Pub Date : 2023-05-05DOI: 10.1109/InCACCT57535.2023.10141727
M. L. Saini, V. Sharma, Ashok Kumar
In digital communication there are various single and double bit error correcting and detecting codes are available. The efficiency of an error correcting code is evaluated by its errors correction capabilities and redundancy. This paper presents a new single bit and double bit error correcting codes which have lower redundancy compare to the other existing codes. In this paper the number of parity bits over the message bits for Hamming, BCH,RS Code, and DEC are examined and overhead is calculated. The proposed codes having less parity bits compared to other existing and having up to double bit error correction capabilities and minimize the encoding/decoding time delay.
{"title":"An Efficient Single and Double Error Correcting Block Codes with Low Redundancy for Digital Communications","authors":"M. L. Saini, V. Sharma, Ashok Kumar","doi":"10.1109/InCACCT57535.2023.10141727","DOIUrl":"https://doi.org/10.1109/InCACCT57535.2023.10141727","url":null,"abstract":"In digital communication there are various single and double bit error correcting and detecting codes are available. The efficiency of an error correcting code is evaluated by its errors correction capabilities and redundancy. This paper presents a new single bit and double bit error correcting codes which have lower redundancy compare to the other existing codes. In this paper the number of parity bits over the message bits for Hamming, BCH,RS Code, and DEC are examined and overhead is calculated. The proposed codes having less parity bits compared to other existing and having up to double bit error correction capabilities and minimize the encoding/decoding time delay.","PeriodicalId":405272,"journal":{"name":"2023 International Conference on Advancement in Computation & Computer Technologies (InCACCT)","volume":"8 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-05-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115318281","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 : 2023-05-05DOI: 10.1109/InCACCT57535.2023.10141758
Saparna P, A. Mary
Pneumonia is an inflammation of the lungs caused by a bacterial or viral infection. The air bags of the lungs fill with pus when infected with bacteria or viruses. It can affect both lungs or a single. It can also be caused by fungi or parasites. This is an illness that threatens the lives of millions of people worldwide.. At present, the main challenge is to detect the disease in itsearliest stages. It is typically diagnosed by examining a chest X-ray taken by a trained physician or radiologist. In this review paper, a database of X-ray, CT-Scan images from patients was used to automatically detect pneumonia.The patient’s breathing becomes progressively unpleasant and difficult as a result of pneumonia. Machine learning-based diagnosis techniques can aid in the early and efficient detection of disease. Medical imaging research is utilizing computer vision-related automatic detection algorithm.
{"title":"A Comprehensive study on the Detection of Pneumonia using Machine Learning and Deep Learning Approaches","authors":"Saparna P, A. Mary","doi":"10.1109/InCACCT57535.2023.10141758","DOIUrl":"https://doi.org/10.1109/InCACCT57535.2023.10141758","url":null,"abstract":"Pneumonia is an inflammation of the lungs caused by a bacterial or viral infection. The air bags of the lungs fill with pus when infected with bacteria or viruses. It can affect both lungs or a single. It can also be caused by fungi or parasites. This is an illness that threatens the lives of millions of people worldwide.. At present, the main challenge is to detect the disease in itsearliest stages. It is typically diagnosed by examining a chest X-ray taken by a trained physician or radiologist. In this review paper, a database of X-ray, CT-Scan images from patients was used to automatically detect pneumonia.The patient’s breathing becomes progressively unpleasant and difficult as a result of pneumonia. Machine learning-based diagnosis techniques can aid in the early and efficient detection of disease. Medical imaging research is utilizing computer vision-related automatic detection algorithm.","PeriodicalId":405272,"journal":{"name":"2023 International Conference on Advancement in Computation & Computer Technologies (InCACCT)","volume":"3 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-05-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121209183","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 : 2023-05-05DOI: 10.1109/InCACCT57535.2023.10141703
Mrinal Paliwal, Punit Soni, Sharad Chauhan
Digit recognition using the Artificial Neural Network method is discussed in this study. Due to the enormous volumes of data and algorithms, the neural network can now be used to train the network and get the desired result. With the advancement in information and communication technology, internet access has increased as the use of technology increases the demand for digit recognition systems has gained popularity. This paper will discuss one of the techniques for digit recognition. We will train our model with the MNIST dataset & then test our model. Programming in Python is used to perform digit recognition. We have taken a dataset of 28,000-digit images, that will be used for training and 14,000-digit images for testing. The test performance accuracy of our multi-layer artificial neural network is 99.59 %.
