Chaotic multi-swarm whale optimizer boosted support vector machine for medical diagnosis

Polymers as Efficient Non-Viral Gene Delivery Vectors: The Role of the Chemical and Physical Architecture of Macromolecules

Gene therapy is the technique of inserting foreign genetic elements into host cells to achieve a therapeutic effect. Although gene therapy was initially formulated as a potential remedy for specific genetic problems, it currently offers solutions for many diseases with varying inheritance patterns and acquired diseases. There are two major groups of vectors for gene therapy: viral vector gene therapy and non-viral vector gene therapy. This review examines the role of a macromolecule's chemical and physical architecture in non-viral gene delivery, including their design and synthesis. Polymers can boost circulation, improve delivery, and control cargo release through various methods. The prominent examples discussed include poly-L-lysine, polyethyleneimine, comb polymers, brush polymers, and star polymers, as well as hydrogels and natural polymers and their modifications. While significant progress has been made, challenges still exist in gene stabilization, targeting specificity, and cellular uptake. Overcoming cytotoxicity, improving delivery efficiency, and utilizing natural polymers and hybrid systems are vital factors for prospects. This comprehensive review provides an illuminating overview of the field, guiding the way toward innovative non-viral-based gene delivery solutions.

A deep neural network prediction method for diabetes based on Kendall's correlation coefficient and attention mechanism

Diabetes is a chronic disease, which is characterized by abnormally high blood sugar levels. It may affect various organs and tissues, and even lead to life-threatening complications. Accurate prediction of diabetes can significantly reduce its incidence. However, the current prediction methods struggle to accurately capture the essential characteristics of nonlinear data, and the black-box nature of these methods hampers its clinical application. To address these challenges, we propose KCCAM_DNN, a diabetes prediction method that integrates Kendall's correlation coefficient and an attention mechanism within a deep neural network. In the KCCAM_DNN, Kendall's correlation coefficient is initially employed for feature selection, which effectively filters out key features influencing diabetes prediction. For missing values in the data, polynomial regression is utilized for imputation, ensuring data completeness. Subsequently, we construct a deep neural network (KCCAM_DNN) based on the self-attention mechanism, which assigns greater weight to crucial features affecting diabetes and enhances the model's predictive performance. Finally, we employ the SHAP model to analyze the impact of each feature on diabetes prediction, augmenting the model's interpretability. Experimental results show that KCCAM_DNN exhibits superior performance on both PIMA Indian and LMCH diabetes datasets, achieving test accuracies of 99.090% and 99.333%, respectively, approximately 2% higher than the best existing method. These results suggest that KCCAM_DNN is proficient in diabetes prediction, providing a foundation for informed decision-making in the diagnosis and prevention of diabetes.

The path from task-specific to general purpose artificial intelligence for medical diagnostics: A bibliometric analysis

Artificial intelligence (AI) has revolutionized many fields, and its potential in healthcare has been increasingly recognized. Based on diverse data sources such as imaging, laboratory tests, medical records, and electrophysiological data, diagnostic AI has witnessed rapid development in recent years. A comprehensive understanding of the development status, contributing factors, and their relationships in the application of AI to medical diagnostics is essential to further promote its use in clinical practice. In this study, we conducted a bibliometric analysis to explore the evolution of task-specific to general-purpose AI for medical diagnostics. We used the Web of Science database to search for relevant articles published between 2010 and 2023, and applied VOSviewer, the R package Bibliometrix, and CiteSpace to analyze collaborative networks and keywords. Our analysis revealed that the field of AI in medical diagnostics has experienced rapid growth in recent years, with a focus on tasks such as image analysis, disease prediction, and decision support. Collaborative networks were observed among researchers and institutions, indicating a trend of global cooperation in this field. Additionally, we identified several key factors contributing to the development of AI in medical diagnostics, including data quality, algorithm design, and computational power. Challenges to progress in the field include model explainability, robustness, and equality, which will require multi-stakeholder, interdisciplinary collaboration to tackle. Our study provides a holistic understanding of the path from task-specific, mono-modal AI toward general-purpose, multimodal AI for medical diagnostics. With the continuous improvement of AI technology and the accumulation of medical data, we believe that AI will play a greater role in medical diagnostics in the future.

GOA-optimized deep learning for soybean yield estimation using multi-source remote sensing data

Accurately estimating large-area crop yields, especially for soybeans, is essential for addressing global food security challenges. This study introduces a deep learning framework that focuses on precise county-level soybean yield estimation in the United States. It utilizes a wide range of multi-variable remote sensing data. The model used in this study is a state-of-the-art CNN-BiGRU model, which is enhanced by the GOA and a novel attention mechanism (GCBA). This model excels in handling intricate time series and diverse remote sensing datasets. Compared to five leading machine learning and deep learning models, our GCBA model demonstrates superior performance, particularly in the 2019 and 2020 evaluations, achieving remarkable R2, RMSE, MAE and MAPE values. This sets a new benchmark in yield estimation accuracy. Importantly, the study highlights the significance of integrating multi-source remote sensing data. It reveals that synthesizing information from various sensors and incorporating photosynthesis-related parameters significantly enhances yield estimation precision. These advancements not only provide transformative insights for precision agricultural management but also establish a solid scientific foundation for informed decision-making in global agricultural production and food security.

A hybrid feature selection algorithm combining information gain and grouping particle swarm optimization for cancer diagnosis

BACKGROUND

Cancer diagnosis based on machine learning has become a popular application direction. Support vector machine (SVM), as a classical machine learning algorithm, has been widely used in cancer diagnosis because of its advantages in high-dimensional and small sample data. However, due to the high-dimensional feature space and high feature redundancy of gene expression data, SVM faces the problem of poor classification effect when dealing with such data.

METHODS

Based on this, this paper proposes a hybrid feature selection algorithm combining information gain and grouping particle swarm optimization (IG-GPSO). The algorithm firstly calculates the information gain values of the features and ranks them in descending order according to the value. Then, ranked features are grouped according to the information index, so that the features in the group are close, and the features outside the group are sparse. Finally, grouped features are searched using grouping PSO and evaluated according to in-group and out-group.

