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16th International Conference Management of Large-Scale System Development, MLSD 2023

作者： Laryushin, Ivan Dmitrievich Pashin, Dmitrii Sergeevich Koltochenko, Yaroslav Alekseevich Ereshko, Felix Ivanovich Gaponov-Grekhov Institute of Applied Physics Ras Plasma Physics Department Nizhny Novgorod Russia State University of Nizhny Novgorod Faculty of Physics National Research Lobachevsky Nizhny Novgorod Russia Moscow Institute of Physics and Technology Department of Mathematical Modeling of Complex Systems and Optimization Moscow Russia Federal Research Center 'Computer Science and Control' of the Ras Department of Information and Computing Systems Moscow Russia

ISBN: (纸本)9798350337907

A set of new models that describe the dynamics of battles in strategic computer games is presented. These models not only have descriptive functions, but also allow solving problems of optimizing army control under given conditions, as well as calculating the most efficient composition of the army for the planned operation within a computer game. © 2023 IEEE.

关键词： Computer games

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Dataset transformation using hybrid method of polar-based cartesian and image filtering technique for annual rainfall clustering

Dataset transformation using hybrid method of polar-based ca...

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2021 International Seminar on Intelligent Technology and Its Application, ISITIA 2021

作者： Suprapty, Bedi Wiguna, Anggri Sartika Wajiansyah, Agusma Malani, Rheo Politeknik Negeri Samarinda The Applied Modern Computing Robotic Systems Unit East Kalimantan Indonesia Universitas Pgri Kanjuruhan Malang Faculty of Science Technology Informatics Department East Java Indonesia

ISBN: (纸本)9781665428477

Clustering is categorized as unsupervised learning because there is no class label information available in grouping a dataset. For this reason, an assessment of the quality of the clustering results is critical. In general, two essential clustering parameters are the similarities between cluster members in a cluster and the cluster centers' separation. Various approaches can be used to improve a clustering algorithm's performance: raw data pre-processing, cluster center initialization techniques, objective function assignment, modification of specific steps, and others. The subject of this study is the pattern of rainfall every month during the year of the observation period obtained from the clustering process. This study aims to improve the performance of K-Mean Clustering through manipulation of raw data pre-processing into certain datasets. Image filtering technique is used to generate a dataset based on the relationship between neighboring rainfall values. Polar-based Cartesian data space transformation is used to generate a dataset based on a range of rainfall values for each month during the year of the observation period. Four scenarios have been used to test the performance of the proposed method. The study results show that the proposed method produces the highest performance ratio (54.79%) of all scenarios' total average GOS (Global Optimum Solution). Meanwhile, increasing GOS to the original method also resulted in the highest increase in GOS ratio (63.68%) compared to other methods. Further studies will focus on the application of the proposed methods for improving the performance of SOM and Fuzzy C-Mean Clustering. © 2021 IEEE.

关键词： K-means clustering

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Two-stage CNN-based framework for leukocytes classification

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Computers in Biology and Medicine 2025年 187卷 109616-109616页

作者： Khan, Siraj Sajjad, Muhammad Escorcia-Gutierrez, José Dhahbi, Sami Hijji, Mohammad Muhammad, Khan Department of Computer Science Islamia College University Peshawar25120 Pakistan Department of Computational Science and Electronics Universidad de la Costa CUC Barranquilla080002 Colombia Applied College of Mahail Aseer King Khalid University Muhayil Aseer62529 Saudi Arabia Faculty of Computers and Information Technology University of Tabuk Tabuk71491 Saudi Arabia Department of Applied AI School of Convergence College of Computing and Informatics Sungkyunkwan University Seoul03063 Korea Republic of

Leukocytes are pivotal markers in health, crucial for diagnosing diseases like malaria and viral infections. Peripheral blood smear tests provide pathologists with vital insights into various medical conditions. Manual leukocyte counting is challenging and error-prone due to their complex structure. Accurate segmentation and classification of leukocytes remain challenging, impacting both accuracy and efficiency in blood microscopic image analysis. To overcome these limitations, we propose a robust two-stage CNN framework that integrates YOLOv8 for precise segmentation and MobileNetV3 for effective classification. Initially, WBCs are segmented using YOLOv8m-seg, extracting ROIs for subsequent analysis. Then, features from segmented ROIs are used to train MobileNetV3, classifying WBCs into lymphocytes, monocytes, basophils, eosinophils, and neutrophils. This framework significantly advances leukocyte categorization, enhancing diagnostic performance and patient outcomes. The proposed technique achieved impressive accuracy rates of 99.56 %, 99.19 % and 98.89 % during segmentation and 99.28 %, 99.63 % and 98.49 % during classification on Raabin-WBC, PBC and LISC datasets, respectively, outperforming state-of-the-art methods. © 2024 Elsevier Ltd

