International Journal of Advanced Trends in Computer Science and Engineering最新文献_第7页

Efficient Query Processing For Imprecise Data 不精确数据的高效查询处理

International Journal of Advanced Trends in Computer Science and Engineering Pub Date : 2022-08-10 DOI: 10.30534/ijatcse/2022/031142022

引用次数: 0

Electronic Document Securisation based on Document Structure 基于文档结构的电子文档安全

International Journal of Advanced Trends in Computer Science and Engineering Pub Date : 2022-08-10 DOI: 10.30534/ijatcse/2022/041142022

引用次数: 0

Prediction of software defects by knowledge graph and genetic algorithm 基于知识图谱和遗传算法的软件缺陷预测

International Journal of Advanced Trends in Computer Science and Engineering Pub Date : 2022-08-10 DOI: 10.30534/ijatcse/2022/011142022

{"title":"Prediction of software defects by knowledge graph and genetic algorithm","authors":"","doi":"10.30534/ijatcse/2022/011142022","DOIUrl":"https://doi.org/10.30534/ijatcse/2022/011142022","url":null,"abstract":"Software defect detection is one of the biggest software development challenges and accounts for the largest budget in the software development process. One of the effective activities for software development and increasing its reliability is to predict software defects before reaching the test stage, which helps to save time in the production, maintenance and cost process. This research aims to present a software defect prediction method based on knowledge graphs and automated machine learning. We use knowledge acquisition, knowledge fusion, knowledge storage and knowledge calculation and other knowledge map construction technology research, to realize the knowledge map recommends high-quality software defect prediction models as the hot-start input conditions for automatic search. The empirical study uses NASA's open-source dataset experimental objects and six performance evaluation indicators include Precision, Recall, PRC (Precision Recall Characteristic), ROC (Receiver Operating Characteristic), F-Measure, MCC (Matthews Correlation Coefficient). The experimental results show that the proposed model performs better than the traditional classic software defect prediction model recommended by the knowledge map in terms of different datasets and evaluation indicators","PeriodicalId":129636,"journal":{"name":"International Journal of Advanced Trends in Computer Science and Engineering","volume":"8 5","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-08-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"120904308","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

Comparative study of Sentiment Analysis on Amazon Product Reviews using Recurrent Neural Network (RNN) 基于递归神经网络(RNN)的亚马逊产品评论情感分析比较研究

International Journal of Advanced Trends in Computer Science and Engineering Pub Date : 2022-06-15 DOI: 10.30534/ijatcse/2022/111132022

引用次数: 0

IoT and Machine Learning approach for Early Heart disease Prediction & Diagnosis 物联网和机器学习方法用于早期心脏病预测和诊断

International Journal of Advanced Trends in Computer Science and Engineering Pub Date : 2022-06-15 DOI: 10.30534/ijatcse/2022/101132022

{"title":"IoT and Machine Learning approach for Early Heart disease Prediction & Diagnosis","authors":"","doi":"10.30534/ijatcse/2022/101132022","DOIUrl":"https://doi.org/10.30534/ijatcse/2022/101132022","url":null,"abstract":"Heart disease is one of the most prominent causes of deaths globally. Every year almost 17.9 million people lose their life due to heart disease which account for 31% of total deaths worldwide. Most of the time patients know about their heart disease after reaching a severe heart condition from where total recovery is impossible. However, if the Cardiovascular state is monitored regularly, heart disease can be detected at an early stage and early detection can prevent the severity of most heart diseases. In most cases patients do not feel any kind of pain when the cardiovascular diseases grow slowly. By the time someone feels uneasiness and pain, their heart condition gets seriously bad. Moreover, it is also not feasible for everyone to check up on their heart condition periodically by visiting a heart specialist. Our proposed system will work for the early detection of heart diseases using Machine learning classifiers and IoT technologies. Our system has two subsystems. First one is our trained machine learning model which will be implemented as a WebApi. Second one is our IoT setup with heartbeat sensors. Sensors will collect data from the user's body and send those to the machine learning model. Then, the model will predict the result about the user’s heart condition and send it back to the IoT device. Model will classify the user’s heart condition either as “Normal” or “Abnormal”. Based on the result, the user should go to a cardiologist for a checkup. We have used the Heart Disease Dataset from UCI Machine Learning Repository. In addition, we trained seven machine learning algorithms after preprocessing the dataset. Further we will also build an IoT setup with sensors to communicate with the WebApi and complete our proposed system of predicting heart diseases.","PeriodicalId":129636,"journal":{"name":"International Journal of Advanced Trends in Computer Science and Engineering","volume":"11 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-06-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132512177","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

Handwritten Digits Image Generation with help of Generative Adversarial Network: Machine Learning Approach 基于生成对抗网络的手写数字图像生成:机器学习方法

International Journal of Advanced Trends in Computer Science and Engineering Pub Date : 2022-06-10 DOI: 10.30534/ijatcse/2022/091132022