{"title":"Digit Recognition using the Artificial Neural Network","authors":"Mrinal Paliwal, Punit Soni, Sharad Chauhan","doi":"10.1109/InCACCT57535.2023.10141703","DOIUrl":"https://doi.org/10.1109/InCACCT57535.2023.10141703","url":null,"abstract":"Digit recognition using the Artificial Neural Network method is discussed in this study. Due to the enormous volumes of data and algorithms, the neural network can now be used to train the network and get the desired result. With the advancement in information and communication technology, internet access has increased as the use of technology increases the demand for digit recognition systems has gained popularity. This paper will discuss one of the techniques for digit recognition. We will train our model with the MNIST dataset & then test our model. Programming in Python is used to perform digit recognition. We have taken a dataset of 28,000-digit images, that will be used for training and 14,000-digit images for testing. The test performance accuracy of our multi-layer artificial neural network is 99.59 %.","PeriodicalId":405272,"journal":{"name":"2023 International Conference on Advancement in Computation & Computer Technologies (InCACCT)","volume":"56 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-05-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116573302","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 : 2023-05-05DOI: 10.1109/InCACCT57535.2023.10141734
Srishti Mahajan, P. Sarangi, A. Sahoo, Mukesh Rohra
Diabetes is a long-term condition that occurs when either the body cannot use insulin properly or the pancreas does not produce sufficient amounts of hormone to control blood glucose levels. High blood sugar levels are a hallmark of diabetes, which belongs to a group of metabolic diseases. The two most prevalent varieties of diabetes are type 1 and type 2, but there are other types as well, such as gestational diabetes, which develops during pregnancy. The number of people with type 1 diabetes has significantly increased. The genetic condition known as type 1 diabetes has a long incubation period and frequently manifests early in life. Cells in people with type 2 diabetes do not properly respond to insulin. It changes over time and mostly depends on how people live their lives. According to a 2022 report by the International Diabetes Federation, currently around 382 million people worldwide have diabetes. By 2035, the Figure is expected to increase to 592 million. One of the most common causes of tissue and organ damage and dysfunction, including blindness, kidney failure, heart failure, and stroke, is diabetes. As a result, early detection of diabetes is critical. This work aims at implementing two machine learning methods like Logistic Regression and Random Forest for diabetes prediction. Each algorithm is calculated to determine the model’s accuracy. Furthermore, the highest accuracy of 99.03% is received by Random Forest.
{"title":"Diabetes Mellitus Prediction using Supervised Machine Learning Techniques","authors":"Srishti Mahajan, P. Sarangi, A. Sahoo, Mukesh Rohra","doi":"10.1109/InCACCT57535.2023.10141734","DOIUrl":"https://doi.org/10.1109/InCACCT57535.2023.10141734","url":null,"abstract":"Diabetes is a long-term condition that occurs when either the body cannot use insulin properly or the pancreas does not produce sufficient amounts of hormone to control blood glucose levels. High blood sugar levels are a hallmark of diabetes, which belongs to a group of metabolic diseases. The two most prevalent varieties of diabetes are type 1 and type 2, but there are other types as well, such as gestational diabetes, which develops during pregnancy. The number of people with type 1 diabetes has significantly increased. The genetic condition known as type 1 diabetes has a long incubation period and frequently manifests early in life. Cells in people with type 2 diabetes do not properly respond to insulin. It changes over time and mostly depends on how people live their lives. According to a 2022 report by the International Diabetes Federation, currently around 382 million people worldwide have diabetes. By 2035, the Figure is expected to increase to 592 million. One of the most common causes of tissue and organ damage and dysfunction, including blindness, kidney failure, heart failure, and stroke, is diabetes. As a result, early detection of diabetes is critical. This work aims at implementing two machine learning methods like Logistic Regression and Random Forest for diabetes prediction. Each algorithm is calculated to determine the model’s accuracy. Furthermore, the highest accuracy of 99.03% is received by Random Forest.","PeriodicalId":405272,"journal":{"name":"2023 International Conference on Advancement in Computation & Computer Technologies (InCACCT)","volume":"6 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-05-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129717997","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 : 2023-05-05DOI: 10.1109/InCACCT57535.2023.10141842
