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Science, engineering and Business for Sustainable Development Goals (SEB-SDG), International Conference on

作者： Emmanuel Adetiba John S. Wejin Oluwadamilola Oshin Ayodele H. Ifijeh Comfort Lawal Surendra Colin Thakur Ayokunle A. Awelewa Raymond Jules Kala Priscilla O. Ajayi Matthew B. Akanle Faith O. Sweetwiliams Uche Nnaji Emmanuel Owolabi Olabode Idowu-Bismark Gabriel Sobola Department of Electrical and Information Engineering & Covenant Applied Informatics & Communication African Center of Excellene (CApIC-ACE) Covenant University Ota Nigeria HRA Institute for Systems Science Durban University of Technology Durban South Africa Department of Electrical and Information Engineering & CApIC-ACE Covenant University Jalingo Nigeria Department of Computer Science Taraba State University Jalingo Nigeria Department of Electrical and Information Engineering & CApIC-ACE Covenant University Ota Nigeria Department of Information Technology and KZN E-Skills Co-Laboratory Durban University of Technology Durban South Africa Department of Electrical and Information Engineering Covenant University Ota Nigeria International University of Grand Bassam Grand Bassam Ivory Coast Center for Systems and Information Services & CApIC-ACE Covenant University Ota Nigeria Department of Computer and Information Science & CApIC-ACE Covenant University Ota Nigeria Smartyou Integral (PTY) LTD Pretoria Gauteng South Africa

ISBN: (数字)9798350358155

ISBN: (纸本)9798350358162

In transiting from one civilization to another, technology has played a vital and positive role. In the 21 st century, one of the digital developments that is paving ways for human life improvement is machine-assisted technology using Artificial Intelligence (AI). Artificial Intelligence has successfully enhanced man’s capacity in solving complex problems and processes. However, as developed nations continue to reap from the adoption of AI in various fields of human endeavors, the continent of Africa has remained behind, especially in AI-based skills and research. Various governments in developing nations have encouraged the adoption of AI, especially in institutions of learning. However, theoretical adoption without practical experience has remained an ineffective way of bridging the digital divide. In this paper we present the outcome of a practical approach to bridging the AI divide among students and researchers in Africa through funding support from the Google TensorFlow College Outreach Award. A 3-day hybrid bootcamp was organized (11 th to 13 th December, 2023) using the Google funding in order to equip postgraduate students and researchers with AI and collaborative research skills. A pre-survey method was employed to ascertain the knowledge level of the bootcamp participants. From the pre-surveyed feedback, training sessions on various AI domains were presented, and participant equipped with practical AI skills using a deployed AI-based cloud programming platform running on the private Federated Genomic Cloud (FEDGEN) infrastructure at Covenant university. A post-survey feedback was used to ascertain the effectiveness of this approach. A comparative analysis of the pre-survey and post-survey reveals a 70% improvement of AI skills among participants. This shows that having continuous training session for students and researchers is an effective method in closing the AI skills gap between developed and developing nations.

关键词： Training Surveys Knowledge based systems Government Genomics Africa Programming

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Research on reversible functions having component functions with specified properties: An overview 12th

Research on reversible functions having component functions ...

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12th International Conference on Reversible Computation, RC 2020

作者： Kerntopf, Pawel Moraga, Claudio Podlaski, Krzysztof Stanković, Radomir Institute of Computer Science Warsaw University of Technology Warsaw Poland Faculty of Computer Science Technical University of Dortmund Dortmund Germany Faculty of Physics and Applied Informatics University of Lódź Lódź Poland Department of Computer Science Faculty of Electronic Engineering University of Niš Niš Serbia

ISBN: (纸本)9783030473600

In the traditional logic synthesis, different classifications of non-reversible Boolean functions have found many applications. Recently, some attempts to deal with classifications of reversible functions have been published. In this paper, an overview of our results towards constructing a new classification of reversible functions is presented. These results were obtained due to our discussions during two Short Term Scientific Missions (STSMs) as well as during our further research in the framework of COST Action IC1405 "Reversible Computation - Extending Horizons of Computing" and were published in five papers. © The Author(s) 2020.

