A Novel Switching Delayed PSO Algorithm for Estimating Unknown Parameters of Lateral Flow Immunoassay

Convolutional Neural Network Classification of Rest EEG Signals among People with Epilepsy, Psychogenic Non Epileptic Seizures and Control Subjects

Identifying subjects with epileptic seizures or psychogenic non-epileptic seizures from healthy subjects via interictal EEG analysis can be a very challenging issue. Indeed, at visual inspection, EEG can be normal in both cases. This paper proposes an automatic diagnosis approach based on deep learning to differentiate three classes: subjects with epileptic seizures (ES), subjects with non-epileptic psychogenic seizures (PNES) and control subjects (CS), analyzed by non-invasive low-density interictal scalp EEG recordings. The EEGs of 42 patients with new-onset ES, 42 patients with PNES video recorded and 19 patients with CS all with normal interictal EEG on visual inspection, were analyzed in the study; none of them was taking psychotropic drugs before registration. The processing pipeline applies empirical mode decomposition (EMD) to 5s EEG segments of 19 channels in order to extract enhanced features learned automatically from the customized convolutional neural network (CNN). The resulting CNN has been shown to perform well during classification, with an accuracy of 85.7%; these results encourage the use of deep processing systems to assist clinicians in difficult clinical settings.

A Dynamic Neighborhood-Based Switching Particle Swarm Optimization Algorithm

In this article, a dynamic-neighborhood-based switching PSO (DNSPSO) algorithm is proposed, where a new velocity updating mechanism is designed to adjust the personal best position and the global best position according to a distance-based dynamic neighborhood to make full use of the population evolution information among the entire swarm. In addition, a novel switching learning strategy is introduced to adaptively select the acceleration coefficients and update the velocity model according to the searching state at each iteration, thereby contributing to a thorough search of the problem space. Furthermore, the differential evolution algorithm is successfully hybridized with the particle swarm optimization (PSO) algorithm to alleviate premature convergence. A series of commonly used benchmark functions (including unimodal, multimodal, and rotated multimodal cases) is utilized to comprehensively evaluate the performance of the DNSPSO algorithm. The experimental results demonstrate that the developed DNSPSO algorithm outperforms a number of existing PSO algorithms in terms of the solution accuracy and convergence performance, especially for complicated multimodal optimization problems.

Stress Optimization of Vent Holes with Different Shapes Using Efficient Switching Delayed PSO Algorithm

An effective integrated design optimization method is developed to reduce the maximum von Mises stress around vent holes of a high-pressure turbine sealing disk. It mainly includes four different shape designs (circular, elliptical, race-track, and four-arc) for holes, an updated self-developed modelling and meshing tool, an APDL-based strength analysis, and a self-proposed efficient switching delayed particle swarm optimization (SDPSO) algorithm. The main idea of SDPSO is: (1) by evaluating an evolutionary factor and utilizing a probability transition matrix, a non-homogeneous Markov chain is determined and auto-updated in each generation; (2) the evolutionary factor and the Markov chain are used to adaptively select the inertia weight, acceleration coefficients, and delayed information to adjust the particle’s velocity. The performance of SDPSO is evaluated through two benchmark optimization problems with constraints. The results show that SDPSO is superior to two well-known PSO algorithms in optimization capability, numerical robustness, and convergence speed. Furthermore, SDPSO is used for the stress optimization of vent holes with four different shapes. The results show that: (1) SDPSO is suitable and valuable for practical engineering optimization problems with constraints; (2) the developed integrated design optimization method is effective and advanced for reducing the maximum von Mises stress around the vent holes; and (3) the four-arc hole has more tremendous advantages in reducing the maximum von Mises stress, followed by the elliptical hole, the race-track hole, and the circular hole.

A Hybrid Preaching Optimization Algorithm Based on Kapur Entropy for Multilevel Thresholding Color Image Segmentation

Multilevel thresholding segmentation of color images plays an important role in many fields. The pivotal procedure of this technique is determining the specific threshold of the images. In this paper, a hybrid preaching optimization algorithm (HPOA) for color image segmentation is proposed. Firstly, the evolutionary state strategy is adopted to evaluate the evolutionary factors in each iteration. With the introduction of the evolutionary state, the proposed algorithm has more balanced exploration-exploitation compared with the original POA. Secondly, in order to prevent premature convergence, a randomly occurring time-delay is introduced into HPOA in a distributed manner. The expression of the time-delay is inspired by particle swarm optimization and reflects the history of previous personal optimum and global optimum. To better verify the effectiveness of the proposed method, eight well-known benchmark functions are employed to evaluate HPOA. In the interim, seven state-of-the-art algorithms are utilized to compare with HPOA in the terms of accuracy, convergence, and statistical analysis. On this basis, an excellent multilevel thresholding image segmentation method is proposed in this paper. Finally, to further illustrate the potential, experiments are respectively conducted on three different groups of Berkeley images. The quality of a segmented image is evaluated by an array of metrics including feature similarity index (FSIM), peak signal-to-noise ratio (PSNR), structural similarity index (SSIM), and Kapur entropy values. The experimental results reveal that the proposed method significantly outperforms other algorithms and has remarkable and promising performance for multilevel thresholding color image segmentation.

