Breast cancer is highlighted in recent research as one of the most prevalent types of cancer. Timely identification is essential for enhancing patient results and decreasing fatality rates. Utilizing computer-assisted detection and diagnosis early on may greatly improve the chances of recovery by accurately predicting outcomes and developing suitable treatment plans. Grading breast cancer properly, especially evaluating nuclear atypia, is difficult owing to faults and inconsistencies in slide preparation and the intricate nature of tissue patterns. This work explores the capability of deep learning to extract characteristics from histopathology photos of breast cancer. The research introduces a new method called SMOTE-based Convolut

In this study, we made a comparison between LASSO & SCAD methods, which are two special methods for dealing with models in partial quantile regression. (Nadaraya & Watson Kernel) was used to estimate the non-parametric part ;in addition, the rule of thumb method was used to estimate the smoothing bandwidth (h). Penalty methods proved to be efficient in estimating the regression coefficients, but the SCAD method according to the mean squared error criterion (MSE) was the best after estimating the missing data using the mean imputation method

The issue of image captioning, which comprises automatic text generation to understand an image’s visual information, has become feasible with the developments in object recognition and image classification. Deep learning has received much interest from the scientific community and can be very useful in real-world applications. The proposed image captioning approach involves the use of Convolution Neural Network (CNN) pre-trained models combined with Long Short Term Memory (LSTM) to generate image captions. The process includes two stages. The first stage entails training the CNN-LSTM models using baseline hyper-parameters and the second stage encompasses training CNN-LSTM models by optimizing and adjusting the hyper-parameters of

In this study, novel Schiff base complexes with Zn(II) and Co(II) ions were successfully synthesized. The malonic acid dihydrazide was converted into the Schiff base ligand by combining it with 1-hydroxy-2-naphthaldehyde, and the last step required reacting it with the appropriate metal(II) chloride to produce pure target complexes. The generated complexes were thoroughly characterized using FTIR, 1H-NMR, 13C-NMR, GC-mass, and UV-Vis spectroscopies. In order to photo-stabilize polystyrene (PS) and reduce the photodegradation of its polymeric chains, these chemicals have been used in this work. The efficiency of the generated complexes as photo-stabilizers was evaluated using a variety of techniques, including FTIR, weight loss, visc

      Field trial was conducted with the aim of utilizing extract of allelopathic crop to reduce the use of synthetic herbicides in wheat fields. Sorghum extract at 12 L /ha, sunflower extract at 12 L /ha, combination of sorghum and sunflower extracts at 12 L /ha and chevalier at 25, 50 and 100% of recommended dose were applied alone or in combination with each other. Weed free and weedy check treatments were included for comparison. The experiment was conducted in a randomized complete block design with three replications. The results showed that the recommended  dose of chevalier  treatment recorded lowest means of weed density 15.7, 23.7, 25.3 and 27.9 weeds m-2and weeds dry weight 13.4, 16.4, 23.3 and 29.2 g m-2 and gave

Background: This study was carried out in response to the Zika virus epidemic, which constituted a public health emergency, and to the 2019 WHO calling for strengthened surveillance for the early detection of related microcephaly. The main aim of the study was to phenotype the craniofacial morphology of microcephaly using a novel approach and new measurements, and relate the characteristics to brain abnormalities in Zika-infected infants in Brazil to improve clinical surveillance.

Methods: 3D images of the face and the cranial vault of 44 Zika-infected infants and matched healthy controls were captured using a 3D stereophotogrammetry system. The CT scans of the brain of t