检索结果-内蒙古大学图书馆

Wind Power Prediction Based on Machine Learning and Deep Learning Models

computers, Materials & Continua 2023年第1期74卷 715-732页

作者： Zahraa Tarek Mahmoud Y.Shams Ahmed M.Elshewey El-Sayed M.El-kenawy Abdelhameed Ibrahim Abdelaziz A.Abdelhamid Mohamed A.El-dosuky Faculty of Computers and Information Computer Science DepartmentMansoura UniversityMansoura35561Egypt Faculty of Artificial Intelligence Kafrelsheikh UniversityKafrelsheikh33511Egypt Faculty of Computers and Information Computer Science DepartmentSuez UniversitySuezEgypt 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 Science Faculty of Computer and Information SciencesAin Shams UniversityCairo11566Egypt Department of Computer Science College of Computing and Information TechnologyShaqra University11961Saudi Arabia Department of Computer Science Arab East CollegesRiyadh13544Saudi Arabia

Wind power is one of the sustainable ways to generate renewable *** recent years,some countries have set renewables to meet future energy needs,with the primary goal of reducing emissions and promoting sustainable growth,primarily the use of wind and solar *** achieve the prediction of wind power generation,several deep and machine learning models are constructed in this article as base *** regression models are Deep neural network(DNN),k-nearest neighbor(KNN)regressor,long short-term memory(LSTM),averaging model,random forest(RF)regressor,bagging regressor,and gradient boosting(GB)*** addition,data cleaning and data preprocessing were performed to the *** dataset used in this study includes 4 features and 50530 *** accurately predict the wind power values,we propose in this paper a new optimization technique based on stochastic fractal search and particle swarm optimization(SFSPSO)to optimize the parameters of LSTM *** evaluation criteria were utilized to estimate the efficiency of the regression models,namely,mean absolute error(MAE),Nash Sutcliffe Efficiency(NSE),mean square error(MSE),coefficient of determination(R2),root mean squared error(RMSE).The experimental results illustrated that the proposed optimization of LSTM using SFS-PSO model achieved the best results with R2 equals 99.99%in predicting the wind power values.

关键词： Prediction of wind power data preprocessing performance evaluation

来源：评论

学校读者我要写书评

暂无评论

Shadow Extraction and Elimination of Moving Vehicles for Tracking Vehicles

引用

computers, Materials & Continua 2023年第11期77卷 2009-2030页

作者： Kalpesh Jadav Vishal Sorathiya Walid El-Shafai Torki Altameem Moustafa HAly Vipul Vekariya Kawsar Ahmed Francis MBui Department of Electronics&Communication Engineering Faculty of Engineering and TechnologyParul Institute of Engineering and TechnologyParul UniversityVadodaraGujarat391760India Department Electronics and Electrical Communications Engineering Faculty of Electronic EngineeringMenoufia UniversityMenouf32952Egypt Computer Science Department Community CollegeKing Saud UniversityRiyadh11362Saudi Arabia Electronics and Communications Engineering Department College of Engineering and TechnologyArab Academy for ScienceTechnology and Maritime TransportAlexandria1029Egypt Department of Computer Engineering Faculty of Engineering and TechnologyParul Institute of Engineering and TechnologyParul UniversityVadodaraGujarat391760India Department of Electrical and Computer Engineering University of Saskatchewan57 Campus DriveSaskatoonSK S7N5A9Canada

Shadow extraction and elimination is essential for intelligent transportation systems(ITS)in vehicle tracking *** shadow is the source of error for vehicle detection,which causes misclassification of vehicles and a high false alarm rate in the research of vehicle counting,vehicle detection,vehicle tracking,and *** of the existing research is on shadow extraction of moving vehicles in high intensity and on standard datasets,but the process of extracting shadows from moving vehicles in low light of real scenes is *** real scenes of vehicles dataset are generated by self on the Vadodara–Mumbai highway during periods of poor illumination for shadow extraction of moving vehicles to address the above *** paper offers a robust shadow extraction of moving vehicles and its elimination for vehicle *** method is distributed into two phases:In the first phase,we extract foreground regions using a mixture of Gaussian model,and then in the second phase,with the help of the Gamma correction,intensity ratio,negative transformation,and a combination of Gaussian filters,we locate and remove the shadow region from the foreground *** to the outcomes proposed method with outcomes of an existing method,the suggested method achieves an average true negative rate of above 90%,a shadow detection rate SDR(η%),and a shadow discrimination rate SDR(ξ%)of 80%.Hence,the suggested method is more appropriate for moving shadow detection in real scenes.

