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Automated approach to predict cerebral stroke based on fuzzy inference and convolutional neural network

Multimedia Tools and Applications 2024年 1-22页

作者： Alrowais, Fadwa Jamjoom, Arwa A. Karamti, Hanen Umer, Muhammad Alsubai, Shtwai Abate, Andrea F. Ashraf, Imran Department of Information Systems College of Computer and Information Sciences Princess Nourah bint Abdulrahman University P.O. Box 84428 Riyadh11671 Saudi Arabia Department of Information Systems Faculty of Computing and Information Technology King Abdulaziz University Jeddah21589 Saudi Arabia Department of Computer Science The Islamia University of Bahawalpur Bahawalpur Pakistan Department of Computer Science College of Computer Engineering and Sciences Prince Sattam bin Abdulaziz University P.O. Box 151 Al-Kharj11942 Saudi Arabia Department of Computer Science University of Salerno Fisciano84084 Italy Department of Information and Communication Engineering Yeungnam University Gyeongsan38541 Korea Republic of

Cerebral stroke indicates a neurological impairment caused by a localized injury to the central nervous system resulting from a diminished blood supply to the brain. Today, stroke stands as a global menace linked to the premature mortality of millions of people globally. Consequently, it is crucial to simulate how different risk factors impact the incidence of strokes and artificial intelligence is emerging as a suitable tool for achieving this goal. This study aims to construct dependable learning prediction models for stroke illness. The proposed approach can handle the inherent difficulty posed by a significant imbalance in classes, where the number of stroke patients is notably smaller compared to the healthy class. The study also provides a model based on an adaptive neuro-fuzzy inference system logic and convolutional neural networks (CNN) for accurate stroke prediction. An adaptive neuro-fuzzy inference system logic approach is adopted as it incorporates the capabilities of artificial intelligence and fuzzy inference, thereby having the potential to yield superior outcomes. The efficacy of the suggested method is extensively analyzed involving machine and deep learning classifiers and considering metrics relevant to encompass both the capacity for generalization and the accuracy in predicting. The proposed fuzzy-CNN model outperforms with the most considerable accuracy of 98.97% when using the original dataset, resampled dataset, and data imputation. K-fold cross-validation also shows superior results with an average accuracy of 99.6% for five folds. © The Author(s), under exclusive licence to Springer science+Business Media, LLC, part of Springer Nature 2024.

关键词： Forecasting

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information technology Project Management Research: A Review of Works by Influential Pioneers

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Project Management Journal 2023年第4期54卷 366-391页

作者： Wu, Xiaosong Tsai, Jacob Chia-An Lei, Yang Bay Area International Business School Beijing Normal University China Department of Information and Management College of Management National Yunlin University of Science and Technology Taiwan Department of Information Systems and Management Engineering Southern University of Science and Technology China

information technology project management practices effectively help organizations achieve IT value. We employed a semistructured review with the practice of jizhuanti by tracing the development of the research intersection of IT and projects through the works of seven influential authors. From the analysis of the review, we build representative models of the intersection and suggest open lines of research. The results unveil stakeholder integration, knowledge management, and risk controls as principal themes for the sampling subset. Future scholars can continue to delve into these areas over more complex systems, consider other categories, or open new directions revealed by practice. © 2023 Project Management Institute, Inc.

关键词： IT project jizhuanti knowledge management risk control stakeholder integration

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TMRE:Novel Algorithm for Computing Daily Reference Evapotranspiration Using Transformer-Based Models

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Computers, Materials & Continua 2025年第5期83卷 2851-2864页

作者： Bushra Tayyaba Muhammad Usman Ghani Khan Talha Waheed Shaha Al-Otaibi Tanzila Saba National Center of Artificial Intelligence(NCAI) Al Khwarizmi Institute of Computer Sciences(KICS)Lahore54890Pakistan Department of Computer Science University of Engineering and Technology LahoreLahore54890Pakistan Artificial Intelligence&Data Analytics Lab Prince Sultan UniversityRiyadh11586Saudi Arabia Department of Information Systems College of Computer and Information SciencesPrincess Nourah bint Abdulrahman UniversityRiyadh11671Saudi Arabia

