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Artificial Intelligence is widely used in solar applications. Adaptive Neural Fuzzy Inference System (ANFIS) principle is one of the intelligent techniques that is sufficient to be used in control systems. This paper proposes two new efficient intelligent solar tracking control systems based on ANFIS principle. The aim of this paper is to design and implement efficient single and dual-axis solar tracking control systems that can increase the performance of solar trackers, predict the trajectory of the sun across the sky accurately, and minimize the error, therefore, maximize the energy output of solar tracking systems. Experimental data are used to train and test the proposed solar tracking controllers by using month, day and time as input variables to predict the optimum positions for solar tracking systems (tilt/orientation angles). The proposed ANFIS models have been evaluated to find its capability and robustness in tracking the optimum angles that gain the maximum solar radiation. It is found that the proposed controllers are optimum to control solar tracking systems with high prediction rate and the low error rate. Besides, the selected variables along with the selected architecture could successfully predict the optimum tilt and orientation angles. The proposed models provide superior results with five membership functions, and it could obtain high performance for both single-axis and dual-axis solar tracking systems. [ABSTRACT FROM AUTHOR]

The differential evolution optimization-based clustering techniques are powerful, robust and more sophisticated than the conventional clustering methods due to their stochastic and heuristic characteristics. Unfortunately, these algorithms suffer from several drawbacks such as the tendency to be trapped or stagnated into local optima and slow convergence rates. These drawbacks are consequences of the difficulty in balancing the exploitation and exploration processes which directly affects the final quality of the clustering solutions. Hence, a variance-based differential evolution algorithm with an optional crossover for data clustering is presented in this paper to further enhance the quality of the clustering solutions along with the convergence speed. The proposed algorithm considers the balance between the exploitation and exploration processes by introducing (i) a single-based solution representation, (ii) a switchable mutation scheme, (iii) a vector-based estimation of the mutation factor, and (iv) an optional crossover strategy. The performance of the proposed algorithm is compared with current state-of-the-art differential evolution-based clustering techniques on 15 benchmark datasets from the UCI repository. The experimental results are also thoroughly evaluated and verified via non-parametric statistical analysis. Based on the obtained experimental results, the proposed algorithm achieves an average enhancement up to 11.98% of classification accuracy and obtains a significant improvement in terms of cluster compactness over the competing algorithms. Moreover, the proposed algorithm outperforms its peers in terms of the convergence speed and provides repeatable clustering results over 50 independent runs. • A single solution representation is adopted to avoid setting the initial solutions' sizes and positions. • A new switchable mutation scheme is employed to enhance the balance of the search behavior. • Multidimensional mutation factor is introduced to enhance the mutant solution quality. • A new optional crossover strategy is proposed to increase the convergence rate. • Integration of the four proposals in one DE-based clustering algorithm. [ABSTRACT FROM AUTHOR]

The shifting of image mean brightness and the domination of high-frequency bins during histogram equalization (HE) often result in the deteriorating quality of enhanced images and a considerable amount of information loss. This study proposes a novel approach based on bi-histogram equalization to improve its abilities in preserving information entropy and mean brightness. The proposed technique, named Bi-histogram Equalization using Modified Histogram Bins (BHEMHB), segments the input histogram based on the median brightness of an image and alters the histogram bins before HE is applied. Histogram segmentation enables mean brightness preservation, whereas the modification of histogram bins restricts the enhancement rate, thus minimizing the domination effects of high-frequency histogram bins. Simulation results show that BHEMHB significantly outperforms its peers in preserving the details and mean brightness of an image. The output image is visually pleasant with a natural appearance. [ABSTRACT FROM AUTHOR]

The attenuation of the light that travels through a water medium subjects underwater images to several problems. As a result of low contrast and color performance, images are unclear and lose important information. Therefore, the objects in these images can hardly be differentiated from the background. This study proposes a new method called dual-image Rayleigh-stretched contrast-limited adaptive histogram specification, which integrates global and local contrast correction. The aims of the proposed method are to increase image details and to improve the visibility of underwater images while enhancing image contrasts. The two main steps of the proposed method are contrast and color corrections; an underwater image undergoes the former before the latter. Global contrast correction generates dual-intensity images, which are then integrated to produce contrast-enhanced resultant images. Subsequently, such images are processed locally to enhance details. The color of the images is also corrected to improve saturation and brightness. Qualitative and quantitative results show that the contrast of the resultant image improves significantly. Moreover, image detail and color are adequately enhanced; thus, the proposed approach outperforms current state-of-the-art methods. [ABSTRACT FROM AUTHOR]

