Breast cancer prediction using a hybrid method based on Butterfly Optimization Algorithm and Ant Lion Optimizer

Cross vision transformer with enhanced Growth Optimizer for breast cancer detection in IoMT environment

The recent advances in artificial intelligence modern approaches can play vital roles in the Internet of Medical Things (IoMT). Automatic diagnosis is one of the most important topics in the IoMT, including cancer diagnosis. Breast cancer is one of the top causes of death among women. Accurate diagnosis and early detection of breast cancer can improve the survival rate of patients. Deep learning models have demonstrated outstanding potential in accurately detecting and diagnosing breast cancer. This paper proposes a novel technology for breast cancer detection using CrossViT as the deep learning model and an enhanced version of the Growth Optimizer algorithm (MGO) as the feature selection method. CrossVit is a hybrid deep learning model that combines the strengths of both convolutional neural networks (CNNs) and transformers. The MGO is a meta-heuristic algorithm that selects the most relevant features from a large pool of features to enhance the performance of the model. The developed approach was evaluated on three publicly available breast cancer datasets and achieved competitive performance compared to other state-of-the-art methods. The results show that the combination of CrossViT and the MGO can effectively identify the most informative features for breast cancer detection, potentially assisting clinicians in making accurate diagnoses and improving patient outcomes. The MGO algorithm improves accuracy by approximately 1.59% on INbreast, 5.00% on MIAS, and 0.79% on MiniDDSM compared to other methods on each respective dataset. The developed approach can also be utilized to improve the Quality of Service (QoS) in the healthcare system as a deployable IoT-based intelligent solution or a decision-making assistance service, enhancing the efficiency and precision of the diagnosis.

An enhanced machine learning approach for effective prediction of IgA nephropathy patients with severe proteinuria based on clinical data

IgA Nephropathy (IgAN) is a disease of the glomeruli that may eventually lead to chronic kidney disease or kidney failure. The signs and symptoms of IgAN nephropathy are usually not specific enough and are similar to those of other glomerular or inflammatory diseases. This makes a correct diagnosis more difficult. This study collected data from a sample of adult patients diagnosed with primary IgAN at the First Affiliated Hospital of Wenzhou Medical University, with proteinuria ≥1 g/d at the time of diagnosis. Based on these samples, we propose a machine learning framework based on weIghted meaN oF vectOrs (INFO). An enhanced COINFO algorithm is proposed by merging INFO, Cauchy Mutation (CM) and Oppositional-based Learning (OBL) strategies. At the same time, COINFO and Support Vector Machine (SVM) were integrated to construct the BCOINFO-SVM framework for IgAN diagnosis and prediction. Initially, the proposed enhanced COINFO is evaluated using the IEEE CEC2017 benchmark problems, with the outcomes demonstrating its efficient optimization capability and accuracy in convergence. Furthermore, the feature selection capability of the proposed method is verified on the public medical datasets. Finally, the auxiliary diagnostic experiment was carried out through IgAN real sample data. The results demonstrate that the proposed BCOINFO-SVM can screen out essential features such as High-Density Lipoprotein (HDL), Uric Acid (UA), Cardiovascular Disease (CVD), Hypertension and Diabetes. Simultaneously, the BCOINFO-SVM model achieves an accuracy of 98.56%, with sensitivity at 96.08% and specificity at 97.73%, making it a potential auxiliary diagnostic model for IgAN.

Lie symmetry, chaos optimal control in non-linear fractional-order diabetes mellitus, human immunodeficiency virus, migraine Parkinson's diseases models: using evolutionary algorithms

The purpose of this article is to investigate the optimal control of nonlinear fractional order chaotic models of diabetes mellitus, human immunodeficiency virus, migraine and Parkinson's diseases using genetic algorithms and particle swarm optimization. Mathematical chaotic models of nonlinear fractional order type of the above diseases were presented. Then optimal control for each of the models and numerical simulation was done using genetic algorithm and particle swarm optimization algorithm. The results of the genetic algorithm method are excellent. All the results obtained for the particle swarm optimization method show that this method is also very successful and the results are very close to the genetic algorithm method. Very low values of MSE and RMSE errors indicate that the simulation is effective and efficient. Also, Lie symmetry was calculated for the proposed models and the results were presented.