Reference Evapotranspiration(ETo)iswidely used to assess totalwater loss between land and atmosphere due to its importance in maintaining the atmospheric water balance,especially in agricultural and environmental *** estimation of ETo is challenging due to its dependency onmultiple climatic variables,including temperature,humidity,and solar radiation,making it a complexmultivariate time-series *** machine learning and deep learning models have been applied to forecast ETo,achieving moderate ***,the introduction of transformer-based architectures in time-series forecasting has opened new possibilities formore precise ETo *** this study,a novel algorithm for ETo forecasting is proposed,focusing on four transformer-based models:Vanilla Transformer,Informer,Autoformer,and FEDformer(Frequency Enhanced Decomposed Transformer),applied to an ETo dataset from the Andalusian *** novelty of the proposed algorithm lies in determining optimized window sizes based on seasonal trends and variations,which were then used with each model to enhance prediction *** custom window-sizing method allows the models to capture ETo’s unique seasonal patterns more ***,results demonstrate that the Informer model outperformed other transformer-based models,achievingmean square error(MSE)values of 0.1404 and 0.1445 for forecast windows(15,7)and(30,15),*** Vanilla Transformer also showed strong performance,closely following the *** findings suggest that the proposed optimized window-sizing approach,combined with transformer-based architectures,is highly effective for ETo *** novel strategy has the potential to be adapted in othermultivariate time-series forecasting tasks that require seasonality-sensitive approaches.

关键词： Reference evapotranspiration ETo transformer informer autoformer FEDformer timeseries forecasting self-attention

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An aircraft brake control algorithm with torque compensation based on RBF neural network

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Chinese Journal of Aeronautics 2024年第1期37卷 438-450页

作者： Ning BAI Xiaochao LIU Juefei LI Zhuangzhuang WANG Pengyuan QI Yaoxing SHANG Zongxia JIAO School of Automation Science and Electrical Engineering Beihang UniversityBeijing 100191China Science and Technology on Aircraft Control Laboratory Beihang UniversityBeijing 100191China Key Laboratory of Advanced Aircraft Systems(Beihang University) Ministry of Industry and Information TechnologyBeijing 100091China Research Institute for Frontier Science Beihang UniversityBeijing 100191China Ningbo Institute of Technology of Beihang University Ningbo 315800China Tianmushan Laboratory Xixi Octagon CityYuhang DistrictHangzhou 310023China

The wheel brake system of an aircraft is the key to ensure its safe landing and rejected takeoff.A wheel’s slip state is determined by the brake torque and ground adhesion torque,both of which have a large degree of *** is this nature that brings upon the challenge of obtaining high deceleration rate for aircraft brake *** overcome the disturbances caused by the above uncertainties,a braking control law is designed,which consists of two parts:runway surface recognition and wheel’s slip state *** runway surface recognition,the identification rules balancing safety and braking efficiency are defined,and the actual identification process is realized through recursive least square method with forgetting *** slip state tracking,the LuGre model with parameter adaptation and a brake torque compensation method based on RBF neural network are proposed,and their convergence are *** effectiveness of our control law is verified through simulation and ground *** in the experiments on the ground inertial test bench,compared to the improved pressure-biased-modulation(PBM)anti-skid algorithm,fewer wheel slips occur,and the average deceleration rate is increased by 5.78%,which makes it a control strategy with potential for engineering applications.

关键词： Aircraft landing Brakes Disturbance observer Neural networks Runway identification

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A Silicon-Based Integrated Photonic-Assisted Emitter for High Date-Rate Microwave Wireless Communication

A Silicon-Based Integrated Photonic-Assisted Emitter for Hig...

