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2020 Genetic and Evolutionary Computation Conference, GECCO 2020

作者： Opara, Karol R. Hadi, Anas A. Mohamed, Ali W. Systems Research Institute Polish Academy of Sciences Warsaw Poland Faculty of Computing and Information Technology King Abdulaziz University Jeddah Saudi Arabia School of Engineering and Applied Sciences Nile University Giza Egypt

ISBN: (纸本)9781450371278

Performance of real-parameter global optimization algorithms is typically evaluated using sets of test problems. We propose a new methodology of extending these benchmarks to obtain a more balanced experimental design. This can be done by selectively removing some of the transformations originally used in the definitions of the test problems such as rotation, scaling, or translation. In this way, we obtain several variants of each problem parametrized by interpretable, high-level characteristics. These binary parameters are used as predictors in a multiple regression model explaining the algorithmic performance. Linear models allow for the attribution of strength and direction of performance changes to particular characteristics of the optimization problems and thus provide insight into the underlying mechanics of the investigated algorithms. The proposed ideas are illustrated with an application example showing the feasibility of the new benchmark. Parametrized benchmarking is a step towards obtaining multi-faceted insight into algorithmic performance and the optimization problems. The overall goal is to systematize a method of matching problems to algorithms and in this way constructively address the limitations imposed by the no free lunch theorem. © 2020 Owner/Author.

关键词： Benchmarking

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Linear Programming Model for Investment Problem in Maximizing the Total Return 28

Linear Programming Model for Investment Problem in Maximizin...

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28th Simposium Kebangsaan Sains Matematik, SKSM 2021

作者： Nordin, Syarifah Zyurina Johar, Farhana Abu, Noratikah Malaysia-Japan International Institute of Technology Universiti Teknologi Malaysia Wilayah Persekutuan Kuala Lumpur Kuala Lumpur54100 Malaysia Mathematical Sciences Department Faculty of Science Universiti Teknologi Malaysia Johor Johor Bahru81310 Malaysia Centre for Matematical Sciences College of Computing and Applied Sciences Universiti Malaysia Pahang Pahang Kuantan Gambang26300 Malaysia

In this paper, we concentrate on the investment problem of fixed deposit (FD). Our problem is to allocate the amount invested to a suitable tenure and obtain the optimal investment. There are two types of maturity that need to be considered which are short-term and long-term. Our objective is to maximize the total return of the total amount invested with a different percentage of annual return. A linear programming (LP) model is proposed to solve this investment problem using scheduling methodology. We conduct a computational experiment of a real case study for one company located in Kuala Lumpur with RM 20.6 million of investment to see the performance of the model by using the Excel Solver Parameter package. The results show that a significant improvement obtains by our model compared to the original investment practice by the company. © Published under licence by IOP Publishing Ltd.

关键词： Investments

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Infinite-time incompleteness of noncompact Yamabe flow

arXiv

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arXiv 2021年

作者： Takahashi, Jin Yamamoto, Hikaru Department of Mathematical and Computing Science Tokyo Institute of Technology Tokyo152-8552 Japan Department of Mathematics Faculty of Pure and Applied Science University of Tsukuba Ibaraki305-8571 Japan

We show the noninheritance of the completeness of the noncompact Yamabe flow. Our main theorem states the existence of a long time solution which is complete for each time and converges to an incomplete Riemannian metric. This shows the occurrence of the infinite-time *** Codes 53E99 (Primary) 35K67, 35A21, 53C18 (Secondary) Copyright © 2021, The Authors. All rights reserved.

