Cognitively Enhanced Versions of Capuchin Search Algorithm for Feature Selection in Medical Diagnosis: a COVID-19 Case Study.

Feature selection (FS) is a crucial area of cognitive computation that demands further studies. It has recently received a lot of attention from researchers working in machine learning and data mining. It is broadly employed in many different applications. Many enhanced strategies have been created for FS methods in cognitive computation to boost the performance of the methods. The goal of this paper is to present three adaptive versions of the capuchin search algorithm (CSA) that each features a better search ability than the parent CSA. These versions are used to select optimal feature subset based on a binary version of each adapted one and the k-Nearest Neighbor (k-NN) classifier. These versions were matured by applying several strategies, including automated control of inertia weight, acceleration coefficients, and other computational factors, to ameliorate search potency and convergence speed of CSA. In the velocity model of CSA, some growth computational functions, known as exponential, power, and S-shaped functions, were adopted to evolve three versions of CSA, referred to as exponential CSA (ECSA), power CSA (PCSA), and S-shaped CSA (SCSA), respectively. The results of the proposed FS methods on 24 benchmark datasets with different dimensions from various repositories were compared with other k-NN based FS methods from the literature. The results revealed that the proposed methods significantly outperformed the performance of CSA and other well-established FS methods in several relevant criteria. In particular, among the 24 datasets considered, the proposed binary ECSA, which yielded the best overall results among all other proposed versions, is able to excel the others in 18 datasets in terms of classification accuracy, 13 datasets in terms of specificity, 10 datasets in terms of sensitivity, and 14 datasets in terms of fitness values. Simply put, the results on 15, 9, and 5 datasets out of the 24 datasets studied showed that the performance levels of the binary ECSA, PCSA, and SCSA are over 90% in respect of specificity, sensitivity, and accuracy measures, respectively. The thorough results via different comparisons divulge the efficiency of the proposed methods in widening the classification accuracy compared to other methods, ensuring the ability of the proposed methods in exploring the feature space and selecting the most useful features for classification studies.

A prospective multicentre study evaluating the outcomes of the abdominal wall dehiscence repair using posterior component separation with transversus abdominis muscle release reinforced by a retro-muscular mesh: filling a step.

BACKGROUND: This study aimed to evaluate the results of posterior component separation (CS) and transversus abdominis muscle release (TAR) with retro-muscular mesh reinforcement in patients with primary abdominal wall dehiscence (AWD). The secondary aims were to detect the incidence of postoperative surgical site occurrence and risk factors of incisional hernia (IH) development following AWD repair with posterior CS with TAR reinforced by retromuscular mesh. METHODS: Between June 2014 and April 2018, 202 patients with grade IA primary AWD (Björck's first classification) following midline laparotomies were treated using posterior CS with TAR release reinforced by a retro-muscular mesh in a prospective multicenter cohort study. RESULTS: The mean age was 42 ± 10 years, with female predominance (59.9%). The mean time from index surgery (midline laparotomy) to primary AWD was 7 ± 3 days. The mean vertical length of primary AWD was 16 ± 2 cm. The median time from primary AWD occurrence to posterior CS + TAR surgery was 3 ± 1 days. The mean operative time of posterior CS + TAR was 95 ± 12 min. No recurrent AWD occurred. Surgical site infections (SSI), seroma, hematoma, IH, and infected mesh occurred in 7.9%, 12.4%, 2%, 8.9%, and 3%, respectively. Mortality was reported in 2.5%. Old age, male gender, smoking, albumin level < 3.5 gm%, time from AWD to posterior CS + TAR surgery, SSI, ileus, and infected mesh were significantly higher in IH. IH rate was 0.5% and 8.9% at two and three years, respectively. In multivariate logistic regression analyses, the predictors of IH were time from AWD till posterior CS + TAR surgical intervention, ileus, SSI, and infected mesh. CONCLUSION: Posterior CS with TAR reinforced by retro-muscular mesh insertion resulted in no AWD recurrence, low IH rates, and low mortality of 2.5%. Trial registration Clinical trial: NCT05278117.

Recycling rice straw ash to produce low thermal conductivity and moisture-resistant geopolymer adobe bricks.

