IOT Based Water Management System For Crops Using Conventional Machine Learning Techniques

2021 ◽  
Author(s):  
Tiramareddy Manasa Swetha ◽  
Tekkali Yogitha ◽  
Manche Kuruba Sai Hitha ◽  
Puppala Syamanthika ◽  
S S Poorna ◽  
...  
Keyword(s):  
Water Management ◽  
Machine Learning ◽  
Management System ◽  
Machine Learning Techniques ◽  
Water Management System ◽  
Learning Techniques ◽  
Conventional Machine

scholarly journals Prediction of defects using machine learning techniques in order to improve quality management system – A case study

2021 ◽  
Vol 343 ◽  
pp. 05010
Author(s):  
Adina Sârb ◽  
Cristina Burja Udrea ◽  
Daniela Nagy – Oniţa ◽  
Liliana Itul ◽  
Maria Popa
Keyword(s):  
Machine Learning ◽  
Quality Management ◽  
Management System ◽  
Quality Management System ◽  
Real Data ◽  
Machine Learning Techniques ◽  
Data Sets ◽  
Learning Techniques ◽  
Different Types ◽  
The Future

According to ISO 9000, a quality management system is part of a set of related or interacting elements of an organization that sets policies and objectives, as well as the processes necessary to achieve the quality objectives. Quality is the extent to which a set of intrinsic characteristics of an object meets the requirements. Based on these definitions, the factory, considered in this paper, S.C. APULUM S.A.,decided to implement a quality management system since 1998. Subsequently, the organization’s attention is focus on the continuous improvement of the implemented quality management system. The purpose of this paper is to study the percent of specified defects specific to ceramic products in the future to improve the quality management system. In this regard, machine learning techniques were applied for defects forecasting for different types of products: mugs, pressed plates and jiggered plates. The experimental evaluation was performed on real data sets that contain percentages about different types of defects collected in 2018-2019. The experimental results show that for each type of product exists an algorithm that forecasts the future defects.

Automated Corn Seed Fusarium Disease Classification System Using Hybrid Feature Space and Conventional Machine Learning Techniques

2021 ◽  
Vol 58 (2) ◽  
pp. 1-10
Author(s):  
Samreen Naeem ◽  
Aqib Ali ◽  
Jamal Abdul Nasir ◽  
Arooj Fatima ◽  
Farrukh Jamal ◽  
...  
Keyword(s):  
Machine Learning ◽  
Feature Space ◽  
Disease Classification ◽  
Machine Learning Techniques ◽  
Corn Seed ◽  
Area Of Interest ◽  
Learning Techniques ◽  
Machine Learning Approach ◽  
Correlation Based Feature Selection ◽  
Conventional Machine

The purpose of this learning is to detect the Corn Seed Fusarium Disease using Hybrid Feature Space and Conventional machine learning (ML) approaches. A novel machine learning approach is employed for the classification of a total of six types of corn seed are collected which contain Infected Fusarium (moniliforme, graminearum, gibberella, verticillioides, kernel) as well as healthy corn seed, based on a multi-feature dataset, which is the grouping of geometric, texture and histogram features extracted from digital images. For each corn seed image, a total of twenty-five multi-features have been developed on every area of interest (AOI), sizes (50 × 50), (100 × 100), (150 × 150), and (200 × 200). A total of seven optimized features were selected by using a machine learning-based algorithm named “Correlation-based Feature Selection”. For experimentation, “Random forest”, “BayesNet” and “LogitBoost” have been employed using an optimized multi-feature user-supplied dataset divided with 70% training and 30 % testing. A comparative analysis of three ML classifiers RF, BN, and LB have been used and a considerably very high classification ratio of 96.67 %, 97.22 %, and 97.78 % have been achieved respectively when the AOI size (200×200) have been deployed to the classifiers.

scholarly journals TaxoNN: ensemble of neural networks on stratified microbiome data for disease prediction

2020 ◽  
Vol 36 (17) ◽  
pp. 4544-4550 ◽  
Author(s):  
Divya Sharma ◽  
Andrew D Paterson ◽  
Wei Xu
Keyword(s):  
Machine Learning ◽  
Neural Networks ◽  
Human Microbiome ◽  
Machine Learning Techniques ◽  
Supplementary Information ◽  
Operational Taxonomic Units ◽  
Learning Techniques ◽  
Conventional Machine ◽  
Risk Of Disease ◽  
Microbiome Data

Abstract Motivation Research supports the potential use of microbiome as a predictor of some diseases. Motivated by the findings that microbiome data is complex in nature, and there is an inherent correlation due to hierarchical taxonomy of microbial Operational Taxonomic Units (OTUs), we propose a novel machine learning method incorporating a stratified approach to group OTUs into phylum clusters. Convolutional Neural Networks (CNNs) were used to train within each of the clusters individually. Further, through an ensemble learning approach, features obtained from each cluster were then concatenated to improve prediction accuracy. Our two-step approach comprising stratification prior to combining multiple CNNs, aided in capturing the relationships between OTUs sharing a phylum efficiently, as compared to using a single CNN ignoring OTU correlations. Results We used simulated datasets containing 168 OTUs in 200 cases and 200 controls for model testing. Thirty-two OTUs, potentially associated with risk of disease were randomly selected and interactions between three OTUs were used to introduce non-linearity. We also implemented this novel method in two human microbiome studies: (i) Cirrhosis with 118 cases, 114 controls; (ii) type 2 diabetes (T2D) with 170 cases, 174 controls; to demonstrate the model’s effectiveness. Extensive experimentation and comparison against conventional machine learning techniques yielded encouraging results. We obtained mean AUC values of 0.88, 0.92, 0.75, showing a consistent increment (5%, 3%, 7%) in simulations, Cirrhosis and T2D data, respectively, against the next best performing method, Random Forest. Availability and implementation https://github.com/divya031090/TaxoNN_OTU. Supplementary information Supplementary data are available at Bioinformatics online.

A New Student Modeling Technique With Convolutional Neural Networks: LearnerPrints

2020 ◽  
pp. 073563312096921
Author(s):  
Şeyhmus Aydoğdu
Keyword(s):  
Machine Learning ◽  
Neural Networks ◽  
Convolutional Neural Networks ◽  
Management System ◽  
Learning Management System ◽  
Modeling Technique ◽  
Machine Learning Techniques ◽  
Student Modeling ◽  
Student Model ◽  
Learning Techniques

Student modeling is one of the most important processes in adaptive systems. Although learning is individual, a model can be created based on patterns in student behavior. Since a student model can be created for more than one student, the use of machine learning techniques in student modeling is increasing. Artificial neural networks (ANNs), which form one group of machine learning techniques, are among the methods most frequently used in learning environments. Convolutional neural networks (CNNs), which are specific types of these networks, are used effectively for complex problems such as image processing, computer vision and speech recognition. In this study, a student model was created using a CNN due to the complexity of the learning process, and the performance of the model was examined. The student modeling technique used was named LearnerPrints. The navigation data of the students in a learning management system were used to construct the model. Training and test data were used to analyze the performance of the model. The classification results showed that CNNs can be used effectively for student modeling. The modeling was based on the students’ achievement and used the students’ data from the learning management system. The study found that the LearnerPrints technique classified students with an accuracy of over 80%.

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