Non-Linear Machine Learning Techniques for Multi-Label Image Data Classification

<p>Curve fitting is one of the procedures in data analysis and is helpful for prediction analysis showing graphically how the data points are related to one another whether it is in linear or non-linear model. Usually, the curve fit will find the concentrates along the curve or it will just use to smooth the data and upgrade the presence of the plot. Curve fitting checks the relationship between independent variables and dependent variables with the objective of characterizing a good fit model. Curve fitting finds mathematical equation that best fits given information. In this paper, 150 unorganized data points of environmental variables are used to develop Linear and non-linear data modelling which are evaluated by utilizing 3 dimensional ‘Sftool’ and ‘Labfit’ machine learning techniques. In Linear model, the best estimations of the coefficients are realized by the estimation of R- square turns in to one and in Non-Linear models with least Chi-square are the criteria. </p>

Download Full-text

Medical Data Classification using Machine Learning Techniques

International Journal of Computer Applications ◽

10.5120/ijca2021921339 ◽

2021 ◽

Vol 183 (6) ◽

pp. 1-8

Author(s):

Koby Bond ◽

Alaa Sheta

Keyword(s):

Machine Learning ◽

Data Classification ◽

Medical Data ◽

Machine Learning Techniques ◽

Learning Techniques ◽

Medical Data Classification

Download Full-text

Geographic Origin Discrimination of Millet Using Vis-NIR Spectroscopy Combined with Machine Learning Techniques

Foods ◽

10.3390/foods10112767 ◽

2021 ◽

Vol 10 (11) ◽

pp. 2767

Author(s):

Muhammad Hilal Kabir ◽

Mahamed Lamine Guindo ◽

Rongqin Chen ◽

Fei Liu

Keyword(s):

Machine Learning ◽

Geographic Origin ◽

Discrimination Performance ◽

Nir Spectroscopy ◽

Machine Learning Algorithms ◽

Machine Learning Techniques ◽

Support Vector ◽

Protected Designation Of Origin ◽

Learning Techniques ◽

Non Linear

Millet is a primary food for people living in the dry and semi-dry regions and is dispersed within most parts of Europe, Africa, and Asian countries. As part of the European Union (EU) efforts to establish food originality, there is a global need to create Protected Geographical Indication (PGI) and Protected Designation of Origin (PDO) of crops and agricultural products to ensure the integrity of the food supply. In the present work, Visible and Near-Infrared Spectroscopy (Vis-NIR) combined with machine learning techniques was used to discriminate 16 millet varieties (n = 480) originating from various regions of China. Five different machine learning algorithms, namely, K-nearest neighbor (K-NN), Linear discriminant analysis (LDA), Logistic regression (LR), Random Forest (RF), and Support vector machine (SVM), were used to train the NIR spectra of these millet samples and to assess their discrimination performance. Visible cluster trends were obtained from the Principal Component Analysis (PCA) of the spectral data. Cross-validation was used to optimize the performance of the models. Overall, the F-Score values were as follows: SVM with 99.5%, accompanied by RF with 99.5%, LDA with 99.5%, K-NN with 99.1%, and LR with 98.8%. Both the linear and non-linear algorithms yielded positive results, but the non-linear models appear slightly better. The study revealed that applying Vis-NIR spectroscopy assisted by machine learning technique can be an essential tool for tracing the origins of millet, contributing to a safe authentication method in a quick, relatively cheap, and non-destructive way.

Download Full-text

Efficient Machine Learning Techniques to Diagnose and Predict Alzheimer’s disease

International Journal of Engineering and Advanced Technology - Regular Issue ◽

10.35940/ijeat.c6508.029320 ◽

2020 ◽

Vol 9 (3) ◽

pp. 3953-3960

Keyword(s):

