Bonfring International Journal of Networking Technologies and Applications最新文献

Modified Fuzzy Neural Network Approach for Academic Performance Prediction of Students in Early Childhood Education 用于幼儿教育学生学业成绩预测的改进型模糊神经网络方法

Bonfring International Journal of Networking Technologies and Applications Pub Date : 2024-02-13 DOI: 10.9756/bijnta/v11i1/bij24007

Marwah Hameed

{"title":"Modified Fuzzy Neural Network Approach for Academic Performance Prediction of Students in Early Childhood Education","authors":"Marwah Hameed","doi":"10.9756/bijnta/v11i1/bij24007","DOIUrl":"https://doi.org/10.9756/bijnta/v11i1/bij24007","url":null,"abstract":"Modern education relies heavily on educational technology, which provides students with unique learning opportunities and enhances their ability to learn. For many years now, computers and other technological tools have been an integral part of education. However, compared to other educational levels, the incorporation of educational technology in early childhood education is a more recent trend. It is because of this that materials and procedures tailored to young children must be created, implemented, and studied. The use of artificial intelligence techniques in educational technology resources has resulted in better engagement for students. Early childhood special education students' academic achievement is predicted using a Modified Fuzzy Neural Network (MFNN). Before constructing the classifier, the dataset had to be preprocessed to remove any extraneous information. As a follow-up, this study will put to the test an organized approach to the implementation of customized fuzzy neural networks for the prediction of academic achievement in early childhood settings. Considerations for the analysis of academic achievement in early childhood education are discussed in this article, including recommendations for the implementation of proposed modified fuzzy neural networks. In terms of evaluation metrics such as Precision, recall, accuracy, and the F1 coefficient, the proposed model outperforms conventional machine-learning (ML) techniques.","PeriodicalId":105712,"journal":{"name":"Bonfring International Journal of Networking Technologies and Applications","volume":"39 3","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-02-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139840743","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Modified Fuzzy Neural Network Approach for Academic Performance Prediction of Students in Early Childhood Education 用于幼儿教育学生学业成绩预测的改进型模糊神经网络方法

Bonfring International Journal of Networking Technologies and Applications Pub Date : 2024-02-13 DOI: 10.9756/bijnta/v11i1/bij24007

Marwah Hameed

{"title":"Modified Fuzzy Neural Network Approach for Academic Performance Prediction of Students in Early Childhood Education","authors":"Marwah Hameed","doi":"10.9756/bijnta/v11i1/bij24007","DOIUrl":"https://doi.org/10.9756/bijnta/v11i1/bij24007","url":null,"abstract":"Modern education relies heavily on educational technology, which provides students with unique learning opportunities and enhances their ability to learn. For many years now, computers and other technological tools have been an integral part of education. However, compared to other educational levels, the incorporation of educational technology in early childhood education is a more recent trend. It is because of this that materials and procedures tailored to young children must be created, implemented, and studied. The use of artificial intelligence techniques in educational technology resources has resulted in better engagement for students. Early childhood special education students' academic achievement is predicted using a Modified Fuzzy Neural Network (MFNN). Before constructing the classifier, the dataset had to be preprocessed to remove any extraneous information. As a follow-up, this study will put to the test an organized approach to the implementation of customized fuzzy neural networks for the prediction of academic achievement in early childhood settings. Considerations for the analysis of academic achievement in early childhood education are discussed in this article, including recommendations for the implementation of proposed modified fuzzy neural networks. In terms of evaluation metrics such as Precision, recall, accuracy, and the F1 coefficient, the proposed model outperforms conventional machine-learning (ML) techniques.","PeriodicalId":105712,"journal":{"name":"Bonfring International Journal of Networking Technologies and Applications","volume":"135 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-02-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139780642","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Node Localization Using Modified Wild Horse Optimization and Energy Efficient Secured Routing Protocol for IoT Based Wireless Sensor Networks 基于物联网的无线传感器网络的节点定位（使用改良野马优化和高能效安全路由协议

Bonfring International Journal of Networking Technologies and Applications Pub Date : 2024-02-03 DOI: 10.9756/bijnta/v11i1/bij24004

Prakash M., Prakash A.

