Communications on Applied Nonlinear Analysis最新文献_第6页

Flowers Images Classification with Deep Learning: A Review 利用深度学习进行花卉图像分类：综述

Communications on Applied Nonlinear Analysis Pub Date : 2024-07-05 DOI: 10.52783/cana.v31.851

Asia Kamal Mustfa, S. Abdulateef, Qabas A. Hameed

引用次数: 0

Significant Differences Between the IQ Scores of Mathematics Students based on Regression Analysis and Factorial Experimental Design 基于回归分析和因子实验设计的数学学生智商得分的显著差异

Communications on Applied Nonlinear Analysis Pub Date : 2024-07-05 DOI: 10.52783/cana.v31.829

Hadeel Salim Alkutubi

{"title":"Significant Differences Between the IQ Scores of Mathematics Students based on Regression Analysis and Factorial Experimental Design","authors":"Hadeel Salim Alkutubi","doi":"10.52783/cana.v31.829","DOIUrl":"https://doi.org/10.52783/cana.v31.829","url":null,"abstract":"In this research, data on general intelligence and emotional intelligence scores were analyzed and the difference between them was studied in terms of their application to students of the Mathematics Department in the four stages in the College of Computer Science and Mathematics at the University of Kufa for the academic year 2023-2024. Four statistical methods were used. The first was multiple regression analysis to study the extent to which each student's mathematics score was affected on the general intelligence and emotional intelligence tests (Y), by the student's gender (X1) and his type of residence (X2). The second statistical method is to analyze the design of a factorial experiment to study the presence or absence of significant differences between the grades of students in the four stages by classifying the students in the four stages into males and females, as well as studying the significant difference in the scores of the two intelligence tests (general and emotional) for students in the Mathematics Department in general and divided according to gender. requester. The third method of analysis was to analyze the design of a completely randomized experiment to study the significant differences in students’ intelligence scores between the four academic levels in the mathematics department. If there is a significant difference in the results of analyzing the data in the above methods, then we use the fourth statistical method in this research, which is the analysis of the least significant difference test to study the differences between each two groups separately. The results of the data analysis were numerous, the most important of which is that the intelligence scores in the two tests (general intelligence and emotional intelligence) do not depend on nor are affected by the student’s gender or type of residence. In addition, intelligence scores were not affected for the four educational levels.","PeriodicalId":40036,"journal":{"name":"Communications on Applied Nonlinear Analysis","volume":" 3","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-07-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141674570","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

Optimizing Graph Theory Algorithms for Social Network Analysis 为社交网络分析优化图论算法

Communications on Applied Nonlinear Analysis Pub Date : 2024-07-05 DOI: 10.52783/cana.v31.834

S. Sahoo, Sasmita Mishra

引用次数: 0

Exploring Fractional Quantum Mechanics: Stability Analysis and Wave Propagation in Coupled Schrödinger Equations 探索分数量子力学：耦合薛定谔方程中的稳定性分析和波传播

Communications on Applied Nonlinear Analysis Pub Date : 2024-07-05 DOI: 10.52783/cana.v31.935

Iftekher S. Chowdhury, Dr. Eric Howard, Dr Nand Kumar

引用次数: 0

Applying a Fuzzy Ordering Approach in Transportation Problems with Decagonal Intuitionistic Fuzzy Numbers 在使用十边形直觉模糊数的交通问题中应用模糊排序法

Communications on Applied Nonlinear Analysis Pub Date : 2024-07-05 DOI: 10.52783/cana.v31.975

KR Balasubramanian

引用次数: 0

Computational Machine Learning Analytics for Prediction of Water Quality 用于水质预测的计算机器学习分析技术

Communications on Applied Nonlinear Analysis Pub Date : 2024-07-05 DOI: 10.52783/cana.v31.942

Nitya Nand Jha, R. Singh, Sushila Sharma, Abhishek Kumar

{"title":"Computational Machine Learning Analytics for Prediction of Water Quality","authors":"Nitya Nand Jha, R. Singh, Sushila Sharma, Abhishek Kumar","doi":"10.52783/cana.v31.942","DOIUrl":"https://doi.org/10.52783/cana.v31.942","url":null,"abstract":"In terms of impacts on ecosystems, industry, people, and flora and fauna, water quality is paramount. Contamination and pollution have degraded water quality in recent decades. Predicting WQC and Water Quality Index (WQI) is the problem of this article; WQI is an important measure of water validity. This research use machine learning approaches to forecast WQI and WQC, and it does so by optimizing and tweaking the parameters of several machine learning models. Parameter optimization and tuning for four classification models and four regression models both make use of grid search, an essential tool in both contexts. To forecast WQC, classification models such as Random Forest (RF), Extreme Gradient Boosting (Xgboost), Gradient Boosting (GB), and Adaptive Boosting (Ada-Boost) are used. Predicting WQI is done using regression models such as K-nearest neighbour (KNN), decision tree (DT), support vector regression (SVR), and multi-layer perceptron (MLP). Data normalization and data imputation (mean imputation) were also executed as pretreatment steps to suit the data and make it convenient for any further processing. Seven characteristics and ninety-one cases make up the dataset used for this research. Five evaluation measures were calculated to evaluate the classification systems' effectiveness: accuracy, recall, precision, Matthews' Correlation Coefficient (MCC), and F1 score. A total of four evaluation metrics were calculated to measure the efficacy of the regression models: MAE, MedAE,MSE, and R2. The results of the testing showed that the GB model yielded the most accurate predictions of WQC values (99.50%), making it the top performer in terms of categorization. The experimental findings show that the MLP regressor model got a value of 99.8 percent R2 when predicting WQI values, making it the best performing model in regression.","PeriodicalId":40036,"journal":{"name":"Communications on Applied Nonlinear Analysis","volume":" 7","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-07-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141677046","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

A Fuzzy Optimal Base Stock System for Tolerant Clients 面向宽容客户的模糊优化基础库存系统

Communications on Applied Nonlinear Analysis Pub Date : 2024-07-05 DOI: 10.52783/cana.v31.952

Jagatheesan. R

引用次数: 0

Homotopy on Smooth Fuzzy Fréchet Manifold 光滑模糊弗雷谢特流形上的同向性

Communications on Applied Nonlinear Analysis Pub Date : 2024-07-05 DOI: 10.52783/cana.v31.859

Ahmed Ghanawi Jasim, Intisar Harbi, Ali Salim Mohammed

引用次数: 0

Analysis and Prediction of Stock Price using HMM and Facebook’s Prophet Computational Models 使用 HMM 和 Facebook 的先知计算模型分析和预测股票价格

Communications on Applied Nonlinear Analysis Pub Date : 2024-07-05 DOI: 10.52783/cana.v31.813

K. Senthamarai Kannan

引用次数: 0

IoT-Based Smart Glove for Pollution Monitoring and Potholes mapping using Node-MCU 使用 Node-MCU 绘制基于物联网的污染监测和坑洼地图的智能手套

Communications on Applied Nonlinear Analysis Pub Date : 2024-07-05 DOI: 10.52783/cana.v31.947

Dr.Kaushalya Thopate, Dr.Deepali S. Jadhav, Ms.Kalyani Ghuge, Dr.Virat V Giri, Dr. Ganesh B. Dongre, Mrs.Archana, Bhushan Burujwale

引用次数: 0