检索结果-内蒙古大学图书馆

3rd International Conference on Digital Transformation and Applications, ICDXA 2024

作者： Allias, Noormadinah Kadir, Diyana Ab Abdullahi, Akibu Mahmoud Ismail, Shahrinaz Tunku Abdul Rahman University of Management and Technology Faculty of Computing and Information Technology Kuala Lumpur Malaysia Computer Engineering Technology Universiti Kuala Lumpur- Malaysia Institute of Information Technology Kuala Lumpur Malaysia School of Computing and Informatics Albukhary International University Kedah Malaysia Kuala Lumpur Malaysia

ISBN: (纸本)9798350373424

Fifth generation and beyond (5G/B5G) is part of the next generation of mobile technology, which is expected to offer more capacity and faster speeds than the previous generation Long-Term Evolution (LTE) network. These features enable 5G/B5G to offer users a wide range of services, including smart cities, entertainment and multimedia, healthcare and mission-critical applications that benefit the entire economy and communities. However, due to growing demand, telecom service providers need to provide better Quality of service (QoS) on their networks to meet user expectations. Therefore, it is necessary to implement service classification that enables service providers to select the right network slices for each service to improve network QoS. Previous studies have predicted the classification of 5G/B5G services using traditional machine learning algorithms such as random forest and decision tree, achieving good accuracy. However, prediction based on black-box machine learning models was not transparent enough, so mobile service providers could not understand the reliability and interpretation of the results. Therefore, this article aims to model the system performance of 5G Key Performance Indicators (KPI) and extend the experiment to Explainable Artificial Intelligence (XAI) by using the Explainable AI to look behind the scenes of the chosen black-box models for service classification of 5G/B5G networks, as this is crucial for telecom service providers to make critical investment decisions around customer and employee experience. The results from the experiments show that the random forest has the highest confidence towards class Label 3 with the probability of 35 percent. © 2024 IEEE.

关键词： Decision trees

来源：评论

学校读者我要写书评

暂无评论

Assessing Predictor Influence in LSTM Models for Enhanced Solar Energy Forecasting

Assessing Predictor Influence in LSTM Models for Enhanced So...

引用

International Symposium on Symbolic and Numeric Algorithms for Scientific Computing (SYNASC)

作者： Alexandru-Adrian Ciobanu Nebojsa Bacanin Tilman Sanders Catalin Stoean Department of Computer Science University of Craiova Craiova Romania Faculty of Informatics and Computing Singidunum University Belgrade Serbia Department of Electrical Engineering and Computer Science FH Muenster Steinfurt Germany

ISBN: (数字)9798331532833

ISBN: (纸本)9798331532840

Accurate forecasting of solar energy production is highly important for an adequate integration of renewable energy into the power grid. This study explores the importance of various predictors for enhancing the accuracy of solar energy forecasting using two distinct Long Short-Term Memory models: one that relies solely on historical power production data and past exogenous weather data, and another that additionally incorporates future exogenous weather data. Various settings are tried to improve the results and relatively similar parameters are used by both, yielding more accurate results for the model that also uses information from the future. The results also demonstrate that predictor importance varies significantly between the two models. These findings suggest that different models benefit from distinct predictor combinations and incorporating exogenous data can significantly enhance forecasting performance.

关键词： Accuracy Computational modeling Solar energy Production Predictive models Data models Numerical models Forecasting Long short term memory Meteorology

来源：评论

学校读者我要写书评

暂无评论

Enhance Object Tracking on Augmented Reality Markerless using FAST Corner Detection 5

Enhance Object Tracking on Augmented Reality Markerless usin...

引用

5th IEEE International Conference on Information Technology, Information Systems and Electrical engineering, ICITISEE 2021

作者： Nurhadi Winanto, Eko Arip Saparudin Dinamika Bangsa University Faculty Of Engineering Sriwijaya University Dept. Of Informatics Engineering Indonesia University Of Technology Malaysia School Of Computing Malaysia Telkom University Faculty Of Computer Science Dept. Of Informatics Engineering Indonesia

ISBN: (纸本)9781665401968

Markerless-based is the method in Augmented Reality (AR) to augmentation 3D objects in the frame. One of the challenges in markerless AR is to extract features as input data training on markerless AR. One of algorithm for feature extraction of object target is FAST corner detection in image processing. This paper uses FAST corner detection for feature extraction on Augmented Reality Markerless based with the aim to improve performance of augmentation objects in AR. The User Defined Target and Extended Tracking are to improve convenience and reliability of the AR. The aim is can trace 3D objects even the 3D object invisible. The results show that maximum environment for the best augmentation is intensity in of 80-190 cd. © 2021 IEEE.

关键词： Augmented reality

来源：评论

学校读者我要写书评

暂无评论

The Effect of Data Augmentation and Optimization Technique on the Performance of EfficientNetV2 for Plant-Parasitic Nematode Identification

The Effect of Data Augmentation and Optimization Technique o...

