Pub Date : 2022-10-10DOI: 10.1109/ICTACS56270.2022.9988212
Abhishek Bhola, S. Athithan, K. Srinivas, Naresh Poloju, S. Mittal, Yogesh Kumar Sharma
Multi-label classification is an important but difficult topic that involves assigning the most appropriate subset of class labels to each document from a large label collection. The enormous label space presents a number of research obstacles, including data sparsity and scalability. In recent years, breakthrough machine learning algorithms such as tree induction using large margin partitions of the instance spaces and label vector embedding in the target space have resulted in substantial progress. Example: The input text may be a narrative document from chinastory.cn, with the labels representing storey categories that infer the possible meaning of the content. However, applying standard neural network models to the Multi-label classification problem in a haphazard manner results in sub-optimal performance because to the wide output space as well as the label sparsity problem. Despite its widespread success in other fields, Q-learning has not been investigated for multi-label classification. This paper presents the Q-learning algorithm to Multi-label classification, which was the first attempt of applying to Multi-label classification.
{"title":"Multi-Label Classification using Q-Learning","authors":"Abhishek Bhola, S. Athithan, K. Srinivas, Naresh Poloju, S. Mittal, Yogesh Kumar Sharma","doi":"10.1109/ICTACS56270.2022.9988212","DOIUrl":"https://doi.org/10.1109/ICTACS56270.2022.9988212","url":null,"abstract":"Multi-label classification is an important but difficult topic that involves assigning the most appropriate subset of class labels to each document from a large label collection. The enormous label space presents a number of research obstacles, including data sparsity and scalability. In recent years, breakthrough machine learning algorithms such as tree induction using large margin partitions of the instance spaces and label vector embedding in the target space have resulted in substantial progress. Example: The input text may be a narrative document from chinastory.cn, with the labels representing storey categories that infer the possible meaning of the content. However, applying standard neural network models to the Multi-label classification problem in a haphazard manner results in sub-optimal performance because to the wide output space as well as the label sparsity problem. Despite its widespread success in other fields, Q-learning has not been investigated for multi-label classification. This paper presents the Q-learning algorithm to Multi-label classification, which was the first attempt of applying to Multi-label classification.","PeriodicalId":385163,"journal":{"name":"2022 2nd International Conference on Technological Advancements in Computational Sciences (ICTACS)","volume":"15 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-10-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"120963808","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 : 2022-10-10DOI: 10.1109/ICTACS56270.2022.9988667
Namrata Nagpal
Internet in today's times is the daily need of people. To retrieve right information efficiently is the constant desire. Expanding user queries by transforming some keywords to retrieve specific domain keywords has been a probable solution for information retrieval. Various methods have been combined with query expansion from time to time to improve the information retrieval results right from Classical IR methods to semantic methods or to natural language processing methods. All the methods have eventually minimized the mismatch problems and gave better retrieval results. This paper discusses the performance of various such methods that can be implemented to expand user query such that it gives high precision search results. The paper mainly focuses on word embeddings methods like Word2Vec - CBOW or Skip gram and Glove that are trained on real estate related legal datasets over classical methods. Experimental results show that word embeddings give better results with 87% mean average precision (mAP) values on all datasets.
