检索结果-内蒙古大学图书馆

Congress on Evolutionary Computation

作者： Yuto Inokuchi Pedro Henrique González Israel Mendonça Masayoshi Aritsugi Graduate School of Science and Technology Kumamoto University Kumamoto Japan System Engineering and Computer Science Program Federal University of Rio de Janeiro Rio de Janeiro Brazil Faculty of Advanced Science and Technology Kumamoto University Kumamoto Japan

ISBN: (数字)9798350308365

ISBN: (纸本)9798350308372

While Spatio-Temporal Graph Convolutional Networks (STGCNs) are an effective method for traffic speed fore-casting, their training and inference tend to be time-consuming. In this paper, we aim to refine these networks by strategically reducing their number of nodes, thereby boosting computational efficiency. The nodes in these graphs represent data observed for road segments, and by analyzing the interconnections and layout of the graph, we can identify nodes with minimal contribution to overall performance. Removing these nodes can potentially decrease computation time while maintaining the prediction accuracy. We employ the Biased Random-Key Genetic Algorithm (BRKGA) to identify a good set of nodes for removal, based on a predefined percentage reduction of the original graph size (e.g., retaining 95 % of the original graph). We evaluate different graph size configurations, ranging from 95 % to 70 % node retention, to determine the least impactful node set performance. Our experiments on three real-world datasets reveal that reducing nodes can decrease computation time by up to 29%, and as a byproduct of removing noise, even improve the prediction accuracy.

关键词： Training Accuracy Graph convolutional networks Roads Noise Layout Evolutionary computation

来源：评论

学校读者我要写书评

暂无评论

Efficient Hyperparameter Optimization Using Deep Q-Network and BRKGA

Efficient Hyperparameter Optimization Using Deep Q-Network a...

引用

Congress on Evolutionary Computation

作者： Kosei Kobayashi Masayoshi Aritsugi Pedro Henrique González Israel Mendonça Graduate School of Science and Technology Kumamoto University Kumamoto Japan Faculty of Advanced Science and Technology Kumamoto University Kumamoto Japan System Engineering and Computer Science Program Federal University of Rio de Janeiro Rio de Janeiro Brazil

ISBN: (数字)9798350308365

ISBN: (纸本)9798350308372

Hyperparameter optimization (HPO) is paragon to maximize performance when designing machine learning models. Among different HPO methods, Genetic Algorithm (GA) based optimization is considered effective because it allows a wide and diverse range of solutions to be explored. However, GA's exploratory nature makes this type of algorithm to evaluate many solutions that do not improve the overall performance. This is specially costly when the objective function to be evaluated is time-consuming, like in the HPO field. In this paper, we propose an efficient hybrid algorithm that is able to reduce computational cost by combining deep reinforcement learning with the Biased Random Key Genetic Algorithm (BRKGA), a variant of genetic algorithms. Our reinforcement learning agent has a decision-making role during the population's fitness calculation, in which it filters out chromosomes that would not improve the overall fitness of the population. The agent uses small amounts of pre-trained data to identify trends in potentially good solutions, and carry out its decision process. We conduct experiments on eight different datasets to assess the effectiveness of the proposed method, and the results show that the proposed method can significantly reduce the computation time of hyperparameter search using BRKGA (up to 44% reduction in computational time) without compromising the quality of the solution (no statistically difference in results).

关键词： Machine learning algorithms Filters Sociology Evolutionary computation Hyperparameter optimization Market research Linear programming

来源：评论

学校读者我要写书评

暂无评论

Evaluation of the Application of Hospital Management Information system (SIMRS) in RSUD Dr. Kanujoso Djatiwibowo Using the HOT-Fit Method 3

Evaluation of the Application of Hospital Management Informa...

引用

3rd Borneo International Conference on Applied Mathematics and Engineering 2020, BICAME 2020

作者： Setiorini, Adani Natasia, Sri R Wiranti, Yuyun Tri Ramadhan, Dean A Department of Information System Majoring in Mathematics and Information Technology Kalimantan Institute of Technology Balikpapan Indonesia Department of Computer Science Faculty of Mathematics and Natural Sciences IPB University Bogor Indonesia

Hospital Management Information system (SIMRS) is a system that can process and integrate the entire hospital service process flow in the form of a network of coordination, reporting and administrative procedures to obtain accurate data and information. At the RSUD Dr. Kanujoso Djatiwibowo (RSKD) of Balikpapan City, the application of SIMRS experienced problems such as the high number of troubleshooting cases and the redundancy of patient medical record numbers. This case is consideredto inhibit the performance of hospital services related to efficiency. Based on this problem, the evaluation of SIMRS in the RSKD was carried out by using the constructed Human, Organization, technology-Fit (HOT-Fit) model. This model involves eight variables which consists of system Quality, Information Quality, and Service Quality, system Use, User Satisfaction, Structure, and Environment, and Net Benefits. The results indicate that the proposed model either directly or indirectly affectsthe benefits obtained from implementing the SIMRS. Further implications of the results toward improvement of the system are provided. © Published under licence by IOP Publishing Ltd.

