检索结果-内蒙古大学图书馆

Hybrid Sine Cosine and Stochastic Fractal Search for Hemoglobin Estimation

computers, Materials & Continua 2022年第8期72卷 2467-2482页

作者： Marwa M.Eid Fawaz Alassery Abdelhameed Ibrahim Bandar Abdullah Aloyaydi Hesham Arafat Ali Shady Y.El-Mashad Faculty of Artificial Intelligence Delta University for Science and TechnologyMansoura35712Egypt Department of Computer Engineering College of Computers and Information TechnologyTaif UniversityTaif21944Saudi Arabia Computer Engineering and Control Systems Department Faculty of EngineeringMansoura UniversityMansoura35516Egypt Mechanical Engineering Department Qassim UniversityBuraidah51452Saudi Arabia Department of Computer Systems Engineering Faculty of Engineering at ShoubraBenha UniversityEgypt

The sample’s hemoglobin and glucose levels can be determined by obtaining a blood sample from the human body using a needle and analyzing ***(HGB)is a critical component of the human body because it transports oxygen from the lungs to the body’s tissues and returns carbon dioxide from the tissues to the *** the HGB level is a critical step in any blood analysis *** often indicate whether a person is anemic or polycythemia *** ensemble models by combining two or more base machine learning(ML)models can help create a more improved *** purpose of this work is to present a weighted average ensemble model for predicting hemoglobin *** optimization method is utilized to get the ensemble’s optimum *** optimum weight for this work is determined using a sine cosine algorithm based on stochastic fractal search(SCSFS).The proposed SCSFS ensemble is compared toDecision Tree,Multilayer perceptron(MLP),Support Vector Regression(SVR)and Random Forest Regressors as model-based approaches and the average ensemble *** SCSFS results indicate that the proposed model outperforms existing models and provides an almost accurate hemoglobin estimate.

关键词： Sine cosine optimization metaheuristics optimization hemoglobin estimation weight average ensemble

来源：评论

学校读者我要写书评

暂无评论

CHANGES IN MOBILITY OF SYNAPTIC VESICLES WITH ASSEMBLY AND DISASSEMBLY OF ACTIN NETWORK

引用

BIOCHIMICA ET BIOPHYSICA ACTA-GENERAL SUBJECTS 1995年第1期1244卷 85-91页

作者： MIYAMOTO, S Department of Biochemical Engineering Faculty of Computer Science and Systems Engineering Kyushu Institute of Technology Iizuka Fukuoka 820 Japan

In a presynaptic terminal, neurotransmitters are stored in synaptic vesicles and secreted into the synaptic cleft as a final step of cell signal transduction. At a static state, the vesicles are retained in the highly dense actin network. Prior to exocytosis, the dense actin network must disassemble or largely be organized. Actin networks are formed in vitro which retain synaptic vesicles prepared from rat cerebral cortex. Dynamic behaviors of synaptic vesicles are measured by the dynamic light scattering method. The D-app values of synaptic vesicles confined in actin network became less than 1/4 those of free vesicles. The motions of synaptic vesicles are substantially restricted. This means that synaptic vesicles which are liberated from the actin network by detachment of synapsin 1 molecules are still trapped in the cage-like space of actin filaments. The actin network is disassembled by the actin severing protein, gelsolin, which is activated in the presence of mu M level free Ca2+ ions. The D-app(v) values of synaptic vesicles after severing the actin network return to those of free vesicles in the presence of short actin fragments. A molecular model for exocytosis in the synaptic terminal is constructed on the basis of these results.

关键词： SYNAPTIC VESICLE ACTIN NETWORK GELSOLIN DYNAMIC LIGHT SCATTERING

来源：评论

学校读者我要写书评

暂无评论

Arrhythmia Detection by Using Chaos Theory with Machine Learning Algorithms

引用

computers, Materials & Continua 2024年第6期79卷 3855-3875页

作者： Maie Aboghazalah Passent El-kafrawy Abdelmoty M.Ahmed Rasha Elnemr Belgacem Bouallegue Ayman El-sayed Math and Computer Science Department Faculty of ScienceMenoufia UniversityShebin El-komEgypt College of Engineering Computer Science DepartmentEffat UniversityJeddahKingdom of Saudi Arabia Department of Computer Engineering College of Computer ScienceKing Khalid UniversityAbha61421Saudi Arabia Computer Science and Engineering Department Faculty of Electronic EngineeringMenoufia UniversityShebin El-komEgypt Climate Change Information Center and Expert Systems Agriculture Research CenterGizaEgypt

