, is the history of the creator just as the other company's foundation has been pointed out to characterize how organizations, especially FashionValet, have moved from their primary stage to where they are today as the most aggressive design project. In addition, the technologies and innovations used by FashionValet are shown to provide a comprehensive detailed blueprint. To understand the improvement in style organizations in Malaysia, it was examined further. The paper outlines the critical elements to FashionValet's thriving as an end. © 2021 IEEE.

关键词： Online systems

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Efficient Privacy-Preserving Multi-Location Task Allocation in Fog-Assisted Vehicular Crowdsourcing

Efficient Privacy-Preserving Multi-Location Task Allocation ...

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IEEE International Conference on Communications (ICC)

作者： Yantao Yu Yunguo Guan Xiaoping Xue Jingxiao Ma Ellen Z. Zhang Rongxing Lu College of Electronic and Information Engineering Tongji University Shanghai China Faculty of Computer Science University of New Brunswick Frederiction NB Canada School of Information Security and Applied Computing Eastern Michigan University Ypsilanti MI USA

ISBN: (数字)9781728190549

ISBN: (纸本)9781728190556

Multi-location task allocation is one of the most crucial issues in vehicular crowdsourcing (VCS). To ensure service quality, the VCS service provider prefers to assign multi-location tasks to the workers whose future trajectories have high spatial proximity with the task locations. However, this process requires workers and task owners to upload their precise locations to a not-fully-trusted service provider, thereby raising location privacy concerns. Although several privacy-preserving trajectory similarity evaluation schemes have been proposed, they either fail to match the multi-location task allocation scenario, or incur nontrivial computational costs due to homomorphic encryption. To address these challenges, we propose a novel efficient privacy-preserving multi-location task allocation scheme in fog-assisted VCS. Specifically, we design a lightweight secure Euclidean distance computation protocol based on arithmetic secret sharing (ASS), which can compute Euclidean distance without revealing the two input locations. Then, based on this protocol, we build our scheme that supports multi-location task allocation based on Hausdorff semi-distance (HSD). Our security analysis demonstrates the location privacy preservation of our scheme, and the experiment results on a real dataset also validate the efficiency of our scheme.

关键词： Crowdsourcing Privacy Data privacy Protocols Euclidean distance Trajectory Computational efficiency

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ANFIS Model for Cost Analysis in a Dual Source Multi-Destination System 4

ANFIS Model for Cost Analysis in a Dual Source Multi-Destina...

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4th International Conference on Industry 4.0 and Smart Manufacturing, ISM 2022

作者： Okwu, M.O. Tartibu, L.K. Ojo, E.O. Adume, S. Gidiagba, J.O. Fadeyi, J. Department of Mechanical Industrial Engineering Tech. University of Johannesburg South Africa Department of Mechanical Engineering Federal University of Petroleum Resources Effurun Nigeria Mathematics and Data Science Dept. Computing Science and Mathematics Division Faculty of Natural Science University of Stirling United Kingdom Faculty of Engineering and Applied Sc. Memorial University Newfoundland Canada Department of Industrial and Systems Engineering University of Buffalo United States

Managers face uncertainties while making allocation decisions, especially in dual or multi-source multi-destination inventory systems. Regrettably, many studies focus on classical methods as a technique for product distribution, which has never guaranteed a satisfactory solution. Real-life problems are non-deterministic polynomial-time hard (NP-hard), and solving such problems is relatively challenging. Such complicated problems need an efficient and robust computational hybrid algorithm. This study emphasises the need for a hybrid intelligent technique for effective product distribution. Soft computing hybrid algorithm, ANFIS was applied to product distribution in a double source multi-destination system. Rules were developed from available input datasets. Distributing products from dual manufacturing plants to fifteen available depots using the creative algorithm resulted in an overall 13.5% decrease in cost compared to the existing method adopted by the company. The result showed that the proposed method is relatively satisfactory and adequate for cost modelling. In addition, it is easy to use and outperforms the classical approach. © 2022 The Authors. Published by ELSEVIER B.V.

