Granular computing is a computational paradigm that mimics human cognition in terms of grouping similar information together. Compatibility operators such as cardinality, orientation, density, and multidimensional length act on both in raw data and information granules which are formed from raw data providing a framework for human-like information processing where information granulation is intrinsic. Granular computing, as a computational concept, is not new, however it is only relatively recent when this concept has been formalised computationally via the use of Computational Intelligence methods such as Fuzzy Logic and Rough Sets. Neutrosophy is a unifying field in logics that extents the concept of fuzzy sets into a three-valued logic that uses an indeterminacy value, and it is the basis of neutrosophic logic, neutrosophic probability, neutrosophic statistics and interval valued neutrosophic theory. In this paper we present a new framework for creating Granular Computing Neural-Fuzzy modelling structures via the use of Neutrosophic Logic to address the issue of uncertainty during the data granulation process. The theoretical and computational aspects of the approach are presented and discussed in this paper, as well as a case study using real industrial data. The case study under investigation is the predictive modelling of the Charpy Toughness of heat-treated steel; a process that exhibits very high uncertainty in the measurements due to the thermomechanical complexity of the Charpy test itself. The results show that the proposed approach leads to more meaningful and simpler granular models, with a better generalisation performance as compared to other recent modelling attempts on the same data set.
Deep learning classifiers have demonstrated their ability to provide robust accuracy for the treatment of com- bined signals including electroencephalography (EEG) and functional near infrared spectroscopy (fNIRS) [1], [2]. In this work, an evolutionary deep learning strategy is applied to classify different cognitive workload states that surgeons experience during laparoscopic surgery. The proposed learning strategy is applied to train an Evolutionary Multilayer Perceptron Neural Network (E- MLPNN), where multimodal raw data of EEG, fNIRS and Electrocardiogram (ECG) signals were collected and concatenated from a series of ten experiments using the back-end platform Multi-sensing AI Environment for Surgical Task & Role Optimisation (MAESTRO) as shown in Figure 1(a). Each experiment required surgical trainees to perform a simulated laparoscopic cholecystec- tomy (LCH), i.e. the removal of a gallbladder in a porcine model using a minimally invasive surgical technique as demonstrated in Figure 1(b). At each experiment, the level of Cognitive Workload (CWL) is assumed to increase as the mental activity increases during the surgical operation. As presented in Figure 1c, a number of tasks performed during the LCH were defined to measure the level of CWL
Surgery is a mentally demanding task that is focused on patient safety and requires the precise execution of motor control and decision making in a timely manner. Episodes of high Cognitive Workload (CWL) induced by stressors or distractions have been shown to lead to inferior performance potentially compromising patient safety [1]. We have proposed a promising CWL assess- ment platform utilising a wide range of physiological sensors [2]. However, there are some disadvantages associated with a complex multimodal sensing design, including high device cost, long set up time and the dis- comfort caused by wearing multiple wearable sensors for long periods during surgery. To address this problem, the proposed one-dimensional convolutional neural network (1D-CNN) model discussed here, offers an alternative solution to recognising CWL states, achieving satisfac- tory performance (91.3% accuracy) with the use of a wireless ECG sensor alone, showing great potential for widespread deployment in the operating room (OR). MATERIALS AND METHODS
Via Granular Computing (GrC), one can create effective computational frameworks for obtaining information from data, motivated by the human perception of combining similar objects. Combining knowledge gained via GrC with a Fuzzy inference engine (Neural-Fuzzy) enable us to develop a transparent system. While weighting variables based on their importance during the iterative data granulation process has been proposed before (W-GrC), there is no work in the literature to demonstrate effectiveness and impact on Type-2 Fuzzy Logic systems (T2-FLS). The main contribution of this paper is to extend W-GrC, for the first time, to both Type-1 and Type-2 models known as Radial Basis Function Neural Network (RBFNN) and General Type-2 Radial Basis Function Neural Network (GT2-RBFNN). The proposed framework is validated using popular datasets: Iris, Wine, Breast Cancer, Heart and Cardiotocography. Results show that with the appropriate selection of feature weight parameter, the new computational framework achieves better classification accuracy outcomes. In addition, we also introduce in this research work an investigation on the modelling structure's interpretability (via Nauck's index) where it is shown that a good balance of interpretability and accuracy can be maintained.
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