Cervical cancer has caused many deaths each year. Screening tests, such as Pap smear test used for the detection of the precancerous stage are able to avoid the occurrence of cervical cancer. However, the Pap smear test has several disadvantages such as less effective slides preparation and human error. Therefore, a computer-aided diagnosis system is introduced as a solution to the problem. One of the diagnostic systems that has been built is NeuralPap. However, the NeuralPap performance is limited by several constraints. This research proposed several new image processing algorithms to reduce these constraints. The Adaptive Fuzzy-k-Means (AFKM) clustering algorithm is proposed to replace the Moving k-Means (MKM) to segment Pap smear images into the nucleus, cytoplasm and background regions. Next, the feature extraction algorithm based on pseudo colouring called the Pseudo Colour Feature Extraction (PCFE) manual and Semi-Automatic PCFE are designed to replace the Region Growing Based Feature Extraction (RGBFE) which uses monochromatic images. This research is a step forward compared with the NeuralPap system by proposing the feature extraction algorithm for overlapping cells by combining the concept of colour space with Semi-Automatic PCFE algorithm. In addition, this research has also suggested the AFKM algorithm as a new centre positioning algorithm for the Radial Basis Function (RBF) and Hybrid RBF (HRBF) networks replacing the MKM algorithm. The entire proposed algorithm has been proven to produce better performance than the corresponding algorithm used in the NeuralPap. In addition, the combination of all algorithms has managed to increase the accuracy of the classification of cervical cancer to 76.35%, compared with 73.40% which is obtained from the previous NeuralPap system. [ABSTRACT FROM AUTHOR]

An information granule has to be translated into significant frameworks of granular computing to realize interpretability–accuracy tradeoff. These two objectives are in conflict and constitute an open problem. A new operational framework to form the evolving information granule (EIG) is developed in this paper, which ensures a compromise between interpretability and reasonable accuracy. The evolving information granule is initiated with the first information granule by translating the knowledge of the entire output domain. The initial information granule is considered an underfitting state with a high approximation error. Then, the EIG starts evolving in the information granule by partitioning the output domain and uses a dynamic constraint to maintain semantic interpretability in the output-contexts. The important criterion in the EIG is to determine the prominent distinction (output-context) in the output domain and realize the distinct information granule that depicts the semantics at the fuzzy partition level. The EIG tends to evolve toward the lower error region and realizes the effective rulebase by avoiding overfitting. The outcome on the synthetic and real-world data using the EIG shows the effectiveness of the proposed system, which outperforms state-of-the art methods. [ABSTRACT FROM AUTHOR]

The physical properties of water cause light-induced degradation of underwater images. Light rapidly loses intensity as it travels in water, depending on the color spectrum wavelength. Visible light is absorbed at the longest wavelength first. Red and blue are the most and least absorbed, respectively. Underwater images with low contrast are captured due to the degradation effects of light spectrum. Therefore, the valuable information from these images cannot be fully extracted for further processing. The current study proposes a new method to improve the contrast and reduce the noise of underwater images. The proposed method integrates the modification of image histogram into two main color models, Red–Green–Blue (RGB) and Hue-Saturation-Value (HSV). In the RGB color model, the histogram of the dominant color channel (i.e., blue channel) is stretched toward the lower level, with a maximum limit of 95%, whereas the inferior color channel (i.e., red channel) is stretched toward the upper level, with a minimum limit of 5%. The color channel between the dominant and inferior color channels (i.e., green channel) is stretched to both directions within the whole dynamic range. All stretching processes in the RGB color model are shaped to follow the Rayleigh distribution. The image is converted into the HSV color model, wherein the S and V components are modified within the limit of 1% from the minimum and maximum values. Qualitative analysis reveals that the proposed method significantly enhances the image contrast, reduces the blue-green effect, and minimizes under- and over-enhanced areas in the output image. For quantitative analysis, the test with 300 underwater images shows that the proposed method produces average mean square error (MSE) and peak signal to noise ratio (PSNR) of 76.76 and 31.13, respectively, which outperform six state-of-the-art methods. [ABSTRACT FROM AUTHOR]

Particle swarm optimization (PSO) has shown its competitive performance for solving benchmark and real-world optimization problems. Nevertheless, it requires better control of exploration/exploitation searches to prevent the premature convergence of swarms. Thus, this paper proposes a new PSO variant called PSO with adaptive time-varying topology connectivity (PSO-ATVTC) that employs an ATVTC module and a new learning framework. The proposed ATVTC module specifically aims to balance the algorithm's exploration/exploitation searches by varying the particle's topology connectivity with time according to its searching performance. The proposed learning framework consists of a new velocity update mechanism and a new neighborhood search operator to improve the algorithm's performance. A comprehensive study was conducted on 24 benchmark functions and one real-world problem. Compared with nine well-established PSO variants and six other cutting-edge metaheuristic search algorithms, the searching performance of PSO-ATVTC was proven to be more prominent in majority of the tested problems. [ABSTRACT FROM AUTHOR]