RESULTS

Experimental results show that the average accuracy (ACC) of the SVM on the feature subset selected by the IG-GPSO is 98.50%, which is significantly better than the traditional feature selection algorithm. Compared with KNN, the classification effect of the feature subset selected by the IG-GPSO is still optimal. In addition, the results of multiple comparison tests show that the feature selection effect of the IG-GPSO is significantly better than that of traditional feature selection algorithms.

CONCLUSION

The feature subset selected by IG-GPSO not only has the best classification effect, but also has the least feature scale (FS). More importantly, the IG-GPSO significantly improves the ACC of SVM in cancer diagnostic.

Highly accurate brain tumor detection with high sensitivity using transform-based functions and machine learning algorithms

BACKGROUND

Brain tumor is an extremely dangerous disease with a very high mortality rate worldwide. Detecting brain tumors accurately is crucial due to the varying appearance of tumor cells and the dimensional irregularities in their growth. This poses a significant challenge for detection algorithms. Currently, there are numerous algorithms utilized for this purpose, ranging from transform-based methods to those rooted in machine learning techniques. These algorithms aim to enhance the accuracy of detection despite the complexities involved in identifying brain tumor cells. The major limitation of these algorithms is the mapping of extracted features of a brain tumor in the classification algorithms.

OBJECTIVE

To employ a combination of transform methods to extract texture feature from brain tumor images.

METHODS

This paper employs a combination of transform methods based on sub band decomposition for texture feature extraction from MRI scans, hybrid feature optimization methods using firefly and glow-worm algorithms for selection of feature, employment of MKSVM algorithm and stacking ensemble classifier for classification and application of the feature of fusion of different feature extraction methods.

RESULTS

The algorithm under consideration has been put into practice using MATLAB, utilizing datasets from BRATS (Brain Tumor Segmentation) for the years 2013, 2015, and 2018. These datasets serve as the foundation for testing and validating the algorithm's performance across different time periods, providing a comprehensive assessment of its effectiveness in detecting brain tumors. The proposed algorithm achieves maximum detection accuracy, detection sensitivity and specificity up to 98%, 99% and 99.5% respectively. The experimental outcomes showcase the efficiency of the algorithm in detection of brain tumor.

CONCLUSION

The proposed work mainly contributes in brain tumor detection in the following aspects: a) use of combination of transform methods for texture feature extraction from MRI scans b) hybrid feature selection methods using firefly and glow-worm optimization algorithms for selection of feature c) employment of MKSVM algorithm and stacking ensemble classifier for classification and application of the feature of fusion of different feature extraction methods.

Performance evaluation of metaheuristics-tuned recurrent neural networks for electroencephalography anomaly detection

Electroencephalography (EEG) serves as a diagnostic technique for measuring brain waves and brain activity. Despite its precision in capturing brain electrical activity, certain factors like environmental influences during the test can affect the objectivity and accuracy of EEG interpretations. Challenges associated with interpretation, even with advanced techniques to minimize artifact influences, can significantly impact the accurate interpretation of EEG findings. To address this issue, artificial intelligence (AI) has been utilized in this study to analyze anomalies in EEG signals for epilepsy detection. Recurrent neural networks (RNNs) are AI techniques specifically designed to handle sequential data, making them well-suited for precise time-series tasks. While AI methods, including RNNs and artificial neural networks (ANNs), hold great promise, their effectiveness heavily relies on the initial values assigned to hyperparameters, which are crucial for their performance for concrete assignment. To tune RNN performance, the selection of hyperparameters is approached as a typical optimization problem, and metaheuristic algorithms are employed to further enhance the process. The modified hybrid sine cosine algorithm has been developed and used to further improve hyperparameter optimization. To facilitate testing, publicly available real-world EEG data is utilized. A dataset is constructed using captured data from healthy and archived data from patients confirmed to be affected by epilepsy, as well as data captured during an active seizure. Two experiments have been conducted using generated dataset. In the first experiment, models were tasked with the detection of anomalous EEG activity. The second experiment required models to segment normal, anomalous activity as well as detect occurrences of seizures from EEG data. Considering the modest sample size (one second of data, 158 data points) used for classification models demonstrated decent outcomes. Obtained outcomes are compared with those generated by other cutting-edge metaheuristics and rigid statistical validation, as well as results' interpretation is performed.

Ensemble model for predicting chronic non-communicable diseases using Latin square extraction and fuzzy-artificial neural networks from 2013 to 2019

Background

The presented study tracks the increase or decrease in the prevalence of seventeen different chronic non-communicable diseases in Serbia. This analysis considers factors such as region, age, and gender and is based on data from two national cross-sectional studies conducted in 2013 and 2019. The research aims to accurately identify the regions with the highest percentage of affected individuals, as well as their respective age and gender groups. The ultimate goal is to facilitate organized, free preventive screenings for these population categories within a very short time-frame in the future.

Materials and methods

The study analyzed two cross-sectional studies conducted between 2013 and 2019, using data obtained from the Institute of Public Health of Serbia. Both studies involved a total of 27801 participants. The study compared the performance of Decision Tree and Support Vector Regressor models with artificial neural network (ANN) models that employed two encoding functions. The new methodology for the ANN-L36 model was based on artificial neural networks constructed using a Latin square (L36) design, incorporating Taguchi's robust design optimization.

Results

The results of the analysis from three different models have shown that cardiovascular diseases are the most prevalent illnesses among the population in Serbia, with hypertension as the leading condition in all regions, particularly among individuals aged 64 to 75 years, and more prevalent among females. In 2019, there was a decrease in the percentage of the leading disease, hypertension, compared to 2013, with a decrease from 34.0% to 32.2%. The ANN-L36 model with Fuzzy encoding function demonstrated the highest precision, achieving the smallest relative error of 0.1%.