Rahul Pandya, V. Shah, Neel Macwan, Maithili Rajesh Vartak, Dhruvisha J. Patel
Parkinson’s is one of the most common diseases in which the patient suffers from a disorder involving shaking and improper muscle balance and coordination. This makes their daily life activities quite different and troublesome from healthy normal individuals. This paper deals with the detection of patients afflicted with Parkinson’s disease and a normal healthy person based on a dataset that involves hand-drawn spiral and wave structures by them. After the image processing of these hand-drawn structures, a deep learning algorithmic approach is implemented to detect how accurately a model can predict whether the drawing would be made by a healthy person or a person suffering from Parkinson’s disease. The model incorporated here is Resnet-50 architecture having enhanced performance owing to the large number of layers used and has a higher speed. The results were obtained over a range of iterations performed using this model concerning several parameters. Significant and accurate predictions for the disease detection were achieved therefore making this approach more effective to be implemented while using more complicated datasets with larger deep learning architectures.
{"title":"Investigating ResNet deep features for Parkinson’s disease diagnosis using hand-drawn pattern","authors":"Rahul Pandya, V. Shah, Neel Macwan, Maithili Rajesh Vartak, Dhruvisha J. Patel","doi":"10.1109/InCACCT57535.2023.10141842","DOIUrl":"https://doi.org/10.1109/InCACCT57535.2023.10141842","url":null,"abstract":"Parkinson’s is one of the most common diseases in which the patient suffers from a disorder involving shaking and improper muscle balance and coordination. This makes their daily life activities quite different and troublesome from healthy normal individuals. This paper deals with the detection of patients afflicted with Parkinson’s disease and a normal healthy person based on a dataset that involves hand-drawn spiral and wave structures by them. After the image processing of these hand-drawn structures, a deep learning algorithmic approach is implemented to detect how accurately a model can predict whether the drawing would be made by a healthy person or a person suffering from Parkinson’s disease. The model incorporated here is Resnet-50 architecture having enhanced performance owing to the large number of layers used and has a higher speed. The results were obtained over a range of iterations performed using this model concerning several parameters. Significant and accurate predictions for the disease detection were achieved therefore making this approach more effective to be implemented while using more complicated datasets with larger deep learning architectures.","PeriodicalId":405272,"journal":{"name":"2023 International Conference on Advancement in Computation & Computer Technologies (InCACCT)","volume":"5 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-05-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124448382","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 : 2023-05-05DOI: 10.1109/InCACCT57535.2023.10141847
P. Bachhal, V. Kukreja, S. Ahuja
Recent years have seen a significant increase in interest from both academic and commercial premises due to the benefits of autonomous learning and features extraction. Natural language processing, voice processing, picture and video processing all make extensive use of it. In addition, it has developed into a hub for research in the field of agricultural plant protection, including the identification of plant diseases and the evaluation of pest ranges. To increase agricultural productivity in a sustainable way, it’s critical to identify crop leaf diseases quickly and precisely. In this paper, we present a comprehensive assessment of recent work on crop leaf disease prediction using machine learning, image processing and deep learning techniques. Deep learning (DL) techniques, particularly those built on convolutional neural networks (CNN), are now widely used to classify plant diseases. The research articles that presented the various techniques are surveyed in this article, which assesses them in terms of the dataset, the quantity of images, the quantity of classes, the techniques applied, the convolutional neural networks (CNN) models employed, and the final results obtained. Modified DL techniques outperform conventional ML techniques in terms of performance. In order to expand the real-time autonomous system for identifying maize leaf disease, we addressed the performance measurements that were employed as well as some of the limits and future work that needs to be focused on.