互联网在当今时代是人们的日常需要。有效地检索正确的信息是人们一直以来的愿望。通过将某些关键字转换为检索特定领域关键字来扩展用户查询已成为信息检索的一种可能解决方案。从经典IR方法到语义方法或自然语言处理方法，各种方法不时结合查询扩展来改进信息检索结果。所有的方法最终都最小化了不匹配问题，并给出了更好的检索结果。本文讨论了各种此类方法的性能，这些方法可以实现扩展用户查询，从而提供高精度的搜索结果。本文主要关注Word2Vec - CBOW或Skip gram and Glove等词嵌入方法，这些方法是在房地产相关法律数据集上进行训练的，而不是经典方法。实验结果表明，在所有数据集上，词嵌入的平均精度(mAP)达到了87%。
{"title":"Query Expansion for Information Retrieval using Word Embeddings: A Comparative Study","authors":"Namrata Nagpal","doi":"10.1109/ICTACS56270.2022.9988667","DOIUrl":"https://doi.org/10.1109/ICTACS56270.2022.9988667","url":null,"abstract":"Internet in today's times is the daily need of people. To retrieve right information efficiently is the constant desire. Expanding user queries by transforming some keywords to retrieve specific domain keywords has been a probable solution for information retrieval. Various methods have been combined with query expansion from time to time to improve the information retrieval results right from Classical IR methods to semantic methods or to natural language processing methods. All the methods have eventually minimized the mismatch problems and gave better retrieval results. This paper discusses the performance of various such methods that can be implemented to expand user query such that it gives high precision search results. The paper mainly focuses on word embeddings methods like Word2Vec - CBOW or Skip gram and Glove that are trained on real estate related legal datasets over classical methods. Experimental results show that word embeddings give better results with 87% mean average precision (mAP) values on all datasets.","PeriodicalId":385163,"journal":{"name":"2022 2nd International Conference on Technological Advancements in Computational Sciences (ICTACS)","volume":"9 5 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-10-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126192251","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 : 2022-10-10DOI: 10.1109/ICTACS56270.2022.9988692
D. Ather, N. Rashevskiy, D. Parygin, A. Gurtyakov, S. Katerinina
The article is devoted to the study of the visual ecology of the urban environment. The main factors of visual pollution are analyzed and a classification of sources of visual pollution is made. A three-stage approach to the intellectual assessment of the state of the visual ecology of the urban environment is proposed. The technology for collecting, preparing and analyzing data for calculating the integral level of area visual ecology using machine learning and geo information methods is described. The developed approach was tested on two cases: survey of visual pollution of vertical surfaces (walls, fences) for the presence of unauthorized graffiti; control over the filling of garbage containers and waste collection sites. Software solutions for monitoring urban infrastructure for violations of the normal state and notification of city services with visualization of information on a city map are described in the paper.
{"title":"Intelligent Assessment of the Visual Ecology of the Urban Environment","authors":"D. Ather, N. Rashevskiy, D. Parygin, A. Gurtyakov, S. Katerinina","doi":"10.1109/ICTACS56270.2022.9988692","DOIUrl":"https://doi.org/10.1109/ICTACS56270.2022.9988692","url":null,"abstract":"The article is devoted to the study of the visual ecology of the urban environment. The main factors of visual pollution are analyzed and a classification of sources of visual pollution is made. A three-stage approach to the intellectual assessment of the state of the visual ecology of the urban environment is proposed. The technology for collecting, preparing and analyzing data for calculating the integral level of area visual ecology using machine learning and geo information methods is described. The developed approach was tested on two cases: survey of visual pollution of vertical surfaces (walls, fences) for the presence of unauthorized graffiti; control over the filling of garbage containers and waste collection sites. Software solutions for monitoring urban infrastructure for violations of the normal state and notification of city services with visualization of information on a city map are described in the paper.","PeriodicalId":385163,"journal":{"name":"2022 2nd International Conference on Technological Advancements in Computational Sciences (ICTACS)","volume":"182 2 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-10-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123575526","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 : 2022-10-10DOI: 10.1109/ICTACS56270.2022.9988589
Awakash Prasad, Sapna Kumari, A. Rao, V. Chaubey, Arun Kumar
During this Covid-19-time number of patients in hospital are increased. The covid-19 patients are mainly of two types one who has a serious condition and the other who has a mild covid-19 symptom. On the other way, the patients who have very serious conditions generally have all facilities, they have doctors around them and other medical staff also there to take care of the situation, but the patients who have not very serious conditions are generally isolated in their home, a problem with this home isolated patients are they do not consult with doctor day to day and what happens if patients condition become serious basically this paper is going to solve this two problem and also monitoring the patients while getting different data like a heartbeat, SpO2, body temperature, etc. along with that we used a location sharing and nearest hospitals identification. Here we design IoT, GSM/GPS-based system through which we send the patients' health data directly to the hospital and if the patients' conduction becomes serious then it sends an alert to the hospital along with the patient's location and it also sends the hospital location to the patients.
