scholarly journals Comparison of Night, Day and 24 h Motor Activity Data for the Classification of Depressive Episodes

Diagnostics ◽  
2020 ◽  
Vol 10 (3) ◽  
pp. 162 ◽  
Author(s):  
Julieta G. Rodríguez-Ruiz ◽  
Carlos E. Galván-Tejada ◽  
Laura A. Zanella-Calzada ◽  
José M. Celaya-Padilla ◽  
Jorge I. Galván-Tejada ◽  
...  
Keyword(s):  
Data Mining ◽  
Motor Activity ◽  
Random Forest Classifier ◽  
Machine Learning Algorithms ◽  
World Population ◽  
Activity Data ◽  
The World ◽  
Depressive Episodes ◽  
Full Day

Major Depression Disease has been increasing in the last few years, affecting around 7 percent of the world population, but nowadays techniques to diagnose it are outdated and inefficient. Motor activity data in the last decade is presented as a better way to diagnose, treat and monitor patients suffering from this illness, this is achieved through the use of machine learning algorithms. Disturbances in the circadian rhythm of mental illness patients increase the effectiveness of the data mining process. In this paper, a comparison of motor activity data from the night, day and full day is carried out through a data mining process using the Random Forest classifier to identified depressive and non-depressive episodes. Data from Depressjon dataset is split into three different subsets and 24 features in time and frequency domain are extracted to select the best model to be used in the classification of depression episodes. The results showed that the best dataset and model to realize the classification of depressive episodes is the night motor activity data with 99.37% of sensitivity and 99.91% of specificity.

scholarly journals Computational classification of animals for a highway detection system

2021 ◽  
Vol 58 ◽  
pp. e174951
Author(s):  
Denis Sato ◽  
Adroaldo José Zanella ◽  
Ernane Xavier Costa
Keyword(s):  
Neural Network ◽  
Machine Learning ◽  
Computer Vision ◽  
Convolutional Neural Network ◽  
Road Safety ◽  
Detection System ◽  
Learning Algorithms ◽  
Machine Learning Algorithms ◽  
The World

Vehicle-animal collisions represent a serious problem in roadway infrastructure. To avoid these roadway collisions, different mitigation systems have been applied in various regions of the world. In this article, a system for detecting animals on highways is presented using computer vision and machine learning algorithms. The models were trained to classify two groups of animals: capybaras and donkeys. Two variants of the convolutional neural network called Yolo (You only look once) were used, Yolov4 and Yolov4-tiny (a lighter version of the network). The training was carried out using pre-trained models. Detection tests were performed on 147 images. The accuracy results obtained were 84.87% and 79.87% for Yolov4 and Yolov4-tiny, respectively. The proposed system has the potential to improve road safety by reducing or preventing accidents with animals.

Machine Learning and Data Mining in Bioinformatics

2012 ◽  
pp. 695-703
Author(s):  
George Tzanis ◽  
Christos Berberidis ◽  
Ioannis Vlahavas
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Data Mining ◽  
Reinforcement Learning ◽  
Supervised Learning ◽  
Learning Algorithms ◽  
Machine Learning Algorithms ◽  
The World ◽  
Supervised Learning Algorithms ◽  
Computational Systems

Machine learning is one of the oldest subfields of artificial intelligence and is concerned with the design and development of computational systems that can adapt themselves and learn. The most common machine learning algorithms can be either supervised or unsupervised. Supervised learning algorithms generate a function that maps inputs to desired outputs, based on a set of examples with known output (labeled examples). Unsupervised learning algorithms find patterns and relationships over a given set of inputs (unlabeled examples). Other categories of machine learning are semi-supervised learning, where an algorithm uses both labeled and unlabeled examples, and reinforcement learning, where an algorithm learns a policy of how to act given an observation of the world.

Machine Learning and Data Mining in Bioinformatics

2009 ◽  
pp. 612-621 ◽  
Author(s):  
George Tzanis ◽  
Christos Berberidis ◽  
Ioannis Vlahavas
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Data Mining ◽  
Reinforcement Learning ◽  
Supervised Learning ◽  
Learning Algorithms ◽  
Machine Learning Algorithms ◽  
The World ◽  
Supervised Learning Algorithms ◽  
Computational Systems

Machine learning is one of the oldest subfields of artificial intelligence and is concerned with the design and development of computational systems that can adapt themselves and learn. The most common machine learning algorithms can be either supervised or unsupervised. Supervised learning algorithms generate a function that maps inputs to desired outputs, based on a set of examples with known output (labeled examples). Unsupervised learning algorithms find patterns and relationships over a given set of inputs (unlabeled examples). Other categories of machine learning are semi-supervised learning, where an algorithm uses both labeled and unlabeled examples, and reinforcement learning, where an algorithm learns a policy of how to act given an observation of the world.

scholarly journals Impact of Lexical Features on Answer Detection Model in Discussion Forums

