Indoor air quality (IAQ) has a crucial impact on health, yet many spaces suffer from unnoticed pollution. This study introduces “Indoor Air Wellness”, a predictive model that utilizes advanced AI techniques, specifically integrating Kalman filtering (KF) and artificial neural networks (ANN), to enhance indoor air pollution prediction and management. The model excels in dynamic environments by refining the accuracy of the proposed Kal-ANN algorithm achieving a predictive accuracy (R²) of up to 96. 55%, a root mean square error (RMSE), a Mean Absolute Error (MAE), and a Mean Squared Prediction Error (MSPE) as low as 9.85, 6.12, and 3.15 g/m. ‘‘Indoor AirWellness" operates in three phases: IAQ prediction, control, and energy-efficient green building design. IAQ prediction consists of Learning and Analysis subphases, continuously monitoring and forecasting indoor parameters such as temperature, humidity, and CO levels while considering external factors. The Analysis phase implements control measures based on these predictions, leading to a 60% improvement in energy utilization in green buildings compared to traditional methods. Simulations and real-world applications demonstrate its effectiveness in reducing pollutants by up to 23% and minimizing energy consumption by up to 48%. Overall, the “Indoor Air Wellness” model enhances air quality and promotes energy efficiency, positioning it as a valuable tool for residential, commercial, and industrial applications. This study contributes to advancing smart environmental management, advocating for healthier indoor environments through AI-driven solutions.

Accurate identification of the newest 2019 coronavirus (COVID-19) disease is required for effective illness behavior and management. To categorize and analyses COVID-19 in the prevalent region, for (COVID-19) detection using medical images, computed tomography (CT) imaging is informative, reliable, and quick. Chest CT images are readily available in nearly all hospitals, making it possible to use them to classify COVID-19 patients early on. When COVID-19 infection spreads quickly, a significant amount of time are required for the chest CT-based COVID-19 categorization. Since medical practitioners have scarce time, a computerized analysis of CT scans is required. In this paper, we construct a classification framework involving the extraction of CT image characteristics and categorization. The framework is separated into training and testing modules, with training the classifier aiding in the development of a model that efficiently classifies CT images as input. Deep convolutional neural network including VGG16, ResNet50, DensNet121, and inspectionResNetV2, variations were utilized as classifiers in the present investigation (DCNN) different machine learning and Statistical modeling techniques to identify COVID-19 infections probability and discover lesion for training.

Urban air pollution poses a considerable risk to both public health and environmental sustainability, highlighting the importance of precise, low-latency forecasting systems that can be integrated into real-world infrastructures. This research presents the Urban Air Quality Forecasting with Grid-Embedded Recurrent MLP Model (AiM), a hybrid model that merges a recurrent Multi-Layer Perceptron (R-MLP) with a Grid-Embedded framework that takes spatial factors into account, aiming to improve air quality predictions across space and time. It employs a grid-based partitioning strategy for urban monitoring areas, allowing it to effectively capture localized patterns in pollutant dispersion, while the R-MLP aspect addresses the complex temporal dependencies found in multi-pollutant and meteorological time series data. A customized feature engineering pipeline is designed to incorporate pollutant interactions, meteorological variability, and grid adjacency relationships, facilitating the robust learning of cross-regional correlations. Experiments conducted on multi-season, multi-station datasets reveal that AiM achieves superior forecasting accuracy compared to conventional LSTM, GRU, and CNN-RNN hybrids, reducing RMSE by as much as 12.4% and inference latency by 35% on edge devices. Furthermore, the architecture demonstrates high scalability, accommodating dynamic grid reconfiguration and integration with low-power IoT nodes, making it well-suited for real-time deployment in smart city air quality management systems.