scholarly journals Analysing the Accuracy of Crop Yield Prediction Using Deep Learning Algorithm

2021 ◽  
Vol 9 (9) ◽  
pp. 201-227
Author(s):  
Sehkammal A
Keyword(s):  
Data Mining ◽  
Deep Learning ◽  
Crop Yield ◽  
Learning Algorithm ◽  
Crop Protection ◽  
Yield Prediction ◽  
Ann Model ◽  
Deep Learning Algorithm ◽  
Yield Forecast ◽  
The Creation

Abstract: The Indian farming level decreases step by step inferable from certain components like inordinate usage of pesticides, water level decrement, environment changes, and unpredicted precipitation, and so forth on the farming information, elucidating investigation is performed to comprehend the creation level. The creation of yields isn't expanded inferable from these issues that influences the economy of farming. By utilizing AI strategies, the harvest from given dataset need to foresee by farming areas for forestalling this issue. Yield forecast is of extraordinary importance for yield planning, crop market arranging, crop protection, and gather the executives. Data mining based deep learning is turning out to be progressively significant in crop yield forecast. This strip-mined information will be wont to inform promoting selections, improve sales and abate on prices that has been made in this field by utilizing AI, particularly the Deep Learning (DL) strategy. Profound learning-based models are extensively used to extricate critical yield highlights for forecast. However, these techniques could resolve the yield forecast issue there exist the accompanying insufficiencies: Unable to make a direct non-straight or straight planning between the crude information and harvest yield esteems accuracy; and the exhibition of those models profoundly depends on the nature of the separated elements. Profound deep learning gives guidance and inspiration for the previously mentioned deficiencies. In this paper, I proposed two deep learning models namely ANN and LSTM considering into account the following parameters such as temperature, humidity, pH, rainfall respectively in each model which in turn were compared based on their accuracy level by limiting the blunder and expanding the conjecture accuracy. From my proposed work, I found LSTM is the model that provides us with the better accuracy that that of the ANN. The accuracy of the ANN model is 96.93548387096774 that is approximately 97% and that of the LSTM is 100 % which is obviously the highest. Keywords: Crop yield prediction, Deep learning, Data mining, ANN, LSTM, Accuracy.

scholarly journals Prediction of Merchandise Sales on E-Commerce Platforms Based on Data Mining and Deep Learning

2021 ◽  
Vol 2021 ◽  
pp. 1-9
Author(s):  
Xiaoting Yin ◽  
Xiaosha Tao
Keyword(s):  
Data Mining ◽  
Deep Learning ◽  
Learning Algorithm ◽  
Research Process ◽  
Deep Learning Algorithm ◽  
Sales Prediction ◽  
Different Types ◽  
Online Business ◽  
Product Sales ◽  
Deep Learning Model

Online business has grown exponentially during the last decade, and the industries are focusing on online business more than before. However, just setting up an online store and starting selling might not work. Different machine learning and data mining techniques are needed to know the users’ preferences and know what would be best for business. According to the decision-making needs of online product sales, combined with the influencing factors of online product sales in various industries and the advantages of deep learning algorithm, this paper constructs a sales prediction model suitable for online products and focuses on evaluating the adaptability of the model in different types of online products. In the research process, the full connection model is compared with the training results of CNN, which proves the accuracy and generalization ability of CNN model. By selecting the non-deep learning model as the comparison baseline, the performance advantages of CNN model under different categories of products are proved. In addition, the experiment concludes that the unsupervised pretrained CNN model is more effective and adaptable in sales forecasting.

scholarly journals Liver Disease Classification using Deep Learning Algorithm

2019 ◽  
Vol 8 (12) ◽  
pp. 5105-5111 ◽  
Keyword(s):  
Data Mining ◽  
Deep Learning ◽  
Liver Disease ◽  
Learning Algorithm ◽  
Liver Disorder ◽  
Disease Classification ◽  
Human Culture ◽  
Deep Learning Algorithm

Data Mining is one of the prevalent elucidating portions of programmed request and distinguishing proof. It involves data mining counts and strategies to examine helpful data. Of late, liver dissents have disproportionately expanded and liver infections are complimenting one of the most human pains in different countries. Early assurance of Liver Disorder is essential for the welfare of human culture. This complaint should be considered sincerely by setting up watchful structures for the early break down and expectation of Liver contaminations. The robotized gathering system suffers with non attendance of precision results when differentiated and cautious biopsy. We propose another model for liver issue request for separating the patient's helpful, data using ANN algorithm. The remedial records are organized whether there is a believability of essence of disorder or not. This proposed methodology uses extracted features using M-PSO and ANN for classifying the features. The ANN methodology improves the accuracy when appeared differently in relation to existing request computations. This paper focuses classification of selected features for classification.

Music Emotion Recognition

2021 ◽  
Author(s):  
tejaswini kambaiahgari ◽  
Uma Rao K
Keyword(s):  
Deep Learning ◽  
Nearest Neighbor ◽  
Learning Algorithm ◽  
Machine Learning Algorithms ◽  
Training Data ◽  
Ann Model ◽  
K Nearest Neighbor ◽  
Data Set ◽  
Deep Learning Algorithm ◽  
Present World

Abstract In the present world, there are many songs over the internet. But the information retrieval on these songs can be complicated. This paper intends to classify songs based on emotions using deep learning. We propose a strategy to recognize the emotion present in a song by classifying their spectrograms, which contains both time and frequency information. According to human psychology, neurons within a sub pop- ulation of our brain did not react the same way for all the emotions.So only specific neurons need to be triggered for identifying an emotion. Dif- ferent deep learning and machine learning algorithms are implemented to build music emotion recognizer. The main objective of this study is to study about the features which are important for audio file ,to de- velop a music emotion classifier using deep learning algorithm and also to validate the model.The datasets are split into training and testing sets, models are trained with training data set. The accuracy of Artifi- cial Neural Network (ANN) model is 79.7% ,K-Nearest Neighbor (KNN) model is 78.26% and logistic regression for gender classification is 81%.

Analysis of Crop Yield Prediction of Kharif & Rabi Jowar Crops Using Data Mining Techniques

2017 ◽  
Vol 7 (11) ◽  
pp. 79
Author(s):  
Sujata Mulik
Keyword(s):  
Data Mining ◽  
Crop Yield ◽  
Crop Production ◽  
Climatic Factors ◽  
Crop Productivity ◽  
Yield Prediction ◽  
Data Mining Techniques ◽  
Agriculture Sector ◽  
Using Data ◽  
Rabi Crops

Agriculture sector in India is facing rigorous problem to maximize crop productivity. More than 60 percent of the crop still depends on climatic factors like rainfall, temperature, humidity. This paper discusses the use of various Data Mining applications in agriculture sector. Data Mining is used to solve various problems in agriculture sector. It can be used it to solve yield prediction.  The problem of yield prediction is a major problem that remains to be solved based on available data. Data mining techniques are the better choices for this purpose. Different Data Mining techniques are used and evaluated in agriculture for estimating the future year's crop production. In this paper we have focused on predicting crop yield productivity of kharif & Rabi Crops. 

Sign in / Sign up

Export Citation Format

Share Document