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In the last decade, we observe an increasing number of nature-inspired optimization algorithms, with authors often claiming their novelty and their capabilities of acting as powerful optimization techniques. However, a considerable number of these algorithms do not seem to draw inspiration from nature or to incorporate successful tactics, laws, or practices existing in natural systems, while also some of them have never been applied in any optimization field, since their first appearance in literature. This paper presents some interesting findings that have emerged after the extensive study of most of the existing nature-inspired algorithms. The need for irrationally introducing new nature inspired intelligent (NII) algorithms in literature is also questioned and possible drawbacks of NII algorithms met in literature are discussed. In addition, guidelines for the development of new nature-inspired algorithms are proposed, in an attempt to limit the misleading appearance of variation of metaheuristics as nature inspired optimization algorithms.

Computer-aided diagnosis system is becoming a more and more important tool in clinical treatment, which can provide a verification of the doctors’ decisions. In this paper, we proposed a novel abnormal brain detection method for magnetic resonance image. Firstly, a pre-trained AlexNet was modified with batch normalization layers and trained on our brain images. Then, the last several layers were replaced with an extreme learning machine. A searching method was proposed to find the best number of layers to be replaced. Finally, the extreme learning machine was optimized by chaotic bat algorithm to obtain better classification performance. Experiment results based on 5 × hold-out validation revealed that our method achieved state-of-the-art performance.

Image segmentation is considered a crucial step required for image analysis and research. Many techniques have been proposed to resolve the existing problems and improve the quality of research, such as region-based, threshold-based, edge-based, and feature-based clustering in the literature. The researchers have moved toward using the threshold technique due to the ease of use for image segmentation. To find the optimal threshold value for a grayscale image, we improved and used a novel meta-heuristic equilibrium algorithm to resolve this scientific problem. Additionally, our improved algorithm has the ability to enhance the accuracy of the segmented image for research analysis with a significant threshold level. The performance of our algorithm is compared with seven other algorithms like whale optimization algorithm, bat algorithm, sine–cosine algorithm, salp swarm algorithm, Harris hawks algorithm, crow search algorithm, and particle swarm optimization. Based on a set of well-known test images taken from Berkeley Segmentation Dataset, the performance evaluation of our algorithm and well-known algorithms described above has been conducted and compared. According to the independent results and analysis of each algorithm, our algorithm can outperform all other algorithms in fitness values, peak signal-to-noise ratio metric, structured similarity index metric, maximum absolute error, and signal-to-noise ratio. However, our algorithm cannot outperform some algorithms in standard deviation values and central processing unit time with the large threshold levels observed.

Over previous decades, many nature-inspired optimization algorithms (NIOAs) have been proposed and applied due to their importance and significance. Some survey studies have also been made to investigate NIOAs and their variants and applications. However, these comparative studies mainly focus on one single NIOA, and there lacks a comprehensive comparative and contrastive study of the existing NIOAs. To fill this gap, we spent a great effort to conduct this comprehensive survey. In this survey, more than 120 meta-heuristic algorithms have been collected and, among them, the most popular and common 11 NIOAs are selected. Their accuracy, stability, efficiency and parameter sensitivity are evaluated based on the 30 black-box optimization benchmarking (BBOB) functions. Furthermore, we apply the Friedman test and Nemenyi test to analyze the performance of the compared NIOAs. In this survey, we provide a unified formal description of the 11 NIOAs in order to compare their similarities and differences in depth and a systematic summarization of the challenging problems and research directions for the whole NIOAs field. This comparative study attempts to provide a broader perspective and meaningful enlightenment to understand NIOAs.

Nanozymes, featuring intrinsic biocatalytic effects and broad‐spectrum antimicrobial properties, are emerging as a novel antibiotic class. However, prevailing bactericidal nanozymes face a challenging dilemma between biofilm penetration and bacterial capture capacity, significantly impeding their antibacterial efficacy. Here, this work introduces a photomodulable bactericidal nanozyme (ICG@hMnOx), composed of a hollow virus‐spiky MnOx nanozyme integrated with indocyanine green, for dually enhanced biofilm penetration and bacterial capture for photothermal‐boosted catalytic therapy of bacterial infections. ICG@hMnOx demonstrates an exceptional capability to deeply penetrate biofilms, owing to its pronounced photothermal effect that disrupts the compact structure of biofilms. Simultaneously, the virus‐spiky surface significantly enhances the bacterial capture capacity of ICG@hMnOx. This surface acts as a membrane‐anchored generator of reactive oxygen species and a glutathione scavenger, facilitating localized photothermal‐boosted catalytic bacterial disinfection. Effective treatment of methicillin‐resistant Staphylococcus aureus‐associated biofilm infections is achieved using ICG@hMnOx, offering an appealing strategy to overcome the longstanding trade‐off between biofilm penetration and bacterial capture capacity in antibacterial nanozymes. This work presents a significant advancement in the development of nanozyme‐based therapies for combating biofilm‐related bacterial infections. A photomodulable bacteriophage‐spike bactericidal nanozyme (ICG@hMnOx) composed of hollow virus‐spiky MnOx nanozyme and indocyanine green is constructed to exhibit dually enhanced biofilm penetration and bacterial capture for catalytic therapy of bacterial infections. This unique construct addresses the long‐standing trade‐off between biofilm penetration and bacterial capture capacity of antibacterial nanozymes and offers a promising strategy for catalytic therapy of bacterial infections.