Optimized fuzzy K-nearest neighbor approach for accurate lung cancer prediction based on radial endobronchial ultrasonography

Radial endobronchial ultrasonography (R-EBUS) has been a surge in the development of new ultrasonography for the diagnosis of pulmonary diseases beyond the central airway. However, it faces challenges in accurately pinpointing the location of abnormal lesions. Therefore, this study proposes an improved machine learning model aimed at distinguishing between malignant lung disease (MLD) from benign lung disease (BLD) through R-EBUS features. An enhanced manta ray foraging optimization based on elite perturbation search and cyclic mutation strategy (ECMRFO) is introduced at first. Experimental validation on 29 test functions from CEC 2017 demonstrates that ECMRFO exhibits superior optimization capabilities and robustness compared to other competing algorithms. Subsequently, it was combined with fuzzy k-nearest neighbor for the classification prediction of BLD and MLD. Experimental results indicate that the proposed modal achieves a remarkable prediction accuracy of up to 99.38%. Additionally, parameters such as R-EBUS1 Circle-dense sign, R-EBUS2 Hemi-dense sign, R-EBUS5 Onionskin sign and CCT5 mediastinum lymph node are identified as having significant clinical diagnostic value.

Parameter optimization of the spiral fertiliser discharger for mango orchards based on the discrete element method and genetic algorithm

Introduction

In order to solve the problems of inaccurate fertilization, unstable fertilization and low fertiliserutilization rate in mango orchard.

Methods

A small spiral fertiliser discharger was designed based on the agronomic characteristics of fertilization in mango orchard. The fertilizing performance test and parameter optimization of thespiral fertiliser discharger were carried out by combining bench and simulation test. Firstly, the main influencing factors of the fertilizing performance of the spiral fertiliser discharger were analyzed by theoretical calculation formula, and the range of its value was preliminarily determined. At the same time, the digital and discrete element models of the spiral fertiliser discharger were established. Then,the discrete element model of granular fertiliser was established on the basis of the physical and related mechanical simulation parameters of granular fertiliser obtained by experimental statistics.Taking the variable coefficient of fertilizing stability as the response value, the method of singlefactor simulation fertilizing test was used to explore the parameters that have a significant influence on the variable coefficient of fertilizing stability. The response surface method (RSM) was used tosimulate the fertilizing performance of three significant parameters. Based on the quadraticregression orthogonal rotation combination design test, a second-order regression mathematicalmodel between the variable coefficient of fertilizing stability and the significant parameters wasestablished. The variable coefficient of fertilizing stability was as small as possible. The geneticalgorithm (GA) was used to optimize the regression model. Finally, the verification test of thefluidity and applicability of different fertilisers was carried out.

Results

The results of single factor test showed that the diameter of spiral blade, pitch and rotationalspeed of fertilizing shaft have significant influence on the variable coefficient of fertilizing stability.The optimal parameter combination of the spiral fertiliser discharger was obtained: 98.44 mm for thediameter of spiral blade, 54.8 mm for the pitch, and 24.43 r/min for the rotational speed of fertilizingshaft. The verification results showed that the average relative error of the test was small, and themass flow rate of different fertilisers and the variable coefficient of fertilizing stability could meetthe agronomic requirements of fertilization in mango orchards. The reliability of the discrete elementsimulation test results and research methods of the spiral fertiliser discharger was verified.

Conclusion

The results and methods of this study can provide reference for the development of mangoorchard fertilization machinery and related fertilizing performance test.

