Pub Date : 2023-08-05DOI: 10.59544/xvtu7545/ngcesi23p37
Misba M, Vivek A, Ratheesh R, Aslin C
Rice cultivation is a crucial industry in India, but it is plagued by various diseases that can damage crops at different stages. These diseases are challenging for farmers to identify accurately due to their limited knowledge and expertise. As a result, the farmers often struggle to take appropriate measures to prevent or manage these diseases, which can result in significant losses in crop yield and quality. Therefore, there is a need for advanced technologies and tools to help farmers accurately identify and manage these diseases, ensuring a sustainable and profitable rice cultivation industry in India. Recent advances in Deep Learning have demonstrated that Convolutional Neural Network models can be highly effective in automatic image recognition tasks. These models have shown great potential in addressing the challenges faced by farmers in identifying diseases in crops such as rice. However, in order to train such models, a large and diverse dataset is required, which may not always be readily available. To address this issue, researchers have created their own dataset of rice leaf disease images, which may be smaller in size but sufficient for the task at hand. To develop their CNN model, they have used a technique called Transfer Learning, which used as a starting point to fine-tune already trained models for a new task. The proposed CNN architecture is based on the VGG-16, a widely used pre-trained model used in computer vision tasks. The researchers trained and tested their model with a dataset obtained from rice fields and the Internet. The results show that the proposed model achieves 92.46% accuracy, demonstrating its potential in accurately detecting rice leaf diseases.
{"title":"Artificial Intelligence based Classification of Diseases for Rice Leaf Using CNN model","authors":"Misba M, Vivek A, Ratheesh R, Aslin C","doi":"10.59544/xvtu7545/ngcesi23p37","DOIUrl":"https://doi.org/10.59544/xvtu7545/ngcesi23p37","url":null,"abstract":"Rice cultivation is a crucial industry in India, but it is plagued by various diseases that can damage crops at different stages. These diseases are challenging for farmers to identify accurately due to their limited knowledge and expertise. As a result, the farmers often struggle to take appropriate measures to prevent or manage these diseases, which can result in significant losses in crop yield and quality. Therefore, there is a need for advanced technologies and tools to help farmers accurately identify and manage these diseases, ensuring a sustainable and profitable rice cultivation industry in India. Recent advances in Deep Learning have demonstrated that Convolutional Neural Network models can be highly effective in automatic image recognition tasks. These models have shown great potential in addressing the challenges faced by farmers in identifying diseases in crops such as rice. However, in order to train such models, a large and diverse dataset is required, which may not always be readily available. To address this issue, researchers have created their own dataset of rice leaf disease images, which may be smaller in size but sufficient for the task at hand. To develop their CNN model, they have used a technique called Transfer Learning, which used as a starting point to fine-tune already trained models for a new task. The proposed CNN architecture is based on the VGG-16, a widely used pre-trained model used in computer vision tasks. The researchers trained and tested their model with a dataset obtained from rice fields and the Internet. The results show that the proposed model achieves 92.46% accuracy, demonstrating its potential in accurately detecting rice leaf diseases.","PeriodicalId":315694,"journal":{"name":"The International Conference on scientific innovations in Science, Technology, and Management","volume":"60 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-08-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126538922","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 : 2023-08-05DOI: 10.59544/axwh3491/ngcesi23p63
Nikhil R
Natural plant fibers have unequivocally contributed economic prosperity and sustainability in our daily lives. Particularly, bamboo fibers have been used for industrial applications as diverse as textiles, paper, and construction. Recent renewed interest in bamboo fiber (BF) is primarily targeted for the replacement or reduction in use of glass fiber from nonrenewable resources. In this project, various mechanical, chemical, and biological approaches for the preparation and separation of bamboo fibers from raw bamboo are summarized. In this work the mechanical properties of Bamboo Fiber Reinforced Composite (BFRC) were studied. The bamboo fibers were prepared through chemical treatment by CUSO4, Borax and Boric acid followed by physical milling method. Compression, tensile, hardness were showed improvement in mechanical properties. Hence this composite material can be used as a manufacturing material for production, manufacturing industries.