Conclusion

To date, no studies have been conducted at the national level in Serbia to comprehensively track and identify chronic diseases in the manner proposed by this study. The model presented in this research will be implemented in practice and is set to significantly contribute to the future healthcare framework in Serbia, shaping and advancing the approach towards addressing these conditions. Furthermore, experimental evidence has shown that Taguchi's optimization approach yields the best results for identifying various chronic non-communicable diseases.

Personalized Risk Assessment of Hepatic Fibrosis after Cholecystectomy in Metabolic-Associated Steatotic Liver Disease: A Machine Learning Approach

Metabolic Associated Fatty Liver Disease (MASLD) is a condition that is often present in patients with a history of cholecystectomy. This is because both situations share interconnected metabolic pathways. This study aimed to establish a predictive model that allows for the identification of patients at risk of developing hepatic fibrosis following this surgery, with potential implications for surgical decision-making. A retrospective cross-sectional analysis was conducted in four hospitals using a database of 211 patients with MASLD who underwent cholecystectomy. MASLD diagnosis was established through liver biopsy or FibroScan, and non-invasive test scores were included for analysis. Various Machine Learning (ML) methods were employed, with the Adaptive Boosting (Adaboost) system selected to build the predictive model. Platelet level emerged as the most crucial variable in the predictive model, followed by dyslipidemia and type-2 diabetes mellitus. FIB-4 score proved to be the most reliable non-invasive test. The Adaboost algorithm improved the results compared to the other methods, excelling in both accuracy and area under the curve (AUC). Moreover, this system holds promise for implementation in hospitals as a valuable diagnostic support tool. In conclusion, platelet level (<150,000/dL), dyslipidemia, and type-2 diabetes mellitus were identified as primary risk factors for liver fibrosis in MASLD patients following cholecystectomy. FIB-4 score is recommended for decision-making, particularly when the indication for surgery is uncertain. This predictive model offers valuable insights into risk stratification and personalized patient management in post-cholecystectomy MASLD cases.

An optimized machine learning method for predicting wogonin therapy for the treatment of pulmonary hypertension

Human health is at risk from pulmonary hypertension (PH), characterized by decreased pulmonary vascular resistance and constriction of the pulmonary vessels, resulting in right heart failure and dysfunction. Thus, preventing PH and monitoring its progression before treating it is vital. Wogonin, derived from the leaves of Scutellaria baicalensis Georgi, exhibits remarkable pharmacological activity. In this study, we examined the effectiveness of wogonin in mitigating the progression of PH in mice using right heart catheterization and hematoxylin-eosin (HE) staining. As an alternative to minimize the possibility of harming small animals, we present a scientifically effective feature selection method (BSCDWOA-KELM) that will allow us to develop a novel simpler noninvasive prediction method for wogonin in treating PH. In this method, we use the proposed enhanced whale optimizer (SCDWOA) in conjunction with the kernel extreme learning machine (KELM). Initially, we let SCDWOA perform global optimization experiments on the IEEE CEC2014 benchmark function set to verify its core advantages. Lastly, 12 public and PH datasets are examined for feature selection experiments using BSCDWOA-KELM. As shown in the experimental results for global optimization, the proposed SCDWOA has better convergence performance. Meanwhile, the proposed binary SCDWOA (BSCDWOA) significantly improves the ability of KELM to classify data. By utilizing the BSCDWOA-KELM, key indicators such as the Red blood cell (RBC), the Haemoglobin (HGB), the Lymphocyte percentage (LYM%), the Hematocrit (HCT), and the Red blood cell distribution width-size distribution (RDW-SD) can be efficiently screened in the Pulmonary hypertension dataset, and one of its most essential points is its accuracy of greater than 0.98. Consequently, the BSCDWOA-KELM introduced in this study can be used to predict wogonin therapy for treating pulmonary hypertension in a simple and noninvasive manner.

IBGJO: Improved Binary Golden Jackal Optimization with Chaotic Tent Map and Cosine Similarity for Feature Selection

Feature selection is a crucial process in machine learning and data mining that identifies the most pertinent and valuable features in a dataset. It enhances the efficacy and precision of predictive models by efficiently reducing the number of features. This reduction improves classification accuracy, lessens the computational burden, and enhances overall performance. This study proposes the improved binary golden jackal optimization (IBGJO) algorithm, an extension of the conventional golden jackal optimization (GJO) algorithm. IBGJO serves as a search strategy for wrapper-based feature selection. It comprises three key factors: a population initialization process with a chaotic tent map (CTM) mechanism that enhances exploitation abilities and guarantees population diversity, an adaptive position update mechanism using cosine similarity to prevent premature convergence, and a binary mechanism well-suited for binary feature selection problems. We evaluated IBGJO on 28 classical datasets from the UC Irvine Machine Learning Repository. The results show that the CTM mechanism and the position update strategy based on cosine similarity proposed in IBGJO can significantly improve the Rate of convergence of the conventional GJO algorithm, and the accuracy is also significantly better than other algorithms. Additionally, we evaluate the effectiveness and performance of the enhanced factors. Our empirical results show that the proposed CTM mechanism and the position update strategy based on cosine similarity can help the conventional GJO algorithm converge faster.

Addressing Internet of Things security by enhanced sine cosine metaheuristics tuned hybrid machine learning model and results interpretation based on SHAP approach

An ever increasing number of electronic devices integrated into the Internet of Things (IoT) generates vast amounts of data, which gets transported via network and stored for further analysis. However, besides the undisputed advantages of this technology, it also brings risks of unauthorized access and data compromise, situations where machine learning (ML) and artificial intelligence (AI) can help with detection of potential threats, intrusions and automation of the diagnostic process. The effectiveness of the applied algorithms largely depends on the previously performed optimization, i.e., predetermined values of hyperparameters and training conducted to achieve the desired result. Therefore, to address very important issue of IoT security, this article proposes an AI framework based on the simple convolutional neural network (CNN) and extreme machine learning machine (ELM) tuned by modified sine cosine algorithm (SCA). Not withstanding that many methods for addressing security issues have been developed, there is always a possibility for further improvements and proposed research tried to fill in this gap. The introduced framework was evaluated on two ToN IoT intrusion detection datasets, that consist of the network traffic data generated in Windows 7 and Windows 10 environments. The analysis of the results suggests that the proposed model achieved superior level of classification performance for the observed datasets. Additionally, besides conducting rigid statistical tests, best derived model is interpreted by SHapley Additive exPlanations (SHAP) analysis and results findings can be used by security experts to further enhance security of IoT systems.