K-means clustering algorithm is a partitional clustering algorithm that has been used widely in many applications for traditional clustering due to its simplicity and low computational complexity. This clustering technique depends on the user specification of the number of clusters generated from the dataset, which affects the clustering results. Moreover, random initialization of cluster centers results in its local minimal convergence. Automatic clustering is a recent approach to clustering where the specification of cluster number is not required. In automatic clustering, natural clusters existing in datasets are identified without any background information of the data objects. Nature-inspired metaheuristic optimization algorithms have been deployed in recent times to overcome the challenges of the traditional clustering algorithm in handling automatic data clustering. Some nature-inspired metaheuristics algorithms have been hybridized with the traditional K-means algorithm to boost its performance and capability to handle automatic data clustering problems. This study aims to identify, retrieve, summarize, and analyze recently proposed studies related to the improvements of the K-means clustering algorithm with nature-inspired optimization techniques. A quest approach for article selection was adopted, which led to the identification and selection of 147 related studies from different reputable academic avenues and databases. More so, the analysis revealed that although the K-means algorithm has been well researched in the literature, its superiority over several well-established state-of-the-art clustering algorithms in terms of speed, accessibility, simplicity of use, and applicability to solve clustering problems with unlabeled and nonlinearly separable datasets has been clearly observed in the study. The current study also evaluated and discussed some of the well-known weaknesses of the K-means clustering algorithm, for which the existing improvement methods were conceptualized. It is noteworthy to mention that the current systematic review and analysis of existing literature on K-means enhancement approaches presents possible perspectives in the clustering analysis research domain and serves as a comprehensive source of information regarding the K-means algorithm and its variants for the research community.

Lung cancer is a deadly disease if not diagnosed in its early stages. However, early detection of lung cancer is a challenging task due to the shape and size of its nodules. Radiologists use automated tools for more precise opinion. Automated detection of the affected lung nodules is complicated because of the shape similarity among healthy and unhealthy tissues. Over the years, several expert systems have been developed that help radiologists to diagnose lung cancer effectively. In this article, we have proposed a framework to precisely detect lungs cancer to classify the benign and malignant nodules. The proposed framework is tested using the subset of the publicly available dataset, i.e., the Lung Image Database Consortium image collection (LIDC-IDRI). We applied filtering and noise removal in the pre-processing phase. Furthermore, the adaptive thresholding technique (OTSU) and the semantic segmentation are used to accurately detect the unhealthy lung nodules. Overall, 13 nodules features have extracted using principal components analysis algorithm. In addition, four optimal features are selected based on the classification performance. In the classification phase, 9 different classifiers are employed for the experimentation. Empirical analysis shows that the proposed system outperformed other techniques and provides 99.23% accuracy using a logit boost classifier.

Service-oriented architecture is becoming a major software framework for complex application and it can be dynamically and flexibly composed by integrating existing component web services provided by different providers with standard protocols. The rapid introduction of new web services into a dynamic business environment can adversely affect the service quality and user satisfaction. Therefore, how to leverage, aggregate and make use of individual component's quality of service (QoS) information to derive the optimal QoS of the composite service which meets the needs of users is still an ongoing hot research problem. This study aims at reviewing the advance of the current state-of-the-art in technologies and inspiring the possible new ideas for web service selection and composition, especially with nature-inspired computing approaches. Firstly, the background knowledge of web services is presented. Secondly, various nature-inspired web selection and composition approaches are systematically reviewed and analysed for QoS-aware web services. Finally, challenges, remarks and discussions about QoS-aware web service composition are presented.

Managing tool-wear is an important issue associated with all material removal processes. This paper deals with the application of two nature-inspired computing techniques, namely, artificial neural network (ANN) and (in silico) DNA-based computing (DBC) for managing the tool-wear. Experimental data (images of worn-zone of cutting tool) has been used to train the ANN and, then, to perform the DBC. It is demonstrated that the ANN can predict the degree of tool-wear from a set of tool-wear images processed under a given procedure whereas the DBC can identify the degree of similarity/dissimilar among the processed images. Further study can be carried out while solving other complex problems integrating ANN and DBC where both prediction and pattern-recognition are two important computational problems that need to be solved simultaneously.

The selection of the most efficient features for glaucoma identification is the subject of our investigation because this disease is rapidly increasing worldwide. This disease causes lifelong blindness due to damage to the eye's optical nerve. Ophthalmologists have traditionally used tonometry, pachymetry, and other methods to measure intraocular pressure in order to diagnose patients. Yet each of these judgments takes time, requires high professional experience, and can be open to human error (inter-observer variability). Therefore, scholars are currently engaged in the domain of medical imaging, specifically focusing on the analysis of retinal images for the purpose of predicting glaucoma. This research also has the same objective and aims to address the aforementioned challenges. This empirical study proposes an artificial intelligence-based computer-assisted diagnosis (CAD) system which is built to overcome these difficulties by providing the best features for machine learning techniques for categorizing subject retinal pictures as \"healthy\" or \"sick\". This study presents a new set of reduced hybrid features that were selected from an initial set of 36 features extracted from fundus images of benchmark datasets that belonged to different classes to categorize patient fundus images into two categories: \"healthy” or \"infected.\" The nature inspired computing-based Emperor Penguin Optimization (EPO) algorithm and the Bacterial Foraging Optimization (BFO) algorithm are utilized to implement feature selection (FS) process. Additionally, a novel hybrid algorithm combining these two techniques is also proposed. Seven machine learning (ML) classifiers are engaged to compute eight statistically based performance metrics along with execution time computation, and a comparison of those metrics is also provided in a detailed fashion. The recommended method exhibits a fortunate performance with the highest specificity of 0.9940, sensitivity of 0.9347, and maximum accuracy of 96.55%. Expert medical practitioners who are overworked may receive assistance from the proposed system in making the optimal decisions to preserve human vision.