Pub Date : 2021-09-24DOI: 10.1109/ICSES52305.2021.9633817
Sejal Bhatia
The internet is increasingly becoming the primary source of news worldwide. Social networking sites have further enabled instantaneous spread of such articles by often allowing single-click user sharing. Majority of the organizations publishing such articles drive revenue through advertisements which is ultimately dependent on the popularity of the article. This popularity is mainly defined in terms of views and shares. One of the emerging applications of Machine Learning is to help organizations predict which articles are most likely to become popular and thus allow them to improve targeted advertising campaigns in order to optimize revenue. This paper proposes and evaluates Machine Learning based approaches alongside Rolling, Growing and a Hybrid training window techniques in order to predict the popularity of news articles.
{"title":"Application and evaluation of Machine Learning for news article popularity prediction","authors":"Sejal Bhatia","doi":"10.1109/ICSES52305.2021.9633817","DOIUrl":"https://doi.org/10.1109/ICSES52305.2021.9633817","url":null,"abstract":"The internet is increasingly becoming the primary source of news worldwide. Social networking sites have further enabled instantaneous spread of such articles by often allowing single-click user sharing. Majority of the organizations publishing such articles drive revenue through advertisements which is ultimately dependent on the popularity of the article. This popularity is mainly defined in terms of views and shares. One of the emerging applications of Machine Learning is to help organizations predict which articles are most likely to become popular and thus allow them to improve targeted advertising campaigns in order to optimize revenue. This paper proposes and evaluates Machine Learning based approaches alongside Rolling, Growing and a Hybrid training window techniques in order to predict the popularity of news articles.","PeriodicalId":6777,"journal":{"name":"2021 International Conference on Innovative Computing, Intelligent Communication and Smart Electrical Systems (ICSES)","volume":"1 1","pages":"1-5"},"PeriodicalIF":0.0,"publicationDate":"2021-09-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"89239506","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 : 2021-09-24DOI: 10.1109/ICSES52305.2021.9633898
Nishq Poorav Desai, Abhijay Wadhwani, Mohammed Farhan Baluch, Nilamadhab Mishra
Heart attack, also known as cardiac arrest, encompasses various heart-related disorders and has been the leading cause of death worldwide in recent decades. Many risk factors are linked to heart illness, and there is a pressing need for accurate, effective, and practical methods to make an early diagnosis and treat the disease. In order to appropriately categorise and predict heart attack patients with minimal features, this study tested alternative algorithms for classification of the dataset. An in-depth comparison is made using pre-processing and standardisation techniques on the UCI dataset, and ensemble algorithms over supervised algorithms, as well as comparing custom neural net design to pre-defined procedures. With the total of 17 used so far, Random Forest (RF) gives a maximum accuracy of 96.5%, which is examined from the survey work. Future study could combine several machine learning techniques to produce a more comprehensive model, which could help health care practitioners make better judgments.
{"title":"A Comparative Assessment Study on Machine Learning Classifiers for Cardiac Arrest Diagnosis and Prediction","authors":"Nishq Poorav Desai, Abhijay Wadhwani, Mohammed Farhan Baluch, Nilamadhab Mishra","doi":"10.1109/ICSES52305.2021.9633898","DOIUrl":"https://doi.org/10.1109/ICSES52305.2021.9633898","url":null,"abstract":"Heart attack, also known as cardiac arrest, encompasses various heart-related disorders and has been the leading cause of death worldwide in recent decades. Many risk factors are linked to heart illness, and there is a pressing need for accurate, effective, and practical methods to make an early diagnosis and treat the disease. In order to appropriately categorise and predict heart attack patients with minimal features, this study tested alternative algorithms for classification of the dataset. An in-depth comparison is made using pre-processing and standardisation techniques on the UCI dataset, and ensemble algorithms over supervised algorithms, as well as comparing custom neural net design to pre-defined procedures. With the total of 17 used so far, Random Forest (RF) gives a maximum accuracy of 96.5%, which is examined from the survey work. Future study could combine several machine learning techniques to produce a more comprehensive model, which could help health care practitioners make better judgments.","PeriodicalId":6777,"journal":{"name":"2021 International Conference on Innovative Computing, Intelligent Communication and Smart Electrical Systems (ICSES)","volume":"113 1","pages":"1-6"},"PeriodicalIF":0.0,"publicationDate":"2021-09-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"79322815","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 : 2021-09-24DOI: 10.1109/ICSES52305.2021.9633773