Heart monitoring improves life ***(ECGs or EKGs)detect heart *** learning algorithms can create a few ECG diagnosis processing *** first method uses raw ECG and time-series *** second method classifies the ECG by patient *** third technique translates ECG impulses into Q waves,R waves and S waves(QRS)features using richer *** ECG signals vary naturally between humans and activities,we will combine the three feature selection methods to improve classification accuracy and *** using all three approaches have not been examined till *** researchers found that Machine Learning(ML)techniques can improve ECG *** study will compare popular machine learning techniques to evaluate ECG *** algorithms—Support Vector Machine(SVM),Decision Tree,Naive Bayes,and Neural Network—compare categorization *** plus prior knowledge has the highest accuracy(99%)of the four ML *** characteristics failed to identify signals without chaos *** 99.8%classification accuracy,the Decision Tree technique outperformed all previous experiments.

关键词： ECG extraction ECG leads time series prior knowledge and arrhythmia chaos theory QRS complex analysis machine learning ECG classification

来源：评论

学校读者我要写书评

暂无评论

Learning environment for designing physics experiment: DEEP

Learning environment for designing physics experiment: DEEP

引用

14th International Conference on computers in Education, ICCE 2006

作者： Toumoto, Takahito Horiguchi, Tomoya Hirashima, Tsukasa Takeuchi, Akira Faculty of Engineering Hiroshima University Japan Faculty of Computer Science and Systems Engineering Kobe University Japan Graduate School of Computer Science and Systems Engineering Kyushu Institute of Technology Japan

In this paper, we explain the importance of the task to design physics experiment in learning of physics. The design and development of a learning environment that supports a learner to design physics experiments, whi... 详细信息

ISBN: (纸本)9781586036874

关键词： E-learning

来源：评论

学校读者我要写书评

暂无评论

IP SafeGuard - An AI-Driven Malicious IP Detection Framework

引用

IEEE Access 2025年 13卷 90249-90261页

作者： Siam, Abdullah Al Alazab, Moutaz Awajan, Albara Hasan, Md Rakibul Obeidat, Areej Faruqui, Nuruzzaman Daffodil International University Daffodil Smart City Birulia Department of Software Engineering Dhaka 1216 Bangladesh Oryx Universal College with Liverpool John Moores University School of Computing and Data Sciences Cybersecurity Department Doha 34110 Qatar Al-Balqa Applied University Faculty of Artificial Intelligence Department of Intelligent Systems Al-Salt 19385 Jordan Bangladesh University of Professionals (BUP) Department of Computer Science and Engineering Dhaka Bangladesh Amman Jordan

The rising frequency and sophistication of cyberattacks have made real-time malicious IP detection a critical challenge for modern Security Operations Center (SOC). Traditional solutions, such as static blacklists and manual IP reputation checks, are no longer sufficient in today's dynamic threat scenario. To overcome these constraints, we present IP SafeGuard, an AI-driven platform that incorporates multi-source threat intelligence, sophisticated feature engineering, and machine learning (ML)for real-time IP categorization. The framework collects data from AbuseIPDB, VirusTotal, and other sources to compute a Dynamic Threat Score (DTS) for each IP address. It leverages an XGBoost-based classification model to achieve high accuracy and low false-positive rates, even in skewed datasets. Experimental findings indicate the improved performance of IP SafeGuard, with an accuracy of 98.2%, a precision of 97.8%, and a recall of 98.5%. The average detection duration of 45 milliseconds makes it appropriate for real-time SOC integration, enabling automated incident response through Security Information and Event Management (SIEM) alerting and firewall blocking. The framework's modular design assures scalability and adaptability, making it a vital tool for high-volume situations. By overcoming the limits of old approaches and using the power of ML, IP SafeGuard considerably boosts the efficiency and efficacy of current cybersecurity systems. Future work involves expanding the system to enable new threat intelligence sources and studying federated learning for secure and privacy-preserving threat information exchange. © 2013 IEEE.

关键词： Artificial Intelligence Cyber attack Cyber defense Cyber Security IP Address SOC Threat Intelligence

来源：评论

学校读者我要写书评

暂无评论

Hybrid control strategy for the autonomous transition flight of a fixed-wing aircraft

引用

IEEE Transactions on Control systems Technology 2013年第6期21卷 2194-2211页

作者： Casau, Pedro Cabecinhas, David Silvestre, Carlos Department of Electrical Engineering and Computer Science Institute for Robotics and Systems in Engineering and Science Universidade Técnica de Lisboa Lisbon 1049-001 Portugal Department of Electrical and Computer Engineering Faculty of Science and Technology University of Macau 999078 China