{"title":"Maize Disease classification using Deep Learning Techniques: A Review","authors":"P. Bachhal, V. Kukreja, S. Ahuja","doi":"10.1109/InCACCT57535.2023.10141847","DOIUrl":"https://doi.org/10.1109/InCACCT57535.2023.10141847","url":null,"abstract":"Recent years have seen a significant increase in interest from both academic and commercial premises due to the benefits of autonomous learning and features extraction. Natural language processing, voice processing, picture and video processing all make extensive use of it. In addition, it has developed into a hub for research in the field of agricultural plant protection, including the identification of plant diseases and the evaluation of pest ranges. To increase agricultural productivity in a sustainable way, it’s critical to identify crop leaf diseases quickly and precisely. In this paper, we present a comprehensive assessment of recent work on crop leaf disease prediction using machine learning, image processing and deep learning techniques. Deep learning (DL) techniques, particularly those built on convolutional neural networks (CNN), are now widely used to classify plant diseases. The research articles that presented the various techniques are surveyed in this article, which assesses them in terms of the dataset, the quantity of images, the quantity of classes, the techniques applied, the convolutional neural networks (CNN) models employed, and the final results obtained. Modified DL techniques outperform conventional ML techniques in terms of performance. In order to expand the real-time autonomous system for identifying maize leaf disease, we addressed the performance measurements that were employed as well as some of the limits and future work that needs to be focused on.","PeriodicalId":405272,"journal":{"name":"2023 International Conference on Advancement in Computation & Computer Technologies (InCACCT)","volume":"13 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-05-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128158274","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 : 2023-05-05DOI: 10.1109/InCACCT57535.2023.10141823
Shashwat Rai, R. Joshi, M. Dutta
Human monkeypox outbreaks have recently been recorded in several nations throughout the world, with rapidly rising number of cases. Monkeypox may be difficult to diagnose clinically in its early stages because of its similarities to both chickenpox and measles. Since confirmatory Polymerase Chain Reaction (PCR) tests are not readily available and various deep-learning techniques have shown promising results in medical diagnosis, computer-assisted monkeypox lesion detection may be beneficial for monitoring and early identification of suspected cases. This research work presents a precise, computationally fast and reliable alternative for monkeypox diagnosis which facilitates quick processing by integrating deep transfer-learning based methods with deployment in android platform that helps in assisting the situation. Images captured by the camera with live feed or user selected images can be analysed using a deep convolutional neural network running on the same device. Following that, the network categorises images for the identification of either chickenpox, measles, monkeypox or normal skin type. An openly accessible dataset has been utilised for this purpose which results in an accuracy of 88.54 (±2.1%) which outperforms all the other existing models for this task. These positive findings, which exceed the most advanced techniques, imply that the suggested method may be used by the general public for mass screening as well as by the health practitioners to rank the seriousness of a case based on the results provided by this model to provide better attention to them accordingly.