V. Ganesh, Johnson Kolluri, K. V. Kumar
Diabetes is one of the many major issues in medical field and lakhs of people are affected due to this diabetes. From many years many researches are going on this problem to detect this diabetes. Here we are mainly concerned towards women because during pregnancy they may get diabetes which is also termed as gestational diabetes and due to this there is a higher chance of getting diabetes called type2 in future and this occurs when our human body doesn't use the insulin hormone and it is unable to prepare it. Therefore many methods are there in literature that is used to classify whether a particular human being gets diabetes in future or not. Generally the dataset used for this purpose is Pima Indian diabetes dataset and it is mainly used by the researchers to classify whether an instance has diabetes or not. There are a lot of problems if this diabetes is not treated and it may leads to other organ related diseases. The main problems occur to kidneys, eyes and heart etc. the normal method that is used for this diabetes detection is to visit a hospital or any health care center and we have to reach doctor for treatment. Many researches in machine learning are going on for this purpose and many methods are proposed using the data of people of past and tries to develop models that is used to predict diabetes. In this we are going to propose a method using logistic regression which is technique that is used for detection of diabetes.
{"title":"Diabetes Prediction using Logistic Regression and Feature Normalization","authors":"V. Ganesh, Johnson Kolluri, K. V. Kumar","doi":"10.1109/ICSES52305.2021.9633773","DOIUrl":"https://doi.org/10.1109/ICSES52305.2021.9633773","url":null,"abstract":"Diabetes is one of the many major issues in medical field and lakhs of people are affected due to this diabetes. From many years many researches are going on this problem to detect this diabetes. Here we are mainly concerned towards women because during pregnancy they may get diabetes which is also termed as gestational diabetes and due to this there is a higher chance of getting diabetes called type2 in future and this occurs when our human body doesn't use the insulin hormone and it is unable to prepare it. Therefore many methods are there in literature that is used to classify whether a particular human being gets diabetes in future or not. Generally the dataset used for this purpose is Pima Indian diabetes dataset and it is mainly used by the researchers to classify whether an instance has diabetes or not. There are a lot of problems if this diabetes is not treated and it may leads to other organ related diseases. The main problems occur to kidneys, eyes and heart etc. the normal method that is used for this diabetes detection is to visit a hospital or any health care center and we have to reach doctor for treatment. Many researches in machine learning are going on for this purpose and many methods are proposed using the data of people of past and tries to develop models that is used to predict diabetes. In this we are going to propose a method using logistic regression which is technique that is used for detection of diabetes.","PeriodicalId":6777,"journal":{"name":"2021 International Conference on Innovative Computing, Intelligent Communication and Smart Electrical Systems (ICSES)","volume":"75 1","pages":"1-6"},"PeriodicalIF":0.0,"publicationDate":"2021-09-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"83845531","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}
In Image Processing, an object is an identifiable portion of a particular image that can be interpreted as a single unit. Humans have the ability to recognize any type of objects whether they are alphabets, digits or any living and non-living things irrespective of their forms. When it comes to a machine, it detects an object by extracting its features. Feature Extraction is the most popular research area in the field of image analysis, and it is the primary requirement for representing an object. By these feature extraction techniques, the objects will be represented as a group of features in the form of feature vectors and then they are used for the recognition of objects and for classifying them. In this paper, we have proposed geometrical features from the set of training images using triangular area and perimeter. These features of the training images are stored in the database and used for classifying the test images and Chi-Square statistics is used as classification method