Clinical Decision Support System for Diabetic Patients by Predicting Type 2 Diabetes Using Machine Learning Algorithms

Diabetes is one of the most serious chronic diseases that result in high blood sugar levels. Early prediction can significantly diminish the potential jeopardy and severity of diabetes. In this study, different machine learning (ML) algorithms were applied to predict whether an unknown sample had diabetes or not. However, the main significance of this research was to provide a clinical decision support system (CDSS) by predicting type 2 diabetes using different ML algorithms. For the research purpose, the publicly available Pima Indian Diabetes (PID) dataset was used. Data preprocessing, K-fold cross-validation, hyperparameter tuning, and various ML classifiers such as K-nearest neighbor (KNN), decision tree (DT), random forest (RF), Naïve Bayes (NB), support vector machine (SVM), and histogram-based gradient boosting (HBGB) were used. Several scaling methods were also used to improve the accuracy of the result. For further research, a rule-based approach was used to escalate the effectiveness of the system. After that, the accuracy of DT and HBGB was above 90%. Based on this result, the CDSS was implemented where users can give the required input parameters through a web-based user interface to get decision support with some analytical results for the individual patient. The CDSS, which was implemented, will be beneficial for physicians and patients to make decisions about diabetes diagnosis and offer real-time analysis-based suggestions to improve medical quality. For future work, if daily data of a diabetic patient can be put together, then a better clinical support system can be implemented for daily decision support for patients worldwide.

Whale optimization with random contraction and Rosenbrock method for COVID-19 disease prediction

Coronavirus Disease 2019 (COVID-19), instigated by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), has hugely impacted global public health. To identify and intervene in critically ill patients early, this paper proposes an efficient, intelligent prediction model based on the machine learning approach, which combines the improved whale optimization algorithm (RRWOA) with the k-nearest neighbor (KNN) classifier. In order to improve the problem that WOA is prone to fall into local optimum, an improved version named RRWOA is proposed based on the random contraction strategy (RCS) and the Rosenbrock method. To verify the capability of the proposed algorithm, RRWOA is tested against nine classical metaheuristics, nine advanced metaheuristics, and seven well-known WOA variants based on 30 IEEE CEC2014 competition functions, respectively. The experimental results in mean, standard deviation, the Friedman test, and the Wilcoxon signed-rank test are considered, proving that RRWOA won first place on 18, 24, and 25 test functions, respectively. In addition, a binary version of the algorithm, called BRRWOA, is developed for feature selection problems. An efficient prediction model based on BRRWOA and KNN classifier is proposed and compared with seven existing binary metaheuristics based on 15 datasets of UCI repositories. The experimental results show that the proposed algorithm obtains the smallest fitness value in eleven datasets and can solve combinatorial optimization problems, indicating that it still performs well in discrete cases. More importantly, the model was compared with five other algorithms on the COVID-19 dataset. The experiment outcomes demonstrate that the model offers a scientific framework to support clinical diagnostic decision-making. Therefore, RRWOA is an effectively improved optimizer with efficient value.

Triple-DRNet: A triple-cascade convolution neural network for diabetic retinopathy grading using fundus images

Diabetic Retinopathy (DR) is a universal ocular complication of diabetes patients and also the main disease that causes blindness in the world wide. Automatic and efficient DR grading acts a vital role in timely treatment. However, it is difficult to effectively distinguish different types of distinct lesions (such as neovascularization in proliferative DR, microaneurysms in mild NPDR, etc.) using traditional convolutional neural networks (CNN), which greatly affects the ultimate classification results. In this article, we propose a triple-cascade network model (Triple-DRNet) to solve the aforementioned issue. The Triple-DRNet effectively subdivides the classification of five types of DR as well as improves the grading performance which mainly includes the following aspects: (1) In the first stage, the network carries out two types of classification, namely DR and No DR. (2) In the second stage, the cascade network is intended to distinguish the two categories between PDR and NPDR. (3) The final cascade network will be designed to differentiate the mild, moderate and severe types in NPDR. Experimental results show that the ACC of the Triple-DRNet on the APTOS 2019 Blindness Detection dataset achieves 92.08%, and the QWK metric reaches 93.62%, which proves the effectiveness of the devised Triple-DRNet compared with other mainstream models.

Ensemble filters with harmonize PSO-SVM algorithm for optimal hearing disorder prediction

Discovering a hearing disorder at an earlier intervention is critical for reducing the effects of hearing loss and the approaches to increase the remaining hearing ability can be implemented to achieve the successful development of human communication. Recently, the explosive dataset features have increased the complexity for audiologists to decide the proper treatment for the patient. In most cases, data with irrelevant features and improper classifier parameters causes a crucial influence on the audiometry system in terms of accuracy. This is due to the dependent processes of these two, where the classification accuracy performance could be worsened if both processes are conducted independently. Although the filter algorithm is capable of eliminating irrelevant features, it still lacks the ability to consider feature reliance and results in a poor selection of significant features. Improper kernel parameter settings may also contribute to poor accuracy performance. In this paper, an ensemble filters feature selection based on Information Gain (IG), Gain Ratio (GR), Chi-squared (CS), and Relief-F (RF) with harmonize optimization of Particle Swarm Optimization (PSO) and Support Vector Machine (SVM) is presented to mitigate these problems. Ensemble filters are utilized so that the initial top dominant features relevant for classification can be considered. Then, PSO and SVM are optimized simultaneously to achieve the optimal solution. The results on a standard Audiology dataset show that the proposed method produces 96.50% accuracy with optimal solution compared to classical SVM, which signifies the proposed method is effective in handling high dimensional data for hearing disorder prediction.