{"title":"Processing Of a Manufacturing Material Using Treated Bamboo","authors":"Nikhil R","doi":"10.59544/axwh3491/ngcesi23p63","DOIUrl":"https://doi.org/10.59544/axwh3491/ngcesi23p63","url":null,"abstract":"Natural plant fibers have unequivocally contributed economic prosperity and sustainability in our daily lives. Particularly, bamboo fibers have been used for industrial applications as diverse as textiles, paper, and construction. Recent renewed interest in bamboo fiber (BF) is primarily targeted for the replacement or reduction in use of glass fiber from nonrenewable resources. In this project, various mechanical, chemical, and biological approaches for the preparation and separation of bamboo fibers from raw bamboo are summarized. In this work the mechanical properties of Bamboo Fiber Reinforced Composite (BFRC) were studied. The bamboo fibers were prepared through chemical treatment by CUSO4, Borax and Boric acid followed by physical milling method. Compression, tensile, hardness were showed improvement in mechanical properties. Hence this composite material can be used as a manufacturing material for production, manufacturing industries.","PeriodicalId":315694,"journal":{"name":"The International Conference on scientific innovations in Science, Technology, and Management","volume":"35 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-08-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129586306","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 : 2023-08-05DOI: 10.59544/iuhj8423/ngcesi23p48
Akhila K. S, Anuja S. B
The recognition of medical images, especially endoscopic ultrasound images, has the characteristics of changing images and insignificant gray-scale changes, which requires repeated observation and comparison by medical staff. In view of the above- mentioned characteristics of ultrasound imaging, a system scheme suitable for image processing is proposed, which can analyse the biliary tract, gallbladder, abdominal lymph nodes, liver, descending duodenum, duodenal bulb, stomach, pancreas, pancreatic lymph nodes, there are a total of 10 ultrasonic organs, including 21 kinds of sub-categories and 3510 images. The images are pre-processed using binarization, histogram equalization, median filtering and edge enhancement algorithms. The improved YoloV4 convolutional neural network algorithm is used to train the data set and perform high accuracy is detected in real time. Finally, the average accuracy of this algorithm has reached 91.59%. The algorithm proposed in this Paper can make up for the shortcomings of manual detection in the original image detection system, improve the efficiency of detection, and at the same time as an auxiliary system can reduce detection misjudgments, and promote the development of automated and intelligent detection in the medical field.
{"title":"Endoscopic Ultrasound Image Recognition Using Improved You Only Look Once (Yolov4) Convolutional Neural Network","authors":"Akhila K. S, Anuja S. B","doi":"10.59544/iuhj8423/ngcesi23p48","DOIUrl":"https://doi.org/10.59544/iuhj8423/ngcesi23p48","url":null,"abstract":"The recognition of medical images, especially endoscopic ultrasound images, has the characteristics of changing images and insignificant gray-scale changes, which requires repeated observation and comparison by medical staff. In view of the above- mentioned characteristics of ultrasound imaging, a system scheme suitable for image processing is proposed, which can analyse the biliary tract, gallbladder, abdominal lymph nodes, liver, descending duodenum, duodenal bulb, stomach, pancreas, pancreatic lymph nodes, there are a total of 10 ultrasonic organs, including 21 kinds of sub-categories and 3510 images. The images are pre-processed using binarization, histogram equalization, median filtering and edge enhancement algorithms. The improved YoloV4 convolutional neural network algorithm is used to train the data set and perform high accuracy is detected in real time. Finally, the average accuracy of this algorithm has reached 91.59%. The algorithm proposed in this Paper can make up for the shortcomings of manual detection in the original image detection system, improve the efficiency of detection, and at the same time as an auxiliary system can reduce detection misjudgments, and promote the development of automated and intelligent detection in the medical field.","PeriodicalId":315694,"journal":{"name":"The International Conference on scientific innovations in Science, Technology, and Management","volume":"73 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-08-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116354785","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 : 2023-08-05DOI: 10.59544/slmx2075/ngcesi23p55
Siva Sankar V, S. S. T
Today’s organ donation and transplantation systems pose different requirements and challenges in terms of registration, donor-recipient matching, organ removal, organ delivery, and transplantation with legal, clinical, ethical, and technical constraints. Therefore, an end-to-end organ donation and transplantation system is required to guarantee a fair and efficient process to enhance patient experience and trust. Propose a private Ethereum blockchain-based solution to enable organ donation and transplantation management in a manner that is fully decentralized, secure, traceable, auditable, private, and trustworthy. Develop smart contracts and present six algorithms along with their implementation, testing, and validation details. Evaluate the performance of the proposed solution by performing privacy, security, and confidentiality analyses as well as comparing our solution with the existing solutions. As a result, a ranked list is generated as an output and provided to the transplantation surgeons. Next, the transplant surgeon decides whether the organ is appropriate for the patient based on various considerations, such as the donor’s medical records and the current health of the prospective recipient. Later, when a transplant surgeon accepts the donated organ, the donor’s surgeon is notified to remove the donated organ.