A new population initialization of metaheuristic algorithms based on hybrid fuzzy rough set for high-dimensional gene data feature selection

In the realm of modern medicine and biology, vast amounts of genetic data with high complexity are available. However, dealing with such high-dimensional data poses challenges due to increased processing complexity and size. Identifying critical genes to reduce data dimensionality is essential. The filter-wrapper hybrid method is a commonly used approach in feature selection. Most of these methods employ filters such as MRMR and ReliefF, but the performance of these simple filters is limited. Rough set methods, on the other hand, are a type of filter method that outperforms traditional filters. Simultaneously, many studies have pointed out the crucial importance of good initialization strategies for the performance of the metaheuristic algorithm (a type of wrapper-based method). Combining these two points, this paper proposes a novel filter-wrapper hybrid method for high-dimensional feature selection. To be specific, we utilize the variant of bWOA (binary Whale Optimization Algorithm) based on Hybrid Fuzzy Rough Set to perform attribute reduction, and the reduced attributes are used as prior knowledge to initialize the population. We then employ metaheuristics for further feature selection based on this initialized population. We conducted experiments using five different algorithms on 14 UCI datasets. The experiment results show that after applying the initialization method proposed in this article, the performance of five enhanced algorithms, has shown significant improvement. Particularly, the improved bMFO using our initialization method: fuzzy_bMFO outperformed six currently advanced algorithms, indicating that our initialization method for metaheuristic algorithms is suitable for high-dimensional feature selection tasks.

Liver Cancer Algorithm: A novel bio-inspired optimizer

This paper introduces a new bio-inspired optimization algorithm named the Liver Cancer Algorithm (LCA), which mimics the liver tumor growth and takeover process. It uses an evolutionary search approach that simulates the behavior of liver tumors when taking over the liver organ. The tumor's ability to replicate and spread to other organs inspires the algorithm. LCA algorithm is developed using genetic operators and a Random Opposition-Based Learning (ROBL) strategy to efficiently balance local and global searches and explore the search space. The algorithm's efficiency is tested on the IEEE Congress of Evolutionary Computation in 2020 (CEC'2020) benchmark functions and compared to seven widely used metaheuristic algorithms, including Genetic Algorithm (GA), particle swarm optimization (PSO), Differential Evolution (DE), Adaptive Guided Differential Evolution Algorithm (AGDE), Improved Multi-Operator Differential Evolution (IMODE), Harris Hawks Optimization (HHO), Runge-Kutta Optimization Algorithm (RUN), weIghted meaN oF vectOrs (INFO), and Coronavirus Herd Immunity Optimizer (CHIO). The statistical results of the convergence curve, boxplot, parameter space, and qualitative metrics show that the LCA algorithm performs competitively compared to well-known algorithms. Moreover, the versatility of the LCA algorithm extends beyond mathematical benchmark problems. It was also successfully applied to tackle the feature selection problem and optimize the support vector machine for various biomedical data classifications, resulting in the creation of the LCA-SVM model. The LCA-SVM model was evaluated in a total of twelve datasets, among which the MonoAmine Oxidase (MAO) dataset stood out, showing the highest performance compared to the other datasets. In particular, the LCA-SVM model achieved an impressive accuracy of 98.704% on the MAO dataset. This outstanding result demonstrates the efficacy and potential of the LCA-SVM approach in handling complex datasets and producing highly accurate predictions. The experimental results indicate that the LCA algorithm surpasses other methods to solve mathematical benchmark problems and feature selection.

A face recognition algorithm based on the combine of image feature compensation and improved PSO

Face recognition systems have been widely applied in various scenarios in people's daily lives. The recognition rate and speed of face recognition systems have always been the two key technical factors that researchers focus on. Many excellent recognition algorithms achieve high recognition rates or good recognition speeds. However, more research is needed to develop algorithms that can effectively balance these two indicators. In this study, we introduce an improved particle swarm optimization algorithm into a face recognition algorithm based on image feature compensation techniques. This allows the system to achieve high recognition rates while simultaneously enhancing the recognition efficiency, aiming to strike a balance between the two aspects. This approach provides a new perspective for the application of image feature compensation techniques in face recognition systems. It helps achieve a broader range of applications for face recognition technology by reducing the recognition speed as much as possible while maintaining a satisfactory recognition rate. Ultimately, this leads to an improved user experience.

