Preventive and curative personality profiling based on EEG, ERP, and big five personality traits: a literature review

Healthy lifestyle is a significant factor that impacts on the budget for medicine. According to psychological studies, personality traits based on the Big Five personality traits especially the neuroticism and conscientiousness, have the ability to predict healthy lifestyle profiling. Electrophysiological signals have been used to explore the nature of individual differences and personality that are related to perception. In this paper, we reviewed studies examining healthy lifestyle profile i.e., preventive and curative using electroencephalography (EEG) and event-related potential (ERP) signals. This study proposed a general experimental model by reviewing the literature to build suitable experimental design for implementing artificial intelligence techniques based on the machine learning

Consensus by High Gegree of DeGroot model for multi-agent systems

Nonlinear distributions by the high degree of DeGroot model has been studied in this for consensus problem of multi-agent systems (MAS). The idea behind the convergence of nonlinear distribution is that when the degree of nonlinear distribution is increasing the number of iterations is in turn decreasing. From these viewpoints, the efficient aspects of the proposed nonlinearity model by high degree are that the resulting process is of fast convergence and the consensus could not depend on the kind of transition matri

Brain and artificial intelligence: from the viewpoint of spontaneous and task-evoked brain dynamics

Recently, the field of brain science often yields ‘big’ data and utilizes machine learning, which is central for the present artificial intelligence (AI) field and starts usually from extracting the hidden features. However, the data recorded from the brain are dynamic where the property of the data changes with time, different from photos that are static over the time. Then, the following question emerges: Are brain’s dynamic data really suitable for the present AI techniques? More specifically, can we extract exact features from brain’s dynamic data and what kind of dynamics makes this feature extraction more reliable? To answer these questions, in this study, we generated two kinds of the brain dynamics computationally, i.e., spontaneous and task-evoked brain dynamics, and both dynamics were applied to a fundamental technique for most feature extraction methods, that is, the principal component analysis (PCA). We suggest that the task-evoked brain dynamics can give rise to a feature space where different features, possibly related to personality traits, are classified more robustly and may lead to a better brain-AI syste

An approach of classifying waste using transfer learning method

One of the most critical issues facing the world is waste management, regardless of whether the region is being established or becoming established. There is a waste partitioning process in waste management, and the main challenge is that the garbage space is flooded long before the beginning of the following cleaning process at clear spots. Only unskilled workers conduct waste separation, which is less accurate, time-consuming, and not utterly possible due to the enormous amount of waste. Using the Convolutional Neural Network, we propose an artificial waste classification problem to compile and organize a dataset into seven categories consisting of metal, plastic, glass, paper, cardboard, trash, and E-waste. We then distinguished between specific transfer learning algorithms for our project: Xception, DenseNet121, Resnet-50, MobilenetV2, and EffiecienNetB7. DenseNet121 achieved a high precision characterization of about 93.3% for our model, while Mobilenet also demonstrated an incredible conversion to different forms of waste of 93% and Resnet-50, Xception and EfffiecienNetB7 achieved 92%, 92.5%, and 87%, respectively. In the future, we would like to increase the accuracy by using some other hyperparameter tuning, and we would like to deploy the project on mobile devices. We will use dockers or Kubernetes to deploy and YOLO real-time object detection as a framework for the post

The nonlinear limit control of EDSQOs on finite dimensional simplex

Consensus problems in multi agent systems (MAS) are theoretical aspect convergence of doubly stochastic quadratic operators. This work has presented the dynamic classifications of extreme doubly stochastic quadratic operators (EDSQOs) on finite-dimensional simplex (FDS) based on the limit behaviour of the trajectories. The limit behaviour of the trajectories of EDSQOs, on FDS is either in state of convergence, or fixed or periodic. This paper aimed at examining the behaviour of these states. The paper modelled the states and proves theoretically the characteristics of each state. The results indicate that convergence operators converge to the centre (1/m), and EDSQOs point are fixed with two or more points whereas periodic states exhibit sinusoidal behaviour. This work has contributed in understanding the limit of EDSQOs of the exterior initial points as fixed and periodic points developed spread attribute toward a fixed point

Brain tumor MRI medical images classification with data augmentation by transfer learning of VGG16

The ability to estimate conclusions without direct human input in healthcare systems via computer algorithms is known as Artificial intelligence (AI) in healthcare. Deep learning (DL) approaches are already being employed or exploited for healthcare purposes, and in the case of medical images analysis, DL paradigms opened a world of opportunities. This paper describes creating a DL model based on transfer learning of VGG16 that can correctly classify MRI images as either (tumorous) or (non-tumorous). In addition, the model employed data augmentation in order to balance the dataset and increase the number of images. The dataset comes from the brain tumour classification project, which contains publicly available tumorous and non-tumorous images. The result showed that the model performed better with the augmented dataset, with its validation accuracy reaching ~100 %

Brain tumor MRI medical images classification model based on CNN (BTMIC-CNN)

This research discusses a fully automatic brain tumour MRI medical images classification model that use Convolutional Neural Network (BTMIC-CNN). The proposed neural model adopted Design Science Research Methodology (DSRM) to classify MRI medical images from two datasets. One for binary classification task (contains tumorous and non-tumorous images). And the second for multiclass classification task (contains three types of brain tumor MRI medical images namely: Glioma, meningioma, and pituitary). The model's excellent performance was confirmed using the evaluation metrics and reported an overall accuracy of 99%. It outperforms existing methods in terms of classification accuracy and is expected to help radiologists and doctors accurately classify brain tumours’ images. This study contributes to goal three of the Sustainable Development Goals (SDGs), which involves excellent health and well-being

Prediction of agricultural emissions in Malaysia using the arima, LSTM, and regression models

Agriculture has always been an important economical factor for a country, which is causing emissions every day, without realizing how much it is leading towards an increasing number of Greenhouse Gas (GHG). Agricultural emissions have been forecasted for Malaysia to have a better understanding and to take measures right away. This can be done through a machine learning model including collecting data, pre- processing, training, building a model, and testing the model for accuracy. This project aims to develop a model to forecast agricultural emissions using the three most accurate forecasting models. The time series analysis consists of two models, autoregressive integrated moving average(ARIMA) and long short-term memory(LSTM) and simple linear regression model. These models illustrate the forecasted upward trend values until 2040 in Malaysia. The ARIMA model provides good prediction curves which are close to the actual values taken since 1960 and the LSTM model provides a decreasing curve for every value loss epochs which concludes to be a good forecasting model. It was concluded that agricultural emission is causing the soaring temperature in Malaysia and an immense amount of emissions causing by agriculture. The techniques used in this paper can be enhanced more in the future and the visualizations can help the Malaysian agricultural sectors to take proper measurements to prevent this uprising agricultural emission