{"title":"Using Hashing Algorithm for the Organ Procurement and Transplant Network","authors":"Siva Sankar V, S. S. T","doi":"10.59544/slmx2075/ngcesi23p55","DOIUrl":"https://doi.org/10.59544/slmx2075/ngcesi23p55","url":null,"abstract":"Today’s organ donation and transplantation systems pose different requirements and challenges in terms of registration, donor-recipient matching, organ removal, organ delivery, and transplantation with legal, clinical, ethical, and technical constraints. Therefore, an end-to-end organ donation and transplantation system is required to guarantee a fair and efficient process to enhance patient experience and trust. Propose a private Ethereum blockchain-based solution to enable organ donation and transplantation management in a manner that is fully decentralized, secure, traceable, auditable, private, and trustworthy. Develop smart contracts and present six algorithms along with their implementation, testing, and validation details. Evaluate the performance of the proposed solution by performing privacy, security, and confidentiality analyses as well as comparing our solution with the existing solutions. As a result, a ranked list is generated as an output and provided to the transplantation surgeons. Next, the transplant surgeon decides whether the organ is appropriate for the patient based on various considerations, such as the donor’s medical records and the current health of the prospective recipient. Later, when a transplant surgeon accepts the donated organ, the donor’s surgeon is notified to remove the donated organ.","PeriodicalId":315694,"journal":{"name":"The International Conference on scientific innovations in Science, Technology, and Management","volume":"48 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-08-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127044073","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 : 2023-08-05DOI: 10.59544/ijux3686/ngcesi23p31
Akash R, S. S. T
Kinship verification, which is a challenging problem in computer vision and pattern discovery. It has several applications, such as organizing photo albums, recognizing resemblances among humans, and finding missing children. A system for facial kinship verification based on several kinds of texture descriptors (local binary patterns, local ternary patterns, local directional patterns, local phase quantization, and binarized statistical image features) with pyramid multilevel (PML) face representation for feature extraction along with our proposed paired feature representation and our proposed robust feature selection to reduce the number of features. The proposed approach consists of the following three main stages: (1) face pre-processing, (2) feature extraction and selection, and (3) kinship verification. Extensive experiments are conducted on five publicly available databases (Cornell, UB KinFace, Family 101, KinFace W-I, and KinFace W-II). Additionally, a wide experiment for each stage to find the best and most suitable settings. Many comparisons with state-of-the-art methods and through these comparisons, it appears that our experiments show stable and good results.
亲属关系验证是计算机视觉和模式发现领域的一个具有挑战性的问题。它有几个应用程序，如组织相册，识别人类之间的相似之处，以及寻找失踪的儿童。一个基于几种纹理描述符(局部二值模式、局部三值模式、局部方向模式、局部相位量化和二值化统计图像特征)的面部亲属关系验证系统，采用金字塔多层(PML)人脸表示进行特征提取，并采用我们提出的配对特征表示和我们提出的鲁棒特征选择来减少特征数量。该方法包括三个主要阶段:(1)人脸预处理;(2)特征提取与选择;(3)亲属关系验证。在五个公开可用的数据库(Cornell, UB KinFace, Family 101, KinFace W-I和KinFace W-II)上进行了广泛的实验。此外，在每个阶段进行广泛的实验，以找到最佳和最合适的设置。与最先进的方法进行了多次比较，通过这些比较，我们的实验显示出稳定而良好的结果。
{"title":"Kinship Measurement on Face Images by Structured Similarity Fusion","authors":"Akash R, S. S. T","doi":"10.59544/ijux3686/ngcesi23p31","DOIUrl":"https://doi.org/10.59544/ijux3686/ngcesi23p31","url":null,"abstract":"Kinship verification, which is a challenging problem in computer vision and pattern discovery. It has several applications, such as organizing photo albums, recognizing resemblances among humans, and finding missing children. A system for facial kinship verification based on several kinds of texture descriptors (local binary patterns, local ternary patterns, local directional patterns, local phase quantization, and binarized statistical image features) with pyramid multilevel (PML) face representation for feature extraction along with our proposed paired feature representation and our proposed robust feature selection to reduce the number of features. The proposed approach consists of the following three main stages: (1) face pre-processing, (2) feature extraction and selection, and (3) kinship verification. Extensive experiments are conducted on five publicly available databases (Cornell, UB KinFace, Family 101, KinFace W-I, and KinFace W-II). Additionally, a wide experiment for each stage to find the best and most suitable settings. Many comparisons with state-of-the-art methods and through these comparisons, it appears that our experiments show stable and good results.","PeriodicalId":315694,"journal":{"name":"The International Conference on scientific innovations in Science, Technology, and Management","volume":"17 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-08-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127625093","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 : 2023-08-05DOI: 10.59544/flzw2410/ngcesi23p22
