Decision trees learning is one of the most practical classification methods in machine learning, which is used for approximating discrete-valued target functions. However, they may overfit the training data, which limits their ability to generalize to unseen instances. In this study, we investigated the use of instance reduction techniques to smooth the decision boundaries before training the decision trees. Noise filters such as ENN, RENN, and ALLKNN remove noisy instances while DROP3 and DROP5 may remove genuine instances. Extensive empirical experiments were conducted on 13 benchmark datasets from UCI machine learning repository with and without intentionally introduced noise. Empirical results show that eliminating border instances improves the classification accuracy of decision trees and reduces the tree size, which reduces the training and classification times. In datasets without intentionally added noise, applying noise filters without the use of the built-in Reduced Error Pruning gave the best classification accuracy. ENN, RENN, and ALLKNN outperformed decision trees learning without pruning in 9, 9, and 8 out of 13 datasets, respectively. The datasets reduced using ENN and RENN without built-in pruning were more effective when noise was intentionally introduced in different ratios.

In recent years, the number of Gun-related incidents has crossed over 250,000 per year and over 85% of the existing 1 billion firearms are in civilian hands, manual monitoring has not proven effective in detecting firearms. which is why an automated weapon detection system is needed. Various automated convolutional neural networks (CNN) weapon detection systems have been proposed in the past to generate good results. However, These techniques have high computation overhead and are slow to provide real-time detection which is essential for the weapon detection system. These models have a high rate of false negatives because they often fail to detect the guns due to the low quality and visibility issues of surveillance videos. This research work aims to minimize the rate of false negatives and false positives in weapon detection while keeping the speed of detection as a key parameter. The proposed framework is based on You Only Look Once (YOLO) and Area of Interest (AOI). Initially, the models take pre-processed frames where the background is removed by the use of the Gaussian blur algorithm. The proposed architecture will be assessed through various performance parameters such as False Negative, False Positive, precision, recall rate, and F1 score. The results of this research work make it clear that due to YOLO-v5s high recall rate and speed of detection are achieved. Speed reached 0.010 s per frame compared to the 0.17 s of the Faster R-CNN. It is promising to be used in the field of security and weapon detection.

Purpose: To biosynthesize silver nanoparticles (AgNPs) using Psidium guajava L. and Lawsonia inermis L. leaf extracts, and investigate their antioxidant and cytotoxic activities.Methods: The aqueous extracts were prepared by maceration in distilled H2O followed by partitioning with EtOAc. AgNPs were prepared by treating the extracts with 1 mM AgNO3 and then were characterized by UV-vis and FTIR analyses, and transmission electron microscopy (TEM). MTT cytotoxicity and 2,2`-azinobis(3-ethylbenzothiazoline-6-sulphonic acid (ABTS) antioxidant assays were used to assess their cytotoxic and antioxidant properties, respectively.Results: AgNPs from P. guajava and L. inermis extracts exhibited good morphological stability and showed moderate antioxidant activity (68.1 and 71.9%, respectively) compared to their extracts. Equipotent cytotoxicity against HCT-116 and MCF-7 cells was observed for AgNPs derived from P.guajava, while AgNPs derived from L. inermis possessed two-fold cytotoxicity compared to their corresponding extracts. Phytochemical analysis of P. guajava afforded pyrogallol, quercetin, quercetin-3-O-β-xylopyranoside, quercetin-3-O-β-arabinopyranoside, and quercetin-3-O-α-rabinofuranoside, while L. inermis afforded lawsone and luteolin.Conclusion: Flavonoids and phenolics play a major role in reducing Ag+ ions, surface coating, antioxidant, and cytotoxic activities of AgNPs. The biocompatible AgNPs produced by L. inermis demonstrate promising cytotoxic activity that could contribute to new cancer treatments.

Similarity detection in the text is the main task for a number of Natural Language Processing (NLP) applications. As textual data is comparatively large in quantity and huge in volume than the numeric data, therefore measuring textual similarity is one of the important problems. Most of the similarity detection algorithms are based upon word to word matching, sentence/paragraph matching, and matching of the whole document. In this research, a novel approach is proposed using deep learning models, combining Long Short Term Memory network (LSTM) with Convolutional Neural Network (CNN) for measuring semantics similarity between two questions. The proposed model takes sentence pairs as input to measure the similarity between them. The model is tested on publicly available Quora’s dataset. The model in comparison to the existing techniques gave 87.50 % accuracy which is better than the previous approaches.

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