Rice straw ash (RSA) geopolymer adobe bricks were produced using the geopolymerization reaction among the RSA, soil, and alkaline activator at the Biosystem Engineering Department, Faculty of Agriculture, Alexandria University, Egypt, to optimize adobe brick advantages. The bulk density, water absorption, compressive strength, and thermal conductivity of the new composite were measured at RSA contents of 0%, 5%, 10%, and 20% and sodium hydroxide contents of 2.5%, 5%, 7.5%, and 10% after curing the composite for 28 days. Results indicated that increasing RSA from 0% to 20% increased the compressive strength and decreased the bulk density, water absorption, and thermal conductivity. Further, increasing sodium hydroxide from 2.5% to 10% increased the bulk density and compressive strength and decreased the water absorption. Significant effects of RSA and sodium hydroxide percentages and their interaction on all the studied characters were reported. The best conditions to minimize bulk density, water absorption, thermal conductivity, and optimize compressive strength of the composite were at 10% sodium hydroxide and 20% RSA. The minimum bulk density, water absorption, and thermal conductivity were 1.463 g/cm3, 8.3%, and 0.46 W/(m·K), respectively, while the maximum CS was 2.1 MPa after 28 days. Using RSA geopolymer adobe bricks on building interior walls is recommended to decrease bricks' thermal conductivity, water absorption, and weight.

An enhanced binary Rat Swarm Optimizer based on local-best concepts of PSO and collaborative crossover operators for feature selection.

In this paper, an enhanced binary version of the Rat Swarm Optimizer (RSO) is proposed to deal with Feature Selection (FS) problems. FS is an important data reduction step in data mining which finds the most representative features from the entire data. Many FS-based swarm intelligence algorithms have been used to tackle FS. However, the door is still open for further investigations since no FS method gives cutting-edge results for all cases. In this paper, a recent swarm intelligence metaheuristic method called RSO which is inspired by the social and hunting behavior of a group of rats is enhanced and explored for FS problems. The binary enhanced RSO is built based on three successive modifications: i) an S-shape transfer function is used to develop binary RSO algorithms; ii) the local search paradigm of particle swarm optimization is used with the iterative loop of RSO to boost its local exploitation; iii) three crossover mechanisms are used and controlled by a switch probability to improve the diversity. Based on these enhancements, three versions of RSO are produced, referred to as Binary RSO (BRSO), Binary Enhanced RSO (BERSO), and Binary Enhanced RSO with Crossover operators (BERSOC). To assess the performance of these versions, a benchmark of 24 datasets from various domains is used. The proposed methods are assessed concerning the fitness value, number of selected features, classification accuracy, specificity, sensitivity, and computational time. The best performance is achieved by BERSOC followed by BERSO and then BRSO. These proposed versions are comparatively assessed against 25 well-regarded metaheuristic methods and five filter-based approaches. The obtained results underline their superiority by producing new best results for some datasets.

Binary Horse herd optimization algorithm with crossover operators for feature selection.

This paper proposes a binary version of Horse herd Optimization Algorithm (HOA) to tackle Feature Selection (FS) problems. This algorithm mimics the conduct of a pack of horses when they are trying to survive. To build a Binary version of HOA, or referred to as BHOA, twofold of adjustments were made: i) Three transfer functions, namely S-shape, V-shape and U-shape, are utilized to transform the continues domain into a binary one. Four configurations of each transfer function are also well studied to yield four alternatives. ii) Three crossover operators: one-point, two-point and uniform are also suggested to ensure the efficiency of the proposed method for FS domain. The performance of the proposed fifteen BHOA versions is examined using 24 real-world FS datasets. A set of six metric measures was used to evaluate the outcome of the optimization methods: accuracy, number of features selected, fitness values, sensitivity, specificity and computational time. The best-formed version of the proposed versions is BHOA with S-shape and one-point crossover. The comparative evaluation was also accomplished against 21 state-of-the-art methods. The proposed method is able to find very competitive results where some of them are the best-recorded. Due to the viability of the proposed method, it can be further considered in other areas of machine learning.

Discrete flower pollination algorithm for patient admission scheduling problem.