关键词： Logic Synthesis

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DieT Transformer model with PCA-ADE integration for advanced multi-class brain tumor classification

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Intelligence-Based Medicine 2025年 11卷 100192-100192页

作者： Amin, Mohammad Nahar, Khalid M.O. Gharaibeh, Hasan Nasayreh, Ahmad Alsalmanc, Neda'a Alomar, Alaa Malkawi, Majd Alqasem, Noor Smerat, Aseel Zitar, Raed Abu Nusier, Shawd Ezugwu, Absalom E. Abualigah, Laith Department of Information Technology and Computer Sciences Yarmouk University Irbid 211633 Jordan Faculty of Computer Studies Arab Open University P.O.Box 84901 Riyadh 11681 Saudi Arabia Department of Computer Science Faculty of Information Technology Jordan University of Science and Technology Irbid 22110 Jordan Department of Medicine Faculty of Medicine Jordan University of Science and Technology Irbid 22110 Jordan Department of Biomedical Systems and Informatics Engineering Hijjawi Faculty for Engineering Technology Yarmouk University Irbid 21163 Jordan Centre for Research Impact & Outcome Chitkara University Institute of Engineering and Technology Chitkara University Punjab Rajpura 140401 India Department of Biosciences Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Chennai 602105 India Artificial Intelligence and Sensing Technologies (AIST) Research Center University of Tabuk Tabuk 71491 Saudi Arabia Faculty of Engineering and Computing Liwa College Abu Dhabi United Arab Emirates Department of Health and Clinical Outcomes Research Saint Louis University St. Louis MO United States Unit for Data Science and Computing North-West University 11 Hofman Street Potchefstroom 2520 South Africa Computer Science Department Al al-Bayt University Mafraq 25113 Jordan Hourani Center for Applied Scientific Research Al-Ahliyya Amman University Amman 19328 Jordan

Early and accurate diagnosis of brain tumors is crucial to improving patient outcomes and optimizing treatment strategies. Long-term brain injury results from aberrant proliferation of either malignant or nonmalignant tissues in the brain. MRIs, or magnetic resonance imaging, are one of the most used approaches for detecting brain tumors. Professionals physically evaluate people after they have had MRI filtering, the process of enhancing MRI scans for radiologist interpretation, to establish if they have a brain tumor. Because different specialists use different frames to make judgments on the same MRI image, their analyses may yield contradictory results. Furthermore, simply detecting a tumor is insufficient. Inconsistent diagnoses can lead to delays in treatment, impacting survival rates and quality of care. It is also crucial to diagnose the patient's tumor so that treatment can begin as soon as possible. In this research, we investigate the multi-class classification of brain tumors utilizing a cutting-edge methodology that includes feature extraction from pictures using the DieT Transformer model, dimensionality reduction with PCA, and feature selection using the ADE algorithm. The proposed model, known in the publication as ADE_DieT, obtained an accuracy of 96.09 %. In addition, this article analyzes the performance of various pre-trained models, including MobileNetV3, NasNet, ResNet50, VGG16, VGG19, and DeiT. The proposed approach shortens the time required for manual diagnosis by clinicians by assisting in the rapid and accurate identification of brain tumors using MRI data. In oncology, this is important since it allows for early treatment. Integrating ADE_DieT into clinical workflows can support radiologists by reducing diagnosis time and enhancing diagnostic consistency. © 2024 The Authors

关键词： Brain tumor Medical image analysis MRI Vision Transformer

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A Study of Falkner-Skan Flow of Casson Fluid with Development of Empirical Regression Model for Wall Heat and Concentration Flux

SSRN

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SSRN 2022年

作者： Hussain, Dilawar Asghar, Zaheer Zeeshan, Ahmed Alsulami, Hamed Band, Shahab S. Mosavi, Amir Department of Mathematics & Statistics FBAS International Islamic University Islamabad IslamabadH-10 Pakistan Centre for Mathematical Sciences Pakistan Institute of Engineering and Applied Sciences Nilore Islamabad45650 Pakistan Centre for Physics and Applied Mathematics Pakistan Institute of Engineering and Applied Sciences Nilore Islamabad45650 Pakistan -Research Group Department of Mathematics Faculty of Science King Abdulaziz University P.O. Box 80203 Jeddah21589 Saudi Arabia Future Technology Research Center National Yunlin University of Science and Technology Yunlin Douliou64002 Taiwan John von Neumann Faculty of Informatics Obuda University Budapest Hungary Institute of Information Engineering Automation and Mathematics Slovak University of Technology in Bratislava Bratislava Slovakia

In this communication, authors ambition is to investigate the sensitivity of heat flux at solid liquid interface of Falkner-Skan (FS) flow of Casson fluid on a moving wedge. Alongside, authors intended to develop an empirical model in terms of involving parameters. Sensitivity analysis is an essential toll in engineering and manufacturer process to examine the effect of different input factors and reduces the experimental cost and save valuable time. The FS flow in this case is observed under the influence of induced magnetic field. The mathematical model of the flow experiments are adopted from El-Dabe et al. [1] which was solved numerically by using computational software MATLAB and achieved reliable solutions- validated by comparing the numerical results with published results. Researchers and scientists have used a variety of empirical methods to investigate the sensitivity of the output variables. The most effective method for determining the relationship between input parameters and output responses is the correlation development method. A data set is extracted for wall heat flux and Sherwood number at random points, and finally by using RSM the correlation of input parameter and output responses are developed. The statistical results of ANOVA are presented in tabular form by using RSM. © 2022, The Authors. All rights reserved.