{"title":"PoxDetector: A Deep Convolutional Neural Network for Skin Lesion Classification using Android Application","authors":"Shashwat Rai, R. Joshi, M. Dutta","doi":"10.1109/InCACCT57535.2023.10141823","DOIUrl":"https://doi.org/10.1109/InCACCT57535.2023.10141823","url":null,"abstract":"Human monkeypox outbreaks have recently been recorded in several nations throughout the world, with rapidly rising number of cases. Monkeypox may be difficult to diagnose clinically in its early stages because of its similarities to both chickenpox and measles. Since confirmatory Polymerase Chain Reaction (PCR) tests are not readily available and various deep-learning techniques have shown promising results in medical diagnosis, computer-assisted monkeypox lesion detection may be beneficial for monitoring and early identification of suspected cases. This research work presents a precise, computationally fast and reliable alternative for monkeypox diagnosis which facilitates quick processing by integrating deep transfer-learning based methods with deployment in android platform that helps in assisting the situation. Images captured by the camera with live feed or user selected images can be analysed using a deep convolutional neural network running on the same device. Following that, the network categorises images for the identification of either chickenpox, measles, monkeypox or normal skin type. An openly accessible dataset has been utilised for this purpose which results in an accuracy of 88.54 (±2.1%) which outperforms all the other existing models for this task. These positive findings, which exceed the most advanced techniques, imply that the suggested method may be used by the general public for mass screening as well as by the health practitioners to rank the seriousness of a case based on the results provided by this model to provide better attention to them accordingly.","PeriodicalId":405272,"journal":{"name":"2023 International Conference on Advancement in Computation & Computer Technologies (InCACCT)","volume":"05 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-05-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125459797","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 : 2023-05-05DOI: 10.1109/InCACCT57535.2023.10141808
Voshma Reddy Vuyyala, Michael Sadgun Rao Kona, Sai Bhargavi Pusuluri, Swetha Variganji, Bhavani Nenavathu
Farmers are facing problems because they are unable to manage cultivation because of bad weather conditions and uneven rainfall. Thus, to reduce the problems of farmers, the latest technologies are introduced such as machine learning to implement crop recommendation systems. A wide range of classification techniques are used, and a specific model is selected based on their accuracy levels. By using feature selection techniques, the raw data is converted into a dataset which is useful for efficiently training the model with relevant data. Reducing redundant data and utilizing just the aspects that are significantly relevant in deciding the model’s final output will improve the model’s accuracy. The findings show that, compared to other classifiers, the ensemble approach delivers better prediction with a 99.54% accuracy rate. document is a ‘‘live’’ template and already defines the components of your paper [title, text, heads, etc.] in its style sheet.
{"title":"Crop Recommender System Based on Ensemble Classifiers","authors":"Voshma Reddy Vuyyala, Michael Sadgun Rao Kona, Sai Bhargavi Pusuluri, Swetha Variganji, Bhavani Nenavathu","doi":"10.1109/InCACCT57535.2023.10141808","DOIUrl":"https://doi.org/10.1109/InCACCT57535.2023.10141808","url":null,"abstract":"Farmers are facing problems because they are unable to manage cultivation because of bad weather conditions and uneven rainfall. Thus, to reduce the problems of farmers, the latest technologies are introduced such as machine learning to implement crop recommendation systems. A wide range of classification techniques are used, and a specific model is selected based on their accuracy levels. By using feature selection techniques, the raw data is converted into a dataset which is useful for efficiently training the model with relevant data. Reducing redundant data and utilizing just the aspects that are significantly relevant in deciding the model’s final output will improve the model’s accuracy. The findings show that, compared to other classifiers, the ensemble approach delivers better prediction with a 99.54% accuracy rate. document is a ‘‘live’’ template and already defines the components of your paper [title, text, heads, etc.] in its style sheet.","PeriodicalId":405272,"journal":{"name":"2023 International Conference on Advancement in Computation & Computer Technologies (InCACCT)","volume":"32 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-05-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134129290","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 : 2023-05-05DOI: 10.1109/InCACCT57535.2023.10141729
Keshav Allawadi, Mayank Singh, Charvi Vij
For better patient diagnosis and treatment, medical facilities need to be advanced. With the assistance of machine learning, we can large and sophisticated medical datasets for analyzing them and getting clinical insights. Then, doctors can use this to continue offering medical care. Therefore, machine learning can boost patient happiness when it is used in healthcare. We try to incorporate machine learning skills into a single healthcare system in this work. By using precise machine learning predictive algorithms to replace diagnosis with disease prediction, healthcare can be made smarter. In some situations, a disease cannot be detected in its earliest stages. Therefore, disease prediction can be applied successfully. Prediction of diseases and epidemic outbreaks might result in an early prevention of a disease’s emergence, as said by the wise, “Prevention is better than cure." The major focus of this paper is the development of an enhanced system, or more accurately, an urgent medical provision that would incorporate symptoms. Because there is so much medical metadata available in different formats, the user becomes perplexed. The recommender system’s purpose is to adapt to the particular user-related demands of the health department.