关键词： Change illuminations ImageJ software intelligent traffic systems mixture of Gaussian model National Institute of Health vehicle tracking

来源：评论

学校读者我要写书评

暂无评论

Fine-Tuning Cyber Security Defenses: Evaluating Supervised Machine Learning Classifiers for Windows Malware Detection

引用

computers, Materials & Continua 2024年第8期80卷 2917-2939页

作者： Islam Zada Mohammed Naif Alatawi Syed Muhammad Saqlain Abdullah Alshahrani Adel Alshamran Kanwal Imran Hessa Alfraihi Department of Software Engineering International Islamic UniversityIslamabad25000Pakistan Information Technology Department Faculty of Computers and Information TechnologyUniversity of TabukTabuk71491Saudi Arabia Department of Computer Science and Artificial Intelligence College of Computer Science and EngineeringUniversity of JeddahJeddah21493Saudi Arabia Department of Cybersecurity College of Computer Science and EngineeringUniversity of JeddahJeddah21493Saudi Arabia Department of Computer Science University of PeshawarPeshawar25121Pakistan Department of Information Systems College of Computer and Information SciencesPrincess Nourah bint Abdulrahman UniversityRiyadh11671Saudi Arabia

Malware attacks on Windows machines pose significant cybersecurity threats,necessitating effective detection and prevention *** machine learning classifiers have emerged as promising tools for malware ***,there remains a need for comprehensive studies that compare the performance of different classifiers specifically for Windows malware *** this gap can provide valuable insights for enhancing cybersecurity *** numerous studies have explored malware detection using machine learning techniques,there is a lack of systematic comparison of supervised classifiers for Windows malware *** the relative effectiveness of these classifiers can inform the selection of optimal detection methods and improve overall security *** study aims to bridge the research gap by conducting a comparative analysis of supervised machine learning classifiers for detecting malware on Windows *** objectives include Investigating the performance of various classifiers,such as Gaussian Naïve Bayes,K Nearest Neighbors(KNN),Stochastic Gradient Descent Classifier(SGDC),and Decision Tree,in detecting Windows *** the accuracy,efficiency,and suitability of each classifier for real-world malware detection *** the strengths and limitations of different classifiers to provide insights for cybersecurity practitioners and *** recommendations for selecting the most effective classifier for Windows malware detection based on empirical *** study employs a structured methodology consisting of several phases:exploratory data analysis,data preprocessing,model training,and *** data analysis involves understanding the dataset’s characteristics and identifying preprocessing *** preprocessing includes cleaning,feature encoding,dimensionality reduction,and optimization to prepare the data for *** training utilizes various

关键词： Security and privacy challenges in the context of requirements engineering supervisedmachine learning malware detection windows systems comparative analysis Gaussian Naive Bayes K Nearest Neighbors Stochastic Gradient Descent Classifier Decision Tree

来源：评论

学校读者我要写书评

暂无评论

Hybrid Dipper Throated and Grey Wolf Optimization for Feature Selection Applied to Life Benchmark Datasets

