In this data article, we present the data used to evaluate the statistical success of the backtracking search optimisation algorithm (BSA) in comparison with the other four evolutionary optimisation algorithms. The data presented in this data article is related to the research article entitles 'Operational Framework for Recent Advances in Backtracking Search Optimisation Algorithm: A Systematic Review and Performance Evaluation' [1]. Three statistical tests conducted on BSA compared to differential evolution algorithm (DE), particle swarm optimisation (PSO), artificial bee colony (ABC), and firefly algorithm (FF). The tests are used to evaluate these mentioned algorithms and to determine which one could solve a specific optimisation problem concerning the statistical success of 16 benchmark problems taking several criteria into account. The criteria are initializing control parameters, dimension of the problems, their search space, and number of iterations needed to minimise a problem, the performance of the computer used to code the algorithms and their programming style, getting a balance on the effect of randomization, and the use of different type of optimisation problem in terms of hardness and their cohort. In addition, all the three tests include necessary statistical measures (Mean: mean-solution, S.D.: standard-deviation of mean-solution, Best: the best solution, Worst: the worst solution, Exec. Time: mean runtime in seconds, No. of succeeds: number of successful minimisation, and No. of Failure: number of failed minimisation).

It is beneficial to automate the process of deriving concept hierarchies from corpora since a manual construction of concept hierarchies is typically a time-consuming and resource-intensive process. As such, the overall process of learning concept hierarchies from corpora encompasses a set of steps: parsing the text into sentences, splitting the sentences and then tokenising it. After the lemmatisation step, the pairs are extracted using formal context analysis (FCA). However, there might be some uninteresting and erroneous pairs in the formal context. Generating formal context may lead to a time-consuming process, so formal context size reduction is require to remove uninterested and erroneous pairs, taking less time to extract the concept lattice and concept hierarchies accordingly. In this premise, this study aims to propose two frameworks: (1) A framework to review the current process of deriving concept hierarchies from corpus utilising formal concept analysis (FCA); (2) A framework to decrease the formal context’s ambiguity of the first framework using an adaptive version of evolutionary clustering algorithm (ECA*). Experiments are conducted by applying 385 sample corpora from Wikipedia on the two frameworks to examine the reducing size of formal context, which leads to yield concept lattice and concept hierarchy. The resulting lattice of formal context is evaluated to the standard one using concept lattice-invariants. Accordingly, the homomorphic between the two lattices preserves the quality of resulting concept hierarchies by 89% in contrast to the basic ones, and the reduced concept lattice inherits the structural relation of the standard one. The adaptive ECA* is examined against its four counterpart baseline algorithms (Fuzzy K-means, JBOS approach, AddIntent algorithm, and FastAddExtent) to measure the execution time on random datasets with different densities (fill ratios). The results show that adaptive ECA* performs concept lattice faster than other mentioned competitive techniques in different fill ratios.

Immunohistochemistry (IHC) slides are graded for breast cancer based on visual markers and morphological characteristics of stained membrane regions. The usage of whole slide images (WSIs) from histology in digital pathology algorithms for computer-assisted evaluations has increased recently. Human epidermal growth factor receptor 2 (HER2)-stained microscopic images are challenging, time-consuming, and error-prone to evaluate manually. This is due to different staining, overlapped regions, and huge, non-homogeneous slides. Additionally, the classification of HER2 images by the selection of fundamental features must be used to capture the difficult elements of the images, such as the irregular cell structure and the coloring of the tissue of the cells. To solve the above problems, a transfer learning model-based, trainable metaheuristic method for choosing the best features is suggested in this paper. Moreover, the suggested model is efficient in reducing model complexity and computational costs as well as avoiding overfitting. The four main components of the proposed cascaded design are: (a) converting WSIs to tiled images and enhancing contrast with fast local Laplacian filtering (FlLpF); (b) extracting features with a ResNet50 CNN technique based on transfer learning; (c) selecting the most informative features with the help of a non-dominated sorting genetic algorithm (NSGA-II) optimizer; and (d) using a support vector machine (SVM) to classify HER2 scores. Results from the HER2SC and HER2GAN datasets show that the suggested model is superior to other methods already in use, with 94.4% accuracy, 93.71% precision, 98.07% specificity, 93.83% sensitivity, and a 93.71% F1-score for the HER2SC dataset being achieved.