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Process parameters optimization of dry reforming of methane over Mg/NAA zeolite catalyst using design of experiment

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AIP Conference Proceedings 2024年第1期3027卷

作者： Abdul Hadi Abdullah Ahmad Zamani Ab Halim Mohd Abu Asshaary Daud Amri Hj Mohammed Faculty of Industrial Sciences & Technology College of Computing and Applied Science Universiti Malaysia Pahang 26300 Gambang. Kuantan. Pahang Malaysia Faculty of Engineering University College TATI 24000 Kemaman Terengganu Malaysia

In the present work, the RSM method-based design of experiment with BBD was used to optimise the process parameters for dry reforming of methane over a 2.75 wt% Ni/3 wt% Mg/NaA Zeolite catalyst. Control parameters were the reaction temperature, catalyst weight, and inlet flow rate. As the experiment’s response, the CH4 conversion, the product gas H2/CO ratio, and carbon production were examined. The metals Ni and Mg were added during the zeolite synthesis process, and the catalytic support NaA zeolite was made from local kaolin. The catalysts were evaluated catalytically in a packed bed reactor, and the carbon production was determined by gravimetry. The results demonstrated that the chosen parameter has a substantial impact on the response, particularly reaction temperature. Based on statistical research, the ideal parameters were an operating temperature 587.38 °C reaction temperature, 50 mg catalyst weight, and 15 ml/min input flow rate. Using the reduce cubic model, the 2.5 wt% Ni/3.0 wt% Mg-Zeolite for the DRM process may be described.

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Carbon-doped percentage effect on the mechanical properties of nanoporous silicon sample using molecular dynamics simulation

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Case Studies in Chemical and Environmental Engineering 2025年 11卷

作者： Sawaran Singh, Narinderjit Singh Ali, Ali B.M. Ameen, Hawzhen Fateh M. Al-Zahiwat, Murtadha M. Salahshour, Soheil Emami, Nafiseh Faculty of Data Science and Information Technology INTI International University Persiaran Perdana BBN Putra Nilai Nilai 71800 Malaysia Air Conditioning Engineering Department College of Engineering University of Warith Al-Anbiyaa Karbala Iraq Department of Petroleum Technology Erbil Technology College Erbil Polytechnic University Kurdistan Region Erbil Iraq Department of Petroleum Engineering Knowledge University Erbil Iraq Department of Chemical engineering College of Engineering University of Misan Amarah Iraq Faculty of Engineering and Natural Sciences Istanbul Okan University Istanbul Türkiye Faculty of Engineering and Natural Sciences Bahcesehir University Istanbul Türkiye Research Center of Applied Mathematics Khazar University Baku Türkiye Fast Computing Center Shabihsazan Ati Pars Tehran Iran

Porous materials have attracted considerable attention from researchers due to its many uses in molecular separation, heterogeneous catalysis, absorption technologies, and electronic improvements. These solid materials, often defined by their structural voids, are essential in several sectors. This research investigated the impact of carbon doping on the mechanical characteristics of nanoporous silicon matrices. The use of high-purity silicon doping is very beneficial in the semiconductor industry and is crucial for high-power devices and automotive applications. This study simulates a nanoporous silicon sample by molecular dynamics methods, adding carbon doping at different concentrations. The findings demonstrate that when the carbon doping concentration escalated from 1 % to 30 %, the mechanical resistance of the system decreased correspondingly. The ultimate tensile strength fell from 10.26 to 9.02 GPa. Furthermore, Young's modulus rose from 83.47 to 98.37 GPa. The decline in mechanical stability was associated with a drop in the model's total weight, which had considerable ramifications for industrial applications. Thus, incorporating C-doped nanoporous silicon into real applications not only lowered the weight of target materials but also improved their use. © 2025 The Author(s)

关键词： Carbon doping Mechanical properties Molecular dynamics simulation Nanoporous Porous materials

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Dialog chatbot as an interactive online tool in enhancing ESP vocabulary learning

Saudi Journal of Language Studies

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Saudi Journal of Language Studies 2023年第2期3卷 76-86页

作者： Fawaz Qasem Mukhtar Ghaleb Hassan Saleh Mahdi Ahmed Al Khateeb Hind Al Fadda Department of English College of Sciences and Arts and the Applied College at Al-Namas University of Bisha Bisha Saudi Arabia Department of Information Systems College of Computing and Information Technology University of Bisha Al Namas Saudi Arabia Faculty of Computer Science and Information Technology Sana'a University Sana'a Yemen Department of English Hodeidah University Hodeidah Yemen English Language Centre Taif University Taif Saudi Arabia Department of English Language College of Arts King Faisal University Al-Ahsa Saudi Arabia Department of Curriculum and Instructions King Saud University Riyadh Saudi Arabia

