Pub Date : 2023-07-09DOI: 10.55524/ijircst.2023.11.4.7
M. Koohi, Behzad Moshiri, Abbas Shakery
In this modern era, medical image processing is an indispensable part of many applications and practices in the medical domain. The images that are used should meet certain criteria, including having more accurate details and information than each individual image, which can help medical scientists with analysis and treatment. Medical image fusion is among the techniques that offer high-quality images, which are combined from different modalities. Multimodal medical image fusion provides remarkable improvement in the quality of the fused images. In this paper, we describe an image fusion method for magnetic resonance imaging (MRI) and computed tomography (CT) utilizing local features and fuzzy logic methods. The aim of the proposed technique is to create the maximum combination of useful information present in MRI and CT images. Image local features are distinguished and combined with fuzzy logic to calculate weights for each pixel. Simulation outcomes show that the proposed method produces considerably better results compared to cutting-edge techniques. The method is also used to detect and highlight tumorous areas, followed by morphology filters used to eliminate any noise and disturbance.
{"title":"Design of a Robust Hybrid Fuzzy Method for Medical Image Fusion","authors":"M. Koohi, Behzad Moshiri, Abbas Shakery","doi":"10.55524/ijircst.2023.11.4.7","DOIUrl":"https://doi.org/10.55524/ijircst.2023.11.4.7","url":null,"abstract":"In this modern era, medical image processing is an indispensable part of many applications and practices in the medical domain. The images that are used should meet certain criteria, including having more accurate details and information than each individual image, which can help medical scientists with analysis and treatment. Medical image fusion is among the techniques that offer high-quality images, which are combined from different modalities. Multimodal medical image fusion provides remarkable improvement in the quality of the fused images. In this paper, we describe an image fusion method for magnetic resonance imaging (MRI) and computed tomography (CT) utilizing local features and fuzzy logic methods. The aim of the proposed technique is to create the maximum combination of useful information present in MRI and CT images. Image local features are distinguished and combined with fuzzy logic to calculate weights for each pixel. Simulation outcomes show that the proposed method produces considerably better results compared to cutting-edge techniques. The method is also used to detect and highlight tumorous areas, followed by morphology filters used to eliminate any noise and disturbance.","PeriodicalId":218345,"journal":{"name":"International Journal of Innovative Research in Computer Science and Technology","volume":"19 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-07-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124527225","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}
Pub Date : 2023-07-07DOI: 10.55524/ijircst.2023.11.4.5
K. Kumar, K. Tripathi, Rashmi Gupta
The increasing threat of plant diseases poses a significant challenge to global food security. Rapid and accurate identification of plant diseases is crucial for effective disease management and prevention. In recent years, deep learning techniques have shown great promise in automating the process of plant disease identification through image analysis. This report presents a comprehensive study on image-based plant disease classification using deep learning techniques. The report begins by providing an overview of plant diseases and their impact on agriculture. It discusses the limitations of traditional disease identification methods and highlights the potential of deep learning algorithms in revolutionizing the field. The importance of image-based approaches is emphasized due to their non-destructive and scalable nature. Next, the report delves into the methodology of deep learning for plant disease classification. It explores various architectures such as convolutional neural networks (CNNs) and their variants, including transfer learning and ensemble methods. The training process, data augmentation techniques, and hyperparameter tuning are discussed in detail.
{"title":"Plant Disease Detection from Image Using CNN","authors":"K. Kumar, K. Tripathi, Rashmi Gupta","doi":"10.55524/ijircst.2023.11.4.5","DOIUrl":"https://doi.org/10.55524/ijircst.2023.11.4.5","url":null,"abstract":"The increasing threat of plant diseases poses a significant challenge to global food security. Rapid and accurate identification of plant diseases is crucial for effective disease management and prevention. In recent years, deep learning techniques have shown great promise in automating the process of plant disease identification through image analysis. This report presents a comprehensive study on image-based plant disease classification using deep learning techniques. The report begins by providing an overview of plant diseases and their impact on agriculture. It discusses the limitations of traditional disease identification methods and highlights the potential of deep learning algorithms in revolutionizing the field. The importance of image-based approaches is emphasized due to their non-destructive and scalable nature. Next, the report delves into the methodology of deep learning for plant disease classification. It explores various architectures such as convolutional neural networks (CNNs) and their variants, including transfer learning and ensemble methods. The training process, data augmentation techniques, and hyperparameter tuning are discussed in detail.","PeriodicalId":218345,"journal":{"name":"International Journal of Innovative Research in Computer Science and Technology","volume":"20 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-07-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115893621","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}
Pub Date : 2023-07-06DOI: 10.55524/ijircst.2023.11.4.4
Anas Habib Zuberi, Shish Ahmad
The increasing security threats in public places such as airports, train stations, and shopping malls require the development of smart security systems that can detect potential threats and provide timely alerts to security personnel. This research paper proposes an IoT-based smart alert network security system using machine learning to enhance public safety. The system integrates various sensors and devices that collect data such as motion, which is analyzed using machine learning algorithms to detect anomalies and trigger alerts if any suspicious activity is detected. The proposed system achieves an accuracy rate of 91.12% in detecting suspicious activities, which is significantly higher than the existing security systems used in public places. The system can provide real-time alerts, which can reduce the response time of security personnel and prevent potential security threats. The proposed system can be implemented in various public places to enhance public safety and prevent security breaches. The results of this research paper provide a useful reference for future studies on the development of smart security systems using IoT and machine learning technologies.