关键词： White blood cells Blood smear images Image segmentation Deep learning MobileNetV3 YOLOv8 Image classification

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The Terms Estimation of S Robotics using MTRS as well as FEM

The Terms Estimation of S Robotics using MTRS as well as FEM

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Advance computing and Innovative Technologies in Engineering (ICACITE), International Conference on

作者： R. Gopal Mustafa Mudhafar Amjad Hashem Venkadeshan Ramalingam Dibyahash Bordoloi Muhammad Numan Ali Khan Myasar Mundher Adnan Zahraa N. Abdulhussain Assistant Professor Information and communication Engineering College of Engineering University of Buraimi Sultanate of Oman Department of Medical Physics Faculty of Medical Applied Sciences University of Kerbala Karbala Iraq Computer Engineer Department Al-kunooze University College Basra Iraq Network Computing College of Computing and Information Sciences University of Technology and Applied Sciences– Shinas Sultanate of Oman Dept of CSE Graphic Era Hill University Adjunct professor Graphic Era Deemed to be University Dehradun Uttarakhand Faculty – Network Computing College of Computing and Information Sciences University of Technology and Applied Sciences – Shinas Sultanate of Oman Department of computers Techniques engineering College of technical engineering The Islamic university Najaf Iraq College of Technical Engineering National University of Science and TechnologyDhi Qar Iraq

ISBN: (数字)9798350360165

ISBN: (纸本)9798350360172

This look at addresses the critical project of reaching dependable and precise proprioception in soft robots, it really is essential for his or her effective deployment in complicated environments. By integrating multi-tap resistive sensors in the robot’s form, our technique lets in for the distinct sensing of nearby deformations at various factors throughout the robotics’ frame, moving beyond traditional techniques that anticipate steady curvature. We beautify the decision of these deformations and couple the sensory facts with a strong finite detail technique (FEM) model. This included framework allows the correct estimation of the smooth robot’s form and the significance of outside forces applied to it, attaining a mean relative inaccuracy of about 4%. Additionally, our technique proves its software program in tender manipulation structures, in which it achieves thirteen% relative mistakes in external force estimation inside diverse zero to five N. The approach gives a promising direction for enhancing robotic interactions in sensitive and dynamic responsibilities, providing massive enhancements in robotic proprioception without reliance on outdoor monitoring generation.

关键词： Deformable models Deformation Force Estimation Soft robotics Robot sensing systems Software

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Cotton Leaf Diseases Recognition Using Deep Learning and Genetic Algorithm

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Computers, Materials & Continua 2021年第12期69卷 2917-2932页

作者： Muhammad Rizwan Latif Muhamamd Attique Khan Muhammad Younus Javed Haris Masood Usman Tariq Yunyoung Nam Seifedine Kadry Department of Computer Science HITEC University TaxilaTaxilaPakistan Wah Engineering College University of WahWah Cantt47080Pakistan College of Computer Engineering and Sciences Prince Sattam Bin Abdulaziz UniversityAl-KhrajSaudi Arabia Department of Computer Science and Engineering Soonchunhyang UniversityAsanKorea Faculty of Applied Computing and Technology Noroff University CollegeKristiansandNorway

Globally,Pakistan ranks 4th in cotton production,6th as an importer of raw cotton,and 3rd in cotton *** 10%of GDP and 55%of the country’s foreign exchange earnings depend on cotton *** 1.5 million people in Pakistan are engaged in the cotton value ***,several diseases such as Mildew,Leaf Spot,and Soreshine affect cotton *** diagnosis is not a good solution due to several factors such as high cost and unavailability of an ***,it is essential to develop an automated technique that can accurately detect and recognize these diseases at their early *** this study,a new technique is proposed using deep learning architecture with serially fused features and the best feature *** proposed architecture consists of the following steps:(a)a self-collected dataset of cotton diseases is prepared and labeled by an expert;(b)data augmentation is performed on the collected dataset to increase the number of images for better training at the earlier step;(c)a pre-trained deep learning model named ResNet101 is employed and trained through a transfer learning approach;(d)features are computed from the third and fourth last layers and serially combined into one matrix;(e)a genetic algorithm is applied to the combined matrix to select the best points for further *** final recognition,a Cubic SVM approach was utilized and validated on a prepared *** the newly prepared dataset,the highest achieved accuracy was 98.8%using Cubic SVM,which shows the perfection of the proposed framework..