{"title":"Object Recognition using Novel Geometrical Feature Extraction Techniques","authors":"Narasimha Reddy Soora, Snehith Reddy Puli, Venkatramulu Sunkari","doi":"10.1109/ICSES52305.2021.9633971","DOIUrl":"https://doi.org/10.1109/ICSES52305.2021.9633971","url":null,"abstract":"In Image Processing, an object is an identifiable portion of a particular image that can be interpreted as a single unit. Humans have the ability to recognize any type of objects whether they are alphabets, digits or any living and non-living things irrespective of their forms. When it comes to a machine, it detects an object by extracting its features. Feature Extraction is the most popular research area in the field of image analysis, and it is the primary requirement for representing an object. By these feature extraction techniques, the objects will be represented as a group of features in the form of feature vectors and then they are used for the recognition of objects and for classifying them. In this paper, we have proposed geometrical features from the set of training images using triangular area and perimeter. These features of the training images are stored in the database and used for classifying the test images and Chi-Square statistics is used as classification method","PeriodicalId":6777,"journal":{"name":"2021 International Conference on Innovative Computing, Intelligent Communication and Smart Electrical Systems (ICSES)","volume":"25 1","pages":"1-6"},"PeriodicalIF":0.0,"publicationDate":"2021-09-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"81838937","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 : 2021-09-24DOI: 10.1109/ICSES52305.2021.9633794
P. William, Abhishek Badholia
Analysis was made to propose an algorithmic method that is less complex and efficient in the prediction of personality for applying it to machine learning. Prior before applying the machine learning algorithm, Cronbach's Alpha is applied for testingthe questionnaire made usingHEXACO model to check the reliability of the factor and variables considered. By designing a method to change Cronbach's alpha, we aimed to analyse the influence of conflicting responses on the internal reliability of a dataset. Contrary to popular opinion, random reactions can inflate the alpha of Cronbach when their mean differs from that of the true reactions. Except in scales of both positive and negative polarity products, set answers inflate the alpha of Cronbach. There is not much effect on the effects of inconsistent answers by the amount of response groups. For the study, the mean score is calculated compared against the standard value using One sample test to identify there is a significant difference. The result indicates that there is no significant difference in mean score and standard value. It means that all the interviewees has reasonable level personality with respect to Honesty-Humility, Extraversion and Conscientiousness. For the factors; Emotionality, Agreeableness and Openness there is a significant difference in mean score and standard value. Through the Mean score calculated using One-Sample Statistics, it can be interpreted that the Interviewees have more than significant level of Agreeableness and Openness Personality but less Emotionality. This result is compared to the result of many HR professionals. To make the comparison of the resultPearson correlation method is applied, to know is there a significant relationship between the result given by HR managers and personality predicted using the HEXACO Model. The result indicates, there is a significant relationship between HR manager report and the HEXACO model algorithm constructed for personality prediction. Also, the estimated Pearson correlation value (0.819) indicates that there is 81.9% similarity in the result given by HR managers and the HEXACO model algorithm constructed for personality prediction.
{"title":"Analysis of Personality Traits from Text Based Answers using HEXACO Model","authors":"P. William, Abhishek Badholia","doi":"10.1109/ICSES52305.2021.9633794","DOIUrl":"https://doi.org/10.1109/ICSES52305.2021.9633794","url":null,"abstract":"Analysis was made to propose an algorithmic method that is less complex and efficient in the prediction of personality for applying it to machine learning. Prior before applying the machine learning algorithm, Cronbach's Alpha is applied for testingthe questionnaire made usingHEXACO model to check the reliability of the factor and variables considered. By designing a method to change Cronbach's alpha, we aimed to analyse the influence of conflicting responses on the internal reliability of a dataset. Contrary to popular opinion, random reactions can inflate the alpha of Cronbach when their mean differs from that of the true reactions. Except in scales of both positive and negative polarity products, set answers inflate the alpha of Cronbach. There is not much effect on the effects of inconsistent answers by the amount of response groups. For the study, the mean score is calculated compared against the standard value using One sample test to identify there is a significant difference. The result indicates that there is no significant difference in mean score and standard value. It means that all the interviewees has reasonable level personality with respect to Honesty-Humility, Extraversion and Conscientiousness. For the factors; Emotionality, Agreeableness and Openness there is a significant difference in mean score and standard value. Through the Mean score calculated using One-Sample Statistics, it can be interpreted that the Interviewees have more than significant level of Agreeableness and Openness Personality but less Emotionality. This result is compared to the result