scholarly journals K-Nearest Neighbor Classification for Detection of The Effect of Game Addiction on Cognitive Activity in The Late Adolescent Phase based on Brainwave Signals

2019 ◽  
Vol 1 (2) ◽  
pp. 46-62
Author(s):  
Ahmad Azhari ◽  
Ajie Kurnia Saputra Swara
Keyword(s):  
Cognitive Abilities ◽  
Nearest Neighbor ◽  
Cognitive Activity ◽  
World Health ◽  
Gaming Disorder ◽  
Negative Consequences ◽  
K Nearest Neighbor ◽  
Late Adolescent ◽  
Game Addiction ◽  
The Impact

World Health Organization (WHO) has determined that Gaming disorder is included in the International Classification of Diseases (ICD-11). The behavior of playing digital games included in the Gaming disorder category is characterized by impaired control of the game, increasing the priority given to the game more than other activities insofar as the game takes precedence over other daily interests and activities, and the continuation or improvement of the game despite negative consequences. The influence of video games on children's development has always been a polemic because in adolescence not only adopts cognitive abilities in learning activities, but also various strategies related to managing activities in learning, playing and socializing to improve cognitive abilities. Therefore, this research was conducted to analyze the cognitive activity of late teens in learning and playing games based on brainwave signals and to find out the impact of games on cognitive activity in adolescents. Prediction of the effect of the game on cognitive activity will be done by applying Fast Fourier Transform for feature extraction and K-Nearest Neighbor for classification. The results of the expert assessment showed the percentage of respondents with superior cognitive category but game addiction was 63.3% and respondents with cognitive categorization were average but were addicted by 36.6%. The percentage of accuracy produced by the system shows 80% in games and cognitive by using k values of 1, 6, and 7. The correlation test results show a percentage of 0.089, so it is concluded that there is no influence of the game on cognitive activity in late adolescents.

scholarly journals A History and Overview of Video Game Addiction

2020 ◽  
pp. 17-32
Author(s):  
Mark D. Griffiths ◽  
Halley M. Pontes
Keyword(s):  
Video Game ◽  
Internet Gaming Disorder ◽  
Gaming Addiction ◽  
Gaming Disorder ◽  
Negative Consequences ◽  
Video Game Addiction ◽  
The Past ◽  
Internet Gaming ◽  
Game Addiction ◽  
Published Evidence

The past decade has witnessed a significant increase in the number of empirical studies examining various aspects of problematic video game play, video game addiction, and, more recently, gaming disorder. This chapter begins with a brief past history of how research into video game addiction has developed during the past four decades in the 1980s (arcade video game addiction), 1990s (home console video game addiction), and 2000s and beyond (online video game addiction). The chapter also overviews the features of gaming addiction, its prevalence rates, demographics and gaming addiction, negative consequences of excessive video game use, Internet gaming disorder and the DSM-5, and treatment of gaming addiction. Based on the published evidence, particularly from studies conducted in the past decade, it appears that, in extreme cases, excessive gaming can have potentially damaging effects on individuals who appear to display compulsive and/or addictive behavior similar to other more traditional addictions. However, the field has been hindered by the use of inconsistent and nonstandardized criteria to assess and identify problematic and/or addictive video game use.

scholarly journals The Impact of Emotional Music on Active ROI in Patients with Depression Based on Deep Learning: A Task-State fMRI Study

2019 ◽  
Vol 2019 ◽  
pp. 1-14
Author(s):  
Renzhou Gui ◽  
Tongjie Chen ◽  
Han Nie
Keyword(s):  
Neural Network ◽  
Deep Learning ◽  
Correlation Matrix ◽  
Nearest Neighbor ◽  
Learning Model ◽  
Fmri Data ◽  
Support Vector ◽  
K Nearest Neighbor ◽  
The Impact ◽  
Deep Learning Model