Purpose Based on an experimental study on English for Specific Purposes (ESP) students, at the Business Department at the University of Bisha, the purpose of the study is to examine the effect of chatbot use on learning ESP in online classrooms during COVID-19 and find out how Dialogflow chabot can be a useful and interactive online platform to help ESP learners in learning vocabulary well. Design/methodology/approach The research paper is based on an experimental study of two groups, an experiential group and a controlled group. Two tests were carried out. Pre-tests and post-test of vocabulary knowledge were conducted for both groups to explore the usefulness of using the Dialogflow chatbot in learning ESP vocabulary. A designed chatbot content was prepared and included all the vocabulary details related to words' synonyms and a brief explanation of words’ meanings. An informal interview is another tool used in the study. The purpose of using the interview with the participants was to elicit more data from the participants about using the chatbot and about how and in what aspects chatbot using the conversational program was useful and productive. Findings The findings of the study explored that the use of chatbots plays a major role in enhancing and learning ESP vocabulary. That was clear as the results showed that the students who used the chatbot Dialogflow in the experimental group outperformed their counterparts in the control group. Research limitations/implications The study displays an important pedagogical implication as the use of chatbots could be applied in several settings to improve language learning in general or learning ESP courses in particular. Chatbot creates an interesting environment to foster build good interactions where negotiation of meaning takes place clearly seems to be of great benefit to help learners advance in their L2 lexical development. Originality/value Examining and exploring whether the use of chatbots play

关键词： Chatbot English for specific purposes (ESP) ESP vocabulary English as second language (ESL) Online platform

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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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Internet of Things Based Automatic System for the Traffic Violation

Internet of Things Based Automatic System for the Traffic Vi...

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International Conference on Electrical, Electronics, Communication, Computer and Optimization Techniques (ICEECCOT)

作者： A.R.M. Nizzad U.M. Sameer S.M. Suhath Abdul Cader Mohamed Nafrees W. H. Rankothge P. H. Kehelella C.M.M. Mansoor School of Computing British College of Applied Studies Sri Lanka National IT Centre Vocational Training Authority of Sri Lanka Faculty of Graduate Studies Sri Lanka Institute of Information Technology Office of the Dean South Eastern University of Sri Lanka Sri Lanka Faculty of Computing Sri Lanka Institute of Information Technology Faculty of Arts and Culture South Eastern University of Sri Lanka Sri Lanka

ISBN: (纸本)9781665432733

An efficient and effective motor traffic management is crucial for any Intelligent Transportation System (ITS) to reduce traffic violations. Scientific evidence suggests that exceeding the speed limit is the most important factor that impacts the severity, fatality and other risks associated with motor vehicle collisions. However, due to the lack of proper technology used in the transport sector, there are possibilities for traffic law violators get unnoticed. Therefore, this study provides a solution based on Internet of Things with Image Processing technology to process the vehicle registration number to uniquely identify the vehicles that are violating traffic laws. The outcome confirms that the architecture is viable for the low-cost automation of traffic fine for the speed violation. In addition, it is recommended to use more robust mechanism to capture the real time speed of any fast-moving vehicle. In conclusion, the proposed architecture with slight modification can be deployed for many commercial test cases such as traffic management, parking, vehicle counting, data privacy, data security and more.

关键词： Data privacy Wireless sensor networks Automation Ultrasonic variables measurement Smart cities Real-time systems Time measurement

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LI-FPN: Depression and Anxiety Detection from Learning and Imitation

LI-FPN: Depression and Anxiety Detection from Learning and I...

