Pub Date : 2024-06-15DOI: 10.59461/ijitra.v3i2.107
T. Thilagavathi, L. Arockiam, I. Priya Stella Mary
This research paper is dedicated to the comprehensive review and discussion of diverse techniques employed in plant disease detection within the realm of agriculture. Emphasizing notable contributions and showcasing innovative methodologies, the research work takes a critical turn to address the myriad issues and challenges intricately woven into the integration of IoT data analytics in agriculture. The paper meticulously unravels the complexities associated with plant disease detection in the era dominated by IoT and data analytics. Serving as more than just a repository of current methodologies and technologies, this work actively illuminates the challenges that await further exploration. The insights derived from this exploration will provide a substantial foundation for emerging researchers. By shedding light on the evolving landscape of plant disease detection and the nuances of IoT integration in agriculture, this paper empowers researchers to actively contribute to the resilience and sustainability of agricultural practices in the face of ongoing challenges.
{"title":"Smart Agriculture: A Comprehensive Survey on IoT-Enabled Plant Disease Detection and Agricultural Automation","authors":"T. Thilagavathi, L. Arockiam, I. Priya Stella Mary","doi":"10.59461/ijitra.v3i2.107","DOIUrl":"https://doi.org/10.59461/ijitra.v3i2.107","url":null,"abstract":"This research paper is dedicated to the comprehensive review and discussion of diverse techniques employed in plant disease detection within the realm of agriculture. Emphasizing notable contributions and showcasing innovative methodologies, the research work takes a critical turn to address the myriad issues and challenges intricately woven into the integration of IoT data analytics in agriculture. The paper meticulously unravels the complexities associated with plant disease detection in the era dominated by IoT and data analytics. Serving as more than just a repository of current methodologies and technologies, this work actively illuminates the challenges that await further exploration. The insights derived from this exploration will provide a substantial foundation for emerging researchers. By shedding light on the evolving landscape of plant disease detection and the nuances of IoT integration in agriculture, this paper empowers researchers to actively contribute to the resilience and sustainability of agricultural practices in the face of ongoing challenges.","PeriodicalId":503010,"journal":{"name":"International Journal of Information Technology, Research and Applications","volume":"8 18","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-06-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141337748","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}
Today, determining the rent for a property is crucial given that the cost of housing increases annually. Our future generation requires a straightforward method to forecast future property rent. Various factors influence the price of a house, including its physical condition, location, and size. This study utilizes web scraping techniques to collect data from pertinent websites for analytical and predictive purposes. Employing an ensemble strategy, the research predicts housing rents in Bangalore. Seven ensemble models of machine learning algorithms, such as Random Forest, XGBoost, Support Vector Regression (SVR), and Decision Trees, are integrated into the analysis. The objective was to determine the optimal model by evaluating their performance scores obtained from a comparative analysis.
{"title":"Ensemble Approach for Predicting The Price of Residential Property","authors":"Renju K, Freni S","doi":"10.59461/ijitra.v3i2.99","DOIUrl":"https://doi.org/10.59461/ijitra.v3i2.99","url":null,"abstract":"Today, determining the rent for a property is crucial given that the cost of housing increases annually. Our future generation requires a straightforward method to forecast future property rent. Various factors influence the price of a house, including its physical condition, location, and size. This study utilizes web scraping techniques to collect data from pertinent websites for analytical and predictive purposes. Employing an ensemble strategy, the research predicts housing rents in Bangalore. Seven ensemble models of machine learning algorithms, such as Random Forest, XGBoost, Support Vector Regression (SVR), and Decision Trees, are integrated into the analysis. The objective was to determine the optimal model by evaluating their performance scores obtained from a comparative analysis. ","PeriodicalId":503010,"journal":{"name":"International Journal of Information Technology, Research and Applications","volume":"17 2","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-06-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141336733","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}
Accurately predicting stock prices poses a significant challenge due to the dynamic and complex nature of financial markets. This paper introduces a novel method that combines technical indicators with ensemble learning techniques to effectively forecast opening stock prices. Technical indicators offer valuable insights into market trends and patterns, while ensemble learning methods merge multiple models to enhance predictive precision. The study utilizes various technical indicators such as moving averages, Relative Strength Index (RSI), and Bollinger Bands to capture diverse aspects of market behaviour. Ensemble learning techniques like Random Forest, Gradient Boosting, Support Vector Regressor, and ARIMA model are then employed to consolidate the forecasts from these indicators. The proposed framework is assessed using historical stock market data, and extensive experiments showcase its superior performance compared to individual indicators and traditional forecasting approaches. The findings reveal that integrating technical indicators with ensemble learning leads to a significant improvement in accuracy, with a success rate of 91.45% in predicting opening stock prices, thus providing valuable insights for investors and financial analysts.
