International Journal of Advanced Trends in Computer Science and Engineering最新文献_第9页

Mathematical Investigation and Computers Simulation of Plant Protection Process Taking into Account the Temporary Age Structure and Arbitrary Tropic Functions 考虑临时年龄结构和任意热带函数的植物保护过程的数学研究与计算机模拟

International Journal of Advanced Trends in Computer Science and Engineering Pub Date : 2021-12-07 DOI: 10.30534/ijatcse/2021/131062021

Odinaev R.N

{"title":"Mathematical Investigation and Computers Simulation of Plant Protection Process Taking into Account the Temporary Age Structure and Arbitrary Tropic Functions","authors":"Odinaev R.N","doi":"10.30534/ijatcse/2021/131062021","DOIUrl":"https://doi.org/10.30534/ijatcse/2021/131062021","url":null,"abstract":"The problem of describing the process of protecting agricultural crops in mathematical modeling is relevant. Today, the problem of plant protection attracts the attention of a large circle of scientists in connection with its promising use in priority areas of development of science, technology and agriculture. Mathematical modeling of the process of protecting the planned crop is one of the main tools in predicting the state of natural systems and managing them. One of the most important national economic, social and environmental problems at present is the improvement of systems for protecting agricultural crops from pests. Mathematical modeling of the process of protecting the planned harvest obliges to improve regional systems for protecting cotton on the basis of a targeted ecological and biological study, revealing the specifics of the formation and development of agro ecosystems in intensive crop production. The main task of the integrated method of combating agricultural pests of the plant protection process is the management of agrocenoses of “harmful insects” and “beneficial insects” of species based on the use of biological, chemical methods as a means or a management tool.The use of mathematical methods and computer software products to solve the problem of protecting the planned crop significantly increases the efficiency of planned and economic work and ensure optimal results. In this paper, we consider a mathematical model of the plant protection process, taking into account the temporal age structure and arbitrary trophic functions, formulate the task of plant protection. Necessary and sufficient conditions for the solvability of the plant protection problem with arbitrary trophic functions are found","PeriodicalId":129636,"journal":{"name":"International Journal of Advanced Trends in Computer Science and Engineering","volume":"7 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-12-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128581595","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

Framework for Digital Technology Application in Sustainable Waste Management 数码科技应用于可持续废物管理的架构

International Journal of Advanced Trends in Computer Science and Engineering Pub Date : 2021-12-07 DOI: 10.30534/ijatcse/2021/101062021

C. Ngwakwe, S. Mokoena

引用次数: 0

Malware Detection Approaches and Analysis for the Internet of Medical Things Enabled Healthcare Systems 医疗物联网医疗系统的恶意软件检测方法和分析

International Journal of Advanced Trends in Computer Science and Engineering Pub Date : 2021-12-07 DOI: 10.30534/ijatcse/2021/061062021

引用次数: 0

A Machine Learning Approach to Detect Student Dropout at University 一种检测大学学生辍学的机器学习方法

International Journal of Advanced Trends in Computer Science and Engineering Pub Date : 2021-12-07 DOI: 10.30534/ijatcse/2021/041062021

Shiful Islam Shohag, Masum Bakaul

{"title":"A Machine Learning Approach to Detect Student Dropout at University","authors":"Shiful Islam Shohag, Masum Bakaul","doi":"10.30534/ijatcse/2021/041062021","DOIUrl":"https://doi.org/10.30534/ijatcse/2021/041062021","url":null,"abstract":"In universities, student dropout is a major concern that reflects the university's quality. Some characteristics cause students to drop out of university. A high dropout rate of students affects the university's reputation and the student's careers in the future. Therefore, there's a requirement for student dropout analysis to enhance academic plan and management to scale back student's drop out from the university also on enhancing the standard of the upper education system. The machine learning technique provides powerful methods for the analysis and therefore the prediction of the dropout. This study uses a dataset from a university representative to develop a model for predicting student dropout. In this work, machine- learning models were used to detect dropout rates. Machine learning is being more widely used in the field of knowledge mining diagnostics. Following an examination of certain studies, we observed that dropout detection may be done using several methods. We've even used five dropout detection models. These models are Decision tree, Naïve bayes, Random Forest Classifier, SVM and KNN. We used machine-learning technology to analyze the data, and we discovered that the Random Forest classifier is highly promising for predicting dropout rates, with a training accuracy of 94% and a testing accuracy of 86%.","PeriodicalId":129636,"journal":{"name":"International Journal of Advanced Trends in Computer Science and Engineering","volume":"73 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-12-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133550005","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: 2

An Effective Health Information Management System for Optimal Healthcare Delivery 一个有效的健康信息管理系统，以实现最佳的医疗服务

International Journal of Advanced Trends in Computer Science and Engineering Pub Date : 2021-12-07 DOI: 10.30534/ijatcse/2021/081062021

Oguntimilehin, Tope-Oke, O. BabalolaG.