{"title":"Using Machine Learning to Improve Healthcare: A Disease Prediction and Management System","authors":"Keshav Allawadi, Mayank Singh, Charvi Vij","doi":"10.1109/InCACCT57535.2023.10141729","DOIUrl":"https://doi.org/10.1109/InCACCT57535.2023.10141729","url":null,"abstract":"For better patient diagnosis and treatment, medical facilities need to be advanced. With the assistance of machine learning, we can large and sophisticated medical datasets for analyzing them and getting clinical insights. Then, doctors can use this to continue offering medical care. Therefore, machine learning can boost patient happiness when it is used in healthcare. We try to incorporate machine learning skills into a single healthcare system in this work. By using precise machine learning predictive algorithms to replace diagnosis with disease prediction, healthcare can be made smarter. In some situations, a disease cannot be detected in its earliest stages. Therefore, disease prediction can be applied successfully. Prediction of diseases and epidemic outbreaks might result in an early prevention of a disease’s emergence, as said by the wise, “Prevention is better than cure.\" The major focus of this paper is the development of an enhanced system, or more accurately, an urgent medical provision that would incorporate symptoms. Because there is so much medical metadata available in different formats, the user becomes perplexed. The recommender system’s purpose is to adapt to the particular user-related demands of the health department.","PeriodicalId":405272,"journal":{"name":"2023 International Conference on Advancement in Computation & Computer Technologies (InCACCT)","volume":"21 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-05-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134417938","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 : 2023-05-05DOI: 10.1109/InCACCT57535.2023.10141745
Ajay Kumar, Kakoli Banerjee, P. Kumar, Kasaf Aiman, Mukesh Sonkar, R. Rajput, Mohd Rizwan Asif
Moreover half of the population of India relies on agriculture for a living, making it the foundation of the nation’s economy. Agriculture’s future viability is now being threatened by weather, temperature, and other environmental variables. One use of machine learning (ML) is the Crop Yield Prediction (CYP) decision support tool, which provides suggestions about which crops to cultivate and what to perform during the crop’s growth season. Multi-source data for soils, climates, and remotely sensed vegetation indices particular to each site are needed for yield prediction. It is difficult to cope with model uncertainty when using complicated data-model fusion algorithms for crop growth monitoring and yield prediction Several aspects must be considered while developing an accurate and effective model for agricultural yield estimation depending on climate, crop illness, crop classification based on development phase, and other considerations, several research proposals for agricultural development have been made. This study explores severalML techniques for estimating agricultural yields and offers a thorough evaluation of the effectiveness of the methods and we found that the accuracy with Random Forest is higher i.e. 99.31% among all.
{"title":"Comparative Analysis of Crop Yield Prediction Using Machine Learning","authors":"Ajay Kumar, Kakoli Banerjee, P. Kumar, Kasaf Aiman, Mukesh Sonkar, R. Rajput, Mohd Rizwan Asif","doi":"10.1109/InCACCT57535.2023.10141745","DOIUrl":"https://doi.org/10.1109/InCACCT57535.2023.10141745","url":null,"abstract":"Moreover half of the population of India relies on agriculture for a living, making it the foundation of the nation’s economy. Agriculture’s future viability is now being threatened by weather, temperature, and other environmental variables. One use of machine learning (ML) is the Crop Yield Prediction (CYP) decision support tool, which provides suggestions about which crops to cultivate and what to perform during the crop’s growth season. Multi-source data for soils, climates, and remotely sensed vegetation indices particular to each site are needed for yield prediction. It is difficult to cope with model uncertainty when using complicated data-model fusion algorithms for crop growth monitoring and yield prediction Several aspects must be considered while developing an accurate and effective model for agricultural yield estimation depending on climate, crop illness, crop classification based on development phase, and other considerations, several research proposals for agricultural development have been made. This study explores severalML techniques for estimating agricultural yields and offers a thorough evaluation of the effectiveness of the methods and we found that the accuracy with Random Forest is higher i.e. 99.31% among all.","PeriodicalId":405272,"journal":{"name":"2023 International Conference on Advancement in Computation & Computer Technologies (InCACCT)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-05-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133638346","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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