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In blood or bone marrow, leukemia is a form of cancer. A person with leukemia has an expansion of white blood cells (WBCs). It primarily affects children and rarely affects adults. Treatment depends on the type of leukemia and the extent to which cancer has established throughout the body. Identifying leukemia in the initial stage is vital to providing timely patient care. Medical image-analysis-related approaches grant safer, quicker, and less costly solutions while ignoring the difficulties of these invasive processes. It can be simple to generalize Computer vision (CV)-based and image-processing techniques and eradicate human error. Many researchers have implemented computer-aided diagnostic methods and machine learning (ML) for laboratory image analysis, hopefully overcoming the limitations of late leukemia detection and determining its subgroups. This study establishes a Marine Predators Algorithm with Deep Learning Leukemia Cancer Classification (MPADL-LCC) algorithm on Medical Images. The projected MPADL-LCC system uses a bilateral filtering (BF) technique to pre-process medical images. The MPADL-LCC system uses Faster SqueezeNet with Marine Predators Algorithm (MPA) as a hyperparameter optimizer for feature extraction. Lastly, the denoising autoencoder (DAE) methodology can be executed to accurately detect and classify leukemia cancer. The hyperparameter tuning process using MPA helps enhance leukemia cancer classification performance. Simulation results are compared with other recent approaches concerning various measurements and the MPADL-LCC algorithm exhibits the best results over other recent approaches. [ABSTRACT FROM AUTHOR]

Hybridizing metaheuristic algorithms involves synergistically combining different optimization techniques to effectively address complex and challenging optimization problems. This approach aims to leverage the strengths of multiple algorithms, enhancing solution quality, convergence speed, and robustness, thereby offering a more versatile and efficient means of solving intricate real-world optimization tasks. In this paper, we introduce a hybrid algorithm that amalgamates three distinct metaheuristics: the Beluga Whale Optimization (BWO), the Honey Badger Algorithm (HBA), and the Jellyfish Search (JS) optimizer. The proposed hybrid algorithm will be referred to as BHJO. Through this fusion, the BHJO algorithm aims to leverage the strengths of each optimizer. Before this hybridization, we thoroughly examined the exploration and exploitation capabilities of the BWO, HBA, and JS metaheuristics, as well as their ability to strike a balance between exploration and exploitation. This meticulous analysis allowed us to identify the pros and cons of each algorithm, enabling us to combine them in a novel hybrid approach that capitalizes on their respective strengths for enhanced optimization performance. In addition, the BHJO algorithm incorporates Opposition-Based Learning (OBL) to harness the advantages offered by this technique, leveraging its diverse exploration, accelerated convergence, and improved solution quality to enhance the overall performance and effectiveness of the hybrid algorithm. Moreover, the performance of the BHJO algorithm was evaluated across a range of both unconstrained and constrained optimization problems, providing a comprehensive assessment of its efficacy and applicability in diverse problem domains. Similarly, the BHJO algorithm was subjected to a comparative analysis with several renowned algorithms, where mean and standard deviation values were utilized as evaluation metrics. This rigorous comparison aimed to assess the performance of the BHJO algorithm about its counterparts, shedding light on its effectiveness and reliability in solving optimization problems. Finally, the obtained numerical statistics underwent rigorous analysis using the Friedman post hoc Dunn's test. The resulting numerical values revealed the BHJO algorithm's competitiveness in tackling intricate optimization problems, affirming its capability to deliver favorable outcomes in challenging scenarios. [ABSTRACT FROM AUTHOR]

Geopolymer concrete emerges as a promising avenue for sustainable development and offers an effective solution to environmental problems. Its attributes as a non-toxic, low-carbon, and economical substitute for conventional cement concrete, coupled with its elevated compressive strength and reduced shrinkage properties, position it as a pivotal material for diverse applications spanning from architectural structures to transportation infrastructure. In this context, this study sets out the task of using machine learning (ML) algorithms to increase the accuracy and interpretability of predicting the compressive strength of geopolymer concrete in the civil engineering field. To achieve this goal, a new approach using convolutional neural networks (CNNs) has been adopted. This study focuses on creating a comprehensive dataset consisting of compositional and strength parameters of 162 geopolymer concrete mixes, all containing Class F fly ash. The selection of optimal input parameters is guided by two distinct criteria. The first criterion leverages insights garnered from previous research on the influence of individual features on compressive strength. The second criterion scrutinizes the impact of these features within the model's predictive framework. Key to enhancing the CNN model's performance is the meticulous determination of the optimal hyperparameters. Through a systematic trial-and-error process, the study ascertains the ideal number of epochs for data division and the optimal value of k for k-fold cross-validation--a technique vital to the model's robustness. The model's predictive prowess is rigorously assessed via a suite of performance metrics and comprehensive score analyses. Furthermore, the model's adaptability is gauged by integrating a secondary dataset into its predictive framework, facilitating a comparative evaluation against conventional prediction methods. To unravel the intricacies of the CNN model's learning trajectory, a loss plot is deployed to elucidate its learning rate. The study culminates in compelling findings that underscore the CNN model's accurate prediction of geopolymer concrete compressive strength. To maximize the dataset's potential, the application of bivariate plots unveils nuanced trends and interactions among variables, fortifying the consistency with earlier research. Evidenced by promising prediction accuracy, the study's outcomes hold significant promise in guiding the development of innovative geopolymer concrete formulations, thereby reinforcing its role as an eco-conscious and robust construction material. The findings prove that the CNN model accurately estimated geopolymer concrete's compressive strength. The results show that the prediction accuracy is promising and can be used for the development of new geopolymer concrete mixes. The outcomes not only underscore the significance of leveraging technology for sustainable construction practices but also pave the way for innovation and efficiency in the field of civil engineering. [ABSTRACT FROM AUTHOR]