{"title":"An Effective Health Information Management System for Optimal Healthcare Delivery","authors":"Oguntimilehin, Tope-Oke, O. BabalolaG.","doi":"10.30534/ijatcse/2021/081062021","DOIUrl":"https://doi.org/10.30534/ijatcse/2021/081062021","url":null,"abstract":"The effect of security of health information on patients as well as the society at large cannot be overemphasized. If information gets to the wrong hands, it can lead to various disaster ranging from blackmail, financial loss, stigmatization, medio-legal issues and ultimately death. This research work addresses the problems associated with existing records management system in Federal Teaching Hospital, Ekiti State Nigeria (FETHI), which is manual based. At present, approach to file handling involves junior cadre staff moving files between departments and this is often subject to various degree of mishandling which could lead to loss of important documents, leakage of confidential information among others.The model proposed and implemented in this research addresses the issue of security and privacy in health information system and proffer advantages such as easy storage and retrieval, easy transfer of patients ‘records and it also guarantee confidentiality of information at all stages. The user-friendly web-based system was developed using Hypertext Preprocessor (PHP) as the front –end and MySQL as the backend.","PeriodicalId":129636,"journal":{"name":"International Journal of Advanced Trends in Computer Science and Engineering","volume":"144 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-12-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116367708","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

The Crossbreed Invariant Optimization MSVM Method for Effective Diagnosis of Pneumonia from Chest X-Ray Images x线胸片肺炎有效诊断的杂交不变量优化MSVM方法

International Journal of Advanced Trends in Computer Science and Engineering Pub Date : 2021-12-07 DOI: 10.30534/ijatcse/2021/031062021

{"title":"The Crossbreed Invariant Optimization MSVM Method for Effective Diagnosis of Pneumonia from Chest X-Ray Images","authors":"","doi":"10.30534/ijatcse/2021/031062021","DOIUrl":"https://doi.org/10.30534/ijatcse/2021/031062021","url":null,"abstract":"The survival percentage of pulmonary sufferers can be improved if pneumonia is detected in time. Imaging of the chest x-Ray is the most common way of finding as well as identifying pneumonia. A competent radiologist poses a severe problem while identifying pneumonia using CXR scans. To maximize classification precision, it requires an autonomous computer-aided detection approach. Designing a lightweight autonomous pneumonia detection mechanism for resource-efficient healthcare devices is critical for enhancing healthcare quality while lowering expenses and increasing reaction time. In this proposed work, a machine learning-based hybridization approach is implemented for the identification of pneumonia in the chest x-Ray scans. The proposed methodology is divided into different segments: the 1st segment is to remove noise from the chest x-Ray scans (pre-processing). After the pre-processing of CXR scans, the second module is to extract features from the pre-processed scans. The scale-invariant feature transform (SIFT) method is implemented for the extraction of essential features. This CIO-MSVM (Crossbreed Invariant Optimization-MSVM) method will select the valuable feature with the help of FF (fitness function). This function will help to select the feature matrix and then implement the MSVM algorithm. It will pass the instance selected feature set to the train model and test model. It will classify the feature sets. If feature sets will match then detect or classify the Chest X-ray image and evaluate the performance metrics such as accuracy, spec, sens., etc and compared with the existing methods.","PeriodicalId":129636,"journal":{"name":"International Journal of Advanced Trends in Computer Science and Engineering","volume":"28 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-12-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125230594","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

Analysis and Prediction of Graduate Admissions Based on Pre-COVID and post-COVID Scenario 基于疫情前和疫情后情景的研究生招生分析与预测

International Journal of Advanced Trends in Computer Science and Engineering Pub Date : 2021-12-07 DOI: 10.30534/ijatcse/2021/051062021

Sagnik Sarkar, Sriya I. Reddi, R. Manjula

{"title":"Analysis and Prediction of Graduate Admissions Based on Pre-COVID and post-COVID Scenario","authors":"Sagnik Sarkar, Sriya I. Reddi, R. Manjula","doi":"10.30534/ijatcse/2021/051062021","DOIUrl":"https://doi.org/10.30534/ijatcse/2021/051062021","url":null,"abstract":"Graduate admissions is one of the events that attracts a lot of attraction from prospective students and universities alike. Be it the university conducting graduate admissions or an aspiring student; both yearn for a prediction system to aid in the process of selecting admits. On one hand, the university can get an insight on the probability of a student's admit thus aiding the graduate admissions office in their workload, and on the other hand the student can get a forecast on the chance of admit and can take preemptive decisions to facilitate the process. However, due to the COVID-19 pandemic, the graduate admissions has seen a slight change in paradigm. This change creates confusion among the related masses. A probing analysis on this change serves as a reference to act upon. In this study, prediction models are built with an extra parameter signifying whether a record in the dataset belongs to the COVID-19 pandemic period. Various models such as Logistic Regression, Decision Tree, Random Forest, Gaussian Naive Bayes and Artificial Neural Networks are used to determine the change in probability of admission due to the effect of the pandemic. All the models provide an accuracy score in the range of about 55% to 80%, with the Neural Network outperforming all the other models with a test accuracy score of 79.03%. The effect of the pandemic has caused an ambiguous response to various factors, but it can be stated the chances of admits of students have generally increased likely due to the lower number of applicants","PeriodicalId":129636,"journal":{"name":"International Journal of Advanced Trends in Computer Science and Engineering","volume":"63 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-12-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131376784","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