An improved particle swarm optimization combined with double-chaos search

Particle swarm optimization (PSO) has been successfully applied to various complex optimization problems due to its simplicity and efficiency. However, the update strategy of the standard PSO algorithm is to learn from the global best particle, making it difficult to maintain diversity in the population and prone to premature convergence due to being trapped in local optima. Chaos search mechanism is an optimization technique based on chaotic dynamics, which utilizes the randomness and nonlinearity of a chaotic system for global search and can escape from local optima. To overcome the limitations of PSO, an improved particle swarm optimization combined with double-chaos search (DCS-PSO) is proposed in this paper. In DCS-PSO, we first introduce double-chaos search mechanism to narrow the search space, which enables PSO to focus on the neighborhood of the optimal solution and reduces the probability that the swarm gets trapped into a local optimum. Second, to enhance the population diversity, the logistic map is employed to perform a global search in the narrowed search space and the best solution found by both the logistic and population search guides the population to converge. Experimental results show that DCS-PSO can effectively narrow the search space and has better convergence accuracy and speed in most cases.

A new parallel multi-objective Harris hawk algorithm for predicting the mortality of COVID-19 patients

Harris' Hawk Optimization (HHO) is a novel metaheuristic inspired by the collective hunting behaviors of hawks. This technique employs the flight patterns of hawks to produce (near)-optimal solutions, enhanced with feature selection, for challenging classification problems. In this study, we propose a new parallel multi-objective HHO algorithm for predicting the mortality risk of COVID-19 patients based on their symptoms. There are two objectives in this optimization problem: to reduce the number of features while increasing the accuracy of the predictions. We conduct comprehensive experiments on a recent real-world COVID-19 dataset from Kaggle. An augmented version of the COVID-19 dataset is also generated and experimentally shown to improve the quality of the solutions. Significant improvements are observed compared to existing state-of-the-art metaheuristic wrapper algorithms. We report better classification results with feature selection than when using the entire set of features. During experiments, a 98.15% prediction accuracy with a 45% reduction is achieved in the number of features. We successfully obtained new best solutions for this COVID-19 dataset.

Enhancing sensor duty cycle in environmental wireless sensor networks using Quantum Evolutionary Golden Jackal Optimization Algorithm

Environmental wireless sensor networks (EWSNs) are essential in environmental monitoring and are widely used in gas monitoring, soil monitoring, natural disaster early warning and other fields. EWSNs are limited by the sensor battery capacity and data collection range, and the usual deployment method is to deploy many sensor nodes in the monitoring zone. This deployment method improves the robustness of EWSNs, but introduces many redundant nodes, resulting in a problem of duty cycle design, which can be effectively solved by duty cycle optimization. However, the duty cycle optimization in EWSNs is an NP-Hard problem, and the complexity of the problem increases exponentially with the number of sensor nodes. In this way, non-heuristic algorithms often fail to obtain a deployment solution that meets the requirements in reasonable time. Therefore, this paper proposes a novel heuristic algorithm, the Quantum Evolutionary Golden Jackal Optimization Algorithm (QEGJOA), to solve the duty cycle optimization problem. Specifically, QEGJOA can effectively prolong the lifetime of EWSNs by duty cycle optimization and can quickly get a deployment solution in the face of multi-sensor nodes. New quantum exploration and exploitation operators are designed, which greatly improves the global search ability of the algorithm and enables the algorithm to effectively solve the problem of excessive complexity in duty cycle optimization. In addition, this paper designs a new sensor duty cycle model, which has the advantages of high accuracy and low complexity. The simulation shows that the QEGJOA proposed in this paper improves by 18.69, 20.15 and 26.55 compared to the Golden Jackal Optimization (GJO), Whale Optimization Algorithm (WOA) and the Simulated Annealing Algorithm (SA).