{"title":"Handwritten Digits Image Generation with help of Generative Adversarial Network: Machine Learning Approach","authors":"","doi":"10.30534/ijatcse/2022/091132022","DOIUrl":"https://doi.org/10.30534/ijatcse/2022/091132022","url":null,"abstract":"In recent years, research into Generative Adversarial Nets (GANs) has increased dramatically. GAN was first proposed in 2014 and has since used in various real-time applications,Includes computer vision and natural language processing for approximately accurate results. Image composition is the most popular study of the many applications of GAN,Studies in this area have already shown the great future of using GAN for image composition.This article shows how to classify image composition methods, reviews different models of text-to-image composition and image-to-image conversionand provides some metrics and future research on image composition using GAN. I will explain the direction of in this paper. In current years, frameworks using Generative Adversarial Networks (GAN) have been very successful in many areas many areas, especially in image generation, asthey can create very realistic and crisp images and train on large datasets. However, successful GAN training can be very difficult if you need high resolution images. Text-to-image compositing, image-to-image conversion, face manipulation, 3D image compositing, and deep master printing are five interesting areas that can be applied to image compositing based on the state-of-the-art GAN technology described in this article. It presents a comprehensive analysis of current GAN-based imaging models, including their strengths and weaknesses. At the same time, recent rediscovery of deep learning and widespread interest in generation methods in the scientific community have made it possible to generate realistic images by learning the data distribution from noise. If the input data contains information about the visual content of the image, the quality of the generated image will improve.","PeriodicalId":129636,"journal":{"name":"International Journal of Advanced Trends in Computer Science and Engineering","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-06-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122644779","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

THESISQUO: Research Management System with Plagiarism Checker and Prescriptive Analytics 论文:具有抄袭检查和规范分析的科研管理系统

International Journal of Advanced Trends in Computer Science and Engineering Pub Date : 2022-06-09 DOI: 10.30534/ijatcse/2022/051132022

引用次数: 0

Public Health Record Management System: An Up-Close Monitoring System 公共卫生档案管理系统:一个近距离监测系统

International Journal of Advanced Trends in Computer Science and Engineering Pub Date : 2022-06-09 DOI: 10.30534/ijatcse/2022/041132022

{"title":"Public Health Record Management System: An Up-Close Monitoring System","authors":"","doi":"10.30534/ijatcse/2022/041132022","DOIUrl":"https://doi.org/10.30534/ijatcse/2022/041132022","url":null,"abstract":"The researchers developed a public health record management system for the barangay health center of Sto. Rosario, Paombong, Bulacan, recognize the existing problem, circumstances and reduce the risk of data lost due to unprecedented accidents or human errors and decrease the probability of data redundancy for the patients. The developers used an illustrative and user-friendly design that presents the data in the easiest way possible. Public Health Record Management System (PHRMS) aims to elevate the current state of record management of the public health center by incorporating technology to further enhance the productivity securely and a faster way of data processing to save time and serve more patients. For the acceptability of the developed health information system, the researchers consulted with IT professionals from different fields, IT instructors from Bulacan State University, and the endusers or clients of the developed system such as the midwife, and barangay health workers. The evaluation form has the following criteria for software quality evaluation as follows: (1) functional suitability; (2) performance efficiency; (3) usability; (4) reliability; (5) security; and (6) portability.","PeriodicalId":129636,"journal":{"name":"International Journal of Advanced Trends in Computer Science and Engineering","volume":"29 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-06-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127829789","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

Analysis and Design of a Student and Admin Portal with a Pomodoro Technique Feature for Mindanao Kokusai Daigaku (Mindanao International College) 棉兰老岛国际学院番茄工作制学生与管理门户分析与设计

International Journal of Advanced Trends in Computer Science and Engineering Pub Date : 2022-06-09 DOI: 10.30534/ijatcse/2022/021132022

{"title":"Analysis and Design of a Student and Admin Portal with a Pomodoro Technique Feature for Mindanao Kokusai Daigaku (Mindanao International College)","authors":"","doi":"10.30534/ijatcse/2022/021132022","DOIUrl":"https://doi.org/10.30534/ijatcse/2022/021132022","url":null,"abstract":"Academic institutions have become so accustomed to using antiquated paper recording methods to store and process student data that transitioning to a more advanced technological method may not be a top priority at first. Many educational advancements have been demonstrated to be beneficial, however, some educational institutions are falling behind. Mindanao Kokusai Daigaku's lack of a centralized system is the reason for the university's foregoing problems. The college is still managing student data through paper records and an obsolete enrollment information system, which has been very inconvenient for both students and admins, especially during the Covid-19 pandemic. The main objective of this paper is to design a student and admin portal for Mindanao Kokusai Daigaku with a Pomodoro technique feature as an advanced approach for the university's internal operations to improve user convenience, secure documents, and make information more accessible to students while also encouraging productivity. Various methodologies and models were used in this study to further clarify the proposed system's operations and purpose. Based on the results of the findings, the researchers conclude that the suggested portal system is feasible and will address Mindanao Kokusai Daigaku’s current challenges.","PeriodicalId":129636,"journal":{"name":"International Journal of Advanced Trends in Computer Science and Engineering","volume":"8 12","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-06-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"120806988","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

Monochromatic Image Colorization using Machine Learning 使用机器学习的单色图像着色

International Journal of Advanced Trends in Computer Science and Engineering Pub Date : 2022-06-09 DOI: 10.30534/ijatcse/2022/071132022

引用次数: 0