{"title":"IoT Based Smart Alert Network Security System Using Machine Learning","authors":"Anas Habib Zuberi, Shish Ahmad","doi":"10.55524/ijircst.2023.11.4.4","DOIUrl":"https://doi.org/10.55524/ijircst.2023.11.4.4","url":null,"abstract":"The increasing security threats in public places such as airports, train stations, and shopping malls require the development of smart security systems that can detect potential threats and provide timely alerts to security personnel. This research paper proposes an IoT-based smart alert network security system using machine learning to enhance public safety. The system integrates various sensors and devices that collect data such as motion, which is analyzed using machine learning algorithms to detect anomalies and trigger alerts if any suspicious activity is detected. The proposed system achieves an accuracy rate of 91.12% in detecting suspicious activities, which is significantly higher than the existing security systems used in public places. The system can provide real-time alerts, which can reduce the response time of security personnel and prevent potential security threats. The proposed system can be implemented in various public places to enhance public safety and prevent security breaches. The results of this research paper provide a useful reference for future studies on the development of smart security systems using IoT and machine learning technologies.","PeriodicalId":218345,"journal":{"name":"International Journal of Innovative Research in Computer Science and Technology","volume":"47 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-07-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122968385","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}
Pub Date : 2023-07-05DOI: 10.55524/ijircst.2023.11.4.3
Sukwon Kim
The present study evaluated success rate of basketball shooting for different skill levels. A total of 10 subjects (5 skilled and 5 unskilled) participated in this study. The main goal of the study was to provide the preliminary data for further studies in finding mechanisms of improving shooting success rate during basketball games. This larger constraint is thought to require performers to change their motion control strategy to maintain accuracy and create a tremendous urge to push the ball the moment it is released. Still, many players have difficulty adapting to longer distance shooting. The results suggested that there were main effects of group and distance, but, no interaction effect was found. In conclusion, shooting success rate were affected by distance regardless of skill levels in basketball shooting.
{"title":"Exploring the Efficacy of Basketball Shooting: A Comprehensive Analysis of Success Rates","authors":"Sukwon Kim","doi":"10.55524/ijircst.2023.11.4.3","DOIUrl":"https://doi.org/10.55524/ijircst.2023.11.4.3","url":null,"abstract":"The present study evaluated success rate of basketball shooting for different skill levels. A total of 10 subjects (5 skilled and 5 unskilled) participated in this study. The main goal of the study was to provide the preliminary data for further studies in finding mechanisms of improving shooting success rate during basketball games. This larger constraint is thought to require performers to change their motion control strategy to maintain accuracy and create a tremendous urge to push the ball the moment it is released. Still, many players have difficulty adapting to longer distance shooting. The results suggested that there were main effects of group and distance, but, no interaction effect was found. In conclusion, shooting success rate were affected by distance regardless of skill levels in basketball shooting.","PeriodicalId":218345,"journal":{"name":"International Journal of Innovative Research in Computer Science and Technology","volume":"71 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-07-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127274303","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}
Pub Date : 2023-07-01DOI: 10.55524/ijircst.2023.11.4.2
Livingston Jeeva, Ijtaba Saleem Khan
In today’s era, almost everyone is using emails on their daily basis. In our proposed research, we suggest a machine learning-based strategy for enhancing email spam filters' accuracy. Traditional rule-based filters have grown less effective as spam emails have multiplied exponentially. Models can be trained to identify emails as spam or not using machine learning algorithms, particularly supervised learning. We need to create a simple and straightforward machine learning model in order to reach more accurate results while categorizing email spam. We picked the Naive Bayes technique for our model since it is quicker and more accurate than other algorithms. The suggested method can have incorporated into current email systems to enhance spam filtering functionality. This review paper provides an overview of the machine learning model we have suggested.
