检索结果-内蒙古大学图书馆

7th International Conference on information Management and Management Science, IMMS 2024

作者： Cheng, Xijie Feng, Weiqing Pang, Su Hu, Gang Cai, Shimin College of Management Science Chengdu University of Technology China Faculty of Computer Science & Information Technology Universiti Malaya Malaysia Sichuan Tobacco Company Chengdu China School of Computer Science and Engineering University of Electronic Science and Technology of China China

ISBN: (纸本)9798400716997

Traditional methods for assessing tobacco leaf quality rely heavily on experience and subjective judgment, resulting in inefficiency, and a lack of accuracy and reproducibility. To address these challenges, there is an urgent need for a tobacco leaf quality assessment system capable of assessing various aspects of leaf quality in a systematic and quantitative manner. In this paper, we quantitatively analyze the tobacco leaf quality assessment system for Sichuan tobacco, specifically focusing on the Yunyan 87 variety. In further, we employ Grey Relational Analysis and Circle Ill-Condition Index Analysis for indicator selection and optimization. Through quantitative analysis, key indicators that significantly impact the assessment results and exhibit low information redundancy are retained, and weights are reallocated based on correlation analysis. The optimized system retains four core indicators: Oil Content, Delicacy, Total Nicotine, and Sugar-to-Nicotine Ratio, significantly enhancing the scientific rigor and practical utility of the assessment process. This optimized assessment tool provides a scientific basis for quality control in Sichuan tobacco production, ultimately enhancing production efficiency and product quality. © 2024 ACM.

关键词： Quality control

来源：评论

学校读者我要写书评

暂无评论

Feature Model Configuration Reuse Scheme for Self-Adaptive Systems

引用

computers, Materials & Continua 2022年第4期71卷 1249-1262页

作者： Sumaya Alkubaisi Said Ghoul Oguz Ata Electrical and Computer Engineering Department Altinbas UniversityIstanbul34218Turkey Faculty of Information Technology Research Laboratory on Bio-Inspired Software EngineeringPhiladelphia UniversityAmman26160Jordan

Most large-scale systems including self-adaptive systems utilize feature models(FMs)to represent their complex architectures and benefit from the reuse of commonalities and variability ***-adaptive systems(SASs)are capable of reconfiguring themselves during the run time to satisfy the scenarios of the requisite ***,reconfiguration of SASs corresponding to each adaptation of the system requires significant computational time and *** process of configuration reuse can be a better alternative to some contexts to reduce computational time,effort and ***,systems’complexity can be reduced while the development process of systems by reusing elements or *** are considered one of the new ways of reuse process that are able to introduce new opportunities for the reuse process beyond the conventional system *** current FM-based modelling techniques represent,manage,and reuse elementary features to model SASs concepts,modeling and reusing configurations have not yet been *** this context,this study presents an extension to FMs by introducing and managing configuration features and their reuse *** results demonstrate that reusing configuration features reduces the effort and time required by a reconfiguration process during the run time to meet the required scenario according to the current context.

关键词： Self-adaptive system feature model system reuse configuration management variability modeling

来源：评论

学校读者我要写书评

暂无评论

Effect of loads on temperature distribution characteristics of oil-immersed transformer winding

引用

Distributed Generation and Alternative Energy Journal 2022年第2期37卷 237-254页

作者： Zhao, Zhengang Jiang, Zhangnan Li, Yang Li, Chuan Zhang, Dacheng Faculty of Information Engineering and Automation Kunming University of Science and Technology Kunming650000 China Yunnan Key Laboratory of Computer Technology Applications Kunming650000 China

The temperature of the hot-spots on windings is a crucial factor that can limit the overload capacity of the transformer. Few studies consider the impact of the load on the hot-spot when studying the hot-spot temperature and its location. In this paper, a thermal circuit model based on the thermoelectric analogy method is built to simulate the transformer winding and transformer oil temperature distribution. The hot-spot temperature and its location under different loads are qualitatively analyzed, and the hot-spot location is analyzed and compared to the experimental results. The results show that the hot-spot position on the winding under the rated power appears at 85.88% of the winding height, and the hot-spot position of the winding moves down by 5% in turn at 1.3, 1.48, and 1.73 times the rated power respectively. © 2021 River Publishers

关键词： Temperature distribution

来源：评论

学校读者我要写书评

暂无评论

Simulating a Finite-State Markov Chain for Rayleigh Block-Fading Channel based on its Mathematical Model, and Practical Representation 25

Simulating a Finite-State Markov Chain for Rayleigh Block-Fa...