User Engagement and User Design on Online Shopping Apps 在线购物应用的用户粘性和用户设计

International Journal of Advanced Trends in Computer Science and Engineering Pub Date : 2021-12-07 DOI: 10.30534/ijatcse/2021/011062021

John Albert Balansag, Rommel A. Canoy, Troyd B. Puquiz, Honey Marjey V. Curay, Denver F. Divino, Luz Clarisse E. Pejera, Mark Van M. Buladaco

{"title":"User Engagement and User Design on Online Shopping Apps","authors":"John Albert Balansag, Rommel A. Canoy, Troyd B. Puquiz, Honey Marjey V. Curay, Denver F. Divino, Luz Clarisse E. Pejera, Mark Van M. Buladaco","doi":"10.30534/ijatcse/2021/011062021","DOIUrl":"https://doi.org/10.30534/ijatcse/2021/011062021","url":null,"abstract":"Most people nowadays use mobile phones, and they do a lot of things with this device, including online shopping through an app since it saves a lot of time, and they can choose a broad range of products even with just a small screen. User engagement is one of the factors that affect the design of an app. Referring to the quality of the user that emphasizes the interaction between the user and the app. Numerous studies internationally have studied user engagement and app design but not specifically with the user engagement towards shopping apps design. We conducted this study to assess the effectiveness of user engagement towards online shopping apps design. Also, the purpose of this study is to identify user engagement factors that will produce a successful good shopping app design. The results show a high level of user engagement and online shopping apps design. It has also been found that among the indicators of user engagement, attention and satisfaction are the two predictors of the design of online shopping apps","PeriodicalId":129636,"journal":{"name":"International Journal of Advanced Trends in Computer Science and Engineering","volume":"330 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-12-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133988659","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

Review on The Effect of Alcohol Usage as A Fuel Additive in Internal Combustion Engine 乙醇作为燃料添加剂在内燃机中的应用研究进展

International Journal of Advanced Trends in Computer Science and Engineering Pub Date : 2021-12-07 DOI: 10.30534/ijatcse/2021/141062021

Sarbani Daud, M. A. Hamidi, R. Mamat, Senthil Kumar

引用次数: 0

Soil Properties Classification Using Support Vector Machine for Raver Tehsil 基于支持向量机的河流土壤性质分类

International Journal of Advanced Trends in Computer Science and Engineering Pub Date : 2021-12-07 DOI: 10.30534/ijatcse/2021/121062021

Vipin Y. Borole, S. Kulkarni

{"title":"Soil Properties Classification Using Support Vector Machine for Raver Tehsil","authors":"Vipin Y. Borole, S. Kulkarni","doi":"10.30534/ijatcse/2021/121062021","DOIUrl":"https://doi.org/10.30534/ijatcse/2021/121062021","url":null,"abstract":"Soil properties are dynamic in nature and different factors are affecting to the soil quality. It is directly consequence on soil productivity and soil fertility. The heavy use of fertilizers, heavy rain fall, various agricultural practices are responsible for soil quality degradation. The soil assessment is require to maintain the soil quality. The spectroscopic techniques using Remote sensing and GIS gives the fast and accurate results as compare to traditional soil testing methods. The present study is conducted for classification of soil physicochemical properties in pre monsoon and post monsoon season. Soil samples are collected where Organic, Chemical and Mixed fertilizers treatments were applied to banana and cotton crops sites from Raver tehsil of Jalgaon district. Total 220 soil specimens are collected in pre monsoon and post monsoon season for two year respectively. ASD FieldSpec4 spectroradiometer device were used for data acquisition in the controlled laboratory environment. Acquired spectral data were processed for conversion in numeric format then various statistical methods were used for quantitative analysis of the physiochemical soil properties. The support vector machine is used for classification of the collected soil samples in pre-monsoon and post-monsoon season and classification were performed on the basis of training and testing datasets. The soil samples are divide in pre-monsoon training, pre-monsoon testing and post –monsoon training and post-monsoon testing class with support vector. The hyper plane is used for separation of pre-monsoon and post-monsoon soil samples. Misclassification rate and Mean Squared Error were calculated in the SVM classification.","PeriodicalId":129636,"journal":{"name":"International Journal of Advanced Trends in Computer Science and Engineering","volume":"2 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-12-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132509925","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: 1