{"title":"A Review Article On Enhancing Email Spam Filter’s Accuracy Using Machine Learning","authors":"Livingston Jeeva, Ijtaba Saleem Khan","doi":"10.55524/ijircst.2023.11.4.2","DOIUrl":"https://doi.org/10.55524/ijircst.2023.11.4.2","url":null,"abstract":"In today’s era, almost everyone is using emails on their daily basis. In our proposed research, we suggest a machine learning-based strategy for enhancing email spam filters' accuracy. Traditional rule-based filters have grown less effective as spam emails have multiplied exponentially. Models can be trained to identify emails as spam or not using machine learning algorithms, particularly supervised learning. We need to create a simple and straightforward machine learning model in order to reach more accurate results while categorizing email spam. We picked the Naive Bayes technique for our model since it is quicker and more accurate than other algorithms. The suggested method can have incorporated into current email systems to enhance spam filtering functionality. This review paper provides an overview of the machine learning model we have suggested.","PeriodicalId":218345,"journal":{"name":"International Journal of Innovative Research in Computer Science and Technology","volume":"7 8 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127422807","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 reliable fire alarm system (FAS) is crucial for timely reporting and responding to fires. While existing techniques can predict undesirable outcomes, they lack guidance on when and how workers should intervene to minimize the associated costs. Recent advancements in sensors, microelectronics, and information technology have significantly improved fire detection technologies. However, the prevalence of synthetic materials in modern homes has increased the danger of fire-related injuries and deaths due to the release of toxic fumes and gases, including carbon monoxide. This highlights the need for ongoing analysis and development of fire detection techniques to ensure the safety of occupants.
{"title":"Fire Alarm System Through Smoke Detection","authors":"Manya Sinha, Shivank Solanki, Sudeep Batra, Dr. Yojna Arora, Abhishek Tewatia","doi":"10.55524/ijircst.2023.11.4.1","DOIUrl":"https://doi.org/10.55524/ijircst.2023.11.4.1","url":null,"abstract":"A reliable fire alarm system (FAS) is crucial for timely reporting and responding to fires. While existing techniques can predict undesirable outcomes, they lack guidance on when and how workers should intervene to minimize the associated costs. Recent advancements in sensors, microelectronics, and information technology have significantly improved fire detection technologies. However, the prevalence of synthetic materials in modern homes has increased the danger of fire-related injuries and deaths due to the release of toxic fumes and gases, including carbon monoxide. This highlights the need for ongoing analysis and development of fire detection techniques to ensure the safety of occupants.","PeriodicalId":218345,"journal":{"name":"International Journal of Innovative Research in Computer Science and Technology","volume":"20 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125008350","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}
Pub Date : 2023-05-31DOI: 10.55524/ijircst.2023.11.3.4
A. Jatain, S. B. Bajaj, Priyanka Vashisht, Ashima Narang
Deep learning has been evidenced to be a cutting-edge technology for big data scrutiny with a huge figure of effective cases in image processing, speech recognition, object detection, and so on. Lately, it has also been acquainted with in food science and business. In this paper, a fleeting overview of deep learning and detailly labelled the structure of some prevalent constructions of deep neural networks and the method for training a model is provided. Various techniques that used deep learning as the data analysis tool are analyzed to answer the complications and challenges in food sphere together with quality detection of fruits & vegetables. The precise difficulties, the datasets, the pre-processing approaches, the networks and frameworks used, the performance attained, and the evaluation with other prevalent explanations of each research are examined. We also analyzed the potential of deep learning to be used as a cutting-edge data mining tool in food sensory and consume explores. The outcome of our review specifies that deep learning outclasses other approaches such as physical feature extractors, orthodox machine learning algorithms, and deep learning as a capable tool in food quality and safety inspection. The cheering outcomes in classification and regression problems attained by deep learning will fascinate more research exertions to apply deep learning into the arena of food in the forthcoming. The main aim of this work is to facilitate our learning and implement that in real life. Food quality and food security are always issues which are always overlooked. In modern times, this has morphed into more significant concerns relating to optimization of on- demand supply chains and profitability of agri-businesses. But now with the advanced systems and technology, it is possible to resolve this issue efficiently using the power of AI.
