An index for reporting air quality is called the air quality index (AQI). It measures the impact of air pollution on a person’s health over a short period of time. The purpose of the AQI is to educate the public on the negative health effects of local air pollution. The amount of air pollution in Indian cities has significantly increased. There are several ways to create a mathematical formula to determine the air quality index. Numerous studies have found a link between air pollution exposure and adverse health impacts in the population. Data mining techniques are one of the most interesting approaches to forecast AQI and analyze it. The aim of this paper is to find the most effective way for AQI prediction to assist in climate control. The most effective method can be improved upon to find the most optimal solution. Hence, the work in this paper involves intensive research and the addition of novel techniques such as SMOTE to make sure that the best possible solution to the air quality problem is obtained. Another important goal is to demonstrate and display the exact metrics involved in our work in such a way that it is educational and insightful and hence provides proper comparisons and assists future researchers. In the proposed work, three distinct methods—support vector regression (SVR), random forest regression (RFR), and CatBoost regression (CR)—have been utilized to determine the AQI of New Delhi, Bangalore, Kolkata, and Hyderabad. After comparing the results of imbalanced datasets, it was found that random forest regression provides the lowest root mean square error (RMSE) values in Bangalore (0.5674), Kolkata (0.1403), and Hyderabad (0.3826), as well as higher accuracy compared to SVR and CatBoost regression for Kolkata (90.9700%) and Hyderabad (78.3672%), while CatBoost regression provides the lowest RMSE value in New Delhi (0.2792) and the highest accuracy is obtained for New Delhi (79.8622%) and Bangalore (68.6860%). Regarding the dataset that was subjected to the synthetic minority oversampling technique (SMOTE) algorithm, it is noted that random forest regression provides the lowest RMSE values in Kolkata (0.0988) and Hyderabad (0.0628) and higher accuracies are obtained for Kolkata (93.7438%) and Hyderabad (97.6080%) in comparison to SVR and CatBoost regression, whereas CatBoost regression provides the highest accuracies for New Delhi (85.0847%) and Bangalore (90.3071%). This demonstrated definitely that datasets that had the SMOTE algorithm applied to them produced a higher accuracy. The novelty of this paper lies in the fact that the best regression models have been picked through thorough research by analyzing their accuracies. Moreover, unlike most related papers, dataset balancing is carried out through SMOTE. Moreover, all of the implementations have been documented via graphs and metrics, which clearly show the contrast in results and help show what actually caused the improvement in accuracy.

Drone examination has been overall quickly embraced by NDMM (natural disaster mitigation and management) division to survey the state of impacted regions. Manual video analysis by human observers takes time and is subject to mistakes. The human identification examination of pictures caught by drones will give a practical method for saving lives who are being trapped under debris during quakes or in floods and so on. Drone investigation for research and security and search and rescue (SAR) should involve the drone to filter the impacted area using a camera and a model of unmanned area vehicles (UAVs) to identify specific locations where assistance is required. The existing methods (Balmukund et al. 2020) used were faster-region based convolutional neural networks (F-RCNNs), single shot detector (SSD), and region-based fully convolutional network (R-FCN) for the detection of human and recognition of action. Some of the existing methods used 700 images with six classes only, whereas the proposed model uses 1996 images with eight classes. The proposed model is used YOLOv3 (you only look once) algorithm for the detection and recognition of actions. In this study, we provide the fundamental ideas underlying an object detection model. To find the most effective model for human recognition and detection, we trained the YOLOv3 algorithm on the image dataset and evaluated its performance. We compared the outcomes with the existing algorithms like F-RCNN, SSD, and R-FCN. The accuracies of F-RCNN, SSD, R-FCN (existing algorithms), and YOLOv3 (proposed algorithm) are 53%, 73%, 93%, and 94.9%, respectively. Among these algorithms, the YOLOv3 algorithm gives the highest accuracy of 94.9%. The proposed work shows that existing models are inadequate for critical applications like search and rescue, which convinces us to propose a model raised by a pyramidal component extracting SSD in human localization and action recognition. The suggested model is 94.9% accurate when applied to the proposed dataset, which is an important contribution. Likewise, the suggested model succeeds in helping time for expectation in examination with the cutting-edge identification models with existing strategies. The average time taken by our proposed technique to distinguish a picture is 0.40 milisec which is a lot better than the existing method. The proposed model can likewise distinguish video and can be utilized for real-time recognition. The SSD model can likewise use to anticipate messages if present in the picture.

In recent years, nanocrystalline materials have drawn the attention of researchers in the field of materials science engineering due to its enhanced mechanical properties such as high strength and high hardness. However, the cost of nanocrystalline materials is prohibitively high, primarily due to the expensive equipments used and the low output. Recently, researchers have attempted to produce nanocrystalline materials through machining. This research work focuses on the production of nanocrystalline materials through machining and high energy ball milling route. Nanocrystalline materials were generated through oblique machining by the large strain plastic deformation imposed by the cutting tool during machining. Stainless Steel 316L (SS) bar of 50mm diameter and length 300mm was chosen for this study. Machining parameters such as speed, feed depth of cut and rake angle were chosen under different cutting conditions. Taguchi L16 orthogonal array was adopted and optimized the machining parameters. Coated tungsten carbide cutting tool was used for the study. Machined chips were collected and cleaned using ultrasonic machine for microstructure analysis. These chips were characterized using Scanning electron microscope (SEM) and X-ray Diffraction (XRD) analysis. From the optimized machining conditions chips were generated and further processed using the high energy ball mill for preparing nanocrystalline powders. High energy ball mill parameters such as milling speed, milling time and ball to powder ratio were optimized using taguchi method. The milled powders were then characterized using Transmission electron microscope (TEM), SEM and XRD. Results show that the milled powders were in the range of 20 -50 nm.