With the continuous development of science, more and more research results have proved that machine learning is capable of diagnosing and studying the major depressive disorder (MDD) in the brain. We propose a deep learning network with multibranch and local residual feedback, for four different types of functional magnetic resonance imaging (fMRI) data produced by depressed patients and control people under the condition of listening to positive- and negative-emotions music. We use the large convolution kernel of the same size as the correlation matrix to match the features and obtain the results of feature matching of 264 regions of interest (ROIs). Firstly, four-dimensional fMRI data are used to generate the two-dimensional correlation matrix of one person’s brain based on ROIs and then processed by the threshold value which is selected according to the characteristics of complex network and small-world network. After that, the deep learning model in this paper is compared with support vector machine (SVM), logistic regression (LR), k-nearest neighbor (kNN), a common deep neural network (DNN), and a deep convolutional neural network (CNN) for classification. Finally, we further calculate the matched ROIs from the intermediate results of our deep learning model which can help related fields further explore the pathogeny of depression patients.

scholarly journals Application of Deep Convolutional Neural Networks and Smartphone Sensors for Indoor Localization

2019 ◽  
Vol 9 (11) ◽  
pp. 2337 ◽  
Author(s):  
Imran Ashraf ◽  
Soojung Hur ◽  
Yongwan Park
Keyword(s):  
Magnetic Field ◽  
Nearest Neighbor ◽  
Indoor Localization ◽  
Scene Recognition ◽  
Localization Error ◽  
K Nearest Neighbor ◽  
Current Location ◽  
Smartphone Sensors ◽  
The Impact ◽  
Localization Systems

Indoor localization systems are susceptible to higher errors and do not meet the current standards of indoor localization. Moreover, the performance of such approaches is limited by device dependence. The use of Wi-Fi makes the localization process vulnerable to dynamic factors and energy hungry. A multi-sensor fusion based indoor localization approach is proposed to overcome these issues. The proposed approach predicts pedestrians’ current location with smartphone sensors data alone. The proposed approach aims at mitigating the impact of device dependency on the localization accuracy and lowering the localization error in the magnetic field based localization systems. We trained a deep learning based convolutional neural network to recognize the indoor scene which helps to lower the localization error. The recognized scene is used to identify a specific floor and narrow the search space. The database built of magnetic field patterns helps to lower the device dependence. A modified K nearest neighbor (mKNN) is presented to calculate the pedestrian’s current location. The data from pedestrian dead reckoning further refines this location and an extended Kalman filter is implemented to this end. The performance of the proposed approach is tested with experiments on Galaxy S8 and LG G6 smartphones. The experimental results demonstrate that the proposed approach can achieve an accuracy of 1.04 m at 50 percent, regardless of the smartphone used for localization. The proposed mKNN outperforms K nearest neighbor approach, and mean, variance, and maximum errors are lower than those of KNN. Moreover, the proposed approach does not use Wi-Fi for localization and is more energy efficient than those of Wi-Fi based approaches. Experiments reveal that localization without scene recognition leads to higher errors.

Optimized Cost Model for k-NN Queries in R*-Trees over Random Distribution

2011 ◽  
Vol 403-408 ◽  
pp. 3315-3321
Author(s):  
Sirisala Nageswara Rao
Keyword(s):  
Nearest Neighbor ◽  
Cost Model ◽  
Neighbor Join ◽  
Multidimensional Data ◽  
Analysis Model ◽  
K Nearest Neighbor ◽  
Storage And Retrieval ◽  
Key Issues ◽  
Improved Performance ◽  
The Impact