{"title":"Artificial Intelligence Based Predictive Analysis of Customer Churn","authors":"A. Jatain, S. B. Bajaj, Priyanka Vashisht, Ashima Narang","doi":"10.55524/ijircst.2023.11.3.4","DOIUrl":"https://doi.org/10.55524/ijircst.2023.11.3.4","url":null,"abstract":"Deep learning has been evidenced to be a cutting-edge technology for big data scrutiny with a huge figure of effective cases in image processing, speech recognition, object detection, and so on. Lately, it has also been acquainted with in food science and business. In this paper, a fleeting overview of deep learning and detailly labelled the structure of some prevalent constructions of deep neural networks and the method for training a model is provided. Various techniques that used deep learning as the data analysis tool are analyzed to answer the complications and challenges in food sphere together with quality detection of fruits & vegetables. The precise difficulties, the datasets, the pre-processing approaches, the networks and frameworks used, the performance attained, and the evaluation with other prevalent explanations of each research are examined. We also analyzed the potential of deep learning to be used as a cutting-edge data mining tool in food sensory and consume explores. The outcome of our review specifies that deep learning outclasses other approaches such as physical feature extractors, orthodox machine learning algorithms, and deep learning as a capable tool in food quality and safety inspection. The cheering outcomes in classification and regression problems attained by deep learning will fascinate more research exertions to apply deep learning into the arena of food in the forthcoming. The main aim of this work is to facilitate our learning and implement that in real life. Food quality and food security are always issues which are always overlooked. In modern times, this has morphed into more significant concerns relating to optimization of on- demand supply chains and profitability of agri-businesses. But now with the advanced systems and technology, it is possible to resolve this issue efficiently using the power of AI.","PeriodicalId":218345,"journal":{"name":"International Journal of Innovative Research in Computer Science and Technology","volume":"8 4 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-05-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125473927","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}
Pub Date : 2023-05-31DOI: 10.55524/ijircst.2023.11.3.5
Heemakshi Sharma, K. Tripathi
The effectiveness of any digital marketing strategy is strongly dependent on the user experience of the website being promoted. A website that is difficult to browse, confusing or slow to load the content can result in a poor user experience and, as a result there is a loss in leads or consumers. A website that is easy to use, well-organized and optimised for mobile devices, on the other hand can help improve user engagement, raise conversion rates and ultimately boost the success of digital marketing initiatives. This paper shows the significance of website usability in digital marketing and explores the impact of website usability on user experience and how it can affect the key performance indicators such as bounce rates, session duration and conversion rates. By emphasising the importance of website usability in digital marketing, this paper intends to give actionable insights for business looking to improve the user’s website’s user experience and maximise the digital marketing campaigns. Understanding the impact of website usability on the performance of digital marketing efforts allows organisations to make informed decisions regarding website design and functionality to create good user experience that increases engagement and conversion.
{"title":"The Importance of Website Usability in Digital Marketing- A Review","authors":"Heemakshi Sharma, K. Tripathi","doi":"10.55524/ijircst.2023.11.3.5","DOIUrl":"https://doi.org/10.55524/ijircst.2023.11.3.5","url":null,"abstract":"The effectiveness of any digital marketing strategy is strongly dependent on the user experience of the website being promoted. A website that is difficult to browse, confusing or slow to load the content can result in a poor user experience and, as a result there is a loss in leads or consumers. A website that is easy to use, well-organized and optimised for mobile devices, on the other hand can help improve user engagement, raise conversion rates and ultimately boost the success of digital marketing initiatives. This paper shows the significance of website usability in digital marketing and explores the impact of website usability on user experience and how it can affect the key performance indicators such as bounce rates, session duration and conversion rates. By emphasising the importance of website usability in digital marketing, this paper intends to give actionable insights for business looking to improve the user’s website’s user experience and maximise the digital marketing campaigns. Understanding the impact of website usability on the performance of digital marketing efforts allows organisations to make informed decisions regarding website design and functionality to create good user experience that increases engagement and conversion.","PeriodicalId":218345,"journal":{"name":"International Journal of Innovative Research in Computer Science and Technology","volume":"47 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-05-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115426858","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}
Pub Date : 2023-05-31DOI: 10.55524/ijircst.2023.11.3.15
M. Kumar, D. Harikeerthi, G. Rajeswari, D. Manohar, I. Raviteja, T. Upendra, G. Venkateswarlu
Moreover, porous asphalts have volumetric and surface characteristics (e.g., acoustic absorption, drain ability, texture, and friction). These characteristics are influenced by both intrinsic (such as gradation and bitumen content) and extrinsic (such as traffic load) variables, and the complex phenomena and processes that give rise to them determine how they change over time. Deterioration, which consequently impacts budget, noise, and safety. Despite the deterioration of so many different and intricate qualities over time, there aren’t any standards for synergistically improving the pavement system. So, the goal of this study is to establish and validate a design methodology that, when applied to the most important characteristics of friction courses as a component of a pavement structure, works in concert. The aforementioned method is based on extensive literature analyses as well as experiment conducted in a lab and on-site over a period of time to assess how the major surface and volumetric qualities degrade over time.
