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Gene expression datasets offer a wide range of information about various biological processes. However, it is difficult to find the important genes among the high-dimensional biological data due to the existence of redundant and unimportant ones. Numerous Feature Selection (FS) techniques have been created to get beyond this obstacle. Improving the efficacy and precision of FS methodologies is crucial in order to identify significant genes amongst complicated complex biological data. In this work, we present a novel approach to gene selection called the Sine Cosine and Cuckoo Search Algorithm (SCACSA). This hybrid method is designed to work with well-known machine learning classifiers Support Vector Machine (SVM). Using a dataset on breast cancer, the hybrid gene selection algorithm's performance is carefully assessed and compared to other feature selection methods. To improve the quality of the feature set, we use minimum Redundancy Maximum Relevance (mRMR) as a filtering strategy in the first step. The hybrid SCACSA method is then used to enhance and optimize the gene selection procedure. Lastly, we classify the dataset according to the chosen genes by using the SVM classifier. Given the pivotal role gene selection plays in unraveling complex biological datasets, SCACSA stands out as an invaluable tool for the classification of cancer datasets. The findings help medical practitioners make well-informed decisions about cancer diagnosis and provide them with a valuable tool for navigating the complex world of gene expression data.

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Cancer prediction in the early stage is a topic of major interest in medicine since it allows accurate and efficient actions for successful medical treatments of cancer. Mostly cancer datasets contain various gene expression levels as features with less samples, so firstly there is a need to eliminate similar features to permit faster convergence rate of classification algorithms. These features (genes) enable us to identify cancer disease, choose the best prescription to prevent cancer and discover deviations amid different techniques. To resolve this problem, we proposed a hybrid novel technique CSSMO-based gene selection for cancer classification. First, we made alteration of the fitness of spider monkey optimization (SMO) with cuckoo search algorithm (CSA) algorithm viz., CSSMO for feature selection, which helps to combine the benefit of both metaheuristic algorithms to discover a subset of genes which helps to predict a cancer disease in early stage. Further, to enhance the accuracy of the CSSMO algorithm, we choose a cleaning process, minimum redundancy maximum relevance (mRMR) to lessen the gene expression of cancer datasets. Next, these subsets of genes are classified using deep learning (DL) to identify different groups or classes related to a particular cancer disease. Eight different benchmark microarray gene expression datasets of cancer have been utilized to analyze the performance of the proposed approach with different evaluation matrix such as recall, precision, F1-score, and confusion matrix. The proposed gene selection method with DL achieves much better classification accuracy than other existing DL and machine learning classification models with all large gene expression dataset of cancer.

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The most significant groupings of cold-blooded creatures are the fish family. It is crucial to recognize and categorize the most significant species of fish since various species of seafood diseases and decay exhibit different symptoms. Systems based on enhanced deep learning can replace the area's currently cumbersome and sluggish traditional approaches. Although it seems straightforward, classifying fish images is a complex procedure. In addition, the scientific study of population distribution and geographic patterns is important for advancing the field's present advancements. The goal of the proposed work is to identify the best performing strategy using cutting-edge computer vision, the Chaotic Oppositional Based Whale Optimization Algorithm (CO-WOA), and data mining techniques. Performance comparisons with leading models, such as Convolutional Neural Networks (CNN) and VGG-19, are made to confirm the applicability of the suggested method. The suggested feature extraction approach with Proposed Deep Learning Model was used in the research, yielding accuracy rates of 100 %. The performance was also compared to cutting-edge image processing models with an accuracy of 98.48 %, 98.58 %, 99.04 %, 98.44 %, 99.18 % and 99.63 % such as Convolutional Neural Networks, ResNet150V2, DenseNet, Visual Geometry Group-19, Inception V3, Xception. Using an empirical method leveraging artificial neural networks, the Proposed Deep Learning model was shown to be the best model.

