In the Internet of Things (IoT) era, the mobile crowd sensing system (MCS) has become increasingly important. The Internet of Things Auto (IOTA) has evolved rapidly in practically every technology field over the last decade. IOTA-based mobile crowd sensing technology is being developed in this study using machine learning to detect and prevent mobile users from engaging in fake sensing activities. It has been determined through testing and evaluation that our method is effective for both quality estimation and incentive allocation. Using the IOTA Bottleneck dataset, multiple performance metrics were used to demonstrate how well logit-boosted algorithms perform. After applying logit-boosted algorithms on the dataset for the classification, Logi-XGB scored 95.7 percent accuracy, while Logi-GBC scored 90.8 percent accuracy. As a result of this, Logi-ABC had an accuracy rate of 89%. Logi-CBC, on the other hand, got the highest accuracy of 99.8%. Logi-LGBM and Logi-HGBC both scored 91.37 percent accuracy, which is identical. On the given dataset, our Logi-CBC algorithm outperforms earlier Logit-boosted algorithms in terms of accuracy. Using the new IoTA-Botnet 2020 dataset, a new proposed methodology is tested. In comparison to prior Logit-boosted algorithms, the new model Logi-CBC has a highest detection accuracy of 99.8%.

Due to recent developments in the global economy, transportation, and industrialization, air pollution is one of main environmental issues in the 21st century. The current study aimed to predict both short-term and long-term air pollution in Jiangsu Province, China, based on the Prophet forecasting model (PFM). We collected data from 72 air quality monitoring stations to forecast six air pollutants: PM10, PM2.5, SO2, NO2, CO, and O3. To determine the accuracy of the model and to compare its results with predicted and actual values, we used the correlation coefficient (R), mean squared error (MSE), root mean squared error (RMSE), and mean absolute error (MAE). The results show that PFM predicted PM10 and PM2.5 with R values of 0.40 and 0.52, RMSE values of 16.37 and 12.07 μg/m3, and MAE values of 11.74 and 8.22 μg/m3, respectively. Among other pollutants, PFM also predicted SO2, NO2, CO, and O3 with R values are between 5 μg/m3 to 12 μg/m3; and MAE values between 2 μg/m3 to 11 μg/m3. PFM has extensive power to accurately predict the concentrations of air pollutants and can be used to forecast air pollution in other regions. The results of this research will be helpful for local authorities and policymakers to control air pollution and plan accordingly in upcoming years.

Accurate stock market returns are quite difficult for the company because of the unpredictable and non-linear nature of the financial stock markets. With the development of artificial intelligence and increased computer power, programmed prediction approaches have demonstrated that they are increasingly effective in predicting stock values. In this study, the Artificial Neural Network, LSTM, and LR techniques were used to predict the closing price for the following day for five companies belonging to different business sectors. In today's economy, the stock market or equity market has a profound influence. The prediction of stock prices is quite complex, chaotic, and it is a big challenge to have a dynamic environment. Behavioural finance means that investors' decision-making processes are affected by emotions and attitudes in response to particular news. In order to help investors' judgements, we have supplied a technology for the analysis of the stock exchange. The method combines historical price prediction. For predicting, LSTM (Long Short-Term Memory), ANN and LR are employed. It includes the latest information on trade and analytical indicators. Financial data: Open, high, low and close stock prices are used to build new variables needed for model input. The models are validated with standard strategic indicators: RMSE and MAPE. The low values of these two variables indicate that the models are costeffective.

An important measurable indicator of urbanization and its environmental implications has been identified as the urban impervious surface. It presents a strategy based on three-dimensional convolutional neural networks (3D CNNs) for extracting urbanization from the LiDAR datasets using deep learning technology. Various 3D CNN parameters are tested to see how they affect impervious surface extraction. For urban impervious surface delineation, this study investigates the synergistic integration of multiple remote sensing datasets of Azad Kashmir, State of Pakistan, to alleviate the restrictions imposed by single sensor data. Overall accuracy was greater than 95% and overall kappa value was greater than 90% in our suggested 3D CNN approach, which shows tremendous promise for impervious surface extraction. Because it uses multiscale convolutional processes to combine spatial and spectral information and texture and feature maps, we discovered that our proposed 3D CNN approach makes better use of urbanization than the commonly utilized pixel-based support vector machine classifier. In the fast-growing big data era, image analysis presents significant obstacles, yet our proposed 3D CNNs will effectively extract more urban impervious surfaces.

Quantifying atmospheric aerosols and their linkages to climatic repercussions is necessary to understand the dynamics of climate forcing and enhance our knowledge of climate change. Because of this reactivity to precipitation, temperature, topography, and human activity, the atmospheric boundary layer (ABL) is one of the most dynamic atmospheric regions: ABL aerosols have a big impact on the evolution of climate change’s radiative forcing, human health, food security, and, eventually, the local and global economy. Continuous monitoring and instrumental and computational approaches are required for the detection and analysis of ABL pattern behavior. This paper provides a deep learning-based outer layer aerosol detection system based on Light Detection and Ranging (LiDAR) data fusion. The suggested method applies sequential models to turn low-level data into compressed features using object-based analysis, feature-level fusion, and autoencoder-based dimensionality reduction. Convolutional neural networks (CNNs) were used to convert compressed data into high-level properties that could be used to categorize air particles in the outer layer. This research describes deep learning approaches that allowed for detecting 40% more atmospheric features at a horizontal resolution of 5 km during daytime operations when applied to LiDAR data. Compared to existing deep learning algorithms for edges and complicated near-surface sceneries during the day, a convolutional autoencoder (CAE) trained using LiDAR dataset standard data products showed the potential for improved aerosol discrimination with 98% accuracy.