{"title":"Node Localization Using Modified Wild Horse Optimization and Energy Efficient Secured Routing Protocol for IoT Based Wireless Sensor Networks","authors":"Prakash M., Prakash A.","doi":"10.9756/bijnta/v11i1/bij24004","DOIUrl":"https://doi.org/10.9756/bijnta/v11i1/bij24004","url":null,"abstract":"Internet of Things (IoT) devices are being utilized extensively as a result of the development of information and communication technology. Wireless sensor networks (WSNs), which are formed of inexpensive smart devices for information collection, plays a vital function in the establishment of the IoT. These smart devices are not without limitations, though, whether it comes to processing, memory, computing, and energy usage. In addition to these limitations, the core difficulties facing WSN include node localization, reliability, and data security during transmission in a dangerous environment from hostile nodes. The important and difficult problem for researchers to solve in order to improve network longevity, reliability, scalability, connectivity, throughput is accurate localization and routing. To improve the network period and data trustworthiness, this study intends to design an Energy-Efficient and Secure Routing protocol (EESR) and node localization centered on modified wild horse optimization (LMWHO) for intrusion avoidance in IoT utilizing WSN. Initially the suggested protocol bases its creation of various energy-efficient clusters on the inherent characteristics of nodes. Secondly, the base station (BS) and cluster head are able to reliably and securely share sensory data according to the (k,n) threshold-based Shamir secret sharing method. And finally, node localization centered on modified wild horse optimization (EESR-LMWHO), where the fitness function was formed by the development of residual energy and distance estimate. The suggested EESR-LMWHO utilizes less energy and prolongs the life of wireless networks. Lastly, the simulations are run to evaluate the suggested method's efficiency. The suggested approach, according to the experiments, approximates the location of the unknown node, offers a minimal localization error, and is a lightweight way to deal with intrusions caused by hostile nodes.","PeriodicalId":105712,"journal":{"name":"Bonfring International Journal of Networking Technologies and Applications","volume":"1 6","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-02-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139683267","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Fault Prediction Using Fuzzy Convolution Neural Network on IOT Environment with Heterogeneous Sensing Data Fusion 利用模糊卷积神经网络在物联网环境中融合异构传感数据进行故障预测

Bonfring International Journal of Networking Technologies and Applications Pub Date : 2024-01-04 DOI: 10.9756/bijnta/v11i1/bij24001

Gokul S., Madhorubagan G.E., Sasipriya M.

{"title":"Fault Prediction Using Fuzzy Convolution Neural Network on IOT Environment with Heterogeneous Sensing Data Fusion","authors":"Gokul S., Madhorubagan G.E., Sasipriya M.","doi":"10.9756/bijnta/v11i1/bij24001","DOIUrl":"https://doi.org/10.9756/bijnta/v11i1/bij24001","url":null,"abstract":"Because of the developing worldwide familiarity with natural issues, the expansion of sun-based power plants has turned into an unmistakable element of the energy scene. Nonetheless, keeping up with these sunlights-based offices, especially with regards to recognizing breaking down photovoltaic (PV) cells inside huge scope or far off establishments, presents critical difficulties. The focal goal of our exploration project is to resolve this issue by empowering the convenient identification of flaws in PV cells, consequently possibly saving significant time, exertion, and upkeep costs, especially as for the pivoting gear ordinarily utilized in sun-based power plants. I have developed a non-contact vibration pickup system that makes it possible to collect vibration data from PV cells operating at various speeds and loads without having to physically connect them to machine tools. In addition, I rank and select the most relevant features for accurate fault detection using the Sequential Floating Forward Selection (SFFS) method and Principal Component Analysis (PCA) to reduce the extracted features dimensionality. This thorough methodology offers a promising answer for improve the effectiveness and dependability of sun-oriented power plant upkeep while adding to the more extensive objectives of supportable energy creation and natural safeguarding.","PeriodicalId":105712,"journal":{"name":"Bonfring International Journal of Networking Technologies and Applications","volume":"37 7","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-01-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139384861","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Implementation of Advanced Solar Tracking and Cleaning to Improve Efficiency 实施先进的太阳能跟踪和清洁以提高效率

Bonfring International Journal of Networking Technologies and Applications Pub Date : 2019-03-29 DOI: 10.9756/BIJNTA.9001

S. Preethi, K. Suryaprakash, S. Swathi, Bhat Vishnu

引用次数: 2

Implementation of Wireless Underground Sensors Network 无线地下传感器网络的实现

Bonfring International Journal of Networking Technologies and Applications Pub Date : 2019-03-29 DOI: 10.9756/BIJNTA.9009

P. Divyabanu, G. Nalini, Sudhakar, Varsha, M. Vignesh

引用次数: 0

Preventing the Breach of Sniffers in TCP/IP Layer Using Nagles Algorithm 利用Nagles算法防止TCP/IP层嗅探器的入侵

Bonfring International Journal of Networking Technologies and Applications Pub Date : 2019-03-29 DOI: 10.9756/BIJNTA.9005

P. Subhaasini, N. Bhuvaneswari, M. Jerald, M. Madhavakirshnan

引用次数: 0