引用

computers, Materials & Continua 2023年第2期74卷 4531-4545页

作者： Doaa Sami Khafaga El-Sayed M.El-kenawy Faten Khalid Karim Mostafa Abotaleb Abdelhameed Ibrahim Abdelaziz A.Abdelhamid D.L.Elsheweikh Department of Computer Sciences College of Computer and Information SciencesPrincess Nourah bint Abdulrahman UniversityP.O.Box 84428Riyadh11671Saudi Arabia Department of Communications and Electronics Delta Higher Institute of Engineering and TechnologyMansoura35111Egypt Faculty of Artificial Intelligence Delta University for Science and TechnologyMansoura35712Egypt Department of System Programming South Ural State UniversityChelyabinsk454080Russia Computer Engineering and Control Systems Department Faculty of EngineeringMansoura UniversityMansoura35516Egypt Department of Computer Science Faculty of Computer and Information SciencesAin Shams UniversityCairo11566Egypt Department of Computer Science College of Computing and Information TechnologyShaqra University11961Saudi Arabia Department of Computer Science Faculty of Specific EducationMansoura UniversityEgypt

Selecting the most relevant subset of features from a dataset is a vital step in data mining and machine *** feature in a dataset has 2n possible subsets,making it challenging to select the optimum collection of features using typical *** a result,a new metaheuristicsbased feature selection method based on the dipper-throated and grey-wolf optimization(DTO-GW)algorithms has been developed in this *** can result when the selection of features is subject to metaheuristics,which can lead to a wide range of ***,we adopted hybrid optimization in our method of optimizing,which allowed us to better balance exploration and harvesting chores more *** propose utilizing the binary DTO-GW search approach we previously devised for selecting the optimal subset of *** the proposed method,the number of features selected is minimized,while classification accuracy is *** test the proposed method’s performance against eleven other state-of-theart approaches,eight datasets from the UCI repository were used,such as binary grey wolf search(bGWO),binary hybrid grey wolf,and particle swarm optimization(bGWO-PSO),bPSO,binary stochastic fractal search(bSFS),binary whale optimization algorithm(bWOA),binary modified grey wolf optimization(bMGWO),binary multiverse optimization(bMVO),binary bowerbird optimization(bSBO),binary hysteresis optimization(bHy),and binary hysteresis optimization(bHWO).The suggested method is superior 4532 CMC,2023,vol.74,no.2 and successful in handling the problem of feature selection,according to the results of the experiments.

关键词： Metaheuristics dipper throated optimization grey wolf optimization binary optimizer feature selection

来源：评论

学校读者我要写书评

暂无评论

Optimized AI and IoT-Driven Framework for Intelligent Water Resource Management

引用

IEEE Access 2025年 13卷 97628-97646页

作者： Rokaya, Mahmoud Hemdan, Dalia I. Alajmani, Samah Hazzaa Alyami, Raneem Yousif Elmarhomy, Ghada Hashim, Hasan Atlam, El-Sayed College of Computers and Information Technology Taif University Department of Information Technology P.O. Box 11099 Taif21944 Saudi Arabia Taif University Faculty of Science Department of Food Science and Nutrition P.O. Box 11099 Taif21944 Saudi Arabia College of Computer Science and Engineering Taibah University Department of Information Systems Saudi Arabia Tanta University Department Computer Science Faculty of Science Tanta31527 Egypt Taibah University College of Computer Science and Engineering Department of Computer Science Yanbu6421 Saudi Arabia

The scheme of water resources management is a necessity for reducing water scarcity in arid areas and improving water availability in general [1]. However, water leak detection and irrigation scheduling traditional AI models are often computationally intensive and require complex hyperparameter tuning, making them less scalable. This study presents an artificial intelligence-based optimization framework that improves forecasting accuracy, computational speed, and real-time adaptability. The architecture combines the ensemble-learning algorithms (XGBoost, LightGBM), hybrid AIs (XGBoost + Autoencoder), and metaheuristic feature selection (GA, PSO, SA) for making intelligent decisions. Moreover, ontology-based feature structuring enhances interpretability, while hyperparameter tuning (GridSearchCV, Bayesian Optimization) and model compression techniques (pruning, quantization, knowledge distillation) ensure computational efficiency. A large number of experiments on real-world IoT sensor data testify to the effectiveness of the framework. It achieves 0.992 AUC-ROC scores for leak detection, an RMSE of 0.227 hours for irrigation scheduling, and an overall accuracy of 94.8%. Additional performance measures comprise precision (89.0%), recall (95.2%), F1-score (0.92), and inference speed (0.003 ms/sample). Although quantization has reduced the computational overhead, we still see a 13.02% increase in the model size as seen in Experiment 6, leading to a trade-off that needs to be optimized further. This study offers a deployable AI-based model for sustainable water management by tackling the issues of scalability, computational cost, and limitations in benchmark evaluation. By virtue of the empirical validation and comparative analysis of the framework, it has been shown to perform better than the regular methods, proving that the methodology can act as a step forward in the field of real-time, AI-assisted irrigation and leak detection systems. © 2013 IEEE.

