The long-term development goals of most countries face significant challenges in reducing emissions, improving environmental sustainability, and mitigating the negative effects of climate change. This study looks at how the ecological sustainability of BRICS countries is affected by economic growth, financial development, new technologies, and renewable energy consumption with the mediating effect of trade openness. The study covers the years 2004–2023, and it was based on fixed-effect models that use static panel data. Data were collected from the World Development Indicators website. The countries and time frame for this study were selected on the basis of data availability. These findings show that the use of renewable energy sources, technological innovation, and financial development all have a significant and positive impact on environmental sustainability. Nevertheless, environmental sustainability is significantly and negatively impacted by economic growth. Furthermore, trade openness functions as a significant mediator between them. Based on empirical evidence, the paper suggests that the BRICS nations seek sustainable economic development. Moreover, government agencies need to accurately evaluate the connection between financial development and emission reduction when formulating programs to cut emissions.

Public feelings and reactions associated with finance are gaining significant importance as they help individuals, public health, financial and non-financial institutions, and the government understand mental health, the impact of policies, and counter-response. Every individual sentiment linked with a financial text can be categorized, whether it is a headline or the detailed content published in a newspaper. The Guardian newspaper is considered one of the most famous and the biggest websites for digital media on the internet. Moreover, it can be one of the vital platforms for tracking the public’s mental health and feelings via sentimental analysis of news headlines and detailed content related to finance. One of the key purposes of this study is the public’s mental health tracking via the sentimental analysis of financial text news primarily published on digital media to identify the overall mental health of the public and the impact of national or international financial policies. A dataset was collected using The Guardian application programming interface and processed using the support vector machine, AdaBoost, and single layer convolutional neural network. Among all identified techniques, the single layer convolutional neural network with a classification accuracy of 0.939 is considered the best during the training and testing phases as it produced efficient performance and effective results compared to other techniques, such as support vector machine and AdaBoost with associated classification accuracies 0.677 and 0.761, respectively. The findings of this research would also benefit public health, as well as financial and non-financial institutions.

Feature extracting and training module can be done by using face recognition neural learning techniques. Moreover, these techniques are widely employed to extract features from human images. Some detection systems are capable to scan the full body, iris detection, and finger print detection systems. These systems have deployed for safety and security intension. In this research work, we compare different machine learning algorithms for face recognition. Four supervised face recognition machine-learning classifiers such as Principal Component Analysis (PCA), 1-nearest neighbor (1-NN), Linear Discriminant Analysis (LDA), and Support Vector Machine (SVM) are considered. The efficiency of multiple classification systems is also demonstrated and tested in terms of their ability to identify a face correctly. Face Recognition is a technique to identify faces of people whose images are stored in some databases and available in the form of datasets. Extensive experiments conducted on these datasets. The comparative analysis clearly shows that which machine-learning algorithm is the best in terms of accuracy of image detection. Despite the fact, other identification methods are also very effective; face recognition has remained a major focus of research due to its non-meddling nature and being the easy method of personal identification for people. The findings of this work would be useful identification of a suitable machine-learning algorithm in order to achieve better face recognition accuracy.

Smart agriculture is a strategy for restructuring and reorganizing agricultural systems to ensure food security in the face of emerging climate change challenges. Diseases cause problems on agricultural development and yield and they're generally tough to control. It is necessary to have a precise diagnosis of the grape leaf diseases and preventative measures before time. In order to diagnose grape leaf diseases, this research suggests a novel recognition method that is based on enhanced convolutional neural networks. Firstly; addressed the grape leaf disease types into four categories such as Esca, black rot, Leaf Blight, and healthy which cause loss for grape industry every year. A large dataset of labeled images is collected and prepared for training. The images are typically pre-processed to enhance their features and remove any noise or artifacts that might interfere with the CNN's ability to recognize patterns. Data collection, data pre-processing, and image categorization are the three main phases of the study's approach. Secondly; Images are classified and mapped to their respective disease categories on the basis of three features namely, color and texture. Extensive experiments performed on MATLAB using CNN model AlexNet. The CNN training process used learning rate 0.0001 which produced better results and obtained better accuracy. Overall, an accurate diagnosis of grape leaf diseases and the implementation of effective preventative measures will help to reduce the impact of diseases on agricultural development and yield. This will help to ensure a sustainable and profitable grape production industry for farmers and communities.