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As cardiac diseases, which mostly result in heart failure, are increasing rapidly worldwide, heart transplantation seems the only solution for saving lives. However, this practice is not always possible due to several reasons, such as scarcity of donors, rejection of organs from recipient bodies, or costly medical procedures. In the framework of nanotechnology, nanomaterials greatly contribute to the development of these cardiovascular scaffolds as they provide an easy regeneration of the tissues. Currently, functional nanofibers can be used in the production of stem cells and in the regeneration of cells and tissues. The small size of nanomaterials, however, leads to changes in their chemical and physical characteristics that could alter their interaction and exposure to stem cells with cells and tissues. This article aims to review the naturally occurring biodegradable nanomaterials that are used in cardiovascular tissue engineering for the development of cardiac patches, vessels, and tissues. Moreover, this article also provides an overview of cell sources used for cardiac tissue engineering, explains the anatomy and physiology of the human heart, and explores the regeneration of cardiac cells and the nanofabrication approaches used in cardiac tissue engineering as well as scaffolds.

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The design of an optimal framework for the prediction of cancer from high-dimensional and imbalanced microarray data is a challenging job in the fields of bioinformatics and machine learning. There are so many techniques for dimensionality reduction, but it is unclear which of these techniques performs best with different classifiers and datasets. This article focused on the independent component analysis (ICA) features (genes) extraction method for Naïve Bayes (NB) classification of microarray data, because ICA perfectly takes out an independent component from the datasets that satisfy the classification criteria of the NB classifier. A novel hybrid method based on a nature-inspired metaheuristic algorithm is proposed in this article for resolving optimization problems of ICA extracted genes. The cuckoo search (CS) algorithm and artificial bee colony (ABC) for finding the best subset of features to increase the performance of ICA for the NB classifier is designed and executed. According to our investigation, the CS-ABC with ICA was implemented for the first time to resolve the dimensionality reduction problem in high-dimensional microarray biomedical datasets. The CS algorithm improved the local search process of the ABC algorithm, and then the hybrid algorithm CS-ABC provided better optimal gene sets that improved the classification accuracy of the NB classifier. The experimental comparison shows that the CS-ABC approach with the ICA algorithm performs a deeper search in the iterative process, which can avoid premature convergence and produce better results compared with the previously published feature selection algorithm for the NB classifier.

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Identifying a small subset of informative genes from a gene expression dataset is an important process for sample classification in the fields of bioinformatics and machine learning. In this process, there are two objectives: first, to minimize the number of selected genes, and second, to maximize the classification accuracy of the used classifier. In this paper, a hybrid machine learning framework based on a nature-inspired cuckoo search (CS) algorithm has been proposed to resolve this problem. The proposed framework is obtained by incorporating the cuckoo search (CS) algorithm with an artificial bee colony (ABC) in the exploitation and exploration of the genetic algorithm (GA). These strategies are used to maintain an appropriate balance between the exploitation and exploration phases of the ABC and GA algorithms in the search process. In preprocessing, the independent component analysis (ICA) method extracts the important genes from the dataset. Then, the proposed gene selection algorithms along with the Naive Bayes (NB) classifier and leave-one-out cross-validation (LOOCV) have been applied to find a small set of informative genes that maximize the classification accuracy. To conduct a comprehensive performance study, proposed algorithms have been applied on six benchmark datasets of gene expression. The experimental comparison shows that the proposed framework (ICA and CS-based hybrid algorithm with NB classifier) performs a deeper search in the iterative process, which can avoid premature convergence and produce better results compared to the previously published feature selection algorithm for the NB classifier.