Near infrared spectroscopic variable selection by a novel swarm intelligence algorithm for rapid quantification of high order edible blend oil

The quantification of single oil in high order edible blend oil is a challenging task. In this research, a novel swarm intelligence algorithm, discretized whale optimization algorithm (WOA), was first developed for reducing irrelevant variables and improving prediction accuracy of hexanary edible blend oil samples. The WOA is inspired by hunting strategy of humpback whales, which mainly includes three behaviors, i.e., encircling prey, bubble-net attacking and searching for prey. In discretized WOA, positions of whales were updated and then discretized by arctangent function. The whale population performance, iteration number and whale number of WOA were investigated. To validate the performance of selected variables, partial least squares (PLS) was used to build model and predict single oil contents in hexanary blend oil. Results show that WOA-PLS can provide the best prediction accuracy compared with full-spectrum PLS, continuous wavelet transform-PLS (CWT-PLS), uninformative variable elimination-PLS (UVE-PLS), Monte Carlo uninformative variable elimination-PLS (MCUVE-PLS) and randomization test-PLS (RT-PLS). Furthermore, CWT-WOA-PLS can further produce better results with fewer variables compared with WOA-PLS.

An efficient multi-threshold image segmentation for skin cancer using boosting whale optimizer

Due to the terrible manifestations of skin cancer, it seriously disturbs the quality of life status and health of patients, so we needs treatment plans to detect it early and avoid it causing more harm to patients. Medical disease image threshold segmentation technique can well extract the region of interest and effectively assist in disease recognition. Moreover, in multi-threshold image segmentation, the selection of the threshold set determines the image segmentation quality. Among the common threshold selection methods, the selection based on metaheuristic algorithm has the advantages of simplicity, easy implementation and avoidable local optimization. However, different algorithms have different performances for different medical disease images. For example, the Whale Optimization Algorithm (WOA) does not give a satisfactory performance for thresholding skin cancer images. We propose an improved WOA (LCWOA) in which the Levy operator and chaotic random mutation strategy are introduced to enhance the ability of the algorithm to jump out of the local optimum and to explore the search space. Comparing with different existing WOA variants on the CEC2014 function set, our proposed and improved algorithm improves the efficiency of the search. Experimental results show that our method outperforms the extant WOA variants in terms of optimization performances, improving the convergence accuracy and velocity. The method is also applied to solve the threshold selection in the skin cancer image segmentation problem, and LCWOA also gives excellent performance in obtaining optimal segmentation results.

Boosting Whale Optimizer with Quasi-Oppositional Learning and Gaussian Barebone for Feature Selection and COVID-19 Image Segmentation

Whale optimization algorithm (WOA) tends to fall into the local optimum and fails to converge quickly in solving complex problems. To address the shortcomings, an improved WOA (QGBWOA) is proposed in this work. First, quasi-opposition-based learning is introduced to enhance the ability of WOA to search for optimal solutions. Second, a Gaussian barebone mechanism is embedded to promote diversity and expand the scope of the solution space in WOA. To verify the advantages of QGBWOA, comparison experiments between QGBWOA and its comparison peers were carried out on CEC 2014 with dimensions 10, 30, 50, and 100 and on CEC 2020 test with dimension 30. Furthermore, the performance results were tested using Wilcoxon signed-rank (WS), Friedman test, and post hoc statistical tests for statistical analysis. Convergence accuracy and speed are remarkably improved, as shown by experimental results. Finally, feature selection and multi-threshold image segmentation applications are demonstrated to validate the ability of QGBWOA to solve complex real-world problems. QGBWOA proves its superiority over compared algorithms in feature selection and multi-threshold image segmentation by performing several evaluation metrics.

SUPPLEMENTARY INFORMATION

The online version contains supplementary material available at 10.1007/s42235-022-00297-8.

An evolutionary machine learning for multiple myeloma using Runge Kutta Optimizer from multi characteristic indexes

Multiple myeloma (MM) is a malignant plasma cell disease that is the second most prevalent hematological malignancy in high-income nations and accounts for around 1.8% of all cancers and 18% of hematologic malignancies in the United States. In this research, we try to design a machine learning framework for MM diagnosis from multi characteristic indexes using slime mould Runge Kutta Optimizer (MSRUN) and kernel extreme learning machine, which is called as MSRUN-KELM. An efficient slime mould learning operator is introduced to the initial Runge Kutta Optimizer in MSRUN, ensuring that the trade-off between intensity and diversity is satisfied. The MSRUN was evaluated using IEEE CEC2014 benchmark functions, and the statistical results indicate a significant increase in the search performance of MSRUN. In MSRUN-KELM, kernel extreme machine learning is constructed on MM from multi-characteristic indexes with MSRUN, parameter optimization, and feature selection synchronized by MSRUN. The results of MSRUN-KELM on MM are accuracy of 93.88%, a Matthews correlation coefficient of 0.922677, and sensitivities of 93.41% and 93.19%. The suggested MSRUN-KELM may be utilized to analyze MM from multi-characteristic indexes well, and it can be treated as a potential tool for MM diagnosis.