This paper develops a hybrid control strategy that provides autonomous transition between hovered and leveled flights to a model-scale fixed-wing aircraft. The aircraft's closed-loop dynamics are described by means of a hybrid automaton with the hover, transition, level, and recovery operating modes, each one corresponding to a different region of the flight envelope. Linear parameter varying control techniques are employed in hover and level, providing robust local stabilization, and a nonlinear locally input-to-state stable controller provides practical reference tracking to the transition operating mode. These controllers, together with an appropriate choice of reference maneuvers, ensure that a transition from hovered flight to level flight, or vice versa, is achieved. Whenever the aircraft state reaches unexpected values, the recovery controller is triggered in order to drive the aircraft toward stable hovered flight, providing a chance to retry the transition maneuver. The controllers' performance and robustness is assessed within a realistic simulation environment in the presence of sensor noise. © 2013 IEEE.

关键词： Fixed wings

来源：评论

学校读者我要写书评

暂无评论

A State-Migration Particle Swarm Optimizer for Adaptive Latent Factor Analysis of High-Dimensional and Incomplete Data

引用

IEEE/CAA Journal of Automatica Sinica 2024年第11期11卷 2220-2235页

作者： Jiufang Chen Kechen Liu Xin Luo Ye Yuan Khaled Sedraoui Yusuf Al-Turki MengChu Zhou IEEE the College of Computer Science and Technology Chongqing University of Posts and Telecommunications the Department of Computer Science Columbia University the College of Computer and Information Science Southwest University the Department of Electrical and Computer Engineering Faculty of Engineeringand also with K.A.CARE Energy Research and Innovation CenterKing Abdulaziz University the Center of Research Excellence in Renewable Energy and Power Systems the Department of Electrical and Computer EngineeringFaculty of Engineeringand also with K.A.CARE Energy Research and Innovation CenterKing Abdulaziz University the Department of Electrical and Computer Engineering New Jersey Institute of Technology

High-dimensional and incomplete(HDI) matrices are primarily generated in all kinds of big-data-related practical applications. A latent factor analysis(LFA) model is capable of conducting efficient representation learning to an HDI matrix,whose hyper-parameter adaptation can be implemented through a particle swarm optimizer(PSO) to meet scalable ***, conventional PSO is limited by its premature issues,which leads to the accuracy loss of a resultant LFA model. To address this thorny issue, this study merges the information of each particle's state migration into its evolution process following the principle of a generalized momentum method for improving its search ability, thereby building a state-migration particle swarm optimizer(SPSO), whose theoretical convergence is rigorously proved in this study. It is then incorporated into an LFA model for implementing efficient hyper-parameter adaptation without accuracy loss. Experiments on six HDI matrices indicate that an SPSO-incorporated LFA model outperforms state-of-the-art LFA models in terms of prediction accuracy for missing data of an HDI matrix with competitive computational ***, SPSO's use ensures efficient and reliable hyper-parameter adaptation in an LFA model, thus ensuring practicality and accurate representation learning for HDI matrices.

关键词： Data science generalized momentum high-dimensional and incomplete(HDI)data hyper-parameter adaptation latent factor analysis(LFA) particle swarm optimization(PSO)

来源：评论

学校读者我要写书评

暂无评论

Exploring diabetes through the lens of AI and computer vision: Methods and future prospects

引用

computers in Biology and Medicine 2025年 185卷 109537-109537页

作者： Chundi, Ramesh G., Sasikala Basivi, Praveen Kumar Tippana, Anitha Hulipalled, Vishwanath R. N., Prabakaran Simha, Jay B. Kim, Chang Woo Kakani, Vijay Pasupuleti, Visweswara Rao School of Computer Applications Dayananda Sagar University Bangalore India School of Computer Science and Applications REVA University Rukmini Knowledge Park Bangalore560064 India Pukyong National University Industry-University Cooperation Foundation Pukyong National University Busan48513 Korea Republic of Department of Biotechnology School of Applied Sciences REVA University Rukmini Knowledge Park Bangalore560064 India School of Computing and Information Technology REVA University Rukmini Knowledge Park Bangalore560064 India School of Computer Science and Engineering Vellore Institute of Technology Tamilnadu Vellore632014 India Abiba Systems CTO and RACE Labs REVA University Rukmini Knowledge Park Bangalore560064 India Department of Nanotechnology Engineering College of Engineering Pukyong National University Busan48513 Korea Republic of Integrated System Engineering Inha University 100 Inha-ro Nam-gu Incheon22212 Korea Republic of School of Biosciences Taylor's University Lakeside Campus Selangor Subang Jaya47500 Malaysia Faculty of Earth Sciences Universiti Malaysia Kelantan Campus Jeli Jeli Kelantan17600 Malaysia