Jingle Jabha D. F, J. R., Sarath Gokul R. S, Japdrew S
Acoustic Emission (AE) has the unique potential for the real time integrity evaluation of pressurized systems. The technique has found greater importance in its application towards fibre reinforced plastic (FRP) pressure vessels in aerospace use. There is no method till date spelt out in open literature for burst pressure prediction of composite pressure vessels. This paper brings out a methodology for the burst pressure prediction of Glass Fibre Reinforced Plastic (GFRP) pressure vessels using a lucid empirical relation. Acoustic Emission monitoring was carried out during hydrostatic loading of five identical GFRP pressure vessels, about 6- litre capacity. An empirical relation was generated on the basis of the governing AE parameters viz., count rate, duration rate, amplitude rate and felicity ratio exhibited when the h/w was subjected to cyclic proof pressure cum burst test. AE data is acquired up to 50% of the theoretical burst pressure, and then the vessels were pressurized upto failure. The authors have framed an empirical relation to predict the burst performance, solving the typical equations with MAT LAB program for the four identical GFRP vessels. An attempt is made on the fifth hardware to predict its burst pressure. This innovative methodology illustrates the behaviour of GFRP pressure vessels in terms of AE parameters and its derivatives. This can possibly predict in real time the burst pressure of similar hardware if extended to other material systems. The failure is significant even at 50 to 60% of Maximum Expected Operating Pressure (MEOP) with an acceptable error margin.
{"title":"Life Strength Prediction of GFRP Pressure Vessels Using Acoustic Emission Technique","authors":"Jingle Jabha D. F, J. R., Sarath Gokul R. S, Japdrew S","doi":"10.59544/flzw2410/ngcesi23p22","DOIUrl":"https://doi.org/10.59544/flzw2410/ngcesi23p22","url":null,"abstract":"Acoustic Emission (AE) has the unique potential for the real time integrity evaluation of pressurized systems. The technique has found greater importance in its application towards fibre reinforced plastic (FRP) pressure vessels in aerospace use. There is no method till date spelt out in open literature for burst pressure prediction of composite pressure vessels. This paper brings out a methodology for the burst pressure prediction of Glass Fibre Reinforced Plastic (GFRP) pressure vessels using a lucid empirical relation. Acoustic Emission monitoring was carried out during hydrostatic loading of five identical GFRP pressure vessels, about 6- litre capacity. An empirical relation was generated on the basis of the governing AE parameters viz., count rate, duration rate, amplitude rate and felicity ratio exhibited when the h/w was subjected to cyclic proof pressure cum burst test. AE data is acquired up to 50% of the theoretical burst pressure, and then the vessels were pressurized upto failure. The authors have framed an empirical relation to predict the burst performance, solving the typical equations with MAT LAB program for the four identical GFRP vessels. An attempt is made on the fifth hardware to predict its burst pressure. This innovative methodology illustrates the behaviour of GFRP pressure vessels in terms of AE parameters and its derivatives. This can possibly predict in real time the burst pressure of similar hardware if extended to other material systems. The failure is significant even at 50 to 60% of Maximum Expected Operating Pressure (MEOP) with an acceptable error margin.","PeriodicalId":315694,"journal":{"name":"The International Conference on scientific innovations in Science, Technology, and Management","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-08-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128825658","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 : 2023-08-05DOI: 10.59544/yhif3128/ngcesi23p40
Anisha S. S, Shajin Nargunam A
A malignancy known as multiple myeloma develops in a type of white blood cell known as a plasma cell. The plasma cells are responsible for the production of antibodies. In multiple myeloma, healthy blood cells are displaced by malignant plasma cells that build up in the bone marrow. Using machine learning techniques, artificial intelligence has radically changed the world of oncology research. Machine learning is a branch of artificial intelligence that makes use of algorithms to analyse data, draw conclusions from it, and then utilise those conclusions to make decisions in the present. In this study, we explore the potential applications of artificial intelligence in the diagnosis of multiple myeloma and present the most important machine learning and deep learning experiments conducted in the field. One of the most severe haematological cancers worldwide is multiple myeloma.