A Novel Sigmoid-Function-Based Adaptive Weighted Particle Swarm Optimizer

In this paper, a novel particle swarm optimization (PSO) algorithm is put forward where a sigmoid-function-based weighting strategy is developed to adaptively adjust the acceleration coefficients. The newly proposed adaptive weighting strategy takes into account both the distances from the particle to the global best position and from the particle to its personal best position, thereby having the distinguishing feature of enhancing the convergence rate. Inspired by the activation function of neural networks, the new strategy is employed to update the acceleration coefficients by using the sigmoid function. The search capability of the developed adaptive weighting PSO (AWPSO) algorithm is comprehensively evaluated via eight well-known benchmark functions including both the unimodal and multimodal cases. The experimental results demonstrate that the designed AWPSO algorithm substantially improves the convergence rate of the particle swarm optimizer and also outperforms some currently popular PSO algorithms.

Multiobjective Sizing of an Autonomous Hybrid Microgrid Using a Multimodal Delayed PSO Algorithm: A Case Study of a Fishing Village

Renewable energy (RE) systems play a key role in producing electricity worldwide. The integration of RE systems is carried out in a distributed aspect via an autonomous hybrid microgrid (A-HMG) system. The A-HMG concept provides a series of technological solutions that must be managed optimally. As a solution, this paper focuses on the application of a recent nature-inspired metaheuristic optimization algorithm named a multimodal delayed particle swarm optimization (MDPSO). The proposed algorithm is applied to an A-HMG to find the minimum levelized cost of energy (LCOE), the lowest loss of power supply probability (LPSP), and the maximum renewable factor (REF). Firstly, a smart energy management scheme (SEMS) is proposed to coordinate the power flow among the various system components that formed the A-HMG. Then, the MDPSO is integrated with the SEMS to perform the optimal sizing for the A-HMG of a fishing village that is located in the coastal city of Essaouira, Morocco. The proposed A-HMG comprises photovoltaic panels (PV), wind turbines (WTs), battery storage systems, and diesel generators (DGs). The results of the optimization in this location show that A-HMG system can be applied for this location with a high renewable factor that is equal to 90%. Moreover, the solution is very promising in terms of the LCOE and the LPSP indexes that are equal to 0.17$/kWh and 0.12%, respectively. Therefore, using renewable energy can be considered as a good alternative to enhance energy access in remote areas as the fishing village in the city of Essaouira, Morocco. Furthermore, a sensitivity analysis is applied to highlight the impact of varying each energy source in terms of the LCOE index.

Optimization of a Pre-Trained AlexNet Model for Detecting and Localizing Image Forgeries

With the advance of many image manipulation tools, carrying out image forgery and concealing the forgery is becoming easier. In this paper, the convolution neural network (CNN) innovation for image forgery detection and localization is discussed. A novel image forgery detection model using AlexNet framework is introduced. We proposed a modified model to optimize the AlexNet model by using batch normalization instead of local Response normalization, a maxout activation function instead of a rectified linear unit, and a softmax activation function in the last layer to act as a classifier. As a consequence, the AlexNet proposed model can carry out feature extraction and as well as detection of forgeries without the need for further manipulations. Throughout a number of experiments, we examine and differentiate the impacts of several important AlexNet design choices. The proposed networks model is applied on CASIA v2.0, CASIA v1.0, DVMM, and NIST Nimble Challenge 2017 datasets. We also apply k-fold cross-validation on datasets to divide them into training and test data samples. The experimental results achieved prove that the proposed model can accomplish a great performance for detecting different sorts of forgeries. Quantitative performance analysis of the proposed model can detect image forgeries with 98.176% accuracy.