关键词： Information management

来源：评论

学校读者我要写书评

暂无评论

Deep Learning for Automatic Determination of Bone Age in Children 1

Deep Learning for Automatic Determination of Bone Age in Chi...

引用

1st International Engineering Conference on Electrical, Energy, and Artificial Intelligence, EICEEAI 2022

作者： Khomand, Pouya Sabeti, Malihe Boostani, Reza Moradi, Ehsan Odeh, Mahmoud Al-Mousa, Mohammad Rasmi Islamic Azad University North Tehran Branch Deptartment of Computer Engineering Tehran Iran Shiraz University Faculty of Electrical and Computer Engineering CSE and IT Deptartmnet Shiraz Iran Shahid Beheshti University of Medical Sciences Department of Neurosurgery Tehran Iran Zarqa University Department of Management Information System Jordan Zarqa University Faculty of Information Technology Department of Cyber Security Zarqa Jordan

ISBN: (纸本)9798350332742

Skeletal bone age assessment (SBAA) is very important for both sides of parents and physicians to evaluate the irregular growth of children. SBAA process is carried out by radiologists who visually inspect the radiology image of the left hand according to the Greulich and Pyle (GP) or the Tanner-Whitehouse 2 (TW2) methods. However, human eyes have their own limitations and therefore the visual inspection procedure by radiologist involves a degree of error and also intra personal variability. To address these drawbacks, a deep learning-based approach is proposed here to precisely act on these X-ray images. The employed database contains 1391 X-ray left-hand image from Los Angeles children's hospital and 200 left hand x-ray image from different age and gender from Iranian children from Namazi hospital of Shiraz. Our results demonstrate the efficiency of proposed model (mean absolute error of 0.89) in this field. © 2022 IEEE.

关键词： Hospitals

来源：评论

学校读者我要写书评

暂无评论

Food Horticultural Supply Chain Performance Efficiency using Hybrid Model: SCOR-system Dynamic Simulation

Food Horticultural Supply Chain Performance Efficiency using...

引用

2022 International Seminar on Application for technology of Information and Communication, iSemantic 2022

作者： Yusianto, Rindra Casnan, Casnan Sudibyo, Usman Suprihatin, Marimin Hardjomidjojo, Hartrisari Dian Nuswantoro University Faculty of Engineering Department of Industrial Engineering Central Java Semarang53192 Indonesia STKIP Muhammadiyah Kuningan Department of Mathematics Education West Java Kuningan45552 Indonesia Dian Nuswantoro University Faculty of Computer Science Department of Information System Central Java Semarang53192 Indonesia Faculty of Agricultural Engineering and Technology Department of Agro-Industrial Technology West Java Bogor16002 Indonesia

ISBN: (数字)9781665488396

ISBN: (纸本)9781665488396

Today's highly competitive and dynamic business environment has forced horticultural food commodity farmers to implement better supply chain management (SCM) strategies. An essential aspect of SCM is how farmers and collectors can detect changes in supply chain (SC) behavior;these changes affect the efficiency of SC performance. This study aims to improve food horticultural SC performance efficiency. We propose a hybrid method using the supply chain operation reference (SCOR) and system dynamics simulation. To illustrate the methodology, we use a case study of the potato agroindustry in Wonosobo District, Central Java, Indonesia. The results show that the SC performance measurement consists of six perspectives, namely Plan, Source, Make, Deliver, Return, and Enable, with 23 KPIs. The SC value in January-March 2022 is 71.01%, meaning that the average measurement results are in a suitable category. The best scenario we propose is Scenario I, which is a reduction in indirect performance labor costs of IDR 600,000 in three months and an increase in efficiency of 11.08%. For further research, researchers can use this method to increase efficiency in the food processing industry made from food horticultural commodities. © 2022 IEEE.

关键词： Supply chain management

来源：评论

学校读者我要写书评

暂无评论

Python package for causal discovery based on LiNGAM

The Journal of Machine Learning Research

引用

The Journal of Machine Learning Research 2023年第1期24卷 483-490页

作者： Takashi Ikeuchi Mayumi Ide Yan Zeng Takashi Nicholas Maeda Shohei Shimizu SCREEN Advanced System Solutions Co. Ltd. Japan Department of Computer Science and Technology Tsinghua University China School of System Design and Technology Tokyo Denki University Japan and Center for Advanced Intelligence Project RIKEN Japan Faculty of Data Science Shiga University Japan and Center for Advanced Intelligence Project RIKEN Japan

Causal discovery is a methodology for learning causal graphs from data, and LiNGAM is a well-known model for causal discovery. This paper describes an open-source Python package for causal discovery based on LiNGAM. The package implements various LiNGAM methods under different settings like time series cases, multiple-group cases, mixed data cases, and hidden common cause cases, in addition to evaluation of statistical reliability and model assumptions. The source code is freely available under the MIT license at https://***/cdt15/lingam.