{"title":"A Real Time Monitoring System for Home Isolated COVID-19 Patients","authors":"Awakash Prasad, Sapna Kumari, A. Rao, V. Chaubey, Arun Kumar","doi":"10.1109/ICTACS56270.2022.9988589","DOIUrl":"https://doi.org/10.1109/ICTACS56270.2022.9988589","url":null,"abstract":"During this Covid-19-time number of patients in hospital are increased. The covid-19 patients are mainly of two types one who has a serious condition and the other who has a mild covid-19 symptom. On the other way, the patients who have very serious conditions generally have all facilities, they have doctors around them and other medical staff also there to take care of the situation, but the patients who have not very serious conditions are generally isolated in their home, a problem with this home isolated patients are they do not consult with doctor day to day and what happens if patients condition become serious basically this paper is going to solve this two problem and also monitoring the patients while getting different data like a heartbeat, SpO2, body temperature, etc. along with that we used a location sharing and nearest hospitals identification. Here we design IoT, GSM/GPS-based system through which we send the patients' health data directly to the hospital and if the patients' conduction becomes serious then it sends an alert to the hospital along with the patient's location and it also sends the hospital location to the patients.","PeriodicalId":385163,"journal":{"name":"2022 2nd International Conference on Technological Advancements in Computational Sciences (ICTACS)","volume":"12 5 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-10-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123690990","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 : 2022-10-10DOI: 10.1109/ICTACS56270.2022.9988186
S. G, S. Paudel, Riyaz Nakarmi, Prashant Giri, Shanta Karki
Agriculture planning is playing an important role as the economic growth is very high day by day in our daily life. There is lot of research study going on as there are few important issues like soil nutrients, crop prediction, farming system, crop monitoring in agriculture with modern farming system. Crop prediction and crop monitoring is main factor to produce good quality of crops for farmers to predict crop yield based on soil moisture. Prediction of crop yield includes forecasting factors like temperature, humidity, rainfall, etc., and crop yield based on soil moisture includes few measures like NPK (Nitrogen, Phosphorous and potassium) and pH values using various sensors. Machine learning (ML) is a useful decision-making model for estimating crop yields, and also for deciding what crops to plant and what to do during the crop's growing seasons. To aid agricultural yield prediction studies, a number of analytical techniques have been used. In this study Farmers can predict or come to a decision the type of soil moisture values; Farmers can choose the type of crop they want to sow. In this paper, Author proposed decision tree supervised machine learning algorithm to improve the results for the prediction of crop yield based on soil moisture parameters to achieve better error rate and accuracy for economic growth. It also includes the few machine learning algorithms which are discussed in literature survey, further Author highlighted the proposed system in methodology, and compared the analysis in results to give it a balance view. The future scope is also mentioned to improve it for further studies. This paper will be sufficient for those who are keener in learning about the expectation of crop yield based on soil moisture using ML Algorithms.