关键词： Resource allocation

来源：评论

学校读者我要写书评

暂无评论

A Smart CIoT with Secure Healthcare Framework Using Optimized Deep Recuperator Neural Network Long Short-Term Memory

引用

IEEE Internet of Things Journal 2024年第6期11卷 10551-10562页

作者： Narmatha, C. Manimurugan, S. Karthikeyan, P. University of Tabuk Faculty of Computers and Information Technology Tabuk71491 Saudi Arabia Oakland University Department of Electrical and Computer Engineering RochesterMI112345 United States National Chung Cheng University Department of Computer Science and Information Engineering Chiayi62102 Taiwan RV University School of Computer Science and Engineering Bengaluru560059 India

The healthcare system currently relies on the facility to store and process large amounts of health data, supported by efficient management. The Internet of Things (IoT) has driven the growth of Adroit Healthcare, which has vast data processing capabilities and extensive data collection. The consumer IoT (CIoT), also known as the IoT in the context of individual use cases, is dominated by personal healthcare applications. The remarkable expansion of the CIoT can be attributed to the extensive embrace of wearable technology, facilitating everyday monitoring of vital health indicators like blood pressure, heart rate, and respiration rate. However, the CIoT raises concerns about data security, confidentiality, and customer trust. This study proposes an integrating blockchain and deep learning (DL) approach to promote responsible use of CIoT. The study develops an Intelligent IoT (IIoT) and healthcare diagnostic model, named BT-PWO-DRNN-LSTM, using blockchain technology and Prairie Wolf optimization-based deep recuperator neural network (DRNN) and long short-term memory (LSTM). The BT-PWO-DRNN-LSTM approach includes three key processes: 1) encrypted transactions;2) cryptographic hash feature;and 3) medical diagnosis. The PWO technique is used for secure communication of medical images, the neighborhood catalogue disposition (NCD) technique is used in the cryptographic hash feature operation (CHFO), and the DL approach is employed as a classification algorithm for diagnosing disorders. The use of PWO methodology for secure healthcare communication and optimal parameter fine-tuning highlights the originality of the study. The BT-PWO-DRNN-LSTM prototype showed positive results with high sensitivity, specificity, and accuracy during the diagnostic process. © 2014 IEEE.

关键词： Blockchain

来源：评论

学校读者我要写书评

暂无评论

Automatic extraction of lightweight and efficient neural network architecture of heavy convolutional architectures to predict microsatellite instability from hematoxylin and eosin histology in gastric cancer

引用

Neural Computing and Applications 2024年第25期36卷 15295-15321页

作者： Rostami, Habib Ashkpour, Maryam Behzadi-Khormouji, Hamed Mokhtari, Maral Khayati, Armin Keshavarz, Ahmad Talatian Azad, Saeed Tabesh, Yahya Computer Engineering Department Faculty of Intelligent Systems Engineering and Data Science Persian Gulf University Bushehr75168 Iran Artificial Intelligence and Intelligent Healthcare Lab Artificial Intelligence and Data Mining Research Group Faculty of Intelligent Systems Engineering and Data Science ICT Research Institute Persian Gulf University Bushehr75168 Iran Faculty of New Science and Technologies University of Tehran Tehran Iran Department of Computer Science Imec-IDLab University of Antwerp Sint-Pietersvliet 7 Antwerp2000 Belgium Shahid Faghihi Hospital Shiraz University of Medical Sciences Shiraz Iran Department of Computer Science Engineering and IT School of Electrical and Computer Engineering Shiraz University Shiraz Iran Electrical Engineering Department Faculty of Intelligent Systems Engineering and Data Science Persian Gulf University Bushehr75168 Iran IoT and Signal Processing Research Group Faculty of Intelligent Systems Engineering and Data Science ICT Research Institute Persian Gulf University Bushehr75168 Iran Department of Mathematical Sciences Sharif University of Technology Tehran Iran