引用

IEEE International Conference on Industry 4.0, Artificial Intelligence, and Communications Technology (IAICT)

作者： Nabila Husna Shabrina Ryukin Aranta Lika Siwi Indarti Department of Computer Engineering Faculty of Engineering and Informatics Universitas Multimedia Nusantara Tangerang Indonesia Department of Plant Protection Faculty of Agriculture Universitas Gadjah Mada Yogyakarta Indonesia

Plant-parasitic nematodes are major agricultural pathogens contributing to massive crop losses worldwide. It is crucial to identify plant-parasitic nematodes to decide the best pest control and management strategy. The current identification technique is based on visual observation from nematode microscopic images done by the nematologist. However, this method requires a long process and is prone to error. A deep learning-based method can be implemented to speed up the current identification process. This study explores the effect of combining several data augmentation techniques, namely brightness, contrast, blur, and noise, on the performance of the EfficientNetV2B0 and EfficientNetV2M models for identifying plant-parasitic nematodes. Moreover, this study also compared three optimizers while training the models to find the best optimizer for each model and data augmentation. The results show that the EfficientNetV2B0 model yielded the highest test accuracy of 96.91% when employing no augmentation and trained using SGD and RMSProp optimizer. Furthermore, the EfficientNetV2M model gave the highest test accuracy of 96.91% when the combination of brightness and contrast augmentations was applied and trained using the RMSProp optimizer.

关键词：

来源：评论

学校读者我要写书评

暂无评论

ETHACA - A Container-Based Cyber Range for Research and Education 29

ETHACA - A Container-Based Cyber Range for Research and Educ...

引用

29th IEEE International Workshop on computer Aided Modeling and Design of Communication Links and Networks, CAMAD 2024

作者： Chouliaras, Nestoras Kantzavelou, Ioanna Pantziou, Grammati Maglaras, Leandros Chouliara-Sidera, Panagiota Adamakos, Antonis Chalatsakos, Panagiotis University of West Attica Department of Informatics and Computer Engineering Athens Greece School of Computing Edinburgh Napier University EdinburghEH10 5DT United Kingdom University of Thessaly Department of Computer Science and Biomedical Infromatics Lamia Greece Senior Software Engineer at *** Athens Greece Open University of Cyprus Faculty of Pure and Applied Sciences Nicosia Cyprus

ISBN: (纸本)9798350377644

The advent of Industry 4.0 and the Industrial Internet of Things (IIoT) has revolutionized manufacturing and production systems, enhancing efficiency and communication through the integration of advanced technologies such as IoT devices and AI. However, this increased digitization has also led to heightened cybersecurity risks, with a notable rise in the frequency and complexity of cyber-attacks. Cyber Ranges emerges as a critical solution in addressing these challenges, offering a controlled environment for simulating cyber-attack scenarios, training cybersecurity teams, and developing robust defense strategies. The escalating demand for cybersecurity professionals has highlighted the crucial need for effective training solutions. This article presents an open-source Cyber Range constructed on container-based architecture, meticulously engineered for efficient operation with minimal resource consumption. In response to the scarcity of cybersecurity experts, this platform prioritizes extensive scalability and robust isolation between exercises, ensuring secure learning environments, and also integrates a complex developed exercise series aimed at strengthening cybersecurity learning experiences. These exercises encompass a variety of scenarios and challenges, catering to professionals at different skill levels. Each exercise replicates real-world cyber threats, offering practical, hands-on learning opportunities. Additionally, a comprehensive questionnaire accompanies these exercises, designed to gather feedback on participants' experiences. © 2024 IEEE.

关键词： Cyber attacks

来源：评论

学校读者我要写书评

暂无评论

Analyzing the Effectiveness of Smartphone Applications on Disease Management and Healthcare Systems

Analyzing the Effectiveness of Smartphone Applications on Di...

引用

International Conference on Application of Information and Communication Technologies (AICT)

作者： Rabei Raad Ali Wisam Subhi Al-Dayyeni Christy Atika Sari Eko Hari Rachmawanto Candra Irawan Omar M. Jihad Department of Computer Engineering Technology Northern Technical University Mosul Iraq School of Information Technologies and Engineering ADA University Baku Azerbaijan Study Program in Informatics Engineering Faculty of Computer Science University of Dian Nuswantoro Semarang Indonesia Study Program in Information Systems Faculty of Computer Science University of Dian Nuswantoro Semarang Indonesia

ISBN: (数字)9798350387537

ISBN: (纸本)9798350387544

In today’s complex global environments, it is highly challenging to achieve effective healthcare management. The critical challenges faced by healthcare management are being met by the revolution of m-health where mobile cloud computing plays a major role. The taxonomy of mobile computing comprises operational aspects, end-user issues, and service quality and mobility management. The use of smartphone technologies and applications has become a highly significant approach to improving healthcare management. Mobile health services such as mobile pathology, mobile neurosurgery, cancer treatment, and behavioral/psychological disorders are gaining significance where smartphone applications are being used. Portability, flexibility, and convenience are major characteristics of mobile computing that have helped patients and doctors to develop better relationships through coordination and communication. The relationship between smartphone technologies and applications and healthcare management can be understood in several broad aspects. This article aims to analyze the current and future implications of these technologies on healthcare and disease management systems.

