Hindi is the mother tongue of nearly 133 crore Indians. Along with India, it is spoken in Nepal, Fiji, and Bangladesh. Since good knowledge of English is not common, there is a good opportunity for machine translation from English to Hindi and vice versa. Language translation is one task in which machines lag behind human power [1]. One task where machines fall short of human ability is language translation. Rule-Based Translation (RBT) systems and Statistical Machine Translation (SMT) systems are the conventional systems used for language translation. Rule Based Translation requires in-depth knowledge of the language. RBT is a fairly complicated system that can and must include many rules in order to improve quality. SMT is one of the traditional approaches to the machine translation issue. This technique works well with pairs of languages with comparable grammatical structures and requires enormous data sets. This paper proposes a better approach - a neural network model that uses “Recurrent Neural Network” (RNN) and “Gated Recurrent Unit” (GRU). The system consists of an RNN-encoder and RNN-decoder architecture and an attention mechanism to deal with long sentences.
{"title":"A System for Language Translation using Sequence-to-sequence Learning based Encoder","authors":"Sonia Sarode, Raghav Thatte, Kajal Toshniwal, Jatin Warade, Ranjeet Vasant Bidwe, Bhushan Zope","doi":"10.1109/ESCI56872.2023.10099876","DOIUrl":"https://doi.org/10.1109/ESCI56872.2023.10099876","url":null,"abstract":"Hindi is the mother tongue of nearly 133 crore Indians. Along with India, it is spoken in Nepal, Fiji, and Bangladesh. Since good knowledge of English is not common, there is a good opportunity for machine translation from English to Hindi and vice versa. Language translation is one task in which machines lag behind human power [1]. One task where machines fall short of human ability is language translation. Rule-Based Translation (RBT) systems and Statistical Machine Translation (SMT) systems are the conventional systems used for language translation. Rule Based Translation requires in-depth knowledge of the language. RBT is a fairly complicated system that can and must include many rules in order to improve quality. SMT is one of the traditional approaches to the machine translation issue. This technique works well with pairs of languages with comparable grammatical structures and requires enormous data sets. This paper proposes a better approach - a neural network model that uses “Recurrent Neural Network” (RNN) and “Gated Recurrent Unit” (GRU). The system consists of an RNN-encoder and RNN-decoder architecture and an attention mechanism to deal with long sentences.","PeriodicalId":441215,"journal":{"name":"2023 International Conference on Emerging Smart Computing and Informatics (ESCI)","volume":"6 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128277442","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 : 2023-03-01DOI: 10.1109/ESCI56872.2023.10099542
N. K. Trivedi, R. Tiwari, A. Agarwal, Vinay Gautam
A Method of Classification Based on Norms Data mining greatly benefits several subfields within the healthcare industry. Detecting and treating diseases at an early stage is a challenging but essential objective in the healthcare field. If they are discovered early enough, many diseases can be diagnosed and treated while they are still in their early stages. Conditions that affect the thyroid are one example of this type of example. In the past, thyroid disorders were identified through a process that involved observing a patient's symptoms and doing a battery of blood tests. The primary goal is to enhance the accuracy with which diseases are detected in the initial stages of their progression. The healthcare business may gain a significant amount from using data mining techniques for decision-making, disease diagnosis, and the provision of superior treatment to patients at reduced prices. Thyroiditis is ongoing. The act of classifying things into different groups is significant. This study aims to determine the connection between TSH, T3, and T4 and hyperthyroidism and hypothyroidism. It also tries to determine the relationship between TSH, T3, T4, and gender. Additionally, the research will attempt to predict thyroid disease using several classification systems. Our study shows that the Neural network classifier generates the highest classification accuracy of 98.4%.