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OBJECTIVE: To determine the frequency and antimicrobial sensitivity pattern of microbial agents causing neonatal sepsis. STUDY DESIGN: Descriptive study. PLACE AND DURATION OF STUDY: Neonatal ICU, Fauji Foundation Hospital, Rawalpindi; Foundation University, Islabambad, from May 2017 to April 2019. METHODOLOGY: Data of all neonates admitted with sepsis during study period was retrieved from computer database. Age at admission, gender, duration of hospital stay and culture reports were recorded. Culture positive patients were further analysed regarding their antibiotic sensitivity. RESULTS: A total of 1,070 neonates, male:female = 1.36:1, mostly newborn, were included in the study. Total mortality was 182 (17%). Blood culture was positive in 79 (7.4%). Gram positive organisms were identified in 37 (46.8%) Staphylococci in 29 (36.7%), Enterococci 7 (8.9%), Corynebacterium species in 1 (1.3%). Gram negative were isolated in 42 (53.2%) Acinetobacter Baumanni in 14 (17.7%), Klebsiella in 12 (15.2%), Enterobacter spp. In 7 (8.8%), E.coli in 5 (6.3%), Pseudomonas in 2 (2.5%) and Proteus in 1 (1.3%) and Serratia in 1 (1.3%) each. Sensitivity pattern of Gram positive organisms was: vancomycin 30/37 (81.1%), ciprofloxacin 13/37 (35.1%) and Gentimicin 12/37 (32.4%). Gram negative organisms sensitivity pattern was: meropenem 12/42 (28.6%), chloramphenical 10/42 (23.8%), gentimicin 6/42 (14.3%), ciprofloxacin 5/42 (11.9%). Highly resistant strains of Klebsiella (13/14) and Acinitobacter (5/12) were sensitive to colomycin only. CONCLUSION: Common organisms responsible for neonatal sepsis were Styphylococci, Acinitobacter, Klebsiella and E.Coli. Gram positive organisms showed sensitivity to vancomycin and gentamicin. Gram negative organisms were highly sensitive to colomycin. Key Words: Neonatolgy, Neonatal sepsis, Antimicrobial sensitivity, Neonatal mortality.

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This paper proposes a new hybrid search technique for feature (gene) selection (FS) using Independent component analysis (ICA) and Artificial Bee Colony (ABC) called ICA+ABC, to select informative genes based on a Naïve Bayes (NB) algorithm. An important trait of this technique is the optimization of ICA feature vector using ABC. ICA+ABC is a hybrid search algorithm that combines the benefits of extraction approach, to reduce the size of data and wrapper approach, to optimize the reduced feature vectors. This hybrid search technique is facilitated by evaluating the performance of ICA+ABC on six standard gene expression datasets of classification. Extensive experiments were conducted to compare the performance of ICA+ABC with the results obtained from recently published Minimum Redundancy Maximum Relevance (mRMR) +ABC algorithm for NB classifier. Also to check the performance that how ICA+ABC works as feature selection with NB classifier, compared the combination of ICA with popular filter techniques and with other similar bio inspired algorithm such as Genetic Algorithm (GA) and Particle Swarm Optimization (PSO). The result shows that ICA+ABC has a significant ability to generate small subsets of genes from the ICA feature vector, that significantly improve the classification accuracy of NB classifier compared to other previously suggested methods.

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Feature (gene) selection and classification of microarray data are the two most interesting machine learning challenges. In the present work two existing feature selection/extraction algorithms, namely independent component analysis (ICA) and fuzzy backward feature elimination (FBFE) are used which is a new combination of selection/extraction. The main objective of this paper is to select the independent components of the DNA microarray data using FBFE to improve the performance of support vector machine (SVM) and Naïve Bayes (NB) classifier, while making the computational expenses affordable. To show the validity of the proposed method, it is applied to reduce the number of genes for five DNA microarray datasets namely; colon cancer, acute leukemia, prostate cancer, lung cancer II, and high-grade glioma. Now these datasets are then classified using SVM and NB classifiers. Experimental results on these five microarray datasets demonstrate that gene selected by proposed approach, effectively improve the performance of SVM and NB classifiers in terms of classification accuracy. We compare our proposed method with principal component analysis (PCA) as a standard extraction algorithm and find that the proposed method can obtain better classification accuracy, using SVM and NB classifiers with a smaller number of selected genes than the PCA. The curve between the average error rate and number of genes with each dataset represents the selection of required number of genes for the highest accuracy with our proposed method for both the classifiers. ROC shows best subset of genes for both the classifier of different datasets with propose method.