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2024 Conference on Lasers and Electro-Optics, CLEO 2024

作者： Wei, Zhujun Zhang, Qiang Fu, Zhilei Ning, Nannan Huang, Qikai Yang, Jianyi Yu, Hui Institute of Integrated Microelectronic systems College of Information Science and Electronic Engineering Zhejiang University Hangzhou310027 China Zhejiang Lab Hangzhou310027 China

ISBN: (纸本)9781957171395

In this work, we propose and demonstrate a silicon-based integrated photonic-assisted emitter. It consists of an aperiodically distributed traveling wave photodetector (TWPD), a compact Yagi antenna and a bias circuitry network. The measured radiated power of this emitter is above -20 dBm from 31 GHz to 38 GHz. Furthermore, a wireless data transmission at 3/5/8 Gbps is demonstrated. © Optica Publishing Group 2024 © 2024 The Author(s)

关键词： Microwave measurement

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Companion Recognition System for Amnesia Affected Persons

Companion Recognition System for Amnesia Affected Persons

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2024 International Conference on Computing and Intelligent Reality Technologies, ICCIRT 2024

作者： Ignisha Rajathi, G. Devin James, R. Raja Balaji, N. Subhash Hari Krishnan, R. Yogeeshwaran, G. Dept of Information Technology Manipal Institute of Technology Bengaluru Engineering Manipal India Dept of CSE Sri Krishna College of Engineering Coimbatore India Department of Computer Science and Business Systems Sri Krishna College of Engineering and Technology Coimbatore India Dept of CSE and Business Systems Sri Krishna College of Engineeirng and Technology Coimbatore India

ISBN: (纸本)9798331510299

Recogni is an AI-based application designed for the purpose of enhancing the quality for individuals who are suffering from amnesia and other memory-related challenges. This application addresses key challenges in memory management, daily task organization, and social integration through features like geofencing reminders, family sharing, and interactive memory aids and turns out to be a good companion for them. To protect user data, recogni's progressive development integrates advanced functions such as location-based notes, voice-controlled reminders, and multi-language support which are all reinforced by robust security measures. Wearable device integration, predictive analytics, and emergency assistance, are promised to be added in the ongoing development, making Recogni a versatile tool for memory care [1]. © 2024 IEEE.

关键词： Memory management

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Mission Capability Evaluation Testing System for Unmanned Aerial Vehicle Swarm

Mission Capability Evaluation Testing System for Unmanned Ae...

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2023 China Automation Congress, CAC 2023

作者： Ma, Qiongmin Sui, Weishun Bai, Xueying Li, Lechi Du, Linlin Liang, Yueqian Institute of Systems Engineering Military Academy of Science Beijing China China Electronics Technology Group Corporation The Information Science Academy Beijing China

ISBN: (纸本)9798350303759

There is currently a lack of evaluation methods and tools for evaluating the mission capability of unmanned aerial vehicle(UAV) swarm. To solve this problem, a semi-physical simulation evaluation test system is firstly proposed in this work. Then the evaluation framework is established based on evaluation indexes and algorithms. As a case study, the feasibility of this evaluation testing system is verified by using a three-UAV swarm performing city patrol search mission. The results show that the system possesses the capability of evaluating and testing UAV swarm mission capability. © 2023 IEEE.

关键词： Antennas

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A Silicon-Based Integrated Photonic-Assisted Emitter for High Date-Rate Microwave Wireless Communication

A Silicon-Based Integrated Photonic-Assisted Emitter for Hig...

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CLEO: Applications and technology in CLEO 2024, CLEO: A and T 2024 - Part of Conference on Lasers and Electro-Optics

关键词： Silicon photonics

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Integrated Leaf Disease Recognition Across Diverse Crops through Transfer Learning 5

Integrated Leaf Disease Recognition Across Diverse Crops thr...