关键词： Plants diseases data augmentation deep learning features fusion features selection

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Chernobyl Disaster Optimizer Algorithm for Feature Selection Problems

Chernobyl Disaster Optimizer Algorithm for Feature Selection...

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Computational Intelligence Approaches and Applications (ICCIAA), International Conference on

作者： Hossam M. J. Mustafa Hisham A. Shehadeh Ahmad Khasawneh Department of Data Science and Artificial Intelligence Faculty of Information Technology University of Petra Amman Jordan Department of Information Technology Al-Huson University College Al-Balqa Applied University Al-Huson Jordan School of Computing Skyline University College Sharjah UAE

ISBN: (数字)9798331523657

ISBN: (纸本)9798331523664

The rapid growth of high-dimensional data in Machine Learning (ML) applications presents essential computational challenges. This phenomenon manifests primarily through redundant feature space dimensions, requiring robust dimensionality reduction approaches such as Feature Selection (FS). This research introduces an investigation using the Chernobyl Disaster Optimizer (CDO), a physics-inspired metaheuristic optimization algorithm for solving feature selection problems. We propose a CDO algorithm that utilizes dynamic radiation dispersion mechanisms for wrapper-based feature selection, where solution fitness is evaluated through accuracy metrics obtained from a K-nearest-neighbor (KNN) classifier. Comprehensive empirical analysis across eight UCI repository benchmark datasets shows a satisfactory performance of CDO in classification accuracy and feature selection efficiency with an average of 45.8% compared to competing metaheuristic approaches. Friedman tests, with p-value = 0.0302, confirm the algorithm's significant performance advantages. The results demonstrate that CDO is a reliable optimization algorithm for feature selection tasks, particularly effective in high-dimensional classification problems.

关键词： Dimensionality reduction Measurement Accuracy Heuristic algorithms Disasters Metaheuristics Nearest neighbor methods Feature extraction Classification algorithms Reliability

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Forecasting the load flow of Engine Driven Pump based on Light Gradient Boosting Machine Model

Forecasting the load flow of Engine Driven Pump based on Lig...

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2021 IEEE International Conference on Computer science, Electronic Information Engineering and Intelligent Control Technology, CEI 2021

作者： Niu, Wei Song, Xiaogang Wang, Hao Chen, Juan Xi'an Aeronautics Computing Technique Research Institute AVIC Xi'an China Xi'an University of Technology Faculty of Computer Science and Engineering Xi'an China Xi'an Institute of Applied Optics Xi'an China

ISBN: (纸本)9780738146492

The flow rate of aircraft hydraulic system is one of the important parameters to measure the performance of aircraft. The stability of flow rate of hydraulic pipeline is related to the safety of hydraulic system and even aircraft flight. Aiming at the problem that it is difficult to measure the load flow of aircraft hydraulic system, a prediction method based on ensemble learning for the load flow of Engine Driven Pump (EDP) is proposed. According to the different working conditions of EDP, the Light Gradient Boosting Machine (LightGBM) model is established. Through Particle Swarm Optimization algorithm optimizes the super parameters of LightGBM algorithm, and realizes the regression prediction mode. Finally, compared with the multiple linear regression, random forest, experimental results indicate that the proposed model obviously can improve the prediction accuracy. © 2021 IEEE.

关键词： Forecasting

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What Students Want to Have in the Post-Pandemic?: A Study about computing science Study Preferences and Mode of Study in Southeast Asia 22

What Students Want to Have in the Post-Pandemic?: A Study ab...

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Proceedings of the 2022 5th International Conference on Education Technology Management

作者： Peter ChunYu Yau Yongmin Kim Dennis Wong Patrick Pang Stanley Kan Qi Cao School of Computing Science University of Glasgow United Kingdom James Watt School of Engineering University of Glasow United Kingdom Department of Computer Science State University of New York Korea Korea Faculty of Applied Sciences Macao Polytechnic University China

ISBN: (纸本)9781450398015

After two years of online learning, minor changes happened in some tiny places which we may not always notice. These changes may impact the school management, education arrangement, and curriculum design: what do students expect from their teachers, and the school? Are they expecting forever online learning, and getting their degree completely online? In this study, we examined 93 final-year students from the school of computing science which mainly used work-based learning applied to learn pedagogy. We analyze their viewpoint on how COVID-19 changed their understanding of teaching and learning: what is the role of teachers and students? And how do they see the arrangement for the internship program? We found that over 92% of students agree that a creditable degree can better secure a job; over 68% of the students believed that face-to-face teaching is still a better choice, but at the same time, over 73% wish to have the materials recorded so that they can access them at their convenience time. We also examined some of the course design elements, especially those related to the internship program, to achieve a possible curriculum improvement for the students in the Asia Pacific region.