Multi-Strategy Learning Boosted Colony Predation Algorithm for Photovoltaic Model Parameter Identification

Modeling solar systems necessitates the effective identification of unknown and variable photovoltaic parameters. To efficiently convert solar energy into electricity, these parameters must be precise. The research introduces the multi-strategy learning boosted colony predation algorithm (MLCPA) for optimizing photovoltaic parameters and boosting the efficiency of solar power conversion. In MLCPA, opposition-based learning can be used to investigate each individual's opposing position, thereby accelerating convergence and preserving population diversity. Level-based learning categorizes individuals according to their fitness levels and treats them differently, allowing for a more optimal balance between variation and intensity during optimization. On a variety of benchmark functions, the MLCPA's performance is compared to the performance of the best algorithms currently in use. On a variety of benchmark functions, the MLCPA's performance is compared to that of existing methods. MLCPA is then used to estimate the parameters of the single, double, and photovoltaic modules. Last but not least, the stability of the proposed MLCPA algorithm is evaluated on the datasheets of many manufacturers at varying temperatures and irradiance levels. Statistics have demonstrated that the MLCPA is more precise and dependable in predicting photovoltaic mode critical parameters, making it a viable tool for solar system parameter identification issues.

An enhanced whale optimization algorithm for DNA storage encoding

Metaheuristic algorithms have the drawback that local optimal solutions are prone to precocious convergence. In order to overcome the disadvantages of the whale optimization algorithm, we propose an improved selective opposition whale optimization algorithm (ISOWOA) in this paper. Firstly, the enhanced quasi-opposition learning (EQOBL) is applied to selectively update the position of the predator, calculate the fitness of the population before and after, and retain optimal individuals as the food source position; Secondly, an improved time-varying update strategy for inertia weight predator position is proposed, and the position update of the food source is completed by this strategy. The performance of the algorithm is analyzed by 23 benchmark functions of CEC 2005 and 15 benchmark functions of CEC 2015 in various dimensions. The superior results are further shown by Wilcoxon's rank sum test and Friedman's nonparametric rank test. Finally, its applicability is demonstrated through applications to the field of biological computing. In this paper, our aim is to achieve access to DNA files and designs high-quantity DNA code sets by ISOWOA. The experimental results show that the lower bounds of the multi-constraint storage coding sets implemented in this paper equals or surpasses that of previous optimal constructions. The data show that the amount of the DNA storage cods filtered by ISOWOA increased 2-18%, which demonstrates the algorithm's reliability in practical optimization tasks.

Analysis of COVID-19 severity from the perspective of coagulation index using evolutionary machine learning with enhanced brain storm optimization

Coronavirus 2019 (COVID-19) is an extreme acute respiratory syndrome. Early diagnosis and accurate assessment of COVID-19 are not available, resulting in ineffective therapeutic therapy. This study designs an effective intelligence framework to early recognition and discrimination of COVID-19 severity from the perspective of coagulation indexes. The framework is proposed by integrating an enhanced new stochastic optimizer, a brain storm optimizing algorithm (EBSO), with an evolutionary machine learning algorithm called EBSO-SVM. Fast convergence and low risk of the local stagnant can be guaranteed for EBSO with added by Harris hawks optimization (HHO), and its property is verified on 23 benchmarks. Then, the EBSO is utilized to perform parameter optimization and feature selection simultaneously for support vector machine (SVM), and the presented EBSO-SVM early recognition and discrimination of COVID-19 severity in terms of coagulation indexes using COVID-19 clinical data. The classification performance of the EBSO-SVM is very promising, reaching 91.9195% accuracy, 90.529% Matthews correlation coefficient, 90.9912% Sensitivity and 88.5705% Specificity on COVID-19. Compared with other existing state-of-the-art methods, the EBSO-SVM in this paper still shows obvious advantages in multiple metrics. The statistical results demonstrate that the proposed EBSO-SVM shows predictive properties for all metrics and higher stability, which can be treated as a computer-aided technique for analysis of COVID-19 severity from the perspective of coagulation.

GC-CNNnet: Diagnosis of Alzheimer’s Disease with PET Images Using Genetic and Convolutional Neural Network

There is a wide variety of effects of Alzheimer's disease (AD), a neurodegenerative disease that can lead to cognitive decline, deterioration of daily life, and behavioral and psychological changes. A polymorphism of the ApoE gene ε 4 is considered a genetic risk factor for Alzheimer's disease. The purpose of this paper is to demonstrate that single-nucleotide polymorphic markers (SNPs) have a causal relationship with quantitative PET imaging traits. Additionally, the classification of AD is based on the frequency of brain tissue variations in PET images using a combination of k-nearest-neighbor (KNN), support vector machine (SVM), linear discrimination analysis (LDA), and convolutional neural network (CNN) techniques. According to the results, the suggested SNPs appear to be associated with quantitative traits more strongly than the SNPs in the ApoE genes. Regarding the classification result, the highest accuracy is obtained by the CNN with 91.1%. These results indicate that the KNN and CNN methods are beneficial in diagnosing AD. Nevertheless, the LDA and SVM are demonstrated with a lower level of accuracy.

A hybrid greedy political optimizer with fireworks algorithm for numerical and engineering optimization problems

This paper proposes a novel hybrid optimization algorithm named GPOFWA, which integrates political optimizer (PO) with fireworks algorithm (FWA) to solve numerical and engineering optimization problems. The original PO uses subgroup optimal solutions such as party leaders and constituency winners to guide the movement of the search agent. However, the number of such subgroup optimal solutions is limited, which leads to insufficient global exploration capabilities of PO. In addition, the recent past-based position updating strategy (RPPUS) of PO lacks effective verification of the updated candidate solutions, which reduces the convergence speed of the algorithm. The proposed hybrid algorithm uses the spark explosion mechanism in FWA to perform explosion spark and Gauss explosion spark operations on the subgroup optimal solutions (party leader and constituency winner) respectively based on the greedy strategy, which optimizes the subgroup optimal solution and enhances the exploitative ability of the algorithm. Moreover, Gaussian explosion sparks are also used to correct the candidate solutions after RPPUS, which makes up for the shortcomings of the original PO. In addition, a new subgroup optimal solution called the Converged Mobile Center (CMC) based on two-way consideration is designed to guide the movement of search agents and maintain the population diversity. We test the presented hybrid algorithm on 30 well-known benchmark functions, CEC2019 benchmark functions and three engineering optimization problems. The experimental results show that GPOFWA is superior to many statE−of-thE−art methods in terms of the quality of the resulting solution.