Algorithms for immunochromatographic assay: review and impact on future application

Lateral flow immunoassay (LFIA) is a critical choice for applications of point-of-care testing (POCT) in clinical and laboratory environments because of its excellent features and versatility. To obtain authentic values of analyte concentrations and reliable detection results, the relevant research has featured the application of a diversity of methods of mathematical analysis to technical analysis to allow for use with a small quantity of data. Accordingly, a number of signal and image processing strategies have also emerged for the application of gold immunochromatographic and fluorescent strips to improve sensitivity and overcome the limitations of correlative hardware systems. Instead of traditional methods to solve the problem, researchers nowadays are interested in machine learning and its more powerful variant, deep learning technology, for LFIA detection. This review emphasizes different models for the POCT of accurate labels as well as signal processing strategies that use artificial intelligence and machine learning. We focus on the analytical mechanism, procedural flow, and the results of the assay, and conclude by summarizing the advantages and limitations of each algorithm. We also discuss the potential for application of and directions of future research on LFIA technology when combined with Artificial Intelligence and deep learning.

Combining a gravitational search algorithm, particle swarm optimization, and fuzzy rules to improve the classification performance of a feed-forward neural network

BACKGROUND AND OBJECTIVE

A feed-forward neural network (FNN) is a type of artificial neural network that has been widely used in medical diagnosis, data mining, stock market analysis, and other fields. Many studies have used FNN to develop medical decision-making systems to assist doctors in clinical diagnosis. The aim of the learning process in FNN is to find the best combination of connection weights and biases to achieve the minimum error. However, in many cases, FNNs converge to the local optimum but not the global optimum. Using open disease datasets, the purpose of this study was to optimize the connection weights and biases of the FNN to minimize the error and improve the accuracy of disease diagnosis.

METHOD

In this study, the chronic kidney disease (CKD) and mesothelioma (MES) disease datasets from the University of California Irvine (UCI) machine learning repository were used as research objects. This study applied the FNN to learn the features of each datum and used particle swarm optimization (PSO) and a gravitational search algorithm (GSA) to optimize the weights and biases of the FNN classifiers based on the algorithms inspired by the observation of natural phenomena. Moreover, fuzzy rules were used to optimize the parameters of the GSA to improve the performance of the algorithm in the classifier.

RESULTS

When applied to the CKD dataset, the accuracies of PSO and GSA were 99%. By using fuzzy rules to optimize the GSA parameter, the accuracy of fuzzy-GSA was 99.25%. The accuracies of the combined algorithms PSO-GSA and fuzzy-PSO-GSA reached 100%. In the MES disease dataset, all methods exhibited good performance with 100% accuracy.

CONCLUSIONS

This study used PSO, GSA, fuzzy-GSA, PSO-GSA, and fuzzy-PSO-GSA on CKD and MES disease datasets to identify the disease, and the performance of different algorithms was explored. Compared with other methods in the literature, our proposed method achieved higher accuracy.

Cerebral Micro-Bleeding Detection Based on Densely Connected Neural Network

Cerebral micro-bleedings (CMBs) are small chronic brain hemorrhages that have many side effects. For example, CMBs can result in long-term disability, neurologic dysfunction, cognitive impairment and side effects from other medications and treatment. Therefore, it is important and essential to detect CMBs timely and in an early stage for prompt treatment. In this research, because of the limited labeled samples, it is hard to train a classifier to achieve high accuracy. Therefore, we proposed employing Densely connected neural network (DenseNet) as the basic algorithm for transfer learning to detect CMBs. To generate the subsamples for training and test, we used a sliding window to cover the whole original images from left to right and from top to bottom. Based on the central pixel of the subsamples, we could decide the target value. Considering the data imbalance, the cost matrix was also employed. Then, based on the new model, we tested the classification accuracy, and it achieved 97.71%, which provided better performance than the state of art methods.

A New Pulse Coupled Neural Network (PCNN) for Brain Medical Image Fusion Empowered by Shuffled Frog Leaping Algorithm

Recent research has reported the application of image fusion technologies in medical images in a wide range of aspects, such as in the diagnosis of brain diseases, the detection of glioma and the diagnosis of Alzheimer's disease. In our study, a new fusion method based on the combination of the shuffled frog leaping algorithm (SFLA) and the pulse coupled neural network (PCNN) is proposed for the fusion of SPECT and CT images to improve the quality of fused brain images. First, the intensity-hue-saturation (IHS) of a SPECT and CT image are decomposed using a non-subsampled contourlet transform (NSCT) independently, where both low-frequency and high-frequency images, using NSCT, are obtained. We then used the combined SFLA and PCNN to fuse the high-frequency sub-band images and low-frequency images. The SFLA is considered to optimize the PCNN network parameters. Finally, the fused image was produced from the reversed NSCT and reversed IHS transforms. We evaluated our algorithms against standard deviation (SD), mean gradient (Ḡ), spatial frequency (SF) and information entropy (E) using three different sets of brain images. The experimental results demonstrated the superior performance of the proposed fusion method to enhance both precision and spatial resolution significantly.

