检索结果-内蒙古大学图书馆

24th IEEE International Symposium on Computational Intelligence and informatics, CINTI 2024

作者： Kozhanov, Murat Mosavi, Amir Amirov, Azamat Kaibassova, Dinara Poser, Valeria Shakhatova, Aliya Lira, La Eigner, Gyorgy Department of Information and Computing Systems Abylkas Sagninov Karaganda Technical University Karaganda Kazakhstan Obuda University Budapest Hungary Ludovika University of Public Service Budapest Hungary Department of Computer Engineering Astana IT University Astana Kazakhstan John von Neumann Faculty of Informatics Obuda University Budapest Hungary Department of Information Systems L.N.Gumilyov Eurasian National University Astana Kazakhstan

ISBN: (纸本)9798350353433

This article introduces a methodology to identify the fundamental studies carried out using artificial intelligence for syllabus design and planning. A taxonomy of the most relevant studies is presented based on the method and applications. The main studies in the field are reviewed and their contributions are emphasized. The meta-analysis of the results presents the extent and applicability of artificial intelligence in syllabus design and planning. The result shows the essential role of machine learning in the development and improving the process of syllabus design and planning. © 2024 IEEE.

关键词： Adversarial machine learning

来源：评论

学校读者我要写书评

暂无评论

Forecasting the Number of Traffic Accidents in Jakarta Using Artificial Neural Network

Forecasting the Number of Traffic Accidents in Jakarta Using...

引用

Information Technology and Computing (ICITCOM), International Conference on

作者： Henny Yulianti Gatot Tri Pranoto Yaddarabullah Erneza Dewi Krishnasari Michael Marchenko Informatics Engineering Universitas Siber Asia Jakarta Indonesia Informatics Engineering Pelita Bangsa University Bekasi Indonesia Department of Informatics Universitas Trilogi Jakarta Indonesia Department of Visual Communication Design Universitas Trilogi Jakarta Indonesia Faculty of Physics Electronics and Computer Systems Dnipro National University Dnipropetrovsk Oblast Ukraine

Traffic accidents are a problem confronted by means of each a around the arena, Indonesia isn't any exception. The Indonesian national Police recorded an increase inside the number of traffic accidents in 2019 in comparison to 2018. based totally on POLRI information, there had been 107,500 traffic accidents in 2019, an increase from 103,672 incidents in 2018. in particular in Jakarta, that is the capital city of the Republic of Indonesia, traffic accidents are a common phenomenon, in conjunction with the unexpectedly developing use of motorized motors on the dual carriageway. for that reason, a examine is wanted to determine the variety of traffic accidents with the intention to occur in the future. The observe will forecast the number of traffic accidents for every month based totally on datasets from 2015 to 2020 from the Jakarta Metro Jaya Police Traffic Directorate. The approach used on this examine makes use of facts mining methods by using comparing the artificial Neural community Backpropagation, Deep getting to know and Linear Regression algorithms to decide which algorithm is right for predicting the range of traffic injuries inside the Jakarta region in each following month. This examine concludes that the synthetic Neural community Backpropagation set of rules is the first-rate set of rules to be applied in predicting the number of visitors accidents within the Jakarta vicinity with a Root suggest square mistakes (RMSE) of 29.477. studies trying out with the aid of applying the synthetic Neural network Backpropagation algorithm version has verified to expose the price of forecasting the number of traffic accidents in Jakarta with a mean monthly accuracy cost of 97.34%.

关键词：

来源：评论

学校读者我要写书评

暂无评论

Improving the Estimation of Coronary Artery Disease by Classification Machine Learning Algorithm 6

Improving the Estimation of Coronary Artery Disease by Class...

引用

6th IEEE International Conference and Workshop Obuda on Electrical and Power engineering, CANDO-EPE 2023

作者： Ghorbani, Hamzeh Krasnikova, Alla Ghorbani, Parvin Ghorbani, Simin Hovhannisyan, Harutyun S. Minasyan, Arsen Minasian, Natali Alvar, Mehdi Ahmadi Stepanyan, Harutyun Azodinia, Mohammazreza Faculty of General Medicine 38a Marshal Babajanyan St. Yerevan0040 Armenia Yerevan State Medical University Faculty of Medicine Department of Cardiology Yerevan0031 Armenia Ahvaz Jundishapur University of Medical Sciences Faculty of Medicine Department of Cardiology Ahvaz Iran Ahvaz Jundishapur University of Medical Sciences Faculty of Nursing Department of Nursing and Midwifery Ahvaz Iran Yerevan State Medical University Department of Internal Disease Propaedeutics Yerevan Armenia Yaroslavl State Medical University Faculty of General Medicine Yaroslavl Russia Shahid Chamran University Faculty of Engineering Department of Computer Engineering Ahwaz Iran Doctoral School of Applied Informatics and Applied Mathematics Obuda University Budapest Hungary

