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Paraquat (PQ) poisoning seriously harms the health of humanity. An effective diagnostic method for paraquat poisoned patients is a crucial concern. Nevertheless, it's difficult to identify the patients with low intake of PQ or delayed treatment. Here, a new efficient diagnostic approach to integrate machine learning and gas chromatography-mass spectrometry (GC-MS), named GEE, is proposed to identify the PQ poisoned patients. First, GC-MS provides the original data that efficiently identified the paraquat-poisoned patients. According to the high dimensionality of the original data, in the second stage, the chaos enhanced grey wolf optimization (EGWO) is adopted to search the optimal feature sets to improve the accuracy of identification. Finally, the extreme learning machine (ELM) is used to identify the PQ poisoned patients. To efficiently evaluate the proposed method, four measures were used in our experiments and comparisons were made with six other methods. The PQ-poisoned patients and robust volunteers can be well identified by GEE and the values of AUC, accuracy, sensitivity and specificity were 95.14%, 93.89%, 94.44% and 95.83%, respectively. Our experimental results demonstrated that GEE had better performance and might serve as a novel candidate diagnosis of PQ-poisoned patients., (Copyright © 2018 Elsevier Ltd. All rights reserved.)

The prognosis of paraquat (PQ) poisoning is highly correlated to plasma PQ concentration, which has been identified as the most important index in PQ poisoning. This study investigated the predictive value of coagulation, liver, and kidney indices in prognosticating PQ-poisoning patients, when aligned with plasma PQ concentrations. Coagulation, liver, and kidney indices were first analyzed by variance analysis, receiver operating characteristic curves, and Fisher discriminant analysis. Then, a new, intelligent, machine learning-based system was established to effectively provide prognostic analysis of PQ-poisoning patients based on a combination of the aforementioned indices. In the proposed system, an enhanced extreme learning machine wrapped with a grey wolf-optimization strategy was developed to predict the risk status from a pool of 103 patients (56 males and 47 females); of these, 52 subjects were deceased and 51 alive. The proposed method was rigorously evaluated against this real-life dataset, in terms of accuracy, Matthews correlation coefficients, sensitivity, and specificity. Additionally, the feature selection was investigated to identify correlating factors for risk status. The results demonstrated that there were significant differences in the coagulation, liver, and kidney indices between deceased and surviving subjects (p<0.05). Aspartate aminotransferase, prothrombin time, prothrombin activity, total bilirubin, direct bilirubin, indirect bilirubin, alanine aminotransferase, urea nitrogen, and creatinine were the most highly correlated indices in PQ poisoning and showed statistical significance (p<0.05) in predicting PQ-poisoning prognoses. According to the feature selection, the most important correlated indices were found to be associated with aspartate aminotransferase, the aspartate aminotransferase to alanine ratio, creatinine, prothrombin time, and prothrombin activity. The method proposed here showed excellent results that were better than that produced based on blood-PQ concentration alone. These promising results indicated that the combination of these indices can provide a new avenue for prognosticating the outcome of PQ poisoning.

Polycyclic aromatic hydrocarbons (PAHs) are organic pollutants and manufactured substances conferring toxicity to human health. The present study investigated whether pyrene, a type of PAH, harms rats. Our research provides an effective feature selection strategy for the animal dataset from Wenzhou Medical University's Experimental Animal Center to thoroughly examine the impacts of PAH toxicity on rat features. Initially, we devised a high‐performance optimization method (SCBA) and added the Sobol sequence, vertical crossover and horizontal crossover mechanisms to the bat algorithm (BA). The SCBA‐KELM model, which combines SCBA with the kernel extreme learning machine model (KELM), has excellent accuracy and high stability for selecting features. Benchmark function tests are then used in this research to verify the overall optimization performance of SCBA. In this paper, the feature selection performance of SCBA‐KELM is verified using various comparative experiments. According to the results, the features of the genes PXR, CAR, CYP2B1/2 and CYP1A1/2 have the most impact on rats. The SCBA‐KELM model's classification performance for the gene dataset was 100%, and the model's precision value for the public dataset was around 96%, as determined by the classification index. In conclusion, the model utilized in this research is anticipated to be a reliable and valuable approach for toxicological classification and assessment. [ABSTRACT FROM AUTHOR]

The colony Predation Algorithm (CPA) has been proven to be one of the heuristic algorithms that can efficiently solve global optimization problems. Balancing the paradox between exploration and exploitation capabilities while mitigating premature convergence are two key subjects that need to be addressed in CPA research. To effectively alleviate these problems, this study proposes a CPA variant named Covariance Gaussian cuckoo Colony Predation Algorithm (CGCPA). Specifically, the designed gaussian cuckoo variable dimensional strategy is used to decentralize the agent population in CPA to enhance the search agents' population diversity and global search ability. The covariance matrix adaptation evolution strategy is used to enhance the convergence speed of the evolutionary agents and the ability to capture the global optimal solution at a later stage. This study subjects CGCPA to competitive experiments with ten basic metaheuristics and ten state−of−the−art algorithms on the IEEE CEC 2017 function test suite. Experimental results confirm that CGCPA outperforms several state−of−the−art DE variants and the latest proposed algorithms in terms of convergence speed and accuracy. In addition, this study proposes a discrete binary feature selection method to better select features in medical data classification named BCGCPA. Its feature selection capability is evaluated in detail on 12 high−dimensional biomedical datasets in the UCI machine learning repository. BCGCPA achieves the lowest classification error rate on all 12 high−dimensional datasets, realizing the best feature selection classification accuracy. BCGCPA can be an efficient pre−processing tool for the dimensionality reduction of high−dimensional biomedical data. It has crucial applications in search space optimization and feature selection of medical datasets. [ABSTRACT FROM AUTHOR]