关键词： causal structure learning statistical reliability model evaluation

来源：评论

学校读者我要写书评

暂无评论

Opportunities and Conditions in Education on Blockchain Gamification technology

Opportunities and Conditions in Education on Blockchain Gami...

引用

2022 IEEE Creative Communication and Innovative technology, ICCIT 2022

作者： Tjahjono, Budi Soleman, Muhammad Dede Azmi, Aulia Millah, Shofiyul Andriani, Riri Dewi, Elisa Royani Esa Unggul University Computer Science Faculty Tangerang Indonesia University of Raharja Webcenter Sentra Solusindo Ltd. Tangerang Indonesia University of Raharja Department of Science and Technology Tangerang Indonesia University of Raharja Department of Information System Tangerang Indonesia

ISBN: (纸本)9781665468763

In this disruptive 4.0 era, many generations are competing in creating a new innovation, it makes technological growth increase sharply, one of which is blockchain technology. This technology has ledgers that are simultaneously decentralized and distributed. There is a node that functions to record all transaction histories on the Blockchain with all different networks. Blockchain This is certainly a very practical and safe technology for the world of education, one of which is Gamification. Gamification itself means a concept that has layers of features like a game that is connected or integrated with the blockchain. Gamification provides opportunities for the world of education by creating game elements that focus on the orientation of creating new educational spaces. This study uses the analisis Gamifikasi method and SWOT analysis to determine the overall use of the system starting from the strategies, opportunities, strengths and weaknesses. With this research, it is hoped that gamification in the blockchain system can run optimally in improving the implementation of Blockchain technology in the world of education. The novelty of this research is to apply a concept that has a feature layer such as a game that is connected or integrated with a block chain. © 2022 IEEE.

关键词： Blockchain

来源：评论

学校读者我要写书评

暂无评论

Optimal Deep Learning Model Enabled Secure UAV Classification for Industry 4.0

引用

computers, Materials & Continua 2023年第3期74卷 5349-5367页

作者： Khalid A.Alissa Mohammed Maray Areej A.Malibari Sana Alazwari Hamed Alqahtani Mohamed K.Nour Marwa Obbaya Mohamed A.Shamseldin Mesfer Al Duhayyim SAUDI ARAMCO Cybersecurity Chair Networks and Communications DepartmentCollege of Computer Science and Information TechnologyImam Abdulrahman bin Faisal UniversityP.O.Box 1982Dammam31441Saudi Arabia Department of Information Systems College of Computer ScienceKing Khalid UniversityAbhaSaudi Arabia Department of Industrial and Systems Engineering College of EngineeringPrincess Nourah bint Abdulrahman UniversityP.O.Box 84428Riyadh11671Saudi Arabia Department of Information Technology College of Computers and Information TechnologyTaif UniversityTaif P.O.Box 11099Taif21944Saudi Arabia Department of Information Systems College of Computer ScienceCenter of Artificial IntelligenceUnit of CybersecurityKing Khalid UniversityAbhaSaudi Arabia Department of Computer Sciences College of Computing and Information SystemUmm Al-Qura UniversitySaudi Arabia Department of Biomedical Engineering College of EngineeringPrincess Nourah bint Abdulrahman UniversityP.O.Box 84428Riyadh11671Saudi Arabia Department of Mechanical Engineering Faculty of Engineering and TechnologyFuture University in EgyptNew Cairo11835Egypt Department of Computer Science College of Sciences and Humanities-AflajPrince Sattam bin Abdulaziz UniversitySaudi Arabia

Emerging technologies such as edge computing,Internet of Things(IoT),5G networks,big data,Artificial Intelligence(AI),and Unmanned Aerial Vehicles(UAVs)empower,Industry 4.0,with a progressive production methodology that shows attention to the interaction between machine and human *** the literature,various authors have focused on resolving security problems in UAV communication to provide safety for vital *** current research article presents a Circle Search Optimization with Deep Learning Enabled Secure UAV Classification(CSODL-SUAVC)model for Industry 4.0 *** suggested CSODL-SUAVC methodology is aimed at accomplishing two core objectives such as secure communication via image steganography and image ***,the proposed CSODL-SUAVC method involves the following methods such as Multi-Level Discrete Wavelet Transformation(ML-DWT),CSO-related Optimal Pixel Selection(CSO-OPS),and signcryption-based *** proposed model deploys the CSO-OPS technique to select the optimal pixel points in cover *** secret images,encrypted by signcryption technique,are embedded into cover ***,the image classification process includes three components namely,Super-Resolution using Convolution Neural Network(SRCNN),Adam optimizer,and softmax *** integration of the CSO-OPS algorithm and Adam optimizer helps in achieving the maximum performance upon UAV *** proposed CSODLSUAVC model was experimentally validated using benchmark datasets and the outcomes were evaluated under distinct *** simulation outcomes established the supreme better performance of the CSODL-SUAVC model over recent approaches.