关键词： post-covid applied learning work-study program remote learning internship mode of study home-based learning vocational education

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Modified Sine Cosine Optimization with Adaptive Deep Belief Network for Movie Review Classification

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Intelligent Automation & Soft computing 2023年第7期37卷 283-300页

作者： Hala J.Alshahrani Abdulbaset Gaddah Ehab S.Alnuzaili Mesfer Al Duhayyim Heba Mohsen Ishfaq Yaseen Amgad Atta Abdelmageed Gouse Pasha Mohammed 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 English College of Science&Art at MahayilKing Khalid UniversityMuhayel Aseer63311Saudi Arabia Department of Computer Science College of Sciences and Humanities-AflajPrince Sattam bin Abdulaziz UniversityAl-Aflaj16828Saudi Arabia Department of Computer Science Faculty of Computers and Information TechnologyFuture University in EgyptNew Cairo11835Egypt Department of Computer and Self Development Preparatory Year DeanshipPrince Sattam bin Abdulaziz UniversityAlKharjSaudi Arabia

Sentiment analysis(SA)is a growing field at the intersection of computer science and computational linguistics that endeavors to automati-cally identify the sentiment presented in *** linguistics aims to describe the fundamental methods utilized in the formation of computer methods for understanding natural *** is classified as a negative or positive assessment articulated through *** can be commonly used for the movie review classification that involves the automatic determination that a review posted online(of a movie)can be negative or positive toward the thing that has been *** learning(DL)is becoming a powerful machine learning(ML)method for dealing with the increasing demand for precise *** this motivation,this study designs a computational intelligence enabled modified sine cosine optimization with a adaptive deep belief network for movie review classification(MSCADBN-MVC)*** major intention of the MSCADBN-MVC technique is focused on the identification of sentiments that exist in the movie review ***,the MSCADBN-MVC model follows data pre-processing and the word2vec word embedding *** the classification of sentiments that exist in the movie reviews,the ADBN model is utilized in this *** last,the hyperparameter tuning of the ADBN model is carried out using the MSCA technique,which integrates the Levy flight concepts into the standard sine cosine algorithm(SCA).In order to demonstrate the significant performance of the MSCADBN-MVC model,a wide-ranging experimental analysis is performed on three different *** comprehensive study highlighted the enhancements of the MSCADBN-MVC model in the movie review classification process with maximum accuracy of 88.93%.

关键词： Computational linguistics movie review analysis sentiment analysis sentiment classification deep learning

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Explainable-AI-based two-stage solution for WSN object localization using zero-touch mobile transceivers

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science China(Information sciences) 2024年第7期67卷 27-45页

作者： Kai FANG Junxin CHEN Han ZHU Thippa Reddy GADEKALLU Xiaoping WU Wei WANG School of Mathematics and Computer Science Zhejiang A&F University School of Software Dalian University of Technology Faculty of Applied Sciences Macao Polytechnic University Department of Electrical and Computer Engineering Lebanese American University School of Information Engineering Huzhou University Guangdong-Hong Kong-Macao Joint Laboratory for Emotional Intelligence and Pervasive Computing Artificial Intelligence Research Institute Shenzhen MSU-BIT University

Artificial intelligence technology is widely used in the field of wireless sensor networks(WSN).Due to its inexplicability, the interference factors in the process of WSN object localization cannot be effectively eliminated. In this paper, an explainable-AI-based two-stage solution is proposed for WSN object localization. In this solution, mobile transceivers are used to enlarge the positioning range and eliminate the blind area for object localization. The motion parameters of transceivers are considered to be unavailable,and the localization problem is highly nonlinear with respect to the unknown parameters. To address this,an explainable AI model is proposed to solve the localization problem. Since the relationship among the variables is difficult to fully include in the first-stage traditional model, we develop a two-stage explainable AI solution for this localization problem. The two-stage solution is actually a comprehensive consideration of the relationship between variables. The solution can continue to use the constraints unused in the firststage during the second-stage, thereby improving the performance of the solution. Therefore, the two-stage solution has stronger robustness compared to the closed-form solution. Experimental results show that the performance of both the two-stage solution and the traditional solution will be affected by numerical changes in unknown parameters. However, the two-stage solution performs better than the traditional solution, especially with a small number of mobile transceivers and sensors or in the presence of high noise. Furthermore,we have also verified the feasibility of the proposed explainable-AI-based two-stage solution.

关键词： explainable AI object localization semidefinite relaxation mobile transceiver two-stage solution closed-form solution

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