检索结果-内蒙古大学图书馆

Deep Reinforcement Learning for Addressing Disruptions in Traffic Light Control

computers, Materials & Continua 2022年第5期71卷 2225-2247页

作者： Faizan Rasheed Kok-Lim Alvin Yau Rafidah Md Noor Yung-Wey Chong School of Engineering and Computer Science University of HertfordshireHatfieldAL109ABUK Department of Computing and Information Systems Sunway UniversitySubang Jaya47500Malaysia Faculty of Computer Science and Information Technology University of MalayaKuala Lumpur50603Malaysia National Advanced IPv6 Centre Universiti Sains MalaysiaUSMPenang11800Malaysia

This paper investigates the use of multi-agent deep Q-network(MADQN)to address the curse of dimensionality issue occurred in the traditional multi-agent reinforcement learning(MARL)*** proposed MADQN is applied to traffic light controllers at multiple intersections with busy traffic and traffic disruptions,particularly *** is based on deep Q-network(DQN),which is an integration of the traditional reinforcement learning(RL)and the newly emerging deep learning(DL)*** enables traffic light controllers to learn,exchange knowledge with neighboring agents,and select optimal joint actions in a collaborative manner.A case study based on a real traffic network is conducted as part of a sustainable urban city project in the Sunway City of Kuala Lumpur in *** is also performed using a grid traffic network(GTN)to understand that the proposed scheme is effective in a traditional traffic *** proposed scheme is evaluated using two simulation tools,namely Matlab and Simulation of Urban Mobility(SUMO).Our proposed scheme has shown that the cumulative delay of vehicles can be reduced by up to 30%in the simulations.

关键词： Artificial intelligence traffic light control traffic disruptions multi-agent deep Q-network deep reinforcement learning

来源：评论

学校读者我要写书评

暂无评论

Development of a visual programming tool called SmrAI for function testing to mitigate the shortage of programming personnel in the manufacturing

Computer Software

引用

computer Software 2022年第4期39卷 61-74页

作者： Sugiyama, Ko-Ichiro Okuda, Satsuki Tanabe, Tsubasa Takao, Kouji Higashi, Hirohito Tanaka, Kazuaki Dept. of Information Engineering National Institute of Technology Matsue College Japan NIPPO Co. Ltd. Japan Ruby Programming Shounendan Japan Shimane IT Open-innovation Center Japan Faculty of Computer Science and Systems Engineering Kyushu Institute of Technology Japan

We have developed a visual programming tool called SmrAI for function testing, and investigated whether wrapping existing source code with Scratch Blocks is an effective means of mitigating the shortage of programming personnel in the manufacturing. SmrAI is based on Smalruby, which can convert Scratch Blocks into Ruby code. In addition, SmrAI introduces two new sets of Blocks that wrap existing Ruby libraries for function testing. Since existing tools and libraries were utilized, the cost of developing SmrAI was low, and there were no changes in the function testing procedure. We conducted a verification test using staff members who were familiar with the function testing process but had little or no programming experience, and confirmed that they were able to program using SmrAI and that they had confidence in its business applications. These results are expected to contribute to the diversification and increase of programming personnel responsible for function test programming. © 2022 Japan Society for Software science and Technology. All rights reserved.

关键词： Application programs

来源：评论

学校读者我要写书评

暂无评论

Entropy Based Feature Fusion Using Deep Learning for Waste Object Detection and Classification Model

引用

computer systems science & engineering 2023年第12期47卷 2953-2969页

作者： Ehab Bahaudien Ashary Sahar Jambi Rehab B.Ashari Mahmoud Ragab Electrical and Computer Engineering Department Faculty of EngineeringKing Abdulaziz UniversityJeddah21589Saudi Arabia Information Systems Department Faculty of Computing and Information TechnologyKing Abdulaziz UniversityJeddah21589Saudi Arabia Information Technology Department Faculty of Computing and Information TechnologyKing Abdulaziz UniversityJeddah21589Saudi Arabia Department of Mathematics Faculty of ScienceAl-Azhar UniversityNaser CityCairo11884Egypt