Early diagnosis and timely initiation of treatment plans for diabetes are crucial for ensuring individuals’ well-being. Emerging technologies like artificial intelligence (AI) and computer vision are highly regarded for their ability to enhance the accessibility of large datasets for dynamic training and deliver efficient real-time intelligent technologies and predictable models. The application of AI and computer vision techniques to enhance the analysis of clinical data is referred to as eHealth solutions that employ advanced approaches to aid medical applications. This study examines several advancements and applications of machine learning, deep learning, and machine vision in global perception, with a focus on sustainability. This article discusses the significance of utilizing artificial intelligence and computer vision to detect diabetes, as it has the potential to significantly mitigate harm to human life. This paper provides several comments addressing challenges and recommendations for the use of this technology in the field of diabetes. This study explores the potential of employing Industry 4.0 technologies, including machine learning, deep learning, and computer vision robotics, as effective tools for effectively dealing with diabetes related aspects. © 2024 Elsevier Ltd

关键词： Artificial intelligence computer vision Diabetes Deep learning Machine learning

来源：评论

学校读者我要写书评

暂无评论

Real time image processing with an analog vision chip system.

引用

International journal of neural systems 1999年第5期9卷 423-428页

作者： Kameda, S. Honda, A. Yagi, T. Faculty of Computer Science and Systems Engineering Kyushu Institute of Technology Iizuka Fukuoka Japan

A linear analog network model is proposed to characterize the function of the outer retinal circuit in terms of the standard regularization theory. Inspired by the function and the architecture of the model, a vision chip has been designed using analog CMOS Very Large Scale Integrated circuit technology. In the chip, sample/hold amplifier circuits are incorporated to compensate for statistic transistor mismatches. Accordingly, extremely low noise outputs were obtained from the chip. Using the chip and a zero-crossing detector, edges of given images were effectively extracted in indoor illumination.

关键词：

来源：评论

学校读者我要写书评

暂无评论

Improved Prediction of Metamaterial Antenna Bandwidth Using Adaptive Optimization of LSTM

引用

computers, Materials & Continua 2022年第10期73卷 865-881页

作者： Doaa Sami Khafaga Amel Ali Alhussan El-Sayed M.El-kenawy Abdelhameed Ibrahim Said H.Abd Elkhalik Shady Y.El-Mashad Abdelaziz A.Abdelhamid Department of Computer Sciences College of Computer and Information SciencesPrincess Nourah Bint Abdulrahman UniversityRiyadh11671Saudi Arabia Department of Communications and Electronics Delta Higher Institute of Engineering and TechnologyMansoura35111Egypt Faculty of Artificial Intelligence Delta University for Science and TechnologyMansoura35712Egypt Computer Engineering and Control Systems Department Faculty of EngineeringMansoura UniversityMansoura35516Egypt Department of Computer Systems Engineering Faculty of Engineering at ShoubraBenha UniversityEgypt Department of Computer Science Faculty of Computer and Information SciencesAin Shams UniversityCairo11566Egypt Department of Computer Science College of Computing and Information TechnologyShaqra University11961Saudi Arabia

The design of an antenna requires a careful selection of its parameters to retain the desired ***,this task is time-consuming when the traditional approaches are employed,which represents a significant *** the other hand,machine learning presents an effective solution to this challenge through a set of regression models that can robustly assist antenna designers to find out the best set of design parameters to achieve the intended *** this paper,we propose a novel approach for accurately predicting the bandwidth of metamaterial *** proposed approach is based on employing the recently emerged guided whale optimization algorithm using adaptive particle swarm optimization to optimize the parameters of the long-short-term memory(LSTM)deep *** optimized network is used to retrieve the metamaterial bandwidth given a set of *** addition,the superiority of the proposed approach is examined in terms of a comparison with the traditional multilayer perceptron(ML),Knearest neighbors(K-NN),and the basic LSTM in terms of several evaluation criteria such as root mean square error(RMSE),mean absolute error(MAE),and mean bias error(MBE).Experimental results show that the proposed approach could achieve RMSE of(0.003018),MAE of(0.001871),and MBE of(0.000205).These values are better than those of the other competing models.

关键词： Metamaterial antenna long short term memory(LSTM) guided whale optimization algorithm(Guided WOA) adaptive dynamic particle swarm algorithm(AD-PSO)

来源：评论

学校读者我要写书评

暂无评论

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案：

请选择收藏分类：

通借通还

建议与咨询 留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案： 新增检索档案 确定 取消

请选择收藏分类： 新增自定义分类 确定 取消

通借通还

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

请选择保存的检索档案：

请选择收藏分类：