A novel and innovative cancer classification framework through a consecutive utilization of hybrid feature selection.

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.

CO-WOA: Novel Optimization Approach for Deep Learning Classification of Fish Image.

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.

Pain assessment and management in different wards of a tertiary care hospital.

OBJECTIVE: To assess the pain management by medical team, emergency room (ER) team and Acute Pain team in a tertiary care hospital. METHODS: The cross-sectional study was done in Medical Ward, Surgical Ward and Emergency Room of Aga Khan University, Karachi, in March-April 2010. The assigned research medical officer visited the three locations every day and selected patients by way of convenient sampling. The study comprised 75 patients; 25 each in three groups. Information was collected on patient's demographics, general characteristics, type of drugs and modalities used. Specific queries about pain were sorted out like adequacy of pain assessment done by primary physician, pain intensity, any intervention done and pain relief post-intervention. SPSS version 17, analysis of variance and Chi square test were used for statistical purpose. RESULTS: The mean current pain score on the visual analogue score (VAS) was lowest in the Surgical Ward which was being managed by the Acute Pain Management Service (APMS) team followed by the Medical Ward and then Emergency Rooms. The difference was found to be statistically significant. The mean of worst pain score was also the lowest in the Surgical Ward. There was significant difference between wards in terms of the use of pain medications. Proper documentation for pain was done for all patients in the Surgical Ward, followed by the Emergency Room and then the Medical Ward. CONCLUSION: Better pain assessment, re-assessment, documentation and patient satisfaction were observed in the Surgical Ward compared to the other two locations of the study.