Object Detection is the task of localization and classification of objects in a video or *** recent times,because of its widespread applications,it has obtained more *** the modern world,waste pollution is one significant environmental *** prominence of recycling is known very well for both ecological and economic reasons,and the industry needs higher *** object detection utilizing deep learning(DL)involves training a machine-learning method to classify and detect various types of waste in videos or *** technology is utilized for several purposes recycling and sorting waste,enhancing waste management and reducing environmental *** studies of automatic waste detection are difficult to compare because of the need for benchmarks and broadly accepted standards concerning the employed data ***,this study designs an Entropy-based Feature Fusion using Deep Learning forWasteObject Detection and Classification(EFFDL-WODC)*** presented EFFDL-WODC system inherits the concepts of feature fusion and DL techniques for the effectual recognition and classification of various kinds of waste *** the presented EFFDL-WODC system,two major procedures can be contained,such as waste object detection and waste object *** object detection,the EFFDL-WODC technique uses a YOLOv7 object detector with a fusionbased backbone *** addition,entropy feature fusion-based models such as VGG-16,SqueezeNet,and NASNetmodels are ***,the EFFDL-WODC technique uses a graph convolutional network(GCN)model performed for the classification of detected waste *** performance validation of the EFFDL-WODC approach was validated on the benchmark *** comprehensive comparative results demonstrated the improved performance of the EFFDL-WODC technique over recent approaches.

关键词： Object detection object classification waste management deep learning feature fusion

来源：评论

学校读者我要写书评

暂无评论

Comparing Time-Series Analysis Approaches Utilized in Research Papers to Forecast COVID-19 Cases in Africa: A Literature Review

arXiv

引用

arXiv 2023年

作者： Ebadi, Ali Sahafizadeh, Ebrahim Department of Computer Engineering Faculty of Intelligent Systems Engineering and Data Science Persian Gulf University Bushehr Iran

This literature review aimed to compare various time-series analysis approaches utilized in forecasting COVID-19 cases in Africa. The study involved a methodical search for English-language research papers published between January 2020 and July 2023, focusing specifically on papers that utilized time-series analysis approaches on COVID-19 datasets in Africa. A variety of databases including PubMed, Google Scholar, Scopus, and Web of science were utilized for this process. The research papers underwent an evaluation process to extract relevant information regarding the implementation and performance of the time-series analysis models. The study highlighted the different methodologies employed, evaluating their effectiveness and limitations in forecasting the spread of the virus. The result of this review could contribute deeper insights into the field, and future research should consider these insights to improve time series analysis models and explore the integration of different approaches for enhanced public health decision-making. © 2023, CC BY-NC-ND.

关键词： Coronavirus

来源：评论

学校读者我要写书评

暂无评论

Analysis and Assessment of Wind Energy Potential of Al-Hodeidah in Yemen

引用

computers, Materials & Continua 2021年第11期69卷 1995-2011页

作者： Fahd N.Al-Wesabi Murad A.Almekhlafi Mohammed Abdullah Al-Hagery Mohammad Alamgeer Khalid Mahmood Majdy M.Eltahir Ali M.Al-Sharafi Amin M.El-Kustaban Department of Computer Science King Khalid UniversityMuhayel AseerKingdom of Saudi Arabia Faculty of Computer and IT Sana’a UniversitySana’aYemen Department of Electrical Engineering–Communication Engineering Sana’a UniversityYemen Department of Computer Science College of ComputerQassim UniversitySaudi Arabia Department of Information Systems King Khalid UniversityMayahel AseerKSA Department of Computer Science College of Computers and Information TechnologyUniversity of BishaKSA Department of Electronic Faculty of EngineeringUSTSana’aYemen

