Machine learning in optimization of multi-hole drilling using a hybrid combinatorial IGSA algorithm

Foliar disease is common problem in plants; it appears as an abnormal change in the plant’s characteristics, such as the presence of lesions and discolorations, among others. These problems may be related to plant growth, which causes a decrease in crop production, impacting the agricultural economy. The causes of leaf damage can be variable, such as bacteria, viruses, nutritional deficiencies, or even consequences of climate change. Motivated to find a solution for this problem, we aim that using image processing and machine learning algorithms (MLA), these symptomatic characteristics of the leaf can be used to classify diseases. Then, contributions of this research are (i) the use of image processing methods in the feature extraction (characteristics), and (ii) the combination of assembled algorithms with deep learning to classify foliar features of Valencia orange (Citrus Sinensis) tree leaves. Combining these two classification approaches, we get optimal rates in binary datasets and highly competitive percentages in multiclass sets. This, using a database of images of three types of foliar damage of local plants. Result of combination of these two classification strategies is an exceptional reliable alternative for leaf damage identification of orange and other citrus plants.

Section: Introductionmentioning

confidence: 99%

RETRACTED: Deep learning framework for leaf damage identification

Sánchez-DelaCruz

Lopez

Alabazares

et al. 2021

“…Machine learning (ML) and Artificial intelligence (AI) have been used for cancer diagnosis and classification for a huge span of time but research into cancer prognosis has less significance (Polley et al, 2013). Decision support systems based on machine learning can be applied in sales data analysis (Zhang et al, 2020) and manufacturing (Karthikeyan et al, 2020). Linear regression machine learning method, Linear discriminant analysis model, Logistic regression statistical model and many other methods use statistical approaches for the prediction of disease acquisition (Howell et al, 2005; Loh, 2011; Pincus et al, 1991; West et al, 2001).…”

Section: Related Workmentioning

confidence: 99%

Breast cancer prediction using an optimal machine learning technique for next generation sequences

Kurian

Jyothi²

2021

A wide reach on cancer prediction and detection using Next Generation Sequencing (NGS) by the application of artificial intelligence is highly appreciated in the current scenario of the medical field. Next generation sequences were extracted from NCBI (National Centre for Biotechnology Information) gene repository. Sequences of normal Homo sapiens (Class 1), BRCA1 (Class 2) and BRCA2 (Class 3) were extracted for Machine Learning (ML) purpose. The total volume of datasets extracted for the process were 1580 in number under four categories of 50, 100, 150 and 200 sequences. The breast cancer prediction process was carried out in three major steps such as feature extraction, machine learning classification and performance evaluation. The features were extracted with sequences as input. Ten features of DNA sequences such as ORF (Open Reading Frame) count, individual nucleobase average count of A, T, C, G, AT and GC-content, AT/GC composition, G-quadruplex occurrence, MR (Mutation Rate) were extracted from three types of sequences for the classification process. The sequence type was also included as a target variable to the feature set with values 0, 1 and 2 for classes 1, 2 and 3 respectively. Nine various supervised machine learning techniques like LR (Logistic Regression statistical model), LDA (Linear Discriminant analysis model), k-NN (k nearest neighbours’ algorithm), DT (Decision tree technique), NB (Naive Bayes classifier), SVM (Support-Vector Machine algorithm), RF (Random Forest learning algorithm), AdaBoost (AB) and Gradient Boosting (GB) were employed on four various categories of datasets. Of all supervised models, decision tree machine learning technique performed most with maximum accuracy in classification of 94.03%. Classification model performance was evaluated using precision, recall, F1-score and support values wherein F1-score was most similar to the classification accuracy.

“…Nowadays new machines carry out more intelligent tasks with AI (Artificial Intelligence) developments, and industries apply expert systems using novel technologies (Karthikeyan et al, 2020). Among the image recognition technologies, object inspection, feature identification, and malfunction diagnostics are the most promising advances for the manufacturing industries (Hinterreiter et al, 2020; Krüger et al, 2019; Weimer et al, 2016; Wu et al, 2019).…”

Section: Introductionmentioning

confidence: 99%

An optimization strategy for HMI panel recognition of CNC machines using a CNN deep-learning network

Lee

Lin

2021

This paper suggests an optimization strategy to train a CNN deep-learning network, which successfully recognizing working status on the HMI panels of CNC machines. To verify the developed strategy, the research experiments using a prototype that consists of a CNC milling machine and an industrial robot. In the optimization strategy, the research first defines a length-varying hyperparameter list for the deep-learning network, and the entities in the list adjust themselves to optimize the model scales. During the optimization process, this paper adopts a two-stage training scheme that gradually augments image datasets to improve HMI control-panel recognition performances, such as recognition accuracy and recognition speed to identify the CNC machine working status. Using an open-source PyTorch platform, this research establishes a cloud-based distributed architecture to build training codes for the deep-learning network, in which an applicable optimization model is deployed to recognize the CNC control-panel working status. The optimization strategy employs minimal codes to rebuild the architecture and the least efforts to reform the manufacturing system. The optimally trained model provides up to a 99.34% CNC panel-message recognition accuracy and a high-speed recognition of 100 images in 0.6 s. Moreover, the developed optimization strategy enables the prediction of necessitated dataset augmentation to training a practically implemented CNN network.