Analysis of high-dimensional genomic data employing a novel bio-inspired algorithm

A Unified Multi-Class Feature Selection Framework for Microarray Data

In feature selection research, simultaneous multi-class feature selection technologies are popular because they simultaneously select informative features for all classes. Recursive feature elimination (RFE) methods are state-of-the-art binary feature selection algorithms. However, extending existing RFE algorithms to multi-class tasks may increase the computational cost and lead to performance degradation. With this motivation, we introduce a unified multi-class feature selection (UFS) framework for randomization-based neural networks to address these challenges. First, we propose a new multi-class feature ranking criterion using the output weights of neural networks. The heuristic underlying this criterion is that "the importance of a feature should be related to the magnitude of the output weights of a neural network". Subsequently, the UFS framework utilizes the original features to construct a training model based on a randomization-based neural network, ranks these features by the criterion of the norm of the output weights, and recursively removes a feature with the lowest ranking score. Extensive experiments on 15 real-world datasets suggest that our proposed framework outperforms state-of-the-art algorithms. The code of UFS is available at https://github.com/SVMrelated/UFS.git.

Towards an Optimal KELM Using the PSO-BOA Optimization Strategy with Applications in Data Classification

The features of the kernel extreme learning machine-efficient processing, improved performance, and less human parameter setting-have allowed it to be effectively used to batch multi-label classification tasks. These classic classification algorithms must at present contend with accuracy and space-time issues as a result of the vast and quick, multi-label, and concept drift features of the developing data streams in the practical application sector. The KELM training procedure still has a difficulty in that it has to be repeated numerous times independently in order to maximize the model's generalization performance or the number of nodes in the hidden layer. In this paper, a kernel extreme learning machine multi-label data classification method based on the butterfly algorithm optimized by particle swarm optimization is proposed. The proposed algorithm, which fully accounts for the optimization of the model generalization ability and the number of hidden layer nodes, can train multiple KELM hidden layer networks at once while maintaining the algorithm's current time complexity and avoiding a significant number of repeated calculations. The simulation results demonstrate that, in comparison to the PSO-KELM, BBA-KELM, and BOA-KELM algorithms, the PSOBOA-KELM algorithm proposed in this paper can more effectively search the kernel extreme learning machine parameters and more effectively balance the global and local performance, resulting in a KELM prediction model with a higher prediction accuracy.

T-Friedman Test: A New Statistical Test for Multiple Comparison with an Adjustable Conservativeness Measure

To prove that a certain algorithm is superior to the benchmark algorithms, the statistical hypothesis tests are commonly adopted with experimental results on a number of datasets. Some statistical hypothesis tests draw statistical test results more conservative than the others, while it is not yet possible to characterize quantitatively the degree of conservativeness of such a statistical test. On the basis of the existing nonparametric statistical tests, this paper proposes a new statistical test for multiple comparison which is named as t-Friedman test. T-Friedman test combines t test with Friedman test for multiple comparison. The confidence level of the t test is adopted as a measure of conservativeness of the proposed t-Friedman test. A bigger confidence level infers a higher degree of conservativeness, and vice versa. Based on the synthetic results generated by Monte Carlo simulations with predefined distributions, the performance of several state-of-the-art multiple comparison tests and post hoc procedures are first qualitatively analyzed. The influences of the type of predefined distribution, the number of benchmark algorithms and the number of datasets are explored in the experiments. The conservativeness measure of the proposed method is also validated and verified in the experiments. Finally, some suggestions for the application of these nonparametric statistical tests are provided.