Renewable energy is one of the essential elements of the social and economic development in any civilized *** use of fossil fuels and the non-renewable form of energy has many adverse effects on the most of *** the high potential of renewable energy sources in Yemen and the absence of similar studies in the region,this study aimed to examine the wind energy potential of Hodeidah-Yemen Republic by analyzing wind characteristics and assessment,determining the available power density,and calculate the wind energy extracted at different *** average wind speed of Hodeidah was obtained only for the data currently available for the five years 2005–2009(due to the current economic and the political situation in Yemen).The results show that the average wind speed in the five years is(25.2 W/m2 at 10 m,93.9 W/m2 at 30 m,and 173.5 W/m2 at 50 m).The average yearly wind power density(25.2 W/m2 at 10 m,93.9 W/m2 at 30 m and 173.5 W/m2 at 50m),and the average yearly energy density(220.8 KWh/m2/year at 10 m,822.6 KWh/m2/year at 30 m and 1519.9 KWh/m2/year at 50 m).This research is a preliminary assessment of the potential of wind energy in Hodeidah,which provides useful information for developing wind energy and an efficient wind *** to the International Wind Energy Rating criteria,the region of Hodeidah falls under‘Class 2’and is classified as‘Marginal’for most of the year.

关键词： Energy potential wind speed weibull distribution wind power density

来源：评论

学校读者我要写书评

暂无评论

A Light Field Image Quality Assessment Method Based on Stereo Vision

A Light Field Image Quality Assessment Method Based on Stere...

引用

2022 IEEE International Conference on Unmanned systems, ICUS 2022

作者： Fu, Wei Shen, Xingfa Zhou, Wenhui Zhdanov, Andrei D. Geng, Chuntong Itmo Joint Institute Hangzhou Dianzi University Department of Computer Science Hangzhou China School of Computer Science and Technology Hangzhou Dianzi University Hangzhou China Itmo University Faculty of Software Engineering and Computer Systems St.Petersburg Russia

ISBN: (数字)9781665484565

ISBN: (纸本)9781665484565

Light field imaging is an important achievement in visual information exploration in recent years, which can capture more abundant visual information from the real world. However, most existing light field image quality assessment (LF-IQA) indicators rely heavily on feature extraction based on high complexity statistics in quality evaluation tasks, which is not comprehensive in future modern applications or power-limited devices. To solve this problem, the research puts forward a light field image quality evaluation method based on stereo vision. By introducing a brand-new light field image coding method and training the neural network, the purpose of image quality evaluation is finally achieved. Some experiments show that our model achieves good results in open source data sets. © 2022 IEEE.

关键词： Stereo vision

来源：评论

学校读者我要写书评

暂无评论

A novel method for the detection and classification of multiple diseases using transfer learning-based deep learning techniques with improved performance

引用

Neural Computing and Applications 2024年第30期36卷 18979-18997页

作者： Natarajan, Krishnamoorthy Muthusamy, Suresh Sha, Mizaj Shabil Sadasivuni, Kishor Kumar Sekaran, Sreejith Charles Gnanakkan, Christober Asir Rajan A.Elngar, Ahmed School of Computer Science Engineering and Information Systems Vellore Institute of Technology Tamil Nadu Vellore India Tamil Nadu Perundurai Erode India Centre for Advanced Materials Qatar University Doha Qatar Department of Mechanical and Industrial Engineering Qatar University PO Box 2713 Doha Qatar Department of Electrical Engineering National Institute of Technology Assam Silchar India Department of Electrical and Electronics Engineering Puducherry Technological University Puducherry India Faculty of Computers and Artificial Intelligence Beni-Suef University Beni-Suef City62511 Egypt

