Computational Performance Analysis of Neural Network and Regression Models in Forecasting the Societal Demand for Agricultural Food Harvests

Prabhu, B. V. Balaji; Dakshayini, M.

doi:10.4018/ijghpc.2020100103

Cited by 7 publications

(2 citation statements)

References 8 publications

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“…It has been studied for decades by researchers using i. Linear methods e.g., Auto-Regressive Integrated Moving Average(ARIMA) [12], Holt-Winters Exponential Smoothing(HWES) [9], Multiple Linear Regression(MLR) [15][16][17][18], ii. Non-linear methods e.g., Support Vector Machine(SVM) [13], Arti cial Neural Network (ANN) [16] iii.…”

Section: Introductionmentioning

confidence: 99%

WITHDRAWN: DSBAL: Distributed Stacked Bidirectional Attention-based LSTM Method for Time Series Forecasting

Prakash

2022

Preprint

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Accurate time series forecasting is crucial to increase the performance and turnover of every business. However, It’s quite a difficult task due to the non-stationary and high level of uncertainty in the time series data. This paper proposes a new method called the Distributed Stacked Bidirectional Attention Long Short-Term Memory Neural Network (DSBAL) for time series forecasting. The DSBAL method combines the Stacked Bidirectional LSTM (SBiLSTM) and Attention mechanism in distributed computing. The proposed method consists of an SBiLSTM encoder, attention mechanism, and SBiLSTM decoder. SBiLSTM encoder is used to extract the complex features in the daily tomato supply data, in addition, the Attention mechanism is introduced to enhance the performance of SBILSTM by selecting the more appropriate sequence in the data by giving higher weightage to them. SBiLSTM decoder uses the most appropriate sequences from the attention mechanism to predict the daily tomato supply data. The entire process of the proposed method runs in distributed computing to improve efficiency, accuracy, and scalability. Our proposed method allows us to use only appropriate sequences in the data, captures complicated patterns, and addresses computational issues. To prove the efficiency of the proposed methodology, the experiments are conducted with other time series forecasting methods like RNN, LSTM, Stacked LSTM, Bidirectional LSTM, and Attention LSTM using daily tomato supply datasets in terms of SMAPE and RMSE. The results obtained from the experiment demonstrate that our proposed method is more efficient, accurate, and scalable.

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Section: Introductionmentioning

confidence: 99%

WITHDRAWN: DSBAL: Distributed Stacked Bidirectional Attention-based LSTM Method for Time Series Forecasting

Prakash

2022

Preprint

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“…BV and Dakshayini 20 applied machine learning tools to the project market, used Multiple Linear Regression (MLR) and an ANN model, and attempted to forecast demand in agriculture. They found the proposed helpful model reliable and quiet for planning and producing agri-food.…”

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confidence: 99%

A robust and resilience machine learning for forecasting agri-food production

Lotfi

Gholamrezaei

Kadłubek

et al. 2022

Sci Rep

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This research proposes a new framework for agri-food capacity production by considering resiliency and robustness and paying attention to disruption and risk for the first time. It is applied robust stochastic optimization by adding robustness to the constraint's objective function and resiliency situation. This research minimizes the mean absolute deviation and coefficient of standard deviation errors by linear function in the agri-food capacity production. This study suggests agri-food managers and decision-makers use this mathematical method to forecast and improve production management. The results of this research lead to better decision-making and are compared with other sine functions. The main model's Robust and Resiliency Mean Absolute Deviation (RRMAD) value is 1.28% lower than other sine-type functions. The conservativity coefficient, confidence level, weight factor, resiliency coefficient, and probability of the scenario vary. The main model's RRMAD value is 1.28% lower than other sine-type functions. Growing the weight factor will result in an increase in RRMAD and a smooth decline in R-squared. Additionally, as the resilience coefficient rises, the RRMAD function increases while the R-squared declines. By altering the probability of the scenario, the RRMAD function drops, and the R-squared goes up.

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Demand-Based Land Suitability Prediction Model for Sustainable Agriculture

Farzana

Prakash

2022

Artificial Intelligence and Sustainable Computing

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Computational Performance Analysis of Neural Network and Regression Models in Forecasting the Societal Demand for Agricultural Food Harvests

Cited by 7 publications

References 8 publications

WITHDRAWN: DSBAL: Distributed Stacked Bidirectional Attention-based LSTM Method for Time Series Forecasting

WITHDRAWN: DSBAL: Distributed Stacked Bidirectional Attention-based LSTM Method for Time Series Forecasting

A robust and resilience machine learning for forecasting agri-food production

Demand-Based Land Suitability Prediction Model for Sustainable Agriculture

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