Tissue Recognition Based on Electrical Impedance Classified by Support Vector Machine in Spinal Operation Area

OBJECTIVE

One of the major difficulties in spinal surgery is the injury of important tissues caused by tissue misclassification, which is the source of surgical complications. Accurate recognization of the tissues is the key to increase safety and effect as well as to reduce the complications of spinal surgery. The study aimed at tissue recognition in the spinal operation area based on electrical impedance and the boundaries of electrical impedance between cortical bone, cancellous bone, spinal cord, muscle, and nucleus pulposus.

METHODS

Two female white swines with body weight of 40 kg were used to expose cortical bone, cancellous bone, spinal cord, muscle, and nucleus pulposus under general anesthesia and aseptic conditions. The electrical impedance of these tissues at 12 frequencies (in the range of 10-100 kHz) was measured by electrochemical analyzer with a specially designed probe, at 22.0-25.0°C and 50%-60% humidity. Two types of tissue recognition models - one combines principal component analysis (PCA) and support vector machine (SVM) and the other combines combines SVM and ensemble learning - were constructed, and the boundaries of electrical impedance of the five tissues at 12 frequencies of current were figured out. Linear correlation, two-way ANOVA, and paired T-test were conducted to analyze the relationship between the electrical impedance of different tissues at different frequencies.

RESULTS

The results suggest that the differences of electrical impedance mainly came from tissue type (p < 0.0001), the electrical impedance of five kinds of tissue was statistically different from each other (p < 0.0001). The tissue recognition accuracy of the algorithm based on principal component analysis and support vector machine ranged from 83%-100%, and the overall accuracy was 95.83%. The classification accuracy of the algorithm based on support vector machine and ensemble learning was 100%, and the boundaries of electrical impedance of five tissues at various frequencies were calculated.

CONCLUSION

The electrical impedance of cortical bone, cancellous bone, spinal cord, muscle, and nucleus pulposus had significant differences in 10-100 kHz frequency. The application of support vector machine realized the accurate tissue recognition in the spinal operation area based on electrical impedance, which is expected to be translated and applied to tissue recognition during spinal surgery.

Multi-Swarm Algorithm for Extreme Learning Machine Optimization

There are many machine learning approaches available and commonly used today, however, the extreme learning machine is appraised as one of the fastest and, additionally, relatively efficient models. Its main benefit is that it is very fast, which makes it suitable for integration within products that require models taking rapid decisions. Nevertheless, despite their large potential, they have not yet been exploited enough, according to the recent literature. Extreme learning machines still face several challenges that need to be addressed. The most significant downside is that the performance of the model heavily depends on the allocated weights and biases within the hidden layer. Finding its appropriate values for practical tasks represents an NP-hard continuous optimization challenge. Research proposed in this study focuses on determining optimal or near optimal weights and biases in the hidden layer for specific tasks. To address this task, a multi-swarm hybrid optimization approach has been proposed, based on three swarm intelligence meta-heuristics, namely the artificial bee colony, the firefly algorithm and the sine-cosine algorithm. The proposed method has been thoroughly validated on seven well-known classification benchmark datasets, and obtained results are compared to other already existing similar cutting-edge approaches from the recent literature. The simulation results point out that the suggested multi-swarm technique is capable to obtain better generalization performance than the rest of the approaches included in the comparative analysis in terms of accuracy, precision, recall, and f1-score indicators. Moreover, to prove that combining two algorithms is not as effective as joining three approaches, additional hybrids generated by pairing, each, two methods employed in the proposed multi-swarm approach, were also implemented and validated against four challenging datasets. The findings from these experiments also prove superior performance of the proposed multi-swarm algorithm. Sample code from devised ELM tuning framework is available on the GitHub.

Discovering trends and hotspots of biosafety and biosecurity research via machine learning

Coronavirus disease 2019 (COVID-19) has infected hundreds of millions of people and killed millions of them. As an RNA virus, COVID-19 is more susceptible to variation than other viruses. Many problems involved in this epidemic have made biosafety and biosecurity (hereafter collectively referred to as ‘biosafety’) a popular and timely topic globally. Biosafety research covers a broad and diverse range of topics, and it is important to quickly identify hotspots and trends in biosafety research through big data analysis. However, the data-driven literature on biosafety research discovery is quite scant. We developed a novel topic model based on latent Dirichlet allocation, affinity propagation clustering and the PageRank algorithm (LDAPR) to extract knowledge from biosafety research publications from 2011 to 2020. Then, we conducted hotspot and trend analysis with LDAPR and carried out further studies, including annual hot topic extraction, a 10-year keyword evolution trend analysis, topic map construction, hot region discovery and fine-grained correlation analysis of interdisciplinary research topic trends. These analyses revealed valuable information that can guide epidemic prevention work: (1) the research enthusiasm over a certain infectious disease not only is related to its epidemic characteristics but also is affected by the progress of research on other diseases, and (2) infectious diseases are not only strongly related to their corresponding microorganisms but also potentially related to other specific microorganisms. The detailed experimental results and our code are available at https://github.com/KEAML-JLU/Biosafety-analysis.

Tool for Predicting College Student Career Decisions: An Enhanced Support Vector Machine Framework

The goal of this research is to offer an effective intelligent model for forecasting college students’ career decisions in order to give a useful reference for career decisions and policy formation by relevant departments. The suggested prediction model is mainly based on a support vector machine (SVM) that has been modified using an enhanced butterfly optimization approach with a communication mechanism and Gaussian bare-bones mechanism (CBBOA). To get a better set of parameters and feature subsets, first, we added a communication mechanism to BOA to improve its global search capability and balance exploration and exploitation trends. Then, Gaussian bare-bones was added to increase the population diversity of BOA and its ability to jump out of the local optimum. The optimal SVM model (CBBOA-SVM) was then developed to predict the career decisions of college students based on the obtained parameters and feature subsets that are already optimized by CBBOA. In order to verify the effectiveness of CBBOA, we compared it with some advanced algorithms on all benchmark functions of CEC2014. Simulation results demonstrated that the performance of CBBOA is indeed more comprehensive. Meanwhile, comparisons between CBBOA-SVM and other machine learning approaches for career decision prediction were carried out, and the findings demonstrate that the provided CBBOA-SVM has better classification and more stable performance. As a result, it is plausible to conclude that the CBBOA-SVM is capable of being an effective tool for predicting college student career decisions.