College students are the group with the most entrepreneurial vitality and potential. How to cultivate their entrepreneurial and innovative ability is one of the important and urgent issues facing this current social development. This paper proposes a reliable, intelligent prediction model of entrepreneurial intentions, providing theoretical support for guiding college students' positive entrepreneurial intentions. The model mainly uses the improved crow search algorithm (CSA) to optimize the kernel extreme learning machine (KELM) model with feature selection (FS), namely CSA-KELM-FS, to study entrepreneurial intention. To obtain the best fitting model and key features, the gradient search rule, local escaping operator, and levy flight mutation (GLL) mechanism are introduced to enhance the CSA (GLLCSA), and FS is used to extract the key features. To verify the performance of the proposed GLLCSA, it is compared with eight other state-of-the-art methods. Further, the GLLCSA-KELM-FS model and five other machine learning methods have been used to predict the entrepreneurial intentions of 842 students from the Wenzhou Vocational College in Zhejiang, China, in the past five years. The results show that the proposed model can correctly predict the students' entrepreneurial intention with an accuracy rate of 93.2% and excellent stability. According to the prediction results of the proposed model, the key factors affecting the student's entrepreneurial intention are mainly the major studied, campus innovation, entrepreneurship practice experience, and positive personality. Therefore, the proposed GLLCSA-KELM-FS is expected to be an effective tool for predicting students' entrepreneurial intentions. [ABSTRACT FROM AUTHOR]

Blockchain presents a chance to address the security and privacy issues of the Internet of Things; however, blockchain itself has certain security issues. How to accurately identify smart contract vulnerabilities is one of the key issues at hand. Most existing methods require large-scale data support to avoid overfitting; machine learning (ML) models trained on small-scale vulnerability data are often difficult to produce satisfactory results in smart contract vulnerability prediction. However, in the real world, collecting contractual vulnerability data requires huge human and time costs. To alleviate these problems, this paper proposed an ensemble learning (EL)-based contract vulnerability prediction method, which is based on seven different neural networks using contract vulnerability data for contract-level vulnerability detection. Seven neural network (NN) models were first pretrained using an information graph (IG) consisting of source datasets, which then were integrated into an ensemble model called Smart Contract Vulnerability Detection method based on Information Graph and Ensemble Learning (SCVDIE). The effectiveness of the SCVDIE model was verified using a target dataset composed of IG, and then its performances were compared with static tools and seven independent data-driven methods. The verification and comparison results show that the proposed SCVDIE method has higher accuracy and robustness than other data-driven methods in the target task of predicting smart contract vulnerabilities. [ABSTRACT FROM AUTHOR]

Continuous ant colony optimization was a population-based heuristic search algorithm inspired by the pathfinding behavior of ant colonies with a simple structure and few control parameters. However, in the case of multimodal and high-dimensional optimization problems, it was often limited to local regions in the feasible domain space, negatively affecting the computational effort required to find the optimal solution point. To alleviate its limitations in this regard, a random following strategy is proposed to enhance communication among the ant colony search agent and other ant colony members within the search dimension. The proposed algorithm that incorporates this strategy is called Random Following Ant Colony Optimization. Then, to evaluate the global optimization performance of the proposed algorithm, the well-known numerical optimization problem, namely the Congress on Evolutionary Computation 2017 test suite, is used. First, the proposed algorithm's parameters are analyzed for sensitivity, scalability experiments, and balanced diversity. Second, it is compared experimentally with 11 state-of-the-art algorithms in dimensions 10, 30, 50, and 100, respectively, and Wilcoxon signed-rank test, Friedman test, and Bonferroni-Dunn post-hoc statistical test are used to synthesize the experimental comparison results. Finally, to evaluate the ability of the proposed algorithm to handle discrete feature selection problems, comparative experiments are conducted on 24 datasets with eight well-known classification methods and five high-performance classification methods. The benchmark test results show that the global optimization performance of the proposed algorithm is comparable to the winners of the test suite in 50 and 100 dimensions. The results of the feature selection experiments show that the proposed algorithm is much stronger than the well-known and high-performance classification methods on high-dimensional datasets. • An ant colony optimization method with random following (RFACO) is proposed. • A new random following strategy is proposed to improve the global search capability. • RFACO obtains higher quality optimal solutions in IEEE CEC2017 functions. • The binary version of RFACO shows excellent classification performance. [ABSTRACT FROM AUTHOR]

