Deep learning model for temperature prediction: an empirical study

Shrivastava, Virendra Kumar; Shrivastava, Aastik; Sharma, Nonita; Mohanty, Sachi Nandan; Pattanaik, Chinmaya Ranjan

doi:10.1007/s40808-022-01609-x

Cited by 7 publications

(3 citation statements)

References 37 publications

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“…ANN is used to find nonlinear patterns in data. The actual strength of ANN comes from the hidden layers, which are present in between the input and output layers (Jain et al, 1996, Shrivastava et al., 2022). ANN is a feedforward network.…”

Section: Methodsmentioning

confidence: 99%

Deep learning model for temperature prediction: A case study in New Delhi

Shrivastava

Sharma

et al. 2023

Journal of Forecasting

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This study is based on temperature prediction in the capital of India (New Delhi). We have adopted different ML models such as (MPR and DNN) which are designed and implemented for temperature prediction. The MPR models are varied on the degree of the polynomial, whereas the DNN models differ in the number of input parameters. DNNM‐1 takes date, time, and temperature as input, and DNNM‐2 receives date, time, temperature, pressure, humidity, and dew point as input parameters, whereas DNNM‐3, is a complex model that takes date, time, temperature, pressure, humidity, dew point, and 32 weather conditions as input. To evaluate the accuracy of the predictions, a comparison of the predicted temperature and the actual recorded temperature is done, and the performance and accuracy of the models are examined. The MPR models work well in case of fewer input features, but as the number of input features grows, the DNN model outperforms the MPR models. The DNN model (DNNM‐3) outperformed the other models with better accuracy as compared to past evidence.

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Section: Methodsmentioning

confidence: 99%

Deep learning model for temperature prediction: A case study in New Delhi

Shrivastava

Sharma

et al. 2023

Journal of Forecasting

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“…Similar research [19] [20] demonstrated how random forest can be fine-tuned for a high dimensional dataset with large number of features relative to the sample size. Deep learning is one of the widely used that offers variety of models to deal with textual data analysis [21][22][23][24] and image classifications [25]. Deep neural network models, such as Convolutional Neural Networks, have shown great potential in image analysis and have emerged as a powerful tool for feature extraction and classification tasks [26][27][28].…”

Section: Motivation and Related Workmentioning

confidence: 99%

Predicting Probable Product Swaps in Customer Behaviour: An In-depth Analysis of Forecasting Techniques, Factors Influencing Decisions, and Implications for Business Strategies

Rao,

Shrivastava

2023

ICST Transactions on Scalable Information Systems

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Introduction: Factors influencing product swap requests and predict the likelihood of such requests, focusing on product usage, attributes, and customer behaviour, particularly in the IT industry. Objectives: Analyse customer and product data from a leading IT company, aiming to uncover insights and determinants of swap requests Methods: Gather product and customer data, perform data processing, and employ machine learning methods such as Random Forest, Support Vector Machine, and Naive Bayes to discern the variables influencing product swap requests and apply them for classification purposes. Results: Analysed a substantial dataset, comprising 320K product purchase requests and 30K swap requests from a prominent social media company. The dataset encompasses 520 attributes, encompassing customer and product details, usage data, purchase history, and chatter comments related to swap requests. The study compared Random Forest, Support Vector Machine, and Naïve Bayes models, with Random Forest fine-tuned for optimal results and feature importance identified based on F1 scores to understand attribute relevance in swap requests. Conclusion: Evaluated three algorithms: support vector machine, naive Bayes, and Random Forest. The Random Forest, fine-tuned based on feature importance, yielded the best results with an accuracy of 0.83 and an F1 score of 0.86.

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“…are very advantageous to speed up the potato disease prediction process. AI and deep learning have witnessed immense surge in the agriculture domain due to its capabilities of image identification, processing, image classification and image prediction [3].…”

Section: Introductionmentioning

confidence: 99%

Optimized Deep Learning Model for Disease Prediction in Potato Leaves

Shrivastava,

Shelke,

Shrivastava

et al. 2023

EAI Endorsed Trans Perv Health Tech

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Food crops are important for nations and human survival. Potatoes are one of the most widely used foods globally. But there are several diseases hampering potato growth and production as well. Traditional methods for diagnosing disease in potato leaves are based on human observations and laboratory tests which is a cumbersome and time-consuming task. The new age technologies such as artificial intelligence and deep learning can play a vital role in disease detection. This research proposed an optimized deep learning model to predict potato leaf diseases. The model is trained on a collection of potato leaf image datasets. The model is based on a deep convolutional neural network architecture which includes data augmentation, transfer learning, and hyper-parameter tweaking used to optimize the proposed model. Results indicate that the optimized deep convolutional neural network model has produced 99.22% prediction accuracy on Potato Disease Leaf Dataset.

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Deep learning model for temperature prediction: an empirical study

Cited by 7 publications

References 37 publications

Deep learning model for temperature prediction: A case study in New Delhi

Deep learning model for temperature prediction: A case study in New Delhi

Predicting Probable Product Swaps in Customer Behaviour: An In-depth Analysis of Forecasting Techniques, Factors Influencing Decisions, and Implications for Business Strategies

Optimized Deep Learning Model for Disease Prediction in Potato Leaves

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