IQ Classification via Brainwave Features: Review on Artificial Intelligence Techniques

Intelligence study is one of keystone to distinguish individual differences in cognitive psychology. Conventional psychometric tests are limited in terms of assessment time, and existence of biasness issues. Apart from that, there is still lack in knowledge to classify IQ based on EEG signals and intelligent signal processing (ISP) technique. ISP purpose is to extract as much information as possible from signal and noise data using learning and/or other smart techniques. Therefore, as a first attempt in classifying IQ feature via scientific approach, it is important to identify a relevant technique with prominent paradigm that is suitable for this area of application. Thus, this article reviews several ISP approaches to provide consolidated source of information. This in particular focuses on prominent paradigm that suitable for pattern classification in biomedical area. The review leads to selection of ANN since it has been widely implemented for pattern classification in biomedical engineering

Multilayer perceptron artificial neural network for the prediction of heating value of municipal solid waste

Abstract: Energy from municipal solid waste is steadily being integrated into the global energy feedstock, given the huge amount of waste being generated from various sources. This study develops a Multilayer Perceptron Artificial Neural Network for the prediction of High Heating Value of municipal solid waste as a function of moisture content, carbon, hydrogen, oxygen, nitrogen, sulphur, and ash. A total of 123 experimental data were extracted from reliable database for training, testing, and validation of the model. This model was trained, validated and tested with 70%, 20%, and 10% of the municipal solid waste biomass datasets respectively. The predicted High Heating Value was compared with the experimental data for two different training functions: Levenberg Marquardt backpropagation and Resilience backpropagation, and with some correlation from the literature. The accuracy of the model was reported based on some known performance criteria. The values of Root Mean Squared Error (RMSE), Mean Absolute Deviation (MAD), Mean Absolute Percentage Error (MAPE), and Coefficient of Correlation (CC) were 3.587, 2.409, 21.680, 0.970 respectively for RP and 3.095, 0.328, 22.483, 0.986 for LM respectively. Regression analysis was also carried out to determine the level of correlation between the experimental and predicted High Heating Values (HHV). The authors concluded that these models can be a useful tool in the prediction of heating value of MSW in order to facilitate clean energy production from waste

Intelligent Biosignal Processing in Wearable and Implantable Sensors

This reprint provides a collection of papers illustrating the state-of-the-art of smart processing of data coming from wearable, implantable or portable sensors. Each paper presents the design, databases used, methodological background, obtained results, and their interpretation for biomedical applications. Revealing examples are brain–machine interfaces for medical rehabilitation, the evaluation of sympathetic nerve activity, a novel automated diagnostic tool based on ECG data to diagnose COVID-19, machine learning-based hypertension risk assessment by means of photoplethysmography and electrocardiography signals, Parkinsonian gait assessment using machine learning tools, thorough analysis of compressive sensing of ECG signals, development of a nanotechnology application for decoding vagus-nerve activity, detection of liver dysfunction using a wearable electronic nose system, prosthetic hand control using surface electromyography, epileptic seizure detection using a CNN, and premature ventricular contraction detection using deep metric learning. Thus, this reprint presents significant clinical applications as well as valuable new research issues, providing current illustrations of this new field of research by addressing the promises, challenges, and hurdles associated with the synergy of biosignal processing and AI through 16 different pertinent studies. Covering a wide range of research and application areas, this book is an excellent resource for researchers, physicians, academics, and PhD or master students working on (bio)signal and image processing, AI, biomaterials, biomechanics, and biotechnology with applications in medicine

Optimal Model-parameter Determination for Feedforward Artificial Neural Networks

Neural Networks are an immensely versatile tool for state-of-the-art prediction problems. However, they require a training process that involves numerous hyper-parameters. This creates a training process that demands expert knowledge to configure and is often described as a trial-and-error process. The result is a training process that needs to be executed multiple times and this is highly time expensive. Currently, one solution to this problem is to perform a Grid-Search algorithm. This is where a set of possible values (essentially guesses) is declared for each hyper-parameter. Then each combination of hyper-parameters is used to configure the training session. Once the training of each model (hyper-parameter combination) is completed, the best performing model is retained, and the rest are discarded. The problem with this is that it can be wasteful as it explores hyper-parameter combinations that predictably produce poor models. It is also very time consuming and scales poorly with the size of the model. A number of methods are proposed in this {thesis} to efficiently derive hyper-parameters and model parameters and the empirical results are presented. These methods are split into two categories, Weight-Direct Determination (WDD), and Simple Effective Evolutionary Method. The former category exhibits success in certain cases whereas the latter exhibits a broad success across Classification and Regression; amongst a large number of samples and features and small number of samples and features. The thesis concludes that the WDD is only effective on small datasets (both in terms of the number of samples and number of input features). This is due to its dependence on Delaunay Triangulation which exhibits a quadratic time complexity with-respect-to the number of input samples. It is deemed that the WDD methods developed in this research are not optimal for achieving general-purpose application of Multi-Layer Perceptrons. However, the Complete Simple Effective Evolutionary Method (CSEEM) from the SEEM Chapter shows great promise as it is able to perform effectively on the `Knowledge Extraction based on Evolutionary Learning' (KEEL) Datasets for both Regression and Classification. This method can achieve this effectiveness whilst only requiring a single hyper-parameter (the number of children in a population) that is fairly invariant across datasets. In this {thesis}, CSEEM is applied to real-world regression and classification problems. It is also compared to RMSProp (gradient-dependent iterative method) to compare its performance with an existing gradient-dependent method. In both categories, CSEEM consistently performs with a lower normalized square loss and higher classification accuracy, respectively, versus the number hidden nodes when compared to RMSProp

Classificação da segurança de sistemas interligados com elevada penetração eólica com base em redes neuronais artificiais

Tese de mestrado integrado. Engenharia Electrotécnica e de Computadores (Major Energia). Faculdade de Engenharia. Universidade do Porto. 201