of many HR professionals. To make the comparison of the resultPearson correlation method is applied, to know is there a significant relationship between the result given by HR managers and personality predicted using the HEXACO Model. The result indicates, there is a significant relationship between HR manager report and the HEXACO model algorithm constructed for personality prediction. Also, the estimated Pearson correlation value (0.819) indicates that there is 81.9% similarity in the result given by HR managers and the HEXACO model algorithm constructed for personality prediction.","PeriodicalId":6777,"journal":{"name":"2021 International Conference on Innovative Computing, Intelligent Communication and Smart Electrical Systems (ICSES)","volume":"15 1","pages":"1-10"},"PeriodicalIF":0.0,"publicationDate":"2021-09-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"88014266","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 : 2021-09-24DOI: 10.1109/ICSES52305.2021.9633846
Mosiur Rahman, Sharmin Akter, ParizatBinta Kabir
The coronavirus disease of 2019 (COVID-19) eruption has perpetrated desolation on educational systems all across the globe. We conducted a fast longitudinal study to find, evaluate, and synthesize research on the repercussions of this epidemic on university sophomores' psychological disorders. We created an interactive simulation to gain a better understanding of the intellectual well-being of university students. Collaborative boards analyze and exhibit actual data as visualizations, statistics, and prose, with a variety of user involvement possibilities. The widgets make it possible to derive meaningful data and present it in a simple and easy-to- understand style. We created an interactive statistics interface to show not only just the latest trends but also crucial metrics and forecasts for the future fortnight. Our panel is simple to use and optimized for effectiveness. It can forcibly temporize values and deploy on any remote server. In this study, we have compared data of pre and during COVID. The data are divided into four categories such as 1) educational impact, 2) family pressure, 3) social and mental health, and 4) stress. Our study found that during prevalent psychological impact is more negative than pre- stage.
{"title":"Exploring Psychosocial Effects of the COVID-19 on University Students Using Visual Analytics","authors":"Mosiur Rahman, Sharmin Akter, ParizatBinta Kabir","doi":"10.1109/ICSES52305.2021.9633846","DOIUrl":"https://doi.org/10.1109/ICSES52305.2021.9633846","url":null,"abstract":"The coronavirus disease of 2019 (COVID-19) eruption has perpetrated desolation on educational systems all across the globe. We conducted a fast longitudinal study to find, evaluate, and synthesize research on the repercussions of this epidemic on university sophomores' psychological disorders. We created an interactive simulation to gain a better understanding of the intellectual well-being of university students. Collaborative boards analyze and exhibit actual data as visualizations, statistics, and prose, with a variety of user involvement possibilities. The widgets make it possible to derive meaningful data and present it in a simple and easy-to- understand style. We created an interactive statistics interface to show not only just the latest trends but also crucial metrics and forecasts for the future fortnight. Our panel is simple to use and optimized for effectiveness. It can forcibly temporize values and deploy on any remote server. In this study, we have compared data of pre and during COVID. The data are divided into four categories such as 1) educational impact, 2) family pressure, 3) social and mental health, and 4) stress. Our study found that during prevalent psychological impact is more negative than pre- stage.","PeriodicalId":6777,"journal":{"name":"2021 International Conference on Innovative Computing, Intelligent Communication and Smart Electrical Systems (ICSES)","volume":"49 1","pages":"1-6"},"PeriodicalIF":0.0,"publicationDate":"2021-09-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"78917238","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 : 2021-09-24DOI: 10.1109/ICSES52305.2021.9633784
P. Abirami, C. N. Ravi, M. Pushpavalli, V. Geetha, P. Sivagami, M. Chandraleka, S. Deepa
The economic growth of any country is decided based on the per capita consumption of energy. In our busy life style, we have to carry over our essential services without any interruption. The prime objective of this work is to supply the essential loads continuously using hybrid resources such as solar, wind, main supply and battery backup. These four sources are automatically simulated to supply all essential services like banking sector, schools and colleges, medical and domestic appliances and for industrial automation. To fulfill the needs of human, all crucial loads should get continuous power supply. This is achieved by controlling all the sources using Arduino microcontroller and relay driver circuit. If any one of the sources fail to supply the load, then the next source is activated through relay driver IC and supply the load without any disruption.