引用

25th International Arab Conference on information technology, ACIT 2024

作者： Attar, Hani Ababneh, Jafar Al-Hihi, Mohammad Rachini, Ali Solyman, Ahmed Zarqa University Faculty of Engineering Zarqa Jordan University of Business and Technology College of Engineering Jeddah21448 Saudi Arabia Zarqa University Faculty of Information Technology Cyber Security Department Zarqa Jordan Erlangen Germany Department of Computer Science and Information Technology Jounieh Lebanon Glasgow Caledonian University School of Computing Engineering and Built Environment Glasgow United Kingdom

ISBN: (纸本)9798331540012

In the proposed paper, simulating the Finite State Markov Chain (FSMC) to be implemented as Rayleigh Block-fading channel. Indeed, FSMC is regarded as a practical fading channel because of its random choice for future transmission probability. Accordingly, this paper introduces the FSMC technology with the theoretical limits and equations;moreover, the uplink and downlink Rayleigh Block-fading channel scenarios are also investigated and supported by the simulation set-up. The results for the Rayleigh fading channel for the Long-Term Evaluation-Advanced network were obtained as an example of the channel's attitude. The findings demonstrate that FSMC provides such reliable results and is suitable for use as a practical fading channel, particularly in terms of burst error behavior. © 2024 IEEE.

关键词： Markov chains

来源：评论

学校读者我要写书评

暂无评论

Traffic Congestion Prediction Using Machine Learning: Amman City Case Study

Traffic Congestion Prediction Using Machine Learning: Amman ...

引用

2024 International Conference on Medical Imaging, Electronic Imaging, information Technologies, and Sensors, MIEITS 2024

作者： Arabiat, Areen Hassan, Mohammad Al Momani, Omar Department of Communication and Computer Engineering Faculty of Engineering Al Ahliyya Amman University Amman19111 Jordan Department of Networks security Faculty of Information Technology The World Islamic Sciences and Educations University WISE Amman Jordan

ISBN: (纸本)9781510680814

Traffic congestion leads to wasted time, pollution, and increased fuel usage. Traffic congestion prediction has become a developing research topic in recent years, particularly in the field of machine learning (ML). The evaluation of various traffic parameters is used to predict traffic congestion by relying on historical data. In this study, we will predict traffic congestion in Amman city specifically at Northbound Street at 8th Circle using different ML classifiers. The 8th Circle links four main streets: Westbound, Northbound, Eastbound, and Southbound. Because of the heavy traffic on Northbound, it was chosen to predict traffic congestion. Datasets were collected from the greater Amman municipality hourly based. Datasets were divided and per-process to be understandable. The Logistic Regression, Decision Tree (DT), Random Forest, and multilayer perceptron (MLP) classifiers have been chosen to predict traffic congestion at Northbound Street linked with the 8th Circle. Four experiments were run under The Waikato Environment for Knowledge Analysis (WEKA) tools to find the best classifiers to predict traffic congestion at Northbound. The chosen classifiers have been evaluated using F-Measure, sensitivity, precision, and accuracy evaluation metrics. The obtained results from all experiments have demonstrated the Logistic Regression is the best classifier to predict traffic congestion. The accuracy of Logistic Regression to predict traffic congestion at Northbound Street was97.6%. © 2024 SPIE.

关键词： Traffic congestion

来源：评论

学校读者我要写书评

暂无评论

Effective Return Rate Prediction of Blockchain Financial Products Using Machine Learning

引用

computers, Materials & Continua 2023年第1期74卷 2303-2316页

作者： K.Kalyani Velmurugan Subbiah Parvathy Hikmat A.M.Abdeljaber T.Satyanarayana Murthy Srijana Acharya Gyanendra Prasad Joshi Sung Won Kim PG and Research Department of Computer Science Marudupandiyar College(Affiliated to Bharathidasan UniversityTiruchirappalli)Thanjavur613403India Department of Electronics and Communication Engineering Kalasalingam Academy of Research and EducationKrishnankoil626126TamilnaduIndia Department of Computer Science Faculty of Information TechnologyApplied Science Private UniversityAmmanJordan Department of Information Technology Chaitanya Bharathi Institute of TechnologyHyderabadTelangana500075India Department of Convergence Science Kongju National UniversityGongju32588Korea Department of Computer Science and Engineering Sejong UniversitySeoul05006Korea Department of Information and Communication Engineering Yeungnam UniversityGyeongsan-siGyeongbuk-do38541Korea