Complexity ◽  
2021 ◽  
Vol 2021 ◽  
pp. 1-8
Author(s):  
Atif Khan ◽  
Muhammad Adnan Gul ◽  
Abdullah Alharbi ◽  
M. Irfan Uddin ◽  
Shaukat Ali ◽  
...  
Keyword(s):  
Classification Accuracy ◽  
State Of The Art ◽  
Random Forest Classifier ◽  
Machine Learning Algorithms ◽  
Discussion Boards ◽  
Discussion Forums ◽  
Online Forums ◽  
Detection Model ◽  
Web Forum

Online forums have become the main source of knowledge over the Internet as data are constantly flooded into them. In most cases, a question in a web forum receives several responses, making it impossible for the question poster to obtain the most suitable answer. Thus, an important problem is how to automatically extract the most appropriate and high-quality answers in a thread. Prior studies have used different combinations of both lexical and nonlexical features to retrieve the most relevant answers from discussion forums, and hence, there is no standard/general set of features that could be effectively used for relevant answer/reply post classification. However, this study proposed an answer detection model that is exclusively relying on lexical features and employs a random forest classifier for classification of answers in discussion boards. Experimental results showed that the proposed answer detection model outperformed the baseline technique and other state-of-the-art machine learning algorithms in terms of classification accuracy on benchmark forum datasets.

Churn Prediction and Fraud Detection in Dairy Sector Using Machine Learning

2021 ◽  
pp. 391-406
Author(s):  
Hitarth Deepak Shah ◽  
Chintan M. Bhatt ◽  
Shubham Mitul Patel ◽  
Jayshil Bhavin Khajanchi ◽  
Jaimin Narendrakumar Makwana
Keyword(s):  
Machine Learning ◽  
Random Forest ◽  
Research Work ◽  
Dairy Industry ◽  
Fraud Detection ◽  
Random Forest Classifier ◽  
Machine Learning Algorithms ◽  
Financial Capital ◽  
Churn Prediction ◽  
The World

India has globally been the largest milk-producing country in the world for two decades. About 400 million litres of milk is produced every day. It is the responsibility of a dairy sector to look after the farmers by providing them with various services for their livelihood. The growing financial capital of the dairy industry has enticed various fraudulent behaviour. The majority of suspicious activities are seen during the collection at local collection centres, fake farmer entries, tempered quantity and fat entries manually, and adulteration are the profound malpractices exercised by farmers. So, in this research work, the authors present a profound study on the most popular machine learning methods applied to the problems of farmer churn prediction and fraud detection in the dairies. They applied a plethora of machine learning algorithms to get accurate results for churn and fraud detection. XGBoost Classifier was the best for churn prediction with 93% accuracy, while random forest classifier turns out to be effective for fraud detection with 94% accuracy.

scholarly journals A MULTIDIMENSIONAL LOOK ON THE GROWTH OF POPULATION IN ASIA THROUGH DATA MINING

2020 ◽  
pp. 268-272
Author(s):  
Ma. Victoria D. Naboya
Keyword(s):  
Data Mining ◽  
Population Growth ◽  
Turning Point ◽  
Exploratory Data Analysis ◽  
World Population ◽  
Data Sets ◽  
The Earth ◽  
The World ◽  
Exploratory Data ◽  
Socio Economic Factors

The world population is at a critical turning point. Its increasing population is eating away the earth itself wherein its impact has been sufficient to make permanent changes in the environment. Asia is the largest continent in the world, both in terms of area and population that was basically the reason why this study was conducted. The main purpose of this study is to determine what causes the growth of population in it. There are many factors which affect the growth of human population in Asia. These include the geographic, demographic and socio-economic factors. This study employs the exploratory data analysis or data mining which is a statistical procedure for exploring data sets and for formulating theory on the multidimensional look on the growth of population in Asia. The study revealed that population growth in Asia was largely affected by these factors specifically its land area, fertility rate and population literacy of the country. KEYWORDS: Asia, data mining, demography, geography, literacy, population, population growth.

scholarly journals Classification of Depressive Episodes Using Nighttime Data: Multivariate and Univariate Analysis

2021 ◽  
Vol 33 (2) ◽  
pp. 115-124
Author(s):  
Julieta G. Rodríguez-Ruiz ◽  
Carlos Eric Galván-Tejada ◽  
Sodel Vázquez-Reyes ◽  
Jorge Issac Galván-Tejada ◽  
Hamurabi Gamboa-Rosales
Keyword(s):  
Data Mining ◽  
Sleep Disturbances ◽  
Formal Method ◽  
Univariate Analysis ◽  
Knowledge Discovery In Databases ◽  
Random Forest Algorithm ◽  
State Of Mind ◽  
Depressive Episodes ◽  
Depressive Patients

