Design and development of e-farm with S.C.H.E.M.E.

Khandare, Samruddhi; Gawade, Sushopti; Turkar, Varsha

doi:10.1109/rise.2017.8378223

Cited by 5 publications

(4 citation statements)

References 6 publications

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“…There are three main parts in the Levenshtein distance calculation algorithm: initializing the distance matrix, calculating the distance matrix, and returning the value from the distance matrix with the largest value as a result of the Levenshtein distance. In this study, similarity strings were used in the grouping of DPP-4 inhibitor molecules through a comparison of bit vector strings, each of which contained a string of 0 or 1, from each molecule obtained based on the fingerprinting method, ECFP and FCFP [17][18][19].…”

Section: Data Clustering Using K-modesmentioning

confidence: 99%

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Artificial intelligence paradigm for ligand-based virtual screening on the drug discovery of type 2 diabetes mellitus

et al. 2021

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Background New dipeptidyl peptidase-4 (DPP-4) inhibitors need to be developed to be used as agents with low adverse effects for the treatment of type 2 diabetes mellitus. This study aims to build quantitative structure-activity relationship (QSAR) models using the artificial intelligence paradigm. Rotation Forest and Deep Neural Network (DNN) are used to predict QSAR models. We compared principal component analysis (PCA) with sparse PCA (SPCA) as methods for transforming Rotation Forest. K-modes clustering with Levenshtein distance was used for the selection method of molecules, and CatBoost was used for the feature selection method. Results The amount of the DPP-4 inhibitor molecules resulting from the selection process of molecules using K-Modes clustering algorithm is 1020 with logP range value of -1.6693 to 4.99044. Several fingerprint methods such as extended connectivity fingerprint and functional class fingerprint with diameters of 4 and 6 were used to construct four fingerprint datasets, ECFP_4, ECFP_6, FCFP_4, and FCFP_6. There are 1024 features from the four fingerprint datasets that are then selected using the CatBoost method. CatBoost can represent QSAR models with good performance for machine learning and deep learning methods respectively with evaluation metrics, such as Sensitivity, Specificity, Accuracy, and Matthew’s correlation coefficient, all valued above 70% with a feature importance level of 60%, 70%, 80%, and 90%. Conclusion The K-modes clustering algorithm can produce a representative subset of DPP-4 inhibitor molecules. Feature selection in the fingerprint dataset using CatBoost is best used before making QSAR Classification and QSAR Regression models. QSAR Classification using Machine Learning and QSAR Classification using Deep Learning, each of which has an accuracy of above 70%. The QSAR RFC-PCA and QSAR RFR-PCA models performed better than QSAR RFC-SPCA and QSAR RFR-SPCA models because QSAR RFC-PCA and QSAR RFR-PCA models have more effective time than the QSAR RFC-SPCA and QSAR RFR-SPCA models.

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Section: Data Clustering Using K-modesmentioning

confidence: 99%

“…Levenshtein distance is a distance method that can be used in the K-modes clustering as a dissimilarity function. It aims at measuring the similarity of two strings in the form of a sequence, where the order of the elements in one string is considered necessary using insertion, deletion, and substitution operations [17][18][19].…”

Section: Introductionmentioning

confidence: 99%

Artificial intelligence paradigm for ligand-based virtual screening on the drug discovery of type 2 diabetes mellitus

et al. 2021

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“…This failure is not just a direct result of farmers' language limitations and a lack of attendance of instruction; it is an immediate consequence of the inconvenience regarding information access, including details about government plans. Subsequently, the motivation of this assessment is to offer Indian farmers a user-friendly site that gives adaptable applications tailored to their individual situations, so they can easily access information related to necessary government plans (Khandare et al, 2017). The cultivation instruction administration focuses on the dispersal of cultivation data (Chung et al, 2019) gathered from current exercises, cultivation items and the experience of farmers in the fields.…”

Section: Literature Surveymentioning

confidence: 99%

E-farming management system using data mining techniques

Vanitha

2021

IJIUS

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PurposeThe purpose of this paper is to help farmers and get to sale the products in profit way.Design/methodology/approachThe project is developed using visual studio code front end, and back end is developed by SQl server configuration.FindingsThe user friendly website to farmers: they can sell the products to customer in direct relationship in efficient manner.Originality/valueThis is the website developed to help farmers which is a good idea to help farmers and increase productivity and profit earning methodology.

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“…It aims to develop the rural farming communities in India by providing seamless access to vital agricultural information and services. It is a user-centered information system with an ease of use, simple and learnable, community-oriented design that aims to promote professional growth of the rural farmers [1]. The e-Krishimitra online service provider tool is available in the form of mobile app and website as content management system.…”

Section: Introductionmentioning

confidence: 99%

A Role and Potential of E-Krishimitra Tool in Usability Improvement of Agricultural Domain

Gawade¹,

Turkar²

2019

IJCA

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The agricultural sector in India has challenges, such as requirement for increase of food production and produce. In addition, the agricultural sector has important effects due to global factors and rapid changes. These truths show that there is great necessity for information technologies (IT), which can be used to handle the challenges and changes and to progress agricultural production and marketing. Though, in India, the power of IT is not fully exploited in agriculture. Execution of IT in agricultural segment and rural parts is slow in comparison to the other sectors of the economy where IT has been implemented at rapid speed. The e-Krishimitra tool has been developed using information and communication technology to make the life of Indian famers easy. The tool provides various services like location-based service, Best crop suggestion, Crop disease diagnosis, guidance for government scheme and regional language support. Only the tool development is not sufficient, but the effectiveness of the tool should be evaluated to know whether it is used by maximum population or not. This paper presents detailed results of usability evaluation of e-Krishimitra tool.

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Design and development of e-farm with S.C.H.E.M.E.

Cited by 5 publications

References 6 publications

Artificial intelligence paradigm for ligand-based virtual screening on the drug discovery of type 2 diabetes mellitus

Artificial intelligence paradigm for ligand-based virtual screening on the drug discovery of type 2 diabetes mellitus

E-farming management system using data mining techniques

A Role and Potential of E-Krishimitra Tool in Usability Improvement of Agricultural Domain

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