{"title":"A Detailed Investigation and Analysis of Using Machine Learning Techniques for Thyroid Diagnosis","authors":"N. K. Trivedi, R. Tiwari, A. Agarwal, Vinay Gautam","doi":"10.1109/ESCI56872.2023.10099542","DOIUrl":"https://doi.org/10.1109/ESCI56872.2023.10099542","url":null,"abstract":"A Method of Classification Based on Norms Data mining greatly benefits several subfields within the healthcare industry. Detecting and treating diseases at an early stage is a challenging but essential objective in the healthcare field. If they are discovered early enough, many diseases can be diagnosed and treated while they are still in their early stages. Conditions that affect the thyroid are one example of this type of example. In the past, thyroid disorders were identified through a process that involved observing a patient's symptoms and doing a battery of blood tests. The primary goal is to enhance the accuracy with which diseases are detected in the initial stages of their progression. The healthcare business may gain a significant amount from using data mining techniques for decision-making, disease diagnosis, and the provision of superior treatment to patients at reduced prices. Thyroiditis is ongoing. The act of classifying things into different groups is significant. This study aims to determine the connection between TSH, T3, and T4 and hyperthyroidism and hypothyroidism. It also tries to determine the relationship between TSH, T3, T4, and gender. Additionally, the research will attempt to predict thyroid disease using several classification systems. Our study shows that the Neural network classifier generates the highest classification accuracy of 98.4%.","PeriodicalId":441215,"journal":{"name":"2023 International Conference on Emerging Smart Computing and Informatics (ESCI)","volume":"114 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133569461","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}
The quantity, complexity, and variety of malware are all increasing at an alarming rate. Attackers and hackers frequently create systems that can automatically reorder and encrypt their code in order to avoid detection. This paper proposes an improvement in malware detection using a modern neural network model, EfficientNet, determined to achieve higher accuracy and efficiency. The project was implemented using around 2000 samples classified as malicious and benign files imported from the Dike dataset. The portable executable (PE) files were then converted into grayscale images to carry out malware detection using Efficient, an image classification algorithm based on convolutional neural networks. In particular, 4 models - B0 to B3 were implemented in this study. The Agile software development techniques and methodologies were implemented throughout the process.
{"title":"Malware Detection Using Efficientnet","authors":"Sandip Shinde, Aditya Dhotarkar, Dhanshree Pajankar, Kshitij Dhone, Sejal Babar","doi":"10.1109/ESCI56872.2023.10099693","DOIUrl":"https://doi.org/10.1109/ESCI56872.2023.10099693","url":null,"abstract":"The quantity, complexity, and variety of malware are all increasing at an alarming rate. Attackers and hackers frequently create systems that can automatically reorder and encrypt their code in order to avoid detection. This paper proposes an improvement in malware detection using a modern neural network model, EfficientNet, determined to achieve higher accuracy and efficiency. The project was implemented using around 2000 samples classified as malicious and benign files imported from the Dike dataset. The portable executable (PE) files were then converted into grayscale images to carry out malware detection using Efficient, an image classification algorithm based on convolutional neural networks. In particular, 4 models - B0 to B3 were implemented in this study. The Agile software development techniques and methodologies were implemented throughout the process.","PeriodicalId":441215,"journal":{"name":"2023 International Conference on Emerging Smart Computing and Informatics (ESCI)","volume":"45 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132799441","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 : 2023-03-01DOI: 10.1109/ESCI56872.2023.10100278
Snehal R. Rathi, Pawan Wawage, Amrut Kulkarni
In this world of ever growing technology, humans have tried to develop systems to make their work as easy as possible. Education sector is no exception to this as we have got introduced to a lot of Ed-tech systems and can access any information or course just with the help of our smartphones. Carrying out educational activities is not an easy task. There are a lot of things to manage or taken care by the professors. To reduce the work load of the professors we have developed a Web Application to generate automatic questions based on the data being provided to the system. This will not only make the jobs of the professors easy but will also allow them to process huge educational syllabus and generate any type of questions using that data.
{"title":"Automatic Question Generation from Textual data using NLP techniques","authors":"Snehal R. Rathi, Pawan Wawage, Amrut Kulkarni","doi":"10.1109/ESCI56872.2023.10100278","DOIUrl":"https://doi.org/10.1109/ESCI56872.2023.10100278","url":null,"abstract":"In this world of ever growing technology, humans have tried to develop systems to make their work as easy as possible. Education sector is no exception to this as we have got introduced to a lot of Ed-tech systems and can access any information or course just with the help of our smartphones. Carrying out educational activities is not an easy task. There are a lot of things to manage or taken care by the professors. To reduce the work load of the professors we have developed a Web Application to generate automatic questions based on the data being provided to the system. This will not only make the jobs of the professors easy but will also allow them to process huge educational syllabus and generate any type of questions using that data.","PeriodicalId":441215,"journal":{"name":"2023 International Conference on Emerging Smart Computing and Informatics (ESCI)","volume":"27 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133036387","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 : 2023-03-01DOI: 10.1109/ESCI56872.2023.10100091
S. R, S. S, Thangerani Raajaseharan
Diabetic Retinopathy is an ocular sickness resulting in the visual disability, the treatment and cure of this eye disease becomes comfort if the disease is identified at the earliest. The present study conceives an integrated machine learning approach for the multi-level multi-classification of the earliest stage of diabetic retinopathy called Non-Proliferative Diabetic Retinopathy. At the first level, the disease features are classified and at the second level, the disease severities are classified. The implementation of the work ensues with the fundus images undergoing grayscale conversion and median filter for preprocessing. Then, the statistical feature vectors like local binary patterns, histogram of gradients, and gray level co-occurrence matrix are extracted and fed into a multi-class support vector machine for classifying the non-Proliferative diabetic retinopathy disease features called microaneurysm, intra-retinal hemorrhages, and hard exudates. The classified features are classified into non-proliferative-diabetic-retinopathy disease severities namely mild, moderate and severe with the k-Nearest neighbor, random forest, and naive bayes methods. The proposed classifiers are assessed and validated in terms of accuracy and execution time; comparatively the k-Nearest neighbor classifier achieved a better result of 99% accuracy and the least processing time.
