Modelling The Nexus between Parenting Style and Anti Social Behavior using Ensemble Learning Approach

Contemporary society is grappling with issues of anti-social behavior in children and adolescents, one of which is influenced by parenting styles. This research employs machine learning technology, particularly ensemble learning, to model the relationship between parenting styles and anti-social behavior. The research data is derived from previous studies encompassing parenting style parameters and anti-social behavior. This data is preprocessed and feature-engineered, then used in modeling through the Random Forest (RF) and Adaptive Boost (AdaBoost) methods. Modeling is conducted in two phases: vanilla modeling and hyperparameter tuning. The results of the tuned models indicate that RF performs better (accuracy=91%) than AdaBoost (accuracy=72%). In conclusion, RF, as a bagging ensemble learning technique, effectively models the relationship between parenting styles and anti-social behavior. Future studies are recommended to gather more training data and develop an early detection system for use by child psychologists in the field.Contemporary society is grappling with issues of anti-social behavior in children and adolescents, one of which is influenced by parenting styles. This research employs machine learning technology, particularly ensemble learning, to model the relationship between parenting styles and anti-social behavior. The research data is derived from previous studies encompassing parenting style parameters and anti-social behavior. This data is preprocessed and feature-engineered, then used in modelling through the Random Forest (RF) and Adaptive Boost (AdaBoost) methods. Modelling is conducted in two phases: vanilla modelling and hyperparameter tuning. The results of the tuned models indicate that RF performs better (accuracy=91%) than AdaBoost (accuracy=72%). In conclusion, RF, as a bagging ensemble learning technique, effectively models the relationship between parenting styles and anti-social behavior. Future studies are recommended to gather more training data and develop an early detection system for use by child psychologists in the field

DETECTION OF ALZHEIMER'S DISEASE FROM ELECTROENCEPHALOGRAPHY (EEG) SIGNALS USING MULTITAPER AND ENSEMBLE LEARNING METHODS

Alzheimer's disease is a complex brain disease and is also the most common form of dementia that leads to impaired social and intellectual abilities. The disease only manifests itself with a simple forgetfulness, as the disease progresses, the patient forgets the recent events, cannot recognize his family members and close environment, and becomes in need of care in the last stage. Early detection is therefore crucial for medical intervention to prevent brain injury and prolong everyday functioning. In this study is aimed to detection of Alzheimer’s disease from EEG signals using the multitaper and ensemble learning methods. The dataset comprises of 24 healthy people and 24 Alzheimer's patients' EEG signals. 49 features were extracted by calculating the power spectral density (PSD) of the frequencies of the EEG signals between 1-49 Hz using the multitaper method. Then, the performances of AdaboostM1, Total Boost, Gentle Boost, Logit Boost, Robust Boost, and Bagging ensemble learning algorithms were compared. As a result of experiments, the Logit Boost algorithm has the highest performance. The algorithm has achieved a promising performance of 93.04% accuracy, 93.09% f1-score, 92.75% sensitivity, 93.43% precision, and 93.33% specificity

A novel spectral entropy-based index for assessing the depth of anaesthesia

Anaesthesia is a state of temporary controlled loss of awareness induced for medical operations. An accurate assessment of the depth of anaesthesia (DoA) helps anesthesiologists to avoid awareness during surgery and keep the recovery period short. However, the existing DoA algorithms have limitations, such as not robust enough for different patients and having time delay in assessment. In this study, to develop a reliable DoA measurement method, pre-denoised electroencephalograph (EEG) signals are divided into ten frequency bands (α, β1, β2, β3, β4, β, βγ, γ, δ and θ), and the features are extracted from different frequency bands using spectral entropy (SE) methods. SE from the beta-gamma frequency band (21.5–38.5 Hz) and SE from the beta frequency band show the highest correlation (R-squared value: 0.8458 and 0.7312, respectively) with the most popular DoA index, bispectral index (BIS). In this research, a new DoA index is developed based on these two SE features for monitoring the DoA. The highest Pearson correlation coefficient by comparing the BIS index for testing data is 0.918, and the average is 0.80. In addition, the proposed index shows an earlier reaction than the BIS index when the patient goes from deep anaesthesia to moderate anaesthesia, which means it is more suitable for the real-time DoA assessment. In the case of poor signal quality (SQ), while the BIS index exhibits inflexibility with cases of poor SQ, the new proposed index shows reliable assessment results that reflect the clinical observations

Comparative Analysis of Ensemble Learning Methods for Signal Classification

In recent years, the machine learning algorithms commenced to be used widely in signal classification area as well as many other areas. Ensemble learning has become one of the most popular Machine Learning approaches due to the high classification performance it provides. In this study, the application of four fundamental ensemble learning methods (Bagging, Boosting, Stacking, and Voting) with five different classification algorithms (Neural Network, Support Vector Machines, k-Nearest Neighbor, Naive Bayes, and C4.5) with the most optimal parameter values on signal datasets is presented. In the experimental studies, ensemble learning methods were applied on 14 different signal datasets and the results were compared in terms of classification accuracy rates. According to the results, the best classification performance was obtained with the Random Forest algorithm which is a Bagging based method