Cancers have emerged as a significant concern due to their impact on public health and society. The examination and interpretation of tissue sections stained with Hematoxylin and Eosin (H&E) play a crucial role in disease assessment, particularly in cases like gastric cancer. Microsatellite instability (MSI) is suggested to contribute to the carcinogenesis of specific gastrointestinal tumors. However, due to the nonspecific morphology observed in H&E-stained tissue sections, MSI determination often requires costly evaluations through various molecular studies and immunohistochemistry methods in specialized molecular pathology laboratories. Despite the high cost, international guidelines recommend MSI testing for gastrointestinal cancers. Thus, there is a pressing need for a new diagnostic modality with lower costs and widespread applicability for MSI detection. This study aims to detect MSI directly from H&E histology slides in gastric cancer, providing a cost-effective alternative. The performance of well-known deep convolutional neural networks (DCNNs) and a proposed architecture are compared. Medical image datasets are typically smaller than benchmark datasets like ImageNet, necessitating the use of off-the-shelf DCNN architectures developed for large datasets through techniques such as transfer learning. Designing an architecture proportional to a custom dataset can be tedious and may not yield desirable results. In this work, we propose an automatic method to extract a lightweight and efficient architecture from a given heavy architecture (e.g., well-known off-the-shelf DCNNs) proportional to a specific dataset. To predict MSI instability, we extracted the MicroNet architecture from the Xception network using the proposed method and compared its performance with other well-known architectures. The models were trained using tiles extracted from whole-slide images, and two evaluation strategies, tile-based and whole-slide image (WSI)-based, were employed and comp

关键词： Network architecture

来源：评论

学校读者我要写书评

暂无评论

Supervised learning applied to electrocardiogram statistical features for the detection of premature ventricular contraction

引用

Research on Biomedical engineering 2025年第1期41卷 1-12页

作者： Issa, Khouloud Rammal, Abbas Assaf, Rabih Ghandour, Ahmad Electrical and Communication Engineering Department College of Engineering Phoenicia University Zahrani South Lebanon Statistics and Informatics Department Faculty of Science Lebanese University Beirut Lebanon Mathematics and Computer Sciences Department Faculty of Arts and Sciences Lebanese American University Beirut Lebanon Department of Mathematics Faculty of Arts and Sciences Holy Spirit University of Kaslik Jounieh Lebanon Biomedical Engineering Department Faculty of Engineering Islamic University of Lebanon Wardenieh30014 Lebanon

Purpose: The development of an automated premature ventricular contraction (PVC) detection system has significant implications for early intervention and treatment decisions. This study aims to develop a novel approach using various supervised machine learning (ML) methods for detecting PVCs in electrocardiogram (ECG) recordings. Methodology: To achieve this, we extracted ten distinct statistical and temporal features from 33 long-term ECG recordings from the benchmark MIT-BIH arrhythmia database (MIT-BIH-AD), capturing significant characteristics from the signals. We then investigated the effectiveness of traditional ML algorithms in identifying PVCs based on these features, namely support vector machine (SVM), decision tree (DT), naïve Bayes (NB), k-nearest neighbor (KNN), linear discriminant analysis (LDA), and artificial neural network (ANN). Results: Among these classifiers, the SVM, KNN, ANN, and DT classifiers demonstrated exceptional discriminatory power and classification performance, yielding near-perfect area under the receiver operating characteristic curve (AUC-ROC) values of 97.3%, 95.4%, 95%, and 94.1%, respectively. On the other hand, the SVM classifier emerged as the most accurate, achieving an overall accuracy of 97.39%, indicative of making correct predictions. Conclusion: These findings underscore the generalization of the proposed approach, especially since we worked on an extended dataset encompassing different types of heartbeats to discriminate PVCs from other heartbeat types, not only normal ones, and providing closer alignment with real-world scenarios. Subsequently, the robustness and accuracy of these models highlight their suitability for clinical applications and their potential for efficient implementation and deployment in the diagnosis of cardiovascular diseases (CVDs), as they demand minimal computational resources and time. © The Author(s), under exclusive licence to The Brazilian Society of Biomedical engineering 2025.