{"title":"Multi-Classification of Non-Proliferative Diabetic Retinopathy Through Integrated Machine Learning Approach in Fundus Images","authors":"S. R, S. S, Thangerani Raajaseharan","doi":"10.1109/ESCI56872.2023.10100091","DOIUrl":"https://doi.org/10.1109/ESCI56872.2023.10100091","url":null,"abstract":"Diabetic Retinopathy is an ocular sickness resulting in the visual disability, the treatment and cure of this eye disease becomes comfort if the disease is identified at the earliest. The present study conceives an integrated machine learning approach for the multi-level multi-classification of the earliest stage of diabetic retinopathy called Non-Proliferative Diabetic Retinopathy. At the first level, the disease features are classified and at the second level, the disease severities are classified. The implementation of the work ensues with the fundus images undergoing grayscale conversion and median filter for preprocessing. Then, the statistical feature vectors like local binary patterns, histogram of gradients, and gray level co-occurrence matrix are extracted and fed into a multi-class support vector machine for classifying the non-Proliferative diabetic retinopathy disease features called microaneurysm, intra-retinal hemorrhages, and hard exudates. The classified features are classified into non-proliferative-diabetic-retinopathy disease severities namely mild, moderate and severe with the k-Nearest neighbor, random forest, and naive bayes methods. The proposed classifiers are assessed and validated in terms of accuracy and execution time; comparatively the k-Nearest neighbor classifier achieved a better result of 99% accuracy and the least processing time.","PeriodicalId":441215,"journal":{"name":"2023 International Conference on Emerging Smart Computing and Informatics (ESCI)","volume":"69 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133463981","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 : 2023-03-01DOI: 10.1109/ESCI56872.2023.10099987
A. Shinde, R. Bichkar
Consumption of energy in the wireless sensor networks is major constrain that restrict the impact of the application. It becomes crucial when a more nodes are deployed. Several energy-efficient solutions have been proposed by many researchers. Clustering is one of the most energy-efficient solution that has been proven for the large-size network. However, the performance of the clustering algorithm degrades because of the non-uniform cluster formation and non-uniform cluster heads distribution over the network. To resolve this problem, a Energy Efficient and Load Balanced Clustering Approach for Wireless Sensor Network Using Genetic Algorithm is presented in this paper. The proposed algorithm not only focused on the load balancing and uniform distribution of cluster head but also on the optimal cluster head selection which considers residual energy, inter-cluster, and intra-cluster communication distance. The performance parameters like, lifetime of the network and energy consumption of the proposed algorithm is analyzed with the existent algorithms. The outcomes of the experiment demonstrated that the presented algorithm performs better than the existent algorithms.
{"title":"Genetic Algorithm Based Energy Efficient and Load Balanced Clustering Approach for WSN","authors":"A. Shinde, R. Bichkar","doi":"10.1109/ESCI56872.2023.10099987","DOIUrl":"https://doi.org/10.1109/ESCI56872.2023.10099987","url":null,"abstract":"Consumption of energy in the wireless sensor networks is major constrain that restrict the impact of the application. It becomes crucial when a more nodes are deployed. Several energy-efficient solutions have been proposed by many researchers. Clustering is one of the most energy-efficient solution that has been proven for the large-size network. However, the performance of the clustering algorithm degrades because of the non-uniform cluster formation and non-uniform cluster heads distribution over the network. To resolve this problem, a Energy Efficient and Load Balanced Clustering Approach for Wireless Sensor Network Using Genetic Algorithm is presented in this paper. The proposed algorithm not only focused on the load balancing and uniform distribution of cluster head but also on the optimal cluster head selection which considers residual energy, inter-cluster, and intra-cluster communication distance. The performance parameters like, lifetime of the network and energy consumption of the proposed algorithm is analyzed with the existent algorithms. The outcomes of the experiment demonstrated that the presented algorithm performs better than the existent algorithms.","PeriodicalId":441215,"journal":{"name":"2023 International Conference on Emerging Smart Computing and Informatics (ESCI)","volume":"84 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132437553","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}
India has been identified as a hotspot for landslides and related disasters. It is impacting nearly all hilly/mountain regions, particularly the Himalayan region. Disasters are becoming increasingly significant and devastating, both in terms of magnitude and frequency. It is resulting in significant loss of human life and property, as well as stifling development in hilly areas. As a result, landslide catastrophe mitigation is one of India's top concerns. This research proposes a low-fee, electricity-efficient, and reliable Landslide Early Warning System (LEWS) for Himalayan landslides to lessen the probability of such tragedies. It's a landslide monitoring system based on Internet of Things (IoT) protocols, with a Wireless Sensor Network (WSN), records amassing, and analysis unit.