Parkinson's disease (PD) is a common neurodegenerative disease, which has attracted more and more attention. Many artificial intelligence methods have been used for the diagnosis of PD. In this study, an enhanced fuzzy k-nearest neighbor (FKNN) method for the early detection of PD based upon vocal measurements was developed. The proposed method, an evolutionary instance-based learning approach termed CBFO-FKNN, was developed by coupling the chaotic bacterial foraging optimization with Gauss mutation (CBFO) approach with FKNN. The integration of the CBFO technique efficiently resolved the parameter tuning issues of the FKNN. The effectiveness of the proposed CBFO-FKNN was rigorously compared to those of the PD datasets in terms of classification accuracy, sensitivity, specificity, and AUC (area under the receiver operating characteristic curve). The simulation results indicated the proposed approach outperformed the other five FKNN models based on BFO, particle swarm optimization, Genetic algorithms, fruit fly optimization, and firefly algorithm, as well as three advanced machine learning methods including support vector machine (SVM), SVM with local learning-based feature selection, and kernel extreme learning machine in a 10-fold cross-validation scheme. The method presented in this paper has a very good prospect, which will bring great convenience to the clinicians to make a better decision in the clinical diagnosis. [ABSTRACT FROM AUTHOR]

• This paper proposes an improved grey wolf optimization (IGWO) for optimizing KELM. • A new hierarchical mechanism was established in the proposed IGWO. • Effectiveness of IGWO strategy is validated on functions and three practical applications. • Experimental results reveal the improved performance of the proposed algorithm. Since its introduction, kernel extreme learning machine (KELM) has been widely used in a number of areas. The parameters in the model have an important influence on the performance of KELM. Therefore, model parameters must be properly adjusted before they can be put into practical use. This study proposes a new parameter learning strategy based on an improved grey wolf optimization (IGWO) strategy, in which a new hierarchical mechanism was established to improve the stochastic behavior, and exploration capability of grey wolves. In the proposed mechanism, random local search around the optimal grey wolf was introduced in Beta grey wolves, and random global search was introduced in Omega grey wolves. The effectiveness of IGWO strategy is first validated on 10 commonly used benchmark functions. Results have shown that the proposed IGWO can find good balance between exploration and exploitation. In addition, when IGWO is applied to solve the parameter adjustment problem of KELM model, it also provides better performance than other seven meta-heuristic algorithms in three practical applications, including students' second major selection, thyroid cancer diagnosis and financial stress prediction. Therefore, the method proposed in this paper can serve as a good candidate tool for tuning the parameters of KELM, thus enabling the KELM model to achieve more promising results in practical applications. [ABSTRACT FROM AUTHOR]

Tuberculous pleural effusion poses a significant threat to human health due to its potential for severe disease and mortality. Without timely treatment, it may lead to fatal consequences. Therefore, early identification and prompt treatment are crucial for preventing problems such as chronic lung disease, respiratory failure, and death. This study proposes an enhanced differential evolution algorithm based on colony predation and dispersed foraging strategies. A series of experiments conducted on the IEEE CEC 2017 competition dataset validated the global optimization capability of the method. Additionally, a binary version of the algorithm is introduced to assess the algorithm's ability to address feature selection problems. Comprehensive comparisons of the effectiveness of the proposed algorithm with 8 similar algorithms were conducted using public datasets with feature sizes ranging from 10 to 10,000. Experimental results demonstrate that the proposed method is an effective feature selection approach. Furthermore, a predictive model for tuberculous pleural effusion is established by integrating the proposed algorithm with support vector machines. The performance of the proposed model is validated using clinical records collected from 140 tuberculous pleural effusion patients, totaling 10,780 instances. Experimental results indicate that the proposed model can identify key correlated indicators such as pleural effusion adenosine deaminase, temperature, white blood cell count, and pleural effusion color, aiding in the clinical feature analysis of tuberculous pleural effusion and providing early warning for its treatment and prediction. • The CFDE is designed and verified on benchmarks. • The binary version of CFDE (bCFDE) is designed for TBPE. • The bCFDE is verified on 24 datasets and compared to other peers. • The bCFDE-SVM is applied to TBPE successfully. [ABSTRACT FROM AUTHOR]

This study proposes a new kernel extreme learning machine (KELM) parameter tuning strategy using a novel swarm intelligence algorithm called grey wolf optimization (GWO). GWO, which simulates the social hierarchy and hunting behavior of grey wolves in nature, is adopted to construct an effective KELM model for bankruptcy prediction. The derived model GWO-KELM is rigorously compared with three competitive KELM methods, which are typical in a comprehensive set of methods including particle swarm optimization-based KELM, genetic algorithm-based KELM, grid-search technique-based KELM, extreme learning machine, improved extreme learning machine, support vector machines and random forest, on two real-life datasets via 10-fold cross validation analysis. Results obtained clearly confirm the superiority of the developed model in terms of classification accuracy (training, validation, test), Type I error, Type II error, area under the receiver operating characteristic curve (AUC) criterion as well as computational time. Therefore, the proposed GWO-KELM prediction model is promising to serve as a powerful early warning tool with excellent performance for bankruptcy prediction. [ABSTRACT FROM AUTHOR]