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As the volume of data continues to grow, the significance of text classification is on the rise. This vast amount of data majorly exists in the form of texts. Effective data preparation is essential to extract sentiment data from this vast amount of text, as irrelevant and redundant information can impede valuable insights. Feature selection is an important step in the data preparation phase as it eliminates irrelevant and insignificant features from the huge features set. There exist a large body of work related to feature selection for image processing but limited research is done for text data. While some studies recognize the significance of feature selection in text classification, but there is still need for more efficient sentiment analysis models that optimize feature selection and reduce computational. This manuscript aims to bridge these gaps by introducing a hybrid multi-objective evolutionary algorithm as a feature selection mechanism, combining the power of multiple objectives and evolutionary processes. The approach combines two feature selection techniques within a binary classification model: a filter method, Information Gain (IG), and an evolutionary wrapper method, Binary Multi-Objective Grey Wolf Optimizer (BMOGWO). Experimental evaluations are conducted across six diverse datasets. It achieves a reduction of over 90 percent in feature size while improving accuracy by nearly nine percent. These results showcase the model's efficiency in terms of computational time and its efficacy in terms of higher classification accuracy which improves sentiment analysis performance. This improvement can be beneficial for various applications, including recommendation systems, reviews analysis, and public opinion observation. However, it's crucial to acknowledge certain limitations of this study. These encompass the need for broader classifier evaluation, and scalability considerations with larger datasets. These identified limitations serve as directions for future research and the enhancement of the proposed approach. [ABSTRACT FROM AUTHOR]

Introduction: Cancer is the second most prevalent cause of mortality in the world, despite the availability of several medications for cancer treatment. Therefore, the cancer research community emphasized on computational techniques to speed up the discovery of novel anticancer drugs. Methods: In the current study, QSAR-based virtual screening was performed on the Zinc15 compound library (271 derivatives of methotrexate (MTX) and phototrexate (PTX)) to predict their inhibitory activity against dihydrofolate reductase (DHFR), a potential anticancer drug target. The deep learning-based ADMET parameters were employed to generate a 2D QSAR model using the multiple linear regression (MPL) methods with Leave-one-out cross-validated (LOO-CV) Q2 and correlation coefficient R2 values as high as 0.77 and 0.81, respectively. Results: From the QSAR model and virtual screening analysis, the top hits (09,27, 41,68,74,85,99,180) exhibited pIC50 ranging from 5.85 to 7.20 with a minimum binding score of -11.6 to -11.0 kcal/mol and were subjected to further investigation. The ADMET attributes using the message-passing neural network (MPNN) model demonstrated the potential of selected hits as an oral medication based on lipophilic profile Log P (0.19-2.69) and bioavailability (76.30% to 78.46%). The clinical toxicity score was 31.24% to 35.30%, with the least toxicity score (8.30%) observed with compound 180. The DFT calculations were carried out to determine the stability, physicochemical parameters and chemical reactivity of selected compounds. The docking results were further validated by 100 ns molecular dynamic simulation analysis. Conclusion: The promising lead compounds found endorsed compared to standard reference drugs MTX and PTX that are best for anticancer activity and can lead to novel therapies after experimental validations. Furthermore, it is suggested to unveil the inhibitory potential of identified hits via in-vitro and in-vivo approaches. [ABSTRACT FROM AUTHOR]

In this study, cucumber peel (CP) was treated with Calcium Hydroxide i.e. Ca(OH)2 and its thermal behavior was examined. The pre‐treated cucumber peel (PTCP) was thermally degraded mainly through three steps; first, moisture and light volatile compounds loss (30–132°C); second, primary devolatilization (132 to 337°C) involving intense degradation with mainly re‐formation process and third and final, secondary reactions (337 to 800°C). Lower PTCP activation energy (192.77–183.33 KJ/mol) reflects its easy thermal conversion initiation. The non‐condensable compounds (CO2 & CH4) and condensable products (H2O, CH3CH2OH, CH3COOH, C=C, C6H5OH, HCOOH) were found as 29.75% and 70.25% respectively determined by TG‐FTIR analysis. While, with temperature, relative concentration of condensable compounds decrease significantly and found as 61.38% and 42.5% at lower and higher temperature respectively. By Py/GC–MS analysis, presence of the high energy optimizing compounds and valuable chemicals, i.e. toluene (5.12%), benzene (4.31%) and phenols (27.38%) was confirmed. All these datasets shows that pre‐treatment of CP with Ca(OH)2 might be suitable approach to produce Bioenergy and Bio‐chemicals. [ABSTRACT FROM AUTHOR]

Evolutionary machine learning research aims to develop classifier systems that can solve complex and hard tasks. This paper addresses the problem of inferring a regular expression from a given set of strings for automating the task of information extraction. To the best of our knowledge, this paper is the first to propose the extension of accuracy-based classifier system XCS to learn the regular expressions for text extraction. This new system named as XCSREA includes tree-like code fragments to learn regular expressions. The genetic algorithm in action sets uses two-point crossover with uniform mutation and Roulette wheel parent selection method. Seven different datasets, each with three different lengths, are used to compare the performance of the proposed model with standard genetic programming (GP) approach. The experimental results demonstrate that XCSREA outperforms standard GP approach when sufficiently large numbers of classifiers are used. [ABSTRACT FROM AUTHOR]

Background: Polycystic ovary syndrome (PCOS) is mainstream gynaecological problems in adult women. Vitamin D has been linked to propensity of developing PCO. As this hormones has certain effects on insulin sensitivity and restoration of reproductive capacity in women with PCOs. So, a study was planned to establish correlation of deficiency of vitamin D and PCOs. Objectives: To determine the correlation of deficiency of vitamin D with PCOs in patients reporting to a teaching hospital. Study Design: Case control study. Setting: Institute of Shaikh Zayed Hospital, Lahore, Gynae-Obst Department. Period: Six months from July 2015 to December 2015. Material & Methods: If serum level of vitamin D is < 25nmol/L then deficiency was labeled. Odds ratio was calculated between PCOs and vitamin D deficiency. Results: The mean age of the patients was 26.91±6.09 years. In this study, 70% females were married. The mean BMI of patients was 22.59±2.48kg/m², and 76(76%) patients had normal BMI and 24(24%) patients were overweight. Vitamin D deficiency was observed in 47 cases in which 31 were from case group and 16 were from control group with significant association i.e. OR=3.47. Conclusion: Our study results concluded that there is more risk of vitamin D deficiency in females with PCOS as compared to females without PCOS. [ABSTRACT FROM AUTHOR]