Multi-level thresholding image segmentation for rubber tree secant using improved Otsu's method and snake optimizer

The main disease that decreases the manufacturing of natural rubber is tapping panel dryness (TPD). To solve this problem faced by a large number of rubber trees, it is recommended to observe TPD images and make early diagnosis. Multi-level thresholding image segmentation can extract regions of interest from TPD images for improving the diagnosis process and increasing the efficiency. In this study, we investigate TPD image properties and enhance Otsu's approach. For a multi-level thresholding problem, we combine the snake optimizer with the improved Otsu's method and propose SO-Otsu. SO-Otsu is compared with five other methods: fruit fly optimization algorithm, sparrow search algorithm, grey wolf optimizer, whale optimization algorithm, Harris hawks optimization and the original Otsu's method. The performance of the SO-Otsu is measured using detail review and indicator reviews. According to experimental findings, SO-Otsu performs better than the competition in terms of running duration, detail effect and degree of fidelity. SO-Otsu is an efficient image segmentation method for TPD images.

The nonalcoholic fatty liver risk in prediction of unfavorable outcome after stroke: A nationwide registry analysis

Few researches have looked at the relationship between nonalcoholic fatty liver disease (NAFLD) at the time of admission and the long-term outcomes of patients suffering from acute ischemic stroke (AIS). We aimed to probe the relationship between NAFLD risk evaluated by NAFLD indices and long-term endpoints, along with the prognostic value of merging NAFLD indices with established risk markers for the prognosis of AIS patients. The fatty liver index (FLI) and the Hepatic steatosis index (HSI) were used to evaluate NAFLD risk in the Third China National Stroke Registry (CNSR-III), a large, prospective, national, multicenter cohort registry study. NAFLD was defined as FLI ≥35 for males and FLI ≥ 20 for females, as well as HSI>36. Death or major disability (modified Rankin Scale score ≥3) were the primary outcomes following the beginning of a stroke. On patient outcomes, the prognostic performance of two objective NAFLD parameters was evaluated. NAFLD was detected in 32.10-51.90% of AIS patients. After 1-year, 14.5% of the participants had died or suffered a severe outcome. After controlling for known risk factors, NAFLD was associated with a modest probability of adverse outcome (odds ratio,0.72[95% CI, 0.61-0.86] for FLI; odds ratio,0.68[95% CI, 0.55-0.85] for HSI). The inclusion of the two NAFLD indicators in the conventional prediction model was justified by the integrated discrimination index, continuing to increase the model's overall predictive value for long-term adverse outcomes. NAFLD risk was linked to a lower risk of long-term death or major disability in people with AIS. The predictive value of objective NAFLD after AIS was demonstrated in our study.

Enhancing the Breast Histopathology Image Analysis for Cancer Detection Using Variational Autoencoder

A breast tissue biopsy is performed to identify the nature of a tumour, as it can be either cancerous or benign. The first implementations involved the use of machine learning algorithms. Random Forest and Support Vector Machine (SVM) were used to classify the input histopathological images into whether they were cancerous or non-cancerous. The implementations continued to provide promising results, and then Artificial Neural Networks (ANNs) were applied for this purpose. We propose an approach for reconstructing the images using a Variational Autoencoder (VAE) and the Denoising Variational Autoencoder (DVAE) and then use a Convolutional Neural Network (CNN) model. Afterwards, we predicted whether the input image was cancerous or non-cancerous. Our implementation provides predictions with 73% accuracy, which is greater than the results produced by our custom-built CNN on our dataset. The proposed architecture will prove to be a new field of research and a new area to be explored in the field of computer vision using CNN and Generative Modelling since it incorporates reconstructions of the original input images and provides predictions on them thereafter.