Machine learning based accident prediction in secure IoT enable transportation system

Smart city has come a long way since the development of emerging technology like Information and communications technology (ICT), Internet of Things (IoT), Machine Learning (ML), Block chain and Artificial Intelligence. The Intelligent Transportation System (ITS) is an important application in a rapidly growing smart city. Prediction of the automotive accident severity plays a very crucial role in the smart transportation system. The main motive behind this research is to determine the specific features which could affect vehicle accident severity. In this paper, some of the classification models, specifically Logistic Regression, Artificial Neural network, Decision Tree, K-Nearest Neighbors, and Random Forest have been implemented for predicting the accident severity. All the models have been verified, and the experimental results prove that these classification models have attained considerable accuracy. The paper also explained a secure communication architecture model for secure information exchange among all the components associated with the ITS. Finally paper implemented web base Message alert system which will be used for alert the users through smart IoT devices.

Evolving kernel extreme learning machine for medical diagnosis via a disperse foraging sine cosine algorithm

Kernel extreme learning machine (KELM) has been widely used in the fields of classification and identification since it was proposed. As the parameters in the KELM model have a crucial impact on performance, they must be optimized before the model can be applied in practical areas. In this study, to improve optimization performance, a new parameter optimization strategy is proposed, based on a disperse foraging sine cosine algorithm (DFSCA), which is utilized to force some portions of search agents to explore other potential regions. Meanwhile, DFSCA is integrated into KELM to establish a new machine learning model named DFSCA-KELM. Firstly, using the CEC2017 benchmark suite, the exploration and exploitation capabilities of DFSCA were demonstrated. Secondly, evaluation of the model DFSCA-KELM on six medical datasets extracted from the UCI machine learning repository for medical diagnosis proved the effectiveness of the proposed model. At last, the model DFSCA-KELM was applied to solve two real medical cases, and the results indicate that DFSCA-KELM can also deal with practical medical problems effectively. Taken together, these results show that the proposed technique can be regarded as a promising tool for medical diagnosis.

Performance optimization of salp swarm algorithm for multi-threshold image segmentation: Comprehensive study of breast cancer microscopy

Multi-threshold image segmentation (MIS) is now a well known image segmentation technique, and many researchers have applied intelligent algorithms to it, but these methods suffer from local optimal drawbacks. This paper presented a novel approach to improve the Salp Swarm Algorithm (SSA), namely EHSSA, and applied it to MIS. Knowing the inaccuracies and discussions on implementation of this method, a new efficient mechanism is proposed to improve global search capability of the algorithm and avoid falling into a local optimum. Moreover, the excellence of the proposed algorithm was proved by comparative experiments at IEEE CEC2014. Afterward, the performance of EHSSA was demonstrated by testing a set of images selected from the Berkeley segmentation data set 500 (BSDS500), and the experimental results were analyzed by evaluating the parameters, which proved the efficiency of the proposed algorithm in MIS. Furthermore, EHSSA was applied to the microscopic image segmentation of breast cancer. Medical image segmentation is the study of how to quickly extract objects of interest (human organs) from various images to perform qualitative and quantitative analysis of diseased tissues and improve the accuracy of their diagnosis, which assists the physician in making more informed decisions and patient rehabilitation. The results of this set of experiments also proved its superior performance. For any info about this paper, readers can refer to https://aliasgharheidari.com.

A novel bio-inspired hybrid multi-filter wrapper gene selection method with ensemble classifier for microarray data