关键词： Sensitivity analysis

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MagnetoShield: Prototype of a Low-Cost Magnetic Levitation Device for Control Education

MagnetoShield: Prototype of a Low-Cost Magnetic Levitation D...

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IEEE Education engineering (EDUCON)

作者： Gergely Takács Jakub Mihalík Erik Mikuláš Martin Gulan Institute of Automation Measurement and Applied Informatics Faculty of Mechanical Engineering Slovak University of Technology in Bratislava Bratislava Slovakia

ISBN: (数字)9781728109305

ISBN: (纸本)9781728109312

This article presents a prototype reference design for a low-cost miniature magnetic levitation experiment. The proposed device is built as an Arduino expansion module, thus may be installed on a broad range of microcontroller prototyping boards. The open-source hardware design uses off-the-shelf and widely available components and 3D printing technology, thus its overall material cost is minimal. This way, the magnetic levitation experiment is transformed into a pocket laboratory that can be borrowed by students for take-home experiments. In addition to the device itself, we present an open-source application programming interface and the outline of classroom examples in modeling, system identification and closed-loop control.

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A scalable quantum gate-based implementation for causal hypothesis testing

arXiv

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arXiv 2022年

作者： Kundu, Akash Acharya, Tamal Sarkar, Aritra Institute of Theoretical and Applied Informatics Polish Academy of Sciences Joint Doctoral School Silesian University of Technology Gliwice Poland Quantum Intelligence Research Team Department of Quantum & Computer Engineering Delft University of Technology Delft2628 CD Netherlands Quantum Machine Learning Group Quantum Computing Division QuTech Delft2628 CJ Netherlands

In this work, we study quantum computing algorithms for accelerating causal inference. Specifically, we consider the formalism of causal hypothesis testing presented in [Nat Commun 10, 1472 (2019)]. We develop a quantum circuit implementation and use it to demonstrate that the error probability introduced in the previous work requires modification. The practical scenario, which follows a theoretical description, is constructed as a scalable quantum gate-based algorithm on IBM Qiskit. We present the circuit construction of the oracle embedding the causal hypothesis and assess the associated gate complexities. Additionally, our experiments on a simulator platform validate the predicted speedup. We discuss applications of this framework for causal inference use cases in bioinformatics and artificial general intelligence. © 2022, CC BY.

关键词： Inference engines

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Accurate Discharge Coefficient Prediction of Streamlined Weirs by Coupling Linear Regression and Deep Convolutional Gated Recurrent Unit

arXiv

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arXiv 2022年

作者： Chen, Weibin Sharifrazi, Danial Liang, Guoxi Band, Shahab S. Chau, Kwok Wing Mosavi, Amir College of Computer Science and Artificial Intelligence Wenzhou University Zhejiang Wenzhou325035 China Department of Computer Engineering Shiraz Branch Islamic Azad University Shiraz Iran Department of Artificial Intelligence Wenzhou Polytechnic Wenzhou325035 China Future Technology Research Center College of Future National Yunlin University of Science and Technology Yunlin County Taiwan Department of Civil and Environmental Engineering Hong Kong Polytechnic University Hong Kong John von Neumann Faculty of Informatics Obuda University Budapest Hungary Institute of Information Engineering Automation and Mathematics Slovak University of Technology Bratislava Slovakia

Streamlined weirs, as a nature-inspired type of weirs, has gained tremendous attention among hydraulic engineers mainly due to their well-known performance with high discharge coefficient. Computational fluid dynamic (CDF) is considered as a robust tool to predict discharge coefficient. To bypass the computational cost of CFD-based assessment, the present study proposes data-driven modelling techniques, as an alternative to CFD simulation, to predict discharge coefficient based on an experimental dataset. To this end, after splitting the dataset by k-fold cross-validation technique, the performance assessment of classical and hybrid machine-deep learning (ML-DL) algorithms is undertaken. Amongst ML techniques, linear regression (LR), random forest (RF), support vector machine (SVM), k-nearest neighbours (KNN), and decision tree (DT) algorithms are studied . In the context of DL, long short-term memory (LSTM), convolutional neural network (CNN), gated recurrent unit (GRU) and their hybrid forms such as LSTM-GRU, CNN-LSTM and CNN-GRU techniques are compared by different error metrics. It is found that the proposed three-layer hierarchical DL algorithm consisting of a convolutional layer coupled with two subsequent GRU levels, which is also hybridized by LR method (i.e., LR-CGRU), leads to lower error metrics. This paper paves the way for data-driven modelling of streamlined *** Codes 68T05 © 2022, CC BY.