关键词： Pathology Cloud computing Taxonomy Cancer treatment Medical services Benchmark testing Neurosurgery Information and communication technology Mobile computing Diseases

来源：评论

学校读者我要写书评

暂无评论

Metaheuristics Algorithm for Tuning of PID Controller of Mobile Robot System

引用

computers, Materials & Continua 2022年第8期72卷 3481-3492页

作者： Harsh Goud Prakash Chandra Sharma Kashif Nisar Muhammad Reazul Haque Ag.Asri Ag.Ibrahim Narendra Singh Yadav Pankaj Swarnkar Manoj Gupta Laxmi Chand Indian Institute of Information Technology NagpurIndia School of Computing&Information Technology Manipal UniversityJaipurRajasthanIndia Faculty of Computing and Informatics University Malaysia SabahJalan UMSKota Kinabalu88400SabahMalaysia Faculty of Computing&Informatics Multimedia UniversityPersiaran MultimediaCyberjaya63100SelangorMalaysia Maulana Azad National Institute of Technology BhopalIndia Department of Electronics and Communication Engineering JECRC UniversityJaipurRajasthanIndia Department of Computer Science and Engineering Hanyang UniversitySeongdong-guSeoul04763Korea

Robots in the medical industry are becoming more common in daily life because of various advantages such as quick response,less human interference,high dependability,improved hygiene,and reduced aging *** is why,in recent years,robotic aid has emerged as a blossoming solution to many challenges in the medical *** this manuscript,meta-heuristics(MH)algorithms,specifically the Firefly Algorithm(FF)and Genetic Algorithm(GA),are applied to tune PID controller constraints such as Proportional gain Kp Integral gain Ki and Derivative gain *** controller is used to control Mobile Robot System(MRS)at the required set *** FF arrangements are made based on various pre-analysis.A detailed simulation study indicates that the proposed PID controller tuned with Firefly Algorithm(FF-PID)for MRSis beneficial and suitable to achieve desired closed-loop system *** FF is touted as providing an easy,reliable,and efficient tuning technique for PID *** most suitable ideal performance is accomplished with FF-PID,according to the display in the time ***,the observed response is compared to those received by applying GA and conventional off-line tuning *** comparison of all tuning methods exhibits supremacy of FF-PID tuning of the given nonlinear Mobile Robot System than GA-PID tuning and conventional controller.

关键词： Metaheuristic algorithm genetic algorithm PID controller mobile robot system firefly algorithm

来源：评论

学校读者我要写书评

暂无评论

ML_BrainDetection: An Intelligent Model for Brain Tumor Identification Using Machine Learning 3

ML_BrainDetection: An Intelligent Model for Brain Tumor Iden...

引用

3rd International Mobile, Intelligent, and Ubiquitous Computing Conference, MIUCC 2023

作者： Abdelminaam, Diaa Salama Madbouly, Magda M. Farag, Mohamed S Gomaa, Ibrahim A Abd-Elghany Zeid, Magdy Abualigah, Laith Misr International University Faculty of Computer Science Cairo Egypt Benha University Faculty of Computers and Artificial Intelligence Benha Egypt Alexandria University Faculty of Computer and Data Science Alexandria Egypt Alobour High Institute for Computer and Informatics Computer Science Department Cairo Egypt Lebanese American University Department of Electrical and Computer Engineering Byblos13-5053 Lebanon Al-Ahliyya Amman University Hourani Center for Applied Scientific Research Amman19328 Jordan Middle East University Meu Research Unit Amman11831 Jordan School of Computer Sciences Universiti Sains Malaysia Pulau Pinang11800 Malaysia

ISBN: (纸本)9798350306231

Brain tumors are one of the greatest causes of death worldwide. Due to that, early diagnosis and classification of the tumor would surely help increase the chance of survival for patients worldwide. However, classifying and identifying brain tumors requires certain tools, including using Magnetic Resonance Imaging (MRI) to detect brain tumors. Moreover, many researchers in the past have also built up computer-Aided Diagnosis (CAD) systems to aid radiologists in their purpose of detecting brain abnormalities and tumors effectively. In this research, extracted multi-modal features like texture, morphological, entropy-based, Scale Invariant Features are proposed, and consequently, certain robust machine learning techniques, which are k-nearest neighbors (KNN), Decision Tree (DT), Support Vector Machine (SVM), Naive Bayes, as well as the Logistic Regression classifier were used to analyze the two data-sets included in the paper, detect tumors found in the brain, not to mention, examining their results, which have then shown the highest accuracy when using the Logistic regression classifier in both data sets, with the Support Vector Machine (SVM) classifier following it, which further support the argument made in the paper. © 2023 IEEE.