Mental disorders like depression represent 28% of global disability, it affects around 7.5% percent of global disability. Depression is a common disorder that affects the state of mind, normal activities, emotions, and produces sleep disorders. It is estimated that approximately 50% of depressive patients suffering from sleep disturbances. In this paper, a data mining process to classify depressive and not depressive episodes during nighttime is carried out based on a formal method of data mining called Knowledge Discovery in Databases (KDD). KDD guides the process of data mining with stages well established: Pre-KDD, Selection, Pre-processing, Transformation, Data Mining, Evaluation, and Post-KDD. The dataset used for the classification is the DEPRESJON dataset, which contains the motor activity of 23 unipolar and bipolar depressed patients and 32 healthy controls. The classification is carried out with two different approaches; a multivariate and univariate analysis to classify depressive and non-depressive episodes. For the multivariate analysis, the Random Forest algorithm is implemented with a model construct of 8 features, the results of the classification are specificity equal to 0.9927 and sensitivity equal to 0.9991. The univariate analysis shows that the maximum of the activity is the most descriptive characteristic of the model with 0.908 in accuracy for the classification of depressive episodes.

scholarly journals The Classification of Customers’ Sentiment using Data Mining Approaches

2019 ◽  
Vol IV (IV) ◽  
pp. 146-156
Author(s):  
Dost Muhammad Khan ◽  
Tariq Aziz Rao ◽  
Faisal Shahzad
Keyword(s):  
Data Mining ◽  
Service Providers ◽  
Daily Basis ◽  
Machine Learning Algorithms ◽  
Decision Makers ◽  
Supervised Machine Learning ◽  
Market Trends ◽  
Using Data

Data mining is a procedure of extracting the requisite information from unprocessed records by using certain methodologies and techniques. Data having sentiments of customers is of utmost importance for managers and decision-makers who intend to monitor the progress, to maintain the quality of their products or services and to observe the latest market trends for business support. Billions of customers are using micro-blogging websites and social media for sharing their opinions about different topics on daily basis. Therefore, it has become a source of acquiring information but to identify a particular feature of a product is still an issue as the information retrieves from varied sources. We proposed a framework for data acquisition, preprocessing, feature extraction and used three supervised machine-learning algorithms for classification of customers’ sentiments. The proposed framework also tested to evaluate the system’s performance. Our proposed methodology will be helpful for researchers, service providers, and decisionmakers.

Russian Citizens' Representations of the Country's Position in the Geopolitical Space of the Commonwealth of Independent States, Europe, and the World

2003 ◽  
Vol 8 (4) ◽  
pp. 238-251
Author(s):  
Victor F. Petrenko ◽  
Olga V. Mitina ◽  
Kirill A. Bertnikov
Keyword(s):  
Well Being ◽  
Multidimensional Space ◽  
Commonwealth Of Independent States ◽  
Repertory Grids ◽  
Implicit Model ◽  
The World ◽  
Public Consciousness ◽  
Independent States ◽  
Invariant Factors

The aim of this research was the reconstruction of the system of categories through which Russians perceive the countries of the Commonwealth of Independent States (CIS), Europe, and the world as a whole; to study the implicit model of the geopolitical space; to analyze the stereotypes in the perception of different countries and the superposition of mental geopolitical representations onto the geographic map. The techniques of psychosemantics by Petrenko, originating in the semantic differential of Osgood and Kelly's “repertory grids,” were used as working tools. Multidimensional semantic spaces act as operational models of the structures of consciousness, and the positions of countries in multidimensional space reflect the geopolitical stereotypes of respondents about these countries. Because of the transformation of geopolitical reality representations in mass consciousness, the commonly used classification of countries as socialist, capitalist, and developing is being replaced by other structures. Four invariant factors of the countries' descriptions were identified. They are connected with Economic and Political Well-being, Military Might, Friendliness toward Russia, and Spirituality and the Level of Culture. It seems that the structure has not been explained in adequate detail and is not clearly realized by the individuals. There is an interrelationship between the democratic political structure of a country and its prosperity in the political mentality of Russian respondents. Russian public consciousness painfully strives for a new geopolitical identity and place in the commonwealth of states. It also signifies the country's interest and orientation toward the East in the search for geopolitical partners. The construct system of geopolitical perception also depends on the region of perception.

Small Industry, Gimmicks and Moscow Statistics, or How the Small Russian Enterprise Came into the World (Russian Physiological Sketch, 1830-1840)

2009 ◽  
pp. 123-129
Author(s):  
Yu. Golubitsky
Keyword(s):  
Small Businesses ◽  
Statistical Data ◽  
Russian Enterprise ◽  
Small Enterprises ◽  
Business Practices ◽  
Way Of Life ◽  
The World ◽  
Small Industry ◽  
Xix Century

The article considers business practices of Moscow small industry in the XIX century, basing upon physiological sketches of N. Polevoy and I. Kokorev, statistical data and the classification of professions are also presented. The author claims that the heroes of the analyzed sketches are the forefathers of Moscow small businesses and shows what a deep similarity their occupations and a way of life bear to the present-day routine existence of small enterprises.

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