{"title":"Auto Supply to Load from Four Different Sources Using IoT","authors":"P. Abirami, C. N. Ravi, M. Pushpavalli, V. Geetha, P. Sivagami, M. Chandraleka, S. Deepa","doi":"10.1109/ICSES52305.2021.9633784","DOIUrl":"https://doi.org/10.1109/ICSES52305.2021.9633784","url":null,"abstract":"The economic growth of any country is decided based on the per capita consumption of energy. In our busy life style, we have to carry over our essential services without any interruption. The prime objective of this work is to supply the essential loads continuously using hybrid resources such as solar, wind, main supply and battery backup. These four sources are automatically simulated to supply all essential services like banking sector, schools and colleges, medical and domestic appliances and for industrial automation. To fulfill the needs of human, all crucial loads should get continuous power supply. This is achieved by controlling all the sources using Arduino microcontroller and relay driver circuit. If any one of the sources fail to supply the load, then the next source is activated through relay driver IC and supply the load without any disruption.","PeriodicalId":6777,"journal":{"name":"2021 International Conference on Innovative Computing, Intelligent Communication and Smart Electrical Systems (ICSES)","volume":"279 1","pages":"1-4"},"PeriodicalIF":0.0,"publicationDate":"2021-09-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"77333428","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 : 2021-09-24DOI: 10.1109/ICSES52305.2021.9633943
V. Elanangai, Kishorebabu Vasanth
Over the past decade, the detection and classification of Steel surface defect image has been a great challenge in computational methodology. This research work aims at classify the Steel surface defect image which can be used to assess quality of steel surface and also measure the performance metrics using this computational methodology. The Proposed work based on Fractional Jaya Optimizer-based Deep Convolutional Neural Network (FJO-DCNN). The segments are generated through the clustering mechanism named Particle Swarm Optimization (PSO), which ensure the effectiveness of optimal segment selection that yields to detect Steel surface defect image more accurately. However, the optimal segments are effectively selected that yield to detect the Steel surfacedefect regions. This experimentation is carried out using the NEU-DET database. Finally, the results are carried out by using this hybrid algorithm and attained the better performance value. The proposed work achieves in FJO-DCNN for Steel surface defect image computed the best values for accuracy, sensitivity and specificity respectively.
{"title":"Performance Evaluation of Stainless Steel Plate Defects Using Deep Learning Approach","authors":"V. Elanangai, Kishorebabu Vasanth","doi":"10.1109/ICSES52305.2021.9633943","DOIUrl":"https://doi.org/10.1109/ICSES52305.2021.9633943","url":null,"abstract":"Over the past decade, the detection and classification of Steel surface defect image has been a great challenge in computational methodology. This research work aims at classify the Steel surface defect image which can be used to assess quality of steel surface and also measure the performance metrics using this computational methodology. The Proposed work based on Fractional Jaya Optimizer-based Deep Convolutional Neural Network (FJO-DCNN). The segments are generated through the clustering mechanism named Particle Swarm Optimization (PSO), which ensure the effectiveness of optimal segment selection that yields to detect Steel surface defect image more accurately. However, the optimal segments are effectively selected that yield to detect the Steel surfacedefect regions. This experimentation is carried out using the NEU-DET database. Finally, the results are carried out by using this hybrid algorithm and attained the better performance value. The proposed work achieves in FJO-DCNN for Steel surface defect image computed the best values for accuracy, sensitivity and specificity respectively.","PeriodicalId":6777,"journal":{"name":"2021 International Conference on Innovative Computing, Intelligent Communication and Smart Electrical Systems (ICSES)","volume":"41 1","pages":"1-9"},"PeriodicalIF":0.0,"publicationDate":"2021-09-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"77667040","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 : 2021-09-24DOI: 10.1109/ICSES52305.2021.9633796
Piyush Agarwal, Sachin Sharma, Priya Matta