{"title":"Prediction of Crop Yield Based-on Soil Moisture using Machine Learning Algorithms","authors":"S. G, S. Paudel, Riyaz Nakarmi, Prashant Giri, Shanta Karki","doi":"10.1109/ICTACS56270.2022.9988186","DOIUrl":"https://doi.org/10.1109/ICTACS56270.2022.9988186","url":null,"abstract":"Agriculture planning is playing an important role as the economic growth is very high day by day in our daily life. There is lot of research study going on as there are few important issues like soil nutrients, crop prediction, farming system, crop monitoring in agriculture with modern farming system. Crop prediction and crop monitoring is main factor to produce good quality of crops for farmers to predict crop yield based on soil moisture. Prediction of crop yield includes forecasting factors like temperature, humidity, rainfall, etc., and crop yield based on soil moisture includes few measures like NPK (Nitrogen, Phosphorous and potassium) and pH values using various sensors. Machine learning (ML) is a useful decision-making model for estimating crop yields, and also for deciding what crops to plant and what to do during the crop's growing seasons. To aid agricultural yield prediction studies, a number of analytical techniques have been used. In this study Farmers can predict or come to a decision the type of soil moisture values; Farmers can choose the type of crop they want to sow. In this paper, Author proposed decision tree supervised machine learning algorithm to improve the results for the prediction of crop yield based on soil moisture parameters to achieve better error rate and accuracy for economic growth. It also includes the few machine learning algorithms which are discussed in literature survey, further Author highlighted the proposed system in methodology, and compared the analysis in results to give it a balance view. The future scope is also mentioned to improve it for further studies. This paper will be sufficient for those who are keener in learning about the expectation of crop yield based on soil moisture using ML Algorithms.","PeriodicalId":385163,"journal":{"name":"2022 2nd International Conference on Technological Advancements in Computational Sciences (ICTACS)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-10-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125500259","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 : 2022-10-10DOI: 10.1109/ICTACS56270.2022.9987974
Subha T D, Mukhil R, Sree Saran, V. T., Aswin Kumar V V
In the last decade, several initiatives have been launched on every continent with the purpose of incorporating smart metering capabilities into existing electricity grids. Because of certain initiatives, there has been a resurgence in demand about the development of transmitters and receivers again for another transmission of Multiband Grid Synchronization. The International telecommunication union and IEEE were already working together over the last several decades to define a series upcoming generation multiplexing based Program Logic converter communication systems. Many of these transmissions are now under consideration for huge implementations in both Europe and Asia. In addition to discussing the significant part that PLC plays not just in industrial automation although in a wide variety of other purposes for such Power System, this article also provides a summary of the primary distinctions that exist among the different methods in upcoming transition.
{"title":"An Intelligent Power Grid with CENELEC A used in Integrated Electronic Devices","authors":"Subha T D, Mukhil R, Sree Saran, V. T., Aswin Kumar V V","doi":"10.1109/ICTACS56270.2022.9987974","DOIUrl":"https://doi.org/10.1109/ICTACS56270.2022.9987974","url":null,"abstract":"In the last decade, several initiatives have been launched on every continent with the purpose of incorporating smart metering capabilities into existing electricity grids. Because of certain initiatives, there has been a resurgence in demand about the development of transmitters and receivers again for another transmission of Multiband Grid Synchronization. The International telecommunication union and IEEE were already working together over the last several decades to define a series upcoming generation multiplexing based Program Logic converter communication systems. Many of these transmissions are now under consideration for huge implementations in both Europe and Asia. In addition to discussing the significant part that PLC plays not just in industrial automation although in a wide variety of other purposes for such Power System, this article also provides a summary of the primary distinctions that exist among the different methods in upcoming transition.","PeriodicalId":385163,"journal":{"name":"2022 2nd International Conference on Technological Advancements in Computational Sciences (ICTACS)","volume":"84 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-10-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115190889","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 : 2022-10-10DOI: 10.1109/ICTACS56270.2022.9987921
Mrinal Paliwal, D. Singh
Nowadays the laundry service industry is growing each day. The laundry shops customers are usually facing problems like finding laundry shops near to their area. It is difficult for laundry service provider or laundry shops the management and maintenance of information and records related customers laundry clothes, bills, and number of cloth given by customers for dry washing or normal washing, also there is so much chances of customer laundry clothes mix-ups with other customer laundry clothes if the laundry shop do not manage and maintain proper details about the customer cloths, this all problem may cause dissatisfaction of customer and losing of customer trust on the laundry shop. This paper proposes web applications for overcoming all above mentioned problems and issues faced by laundry shops and peoples. Another feature provided by this proposed web the laundry shop owner can advertise his or her laundry shop and advertise offers or discounts offered by the laundry shop owners to their customers. In future this proposed web application can be used by people for searching best laundry shops.