ISBN: (纸本)9798350328752

This research paper aims to predict coronary artery disease (CAD) using data from 350 patients collected at one of the hospitals in Armenia. CAD is a critical parameter which can have a significant impact on patients' life and survival. The study considers several input variables, including level of cholesterol (LOC), patient's age (PA), type of chest pain (TCP), number of arteries blocked (NAB), sex (S), and family history (FH), to make accurate predictions. To achieve this crucial task of CAD prediction, the researchers employed three powerful classification algorithms: Random Forest (RF), Support Vector Machine (SVM), and Logistic Regression (LR). Among these, the Random Forest algorithm stands out for its robustness and numerous advantages, including high accuracy, ability to handle outliers effectively, provision of feature importance insights, and reduced risk of overfitting. The research findings presented in this article demonstrate the impressive performance of the Random Forest algorithm, showcasing an accuracy value of 0.95 and a precision value of 0.94. These results indicate the model's ability to make precise and reliable predictions, essential when dealing with a life-or-death parameter like CAD. By conducting a comparative analysis based on statistical parameters, the researchers establish that Random Forest outperforms both SVM and LR. Thus, the conclusion drawn from the study suggests that the ranking of the algorithms based on their performance is as follows: RF > SVM > LR. © 2023 IEEE.

关键词： Forecasting

来源：评论

学校读者我要写书评

暂无评论

GRU-Aided Intra-Cluster Topology Mapping for Aeronautical Ad-Hoc Networks

GRU-Aided Intra-Cluster Topology Mapping for Aeronautical Ad...

引用

GLOBECOM 2022 - 2022 IEEE Global Communications Conference

作者： Tuğçe Bilen Berk Canberk Computer Engineering Department Artificial Intelligence and Data Engineering Department Faculty of Computer and Informatics Istanbul Technical University Istanbul Turkey School of Engineering and Built Environment Edinburgh Napier University

ISBN: (纸本)9781665435413

Aeronautical Ad-hoc Networks (AANET) is a fairly new concept that connects airplanes via wireless air-to-air links, allowing passengers to access the Internet during a flight. The unstable air-to-air link characteristics and ultra-dynamic topology become the main differences between AANETs and usual ad-hoc architectures. To handle these differences, the AANET topology could be created in the form of clusters by collecting airplanes having similar features under the same set. However, it is also difficult to sustain the cluster topologies since ultra-dynamic characteristics still affect them. Therefore, the current cluster topology must be continuously mapped to the airplanes to notify them as a part of sustainability. If we do not ensure the sustainability of the clusters, the packet transfer success of AANET is decreased with higher end-to-end latency. At that point, to solve this aircraft notification problem and map the current cluster topology to them at each timestamp, in this paper, we propose a Gated Recurrent Unit (GRU)-based topology mapping mechanism for AANETs. Here, the GRU can continuously notify the airplanes at each timestamp about topology changes. Therefore, airplanes can forget the old topology when it changes. Otherwise, the topology taken from the previous timestamp is continuously remembered if it does not change. Finally, the performance evaluations reveal that the GRU-aided topology mapping can achieve roughly 42% higher packet delivery ratio with 34% reduced end-to-end latency.

关键词： Wireless communication Performance evaluation Airplanes Network topology Atmospheric modeling Simulation Logic gates

来源：评论

学校读者我要写书评

暂无评论

Utilizing Machine Learning in Medical Diagnosis: Systematic Review and Empirical Analysis

Utilizing Machine Learning in Medical Diagnosis: Systematic ...