Traffic management system (TMS) plays a very important role in everyone's life. Directly or indirectly maximum of our tasks get affected by or related to the traffic. For a better service and travel experience, it is important to know the different signs and symbols of the road. Therefore, this paper also includes a discussion on the different components of TMS, traffic zones set during the maintenance of the traffic infrastructure. This paper includes 5E's of traffic management for road safety. Different technologies that can be used in TMS to make the system more efficient and effective are also discussed. The various research work related to TMS accomplished in the last few years has also been discussed. This work also includes the traffic control system based on technologies like IoT, Artificial Intelligence, etc. Some Intelligent Traffic Management Systems (ITMS) that are being used in different countries are also included
{"title":"Components, Technologies, and Market of Road Traffic Management System in Global Scenarios: A Complete Study","authors":"Piyush Agarwal, Sachin Sharma, Priya Matta","doi":"10.1109/ICSES52305.2021.9633796","DOIUrl":"https://doi.org/10.1109/ICSES52305.2021.9633796","url":null,"abstract":"Traffic management system (TMS) plays a very important role in everyone's life. Directly or indirectly maximum of our tasks get affected by or related to the traffic. For a better service and travel experience, it is important to know the different signs and symbols of the road. Therefore, this paper also includes a discussion on the different components of TMS, traffic zones set during the maintenance of the traffic infrastructure. This paper includes 5E's of traffic management for road safety. Different technologies that can be used in TMS to make the system more efficient and effective are also discussed. The various research work related to TMS accomplished in the last few years has also been discussed. This work also includes the traffic control system based on technologies like IoT, Artificial Intelligence, etc. Some Intelligent Traffic Management Systems (ITMS) that are being used in different countries are also included","PeriodicalId":6777,"journal":{"name":"2021 International Conference on Innovative Computing, Intelligent Communication and Smart Electrical Systems (ICSES)","volume":"74 1","pages":"1-10"},"PeriodicalIF":0.0,"publicationDate":"2021-09-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"79874463","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 : 2021-09-24DOI: 10.1109/ICSES52305.2021.9633814
M. Pushpavalli, V. Geethal, K. Krithika, S. Jebaseelan, P. Sivagami, M. Abirami, P. Abirami
There's an assortment of advancement which has been made with respect to computerized picture preparing and ML calculations which likewise incorporate its different applications. Presently we are living in a period where the issue with respect to agribusiness is a significant issue these days. The serious issue in crop development is we need to deal with the soundness of the plants and yields In this venture we fundamentally centered around characterization of different leafs as various sorts of illnesses. For this we use HSI shading model and bunching calculation. We likewise utilize MATLAB for our task. Less yield, greater expense of creation because of work shortage and compost cost are the significant difficulties prior the farmers. In current situations it's difficult to supply water to the farmlands manually and is consuming more time and manual power. For that We also included an automatic irrigation system to water the plants automatically. Real time monitoring of thedata is there using the cloud platform.
{"title":"Forecasting the Cloud Cover for Agronomical function Based on Real Time Valuation","authors":"M. Pushpavalli, V. Geethal, K. Krithika, S. Jebaseelan, P. Sivagami, M. Abirami, P. Abirami","doi":"10.1109/ICSES52305.2021.9633814","DOIUrl":"https://doi.org/10.1109/ICSES52305.2021.9633814","url":null,"abstract":"There's an assortment of advancement which has been made with respect to computerized picture preparing and ML calculations which likewise incorporate its different applications. Presently we are living in a period where the issue with respect to agribusiness is a significant issue these days. The serious issue in crop development is we need to deal with the soundness of the plants and yields In this venture we fundamentally centered around characterization of different leafs as various sorts of illnesses. For this we use HSI shading model and bunching calculation. We likewise utilize MATLAB for our task. Less yield, greater expense of creation because of work shortage and compost cost are the significant difficulties prior the farmers. In current situations it's difficult to supply water to the farmlands manually and is consuming more time and manual power. For that We also included an automatic irrigation system to water the plants automatically. Real time monitoring of thedata is there using the cloud platform.","PeriodicalId":6777,"journal":{"name":"2021 International Conference on Innovative Computing, Intelligent Communication and Smart Electrical Systems (ICSES)","volume":"21 1","pages":"1-5"},"PeriodicalIF":0.0,"publicationDate":"2021-09-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"81537180","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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