{"title":"IOT Based Smart LandSlide Detection System (S-LDS)","authors":"Amruta Amune, Swapnil Patil, Devyani Ushir, Akshata Nangare","doi":"10.1109/ESCI56872.2023.10099562","DOIUrl":"https://doi.org/10.1109/ESCI56872.2023.10099562","url":null,"abstract":"India has been identified as a hotspot for landslides and related disasters. It is impacting nearly all hilly/mountain regions, particularly the Himalayan region. Disasters are becoming increasingly significant and devastating, both in terms of magnitude and frequency. It is resulting in significant loss of human life and property, as well as stifling development in hilly areas. As a result, landslide catastrophe mitigation is one of India's top concerns. This research proposes a low-fee, electricity-efficient, and reliable Landslide Early Warning System (LEWS) for Himalayan landslides to lessen the probability of such tragedies. It's a landslide monitoring system based on Internet of Things (IoT) protocols, with a Wireless Sensor Network (WSN), records amassing, and analysis unit.","PeriodicalId":441215,"journal":{"name":"2023 International Conference on Emerging Smart Computing and Informatics (ESCI)","volume":"22 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121735082","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 : 2023-03-01DOI: 10.1109/ESCI56872.2023.10099805
J. Karbhari, P. Mukherji
The enormous potential use of air-writing recognition in intelligent systems has made it highly popular. Some of the most fundamental issues in isolated writing are yet to be fully addressed. Writing a linguistic character or word in free space using a finger, marker, or handheld device is referred to as a trajectory-based writing method. It can be used where traditional pen-up and pen-down writing techniques are inconvenient. It has a significant upper hand over the gesture-based approach due to its simple writing style. However, because of the diverse characters and writing styles, it is a difficult process. In this paper, an alphabet recognition system for alphabets written in air, where the alphabet is recognised based on air trajectories which are three-dimensional (3D) and gathered by a single camera in this study. A reliable and effective colour-based segmentation is proposed to extract air recorded trajectories gathered by a standard web camera,. This solves the problem of push-to-write by removing limits on users' writing without the usage of an illusory box. The trajectory is normalized for improved recognition using convolutional neural network (CNN). We achieve recognition in real time with a high accuracy of 95% and negligible neural network complexity. It beats and surpasses the currently used techniques that involvewritten images as input.
{"title":"Alphabet Recognition using Air written Trajectories","authors":"J. Karbhari, P. Mukherji","doi":"10.1109/ESCI56872.2023.10099805","DOIUrl":"https://doi.org/10.1109/ESCI56872.2023.10099805","url":null,"abstract":"The enormous potential use of air-writing recognition in intelligent systems has made it highly popular. Some of the most fundamental issues in isolated writing are yet to be fully addressed. Writing a linguistic character or word in free space using a finger, marker, or handheld device is referred to as a trajectory-based writing method. It can be used where traditional pen-up and pen-down writing techniques are inconvenient. It has a significant upper hand over the gesture-based approach due to its simple writing style. However, because of the diverse characters and writing styles, it is a difficult process. In this paper, an alphabet recognition system for alphabets written in air, where the alphabet is recognised based on air trajectories which are three-dimensional (3D) and gathered by a single camera in this study. A reliable and effective colour-based segmentation is proposed to extract air recorded trajectories gathered by a standard web camera,. This solves the problem of push-to-write by removing limits on users' writing without the usage of an illusory box. The trajectory is normalized for improved recognition using convolutional neural network (CNN). We achieve recognition in real time with a high accuracy of 95% and negligible neural network complexity. It beats and surpasses the currently used techniques that involvewritten images as input.","PeriodicalId":441215,"journal":{"name":"2023 International Conference on Emerging Smart Computing and Informatics (ESCI)","volume":"19 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127779289","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 : 2023-03-01DOI: 10.1109/ESCI56872.2023.10099791
R. M. Dhokane, O. P. Sharma
Financial market prediction is an important task for placing an investor's hard-earned money in the financial market to earn profit. Many parameters affect the financial market's valuation, making it volatile, which is challenging for investors. This review study gives a full overview of 53 research articles that were chosen based on the trend of machine learning algorithms, calculation methods, and other performance parameters. Primarily, it is seen that artificial neural network (ANN) and support vector machine (SVM) techniques are used for forecasting the financial market. For prediction purposes, stock selection is also an important task. A genetic algorithm (GA) is used to choose stocks, and it is a very important part of managing a portfolio. The K-means algorithm is used to create a group of stocks that have a similar pattern and behavior. Hybrid approaches also provide better results. This review paper makes it easier for researchers to understand the terms and key ideas of predicting the financial market using machine learning so they can make the right choices for their needs.