关键词： Support vector machines

来源：评论

学校读者我要写书评

暂无评论

Learning Features Using an optimized Artificial Neural Network for Breast Cancer Diagnosis

引用

SN computer Science 2022年第3期3卷 229页

作者： AlShourbaji, Ibrahim Kachare, Pramod Zogaan, Waleed Muhammad, L.J. Abualigah, Laith Department of Computer and Network Engineering Jazan University Jazan 45142 Saudi Arabia Department of Electronics and Telecommunication Engineering Ramrao Adik Institute of Technology Nerul Maharashtra Navi Mumbai 400706 India Department of Computer Science Jazan University Jazan 45142 Saudi Arabia Department of Computer Science Faculty of Science Federal University of Kashere P.M.B. 0182 Gombe Nigeria Faculty of Computer Sciences and Informatics Amman Arab University Amman 11953 Jordan

Breast cancer (BC) has been one of the significant causes of death worldwide, and its early detection can play a vital role in increasing the survival rate of this disease. This paper suggests a novel feature learning method for BC diagnosis using the artificial neural network (ANN) by optimizing the hidden layers. It uses a systematic hyper-parameter search method to optimize number of hidden layers, neurons in the hidden layers, activation functions, learning rate, batch size, and training epochs of the ANN. The number of hidden layers and the number of neurons in the hidden layer are investigated using t-distributed stochastic neighbor embedding (t-SNE) technique to specify the best hyper-parameters that achieved the highest testing accuracy while avoiding model overfitting. Comparative analysis showed that the proposed method achieved higher performance than the ANN-based models that utilizes particle swarm optimization (PSO), multi-verse optimizer (MVO), BAT algorithm, and firefly algorithm (FFA) as feature selection (FS) methods, with the best classification accuracy of 0.9948, sensitivity of 0.9815, and specificity of 0.9882 for 10 iterations of holdout cross-validation. The performance of the proposed method is also comparable with several earlier reported state-of-the-art models in the literature. © 2022, The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd.

关键词： ANN Brest cancer Classification Feature selection Machine learning

来源：评论

学校读者我要写书评

暂无评论

Real-Time and Intelligent Flood Forecasting Using UAV-Assisted Wireless Sensor Network

引用

computers, Materials & Continua 2022年第1期70卷 715-738页

作者： Shidrokh Goudarzi Seyed Ahmad Soleymani Mohammad Hossein Anisi Domenico Ciuonzo Nazri Kama Salwani Abdullah Mohammad Abdollahi Azgomi Zenon Chaczko Azri Azmi Centre for Artificial Intelligent(CAIT) Universiti Kebangsaan MalaysiaBangi43600Malaysia School of Computer Engineering Iran University of Science and TechnologyResalat Sq.16846-13114TehranIran School of Computing Faculty of Engineering Universiti Teknologi MalaysiaJohorMalaysia School of Computer Science and Electronic Engineering University of EssexColchester CO43SQUnited Kingdom Department of Electrical Engineering and Information Technologies University of Naples“Federico II”Naples80125Italy Razak Faculty of Technology and Informatics Universiti Teknologi MalaysiaKuala Lumpur54100Malaysia School of Electrical and Data Engineering University of Technology SydneyUltimoNSWAustralia

The Wireless Sensor Network(WSN)is a promising technology that could be used to monitor rivers’water levels for early warning flood detection in the 5G ***,during a flood,sensor nodes may be washed up or become faulty,which seriously affects network *** address this issue,Unmanned Aerial Vehicles(UAVs)could be integrated with WSN as routers or data mules to provide reliable data collection and flood *** light of this,we propose a fault-tolerant multi-level framework comprised of a WSN and a UAV to monitor river *** framework is capable to provide seamless data collection by handling the disconnections caused by the failed nodes during a ***,an algorithm hybridized with Group Method Data Handling(GMDH)and Particle Swarm Optimization(PSO)is proposed to predict forthcoming floods in an intelligent collaborative *** proposed water-level prediction model is trained based on the real dataset obtained fromthe Selangor River *** performance of the work in comparison with other models has been also evaluated and numerical results based on different metrics such as coefficient of determination(R2),correlation coefficient(R),RootMean Square Error(RMSE),Mean Absolute Percentage Error(MAPE),and BIAS are provided.

关键词： Unmanned aerial vehicles wireless sensor networks group method data handling particle swarm optimization river flow prediction

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：