Application of deep and machine learning techniques for multi-label classification performance on psychotic disorder diseases

AbstractIt is 50 years since Sieveking et al. published their pioneering research in Nature on the geochemical analysis of artefacts from Neolithic flint mines in southern Britain. In the decades since, geochemical techniques to source stone artefacts have flourished globally, with a renaissance in recent years from new instrumentation, data analysis, and machine learning techniques. Despite the interest over these latter approaches, there has been variation in the quality with which these methods have been applied. Using the case study of flint artefacts and geological samples from England, we present a robust and objective evaluation of three popular techniques, Random Forest, K-Nearest-Neighbour, and Support Vector Machines, and present a pipeline for their appropriate use. When evaluated correctly, the results establish high model classification performance, with Random Forest leading with an average accuracy of 85% (measured through F1 Scores), and with Support Vector Machines following closely. The methodology developed in this paper demonstrates the potential to significantly improve on previous approaches, particularly in removing bias, and providing greater means of evaluation than previously utilised.

Download Full-text

Application of Machine Learning Techniques to Tweet Polarity Classification with News Topic Analysis

International Journal of Engineering & Technology ◽

10.14419/ijet.v7i4.4.19606 ◽

2018 ◽

Vol 7 (4.4) ◽

pp. 40

Author(s):

Hoyeon Park ◽

Hyeonjeong Seo ◽

Kyoung Jae Kim ◽

Gundoo Moon

Keyword(s):

Machine Learning ◽

Emergency Management ◽

Online Community ◽

Classification Performance ◽

Machine Learning Techniques ◽

Topic Analysis ◽

Online Social Media ◽

Learning Techniques ◽

Social Media Platforms ◽

Polarity Classification

The exponential growth of online community provides the tremendous amount of textual information in terms of human behavioral reaction. Thus, online social media platforms such as Twitters, Facebook and YouTube are reflected as an essential part of human relationship networks. Especially, Twitter is widely applied to the disaster situation as a text and it provides critical insights into emergency management. In this study, we propose a topic analysis and sentiment polarity classification with machine learning techniques for emergency management. In this study, we compared the polarity classification models using three machine learning methods and found that the model with random forests showed the best classification performance.

Download Full-text

Air Pollution Monitoring Using WSN Nodes with Machine Learning Techniques: A Case Study

Logic Journal of IGPL ◽

10.1093/jigpal/jzab005 ◽

2021 ◽

Author(s):

Paul D Rosero-Montalvo ◽

Vivian F López-Batista ◽

Ricardo Arciniega-Rocha ◽

Diego H Peluffo-Ordóñez

Keyword(s):

Machine Learning ◽

Air Pollution ◽

Classification Performance ◽

Pollution Monitoring ◽

Machine Learning Techniques ◽

Related Factors ◽

Air Pollution Monitoring ◽

Pollution Levels ◽

Learning Techniques ◽

High Pollution

Abstract Air pollution is a current concern of people and government entities. Therefore, in urban scenarios, its monitoring and subsequent analysis is a remarkable and challenging issue due mainly to the variability of polluting-related factors. For this reason, the present work shows the development of a wireless sensor network that, through machine learning techniques, can be classified into three different types of environments: high pollution levels, medium pollution and no noticeable contamination into the Ibarra City. To achieve this goal, signal smoothing stages, prototype selection, feature analysis and a comparison of classification algorithms are performed. As relevant results, there is a classification performance of 95% with a significant noisy data reduction.

Download Full-text

A Comparison of Resampling Techniques for Medical Data Using Machine Learning

Journal of Information & Knowledge Management ◽

10.1142/s021964922040016x ◽

2020 ◽

Vol 19 (01) ◽

pp. 2040016

Author(s):

Fahad Alahmari

Keyword(s):

Machine Learning ◽

Class Imbalance ◽

Medical Application ◽

Autism Spectrum ◽

Classification Performance ◽

Machine Learning Algorithms ◽

Machine Learning Techniques ◽

Learning Techniques ◽

Under Sampling ◽

Class Labels

Data imbalance with respect to the class labels has been recognised as a challenging problem for machine learning techniques as it has a direct impact on the classification model’s performance. In an imbalanced dataset, most of the instances belong to one class, while far fewer instances are associated with the remaining classes. Most of the machine learning algorithms tend to favour the majority class and ignore the minority classes leading to classification models being generated that cannot be generalised. This paper investigates the problem of class imbalance for a medical application related to autism spectrum disorder (ASD) screening to identify the ideal data resampling method that can stabilise classification performance. To achieve the aim, experimental analyses to measure the performance of different oversampling and under-sampling techniques have been conducted on a real imbalanced ASD dataset related to adults. The results produced by multiple classifiers on the considered datasets showed superiority in terms of specificity, sensitivity, and precision, among others, when adopting oversampling techniques in the pre-processing phase.