{"title":"A Comprehensive Review of Machine Learning for Financial Market Prediction Methods","authors":"R. M. Dhokane, O. P. Sharma","doi":"10.1109/ESCI56872.2023.10099791","DOIUrl":"https://doi.org/10.1109/ESCI56872.2023.10099791","url":null,"abstract":"Financial market prediction is an important task for placing an investor's hard-earned money in the financial market to earn profit. Many parameters affect the financial market's valuation, making it volatile, which is challenging for investors. This review study gives a full overview of 53 research articles that were chosen based on the trend of machine learning algorithms, calculation methods, and other performance parameters. Primarily, it is seen that artificial neural network (ANN) and support vector machine (SVM) techniques are used for forecasting the financial market. For prediction purposes, stock selection is also an important task. A genetic algorithm (GA) is used to choose stocks, and it is a very important part of managing a portfolio. The K-means algorithm is used to create a group of stocks that have a similar pattern and behavior. Hybrid approaches also provide better results. This review paper makes it easier for researchers to understand the terms and key ideas of predicting the financial market using machine learning so they can make the right choices for their needs.","PeriodicalId":441215,"journal":{"name":"2023 International Conference on Emerging Smart Computing and Informatics (ESCI)","volume":"78 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129173733","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 : 2023-03-01DOI: 10.1109/ESCI56872.2023.10099483
S. Toney, Pathan Mohd. Shafi, P. Dhotre
Foods and vegetables are the most important for the survival of human beings. It provides nutrients (Energy) for daily activities and all our functional needs. This is also required to grow and repair our body parts and keep the immune system strong. Export plays a very crucial role in the economy of our country. Indian exports of fruits and vegetables are rising day by day. The demand for farming products such as vegetables is at a high level. The exporting vegetables include Onion, Potato, Cabbage, cauliflower, Brinjal, etc. From the published three years' horticulture data and analysis performed on it using the analytic hierarchy process (AHP), it was observed that amongst all the vegetable potato (Solanum Tubersum) has a significant impact on the overall export and economy of the country. From AHP analysis, it is clear that Potato (Solanum Tubersum) has a 49.09 % contribution in the overall Indian export. Hence targeted in the proposed research.
{"title":"Critical Analysis of Potato (Solanum Tuberosum) on Indian Overall Economy using Analytic Hierarchy Process","authors":"S. Toney, Pathan Mohd. Shafi, P. Dhotre","doi":"10.1109/ESCI56872.2023.10099483","DOIUrl":"https://doi.org/10.1109/ESCI56872.2023.10099483","url":null,"abstract":"Foods and vegetables are the most important for the survival of human beings. It provides nutrients (Energy) for daily activities and all our functional needs. This is also required to grow and repair our body parts and keep the immune system strong. Export plays a very crucial role in the economy of our country. Indian exports of fruits and vegetables are rising day by day. The demand for farming products such as vegetables is at a high level. The exporting vegetables include Onion, Potato, Cabbage, cauliflower, Brinjal, etc. From the published three years' horticulture data and analysis performed on it using the analytic hierarchy process (AHP), it was observed that amongst all the vegetable potato (Solanum Tubersum) has a significant impact on the overall export and economy of the country. From AHP analysis, it is clear that Potato (Solanum Tubersum) has a 49.09 % contribution in the overall Indian export. Hence targeted in the proposed research.","PeriodicalId":441215,"journal":{"name":"2023 International Conference on Emerging Smart Computing and Informatics (ESCI)","volume":"13 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127899771","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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