Highlights: [•] A PSO algorithm's variant, abbreviated as TPLPSO, is proposed. [•] Teaching and peer-learning framework is proposed to improve PSO's performance. [•] Stagnation prevention strategy is proposed to mitigate the premature convergence. [•] TPLPSO has higher searching accuracy and convergence speed during the optimization. [•] Results show that TPLPSO outperforms other state-of-the-art PSO variants. [ABSTRACT FROM AUTHOR]

Abstract: This paper presents the Region Splitting and Merging-Fuzzy C-means Hybrid Algorithm (RFHA), an adaptive unsupervised clustering approach for color image segmentation, which is important in image analysis and in understanding pattern recognition and computer vision field. Histogram thresholding technique is applied in the formation of all possible cells, used to split the image into multiple homogeneous regions. The merging technique is applied to merge perceptually close homogeneous regions and obtain better initialization for the Fuzzy C-means clustering approach. Experimental results have demonstrated that the proposed scheme could obtain promising segmentation results, with 12% average improvement in clustering quality and 63% reduction in classification error compared with other existing segmentation approaches. [Copyright &y& Elsevier]

Abstract: This paper presents a new method called one-against-all ensemble for solving multiclass pattern classification problems. The proposed method incorporates a neural network ensemble into the one-against-all method to improve the generalization performance of the classifier. The experimental results show that the proposed method can reduce the uncertainty of the decision and it is comparable to the other widely used methods. [Copyright &y& Elsevier]

Abstract: Inspired from the great potential of the Fourier-transform infrared (FTIR) spectroscopy as a screening tool for cervical cancer, this paper proposes an intelligent classification of cervical pre-cancerous cells based on the FTIR spectra. It consists of two parts: the extraction of FTIR characteristics and the intelligent classification of the pre-cancerous cells. Peak-corrected area-based features’ extraction (PCABFE) is introduced as a tool for the first part, while the Hybrid Multilayered Perceptron (HMLP) network is employed to classify the cervical pre-cancerous cells according to the Bethesda classification. Correlation test proves the capability of the proposed PCABFE to be as effective as the manual extraction by human experts, while the HMLP network produces a good classification performance with 97.4% of accuracy. [ABSTRACT FROM AUTHOR]

Abstract: This paper introduces a modified version of the Hybrid Multilayer Perceptron (HMLP) network to improve the performance of the conventional HMLP network. We adopted the Clustering Algorithm from the Radial Basis Function (RBF) network architecture and incorporated it into the conventional HMLP network architecture. The modified model is called Clustered-Hybrid Multilayer Perceptron (Clustered-HMLP) network. The proposed Clustered-HMLP network architecture is trained using modified training algorithm called Clustered-Modified Recursive Prediction Error (Clustered-MRPE). The capability of the Clustered-HMLP network with Clustered-MRPE training algorithm is demonstrated using seven benchmark datasets from the University of California at Irvine (UCI) machine learning repository (i.e. Iris, Ionosphere, Pima Indian Diabetes, Wine, Lung Cancer, Hayes-Roth and Glass) and compared with the performance of other twelve classifiers reported in literature. Further, the new network is implemented to model a Transformer Fault Diagnosis System and Aggregate Shape Identification System. The results indicate that the proposed Clustered-HMLP network outperforms other eleven classifiers and provides a significant improvement to the conventional HMLP network for pattern recognition application. [Copyright &y& Elsevier]

Abstract: In this paper, a new learning algorithm, called the Modified Recursive Least Square (MRLS), is introduced for the Hybrid Multilayered Perceptron (HMLP) network. Adopting the Recursive Least Square (RLS) algorithm as its basis, the MRLS algorithm differs from RLS in the way that the weight of the linear connections for the HMLP network is estimated. The convergence rate of the MRLS algorithm is further improved by varying the forgetting factor, optimizing the way the momentum and learning rate are assigned. To investigate its applicability, the MRLS algorithm is demonstrated on the HMLP network using six benchmark data sets obtained from the UCI repository. The classification performance of the HMLP network trained with the MRLS algorithm is compared with those of the HMLP network trained with the Modified Recursive Prediction Error (MRPE) algorithm and the MLP trained with the standard RLS algorithm as well as with other commonly adopted machine learning classifiers. The comparison results indicated that the proposed MRLS trained HMLP network provides significant improvement over RLS trained MLP network, MRPE trained HMLP network, and other machine learning classifiers in terms of accuracy, convergence rate and mean square error (MSE). [Copyright &y& Elsevier]