Multi-strategy assisted chaotic coot-inspired optimization algorithm for medical feature selection: A cervical cancer behavior risk study

Real-world optimization problems require some advanced metaheuristic algorithms, which functionally sustain a variety of solutions and technically explore the tracking space to find the global optimal solution or optimizer. One such algorithm is the newly developed COOT algorithm that is used to solve complex optimization problems. However, like other swarm intelligence algorithms, the COOT algorithm also faces the issues of low diversity, slow iteration speed, and stagnation in local optimization. In order to ameliorate these deficiencies, an improved population-initialized COOT algorithm named COBHCOOT is developed by integrating chaos map, opposition-based learning strategy and hunting strategy, which are used to accelerate the global convergence speed and boost the exploration efficiency and solution quality of the algorithm. To validate the dominance of the proposed COBHCOOT, it is compared with the original COOT algorithm and the well-known natural heuristic optimization algorithm on the recognized CEC2017 and CEC2019 benchmark suites, respectively. For the 29 CEC2017 problems, COBHCOOT performed the best in 15 (51.72%, 30-Dim), 14 (48.28%, 50-Dim) and 11 (37.93%, 100-Dim) respectively, and for the 10 CEC2019 benchmark functions, COBHCOOT performed the best in 7 of them. Furthermore, the practicability and potential of COBHCOOT are also highlighted by solving two engineering optimization problems and four truss structure optimization problems. Eventually, to examine the validity and performance of COBHCOOT for medical feature selection, eight medical datasets are used as benchmarks to compare with other superior methods in terms of average accuracy and number of features. Particularly, COBHCOOT is applied to the feature selection of cervical cancer behavior risk dataset. The findings testified that COBHCOOT achieves better accuracy with a minimal number of features compared with the comparison methods.

Random Replacement Crisscross Butterfly Optimization Algorithm for Standard Evaluation of Overseas Chinese Associations

The butterfly optimization algorithm (BOA) is a swarm intelligence optimization algorithm proposed in 2019 that simulates the foraging behavior of butterflies. Similarly, the BOA itself has certain shortcomings, such as a slow convergence speed and low solution accuracy. To cope with these problems, two strategies are introduced to improve the performance of BOA. One is the random replacement strategy, which involves replacing the position of the current solution with that of the optimal solution and is used to increase the convergence speed. The other is the crisscross search strategy, which is utilized to trade off the capability of exploration and exploitation in BOA to remove local dilemmas whenever possible. In this case, we propose a novel optimizer named the random replacement crisscross butterfly optimization algorithm (RCCBOA). In order to evaluate the performance of RCCBOA, comparative experiments are conducted with another nine advanced algorithms on the IEEE CEC2014 function test set. Furthermore, RCCBOA is combined with support vector machine (SVM) and feature selection (FS)—namely, RCCBOA-SVM-FS—to attain a standardized construction model of overseas Chinese associations. It is found that the reasonableness of bylaws; the regularity of general meetings; and the right to elect, be elected, and vote are of importance to the planning and standardization of Chinese associations. Compared with other machine learning methods, the RCCBOA-SVM-FS model has an up to 95% accuracy when dealing with the normative prediction problem of overseas Chinese associations. Therefore, the constructed model is helpful for guiding the orderly and healthy development of overseas Chinese associations.

Nature-inspired optimization algorithms and their significance in multi-thresholding image segmentation: an inclusive review

Multilevel Thresholding (MLT) is considered as a significant and imperative research field in image segmentation that can efficiently resolve difficulties aroused while analyzing the segmented regions of multifaceted images with complicated nonlinear conditions. MLT being a simple exponential combinatorial optimization problem is commonly phrased by means of a sophisticated objective function requirement that can only be addressed by nondeterministic approaches. Consequently, researchers are engaging Nature-Inspired Optimization Algorithms (NIOA) as an alternate methodology that can be widely employed for resolving problems related to MLT. This paper delivers an acquainted review related to novel NIOA shaped lately in last three years (2019-2021) highlighting and exploring the major challenges encountered during the development of image multi-thresholding models based on NIOA.