A disease is a distinct abnormal state that significantly affects the functioning of all or part of an individual and is not caused by external harm. Diseases are frequently understood as medical conditions connected with distinct indications and symptoms. According to a fairly wide categorization, diseases can also be categorized as mental disorders, deficient diseases, genetic diseases, degenerative diseases, self-inflicted diseases, infectious diseases, non-infectious diseases, social diseases, and physical diseases. Prevention of the diseases is of multiple instances. Primary prevention seeks to prevent illness or harm before it ever happens. Secondary prevention tries to lessen the effect of an illness or damage that has already happened. This is done through diagnosing and treating illness or injury as soon as feasible to stop or delay its course, supporting personal ways to avoid recurrence or reinjury, and implementing programs to restore individuals to their previous health and function to prevent long-term difficulties. Tertiary prevention tries to lessen the impact of a continuing sickness or injury that has enduring repercussions. Diagnosis of the disease at an earlier stage is important for the treatment of the disease. Hence, in this study, deep learning algorithms, such as VGG16, EfficientNetB4, and ResNet, are utilized to diagnose various diseases, such as Alzheimer's, brain tumors, skin diseases, and lung diseases. Chest X-rays, MRI scans, CT scans, and skin lesions are used to diagnose the mentioned diseases. Transfer learning algorithms, such as VGG16, VGG19, ResNet, InceptionV3, and EfficientNetB4, are utilized to categorize various diseases. EfficientNetB4 with the learning rate annealing, having obtained an accuracy of 94.04% on the test dataset, is observed. As a consequence, we observed that every network has unique particular skills on the multi-disease dataset, which includes chest X-rays, MRI scans, etc., © The Author(s) 2024.

关键词： Diseases

来源：评论

学校读者我要写书评

暂无评论

Identifying Inflammatory Bowel Disease-Associated Gene Ontology Groups Using Biological Knowledge-Based Machine Learning

Identifying Inflammatory Bowel Disease-Associated Gene Ontol...

引用

18th International Joint Conference on Biomedical engineering systems and Technologies, BIOSTEC 2025

作者： Ersoz, Nur Sebnem Bakir-Gungor, Burcu Yousef, Malik Department of Bioengineering Graduate School of Engineering and Science Abdullah Gul University Kayseri Turkey Department of Computer Engineering Faculty of Engineering Abdullah Gul University Kayseri38080 Turkey Department of Bioengineering Faculty of Life and Natural Sciences Abdullah Gul University Kayseri Turkey Department of Information Systems Zefat Academic College Zefat Israel Zefat Academic College Zefat Israel

Inflammatory bowel disease (IBD) is a chronic inflammatory disease. Complex pathogenesis behind disease formation and progression necessitated the development of new approaches to identify disease related genes and affected gene ontology (GO) terms. In this study, via exploiting GeNetOntology method, we have reanalysed a gene expression data including Crohn’s Disease (CD) and Ulcerative colitis (UC) patients and controls. In order to identify IBD related genes and affected GO terms, GeNetOntology uses GO hierarchy as the biological domain knowledge while performing gene expression data analysis based on machine learning (ML). In the training part of GeNetOntology, genes annotated with selected ontology terms have been utilized to perform a two-class classification task which generates an important set of ontologies as an output. IBD data samples were obtained from peripheral blood and colon tissue. In order to investigate the effect of different collection sites, IBD data have been analysed under different scenarios;i.e., all samples, only tissue samples and only blood samples. Experimental findings indicate that GeNetOntology can successfully determine significant disease-related ontology terms. Performance of the model slightly differs according to the sample source. Via analysing the differences/commonalities between affected gene ontologies under different scenarios, we attempt to enlighten IBD development mechanisms. © 2025 by SCITEPRESS– science and Technology Publications, Lda.

