The accumulation of salt through natural causes and human artifice, such as saline inundation or mineral weathering, is marked as salinization, but the hindrance toward spatial mapping of soil salinity has somewhat remained a consistent riddle despite decades of efforts. The purpose of the current study is the spatial mapping of soil salinity in Kot Addu (situated in the south of the Punjab province, Pakistan) using Landsat 8 data in five advanced machine learning regression models, i.e., Random Forest Regressor, AdaBoost Regressor, Decision Tree Regressor, Partial Least Squares Regression and Ridge Regressor. For this purpose, spectral data were obtained between 20 and 27 of January 2017 and a field survey was carried out to gather a total of fifty-five soil samples. To evaluate and compare the model’s performances, the coefficient of determination (R²), Mean Squared Error (MSE), Mean Absolute Error (MAE) and the Root-Mean-Squared Error (RMSE) were used. Spectral data of band values, salinity indices and vegetation indices were employed to study the salinity of soil. The results revealed that the Random Forest Regressor outperformed the other models in terms of prediction, achieving an R² of 0.94, MAE of 1.42 dS/m, MSE of 3.58 dS/m and RMSE of 1.89 dS/m when using the Differential Vegetation Index (DVI). Alternatively, when using the Soil Adjusted Vegetation Index (SAVI), the Random Forest Regressor achieved an R² of 0.93, MAE of 1.46 dS/m, MSE of 3.90 dS/m and RMSE of 1.97 dS/m. Hence, remote sensing technology with machine learning models is an efficient method for the assessment of soil salinity at local scales. This study will contribute to mitigating osmotic stress and minimizing the risk of soil erosion by providing early warnings regarding soil salinity. Additionally, it will assist agriculture officers in estimating soil salinity levels within a shorter time frame and at a reduced cost, enabling effective resource allocation.

The driver sleepiness is common and one of the main causes of road accidents. So, there is a need for automatically detecting of this human behaviour. In any case, if the drivers feel drowsy, they still keep on driving the vehicle, and accidents occur. This study can be implemented using the CNN training model and initiating an alarm if the drowsiness condition is detected. Many of the authors suggested the process of detecting the Drowsiness (Problem Statement) of the drivers using technologies like the Internet of Things (IoT), Deep learning, and Haar cascade (to detect the coordinates of eyes and mouth, which are the target objects). However, this study contributes towards providing the real-time application in co-operating the CNN model and Dlib. Hence, this study proposes a novel embedded system with CNN technology. The CNN model is fed with inputs based on four (4) images related to eye and mouth openings and closings. This application is trained using the CNN model, which takes inputs as images and processes by identifying the features on the face using the Dlib library while representing the change in the state of coordinates of eyes and mouth as Yawning. This approach is achieved using Convolution Neural Network (CNN), pillow, Pygame, OpenCV, and the Dlib, along with providing an alarm when the position of mouth changes. The model is recorded with a maximum validation accuracy of 98% with the minimum recorded loss of less than 0.04% as areal-time application.

The COVID-19 pandemic has imposed a public health crisis across the world. The global efforts lead to the development and deployment of multiple vaccines. The success of ending the COVID-19 pandemic relies on the willingness of people to get the vaccines. Social media platforms prove to be a valuable source to perform experiments on sentiment and emotion towards COVID-19 vaccination in many languages, mainly focusing on English. The people express their opinions and emotion on Twitter briefly, which can have tracked almost instantaneously. This helps the governments, public health officials, and decision-makers to understand public opinions towards vaccines. The goal of this research is to investigate public sentiment on COVID-19 vaccines. Twitter social media extracted all Arabic-language tweets mentioning seven vaccines in 7 months from 1 November 2020 to 31 May 2021. A set of Arabic sentiment lexicons were prepared to perform the sentiment analysis. The tweets' monthly average sentiment were calculated from the collected dataset and evaluated comparatively for each vaccine throughout the 11 months. Out of 5.5 million tweets that have been retrieved using the most frequent keywords and hashtags during the COVID-19 pandemic, 202,427 tweets were only considered and included in the monthly sentiment analysis. We considered tweets that mentioned only one vaccine name of the text. The distribution of tweets shows that 47.5% of the considered tweets mentioned the Pfizer vaccine. It is reported that 64% of the total tweets are non-negative while 35% are negative, with a significant difference in sentiment between the months. We observed an increase of non-negative tweets in parallel with increasing negative tweets on May 2021, reflecting the public's rising confidence towards vaccines. Lexicon-based sentiment analysis is valuable and easy to implement the technique. It can be used to track the sentiment regarding COVID-19 vaccines. The analysis of social media data benefits public health authorities by monitoring public opinions, addressing the people's concerns about vaccines, and building the confidence of individuals towards vaccines.

Ocean ecosystems have spatiotemporal variability and dynamic complexity that require a long-term deployment of an autonomous underwater vehicle for data collection. A new long-range autonomous underwater vehicle called Tethys is adapted to study different oceanic phenomena. Additionally, an ocean environment has external forces and moments along with changing water currents which are generally not considered in a vehicle kinematic model. In this scenario, it is not enough to generate a simple trajectory from an initial location to a goal location in an uncertain ocean as the vehicle can deviate from its intended trajectory. As such, we propose to compute a feedback plan that adapts the vehicle trajectory in the presence of any modeled or unmodeled uncertainties. In this work, we present a feedback motion planning method for the Tethys vehicle by combining a predictive ocean model and its kinematic modeling. Given a goal location, the Tethys kinematic model, and the water flow pattern, our method computes a feedback plan for the vehicle in a dynamic ocean environment that reduces its energy consumption. The computed feedback plan provides the optimal action for the Tethys vehicle to take from any location of the environment to reach the goal location considering its orientation. Our results based on actual ocean model prediction data demonstrate the applicability of our method.