The intuitive fuzzy set has found important application in decision-making and machine learning. To enrich and utilize the intuitive fuzzy set, this study designed and developed a deep neural network-based glaucoma eye detection using fuzzy difference equations in the domain where the retinal images converge. Retinal image detections are categorized as normal eye recognition, suspected glaucomatous eye recognition, and glaucomatous eye recognition. Fuzzy degrees associated with weighted values are calculated to determine the level of concentration between the fuzzy partition and the retinal images. The proposed model was used to diagnose glaucoma using retinal images and involved utilizing the Convolutional Neural Network (CNN) and deep learning to identify the fuzzy weighted regularization between images. This methodology was used to clarify the input images and make them adequate for the process of glaucoma detection. The objective of this study was to propose a novel approach to the early diagnosis of glaucoma using the Fuzzy Expert System (FES) and Fuzzy differential equation (FDE). The intensities of the different regions in the images and their respective peak levels were determined. Once the peak regions were identified, the recurrence relationships among those peaks were then measured. Image partitioning was done due to varying degrees of similar and dissimilar concentrations in the image. Similar and dissimilar concentration levels and spatial frequency generated a threshold image from the combined fuzzy matrix and FDE. This distinguished between a normal and abnormal eye condition, thus detecting patients with glaucomatous eyes. [ABSTRACT FROM AUTHOR]

Pupil dynamics are the important characteristics of face spoofing detection. The face recognition system is one of the most used biometrics for authenticating individual identity. The main threats to the facial recognition system are different types of presentation attacks like print attacks, 3D mask attacks, replay attacks, etc. The proposed model uses pupil characteristics for liveness detection during the authentication process. The pupillary light reflex is an involuntary reaction controlling the pupil's diameter at different light intensities. The proposed framework consistsof two-phasemethodologies. In the first phase, thepupil's diameter is calculatedby applying stimulus (light) in one eye of the subject and calculating the constriction of the pupil size on both eyes in different video frames. The above measurement is converted into feature space using Kohn and Clynes model-defined parameters. The Support Vector Machine is used to classify legitimate subjects when the diameter change is normal (or when the eye is alive) or illegitimate subjects when there is no change or abnormal oscillations of pupil behavior due to the presence of printed photograph, video, or 3D mask of the subject in front of the camera. In the second phase, we perform the facial recognition process. Scale-invariant feature transform (SIFT) is used to find the features from the facial images, with each feature having a size of a 128-dimensional vector. These features are scale, rotation, and orientation invariant and are used for recognizing facial images. The brute force matching algorithm is used for matching features of two different images. The threshold value we considered is 0.08 for good matches. To analyze the performance of the framework, we tested our model in two Face antispoofing datasets named Replay attack datasets and CASIA-SURF datasets, which were used because they contain the videos of the subjects in each sample having three modalities (RGB, IR, Depth). The CASIA-SURF datasets showed an 89.9% Equal Error Rate, while the Replay Attack datasets showed a 92.1% Equal Error Rate. [ABSTRACT FROM AUTHOR]

In this paper, an ensemble metaheuristic algorithm (denoted as LX-BBSCA) is introduced. It combines the strengths of Laplacian biogeography-based optimization (LX-BBO) and the sine cosine algorithm (SCA) to address structural engineering design optimization problems. Our primary objective is to mitigate the risk of getting stuck in local minima and accelerate the algorithm's convergence rate. We evaluate the proposed LX-BBSCA algorithm on a set of 23 benchmark functions, including both unimodal and multimodal problems of varying complexity and dimensions. Additionally, we apply LX-BBSCA to tackle five real-world structural engineering design problems, comparing the results with those obtained using other metaheuristics in terms of objective function values and convergence behavior. To ensure the statistical validity of our findings, we employ rigorous tests such as the t-test and the Wilcoxon rank test. The experimental outcomes consistently demonstrate that the ensemble LX-BBSCA algorithm outperforms not only the basic versions of BBO, SCA and LX-BBO but also other state-of-the-art metaheuristic algorithms. [ABSTRACT FROM AUTHOR]

Hybrid Active Power Filter (HAPF) imbibes the advantages of both passive and active power filters. These filters are considered one of the important technologies for mitigating harmonic pollution in electrical systems. Accurate estimation of filter parameters is a key component to reduce harmonic pollution effectively. In recent years, several optimization approaches have been reported to solve this estimation problem; still, this area is worthy of further investigation. This paper is a proposal for an estimator that can estimate the parameter of HAPF configuration accurately. For evolving this estimator, first, an objective function that mathematically embeds filter parameters and harmonic pollution is presented. For handling the optimization process, an Amended Crow Search Algorithm (ACSA) is proposed. ACSA employs a local search algorithm (in the form of a pattern search) for obtaining optimal results. The analysis of the estimation process is carried out on two HAPF configurations. Various analyses that include harmonic pollution statistical analysis along with fitness function value analysis reveal that the proposed algorithm acquires optimal results as compared with other recently published and reported algorithms. Further, the proposed filter configurations are tested with the existing filter. The results prove that the proposed filter shows promising results. [ABSTRACT FROM AUTHOR]