Alcoholism Identification Based on an AlexNet Transfer Learning Model

Aim: This paper proposes a novel alcoholism identification approach that can assist radiologists in patient diagnosis. Method: AlexNet was used as the basic transfer learning model. The global learning rate was small, at 10^-4, and the iteration epoch number was at 10. The learning rate factor of replaced layers was 10 times larger than that of the transferred layers. We tested five different replacement configurations of transfer learning. Results: The experiment shows that the best performance was achieved by replacing the final fully connected layer. Our method yielded a sensitivity of 97.44%± 1.15%, a specificity of 97.41 ± 1.51%, a precision of 97.34 ± 1.49%, an accuracy of 97.42 ± 0.95%, and an F1 score of 97.37 ± 0.97% on the test set. Conclusion: This method can assist radiologists in their routine alcoholism screening of brain magnetic resonance images.

Multiple Sclerosis Identification by 14-Layer Convolutional Neural Network With Batch Normalization, Dropout, and Stochastic Pooling

Aim: Multiple sclerosis is a severe brain and/or spinal cord disease. It may lead to a wide range of symptoms. Hence, the early diagnosis and treatment is quite important.

Method: This study proposed a 14-layer convolutional neural network, combined with three advanced techniques: batch normalization, dropout, and stochastic pooling. The output of the stochastic pooling was obtained via sampling from a multinomial distribution formed from the activations of each pooling region. In addition, we used data augmentation method to enhance the training set. In total 10 runs were implemented with the hold-out randomly set for each run.

Results: The results showed that our 14-layer CNN secured a sensitivity of 98.77 ± 0.35%, a specificity of 98.76 ± 0.58%, and an accuracy of 98.77 ± 0.39%.

Conclusion: Our results were compared with CNN using maximum pooling and average pooling. The comparison shows stochastic pooling gives better performance than other two pooling methods. Furthermore, we compared our proposed method with six state-of-the-art approaches, including five traditional artificial intelligence methods and one deep learning method. The comparison shows our method is superior to all other six state-of-the-art approaches.

Improving Lateral Flow Assay Performance Using Computational Modeling

The performance, field utility, and low cost of lateral flow assays (LFAs) have driven a tremendous shift in global health care practices by enabling diagnostic testing in previously unserved settings. This success has motivated the continued improvement of LFAs through increasingly sophisticated materials and reagents. However, our mechanistic understanding of the underlying processes that drive the informed design of these systems has not received commensurate attention. Here, we review the principles underpinning LFAs and the historical evolution of theory to predict their performance. As this theory is integrated into computational models and becomes testable, the criteria for quantifying performance and validating predictive power are critical. The integration of computational design with LFA development offers a promising and coherent framework to choose from an increasing number of novel materials, techniques, and reagents to deliver the low-cost, high-fidelity assays of the future.

Mathematical Modeling of Bioassays

The high affinity and specificity of biological receptors determine the demand for and the intensive development of analytical systems based on use of these receptors. Therefore, theoretical concepts of the mechanisms of these systems, quantitative parameters of their reactions, and relationships between their characteristics and ligand-receptor interactions have become extremely important. Many mathematical models describing different bioassay formats have been proposed. However, there is almost no information on the comparative characteristics of these models, their assumptions, and predictive insights. In this review we suggested a set of criteria to classify various bioassays and reviewed classical and contemporary publications on these bioassays with special emphasis on immunochemical analysis systems as the most common and in-demand techniques. The possibilities of analytical and numerical modeling are discussed, as well as estimations of the minimum concentrations that may be detected in bioassays and recommendations for the choice of assay conditions.

Nature-Inspired Chemical Reaction Optimisation Algorithms

Nature-inspired meta-heuristic algorithms have dominated the scientific literature in the areas of machine learning and cognitive computing paradigm in the last three decades. Chemical reaction optimisation (CRO) is a population-based meta-heuristic algorithm based on the principles of chemical reaction. A chemical reaction is seen as a process of transforming the reactants (or molecules) through a sequence of reactions into products. This process of transformation is implemented in the CRO algorithm to solve optimisation problems. This article starts with an overview of the chemical reactions and how it is applied to the optimisation problem. A review of CRO and its variants is presented in the paper. Guidelines from the literature on the effective choice of CRO parameters for solution of optimisation problems are summarised.