In recent times,financial globalization has drastically increased in different ways to improve the quality of services with advanced *** successful applications of bitcoin Blockchain(BC)techniques enable the stockholders to worry about the return and risk of financial *** stockholders focused on the prediction of return rate and risk rate of financial ***,an automatic return rate bitcoin prediction model becomes essential for BC financial *** newly designed machine learning(ML)and deep learning(DL)approaches pave the way for return rate predictive *** study introduces a novel Jellyfish search optimization based extreme learning machine with autoencoder(JSO-ELMAE)for return rate prediction of BC financial *** presented JSO-ELMAE model designs a new ELMAE model for predicting the return rate of financial ***,the JSO algorithm is exploited to tune the parameters related to the ELMAE model which in turn boosts the classification *** application of JSO technique assists in optimal parameter adjustment of the ELMAE model to predict the bitcoin return *** experimental validation of the JSO-ELMAE model was executed and the outcomes are inspected in many *** experimental values demonstrated the enhanced performance of the JSO-ELMAE model over recent state of art approaches with minimal RMSE of 0.1562.

关键词： Financial products blockchain return rate prediction model machine learning parameter optimization

来源：评论

学校读者我要写书评

暂无评论

A New Fuzzy Adaptive Algorithm to Classify Imbalanced Data

引用

computers, Materials & Continua 2022年第1期70卷 73-89页

作者： Harshita Patel Dharmendra Singh Rajput Ovidiu Petru Stan Liviu Cristian Miclea School of Information Technology&Engineering Vellore Institute of TechnologyVellore632014India Technical University of Cluj Napoca Faculty of Automation and Computer ScienceCluj Napoca400114Romania

Classification of imbalanced data is a well explored issue in the data mining and machine learning community where one class representation is overwhelmed by other *** Imbalanced distribution of data is a natural occurrence in real world datasets,so needed to be dealt with carefully to get important *** case of imbalance in data sets,traditional classifiers have to sacrifice their performances,therefore lead to *** paper suggests a weighted nearest neighbor approach in a fuzzy manner to deal with this *** have adapted the‘existing algorithm modification solution’to learn from imbalanced datasets that classify data without manipulating the natural distribution of data unlike the other popular data balancing *** K nearest neighbor is a non-parametric classification method that is mostly used in machine learning *** classification with the nearest neighbor clears the belonging of an instance to classes and optimal weights with improved nearest neighbor concept helping to correctly classify imbalanced *** proposed hybrid approach takes care of imbalance nature of data and reduces the inaccuracies appear in applications of original and traditional *** show that it performs well over the existing fuzzy nearest neighbor and weighted neighbor strategies for imbalanced learning.

关键词： Machine learning fuzzy classification nearest neighbor adaptive approach optimal weights

来源：评论

学校读者我要写书评

暂无评论

Advanced DHCP Strategies for Seamless Inter-Campus Connectivity in Educational Networks 8

Advanced DHCP Strategies for Seamless Inter-Campus Connectiv...

引用

8th International Conference on I-SMAC (IoT in Social, Mobile, Analytics and Cloud), I-SMAC 2024

作者： Patadia, Chintan Patel, Akash Gandhi, Moksha Patel, Sachin Jagadeesh, Namratha Thirupatthi, Thanesh Nagarajan Lakehead University Computer Science Thunder Bay Canada Charotar University of Science and Technology Devang Patel Institute of Advanced Technology and Research Faculty of Technlogy and Engineering Department of Information and Technology Changa India

ISBN: (纸本)9798350376425

In today's educational landscape, resource sharing across different sites is crucial. This research study explores the implementation of Dynamic Host Configuration Protocol (DHCP) relay agents to enhance inter-campus connectivity in a large educational institution. By strategically placing and configuring DHCP relay agents, the study demonstrates their effectiveness in facilitating communication across dispersed network segments. This improves data transfer efficiency and reduces latency, enhancing overall network performance. This research study provides valuable insights for network administrators and stakeholders, offering practical guidance on configuring DHCP relay agents and optimizing network architecture. By implementing these recommendations, educational institutions can establish a scalable, reliable, and user-friendly network infrastructure. © 2024 IEEE.

