Personality Prediction with Hybrid Genetic Programming using Portable EEG Device.

This work suggests a method to identify personality traits regarding the targeted film clips in real-time. Such film clips elicit feelings in people while capturing their brain impulses using the electroencephalogram (EEG) devices and examining personality traits. The Myers-Briggs Type Indicator (MBTI) paradigm for determining personality is employed in this study. The fast Fourier transform (FFT) approach is used for feature extraction, and we have used hybrid genetic programming (HGP) for EEG data classification. We used a single-channel NeuroSky MindWave 2 dry electrode unit to obtain the EEG data. In order to collect the data, thirty Hindi and English video clips were placed in a conventional database. Fifty people volunteered to participate in this study and willingly provided brain signals. Using this dataset, we have generated four two-class HGP classifiers (HGP1, HGP2, HGP3, and HGP4), one for each group of MBTI traits overall classification accuracy of the HGP classifier as 82.25% for 10-fold cross-validation partition.

Serum Glutamic Oxaloacetic Transaminase - Predictor in Detection of Early Neurological Deterioration in Acute Ischemic Stroke.

In 20-40 percent of stroke patients, symptoms evolve during initial hours, resulting in rise of mortality. Early Neurological Deterioration (END) is now an emerging entity. In acute ischemic stroke, it results in increased mortality and functional disability. The incidence rates of early neurological deterioration is 13-37 percent of ischemic stroke. Ischemic stroke is associated with release of glutamate in brain. Serum glutamic oxaloacetic transaminase (SGOT) is the enzyme that metabolise glutamate and facilitates in lowering its level, but its significance is poorly understood. So, SGOT has been studied as predictor in detection of early neurological deterioration of acute ischemic stroke. MATERIAL: This was an observational prospective study in 100 in acute ischemic stroke patients with the age more than 18 years and presenting with in 24 hours after symptom onset. Detailed history of the patients was noted and detailed examination was carried out. National Institute of Health Stroke Scale (NIHSS) score was calculated at admission, at 24 hours and at 72 hours to see change in score and detect the early neurological deterioration. Early neurological deterioration was defined by change in the NIHSS Score of 2 or more than 2 with in 72 hours of stroke onset. SGOT level was sent within 24 hours of admission and studied as a contributing marker in detection of Early neurological deterioration. OBSERVATION: Among 100 patients enrolled in our study, there were more male patients (82%) with mean age of the 59.8 years. 72% of patients were diabetic and 84% were non alcoholic. Increase in SGOT value was found to be associated with early neurological deterioration(p <0.01). END in stroke patients were found to have SGOT > 40 IU/ml (p= 0.05). Higher level of SGOT (>40 IU/m) l had association with NIHSS at admission (p =0.07), at 48 hours (p=0.04) and at 72 hours (p= 0.03) with mean of 9.66,10.45,11.45 respectively. Positive correlation had been there between SGOT with NIHSS at admission, at 48 hours and at 72 hours . CONCLUSION: The increase in SGOT levels during stroke is associated with neurological deterioration. Hence, SGOT may be utilised as predictor in detection of END in acute ischemic stroke as high values of SGOT positively correlated with NIHSS at admission, at 48 hours and 72 hours.

Deep Learning Neural Network Prediction System Enhanced with Best Window Size in Sliding Window Algorithm for Predicting Domestic Power Consumption in a Residential Building.

Buildings are considered to be one of the world's largest consumers of energy. The productive utilization of energy will spare the accessible energy assets for the following ages. In this paper, we analyze and predict the domestic electric power consumption of a single residential building, implementing deep learning approach (LSTM and CNN). In these models, a novel feature is proposed, the "best N window size" that will focus on identifying the reliable time period in the past data, which yields an optimal prediction model for domestic energy consumption known as deep learning recurrent neural network prediction system with improved sliding window algorithm. The proposed prediction system is tuned to achieve high accuracy based on various hyperparameters. This work performs a comparative study of different variations of the deep learning model and records the best Root Mean Square Error value compared to other learning models for the benchmark energy consumption dataset.

Genetic Programming-Based Feature Selection for Emotion Classification Using EEG Signal.

The COVID-19 has resulted in one of the world's most significant worldwide lock-downs, affecting human mental health. Therefore, emotion recognition is becoming one of the essential research areas among various world researchers. Treatment that is efficacious and diagnosed early for negative emotions is the only way to save people from mental health problems. Genetic programming, a very important research area of artificial intelligence, proves its potential in almost every field. Therefore, in this study, a genetic program-based feature selection (FSGP) technique is proposed. A fourteen-channel EEG device gives 70 features for the input brain signal; with the help of GP, all the irrelevant and redundant features are separated, and 32 relevant features are selected. The proposed model achieves a classification accuracy of 85% that outmatches other prior works.

A Modified LSTM Framework for Analyzing COVID-19 Effect on Emotion and Mental Health during Pandemic Using the EEG Signals.