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5th International Conference on Innovative Data Communication Technologies and Application, ICIDCA 2024

作者： Tambe, Utkarsh Yashwant Shanthini, A. Hsiung, Pao-Ann Department of Data Science and Business Systems SRM Institute of Science and Technology Kattankulathur Chennai603203 India Department of Computer Science and Information Engineering National Chung Cheng University Chiayi621301 Taiwan

Global food security is seriously threatened by plant diseases and can cause severe economic losses to farmers. Automated detection of plant diseases using computer vision and machine learning techniques has become a popular research area due to its potential to provide faster and more accurate results than traditional methods. In this research, we propose a plant disease detection model based on transfer learning using the MobileNetV2. We evaluate the suggested method on a dataset consisting of 38 various kinds of diseases across 14 different plants. Our experimental findings indicate that the proposed method has a typical accuracy of 91.98% and outperforms additional cutting-edge CNN models for plant disease detection. The experimental findings support the suggested approach's validity and show that it effectively detects plant diseases. We also have put forth the evaluation metrics to investigate the model. The suggested method has potential applications in real-world scenarios and can help farmers detect diseases in their crops at an early stage, allowing them to take timely action to minimize crop losses. © 2024 The Authors. Published by Elsevier B.V.

关键词： Losses

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Marine Predators Algorithm with Deep Learning-Based Leukemia Cancer Classification on Medical Images

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Computer Modeling in engineering & sciences 2024年第10期141卷 893-916页

作者： Sonali Das Saroja Kumar Rout Sujit Kumar Panda Pradyumna Kumar Mohapatra Abdulaziz S.Almazyad Muhammed Basheer Jasser Guojiang Xiong Ali Wagdy Mohamed Department of Computer Science&Engineering Gandhi Institute for TechnologyBPUTBhubaneswar752054India Department of Information Technology Vardhaman College of Engineering(Autonomous)Hyderabad501218TelanganaIndia Department of Electronic&Communication Engineering Vedang Institute of TechnologyBhubaneswar752010India Department of Computer Engineering College of Computer and Information SciencesKing Saud UniversityRiyadh11543Saudi Arabia Department of Computing and Information Systems School of Engineering and TechnologySunway UniversityJalan UniversitiBandar SunwaySelangor Darul Ehsan47500Malaysia Research Centre for Human-Machine Collaboration(HUMAC) School of Engineering and TechnologySunway UniversityJalan UniversitiBandar SunwaySelangor Darul Ehsan47500Malaysia Guizhou Key Laboratory of Intelligent Technology in Power System College of Electrical EngineeringGuizhou UniversityGuiyang550025China Operations Research Department Faculty of Graduate Studies for Statistical ResearchCairo UniversityGiza12613Egypt Applied Science Research Center Applied Science Private UniversityAmman11931Jordan

In blood or bone marrow,leukemia is a form of cancer.A person with leukemia has an expansion of white blood cells(WBCs).It primarily affects children and rarely affects *** depends on the type of leukemia and the extent to which cancer has established throughout the *** leukemia in the initial stage is vital to providing timely patient *** image-analysis-related approaches grant safer,quicker,and less costly solutions while ignoring the difficulties of these invasive *** can be simple to generalize Computer vision(CV)-based and image-processing techniques and eradicate human *** researchers have implemented computer-aided diagnosticmethods andmachine learning(ML)for laboratory image analysis,hopefully overcoming the limitations of late leukemia detection and determining its *** study establishes a Marine Predators Algorithm with Deep Learning Leukemia Cancer Classification(MPADL-LCC)algorithm onMedical *** projectedMPADL-LCC system uses a bilateral filtering(BF)technique to pre-process medical *** MPADL-LCC system uses Faster SqueezeNet withMarine Predators Algorithm(MPA)as a hyperparameter optimizer for feature ***,the denoising autoencoder(DAE)methodology can be executed to accurately detect and classify leukemia *** hyperparameter tuning process using MPA helps enhance leukemia cancer classification *** results are compared with other recent approaches concerning various measurements and the MPADL-LCC algorithm exhibits the best results over other recent approaches.

关键词： Leukemia cancer medical imaging image classification deep learning marine predators algorithm

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