{"title":"Integrating Technical Indicators and Ensemble Learning for Predicting the Opening Stock Price","authors":"Jency Jose, Varshini P","doi":"10.59461/ijitra.v3i2.96","DOIUrl":"https://doi.org/10.59461/ijitra.v3i2.96","url":null,"abstract":"Accurately predicting stock prices poses a significant challenge due to the dynamic and complex nature of financial markets. This paper introduces a novel method that combines technical indicators with ensemble learning techniques to effectively forecast opening stock prices. Technical indicators offer valuable insights into market trends and patterns, while ensemble learning methods merge multiple models to enhance predictive precision. The study utilizes various technical indicators such as moving averages, Relative Strength Index (RSI), and Bollinger Bands to capture diverse aspects of market behaviour. Ensemble learning techniques like Random Forest, Gradient Boosting, Support Vector Regressor, and ARIMA model are then employed to consolidate the forecasts from these indicators. The proposed framework is assessed using historical stock market data, and extensive experiments showcase its superior performance compared to individual indicators and traditional forecasting approaches. The findings reveal that integrating technical indicators with ensemble learning leads to a significant improvement in accuracy, with a success rate of 91.45% in predicting opening stock prices, thus providing valuable insights for investors and financial analysts.","PeriodicalId":503010,"journal":{"name":"International Journal of Information Technology, Research and Applications","volume":" 42","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-06-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141364712","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}
A. G. Daligcon, Jemima Priyadarshini, Lilibeth Rivera Decena
To reduce failure and personalize instruction, educators work to predict student achievement. For this objective, this study compared several categorization techniques. The study investigated techniques employing datasets from Portuguese schools, even though various circumstances make it difficult to gather full data and achieve high accuracy. Upon evaluating the various algorithms, including Random Forest and Decision Trees, the study determined that Random Forest was the most successful model, attaining a 94.55% accuracy rate. This demonstrates how machine learning—more especially, Random Forest—could forecast student achievement. The study opens the door for applying these techniques to early interventions and personalized learning. But more work needs to be done, such as creating publicly accessible educational datasets and investigating different strategies like regression algorithms to manage the nuances of grading systems more effectively.
{"title":"Unveiling the Best-fit Model: A Comparative Analysis of Classification Methods in Predicting Student Success","authors":"A. G. Daligcon, Jemima Priyadarshini, Lilibeth Rivera Decena","doi":"10.59461/ijitra.v3i1.84","DOIUrl":"https://doi.org/10.59461/ijitra.v3i1.84","url":null,"abstract":"To reduce failure and personalize instruction, educators work to predict student achievement. For this objective, this study compared several categorization techniques. The study investigated techniques employing datasets from Portuguese schools, even though various circumstances make it difficult to gather full data and achieve high accuracy. Upon evaluating the various algorithms, including Random Forest and Decision Trees, the study determined that Random Forest was the most successful model, attaining a 94.55% accuracy rate. This demonstrates how machine learning—more especially, Random Forest—could forecast student achievement. The study opens the door for applying these techniques to early interventions and personalized learning. But more work needs to be done, such as creating publicly accessible educational datasets and investigating different strategies like regression algorithms to manage the nuances of grading systems more effectively.","PeriodicalId":503010,"journal":{"name":"International Journal of Information Technology, Research and Applications","volume":"13 24","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-03-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140397150","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}
Patient medical records in Indonesia are starting to transform into electronic-based medical records. Electronic Medical Records (EMR) are medical records created using an electronic system. It is an electronic repository of information about a patient's health status and health services throughout patient life. The impact if health services have not implemented EMR is that it could hamper patient health services. This research aims to identify factors inhibiting the implementation of EMR at the East Pamulang Community Health Center (Puskesmas). The research method used is descriptive with a qualitative approach. The research subject population was medical records officer and chief administrative officer, while the object population was the medical records, registration, clinics, laboratory, and pharmacy room. The results of the research identified several factors inhibiting the implementation of EMR, such as inadequate infrastructure, no officers with a background in medical records, and still using paper-based manual medical records. The conclusion of this research is the discovery of several factors inhibiting the implementation of EMR, human resource factors where it doesn’t yet have an officer with an educational background in medical records, the number of computers is still limited which supporting EMR in each service unit, and the electronic-based medical record applications are still not used comprehensively in the service units of the Puskesmas. Suggestions from the results of this research are that Puskesmas immediately recruits officers who have an educational background in Medical Records, increases the number of computer devices that support the implementation of EMR, and immediately implements electronic-based medical records.