Download Full-text

Minimizing Device-to-Device Variation in the Spectral Response of Portable Spectrometers

Journal of Sensors ◽

10.1155/2019/8392583 ◽

2019 ◽

Vol 2019 ◽

pp. 1-7

Author(s):

Sunwoong Choi ◽

Youngsik Kim ◽

Jae-Hyung Lee ◽

Hanjong You ◽

Byung-Jun Jang ◽

...

Keyword(s):

Machine Learning ◽

Spectral Response ◽

Critical Issue ◽

Image Sensor ◽

Classification Performance ◽

Machine Learning Techniques ◽

Learning Techniques ◽

Device To Device ◽

Portable Spectrometers ◽

Spectral Responses

As portable spectrometers have been developed, the research of spectral analysis has evolved from a traditional laboratory-based closed environment to a network-connected open environment. Consequently, its application areas are expanding in combination with machine learning techniques. The device-to-device variation in the spectral response of portable spectrometers is a critical issue in a machine learning-based service scenario since the classification performance is highly dependent on the consistency of spectral responses from each spectrometer. To minimize device-to-device variation, a cuboid prism is employed instead of a combination of mirrors and prism to construct an optical system for the spectrometer. The spectral responses are calibrated to correct pixel shift on the image sensor. Experimental results show that the proposed method can minimize the device-to-device variation in spectral response of portable spectrometers.

Download Full-text

Estimating Visibility of Annotations for View Management in Spatial Augmented Reality Based on Machine-Learning Techniques

Sensors ◽

10.3390/s19040939 ◽

2019 ◽

Vol 19 (4) ◽

pp. 939 ◽

Cited By ~ 3

Author(s):

Keita Ichihashi ◽

Kaori Fujinami

Keyword(s):

Machine Learning ◽

Augmented Reality ◽

Mental Workload ◽

Spatial Relationship ◽

Human Perception ◽

Physical Object ◽

Classification Performance ◽

Machine Learning Techniques ◽

Learning Techniques ◽

View Management

Augmented Reality (AR) is a class of “mediated reality” that artificially modifies the human perception by superimposing virtual objects on the real world, which is expected to supplement reality. In visual-based augmentation, text and graphics, i.e., label, are often associated with a physical object or a place to describe it. View management in AR is to maintain the visibility of the associated information and plays an important role on communicating the information. Various view management techniques have been investigated so far; however, most of them have been designed for two dimensional see-through displays, and few have been investigated for projector-based AR called spatial AR. In this article, we propose a view management method for spatial AR, VisLP, that places labels and linkage lines based on the estimation of the visibility. Since the information is directly projected on objects, the nature of optics such as reflection and refraction constrains the visibility in addition to the spatial relationship between the information, the objects, and the user. VisLP employs machine-learning techniques to estimate the visibility that reflects human’s subjective mental workload in reading information and objective measures of reading correctness in various projection conditions. Four classes are defined for a label, while the visibility of a linkage line has three classes. After 88 and 28 classification features for label and linkage line visibility estimators are designed, respectively, subsets of features with 15 and 14 features are chosen to improve the processing speed of feature calculation up to 170%, with slight degradation of classification performance. An online experiment with new users and objects showed that 76.0% of the system’s judgments were matched with the users’ evaluations, while 73% of the linkage line visibility estimations were matched.

Download Full-text

Using machine learning techniques to reduce data annotation time

PsycEXTRA Dataset ◽

10.1037/e577762012-020 ◽

2006 ◽

Author(s):

Christopher Schreiner ◽

Kari Torkkola ◽

Mike Gardner ◽

Keshu Zhang

Keyword(s):

Machine Learning ◽

Machine Learning Techniques ◽

Data Annotation ◽

Learning Techniques

Download Full-text

Using Machine Learning Algorithms on Prediction of Stock Price

Journal of Modeling and Optimization ◽

10.32732/jmo.2020.12.2.84 ◽

2020 ◽

Vol 12 (2) ◽

pp. 84-99

Author(s):

Li-Pang Chen

Keyword(s):

Machine Learning ◽

Stock Price ◽

Short Term Memory ◽

Machine Learning Algorithms ◽

Machine Learning Techniques ◽

Support Vector ◽

Short Term ◽

Learning Techniques ◽

Historical Database ◽

Long Short Term Memory

In this paper, we investigate analysis and prediction of the time-dependent data. We focus our attention on four different stocks are selected from Yahoo Finance historical database. To build up models and predict the future stock price, we consider three different machine learning techniques including Long Short-Term Memory (LSTM), Convolutional Neural Networks (CNN) and Support Vector Regression (SVR). By treating close price, open price, daily low, daily high, adjusted close price, and volume of trades as predictors in machine learning methods, it can be shown that the prediction accuracy is improved.

Download Full-text