{"title":"The Significance of artificial intelligence in the early diagnosis of multiple myeloma","authors":"Anisha S. S, Shajin Nargunam A","doi":"10.59544/yhif3128/ngcesi23p40","DOIUrl":"https://doi.org/10.59544/yhif3128/ngcesi23p40","url":null,"abstract":"A malignancy known as multiple myeloma develops in a type of white blood cell known as a plasma cell. The plasma cells are responsible for the production of antibodies. In multiple myeloma, healthy blood cells are displaced by malignant plasma cells that build up in the bone marrow. Using machine learning techniques, artificial intelligence has radically changed the world of oncology research. Machine learning is a branch of artificial intelligence that makes use of algorithms to analyse data, draw conclusions from it, and then utilise those conclusions to make decisions in the present. In this study, we explore the potential applications of artificial intelligence in the diagnosis of multiple myeloma and present the most important machine learning and deep learning experiments conducted in the field. One of the most severe haematological cancers worldwide is multiple myeloma.","PeriodicalId":315694,"journal":{"name":"The International Conference on scientific innovations in Science, Technology, and Management","volume":"4 1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-08-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116872779","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 : 2023-08-05DOI: 10.59544/puyi9607/ngcesi23p11
Surya R, S. S. T
Farmers use Big Data to get information on changing Weather, Rainfall, Fertilizer Usage, Rainfall, and other factors that impact the crop yield. The yield of a crop is mainly determined by the climatic conditions like Temperature, Rainfall, Soil Conditions, and Fertilizers. All of this information assists farmers in making accurate and dependable decisions that maximize their productivity from cultivating the land. Recently, the Machine Learning Algorithms are used by the researchers to predict the yield of a crop before its actual cultivation. Firstly, Pre-process the data in a Python environment and then apply the Map Reduce Framework, which further analyses and processes the large volume of data. Secondly, K-means Clustering is employed on results gained from Map Reduce and provides a mean result on the data in terms of accuracy. Using Gradient Boosting Algorithm to predict the yield of crops based on the parameters like State, District, Area, Seasons, Rainfall, Temperature, and Area. To enhance the yield, this work study also suggests a fertilizer based on the soil conditions like NPK Values, Soil Type, Soil PH, Humidity, and Moisture. Fertilizer Recommendation is primarily done by using the Naive Bayes [NB] Algorithm.
{"title":"Gradient Boosting and Naive Bayes Crop Yield Prediction and Fertilizer Recommendation","authors":"Surya R, S. S. T","doi":"10.59544/puyi9607/ngcesi23p11","DOIUrl":"https://doi.org/10.59544/puyi9607/ngcesi23p11","url":null,"abstract":"Farmers use Big Data to get information on changing Weather, Rainfall, Fertilizer Usage, Rainfall, and other factors that impact the crop yield. The yield of a crop is mainly determined by the climatic conditions like Temperature, Rainfall, Soil Conditions, and Fertilizers. All of this information assists farmers in making accurate and dependable decisions that maximize their productivity from cultivating the land. Recently, the Machine Learning Algorithms are used by the researchers to predict the yield of a crop before its actual cultivation. Firstly, Pre-process the data in a Python environment and then apply the Map Reduce Framework, which further analyses and processes the large volume of data. Secondly, K-means Clustering is employed on results gained from Map Reduce and provides a mean result on the data in terms of accuracy. Using Gradient Boosting Algorithm to predict the yield of crops based on the parameters like State, District, Area, Seasons, Rainfall, Temperature, and Area. To enhance the yield, this work study also suggests a fertilizer based on the soil conditions like NPK Values, Soil Type, Soil PH, Humidity, and Moisture. Fertilizer Recommendation is primarily done by using the Naive Bayes [NB] Algorithm.","PeriodicalId":315694,"journal":{"name":"The International Conference on scientific innovations in Science, Technology, and Management","volume":"7 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-08-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115393843","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 : 2023-08-05DOI: 10.59544/zkuy9763/ngcesi23p18
Hemp (Cannabis sativa) is an agricultural crop that can be used as a building material in combination with lime and cement. A composite building material that combines a cementitious binder (building limes and cement) with hemp shives, the woody core of the hemp stalk is generally referred to as hemp concrete (HC). However, industrial facilities to separate hemp shives and fibres are currently not available in India. HC has many advantages as a building material but it is not load-bearing and must be used in combination with a load-bearing RCC frame. The aim of this research was to evaluate the feasibility of using both hemp shives and fibres in a HC to determine an optimal mix of the different binding agents and to investigate if adding cement binder would improve the mechanical strength of the material. The effects on compressive strength of pre-mixing the binder or creating perforations in the test specimens were also investigated.
