Comparative analysis of text classification algorithms for automated labelling of quranic verses

The ultimate goal of labelling a Quranic verse is to determine its corresponding theme. However, the existing Quranic verse labelling approach is primarily depending on the availability of Quranic scholars who have expertise in Arabic language and Tafseer. In this paper, we propose to automate the labelling task of the Quranic verse using text classification algorithms. We applied three text classification algorithms namely, k-Nearest Neighbour, Support Vector Machine, and Naïve Bayes in automating the labelling procedure. In our experiment with the classification algorithms English translation of the verses are presented as features. The English translation of the verses are then classified as “Shahadah” (the first pillar of Islam) or “Pray” (the second pillar of Islam). It is found that all of the text classification algorithms are capable to achieve more than 70% accuracy in labelling the Quranic verses

Multi-objective optimisation for minimum quantity lubrication assisted milling process based on hybrid response surface methodology and multi-objective genetic algorithm

© 2019 by SAGE Publications Ltd.Parametric modelling and optimisation play an important role in choosing the best or optimal cutting conditions and parameters during machining to achieve the desirable results. However, analysis of optimisation of minimum quantity lubrication–assisted milling process has not been addressed in detail. Minimum quantity lubrication method is very effective for cost reduction and promotes green machining. Hence, this article focuses on minimum quantity lubrication–assisted milling machining parameters on AISI 1045 material surface roughness and power consumption. A novel low-cost power measurement system is developed to measure the power consumption. A predictive mathematical model is developed for surface roughness and power consumption. The effects of minimum quantity lubrication and machining parameters are examined to determine the optimum conditions with minimum surface roughness and minimum power consumption. Empirical models are developed to predict surface roughness and power of machine tool effectively and accurately using response surface methodology and multi-objective optimisation genetic algorithm. Comparison of results obtained from response surface methodology and multi-objective optimisation genetic algorithm depict that both measured and predicted values have a close agreement. This model could be helpful to select the best combination of end-milling machining parameters to save power consumption and time, consequently, increasing both productivity and profitability.Peer reviewedFinal Published versio

Selection of Significant On-Road Sensor Data for Short-Term Traffic Flow Forecasting Using the Taguchi Method

Over the past two decades, neural networks have been applied to develop short-term traffic flow predictors. The past traffic flow data, captured by on-road sensors, is used as input patterns of neural networks to forecast future traffic flow conditions. The amount of input patterns captured by the on-road sensors is usually huge, but not all input patterns are useful when trying to predict the future traffic flow. The inclusion of useless input patterns is not effective to developing neural network models. Therefore, the selection of appropriate input patterns, which are significant for short-term traffic flow forecasting, is essential. This can be conducted by setting an appropriate configuration of input nodes of the neural network; however, this is usually conducted by trial and error. In this paper, the Taguchi method, which is a robust and systematic optimization approach for designing reliable and high-quality models, is proposed for the purpose of determining an appropriate neural network configuration, in terms of input nodes, in order to capture useful input patterns for traffic flow forecasting. The effectiveness of the Taguchi method is demonstrated by a case study, which aims to develop a short-term traffic flow predictor based on past traffic flow data captured by on-road sensors located on a Western Australia freeway. Three advantages of using the Taguchi method were demonstrated: 1) short-term traffic flow predictors with high accuracy can be designed; 2) the development time for short-term traffic flow predictors is reasonable; and 3) the accuracy of short-term traffic flow predictors is robust with respect to the initial settings of the neural network parameters during the learning phase

Robust Design of Artificial Neural Networks Methodology in Neutron Spectrometry

Applications of artificial neural networks (ANNs) have been reported in literature in various areas. [1–5] The wide use of ANNs is due to their robustness, fault tolerant and the ability to learn and generalize, through training process, from examples, complex nonlinear and multi input/output relationships between process parameters using the process data. [6–10] The ANNs have many other advantageous characteristics, which include: generalization, adaptation, universal function approximation, parallel data processing, robustness, etc. Multilayer perceptron (MLP) trained with backpropagation (BP) algorithm is the most used ANN in modeling, optimization classification and prediction processes. [11, 12] Although BP algorithm has proved to be efficient, its convergence tends to be very slow, and there is a possibility to get trapped in some undesired local minimum. [4, 10, 11, 13] Most literature related to ANNs focused on specific applications and their results rather than the methodology of developing and training the networks. In general, the quality of the developed ANN is highly dependable not only on ANN training algorithm and its parameters but also on many ANN architectural parameters such as the number of hidden layers and nodes per layer which have to be set during training process and these settings are very crucial to the accuracy of ANN model. [8, 14–19

Application of Taguchi method and artificial neural network model for the prediction of reductive leaching of cobalt(III) from oxidised low-grade ores

The leaching process of cobalt using a wide range of experimental variables is described. The treated cobalt samples were from the Kalumbwe Mine in the south of the Democratic Republic of Congo. In this study, a predictive model of cobalt recovery using both the Taguchi statistical method and an artificial neural network (ANN) algorithm was proposed. The Taguchi method utilising a L25 (55) orthogonal array and an ANN multi-layer, feed-forward, back-propagation learning algorithm were adopted to optimise the process parameters (acid concentration, leaching time, temperature, percentage solid, and sodium metabisulfite concentration) responsible for the high recovery of cobalt by reducing sulfuric acid leaching. The ANN was built with a neuron in the output layer corresponding to the cobalt leaching recovery, 10 hidden layers, and 5 input variables. The validation of the ANN model was performed with the results of the Taguchi method. The optimised trained neural network depicts the testing data and validation data with R2 equal to 1 and 0.5676, respectively.Significance: We statistically investigated the main factors (acid concentration, leaching time, temperature, percentage solid, and sodium metabisulfite concentration) that affect the cobalt(III) leaching performance using both the Taguchi method and artificial neural network model. This allowed us to ascertain that it is indeed possible to leach cobalt(III) from oxide ores and to identify the optimum leaching conditions

State-of-the-art in aerodynamic shape optimisation methods

Aerodynamic optimisation has become an indispensable component for any aerodynamic design over the past 60 years, with applications to aircraft, cars, trains, bridges, wind turbines, internal pipe flows, and cavities, among others, and is thus relevant in many facets of technology. With advancements in computational power, automated design optimisation procedures have become more competent, however, there is an ambiguity and bias throughout the literature with regards to relative performance of optimisation architectures and employed algorithms. This paper provides a well-balanced critical review of the dominant optimisation approaches that have been integrated with aerodynamic theory for the purpose of shape optimisation. A total of 229 papers, published in more than 120 journals and conference proceedings, have been classified into 6 different optimisation algorithm approaches. The material cited includes some of the most well-established authors and publications in the field of aerodynamic optimisation. This paper aims to eliminate bias toward certain algorithms by analysing the limitations, drawbacks, and the benefits of the most utilised optimisation approaches. This review provides comprehensive but straightforward insight for non-specialists and reference detailing the current state for specialist practitioners

Comparison of Evolutionary Optimization Algorithms for FM-TV Broadcasting Antenna Array Null Filling

Broadcasting antenna array null filling is a very challenging problem for antenna design optimization. This paper compares five antenna design optimization algorithms (Differential Evolution, Particle Swarm, Taguchi, Invasive Weed, Adaptive Invasive Weed) as solutions to the antenna array null filling problem. The algorithms compared are evolutionary algorithms which use mechanisms inspired by biological evolution, such as reproduction, mutation, recombination, and selection. The focus of the comparison is given to the algorithm with the best results, nevertheless, it becomes obvious that the algorithm which produces the best fitness (Invasive Weed Optimization) requires very substantial computational resources due to its random search nature