{"title":"Fire Detection System with Artificial Intelligence","authors":"Mrinal Paliwal, D. Singh","doi":"10.1109/ICTACS56270.2022.9987921","DOIUrl":"https://doi.org/10.1109/ICTACS56270.2022.9987921","url":null,"abstract":"Nowadays the laundry service industry is growing each day. The laundry shops customers are usually facing problems like finding laundry shops near to their area. It is difficult for laundry service provider or laundry shops the management and maintenance of information and records related customers laundry clothes, bills, and number of cloth given by customers for dry washing or normal washing, also there is so much chances of customer laundry clothes mix-ups with other customer laundry clothes if the laundry shop do not manage and maintain proper details about the customer cloths, this all problem may cause dissatisfaction of customer and losing of customer trust on the laundry shop. This paper proposes web applications for overcoming all above mentioned problems and issues faced by laundry shops and peoples. Another feature provided by this proposed web the laundry shop owner can advertise his or her laundry shop and advertise offers or discounts offered by the laundry shop owners to their customers. In future this proposed web application can be used by people for searching best laundry shops.","PeriodicalId":385163,"journal":{"name":"2022 2nd International Conference on Technological Advancements in Computational Sciences (ICTACS)","volume":"27 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-10-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115568117","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 : 2022-10-10DOI: 10.1109/ICTACS56270.2022.9987788
Srikrishna B.R, R. Sivakumar
In recent decades lake water resources are get deteriorating and declining due to an increase in urbanization and the high effects of anthropogenic activities. Lake is an important ecological asset to the earth system. It is necessary to monitor water resources. Due to the spread of the covid-19 pandemic virus, the global range shutdown was implemented so that all the activities come to hold resulting in recovering nature and its environment from pollution. The on-site monitoring and evaluation of the quality of water resources in the pandemic period are impossible. The satellite remote sensing techniques have been used for the water quality assessment for pre-pandemic and during pandemic periods. The result suggested that there is an up-gradation in the quality of lake water in the lockdown period than the pre pandemic period i.e. 30.60% increase in lake water clarity. The satellite image processing techniques had the potential for the estimation of the lake water quality during these difficult times.
{"title":"Image processing of Sentinel-2MSI data for Lake Water Quality Analysis","authors":"Srikrishna B.R, R. Sivakumar","doi":"10.1109/ICTACS56270.2022.9987788","DOIUrl":"https://doi.org/10.1109/ICTACS56270.2022.9987788","url":null,"abstract":"In recent decades lake water resources are get deteriorating and declining due to an increase in urbanization and the high effects of anthropogenic activities. Lake is an important ecological asset to the earth system. It is necessary to monitor water resources. Due to the spread of the covid-19 pandemic virus, the global range shutdown was implemented so that all the activities come to hold resulting in recovering nature and its environment from pollution. The on-site monitoring and evaluation of the quality of water resources in the pandemic period are impossible. The satellite remote sensing techniques have been used for the water quality assessment for pre-pandemic and during pandemic periods. The result suggested that there is an up-gradation in the quality of lake water in the lockdown period than the pre pandemic period i.e. 30.60% increase in lake water clarity. The satellite image processing techniques had the potential for the estimation of the lake water quality during these difficult times.","PeriodicalId":385163,"journal":{"name":"2022 2nd International Conference on Technological Advancements in Computational Sciences (ICTACS)","volume":"11 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-10-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128483818","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 : 2022-10-10DOI: 10.1109/ICTACS56270.2022.9988387
T. Aravinda, K. Krishnareddy
A large level of interest has been shown in precision farming recently due to the growing demand for water and food worldwide. Producers will thus need there must be adequate water and agriculturally appropriate land to meet this need. Because with the limitations both materials are readily accessible, thus farmers need a strategy that modifies their behaviour. The secret to efficient irrigation is finding a way to provide a greater, better, and more profitable output while using less resources. There are many machine learning based Irrigation methods have been suggested to effectively utilize more water. Unusual weather conditions are not suitable for these algorithms since they have a limited learning ability. This innovation, which integrates intelligence, keeps performing better for longer periods of time despite the weather in any place. DLiSA forecasts the overall soil moisture levels for the next day, the duration of the irrigated, and the geographical extent of the water required to irrigate the field using a lengthy short attention span network. The simulation outcomes demonstrate that DLiSA makes better use of water over cutting-edge technology. prototypes used for research agriculture in the area.