Efficient storage and retrieval of multidimensional data in large volumes has become one of the key issues in the design and implementation of commercial and application software. The kind of queries posted on such data is also multifarious. Nearest neighbor queries are one such category and have more significance in GIS type of application. R-tree and its sequel are data partitioned hierarchical multidimensional indexing structures that help in this purpose. Today’s research has turned towards the development of powerful analytical method to predict the performance of such indexing structures such as for varies categories of queries such as range, nearest neighbor, join, etc .This paper focuses on performance of R*-tree for k nearest neighbor (kNN) queries. While general approaches are available in literature that works better for larger k over uniform data, few have explored the impact of small values of k. This paper proposes improved performance analysis model for kNN query for small k over random data. The results are tabulated and compared with existing models, the proposed model out performs the existing models in a significant way for small k

scholarly journals Impact of a pulsed xenon disinfection system on hospital onset Clostridioides difficile infections in 48 hospitals over a 5-year period

2021 ◽  
Vol 21 (1) ◽  
Author(s):  
Sarah Simmons ◽  
Grady Wier ◽  
Antonio Pedraza ◽  
Mark Stibich
Keyword(s):  
Nearest Neighbor ◽  
Negative Binomial ◽  
Learning Algorithm ◽  
Compliance Rate ◽  
Negative Binomial Regression ◽  
Inverse Association ◽  
K Nearest Neighbor ◽  
Data Set ◽  
Clostridioides Difficile ◽  
The Impact

Abstract Background The role of the environment in hospital acquired infections is well established. We examined the impact on the infection rate for hospital onset Clostridioides difficile (HO-CDI) of an environmental hygiene intervention in 48 hospitals over a 5 year period using a pulsed xenon ultraviolet (PX-UV) disinfection system. Methods Utilization data was collected directly from the automated PX-UV system and uploaded in real time to a database. HO-CDI data was provided by each facility. Data was analyzed at the unit level to determine compliance to disinfection protocols. Final data set included 5 years of data aggregated to the facility level, resulting in a dataset of 48 hospitals and a date range of January 2015–December 2019. Negative binomial regression was used with an offset on patient days to convert infection count data and assess HO-CDI rates vs. intervention compliance rate, total successful disinfection cycles, and total rooms disinfected. The K-Nearest Neighbor (KNN) machine learning algorithm was used to compare intervention compliance and total intervention cycles to presence of infection. Results All regression models depict a statistically significant inverse association between the intervention and HO-CDI rates. The KNN model predicts the presence of infection (or whether an infection will be present or not) with greater than 98% accuracy when considering both intervention compliance and total intervention cycles. Conclusions The findings of this study indicate a strong inverse relationship between the utilization of the pulsed xenon intervention and HO-CDI rates.

scholarly journals Classification of Concentration Levels in Adult-Early Phase using Brainwave Signals by Applying K-Nearest Neighbor

2019 ◽  
Vol 1 (1) ◽  
pp. 14-24
Author(s):  
Ahmad Azhari ◽  
Fathia Irbati Ammatulloh
Keyword(s):  
Early Phase ◽  
Nearest Neighbor ◽  
Concentration Level ◽  
Cognitive Activity ◽  
Statistical Characteristics ◽  
K Nearest Neighbor ◽  
Mental Action ◽  
The Brain ◽  
Concentration Levels

The brain controls the center of human life. Through the brain, all activities of living can be done. One of them is cognitive activity. Brain performance is influenced by mental conditions, lifestyle, and age. Cognitive activity is an observation of mental action, so it includes psychological symptoms that involve memory in the brain's memory, information processing, and future planning. In this study, the concentration level was measured at the age of the adult-early phase (18-30 years) because in this phase, the brain thinks more abstractly and mental conditions influence it. The purpose of this study was to see the level of concentration in the adult-early phase with a stimulus in the form of cognitive activity using IQ tests with the type of Standard Progressive Matrices (SPM) tests. To find out the IQ test results require a long time, so in this study, a recording was done to get brain waves so that the results of the concentration level can be obtained quickly.EEG data was taken using an Electroencephalogram (EEG) by applying the SPM test as a stimulus. The acquisition takes three times for each respondent, with a total of 10 respondents. The method implemented in this study is a classification with the k-Nearest Neighbor (kNN) algorithm. Before using this method, preprocessing is done first by reducing the signal and filtering the beta signal (13-30 Hz).The results of the data taken will be extracted first to get the right features, feature extraction in this study using first-order statistical characteristics that aim to find out the typical information from the signals obtained. The results of this study are the classification of concentration levels in the categories of high, medium, and low. Finally, the results of this study show an accuracy rate of 70%.