{"title":"Design Construction and Performance of Porous and Rubbrised Asphalt Pavement","authors":"M. Kumar, D. Harikeerthi, G. Rajeswari, D. Manohar, I. Raviteja, T. Upendra, G. Venkateswarlu","doi":"10.55524/ijircst.2023.11.3.15","DOIUrl":"https://doi.org/10.55524/ijircst.2023.11.3.15","url":null,"abstract":"Moreover, porous asphalts have volumetric and surface characteristics (e.g., acoustic absorption, drain ability, texture, and friction). These characteristics are influenced by both intrinsic (such as gradation and bitumen content) and extrinsic (such as traffic load) variables, and the complex phenomena and processes that give rise to them determine how they change over time. Deterioration, which consequently impacts budget, noise, and safety. Despite the deterioration of so many different and intricate qualities over time, there aren’t any standards for synergistically improving the pavement system. So, the goal of this study is to establish and validate a design methodology that, when applied to the most important characteristics of friction courses as a component of a pavement structure, works in concert. The aforementioned method is based on extensive literature analyses as well as experiment conducted in a lab and on-site over a period of time to assess how the major surface and volumetric qualities degrade over time.","PeriodicalId":218345,"journal":{"name":"International Journal of Innovative Research in Computer Science and Technology","volume":"53 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-05-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130014099","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}
Pub Date : 2023-05-31DOI: 10.55524/ijircst.2023.11.3.13
G. E. Babu, M. Kumar, B. A. saroja, V. V. Vamsi, K. P. Kumar, K. P. Kalyan
Its poor quality was one of the key problems preventing recycled course aggregate (RCA) from being used in concrete mixes. The quality of recycled aggregate may be impacted by cement mortar on the surface of the material. The examination of recycled aggregate in various test results employing acid soaking treatment procedures utilizing sulfuric acid and phosphoric acid is the subject of this research. Natural aggregate, recycled aggregate, recycled aggregate with H2SO4 and recycled aggregate with H3PO4 are the four types of concrete mixtures that are made. Using these cleaned recycled aggregates in concrete has been proven to be beneficial. The strength and durability of concrete were also evaluated, and aggregate attributes were computed. The test findings showed. The behavior of recycled aggregate has changed according to the test results after being treated with acid. Out of these two acids, sulphuric acid treatment of recycled aggregates produced the best results, followed by phosphoric acid doing only marginally better. According to the overall research, employing treated recycled aggregate in concrete can significantly increase the strength and durability properties of concrete made using recycled aggregate.
{"title":"Analyzing The Performance of Environmental Friendly Concrete That Contains Acid-Treated (H2so4, H3po4) Recycled Aggregate","authors":"G. E. Babu, M. Kumar, B. A. saroja, V. V. Vamsi, K. P. Kumar, K. P. Kalyan","doi":"10.55524/ijircst.2023.11.3.13","DOIUrl":"https://doi.org/10.55524/ijircst.2023.11.3.13","url":null,"abstract":"Its poor quality was one of the key problems preventing recycled course aggregate (RCA) from being used in concrete mixes. The quality of recycled aggregate may be impacted by cement mortar on the surface of the material. The examination of recycled aggregate in various test results employing acid soaking treatment procedures utilizing sulfuric acid and phosphoric acid is the subject of this research. Natural aggregate, recycled aggregate, recycled aggregate with H2SO4 and recycled aggregate with H3PO4 are the four types of concrete mixtures that are made. Using these cleaned recycled aggregates in concrete has been proven to be beneficial. The strength and durability of concrete were also evaluated, and aggregate attributes were computed. The test findings showed. The behavior of recycled aggregate has changed according to the test results after being treated with acid. Out of these two acids, sulphuric acid treatment of recycled aggregates produced the best results, followed by phosphoric acid doing only marginally better. According to the overall research, employing treated recycled aggregate in concrete can significantly increase the strength and durability properties of concrete made using recycled aggregate.","PeriodicalId":218345,"journal":{"name":"International Journal of Innovative Research in Computer Science and Technology","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-05-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131249387","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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