Forecasting the correct stock price is intriguing and difficult for investors due to its irregular, inherent dynamics, and tricky nature. Convolutional neural networks (CNN) have impressive performance in forecasting stock prices. One of the most crucial tasks when training a CNN on a stock dataset is identifying the optimal hyperparameter that increases accuracy. In this research, we propose the use of the Firefly algorithm to optimize CNN hyperparameters. The hyperparameters for CNN were tuned with the help of Random Search (RS), Particle Swarm Optimization (PSO), and Firefly (FF) algorithms on different epochs, and CNN is trained on selected hyperparameters. Different evaluation metrics are calculated for training and testing datasets. The experimental finding demonstrates that the FF method finds the ideal parameter with a minimal number of fireflies and epochs. The objective function of the optimization technique is to reduce MSE. The PSO method delivers good results with increasing particle counts, while the FF method gives good results with fewer fireflies. In comparison with PSO, the MSE of the FF approach converges with increasing epoch.

The huge data generate by the Internet of Things (IOT) are measured of high business worth, and data mining algorithms can be applied to IOT to take out hidden information from data. In this paper, we give a methodical way to review data mining in knowledge, technique and application view, together with classification, clustering, association analysis and time series analysis, outlier analysis. And the latest application luggage is also surveyed. As more and more devices connected to IOT, huge volume of data should be analyzed, the latest algorithms should be customized to apply to big data. We reviewed these algorithms and discussed challenges and open research issues. At last a suggested big data mining system is proposed.

Proposed framework can optimize the medical treatment cost that will help to improve the healthcare system of the developing countries. Framework Analytics techniques used in healthcare decision making

Retrieving Medical Images from a large inter-domain dataset requires multiple high efficiency processing models. These models include, but are not limited to, image classification, domain specific feature extraction & selection, ranking and post processing. A wide variety of system models have been designed to perform these tasks, but have limited accuracy, and retrieval performance due to improper cross-domain feature processing. In order to improve performance of cross-domain medical image retrieval systems, this text proposes a transfer learning mechanism, that learns from features of one domain, and applies the trained models to other domains. The proposed method uses a combination of VGGNet19, AlexNet, InceptionNet and Xception Net models for ensemble learning, along with wavelet and bag of features (WBoF) for efficient feature extraction. Each of the individual models were applied to different medical domains, and their retrieval accuracies were evaluated. Based on this evaluation, it is observed that VGGNet19 has better performance on Computer Tomography (CT) images, AlexNet model has better performance on Magnetic Resonance Imaging (MRI) images, InceptionNet model has better performance on Positron Emission Tomography (PET) images, while Xception Net has better retrieval performance for ultrasound (USG) images. Using this observation, a highly efficient augmentation model is designed, which achieves an accuracy of 98.06%, precision of 65.9%, recall of 76.1%, and area under the curve (AUC) performance of 98.9% on different datasets. This performance is compared with a wide variety of medical image datasets including Center for Invivo Microscopy (CIVM), Embrionic and Neonatal Mouse (H&E, MR), LONI image data archive, The Open Access Series of Imaging Studies (OASIS), & CT scans for Colon Cancer (CSCC). It was observed that the proposed model outperforms most of the recent state-of-the-art models, and achieves consistent parametric results across multiple domain medical images.Categories: H.3.1, H.3.2, H.3.3, H.3.7, H.5.1

India has experienced two severe waves of infection with COVID-19. Since there isn’t any effective drug, emerging variants of the virus, an unknown epidemiological life cycle, lockdown, and restrictions at public places slowed the spread of novel Coronavirus in the country. However, a large number of new COVID-19 cases and deaths have been reported from across the country. We carried out a study to evaluate the effectiveness of vaccination in India, randomly selected five states and a union territory to control the severity of COVID-19 (case fatality rate). During the study period, March 21st 2020 to 6th August 2021, with the initiation of vaccination, the case fatality rate was reduced to less than 1.0% in the selected states and union territory. Thus, vaccination for COVID-19 infection is found to be effective in decreasing the severity of the disease. The eligible population should get themselves inoculated at the nearest health facilities.