关键词： Deep neural networks

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RECOMED: A Comprehensive Pharmaceutical Recommendation System

arXiv

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arXiv 2022年

作者： Zomorodi, Mariam Ghodsollahee, Ismail Martin, Jennifer H. Talley, Nicholas J. Salari, Vahid Plawiak, Pawel Rahimi, Kazem Acharya, U. Rajendra Department of Computer Science Faculty of Computer Science and Telecommunications Cracow University of Technology Krakow Poland Department of Computer Engineering Ferdowsi University of Mashhad Iran The University of Newcastle CallaghanNSW Australia Institute for Quantum Science and Technology Department of Physics and Astronomy University of Calgary Alberta Canada Institute of Theoretical and Applied Informatics Polish Academy of Sciences Gliwice Poland Deep Medicine Nuffield Department of Women’s and Reproductive Health University of Oxford Oxford United Kingdom School of Mathematics Physics and Computing University of Southern Queensland ToowoombaQLD Australia

OBJECTIVES: To build datasets containing useful information from drug databases and recommend a list of drugs to physicians and patients with high accuracy by considering features of a spectrum of people, diseases, and chemicals. METHODS: A comprehensive pharmaceutical recommendation system was designed based on the features of a spectrum of people, diseases, and medicines extracted from two major drug databases, a dataset of patients and drug information. Then the recommendation was given, and the response was analysed using patient and caregiver ratings and the knowledge obtained from drug specifications and interactions. Sentiment analysis was employed by natural language processing approaches in pre-processing, along with neural network-based methods and recommender system algorithms for modelling the system. Patient conditions and medicine features were used for making two models based on matrix factorisation. Then we used drug interaction criteria to filter drugs with severe or mild interactions with other drugs. We developed a deep learning model for recommending drugs by using data from 2304 patients as a training set, and then we used data from 660 patients as our validation set. We used knowledge from critical drug information and combined the model’s outcome into a knowledge-based system with the rules obtained from constraints on taking medicine. RESULTS: The results show that our recommendation system can recommend an acceptable combination of medicines according to the existing real-life prescriptions available. Compared with conventional matrix factorisation, our proposed model improves the accuracy, sensitivity, and hit rate by 26%, 34%, and 40%, respectively. In addition, it improves the accuracy, sensitivity, and hit rate by an average of 31%, 29%, and 28% compared to other machine learning methods. We have open-sourced our implementation in Python. CONCLUSION: Compared to conventional machine learning approaches, we obtained average accuracy, sens

关键词： Recommender systems

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A framework for managing enterprise architecture debts - Outline and research directions 10

A framework for managing enterprise architecture debts - Out...

引用

10th International Workshop on Enterprise Modeling and Information Systems Architectures, EMISA 2020

作者： Alexander, Peter Hacks, Simon Jung, Jürgen Lichter, Horst Steffens, Ulrike Uludağ, Ömer RWTH Aachen University Research Group Software Construction Aachen Germany KTH Royal Institute of Technology Network and Systems Engineering Stockholm Sweden Frankfurt University of Applied Sciences Faculty of Computer Science and Engineering Frankfurt Germany Hamburg University of Applied Sciences Department of Computer Science Hamburg Germany Technical University of Munich Department of Informatics Munich Germany

Even though enterprise architecture management (EAM) offers a wide range of methods and tools for aligning business with IT, an architect’s work is challenged by reality. The evolution of enterprise architecture (EA) and given constraints (e.g. legacy systems and processes) lead to debts which may complicate and hinder opportunities;however, the management of such debts has not been considered in EAM research. This paper presents a framework for strategically managing EA-debt-related issues and propose open questions as well as future research directions in this field. Copyright © 2020 for this paper by its authors.

关键词： Legacy systems

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Correction to: Automatic COVID-19 detection mechanisms and approaches from medical images: a systematic review

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Multimedia tools and applications 2022年第20期81卷 28799-28800页

作者： Amir Masoud Rahmani Elham Azhir Morteza Naserbakht Mokhtar Mohammadi Adil Hussein Mohammed Aldalwie Mohammed Kamal Majeed Sarkhel H Taher Karim Mehdi Hosseinzadeh Future Technology Research Center National Yunlin University of Science and Technology Douliu Yunlin Taiwan. Research and Development Center Mobile Telecommunication Company of Iran Tehran Iran. Mental Health Research Center Psychosocial Health Research Institute Iran University of Medical Sciences Tehran Iran. Department of Information Technology College of Engineering and Computer Science Lebanese French University Kurdistan Region Iraq. Department of Communication and Computer Engineering Faculty of Engineering Cihan University-Erbil Kurdistan Region Iraq. Information Technology Department Faculty of Applied Science Tishk International University Erbil Iraq. Computer Department College of Science University of Halabja Halabja Iraq. Computer Networks Department Sulaimani Polytechnic University Technical College of Informatics Sulaymaniyah Iraq. Computer Science University of Human Development Sulaymaniyah Iraq.

[This corrects the article DOI: 10.1007/s11042-022-12952-7.].

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