COVID-19, a WHO-declared public health emergency of worldwide concern, is quickly spreading over the world, posing a physical and mental health hazard. The COVID-19 has resulted in one of the world's most significant worldwide lockdowns, affecting human mental health. In this research work, a modified Long Short-Term Memory (MLSTM)-based Deep Learning model framework is proposed for analyzing COVID-19 effect on emotion and mental health during the pandemic using electroencephalogram (EEG) signals. The participants of this study were volunteers that recovered from COVID-19. The EEG dataset of 40 people is collected to predict emotion and mental health. The results of the MLSTM model are also compared with the other literature classifiers. With an accuracy of 91.26%, the MLSTM beats existing classifiers when using the 70-30 partitioning technique.

EEG-Based Personality Prediction Using Fast Fourier Transform and DeepLSTM Model.

In this paper, a deep long short term memory (DeepLSTM) network to classify personality traits using the electroencephalogram (EEG) signals is implemented. For this research, the Myers-Briggs Type Indicator (MBTI) model for predicting personality is used. There are four groups in MBTI, and each group consists of two traits versus each other; i.e., out of these two traits, every individual will have one personality trait in them. We have collected EEG data using a single NeuroSky MindWave Mobile 2 dry electrode unit. For data collection, 40 Hindi and English video clips were included in a standard database. All clips provoke various emotions, and data collection is focused on these emotions, as the clips include targeted, inductive scenes of personality. Fifty participants engaged in this research and willingly agreed to provide brain signals. We compared the performance of our deep learning DeepLSTM model with other state-of-the-art-based machine learning classifiers such as artificial neural network (ANN), K-nearest neighbors (KNN), LibSVM, and hybrid genetic programming (HGP). The analysis shows that, for the 10-fold partitioning method, the DeepLSTM model surpasses the other state-of-the-art models and offers a maximum classification accuracy of 96.94%. The proposed DeepLSTM model was also applied to the publicly available ASCERTAIN EEG dataset and showed an improvement over the state-of-the-art methods.

A novel quality prediction model for component based software system using ACO-NM optimized extreme learning machine.

Component-based software engineering is currently a development strategy used to improve complex embedded systems. The engineers have to deal with a large number of quality requirements (e.g. safety, security, availability, reliability, maintainability, portability, performance, and temporal correctness requirements), hence the development of complex embedded systems is becoming a challenging task. Enhancement of the quality prediction in component-based software engineering systems using soft computing techniques is the foremost intention of the research. Therefore, this paper proposes an extreme learning machine (ELM) classifier with the ant colony optimization algorithm and Nelder-Mead (ACO-NM) soft computing approach for component quality prediction. To promote efficient software systems and the ability of the software to work under several computer configurations maintainability, independence, and portability are taken as three core software components metrics for measuring the quality prediction. The ELM uses AC-NM for updating its weight to transform the quality constraints into objective functions for providing a global optimum quality prediction. The experimental results have shown that the proposed work gives an improved performance in terms of Sensitivity, Precision, Specificity, Accuracy, Mathews correlation coefficient, false positive rate, negative predictive value, false discovery rate, and rate of convergence.

Prediction of Software Reliability using Bio Inspired Soft Computing Techniques.

A lot of models have been made for predicting software reliability. The reliability models are restricted to using particular types of methodologies and restricted number of parameters. There are a number of techniques and methodologies that may be used for reliability prediction. There is need to focus on parameters consideration while estimating reliability. The reliability of a system may increase or decreases depending on the selection of different parameters used. Thus there is need to identify factors that heavily affecting the reliability of the system. In present days, reusability is mostly used in the various area of research. Reusability is the basis of Component-Based System (CBS). The cost, time and human skill can be saved using Component-Based Software Engineering (CBSE) concepts. CBSE metrics may be used to assess those techniques which are more suitable for estimating system reliability. Soft computing is used for small as well as large-scale problems where it is difficult to find accurate results due to uncertainty or randomness. Several possibilities are available to apply soft computing techniques in medicine related problems. Clinical science of medicine using fuzzy-logic, neural network methodology significantly while basic science of medicine using neural-networks-genetic algorithm most frequently and preferably. There is unavoidable interest shown by medical scientists to use the various soft computing methodologies in genetics, physiology, radiology, cardiology and neurology discipline. CBSE boost users to reuse the past and existing software for making new products to provide quality with a saving of time, memory space, and money. This paper focused on assessment of commonly used soft computing technique like Genetic Algorithm (GA), Neural-Network (NN), Fuzzy Logic, Support Vector Machine (SVM), Ant Colony Optimization (ACO), Particle Swarm Optimization (PSO), and Artificial Bee Colony (ABC). This paper presents working of soft computing techniques and assessment of soft computing techniques to predict reliability. The parameter considered while estimating and prediction of reliability are also discussed. This study can be used in estimation and prediction of the reliability of various instruments used in the medical system, software engineering, computer engineering and mechanical engineering also. These concepts can be applied to both software and hardware, to predict the reliability using CBSE.