WITHDRAWN: Crop Price Prediction Using Random Forest and Decision Tree Regression:-A Review

Rakhra, Manik; Soniya, Priyansh; Tanwar, Dishant; Singh, Piyush; Bordoloi, Dorothy; Agarwal, Prerit; Takkar, Sakshi; Jairath, Kapil; Verma, Neha

doi:10.1016/j.matpr.2021.03.261

Cited by 38 publications

(15 citation statements)

References 7 publications

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“…In similar studies which investigated disease prediction, the random forest (RF) model was suggested as the superior model with the highest performance compared to other well-known predictive models such as support vector machine (SVM), Naïve Bayes algorithm, and logistic regression [19][20][21][22]. Furthermore, RF is convenient in situations 3 Computational and Mathematical Methods in Medicine including more than two classes [23].…”

Section: Data Manipulation Andmentioning

confidence: 99%

Predicting the Physician’s Specialty Using a Medical Prescription Database

Akhlaghi

Tabesh

Mahaki

et al. 2022

Computational and Mathematical Methods in Medicine

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Purpose. The present study is aimed at predicting the physician's specialty based on the most frequent two medications prescribed simultaneously. The results of this study could be utilized in the imputation of the missing data in similar databases. Patients and Methods. The research is done through the KAy-means for MIxed LArge datasets (KAMILA) clustering and random forest (RF) model. The data used in the study were retrieved from outpatients' prescriptions in the second populous province of Iran (Khorasan Razavi) from April 2015 to March 2017. Results. The main findings of the study represent the importance of each combination in predicting the specialty. The final results showed that the combination of amoxicillin-metronidazole has the highest importance in making an accurate prediction. The findings are provided in a user-friendly R-shiny web application, which can be applied to any medical prescription database. Conclusion. Nowadays, a huge amount of data is produced in the field of medical prescriptions, which a significant section of that is missing in the specialty. Thus, imputing the missing variables can lead to valuable results for planning a medication with higher quality, improving healthcare quality, and decreasing expenses.

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Section: Data Manipulation Andmentioning

confidence: 99%

Predicting the Physician’s Specialty Using a Medical Prescription Database

Akhlaghi

Tabesh

Mahaki

et al. 2022

Computational and Mathematical Methods in Medicine

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“…DTs are tree based models. DT is a popular supervised learning method that has consistently been used in varieties of classification and regression studies [110]. Among the popular machine learning models, DT is one of the simplest to understand, visualize and evaluate.…”

Section: Decision Tree (Dt)mentioning

confidence: 99%

“…Tamy et al [81] explained that DTs are combinations of sets of tree predictors whereby individual tree depends on the values of a random vector that is sampled independently with the same distribution for the trees that makes up the forest. Rakhra et al [110] explained that RFs are collections of multiple DTs whereby the challenge of overfitting which is popular with singular DTs is solved by a voting method, in which the most voted class is the final result for the target observation. Generally, RFs are well-known to be fast, scalable and robust.…”

Section: Random Forest (Rf)mentioning

confidence: 99%

A Review of Research Works on Supervised Learning Algorithms for SCADA Intrusion Detection and Classification

et al. 2021

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Supervisory Control and Data Acquisition (SCADA) systems play a significant role in providing remote access, monitoring and control of critical infrastructures (CIs) which includes electrical power systems, water distribution systems, nuclear power plants, etc. The growing interconnectivity, standardization of communication protocols and remote accessibility of modern SCADA systems have contributed massively to the exposure of SCADA systems and CIs to various forms of security challenges. Any form of intrusive action on the SCADA modules and communication networks can create devastating consequences on nations due to their strategic importance to CIs’ operations. Therefore, the prompt and efficient detection and classification of SCADA systems intrusions hold great importance for national CIs operational stability. Due to their well-recognized and documented efficiencies, several literature works have proposed numerous supervised learning techniques for SCADA intrusion detection and classification (IDC). This paper presents a critical review of recent studies whereby supervised learning techniques were modelled for SCADA intrusion solutions. The paper aims to contribute to the state-of-the-art, recognize critical open issues and offer ideas for future studies. The intention is to provide a research-based resource for researchers working on industrial control systems security. The analysis and comparison of different supervised learning techniques for SCADA IDC systems were critically reviewed, in terms of the methodologies, datasets and testbeds used, feature engineering and optimization mechanisms and classification procedures. Finally, we briefly summarized some suggestions and recommendations for future research works.

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“…With the applications of these five LSTM variants, the present scenario in agriculture is likely to improve and help the farmers in getting some basic knowledge about the best Minimum Support Price (MSP) for their crops. Further, it could act as a nerve centre for both peasants and buyers to delve into several choices and act accordingly (Rakhra et. al., 2021).…”

Section: Introductionmentioning

confidence: 99%

Deep Learning Based Models: Basic LSTM, Bi LSTM, Stacked LSTM, CNN LSTM and Conv LSTM to Forecast Agricultural Commodities Prices

Murugesan

Mishra

2021

Preprint

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The literature argues that an accurate price prediction of agricultural goods is a quintessence to assure a good functioning of the economy all over the world. Research reveals that studies with application of deep learning in the tasks of agricultural price forecast on short historical agricultural prices data are very scarce and insist on the use of different methods of deep learning to predict and to this reaction of filling the gap, this study employs five versions of LSTM deep learning techniques for the task of five agricultural commodities prices prediction on univariate time series dataset of Rice, Wheat, Gram, Banana, and Groundnut spanning January 2000 to July 2020. The study obtained good forecasting results for all five commodities employing all the five LSTM models. The study validated the results with lower values of error metrics, MAE, MAPE, MSE, and RMSE and two paired t-test with hypothesis and confidence level of 95% as a measure of robustness. The study predicted the one month ahead future price for all the five commodities and compared it with actual prices using said LSTM models and obtained promising results.

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WITHDRAWN: Crop Price Prediction Using Random Forest and Decision Tree Regression:-A Review

Cited by 38 publications

References 7 publications

Predicting the Physician’s Specialty Using a Medical Prescription Database

Predicting the Physician’s Specialty Using a Medical Prescription Database

A Review of Research Works on Supervised Learning Algorithms for SCADA Intrusion Detection and Classification

Deep Learning Based Models: Basic LSTM, Bi LSTM, Stacked LSTM, CNN LSTM and Conv LSTM to Forecast Agricultural Commodities Prices

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