Automatic generation of fuzzy classification rules using granulation-based adaptive clustering

A central problem of fuzzy modelling is the generation of fuzzy rules that fit the data to the highest possible extent. In this study, we present a method for automatic generation of fuzzy rules from data. The main advantage of the proposed method is its ability to perform data clustering without the requirement of predefining any parameters including number of clusters. The proposed method creates data clusters at different levels of granulation and selects the best clustering results based on some measures. The proposed method involves merging clusters into new clusters that have a coarser granulation. To evaluate performance of the proposed method, three different datasets are used to compare performance of the proposed method to other classifiers: SVM classifier, FCM fuzzy classifier, subtractive clustering fuzzy classifier. Results show that the proposed method has better classification results than other classifiers for all the datasets used

Granular computing approach for the design of medical data classification systems

Granular computing is a computation theory that imitates human thinking and reasoning by dealing with information at different levels of abstraction/precision. The adoption of granular computing approach in the design of data classification systems improves their performance in dealing with data uncertainty and facilitates handling large volumes of data. In this paper, a new approach for the design of medical data classification systems is proposed. The proposed approach makes use of data granulation in training the classifier. Training data is granulated at different levels and data from each level is used for constructing the classification system. To evaluate performance of the proposed approach, a classification system based on neural network is implemented. Four medical datasets are used to compare performance of the proposed approach to other classifiers: neural network classifier, ANFIS classifier and SVM classifier. Results show that the proposed approach improves classification performance of neural network classifier and produces better accuracy and area under curve than other classifiers for most of the datasets used

Evaluating student levelling based on machine learning model’s performance

In this paper, a novel application of machine learning algorithms is presented for student levelling. In multicultural countries such as UAE, there are various education curriculums where the sector of private schools and quality assurance is supervising various private schools for many nationalities. As there are various education curriculums in United Arab Emirates, specifically Abu Dhabi, to meet expats’ needs, there are different requirements for registration and success. In addition, there are different age groups for starting education in each curriculum. Every curriculum follows different education methods such as assessment techniques, reassessment rules, and exam boards. Currently, students who transfer to other curriculums are not correctly placed to their appropriate year group as a result of the start and end dates of each academic year as well as due to their date of birth, in which students who are either younger or older for that year group can create gaps in their learning and performance. In addition, pupils’ academic journeys are not stored which create a gap for the schools to track their learning process. In this paper, we propose a computational framework applicable in multicultural countries such as United Arab Emirates in which multi-education systems are implemented. Machine Learning are used to provide the appropriate student’ level aiding schools to provide a smooth transition when assigning students to their year groups and provide levelling and differentiation information of pupils for a smooth transition between one education curriculums to another, in which retrieval of their progress is possible. For classification and discriminant analysis of pupils levelling, three machine learning classifiers are utilised including random forest classifier, Artificial Neural Network, and combined classifiers. The simulation results indicated that the proposed machine learning classifiers generated effective performance in terms of accuracy

Novel graph for an appropriate cross section and length for cantilever RC beams

Whether the design is done manually or by software, the designer will have difficulty choosing the economic and strength cross section. The designer, in this case, either relies on their experience or resorts to the method of trial and error. Especially for Cantilever beams with a long span as a result of risk deflections, it is exposed. The current theoretical study was performed on rectangular concrete cross sections of different dimensions and subjected to uniformly distributed loads. Based on a previous study, the sections are reinforced with a specific reinforcement ratio. Through an algorithm, Python 3.4 software, and an output file, the permissible deflections for each cross section were calculated according to the ACI 318M-19. Finally, the authors could draw a graph to choose the appropriate cross section for each required beam length in less time and effort

Hardware-In-the-Loop Validation of Direct MPPT Based Cuckoo Search Optimization for Partially Shaded Photovoltaic System

During partial shading conditions (PSCs), the power-voltage curve becomes more complex, having one global maximum power (GMP) and many local peaks. Traditional maximum power point tracking (MPPT) algorithms are unable to track the GMP under PSCs. Therefore, several optimization tactics based on metaheuristics or artificial intelligence have been applied to deal with GMP tracking effectively. This paper details how a direct control cuckoo search optimizer (CSO) is used to track the GMP for a photovoltaic (PV) system. The proposed CSO addresses the limitations of traditional MPPT algorithms to deal with the PSCs and the shortcomings of the particle swarm optimization (PSO) algorithm, such as low tracking efficiency, steady-state fluctuations, and tracking time. The CSO was implemented using MATLAB/Simulink for a PV array operating under PSCs and its tracking performance was compared to that of the PSO-MPPT. Experimental validation of the CSO-MPPT was performed on a boost DC/DC converter using a real-time Hardware-In-the-Loop (HIL) simulator (OPAL-RT OP4510) and dSPACE 1104. The results show that CSO is capable of tracking GMP within 0.99–1.32 s under various shading patterns. Both the simulation and experimental findings revealed that the CSO outperformed the PSO in terms of steady-state fluctuations and tracking time