scholarly journals The Prevalence of Substance Misuse and its Effects among Omani College Students: A Cross-sectional Study

2021 ◽  
Vol 36 (1) ◽  
pp. e224-e224
Author(s):  
Hazaa Al-Hinaai ◽  
Issa Al-Busaidi ◽  
Badriya Al Farsi ◽  
Yaqoub Al Saidi
Keyword(s):  
College Students ◽  
Substance Abuse ◽  
Substance Misuse ◽  
Cross Sectional Study ◽  
Urban Setting ◽  
World Health ◽  
Negative Consequences ◽  
Sectional Study ◽  
Cross Sectional ◽  
The Impact

Objectives: Many studies have confirmed that the use of alcohol, tobacco, and cannabis is prevalent among university students. This study aimed to assess the prevalence of substance misuse among college students in Oman, identifying the most commonly used substances, and reviewing the effect of substance misuse on the students’ performance. Methods: This cross-sectional study was conducted in a higher learning institution in an urban setting in Oman from April 2018 to December 2018. A descriptive, self-administered online questionnaire, the Alcohol, Smoking, and Substance Involvement Screening Test, version 3.0 (Arabic version), adapted from the World Health Organization was sent to 12 000 students at the college. The sample size was calculated using online software (Raosoft), with a margin of error of 5% and a confidence level of 95%. Results: A total of 375 students responded (response rate = 3.1%). The overall lifetime prevalence for any substance misuse (including tobacco and alcohol) among the participants was 41.3%, with the overall prevalence without tobacco or alcohol at 29.9%. Tobacco was the most common substance used, with a prevalence of around 23.5%, followed by alcohol at 10.7%. Male students had a significantly higher rate of substance abuse, for any substance, compared to female students (p < 0.001). There was a significant correlation between tobacco use, alcohol misuse, and misuse of other substances. Most of the adverse effects attributed to substance misuse reported by the respondents in this study were social (27.7%) and health-related (25.8%) problems. The impact of substance abuse on their performance was also high (23.8%). Furthermore, 15.4% of the respondents had financial problems, and 4.7%% were struggling with legal issues. Notably, only 49.1% of the respondents perceived that substance misuse was a serious problem. Conclusions: Although college students are expected to be more aware of the negative impacts of substance misuse, this study found a high prevalence of smoking, alcohol, and other substance misuses among the group of Omani college students. Further research in this field is essential, and the results of this study have shed light on a critical problem among Omani college students. It is hoped that the findings of this study will be used and built on in future research to recognize students at risk of substance misuse from early school life, leading to early intervention, and potentially preventing the possible negative consequences.

scholarly journals An RSSD-Based Fingerprint Positioning Method for Detection of an Unknown Radio Transmitter Using WLS and Factor Graph

2018 ◽  
Vol 2018 ◽  
pp. 1-11
Author(s):  
Liyang Zhang ◽  
Taihang Du ◽  
Chundong Jiang
Keyword(s):  
Nearest Neighbor ◽  
Access Point ◽  
Reference Points ◽  
Least Square ◽  
Factor Graph ◽  
K Nearest Neighbor ◽  
Positioning Accuracy ◽  
Radio Transmitter ◽  
Error Weight ◽  
The Impact