Machine Learning ◽

Alzheimer’S Disease ◽

Alzheimer's Disease ◽

Early Diagnosis ◽

Image Data ◽

Machine Learning Algorithms ◽

Machine Learning Techniques ◽

Support Vector ◽

Learning Mechanisms ◽

Learning Techniques

Recent research in computational engineering have evidenced the design and development numerous intelligent models to analyze medical data and derive inferences related to early diagnosis and prediction of disease severity. In this context, prediction and diagnosis of fatal neurodegenerative diseases that comes under the class of dementia from medical image data is considered as the challenging area of research for many researchers. Recently Alzheimer’s disease is considered as major category of dementia that affects major population. Despite of the development of numerous machine learning models for early diagnosis of Alzheimer’s disease, it is observed that there is a lot more scope of research. Addressing the same, this article presents a systematic literature review of machine learning techniques developed for early diagnosis of Alzheimer’s disease. Furthermore this article includes major categories of machine learning algorithms that include artificial neural networks, Support vector machines and Deep learning based ensemble models that helps the budding researchers to explore the scope of research in predicting Alzheimer’s disease. Implementation results depict the comparative analysis of state of art machine learning mechanisms.

Download Full-text

Data Analytics and Modeling in IoT-Fog Environment for Resource Constrained IoT-Applications - A Review

Recent Advances in Computer Science and Communications ◽

10.2174/2666255814666210715161630 ◽

2021 ◽

Vol 14 ◽

Author(s):

Omar Farooq ◽

Parminder Singh

Keyword(s):

Machine Learning ◽

Data Science ◽

Data Classification ◽

Machine Learning Techniques ◽

Learning Approaches ◽

Resource Constrained ◽

Learning Techniques ◽

Iot Devices ◽

The Right ◽

Continuous Use

Introduction: The emergence of the concepts like Big Data, Data Science, Machine Learning (ML), and the Internet of Things (IoT) has added the potential of research in today's world. The continuous use of IoT devices, sensors, etc. that collect data continuously puts tremendous pressure on the existing IoT network. Materials and Methods: This resource-constrained IoT environment is flooded with data acquired from millions of IoT nodes deployed at the device level. The limited resources of the IoT Network have driven the researchers towards data Management. This paper focuses on data classification at the device level, edge/fog level, and cloud level using machine learning techniques. Results: The data coming from different devices is vast and is of variety. Therefore, it becomes essential to choose the right approach for classification and analysis. It will help optimize the data at the device edge/fog level to better the network's performance in the future. Conclusion: This paper presents data classification, machine learning approaches, and a proposed mathematical model for the IoT environment.

Download Full-text

A Comparison of Linear and Non-Linear Machine Learning Techniques (PCA and SOM) for Characterizing Urban Nutrient Runoff

Sustainability ◽

10.3390/su13042054 ◽

2021 ◽

Vol 13 (4) ◽

pp. 2054 ◽

Cited By ~ 1

Author(s):

Angela Gorgoglione ◽

Alberto Castro ◽

Vito Iacobellis ◽

Andrea Gioia

Keyword(s):

Machine Learning ◽

Water Quality ◽

Principal Component ◽

Machine Learning Techniques ◽

Computational Time ◽

Self Organizing Map ◽

Nutrient Runoff ◽

Non Linear ◽

Linear Technique ◽

Linear Machine

Urban stormwater runoff represents a significant challenge for the practical assessment of diffuse pollution sources on receiving water bodies. Given the high dimensionality of the problem, the main goal of this study was the comparison of linear and non-linear machine learning (ML) methods to characterize urban nutrient runoff from impervious surfaces. In particular, the principal component analysis (PCA) for the linear technique and the self-organizing map (SOM) for the non-linear technique were chosen and compared considering the high number of successful applications in the water quality field. To strengthen this comparison, these techniques were supported by well-known linear and non-linear methods. Those techniques were applied to a complete dataset with precipitation, flow rate, and water quality (sediments and nutrients) records of 577 events gathered for a watershed located in Southern Italy. According to the results, both linear and non-linear techniques can represent build-up and wash-off, the two main processes that characterize urban nutrient runoff. In particular, non-linear methods are able to capture and represent better the rainfall-runoff process and the transport of dissolved nutrients in urban runoff (dilution process). However, their computational time is higher than the linear technique (0.0054 s vs. 15.24 s, for linear and non-linear, respectively, in our study). The outcomes of this study provide significant insights into the application of ML methods for the water quality field.

Download Full-text