{"title":"Internet of Things and Machine Learning Based Intelligent Irrigation System for Agriculture","authors":"T. Aravinda, K. Krishnareddy","doi":"10.1109/ICTACS56270.2022.9988387","DOIUrl":"https://doi.org/10.1109/ICTACS56270.2022.9988387","url":null,"abstract":"A large level of interest has been shown in precision farming recently due to the growing demand for water and food worldwide. Producers will thus need there must be adequate water and agriculturally appropriate land to meet this need. Because with the limitations both materials are readily accessible, thus farmers need a strategy that modifies their behaviour. The secret to efficient irrigation is finding a way to provide a greater, better, and more profitable output while using less resources. There are many machine learning based Irrigation methods have been suggested to effectively utilize more water. Unusual weather conditions are not suitable for these algorithms since they have a limited learning ability. This innovation, which integrates intelligence, keeps performing better for longer periods of time despite the weather in any place. DLiSA forecasts the overall soil moisture levels for the next day, the duration of the irrigated, and the geographical extent of the water required to irrigate the field using a lengthy short attention span network. The simulation outcomes demonstrate that DLiSA makes better use of water over cutting-edge technology. prototypes used for research agriculture in the area.","PeriodicalId":385163,"journal":{"name":"2022 2nd International Conference on Technological Advancements in Computational Sciences (ICTACS)","volume":"11 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-10-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130574847","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 : 2022-10-10DOI: 10.1109/ICTACS56270.2022.9988582
B. Venkataramanaiah, Sasikar A., V. P. Naveen Kumar Reddy, V. L. Prasanna Kumar
In this world of upgrading technologies many types of software equipment are developed in the cardiology sector which help patients to get better treatment. With the present innovative techniques in Artificial intelligence the demand increases in detecting heart diseases. The main machine learning techniques are used for identifying cardiovascular diseases. We made a productive and exact framework to finding coronary illness and the framework depends on AI procedures. This contains a few AI models to give exact arrangements rather than having just one model. Naive Bayes, knn, Random Forest and Decision Tree are used for analysis and testing cardiovascular diseases. The highlights choice calculations utilized for high light choice to build the order exactness and lessen the execution season of arrangement framework. The framework was executed and prepared in the python stage by utilizing the AI
{"title":"Efficient Prediction of Heart Diseases by using Machine Learning Classifiers","authors":"B. Venkataramanaiah, Sasikar A., V. P. Naveen Kumar Reddy, V. L. Prasanna Kumar","doi":"10.1109/ICTACS56270.2022.9988582","DOIUrl":"https://doi.org/10.1109/ICTACS56270.2022.9988582","url":null,"abstract":"In this world of upgrading technologies many types of software equipment are developed in the cardiology sector which help patients to get better treatment. With the present innovative techniques in Artificial intelligence the demand increases in detecting heart diseases. The main machine learning techniques are used for identifying cardiovascular diseases. We made a productive and exact framework to finding coronary illness and the framework depends on AI procedures. This contains a few AI models to give exact arrangements rather than having just one model. Naive Bayes, knn, Random Forest and Decision Tree are used for analysis and testing cardiovascular diseases. The highlights choice calculations utilized for high light choice to build the order exactness and lessen the execution season of arrangement framework. The framework was executed and prepared in the python stage by utilizing the AI","PeriodicalId":385163,"journal":{"name":"2022 2nd International Conference on Technological Advancements in Computational Sciences (ICTACS)","volume":"30 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-10-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124114971","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}
京公网安备 11010802042870号
京ICP备2023020795号-1
扫码关注我们
求助内容：
应助结果提醒方式：