Microarray technology is known as one of the most important tools for collecting DNA expression data. This technology allows researchers to investigate and examine types of diseases and their origins. However, microarray data are often associated with a small sample size, a significant number of genes, imbalanced data, etc., making classification models inefficient. Thus, a new hybrid solution based on a multi-filter and adaptive chaotic multi-objective forest optimization algorithm (AC-MOFOA) is presented to solve the gene selection problem and construct the Ensemble Classifier. In the proposed solution, a multi-filter model (i.e., ensemble filter) is proposed as preprocessing step to reduce the dataset's dimensions, using a combination of five filter methods to remove redundant and irrelevant genes. Accordingly, the results of the five filter methods are combined using a voting-based function. Additionally, the results of the proposed multi-filter indicate that it has good capability in reducing the gene subset size and selecting relevant genes. Then, an AC-MOFOA based on the concepts of non-dominated sorting, crowding distance, chaos theory, and adaptive operators is presented. AC-MOFOA as a wrapper method aimed at reducing dataset dimensions, optimizing KELM, and increasing the accuracy of the classification, simultaneously. Next, in this method, an ensemble classifier model is presented using AC-MOFOA results to classify microarray data. The performance of the proposed algorithm was evaluated on nine public microarray datasets, and its results were compared in terms of the number of selected genes, classification efficiency, execution time, time complexity, hypervolume indicator, and spacing metric with five hybrid multi-objective methods, and three hybrid single-objective methods. According to the results, the proposed hybrid method could increase the accuracy of the KELM in most datasets by reducing the dataset's dimensions and achieve similar or superior performance compared to other multi-objective methods. Furthermore, the proposed Ensemble Classifier model could provide better classification accuracy and generalizability in the seven of nine microarray datasets compared to conventional ensemble methods. Moreover, the comparison results of the Ensemble Classifier model with three state-of-the-art ensemble generation methods indicate its competitive performance in which the proposed ensemble model achieved better results in the five of nine datasets.

A fusion of data science and feed-forward neural network-based modelling of COVID-19 outbreak forecasting in IRAQ

Background

Iraq is among the countries affected by the COVID-19 pandemic. As of 2 August 2020, 129,151 COVID-19 cases were confirmed, including 91,949 recovered cases and 4,867 deaths. After the announcement of lockdown in early April 2020, situation in Iraq was getting steady until late May 2020, when daily COVID-19 infections have raised suddenly due to gradual easing of lockdown restrictions. In this context, it is important to develop a forecasting model to evaluate the COVID-19 outbreak in Iraq and so to guide future health policy.

Methods

COVID-19 lag data were made available by the University of Anbar through their online analytical platform (https://www.uoanbar.edu.iq/covid/), engaged with the day-to-day figures form the Iraqi health authorities. 154 days of patient data were provided covering the period from 2 March 2020 to 2 August 2020. An ensemble of feed-forward neural networks has been adopted to forecast COVID-19 outbreak in Iraq. Also, this study highlights some key questions about this pandemic using data analytics.

Results

Forecasting were achieved with accuracy of 87.6% for daily infections, 82.4% for daily recovered cases, and 84.3% for daily deaths. It is anticipated that COVID-19 infections in Iraq will reach about 308,996 cases by the end of September 2020, including 228,551 to recover and 9,477 deaths.

Conclusion

The applications of artificial neural networks supported by advanced data analytics represent a promising solution through which to realise intelligent solutions, enabling the space of analytical operations to drive a national health policy to contain COVID-19 pandemic.

Diagnosis of diabetes in pregnant woman using a Chaotic-Jaya hybridized extreme learning machine model

As stated by World Health Organization (WHO) report, 246 million individuals have suffered with diabetes disease over worldwide and it is anticipated that by 2025 this estimation can cross 380 million. So, the proper and quick diagnosis of this disease is turned into a significant challenge for the machine learning researchers. This paper aims to design a robust model for diagnosis of diabetes using a hybrid approach of Chaotic-Jaya (CJaya) algorithm with Extreme Learning Machine (ELM), which is named as CJaya-ELM. In this paper, Jaya algorithm with Chaotic learning approach is used to optimize the random parameters of ELM classifier. Here, to assess the efficacy of the designed model, Pima Indian diabetes dataset is considered. Here, the designed model CJaya-ELM, has been compared with basic ELM, Teaching Learning Based Optimization algorithm (TLBO) optimized ELM (TLBO-ELM), Multi-Layer Perceptron (MLP), Jaya algorithm optimized MLP (Jaya-MLP), TLBO algorithm optimized MLP (TLBO-MLP) and CJaya algorithm optimized MLP models. CJaya-ELM model resulted in the highest testing accuracy of 0.9687, sensitivity of 1, specificity of 0.9688 with 0.9782 area under curve (AUC) value. Results reveal that CJaya-ELM model effectively classifies both the positive and negative samples of Pima and outperforms the competitors.