This paper aims to tackle the Patient Admission Scheduling Problem (PASP) using the Discrete Flower Pollination Algorithm (DFPA), a new, meta-heuristic optimization method based on plant pollination. PASP is one of the most important problems in the field of health care. It is a highly constrained and combinatorial optimization problem of assigning patients to medical resources in a hospital, subject to predefined constraints, while maximizing patient comfort. While the flower pollination algorithm was designed for continuous optimization domains, a discretization of the algorithm has been carried out for application to the PASP. Various neighborhood structures have been employed to enhance the method, and to explore more solutions in the search space. The proposed method has been tested on six instances of benchmark datasets for comparison against another algorithm using the same dataset. The prospective method is shown to be very efficient in solving any scheduling problem.

Healthcare scheduling in optimization context: a review.

This paper offers a summary of the latest studies on healthcare scheduling problems including patients' admission scheduling problem, nurse scheduling problem, operation room scheduling problem, surgery scheduling problem and other healthcare scheduling problems. The paper provides a comprehensive survey on healthcare scheduling focuses on the recent literature. The development of healthcare scheduling research plays a critical role in optimizing costs and improving the patient flow, providing prompt administration of treatment, and the optimal use of the resources provided and accessible in the hospitals. In the last decades, the healthcare scheduling methods that aim to automate the search for optimal resource management in hospitals by using metaheuristics methods have proliferated. However, the reported results are disintegrated since they solved every specific problem independently, given that there are many versions of problem definition and various data sets available for each of these problems. Therefore, this paper integrates the existing results by performing a comprehensive review and analyzing 190 articles based on four essential components in solving optimization problems: problem definition, formulations, data sets, and methods. This paper summarizes the latest healthcare scheduling problems focusing on patients' admission scheduling problems, nurse scheduling problems, and operation room scheduling problems considering these are the most common issues found in the literature. Furthermore, this review aims to help researchers to highlight some development from the most recent papers and grasp the new trends for future directions.

Evaluation of occupational and patient radiation doses in orthopedic surgery.

This study intends to measure the radiation dose to patients and staff during (i) Dynamic Hip Screw (DHS) and (ii) Dynamic Cannula Screw (DCS) and to evaluate entrance surface Air kerma (ESAK) dose and organ doses and effective doses. Calibrated Thermoluminescence dosimeters (TLD-GR200A) were used. The mean patients' doses were 0.46mGy and 0.07mGy for DHS and DCS procedures, respectively. The mean staff doses at the thyroid and chest were 4.69mGy and 1.21mGy per procedure. The mean organ and effective dose for patients and staff were higher in DHS compared to DCS. Orthopedic surgeons were exposed to unnecessary radiation doses due to the lack of protection measures. The radiation dose per hip procedure is within the safety limit and less than the previous studies.

Toxicity of cadmium and protective effect of bee honey, vitamins C and B complex.

The present work aimed to study the toxic effect of cadmium (Cd) on rabbits' blood indices, as well as the therapeutic effect of the antioxidant agents, vitamins C and B complex and bee honey on Cd intoxicated rabbits. Cadmium chloride (CdCl2) was injected subcutaneously at a dose of 3 mg/kg of body weight. The results showed a significant increase in serum glucose, triglycerides, cholesterol, total protein, albumin, globulin, urea and creatinine, compared to the control group. In addition, CdCl2 intoxication increased the levels of uric acid, aspartate aminotransferase, alanine aminotransferase, alkaline phosphatase and bilirubin. Concerning haematological parameters, the more obvious changes were an increase in mean corpuscular volume and a decrease in white blood cells count, platelets, lymphocytes, heamatocrit, haemoglobin and red blood cells count. Treatment of CdCl2-intoxicated animals with vitamins C and B complex and bee honey showed a decrease in the harmful effects of Cd by restoring haematological and biochemical changes. Bee honey treatment was the most effective in providing recoveries in the altered blood parameters.

Breast cancer in Arabic women.

A case-control study of breast cancer was conducted in Cairo, Egypt and in Kuwait. This is the first such report from Arabic countries and the most significant finding is the animal incidence rate of 7.67 per 100,000 in Kuwaiti women, which ranks amongst the lowest thus far reported in the world. Factors which contribute to this low rate are a short period of active menstruation, low family history, first childbirth at very early age and most likely significant dietary factors. More than 45% of Egyptian women give birth to a child before the age of 20, with 56.5% of controls and 42.3% of patients (p less than 0.01). Many patients already have extensive disease at time of diagnosis as a result of considerable delay. However, this delay did not result in a high incidence of inflammatory breast carcinoma, such as has been reported in Tunisia.