关键词： Internet protocols

来源：评论

学校读者我要写书评

暂无评论

An Efficient Authenticated Elliptic Curve Cryptography Scheme for Multicore Wireless Sensor Networks

引用

IEEE Access 2023年 11卷 1306-1323页

作者： Oladipupo, Esau Taiwo Abikoye, Oluwakemi Christiana Imoize, Agbotiname Lucky Awotunde, Joseph Bamidele Chang, Ting-Yi Lee, Cheng-Chi Do, Dinh-Thuan The Federal Polytechnic Bida Department of Computer Science Bida912211 Nigeria University of Ilorin Faculty of Information and Communication Sciences Department of Computer Science Ilorin240003 Nigeria University of Lagos Faculty of Engineering Department of Electrical and Electronics Engineering Akoka Lagos100213 Nigeria Institute of Digital Communication Ruhr University Department of Electrical Engineering and Information Technology Bochum44801 Germany National Changhua University of Education Department of Industrial Education and Technology Changhua City500 Taiwan Fu Jen Catholic University Research and Development Center for Physical Education Health and Information Technology Department of Library and Information Science New Taipei City24205 Taiwan Asia University Department of Computer Science and Information Engineering Taichung City41354 Taiwan University of Mount Union School of Engineering AllianceOH44601 United States

The need to ensure the longevity of Wireless Sensor Networks (WSNs) and secure their communication has spurred various researchers to come up with various WSN models. Prime among the methods for extending the life span of WSNs is the clustering of Wireless Sensors (WS), which reduces the workload of WS and thereby reduces its power consumption. However, a drastic reduction in the power consumption of the sensors when multicore sensors are used in combination with sensors clustering has not been well explored. Therefore, this work proposes a WSN model that employs clustering of multicore WS. The existing Elliptic Curve Cryptographic (ECC) algorithm is optimized for parallel execution of the encryption/decryption processes and security against primitive attacks. The Elliptic Curve Diffie-Helman (ECDH) was used for the key exchange algorithm, and the Elliptic Curve Digital Signature Algorithm (ECDSA) was used to authenticate the communicating nodes. Security analysis of the model and comparative performance analysis with the existing ones were demonstrated. The security analysis results reveal that the proposed model meets the security requirements and resists various security attacks. Additionally, the projected model is scalable, energy-conservative, and supports data freshness. The results of comparative performance analysis show that the proposed WSN model can efficiently leverage multiprocessors and/or many cores for quicker execution and conserves power usage. © 2013 IEEE.

关键词： Electric power utilization

来源：评论

学校读者我要写书评

暂无评论

Analysis of BrainMRI: AI-Assisted Healthcare Framework for the Smart Cities

引用

Intelligent Automation & Soft Computing 2023年第2期35卷 1843-1856页

作者： Walid El-Shafai Randa Ali Ahmed Sedik Taha El-Sayed Taha Mohammed Abd-Elnaby Fathi E.Abd El-Samie Department Electronics and Electrical Communications Engineering Faculty of Electronic EngineeringMenoufia UniversityMenoufia32952Egypt Department of ROBOTICS and Intelligent Machines Faculty of Artificial IntelligenceKafrelsheikh UniversityKafrelsheikhEgypt Department of Computer Engineering College of Computers and Information TechnologyTaif UniversityTaif21944Saudi Arabia

The use of intelligent machines to work and react like humans is vital in emerging smart ***-aided analysis of complex and huge MRI(Mag-netic Resonance Imaging)scans is very important in healthcare *** AI(Artiﬁcial Intelligence)driven healthcare applications,tumor detection is one of the contemporary researchﬁelds that have become attractive to *** are several modalities of imaging performed on the brain for the pur-pose of tumor *** paper offers a deep learning approach for detecting brain tumors from MR(Magnetic Resonance)images based on changes in the division of the training and testing data and the structure of the CNN(Convolu-tional Neural Network)*** proposed approach is carried out on a brain tumor dataset from the National Centre of Image-Guided Therapy,including about 4700 MRI images of ten brain tumor cases with both normal and abnormal *** dataset is divided into test,and train subsets with a ratio of the training set to the validation set of 70:*** main contribution of this paper is introdu-cing an optimum deep learning structure of CNN *** simulation results are obtained for 50 epochs in the training *** simulation results reveal that the optimum CNN architecture consists of four layers.

关键词： Healthcare smart cities clinical automation CNN machine learning brain tumor medical diagnosis

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：