{"title":"Manufacturing of Hempcrete building block","authors":"","doi":"10.59544/zkuy9763/ngcesi23p18","DOIUrl":"https://doi.org/10.59544/zkuy9763/ngcesi23p18","url":null,"abstract":"Hemp (Cannabis sativa) is an agricultural crop that can be used as a building material in combination with lime and cement. A composite building material that combines a cementitious binder (building limes and cement) with hemp shives, the woody core of the hemp stalk is generally referred to as hemp concrete (HC). However, industrial facilities to separate hemp shives and fibres are currently not available in India. HC has many advantages as a building material but it is not load-bearing and must be used in combination with a load-bearing RCC frame. The aim of this research was to evaluate the feasibility of using both hemp shives and fibres in a HC to determine an optimal mix of the different binding agents and to investigate if adding cement binder would improve the mechanical strength of the material. The effects on compressive strength of pre-mixing the binder or creating perforations in the test specimens were also investigated.","PeriodicalId":315694,"journal":{"name":"The International Conference on scientific innovations in Science, Technology, and Management","volume":"138 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-08-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115456433","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 : 2023-08-05DOI: 10.59544/najb3829/ngcesi23p28
Abhirami V
The main contributing factors are change in their use new design standards, deterioration due to corrosion in the steel caused by exposure to an aggressive environment and accident events such as earthquakes. In such circumstances there are only two possible solutions: replacement or retrofitting. Full structure replacement might have determinate disadvantages such as high costs for material and labour, a stronger environmental impact and inconvenience due to interruption of the function of the structure. Whenever possible, it is often better to repair or upgrade the structure by retrofitting or strengthening. In this study, shear behaviour of reinforced concrete beams retrofitted with Ultra High Performance Fibre Reinforced Concrete (UHPFRC) with two types of fibres (crimped and micro steel fibre) and a plain UHPC were compared with control beams. A normal M20 mix was designed for the study. Two point loading system was adopted for the test. And deflection were noted for each load increment. Behaviour of retrofitted beams and control beams were studied by comparing the properties such as first crack load, ultimate load and load deflection plot. The result showed that shear performance was improved by 88% for UHPFRC-C, 78% for UHPFRC-M and 36% for UHPC, showing the effect of fibres which improved the shear performance of UHPFRC retrofitted beams.
{"title":"Shear Behaviour of RCC Beams Retrofitted with Ultra High Performance Concrete","authors":"Abhirami V","doi":"10.59544/najb3829/ngcesi23p28","DOIUrl":"https://doi.org/10.59544/najb3829/ngcesi23p28","url":null,"abstract":"The main contributing factors are change in their use new design standards, deterioration due to corrosion in the steel caused by exposure to an aggressive environment and accident events such as earthquakes. In such circumstances there are only two possible solutions: replacement or retrofitting. Full structure replacement might have determinate disadvantages such as high costs for material and labour, a stronger environmental impact and inconvenience due to interruption of the function of the structure. Whenever possible, it is often better to repair or upgrade the structure by retrofitting or strengthening. In this study, shear behaviour of reinforced concrete beams retrofitted with Ultra High Performance Fibre Reinforced Concrete (UHPFRC) with two types of fibres (crimped and micro steel fibre) and a plain UHPC were compared with control beams. A normal M20 mix was designed for the study. Two point loading system was adopted for the test. And deflection were noted for each load increment. Behaviour of retrofitted beams and control beams were studied by comparing the properties such as first crack load, ultimate load and load deflection plot. The result showed that shear performance was improved by 88% for UHPFRC-C, 78% for UHPFRC-M and 36% for UHPC, showing the effect of fibres which improved the shear performance of UHPFRC retrofitted beams.","PeriodicalId":315694,"journal":{"name":"The International Conference on scientific innovations in Science, Technology, and Management","volume":"23 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-08-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116241155","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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