{"title":"Analysis Of Inhibiting Factors Implementation Of Electronic Medical Records In East Pamulang Community Health Center","authors":"Gama Bagus Kuntoadi1, Rita Dwi Pratiwi, Hasan Sadikin, Iah Bilqiz Khairul Barriyah, Brojo Kishore Mishra","doi":"10.59461/ijitra.v3i1.85","DOIUrl":"https://doi.org/10.59461/ijitra.v3i1.85","url":null,"abstract":"Patient medical records in Indonesia are starting to transform into electronic-based medical records. Electronic Medical Records (EMR) are medical records created using an electronic system. It is an electronic repository of information about a patient's health status and health services throughout patient life. The impact if health services have not implemented EMR is that it could hamper patient health services. This research aims to identify factors inhibiting the implementation of EMR at the East Pamulang Community Health Center (Puskesmas). The research method used is descriptive with a qualitative approach. The research subject population was medical records officer and chief administrative officer, while the object population was the medical records, registration, clinics, laboratory, and pharmacy room. The results of the research identified several factors inhibiting the implementation of EMR, such as inadequate infrastructure, no officers with a background in medical records, and still using paper-based manual medical records. The conclusion of this research is the discovery of several factors inhibiting the implementation of EMR, human resource factors where it doesn’t yet have an officer with an educational background in medical records, the number of computers is still limited which supporting EMR in each service unit, and the electronic-based medical record applications are still not used comprehensively in the service units of the Puskesmas. Suggestions from the results of this research are that Puskesmas immediately recruits officers who have an educational background in Medical Records, increases the number of computer devices that support the implementation of EMR, and immediately implements electronic-based medical records.","PeriodicalId":503010,"journal":{"name":"International Journal of Information Technology, Research and Applications","volume":"352 16","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-03-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140397730","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}
Krishnaveni Veeranan, Vaidhyanathan Pandian, Thamaraiselvi, Antonyraj Martin
This research investigates the crucial role of mathematical models in designing and optimizing modern IT systems. The paper explores how mathematical tools simplify complex systems, enabling efficient design and improved understanding. The impact of mathematical models extends beyond initial design. The research emphasizes the importance of maintaining these models throughout a system's life cycle to ensure ongoing optimization. Fuzzy Logic Controllers and Coverage Path Planning (CPP) algorithms are highlighted as key examples of how mathematical models are utilized in IT system design, particularly for robotic guided vehicles. The research focuses on real-world applications of CPP and the role mathematical models play in finding optimal solutions. This study aims to benefit professionals in robotics research, logistics, and process automation. It explores how mathematical models can contribute to overcoming challenges in modern industries, such as increased production efficiency and reduced costs.
这项研究探讨了数学模型在设计和优化现代信息技术系统中的关键作用。论文探讨了数学工具如何简化复杂系统，从而实现高效设计和提高理解能力。数学模型的影响超出了最初的设计。模糊逻辑控制器和覆盖路径规划（CPP）算法是在 IT 系统设计中如何利用数学模型的重要实例，特别是在机器人制导车辆中。研究重点是 CPP 在现实世界中的应用，以及数学模型在寻找最佳解决方案中发挥的作用。本研究旨在为机器人研究、物流和流程自动化领域的专业人士提供帮助，探讨数学模型如何有助于克服现代工业面临的挑战，如提高生产效率和降低成本。
{"title":"Impact of Mathematical Models in IT System Design and Optimization","authors":"Krishnaveni Veeranan, Vaidhyanathan Pandian, Thamaraiselvi, Antonyraj Martin","doi":"10.59461/ijitra.v3i1.83","DOIUrl":"https://doi.org/10.59461/ijitra.v3i1.83","url":null,"abstract":"This research investigates the crucial role of mathematical models in designing and optimizing modern IT systems. The paper explores how mathematical tools simplify complex systems, enabling efficient design and improved understanding. The impact of mathematical models extends beyond initial design. The research emphasizes the importance of maintaining these models throughout a system's life cycle to ensure ongoing optimization.\u0000Fuzzy Logic Controllers and Coverage Path Planning (CPP) algorithms are highlighted as key examples of how mathematical models are utilized in IT system design, particularly for robotic guided vehicles. The research focuses on real-world applications of CPP and the role mathematical models play in finding optimal solutions.\u0000This study aims to benefit professionals in robotics research, logistics, and process automation. It explores how mathematical models can contribute to overcoming challenges in modern industries, such as increased production efficiency and reduced costs.","PeriodicalId":503010,"journal":{"name":"International Journal of Information Technology, Research and Applications","volume":"130 ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-02-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140462199","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}
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