引用

Information Technology (ACIT)

作者： Raed Alazaidah Mohammad Hassan Lara Al-Rbabah Ghassan Samara Marina Yusof Ala'a Saeb Al-Sherideh Department of Data Science AI Faculty of Information Technology Zarqa University Zarqa Jordan Department of Computer Science Faculty of Information Technology Zarqa University Zarqa Jordan Department of Computer Engineering Faculty of Engineering The Hashemite University Zarqa Jordan College of Computer Informatics and Media Universiti Teknologi MARA (UiTM) Selanger Malaysia Department of Cyber Security Faculty of Information Technology Zarqa University Zarqa Jordan

During the last few years, several real-life applications have attempted to utilize the proven high capabilities of artificial intelligence in general and machine learning in particular. Machine learning has been utilized in several domains, such as spam detection, image recognition, recommendation systems, self-driving cars, and medical diagnosis. This paper aims to survey the most related work of utilizing machine learning in the domain of medical diagnosis. Moreover, the paper proposes a comparative analysis for identifying and determining the best classification model and feature selection method in mind of handling medical datasets. Hence, four different medical datasets have been used to train twenty-three classification models and four well-known feature selection methods with respect to several evaluation metrics such as Accuracy, True Positive ratio, False Positive ratio, Precision, and Recall. The results reveal that RandomForest, J48, and SMO classifiers are the best classifiers when it comes to handling medical datasets respectively. Furthermore, the Gain Ratio method is the best choice for handling the step of feature selection.

关键词：

来源：评论

学校读者我要写书评

暂无评论

Tuning a Mamdani Fuzzy Controller with an Imperialist Competitive Algorithm 7th

Tuning a Mamdani Fuzzy Controller with an Imperialist Compet...

引用

7th International Conference on Modelling and Development of Intelligent Systems, MDIS 2020

作者： Ciurea, Stelian Faculty of Engineering Department of Computer and Electrical Engineering Lucian Blaga University of Sibiu Sibiu Romania Research Center in Informatics and Information Technology Lucian Blaga University of Sibiu Sibiu Romania

ISBN: (纸本)9783030685263

We have implemented a fuzzy controller with a view to regulating a single-input and single-output second-order linear system. The fuzzy controller was a Mamdami proportional-derivative controller. To determine the parameters of the fuzzy controller we have used an imperialist competitive algorithm. This type of algorithm has a long running time so we implemented also a parallel version of the algorithm that we run on HPC Zamolxes located at the engineering faculty of "Lucian Blaga" University from Sibiu. Because we did not have on this computer a version of MATLAB allowing to write parallel algorithms, we implemented the entire application in the C language using the MPI library. © 2021, Springer Nature Switzerland AG.

关键词： Controllers

来源：评论

学校读者我要写书评

暂无评论

Agile Enterprise Geographic Information System (AEGIS) from Design and Development Perspective 8

Agile Enterprise Geographic Information System (AEGIS) from ...

引用

8th International Conference on Mechatronics engineering, ICOM 2022

作者： Taha Ali, Taha Alfadul Saeed, Rashid A. Khalifa, Othman O. Ali, Elmustafa Sayed Odeh, Nidhal Hayder, Gasim Hashim, Aisha A. Geo-informatics Centre Alzaiem Alazhari University Sudan Sudan Department of Computer Engineering College of Computers and Information Technology Taif University P.O. Box 11099 Taif21944 Saudi Arabia Department of Electrical and Electronics Engineering Faculty of Engineering International Islamic University Malaysia Malaysia The Libyan Center for Engineering Research and Information Technology Bani Walid Libya Sudan School of electrical computer and telecommunications engineering University of Wollongong Australia Kajang43000 Malaysia

ISBN: (数字)9781839538063

ISBN: (纸本)9781839538063

Agile Enterprise GIS Development (AEGISD) is an environment with a high collaborative feature, needs to be achieved by active communications between the stakeholders. Recently, a number of researchers and users have been interested in Enterprise GIS due to its capabilities in providing a number of services related to co-locate Business. The benefit of AEGISD is that it enables the distribution of GIS components and the possibility of integrating them with other technologies that achieve the concept of smart cities for smart vehicles automation and transportation. This paper reviews the concepts about developing the GIS systems from the prospective of "Agile", where it focus on five keys: development, migration, integration, adaptation, and training. It also explains the methodology used at several levels related to storage, design, implementation, security and integration capabilities. © 2022 IET Conference Proceedings. All rights reserved.

