Rainfall prediction using Back Propagation Neural Network Model with Improved Flower Pollination Optimization Algorithm

Malik, Nisha

doi:10.46253/j.mr.v3i3.a2

Cited by 5 publications

(3 citation statements)

References 15 publications

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“…In this section, the results attained using the adopted IHSA-based DCNN model were shown and the performance was validated on the basis of the SDME, PSNR, and SSIM. Here, the proposed method was compared with the conventional models such as NN [9], KNN, CNN, SVM, Deep CNN, and ANN [10].…”

Section: Resultsmentioning

confidence: 99%

Improved Harmony Search Approach based DCNN for Image Restoration Model

Vuyyala¹

2022

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In various fields, image restoration has received huge interest and many researchers introduce several image restoration techniques to restore hidden clear images from degraded images. Moreover, aforesaid approaches performances are estimated impartially remnants the huge confront that might delay the furthermore improvement of developed image restoration techniques. Hence, an efficient noisy pixel prediction on the basis of the image restoration is introduced that uses the Deep Convolutional Neural Network (DCNN) classifier to restore the input image from several noises, such as random noise as well as impulse noise. An Improved Harmony Search Algorithm (IHSA) is adopted to train the DCNN optimally based on minimum error. After identifying the noisy pixels, by exploiting the neuro-fuzzy system the enhancement of pixel is performed. Finally, the experimental analysis is performed and the image restoration performance on the basis of IHSA is analyzed based on the SDME, PSNR, and SSIM. Ultimately, the adopted model attains the maximum PSNR SSIM for images with random noise, as well as maximum SDME with impulse noise, correspondingly.

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Section: Resultsmentioning

confidence: 99%

Improved Harmony Search Approach based DCNN for Image Restoration Model

Vuyyala¹

2022

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“…Classically, the analysis of the stock market employed a huge number of statistical techniques that include exponential smoothing (ES), and autoregressive conditional heteroscedasticity (ARCH) 19 . The systems of stock prediction utilize techniques with several statistical postulations that did not attain reasonable outcomes due to the complexity of fulfilling statistical postulations, like normality and linearity postulates and independence amongst input attributes 20–22 . Various techniques in recent days have made forecasting the stock market considering soft computing skills and statistics.…”

Section: Introductionmentioning

confidence: 99%

“…19 The systems of stock prediction utilize techniques with several statistical postulations that did not attain reasonable outcomes due to the complexity of fulfilling statistical postulations, like normality and linearity postulates and independence amongst input attributes. [20][21][22] Various techniques in recent days have made forecasting the stock market considering soft computing skills and statistics. The earlier studies tend to adapt statistical techniques.…”

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confidence: 99%

Hybrid optimization enabled deep learning and spark architecture using big data analytics for stock market forecasting

Kanchanamala

Karnati²,

Reddy

2023

Concurrency and Computation

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Summary The precise forecasting of stock prices is not possible because of the complexity and uncertainty of stock. The effectual model is needed for the triumphant assessment of upcoming stock prices for several companies. Here, an optimized deep model is utilized to effectively predict the stock market using the spark framework. Here, the data partitioning is done using deep embedded clustering, wherein the tuning of parameters is done using the proposed Jaya Anti Coronavirus Optimization (JACO) algorithm in the master node. The proposed JACO is developed by combining Jaya Algorithm and Anti‐Coronavirus Optimization algorithm. Then, important technical indicators are mined from divided data in slave nodes. Here, the technical indicators are considered features for enhanced processing. Then, data augmentation is done to make data suitable for processing in the master node. At last, the prediction was done in the master node using deep long short‐term memory (Deep LSTM), and training is performed with the proposed JACO. The proposed JACO‐based Deep LSTM attains the smallest mean absolute error of 0.113, mean squared error of 0.095, and root mean squared error of 0.309.

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Shuffled shepherd social optimization based deep learning for rice leaf disease classification and severity percentage prediction

Daniya

Srinivasan

2022

Concurrency and Computation

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Summary The earlier diagnosis and classification of plant diseases has the ability to control the spread of illnesses on a variety of crops with the aim of improving crop quality and yield. The automatic system effectively recognizes the plant diseases at less error and cost without the interpretation of farm specialists. In this article, shuffled shepherd social optimization‐based deep learning (SSSO‐based deep learning) technique is developed to classify rice leaf disease and severity percentage prediction. The classification is carried out using deep maxout network and the severity percentage prediction is performed using deep LSTM. The training of both deep learning techniques is achieved using developed SSSO algorithm, which is the combination of shuffled shepherd optimization algorithm (SSOA) and social optimization algorithm. The proposed technique achieved maximum accuracy, sensitivity, specificity of 0.926, 0.935, 0.892, and minimum mean square error, and root mean square error of 0.106, and 0.326. The accuracy of the implemented approach is 7.24%, 5.29%, 4%, and 2.81% improved than the existing techniques, like bacterial leaf streak‐based UNet (BLSNet), multilayer maxout, resistance spot welding‐based deep recurrent neural network (RSW‐based deep RNN), and rider Henry gas solubility optimization_deep neuro fuzzy network (RHGSO_DNFN) + deep LSTM.

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Rainfall prediction using Back Propagation Neural Network Model with Improved Flower Pollination Optimization Algorithm

Cited by 5 publications

References 15 publications

Improved Harmony Search Approach based DCNN for Image Restoration Model

Improved Harmony Search Approach based DCNN for Image Restoration Model

Hybrid optimization enabled deep learning and spark architecture using big data analytics for stock market forecasting

Shuffled shepherd social optimization based deep learning for rice leaf disease classification and severity percentage prediction

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