关键词：

来源：评论

学校读者我要写书评

暂无评论

Energy efficient indoor localisation for narrowband internet of things

引用

CAAI Transactions on Intelligence Technology 2023年第4期8卷 1150-1163页

作者： Ismail Keshta Mukesh Soni Mohammed Wasim Bhatt Azeem Irshad Ali Rizwan Shakir Khan Renato RMaaliw III Arsalan Muhammad Soomar Mohammad Shabaz Computer Science and Information Systems Department College of Applied SciencesAlMaarefa UniversityRiyadhSaudi Arabia Department of CSE University Centre for Research and Development Chandigarh UniversityMohaliPunjabIndia Department of Computer Science and Engineering National Institute of TechnologySrinagarIndia Asghar Mall College Rawalpindi Higher Education Department(HED)Govt.of the PunjabRawalpindiPakistan Department of Industrial Engineering Faculty of EngineeringKing Abdulaziz UniversityJeddahSaudi Arabia College of Computer and Information Sciences Imam Mohammad Ibn Saud Islamic University(IMSIU)RiyadhSaudi Arabia College of Engineering Southern Luzon State UniversityLucbanQuezonPhilippines Department of Automation Electronics and Electrical EngineeringGdansk University of TechnologyGdańskPoland Arba Minch University Arba MinchEthiopia

There are an increasing number of Narrow Band IoT devices being manufactured as the technology behind them develops *** high co‐channel interference and signal attenuation seen in edge Narrow Band IoT devices make it challenging to guarantee the service quality of these *** maximise the data rate fairness of Narrow Band IoT devices,a multi‐dimensional indoor localisation model is devised,consisting of transmission power,data scheduling,and time slot scheduling,based on a network model that employs non‐orthogonal multiple access via a *** on this network model,the optimisation goal of Narrow Band IoT device data rate ratio fairness is first established by the authors,while taking into account the Narrow Band IoT network:The multidimensional indoor localisation optimisation model of equipment tends to minimize data rate,energy constraints and EH relay energy and data buffer constraints,data scheduling and time slot *** a result,each Narrow Band IoT device's data rate needs are met while the network's overall performance is *** investigate the model's potential for convex optimisation and offer an algorithm for optimising the distribution of multiple resources using the KKT *** current work primarily considers the NOMA Narrow Band IoT network under a single EH ***,the growth of Narrow Band IoT devices also leads to a rise in co‐channel interference,which impacts NOMA's performance *** simulation,the proposed approach is successfully *** improvements have boosted the network's energy efficiency by 44.1%,data rate proportional fairness by 11.9%,and spectrum efficiency by 55.4%.

关键词： artificial inteligence detection of moving objects internet of things

来源：评论

学校读者我要写书评

暂无评论

Identification of Damaged Date Palm Tree in a Farm using IoT-based Thermal Image Analysis

Identification of Damaged Date Palm Tree in a Farm using IoT...

引用

2023 IEEE International Conference on computer, Information, and Telecommunication systems, CITS 2023

作者： Nadeem, Adnan Ashraf, Muhammad Mehmood, Amir Siddiqui, Muhammad Shoaib Almoamari, Hani Abbasi, Qammer H. Islamic University of Madinah Faculty of Computer and Information Systems Madinah42351 Saudi Arabia Federal Urdu University of Arts Science and Technology Department of Physics Karachi75300 Pakistan Sir Syed University of Engineering and Technology Department of Computer Science and Information Technology Karachi75300 Pakistan James Watt School of Engineering University of Glasgow Glasgow United Kingdom

ISBN: (纸本)9798350336092

The Kingdom of Saudi Arabia is one of the world's leading fresh date producers. Identifying damaged date palm trees on the farm is important for saving water and stopping the spread of pest which has caused the damage. In this paper, we aim to evaluate the performance of IoT-based thermal image camera input for the automatic detection of damaged trees in a Date Tree farm. The thermal images will be obtained by an IoTbased thermal camera attached to UAV. Then the temperature histogram vector will be generated which will be fed to the SVM for identification of the damaged tree. The system will raise the alarm along with the coordinates of the damaged tree for the farmer to proactively deal with the issue. The initial results of the tested scenario are encouraging for its detailed implementation in the future. © 2023 IEEE.

关键词： Cameras

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：