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IEEE International Conference on Bioinformatics and Biomedicine (BIBM)

作者： Xingyun Li Lin Lu Xinyu Yi Hao Wang Yunshao Zheng Yanhong Yu Qingxiang Wang Key Laboratory of Computing Power Network and Information Security Ministry of Education Shandong Computer Science Center Qilu University of Technology (Shandong Academy of Sciences) Jinan China Shandong Engineering Research Center of Big Data Applied Technology Faculty of Computer Science and Technology Qilu University of Technology (Shandong Academy of Sciences) Jinan China Shandong Provincial Key Laboratory of Computer Networks Shandong Fundamental Research Center for Computer Science Jinan China Shandong Mental Health Center Jinan China Shandong University of Traditional Chinese Medicine Jinan China

With the rise in societal pressures, depression and anxiety have increasingly become prominent mental health conditions impacting people’s lives. To enhance the efficacy of automatic detection for these disorders, we have developed an experimental framework called the Voluntary Facial Expression Mimicry(VFEM). This framework led to the creation of the VFEM Dataset, which supports related research endeavors. Subsequently, we introduce the LI-FPN designed specifically for the automatic identification of depression and anxiety disorders. The LI-FPN comprises two core components: the Learning and Imitation Module(LIM) and the Spatio-temporal Feature Pyramid Network(STFPN). Within the LIM, we leverage sequence features to facilitate comprehensive feature extraction through learning and imitation steps. The STFPN is designed to focus on outliers in multi-scale features for further screening. Compared with traditional attention methods, LI-FPN is more suitable for processing sequence data features and small sample datasets. Upon training using the VFEM Dataset, the LI-FPN achieves impressive accuracies: 0.850 for depression detection, 0.835 for anxiety detection, and 0.786 for co-occurrence detection of depression and anxiety. Meanwhile, LI-FPN also achieves SOAT results on AVEC2014 dataset. The source code for LI-FPN is accessible at https://***/muzixingyun/LI-FPN

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Quantum computing quantum Monte Carlo with hybrid tensor network for electronic structure calculations

arXiv

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arXiv 2023年

作者： Kanno, Shu Nakamura, Hajime Kobayashi, Takao Gocho, Shigeki Hatanaka, Miho Yamamoto, Naoki Gao, Qi Mitsubishi Chemical Corporation Science & Innovation Center Yokohama227-8502 Japan Quantum Computing Center Keio University Yokohama223-8522 Japan IBM Quantum – IBM Research Tokyo Tokyo103-8510 Japan School of Fundamental Science and Technology Faculty of Science and Technology Keio University Yokohama223-8522 Japan Department of Applied Physics and Physico-Informatics Keio University 3-14-1 Hiyoshi Kohoku-ku Yokohama223-8522 Japan

Quantum computers have a potential for solving quantum chemistry problems with higher accuracy than classical computers. Quantum computing quantum Monte Carlo (QC-QMC) is a QMC with a trial state prepared in quantum circuit, which is employed to obtain the ground state with higher accuracy than QMC alone. We propose an algorithm combining QC-QMC with a hybrid tensor network to extend the applicability of QC-QMC beyond a single quantum device size. In a two-layer quantum-quantum tree tensor, our algorithm for the larger trial wave function can be executed than preparable wave function in a device. Our algorithm is evaluated on the Heisenberg chain model, graphite-based Hubbard model, hydrogen plane model, and MonoArylBiImidazole using full configuration interaction QMC. Our algorithm can achieve energy accuracy (specifically, variance) several orders of magnitude higher than QMC, and the hybrid tensor version of QMC gives the same energy accuracy as QC-QMC when the system is appropriately decomposed. Moreover, we develop a pseudo-Hadamard test technique that enables efficient overlap calculations between a trial wave function and an orthonormal basis state. In a real device experiment by using the technique, we obtained almost the same accuracy as the statevector simulator, indicating the noise robustness of our algorithm. These results suggests that the present approach will pave the way to electronic structure calculation for large systems with high accuracy on current quantum devices. © 2023, CC BY-NC-SA.

关键词： Tensors

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