Soft-Boosted Self-Constructing Neural Fuzzy Inference Network

© 2013 IEEE. This correspondence paper proposes an improved version of the self-constructing neural fuzzy inference network (SONFIN), called soft-boosted SONFIN (SB-SONFIN). The design softly boosts the learning process of the SONFIN in order to decrease the error rate and enhance the learning speed. The SB-SONFIN boosts the learning power of the SONFIN by taking into account the numbers of fuzzy rules and initial weights which are two important parameters of the SONFIN, SB-SONFIN advances the learning process by: 1) initializing the weights with the width of the fuzzy sets rather than just with random values and 2) improving the parameter learning rates with the number of learned fuzzy rules. The effectiveness of the proposed soft boosting scheme is validated on several real world and benchmark datasets. The experimental results show that the SB-SONFIN possesses the capability to outperform other known methods on various datasets

Radial basis function neural network for head roll prediction modelling in a motion sickness study

Motion Sickness (MS) is the result of uneasy feelings that occurs when travelling. In MS mitigation studies, it is necessary to investigate and measure the occupant’s Motion Sickness Incidence (MSI) for analysis purposes. One way to mathematically calculate the MSI is by using a 6-DOF Subjective Vertical Conflict (SVC) model. This model utilises the information of the vehicle lateral acceleration and the occupant’s head roll angle to determine the MSI. The data of the lateral acceleration can be obtained by using a sensor. However, it is impractical to use a sensor to acquire the occupant’s head roll response. Therefore, this study presents the occupant’s head roll prediction model by using the Radial Basis Function Neural Network (RBFNN) method to estimate the actual head roll responses. The prediction model is modelled based on the correlation between lateral acceleration and head roll angle during curve driving. Experiments have been conducted to collect real naturalistic data for modelling purposes. The results show that the predicted responses from the model are similar with the real responses from the experiment. In future, it is expected that the prediction model will be useful in measuring the occupant’s MSI level by providing the estimated head roll responses

A new data-driven neural fuzzy system with collaborative fuzzy clustering mechanism

© 2015 Elsevier B.V. In this paper, a novel fuzzy rule transfer mechanism for self-constructing neural fuzzy inference networks is being proposed. The features of the proposed method, termed data-driven neural fuzzy system with collaborative fuzzy clustering mechanism (DDNFS-CFCM) are; (1) Fuzzy rules are generated facilely by fuzzy c-means (FCM) and then adapted by the preprocessed collaborative fuzzy clustering (PCFC) technique, and (2) Structure and parameter learning are performed simultaneously without selecting the initial parameters. The DDNFS-CFCM can be applied to deal with big data problems by the virtue of the PCFC technique, which is capable of dealing with immense datasets while preserving the privacy and security of datasets. Initially, the entire dataset is organized into two individual datasets for the PCFC procedure, where each of the dataset is clustered separately. The knowledge of prototype variables (cluster centers) and the matrix of just one halve of the dataset through collaborative technique are deployed. The DDNFS-CFCM is able to achieve consistency in the presence of collective knowledge of the PCFC and boost the system modeling process by parameter learning ability of the self-constructing neural fuzzy inference networks (SONFIN). The proposed method outperforms other existing methods for time series prediction problems

DRUG TARGET DECONVOLUTION IN CANCER CELL LINES

The deconvolution problem to identify the critical protein targets behind drug sensitivity profiling is an important part of drug development. It helps us to understand the mechanism of action of anti-cancer drugs on the cell lines through protein targets in those cell lines. This problem can be formulated as a matrix deconvolution problem, with two matrices for the cell-based drug sensitivity profiling and drug target interaction data, respectively. The model needs to be solved to identify the vulnerability of the cell lines to inhibition of critical targets. We used drug sensitivity data for 265 anti-cancer compounds over 990 cell models taken from cancer patients and cultivated in the lab. Using the data on interaction of these drugs with the protein targets, we used a novel method called TDSBS (target deconvolution with semi-blind source separation) in order to determine the critical targets for each cell model. The critical protein targets determined using this method were found to be clinically relevant, as we could determine that the driver genes have higher TDSBS values compared to the non-driver genes in the cell models. In this thesis we demonstrate a general statistical model which can be used to identify the protein targets which are inhibited by anti-cancer drugs in drug/cell line sensitivity experiments

Motion sickness mitigation in autonomous vehicle: a mini-review

An autonomous vehicle is a rapidly evolving technology that received attention from researchers due to its potential benefits. Besides the advantages, there are also non-negligible issues that need to be overcome in the middle of the autonomous vehicle development process. Among all the challenges, one of the important topics that have not gained adequate consideration is motion sickness (MS). This paper reviews the benefit and challenges of autonomous vehicles, MS factors, the quantifying methods of MS, and the mitigation strategies of MS. Considering the importance of minimizing MS, it is concluded that the number of strategies to lessen MS's severity is still lacking; hence, requiring more attention from automotive researchers

Machine learning methods for the study of cybersickness: a systematic review

This systematic review offers a world-first critical analysis of machine learning methods and systems, along with future directions for the study of cybersickness induced by virtual reality (VR). VR is becoming increasingly popular and is an important part of current advances in human training, therapies, entertainment, and access to the metaverse. Usage of this technology is limited by cybersickness, a common debilitating condition experienced upon VR immersion. Cybersickness is accompanied by a mix of symptoms including nausea, dizziness, fatigue and oculomotor disturbances. Machine learning can be used to identify cybersickness and is a step towards overcoming these physiological limitations. Practical implementation of this is possible with optimised data collection from wearable devices and appropriate algorithms that incorporate advanced machine learning approaches. The present systematic review focuses on 26 selected studies. These concern machine learning of biometric and neuro-physiological signals obtained from wearable devices for the automatic identification of cybersickness. The methods, data processing and machine learning architecture, as well as suggestions for future exploration on detection and prediction of cybersickness are explored. A wide range of immersion environments, participant activity, features and machine learning architectures were identified. Although models for cybersickness detection have been developed, literature still lacks a model for the prediction of first-instance events. Future research is pointed towards goal-oriented data selection and labelling, as well as the use of brain-inspired spiking neural network models to achieve better accuracy and understanding of complex spatio-temporal brain processes related to cybersickness

Development of a Graphical User Interface for processing and visualization of Brain Computer Interface experiments

Treballs Finals de Grau d'Enginyeria Biomèdica. Facultat de Medicina i Ciències de la Salut. Universitat de Barcelona. Curs: 2020-2021. Director i Tutor: Agustín Gutierrez-Galvez.Learning is the process by which new knowledge or skills are acquired and is known to be based on synaptic plasticity and the expansion of the cortical map. However, today it is still difficult to determine the relationship between a specific organizational change at brain level and the learning of a new behaviour. Knowing the functioning of the brain and, in particular, the neural units responsible for the learning process, could make a big difference to those who have suffered a stroke or an amputation and therefore have to relearn the basic locomotor movements. The project that has been carried out has consisted in the development of a graphical user interface (GUI) that allows the processing and visualization of the data obtained from experiments carried out using Brain Computer Interface (BCI) systems. It has been focused on obtaining the tuning curves, which show the firing rates of the neurons with respect to the angle of perturbation, and the trajectories performed by the subject, in order to subsequently visualize them. The graphical interface developed consists of a multiwindow application created using MATLAB App Designer based on the data and functions obtained from BCI experiments performed at Carnegie Mellon University. It is mainly aimed at the biomedical sector, although it could be useful in other fields