关键词： Decision trees

来源：评论

学校读者我要写书评

暂无评论

NLP Research: A Historical Survey and Current Trends in Global, Indic, and Gujarati Languages 4

NLP Research: A Historical Survey and Current Trends in Glob...

引用

4th International Conference on Ubiquitous Computing and Intelligent Information systems, ICUIS 2024

作者： Panchal, Brijeshkumar Y. Shah, Apurva The Maharaja Sayajirao University of Baroda Faculty of Technology and Engineering Computer Science and Engineering Department Gujarat Vadodara India Computer Engineering Department Gujarat Anand India

ISBN: (纸本)9798331529635

This research study presents a comprehensive survey of Natural Language Processing (NLP) research, tracing its historical evolution from its inception to the present. The survey explores the key milestones and advancements in NLP, focusing on global trends, as well as specific contributions from Indic languages, with a particular focus on Gujarati. The paper analyzes the current state of Gujarati NLP research, identifying existing gaps and challenges. By providing a comprehensive overview of NLP research, this study aims to guide future research directions and foster advancements in the field, particularly in the context of Indic languages. © 2024 IEEE.

关键词： Natural language processing systems

来源：评论

学校读者我要写书评

暂无评论

Traffic Control Based on Integrated Kalman Filtering and Adaptive Quantized Q-Learning Framework for Internet of Vehicles

引用

computer Modeling in engineering & sciences 2024年第3期138卷 2103-2127页

作者： Othman S.Al-Heety Zahriladha Zakaria Ahmed Abu-Khadrah Mahamod Ismail Sarmad Nozad Mahmood Mohammed Mudhafar Shakir Hussein Alsariera Faculty of Electronic and Computer Engineering Universiti Teknikal Malaysia Melaka(UTeM)Durian TunggalMalaysia College of Computing&Informatics Saudi Electronic UniversityRiyadhSaudi Arabia Department of Electrical Electronic and System EngineeringUniversiti Kebangsaan MalaysiaUKM BangiSelangorMalaysia Computer Technology Engineering College of Engineering TechnologyAl-Kitab UniversityKirkukIraq Faculty of Engineering and Electronic Engineering Universiti Tun Hussein Onn Malaysia(UTHM)Batu PahatMalaysia 6Research Center DepartmentThe University of MashreqBaghdadIraq

Intelligent traffic control requires accurate estimation of the road states and incorporation of adaptive or dynamically adjusted intelligent algorithms for making the *** this article,these issues are handled by proposing a novel framework for traffic control using vehicular communications and Internet of Things *** framework integrates Kalman filtering and *** smoothing Kalman filtering,our data fusion Kalman filter incorporates a process-aware model which makes it superior in terms of the prediction *** traditional Q-learning,our Q-learning algorithm enables adaptive state quantization by changing the threshold of separating low traffic from high traffic on the road according to the maximum number of vehicles in the junction *** evaluation,the model has been simulated on a single intersection consisting of four roads:east,west,north,and south.A comparison of the developed adaptive quantized Q-learning(AQQL)framework with state-of-the-art and greedy approaches shows the superiority of AQQL with an improvement percentage in terms of the released number of vehicles of AQQL is 5%over the greedy approach and 340%over the state-of-the-art ***,AQQL provides an effective traffic control that can be applied in today’s intelligent traffic system.

关键词： Q-learning intelligent transportation system(ITS) traffic control vehicular communication kalman filtering smart city Internet of Things

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：