Face recognition technologies, integral to security, surveillance, and biometric identification, are continually advancing, yet the accurate detection and recognition of individuals with evolving facial features due to aging, lifestyle changes, medical procedures, and other factors remained a challenging task. This research embarked on a methodical exploration to address this issue by utilizing Generative Adversarial Networks, specifically the StyleGAN3 model. This study navigated the intricate process of manipulating both local and global facial attributes in synthetic images, creating a rich facial diversity while maintaining the consistency of individual identity. To complement this, this research comprehensively studied the capabilities of state-of-the-art Yolo models through few shots approaches, mimicking real world data paucity. This research underscores the transformative potential of GANs, specifically the StyleGAN3 model, in the constantly evolving domain of facial recognition. The encouraging outcomes from the Yolo models, particularly when furnished with limited data, not only challenge traditional training paradigms but also illuminate the significance of adept synthetic data generation techniques. Such advancements hold potential to augment security, surveillance, and streamline biometric identification processes.

This research explores the potential for reducing the cost of advanced metering infrastructure (AMI) by leveraging raw images captured from the screens of digital energy instruments. The study focuses on the extraction of text and the recognition of 7 -segment numbers through optical character recognition (OCR) techniques. The proposed OCR-based dataset holds promise for facilitating fully automated electricity billing processes. Additionally, the research highlights the impact of utilizing high-resolution smart metre data in enhancing the efficiency, reliability, and resilience of distribution power grids. The “digital metre” dataset, comprised of images of digital energy metres, serves as a valuable resource for advancing the field. The study introduces a methodology for automatically reading dial-metre digits through the utilization of a deep learning model based on the YOLOv5 architecture. Evaluation metrics such as precision, recall, and mean Average Precision (mAP) are employed to assess the model's effectiveness. This research underscores the efficacy of the suggested network model in executing object-detection tasks, showcasing superior recall, mAP, and precision in the context of smart metre reading.

The COVID-19 pandemic, declared by the World Health Organization in March 2020, originated in Wuhan, China and quickly spread worldwide. In response to the global health crisis, scientific cooperation across the globe has intensified, with a critical focus on leveraging machine learning and deep learning for faster and non-invasive medical diagnostics. This study aims to contribute to these efforts by utilizing cough audio signals to detect COVID-19. It proposes a comprehensive study that starts with gathering and preprocessing cough audio data. The research further enhances the performance and generalization of deep learning models through transfer learning, employing VGG19 to adapt pretrained neural networks for this specific task. Alongside evaluating the models’ performance, the study also explores their interpretability and explainability, which are crucial for practical implementation. The outcome of this research is expected to provide a reliable, non-invasive, cost-effective, and scalable method for early detection of COVID-19, potentially easing the heavy reliance on traditional RT-PCR testing. Through the comparative analysis of machine learning and deep learning models in this context, the study also aims to provide deeper insights into the effectiveness of these computational approaches in tackling global health challenges.

Due to growing environmental concerns in relation to global warming and pollutants, it’s become very critical to study about sources which are contributing towards it. Once such source is emissions from the industrial equipment’s like Gas Turbine (GT). GTs are used all over the world across various industries for power generation or mechanical drive operations. Some of these industries belongs to Energy, Utilities, Refineries, Chemical & Fertilizer sectors. In this study, efforts were be made to compare all commonly used ML regression models such as MLR, DTR, RFT, Adaboost Regressor, GBR, XGBoost Regressor using same dataset for emission prediction (CO & NOx). Model’s evaluation metrices were analyzed to predict the best model along with number of significant features. Evaluation metric included MAE, RAE, MAPE & RMSE. In addition, various features were analyzed for patterns using univariate & multivariate analysis graphical tools. This study can contribute towards new PEMS (Prediction Emission Monitoring System) designing using ML Regression Models which will help industrial companies in boosting their operation efficiencies, minimization of emissions (like CO & NOx) and making financial gains due to its cost effectiveness over traditional CEMS (Continuous Emission Monitoring System). Overall, these new PEMS will keep monitoring and optimizing industrial equipment’s operations for emissions control during their lifecycles.

Plant diseases pose a significant threat to global food production and require early identification to ensure global food security and safeguard the economic interests of agriculture stakeholders. Practical implementation in agriculture requires fast, lightweight and accurate models for edge devices. This research presents a tomato leaf disease classification approach using a fast and lightweight pre-trained CNN architecture designed for efficiency. The method incorporates resampling techniques to address the data class imbalance, while pre-processing steps such as resizing, and augmentation enhance model performance. The transfer learning-based NASNetMobile model is trained and evaluated using the plant village dataset, containing nine disease classes and a healthy class. Performance assessment focuses on lightweight characteristics and classification accuracy of the model. Results demonstrate the effectiveness of the approach in balancing lightweight characteristics and accuracy, offering the potential for extending disease classification to other plant species. This research contributes to the development of low-end devices for easy disease identification, benefiting global food security and agricultural stakeholders’ economic well-being.

However, energy forecasting is still a challenging task due to the many unpredictable factors that can impact energy consumption and production, such as changes in weather patterns, economic conditions, and energy policies. Therefore, energy forecasts should be continuously updated and refined as new information becomes available. The purpose of this research is to present a high-level, machine learning-centric viewpoint on data-driven energy forecasting. Challenges and constraints of data-driven energy forecasting will be discussed, along with the many machine learning methods and methodologies that can be implemented. The paper compares the performance of various deep learning and time series analysis techniques such as LSTM, RNN, ARIMA and SARIMA in energy forecasting. This research can provide a more comprehensive understanding of the effectiveness of different models in energy forecasting, which can have significant implications for energy management, policymaking, and infrastructure development.