关键词： Soft computing

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applied Linguistics with Mixed Leader Optimizer Based English Text Summarization Model

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Intelligent Automation & Soft computing 2023年第6期36卷 3203-3219页

作者： Hala J.Alshahrani Khaled Tarmissi Ayman Yafoz Abdullah Mohamed Manar Ahmed Hamza Ishfaq Yaseen Abu Sarwar Zamani Mohammad Mahzari Department of Applied Linguistics College of LanguagesPrincess Nourah Bint Abdulrahman UniversityP.O.Box 84428Riyadh11671Saudi Arabia Department of Computer Sciences College of Computing and Information SystemUmm Al-Qura UniversitySaudi Arabia Department of Information Systems Faculty of Computing and Information TechnologyKing Abdulaziz UniversityJeddahSaudi Arabia Research Centre Future University in EgyptNew Cairo11845Egypt Department of Computer and Self Development Preparatory Year DeanshipPrince Sattam bin Abdulaziz UniversityAlKharjSaudi Arabia Department of English College of Science&HumanitiesPrince Sattam bin Abdulaziz UniversityAlKharjSaudi Arabia

The term‘executed linguistics’corresponds to an interdisciplinary domain in which the solutions are identified and provided for real-time language-related *** exponential generation of text data on the Internet must be leveraged to gain knowledgeable *** extraction of meaningful insights from text data is crucial since it can provide value-added solutions for business organizations and *** Automatic Text Summarization(ATS)process reduces the primary size of the text without losing any basic components of the *** current study introduces an applied Linguistics-based English Text Summarization using a Mixed Leader-Based Optimizer with Deep Learning(ALTS-MLODL)*** presented ALTS-MLODL technique aims to summarize the text documents in the English *** accomplish this objective,the proposed ALTS-MLODL technique pre-processes the input documents and primarily extracts a set of ***,the MLO algorithm is used for the effectual selection of the extracted *** the text summarization process,the Cascaded Recurrent Neural Network(CRNN)model is exploited whereas the Whale Optimization Algorithm(WOA)is used as a hyperparameter *** exploitation of the MLO-based feature selection and the WOA-based hyper-parameter tuning enhanced the summarization *** validate the perfor-mance of the ALTS-MLODL technique,numerous simulation analyses were *** experimental results signify the superiority of the proposed ALTS-MLODL technique over other approaches.

关键词： Text summarization deep learning hyperparameter tuning applied linguistics multi-leader optimizer

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Efficient Privacy-Preserving Edge-based Dynamic Aggregation Query Over Crowdsensed Data

Efficient Privacy-Preserving Edge-based Dynamic Aggregation ...

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IEEE Conference on Global Communications (GLOBECOM)

作者： Yantao Yu Yunguo Guan Xiaoping Xue Rongxing Lu College of Electronic and Information Engineering Tongji University Shanghai China School of Information Security and Applied Computing Eastern Michigan University Ypsilanti MI USA Faculty of Computer Science University of New Brunswick Frederiction NB Canada

ISBN: (数字)9798350351255

ISBN: (纸本)9798350351262

Aggregation query, which is one of the most crucial data analysis tools applied to vehicular crowdsensed data, is expected to extract valuable insights and support smart decision-making. Meanwhile, the edge servers are deployed to cope with the escalation of service scale, which however raises privacy concerns about the query requests and reported data. Previously reported privacy-preserving aggregation query schemes are either tailored for static datasets or lack an index structure for deployed queries, which makes them impractical in vehicular crowdsensing (VCS) scenarios characterized by large volumes of real-time data and high data update frequencies. To tackle these challenges, we propose an efficient privacy-preserving edge-based dynamic aggregation query scheme with an encrypted tree-based index. Concretely, we first design two building blocks, namely the balanced spatial encoding quadtree (BSEQtree) and a predicate encryption scheme for membership testing (PEMT), which enable the edge servers to obliviously match data and queries by traversal over the encrypted BSEQtree. Based on the above blocks and arithmetic secret sharing (ASS), we construct our scheme, in which the edge servers can efficiently and securely aggregate newly reported data to related query results. Our security analysis shows the privacy preservation of our scheme, and the experiment results on a real dataset validate the efficiency of our scheme.

关键词： Data privacy Privacy Crowdsensing Data aggregation Real-time systems Cryptography Servers Indexes Vehicle dynamics Testing

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