An improved adaptive neuro fuzzy inference system model using conjoined metaheuristic algorithms for electrical conductivity prediction

Precise prediction of water quality parameters plays a significant role in making an early alert of water pollution and making better decisions for the management of water resources. As one of the influential indicative parameters, electrical conductivity (EC) has a crucial role in calculating the proportion of mineralization. In this study, the integration of an adaptive hybrid of differential evolution and particle swarm optimization (A-DEPSO) with adaptive neuro fuzzy inference system (ANFIS) model is adopted for EC prediction. The A-DEPSO method uses unique mutation and crossover processes to correspondingly boost global and local search mechanisms. It also uses a refreshing operator to prevent the solution from being caught inside the local optimal solutions. This study uses A-DEPSO optimizer for ANFIS training phase to eliminate defects and predict accurately the EC water quality parameter every month at the Maroon River in the southwest of Iran. Accordingly, the recorded dataset originated from the Tange-Takab station from 1980 to 2016 was operated to develop the ANFIS-A-DEPSO model. Besides, the wavelet analysis was jointed to the proposed algorithm in which the original time series of EC was disintegrated into the sub-time series through two mother wavelets to boost the prediction certainty. In the following, the comparison between statistical metrics of the standalone ANFIS, least-square support vector machine (LSSVM), multivariate adaptive regression spline (MARS), generalized regression neural network (GRNN), wavelet-LSSVM (WLSSVM), wavelet-MARS (W-MARS), wavelet-ANFIS (W-ANFIS) and wavelet-GRNN (W-GRNN) models was implemented. As a result, it was apparent that not only was the W-ANFIS-A-DEPSO model able to rise remarkably the EC prediction certainty, but W-ANFIS-A-DEPSO (R = 0.988, RMSE = 53.841, and PI = 0.485) also had the edge over other models with Dmey mother in terms of EC prediction. Moreover, the W-ANFIS-A-DEPSO can improve the RMSE compared to the standalone ANFIS-DEPSO model, accounting for 80%. Hence, this model can create a closer approximation of EC value through W-ANFIS-A-DEPSO model, which is likely to act as a promising procedure to simulate the prediction of EC data.

An improved remora optimization algorithm with autonomous foraging mechanism for global optimization problems

The remora optimization algorithm (ROA) is a newly proposed metaheuristic algorithm for solving global optimization problems. In ROA, each search agent searches new space according to the position of host, which makes the algorithm suffer from the drawbacks of slow convergence rate, poor solution accuracy, and local optima for some optimization problems. To tackle these problems, this study proposes an improved ROA (IROA) by introducing a new mechanism named autonomous foraging mechanism (AFM), which is inspired from the fact that remora can also find food on its own. In AFM, each remora has a small chance to search food randomly or according to the current food position. Thus the AFM can effectively expand the search space and improve the accuracy of the solution. To substantiate the efficacy of the proposed IROA, twenty-three classical benchmark functions and ten latest CEC 2021 test functions with various types and dimensions were employed to test the performance of IROA. Compared with seven metaheuristic and six modified algorithms, the results of test functions show that the IROA has superior performance in solving these optimization problems. Moreover, the results of five representative engineering design optimization problems also reveal that the IROA has the capability to obtain the optimal results for real-world optimization problems. To sum up, these test results confirm the effectiveness of the proposed mechanism.

Deep learning based diagnosis for cysts and tumors of jaw with massive healthy samples

We aimed to develop an explainable and reliable method to diagnose cysts and tumors of the jaw with massive panoramic radiographs of healthy peoples based on deep learning, since collecting and labeling massive lesion samples are time-consuming, and existing deep learning-based methods lack explainability. Based on the collected 872 lesion samples and 10,000 healthy samples, a two-branch network was proposed for classifying the cysts and tumors of the jaw. The two-branch network is firstly pretrained on massive panoramic radiographs of healthy peoples, then is trained for classifying the sample categories and segmenting the lesion area. Totally, 200 healthy samples and 87 lesion samples were included in the testing stage. The average accuracy, precision, sensitivity, specificity, and F1 score of classification are 88.72%, 65.81%, 66.56%, 92.66%, and 66.14%, respectively. The average accuracy, precision, sensitivity, specificity, and F1 score of classification will reach 90.66%, 85.23%, 84.27%, 93.50%, and 84.74%, if only classifying the lesion samples and healthy samples. The proposed method showed encouraging performance in the diagnosis of cysts and tumors of the jaw. The classified categories and segmented lesion areas serve as the diagnostic basis for further diagnosis, which provides a reliable tool for diagnosing jaw tumors and cysts.

Individual Disturbance and Attraction Repulsion Strategy Enhanced Seagull Optimization for Engineering Design

The seagull optimization algorithm (SOA) is a novel swarm intelligence algorithm proposed in recent years. The algorithm has some defects in the search process. To overcome the problem of poor convergence accuracy and easy to fall into local optimality of seagull optimization algorithm, this paper proposed a new variant SOA based on individual disturbance (ID) and attraction-repulsion (AR) strategy, called IDARSOA, which employed ID to enhance the ability to jump out of local optimum and adopted AR to increase the diversity of population and make the exploration of solution space more efficient. The effectiveness of the IDARSOA has been verified using representative comprehensive benchmark functions and six practical engineering optimization problems. The experimental results show that the proposed IDARSOA has the advantages of better convergence accuracy and a strong optimization ability than the original SOA.