关键词： Geographic information systems

来源：评论

学校读者我要写书评

暂无评论

Optimized Vectorizing of Building Structures with Switch: High-Efficiency Convolutional Channel-Switch Hybridization Strategy

arXiv

引用

arXiv 2023年

作者： Lin, Moule Jing, Weipeng Li, Chao Jung, András College of Computer and Control Engineering Northeast Forestry University Harbin150040 China Faculty of Informatics Eötvös Loránd University Budapest1117 Hungary

The building planar graph reconstruction, a.k.a. footprint reconstruction, which lies in the domain of computer vision and geoinformatics, has been long afflicted with the challenge of redundant parameters in conventional convolutional models. Therefore, in this letter, we proposed an advanced and adaptive shift architecture, namely the"Switch" operator, which incorporates non-exponential growth parameters while retaining analogous functionalities to integrate local feature spatial information, resembling a high-dimensional convolution operation. The"Switch" operator, cross-channel operation, architecture implements the XOR operation to alternately exchange adjacent or diagonal features, and then blends alternating channels through a 1x1 convolution operation to consolidate information from different channels. The SwitchNN architecture, on the other hand, incorporates a group-based parameter-sharing mechanism inspired by the convolutional neural network process and thereby significantly reducing the number of parameters. We validated our proposed approach through experiments on the SpaceNet corpus, a publicly available dataset annotated with 2,001 buildings across the cities of Los Angeles, Las Vegas, and Paris. Our results demonstrate the effectiveness of this innovative architecture in building planar graph reconstruction from 2D building images. © 2023, CC BY-NC-ND.

关键词： Convolution

来源：评论

学校读者我要写书评

暂无评论

Prediction of Heart Disease Based on Robust Artificial Intelligence Techniques 6

Prediction of Heart Disease Based on Robust Artificial Intel...

引用

6th IEEE International Conference and Workshop Obuda on Electrical and Power engineering, CANDO-EPE 2023

ISBN: (纸本)9798350328752

Heart disease (HD) is a concerning health condition that demands immediate attention. This article employs three artificial intelligence classification algorithms: DT, NB, and SGD to address this issue. The primary goal is to determine the most effective algorithm for early-stage prediction of heart disease, using easily accessible patient information. A dataset of 290 simple input variables, including sex, age, blood sugar, fasting blood sugar levels, and maximal heart rate, was collected from a hospital in Armenia. The results reveal that the DT algorithm outperforms NB and SGD in accuracy, with CAtrain of 0.96 and CAtest of 0.93. Accurate early prediction enables timely interventions and support for patients, improving healthcare management. Leveraging artificial intelligence, particularly DT, enables swift identification of potential heart disease cases, aiding healthcare professionals in providing essential care and preventive measures. Furthermore, the pragmatic approach of employing easily accessible patient data for prediction underscores the feasibility and applicability of the proposed methodology. By relying on readily available and cost-effective information, medical practitioners can efficiently screen and diagnose individuals at risk of heart disease, leading to improved healthcare management and better patient outcomes. © 2023 IEEE.

关键词： Decision trees

来源：评论

学校读者我要写书评

暂无评论

引用

8th International Iranian Conference on Signal Processing and Intelligent Systems, ICSPIS 2022

作者： Kazemzadeh, Farzaneh Afsharian, Sanaz Safaei, Ali Asghar Mirzarezaee, Mitra Islamic Azad University Department of Computer Engineering Science and Research branch Tehran Iran Islamic Azad University English Department Damavand Branch Tehran Iran Tarbiat Modares University Faculty of Medical Sciences Department of Medical Informatics Tehran Iran

ISBN: (纸本)9781665476232

Following the invention of the Internet and its expansion among all the people of the world, many software products, sites, games, and even hardware was presented by scientists, programmers, and manufacturers, whose use today is one of the essential and irreplaceable needs. It has become part of people's daily lives. Along with all the ways and means of using the Internet, social networks also spread among people with great power and speed. Today, most people are familiar with social networks and use them differently according to their needs. One of the ways to spread information is using social networks. Through social networks, many users can be informed about information such as news, advertisement, and many other things. This diffusion of information among users has been investigated by the problem of influence maximization to provide a solution to spread the diffusion in the social network. JACCIM algorithm, after detecting communities and selecting candidate nodes from overlapping and non-overlapping nodes based on topology criteria including Jaccard coefficient, has selected a set of k nodes. This collection has been able to maximize the diffusion of information in the network. With experiments on the proposed algorithm, comparing it with some other algorithms, and checking the results, the proposed algorithm has shown a significant impact on its execution time. © 2022 IEEE.

关键词： Topology

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：