Realizing accurate detection of an unknown radio transmitter (URT) has become a challenge problem due to its unknown parameter information. A method based on received signal strength difference (RSSD) fingerprint positioning technique and using factor graph (FG) has been successfully developed to achieve the localization of an URT. However, the RSSD-based FG model is not accurate enough to express the relationship between the RSSD and the corresponding location coordinates since the RSSD variances of reference points are different in practice. This paper proposes an enhanced RSSD-based FG algorithm using weighted least square (WLS) to effectively reduce the impact of RSSD measurement variance difference on positioning accuracy. By the use of stochastic RSSD errors between the measured value and the estimated value of the selected reference points, we utilize the error weight matrix to establish a new WLSFG model. Then, the positioning process of proposed RSSD-WLSFG algorithm is derived with the sum-product principle. In addition, the paper also explores the effects of different access point (AP) numbers and grid distances on positioning accuracy. The simulation experiment results show that the proposed algorithm can obtain the best positioning performance compared with the conventional RSSD-based K nearest neighbor (RSSD-KNN) and RSSD-FG algorithms in the case of different AP numbers and grid distances.

scholarly journals The Predictive Power of K-Nearest Neighbor (KNN): The Effect of Corporate Governance Mechanisms on Earnings Management

SAGE Open ◽  
2020 ◽  
Vol 10 (3) ◽  
pp. 215824402094953
Author(s):  
Mengyun Wu ◽  
Martha Coleman ◽  
Jonas Bawuah
Keyword(s):  
Corporate Governance ◽  
Earnings Management ◽  
Management Practices ◽  
Nearest Neighbor ◽  
K Nearest Neighbor ◽  
Governance Mechanisms ◽  
Corporate Managers ◽  
Long Run ◽  
Corporate Governance Mechanisms ◽  
The Impact

This study investigates the long-run effect of corporate governance mechanisms on earnings management of listed companies in Nigeria and Ghana. The study uses Ant Colony Optimization (ACO) and K-Nearest Neighbor (KNN) in establishing a long-run effect of good corporate mechanisms in reducing earnings management practice by corporate managers. ACO selected four major corporate governance mechanisms: Board Procedure Index, Board Disclosure Index, Ownership Structure Index, and Shareholders’ Rights Index; these were the key corporate governance mechanisms that influence the reduction in earnings management activities. KNN produced a strong significant longitudinal effect of implementing good corporate governance mechanisms in decreasing the manipulating behavior of managers. Quality corporate governance mechanisms’ implementation reduces the opportunistic behavior of corporate managers in manipulating earnings. Therefore, the study alert policymakers the urgency in setting up appropriate policies to enhance the reduction in earnings management practices to provide accurate financial information for stakeholders’ financial decision-making. The use of ACO and KNN in the study is a great novelty, which presents a calibration and prediction of the impact of corporate governance mechanisms on earnings management showing the rate of reduction.

scholarly journals Impact of Online Games on Children's Mental Health

2021 ◽  
Vol 2 (1) ◽  
pp. 13-19
Author(s):  
Kevin Guerada
Keyword(s):  
Mental Health ◽  
Mental Disorders ◽  
Negative Impact ◽  
Online Games ◽  
Children’S Mental Health ◽  
Children's Mental Health ◽  
World Health ◽  
Game Addiction ◽  
As Stress ◽  
The Impact

This article examines the impact of online games on children's mental health. A person can be diagnosed with a game addiction by a psychologist or a psychiatrist if he has a game playing pattern that is severe enough to have a negative impact on himself, his family, social, education, work, and other important things. Psychologists or psychiatrists usually can only provide a diagnosis after a person's game addiction pattern lasts for at least 12 months, although this time requirement can be shortened if the adverse effects of playing games on his daily life are very obvious. The impact of online gaming addiction on children is manifested through a lack of focus on other daily activities, lack of attention in class, and constant thinking about games. In fact, the World Health Organization (WHO) has designated online game addiction as a type of mental disorder. Game addiction can also occur with other mental disorders, such as stress, depression and anxiety disorders. Various efforts can be made to prevent mental disorders, namely doing physical activity and staying physically active, helping others sincerely to maintain positive thoughts.

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