关键词： Unmanned Aerial Vehicles Artificial Intelligence emerging technologies Deep Learning Industry 4.0 image steganography

来源：评论

学校读者我要写书评

暂无评论

Two-Layer SVM, Towards Deep Statistical Learning 1

Two-Layer SVM, Towards Deep Statistical Learning

引用

1st International Engineering Conference on Electrical, Energy, and Artificial Intelligence, EICEEAI 2022

作者： Kazemi, Alireza Boostani, Reza Odeh, Mahmoud Al-Mousa, Mohammad Rasmi Shiraz University Shiraz Faculty of Science Mathematics Department Fars Iran Electrical and Computer Engineering School Shiraz University Shiraz CSEandIT Dept. Fars Iran Zarqa University Department of Management Information System Jordan Zarqa University Faculty of Information Technology Department of Cyber Security Zarqa Jordan

ISBN: (纸本)9798350332742

Support Vector Machine (SVM) is originally a binary large-margin classifier emerged from the concept of structural risk minimization. Multiple solutions such as one-versus-one and one-versus-all have been proposed for creating multi-class SVM using elementary binary SVMs. Also multiple solutions have been proposed for SVM model selection, adjusting margin-parameter C and the Gaussian kernel variance. Here, an improved classifier named SVM-SVM is proposed for multi-class problems which increases accuracy and decreases dependency to margin-parameter selection. SVM-SVM adopts two K-class one-vs-one SVMs in a cascaded two-layer structure. In the first layer, input features are fed to one-vs-one SVM with non-linear kernels. We introduce this layer as a large-margin non-linear feature transform that maps input feature space to a discriminative K(K-1)/2 dimensional space. To assess our hierarchical classifier, some datasets from the UCI repository are evaluated. Standard one-vs-one SVM and one-vs-one fuzzy SVM are used as reference classifiers in experiments. Results show significant improvements of our proposed method in terms of test accuracy and robustness to the model (margin and kernel) parameters in comparison with the reference classifiers. Our observations suggest that a multi-layer (deep) SVM structures can gain the same benefits as is seen in the deep neural nets (DNNs). © 2022 IEEE.

关键词： Support vector machines

来源：评论

学校读者我要写书评

暂无评论

Real-Time Violent Action Recognition Using Key Frames Extraction and Deep Learning

引用

computers, Materials & Continua 2021年第11期69卷 2217-2230页

作者： Muzamil Ahmed Muhammad Ramzan Hikmat Ullah Khan Saqib Iqbal Muhammad Attique Khan Jung-In Choi Yunyoung Nam Seifedine Kadry Deparment of Computer Science and Information Technology The University of LahoreSargodha CampusSargodha40100Pakistan Department of Computer Science COMSATS University IslamabadWah CampusWah Cantt47040Pakistan School of System and Technology University of Management and TechnologyLahore54782Pakistan Department of Computer Science and Information Technology University of SargodhaSargodha40100Pakistan College of Engineering Al Ain UniversityAl AinUnited Arab Emirates Department of Computer Science HITEC University TaxilaTaxilaPakistan Applied Artificial Intelligence Ajou UniversitySuwonKorea Department of Computer Science and Engineering Soonchunhyang UniversityAsanKorea Department of Mathematics and Computer Science Faculty of ScienceBeirut Arab UniversityLebanon

Violence recognition is crucial because of its applications in activities related to security and law *** semi-automated systems have issues such as tedious manual surveillances,which causes human errors and makes these systems less *** approaches have been proposed using trajectory-based,non-object-centric,and deep-learning-based *** studies have shown that deep learning techniques attain higher accuracy and lower error rates than those of other ***,the their performance must be *** study explores the state-of-the-art deep learning architecture of convolutional neural networks(CNNs)and inception V4 to detect and recognize violence using video *** the proposed framework,the keyframe extraction technique eliminates duplicate consecutive *** keyframing phase reduces the training data size and hence decreases the computational cost by avoiding duplicate *** feature selection and classification tasks,the applied sequential CNN uses one kernel size,whereas the inception v4 CNN uses multiple kernels for different layers of the *** empirical analysis,four widely used standard datasets are used with diverse *** results confirm that the proposed approach attains 98%accuracy,reduces the computational cost,and outperforms the existing techniques of violence detection and recognition.

关键词： Violence detection violence recognition deep learning convolutional neural network inception v4 keyframe extraction

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：