Gradient Boosting Machine and Deep Learning Approach in Big Data Analysis

Computer vision has been demonstrated as state-of-the-art technology in precision agriculture in recent years. In this paper, an Alex net model was implemented to identify and classify cotton leaf diseases. Cotton Dataset consists of 2275 images, in which 1952 images were used for training and 324 images were used for validation. Five convolutional layers of the AlexNet deep learning technique is applied for features extraction from raw data. They were remaining three fully connected layers of AlexNet and machine learning classification algorithms such as Ada Boost Classifier (ABC), Decision Tree Classifier (DTC), Gradient Boosting Classifier (GBC). K Nearest Neighbor (KNN), Logistic Regression (LR), Random Forest Classifier (RFC), and Support Vector Classifier (SVC) are used for classification. Three fully connected layers of Alex Net provided the best performance model with a 94.92% F1_score at the training time of about 51min.

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“…DTC [10,11] is a supervised algorithm used for both regression and classification problems. GBC [12,13]…”

Section: Classification Phasementioning

confidence: 99%

Classification of Cotton Leaf Diseases Using AlexNet and Machine Learning Models

Borugadda

Lakshmi

Govindu

2021

CJAST

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Garden Landscape Design Method in Public Health Urban Planning Based on Big Data Analysis Technology

Jia

2022

Journal of Environmental and Public Health

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Aiming at the goal of high-quality development of the landscape architecture industry, we should actively promote the development and integration of digital, networked, and intelligent technologies and promote the intelligent and diversified development of the landscape architecture industry. Due to the limitation of drawing design technology and construction method, the traditional landscape architecture construction cannot really understand the public demands, and the construction scheme also relies on the experience and subjective aesthetics of professionals, resulting in improper connection between design and construction. At present, under the guidance of the national strategy, under the background of the rapid development of digital technologies such as 5G, big data, cloud computing, Internet of Things, and digital twins, the high integration of landscape architecture construction and digital technology has led to the transformation of the production mode of landscape architecture construction. Abundant professional data and convenient information processing platform enable landscape planners, designers, and builders to evaluate the whole life cycle of the project more scientifically and objectively and realize the digitalization of the whole process of investigation, analysis, design, construction, operation, and maintenance. For the landscape architecture industry, the significance of digital technology is not only to change the production tools but also to update the environmental awareness, design response, and construction methods, which makes the landscape architecture planning and design achieve the organic combination of qualitative and quantitative and also makes the landscape architecture discipline more scientific and rational. In this paper, the new method of combining grey relational degree with machine learning is used to provide new guidance for traditional landscape planning by using big data information in landscape design and has achieved very good results. The article analyzes the guidance of landscape architecture design under the big data in China and provides valuable reference for promoting the construction of landscape architecture in China.

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Adaptive trio-ensemble deep neural network for high-frequency stock price prediction

Srivas

Kumar

2022

Preprint

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The analysis and forecasting of stock price is a highly complex task since its inception. Researchers have proposed a hundreds of mathematical and machine learning based models to solve this high frequency prediction problem. The constraints that restricts the effective stock market forecasting method is its dependency on variety of factors like news, announcement of dividends, company policy, drastic changes at management level, launch of new products etc. The characteristics of Deep learning algorithms like choice of network structure, activation function, and other model parameters etc voted it as a best choice for prediction. This paper proposed an ensemble prediction model by exploiting three most promising variant of Deep Neural Network (DNN) namely Gaussian, Poisson, and Gamma out of six available probability distributions (Quantile, Gaussian, Poisson, Laplace, Huber, and Gamma). The experimental results show that the proposed ensemble deep learning model claimed the best accuracy of R2: 0.92 and Root Mean Square Error (RMSE): 0.17 as per the literature reviewed in this category.

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Gradient Boosting Machine and Deep Learning Approach in Big Data Analysis

Cited by 3 publications

References 28 publications

Classification of Cotton Leaf Diseases Using AlexNet and Machine Learning Models

Classification of Cotton Leaf Diseases Using AlexNet and Machine Learning Models

Garden Landscape Design Method in Public Health Urban Planning Based on Big Data Analysis Technology

Adaptive trio-ensemble deep neural network for high-frequency stock price prediction

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