Current and future applications of statistical machine learning algorithms for agricultural machine vision systems

2019 ◽  
Vol 156 ◽  
pp. 585-605 ◽  
Author(s):  
Tanzeel U. Rehman ◽  
Md. Sultan Mahmud ◽  
Young K. Chang ◽  
Jian Jin ◽  
Jaemyung Shin
Keyword(s):  
Machine Learning ◽  
Machine Vision ◽  
Learning Algorithms ◽  
Machine Learning Algorithms ◽  
Statistical Machine Learning ◽  
Vision Systems ◽  
Agricultural Machine

scholarly journals A Comparative Analysis of Machine Learning Algorithms Modeled from Machine Vision-Based Lettuce Growth Stage Classification in Smart Aquaponics

2020 ◽  
Vol 11 (9) ◽  
pp. 442-449 ◽  
Author(s):  
Sandy C. Lauguico ◽  
◽  
Ronnie S. Concepcion II ◽  
Jonnel D. Alejandrino ◽  
Rogelio Ruzcko Tobias ◽  
...  
Keyword(s):  
Machine Learning ◽  
Comparative Analysis ◽  
Machine Vision ◽  
Nearest Neighbor ◽  
Learning Algorithms ◽  
Machine Learning Algorithms ◽  
Support Vector ◽  
Urban Farming ◽  
K Nearest Neighbor ◽  
Lettuce Growth

The arising problem on food scarcity drives the innovation of urban farming. One of the methods in urban farming is the smart aquaponics. However, for a smart aquaponics to yield crops successfully, it needs intensive monitoring, control, and automation. An efficient way of implementing this is the utilization of vision systems and machine learning algorithms to optimize the capabilities of the farming technique. To realize this, a comparative analysis of three machine learning estimators: Logistic Regression (LR), K-Nearest Neighbor (KNN), and Linear Support Vector Machine (L-SVM) was conducted. This was done by modeling each algorithm from the machine vision-feature extracted images of lettuce which were raised in a smart aquaponics setup. Each of the model was optimized to increase cross and hold-out validations. The results showed that KNN having the tuned hyperparameters of n_neighbors=24, weights='distance', algorithm='auto', leaf_size = 10 was the most effective model for the given dataset, yielding a cross-validation mean accuracy of 87.06% and a classification accuracy of 91.67%.

scholarly journals Implement of a 6-DOF manipulator with machine vision and machine learning algorithms

2017 ◽  
Author(s):  
Pi-Yun Chen ◽  
Wei-Lun Chen ◽  
Neng-Sheng Pai ◽  
Min-Hung Chou ◽  
Guo-Shing Huang ◽  
...  
Keyword(s):  
Machine Learning ◽  
Machine Vision ◽  
Learning Algorithms ◽  
Machine Learning Algorithms

scholarly journals Statistical Machine Learning Approaches to Liver Disease Prediction

Livers ◽  
2021 ◽  
Vol 1 (4) ◽  
pp. 294-312
Author(s):  
Fahad Mostafa ◽  
Easin Hasan ◽  
Morgan Williamson ◽  
Hafiz Khan
Keyword(s):  
Machine Learning ◽  
Liver Disease ◽  
Missing Data ◽  
Health Professionals ◽  
Learning Algorithms ◽  
Machine Learning Algorithms ◽  
Support Vector ◽  
Accuracy Score ◽  
Statistical Machine Learning ◽  
Medical Diagnoses

Medical diagnoses have important implications for improving patient care, research, and policy. For a medical diagnosis, health professionals use different kinds of pathological methods to make decisions on medical reports in terms of the patients’ medical conditions. Recently, clinicians have been actively engaged in improving medical diagnoses. The use of artificial intelligence and machine learning in combination with clinical findings has further improved disease detection. In the modern era, with the advantage of computers and technologies, one can collect data and visualize many hidden outcomes such as dealing with missing data in medical research. Statistical machine learning algorithms based on specific problems can assist one to make decisions. Machine learning (ML), data-driven algorithms can be utilized to validate existing methods and help researchers to make potential new decisions. The purpose of this study was to extract significant predictors for liver disease from the medical analysis of 615 humans using ML algorithms. Data visualizations were implemented to reveal significant findings such as missing values. Multiple imputations by chained equations (MICEs) were applied to generate missing data points, and principal component analysis (PCA) was used to reduce the dimensionality. Variable importance ranking using the Gini index was implemented to verify significant predictors obtained from the PCA. Training data (ntrain=399) for learning and testing data (ntest=216) in the ML methods were used for predicting classifications. The study compared binary classifier machine learning algorithms (i.e., artificial neural network, random forest (RF), and support vector machine), which were utilized on a published liver disease data set to classify individuals with liver diseases, which will allow health professionals to make a better diagnosis. The synthetic minority oversampling technique was applied to oversample the minority class to regulate overfitting problems. The RF significantly contributed (p<0.001) to a higher accuracy score of 98.14% compared to the other methods. Thus, this suggests that ML methods predict liver disease by incorporating the risk factors, which may improve the inference-based diagnosis of patients.

scholarly journals Music Signal Analysis: Regression Analysis

2021 ◽  
Author(s):  
V. N. Aditya Datta Chivukula ◽  
Sri Keshava Reddy Adupala
Keyword(s):  
Machine Learning ◽  
Regression Analysis ◽  
Deep Learning ◽  
Learning Algorithms ◽  
Machine Learning Algorithms ◽  
Machine Learning Techniques ◽  
Statistical Machine Learning ◽  
Ongoing Research ◽  
Music Signal ◽  
Learning Techniques

Machine learning techniques have become a vital part of every ongoing research in technical areas. In recent times the world has witnessed many beautiful applications of machine learning in a practical sense which amaze us in every aspect. This paper is all about whether we should always rely on deep learning techniques or is it really possible to overcome the performance of simple deep learning algorithms by simple statistical machine learning algorithms by understanding the application and processing the data so that it can help in increasing the performance of the algorithm by a notable amount. The paper mentions the importance of data pre-processing than that of the selection of the algorithm. It discusses the functions involving trigonometric, logarithmic, and exponential terms and also talks about functions that are purely trigonometric. Finally, we discuss regression analysis on music signals.

scholarly journals Multimodal Biometrics Data Based Gender Classification using Machine Vision

2019 ◽  
Vol 8 (11) ◽  
pp. 1356-1363
Keyword(s):  
Machine Learning ◽  
Data Analysis ◽  
Machine Vision ◽  
Data Fusion ◽  
Learning Algorithms ◽  
Machine Learning Algorithms ◽  
Gender Classification ◽  
Criminal Records ◽  
Multimodal Biometrics ◽  
Significant Step

Gender classification from biometrics data is a significant step in forensics to categorize and minimize the suspects search from the criminal records. In this paper, we present multimodal biometrics data analysis for Gender Classification using machine learning algorithms which take input as a Face, Fingerprints and Iris images. Extensive experiments were conducted using feature level and synthesis of classifiers on the SDMULA-HMT and KVK-Multimodal datasets. Experimental results presented using multimodal biometrics data fusion schemes achieves high gender classification accuracies compared to the contemporary techniques stated in the literature.

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