The health system in today’s real world is significant but difficult and overcrowded. These hurdles can be diminished using improved health record management and blockchain technology. These technologies can handle medical data to provide security by monitoring and maintaining patient records. The processing of medical data and patient records is essential to analyze the earlier prescribed medicines and to understand the severity of diseases. Blockchain technology can improve the security, performance, and transparency of sharing the medical records of the current healthcare system. This paper proposed a novel framework for personal health record (PHR) management using IBM cloud data lake and blockchain platform for an effective healthcare management process. The problem in the blockchain-based healthcare management system can be minimized with the utilization of the proposed technique. Significantly, the traditional blockchain system usually decreases the latency. Therefore, the proposed technique focuses on improving latency and throughput. The result of the proposed system is calculated based on various matrices, such as F1 Score, Recall, and Confusion matrices. Therefore, the proposed work scored high accuracy and provided better results than existing techniques.

Data is the biggest asset after people for businesses, and it is a new driver of the world economy. The volume of data that enterprises gather every day is growing rapidly. This kind of rapid growth of data in terms of volume, variety, and velocity is known as Big Data. Big Data is a challenge for enterprises, and the biggest challenge is how to store Big Data. In the past and some organizations currently, data warehouses are used to store Big Data. Enterprise data warehouses work on the concept of schema-on-write but Big Data analytics want data storage which works on the schema-on-read concept. To fulfill market demand, researchers are working on a new data repository system for Big Data storage known as a data lake. The data lake is defined as a data landing area for raw data from many sources. There is some confusion and questions which must be answered about data lakes. The objective of this article is to reduce the confusion and address some question about data lakes with the help of architecture.

Air pollution poses a serious threat to public health, making accurate and timely air quality prediction essential for effective mitigation and planning. This study presents a deep learning–based framework for short-term Air Quality Index (AQI) forecasting that integrates structured feature engineering with advanced neural architectures. Engineered features include lagged pollutant indicators, multi-scale moving averages, seasonal cyclic encodings, pollutant ratios, and date-based temporal variables, designed to capture nonlinear temporal dependencies and pollutant interactions. Air quality data collected from the OpenWeather API are used to evaluate Convolutional Neural Networks (CNN), Long Short-Term Memory (LSTM) networks, and a hybrid CNN–LSTM architecture. Model training is performed using Adam and RMSprop optimizers, and performance is assessed using precision, recall, and F1-score. Experimental results indicate that, within the scope of the evaluated dataset, the hybrid CNN-LSTM model achieves the strongest overall performance for short-term AQI forecasting, attaining an F1-score of approximately 91%, compared with 87.9% for LSTM and 86.7% for CNN under identical configurations. The results further demonstrate that the incorporation of engineered temporal and ratio-based features consistently improves predictive performance across all models. While the study is limited to a region-specific dataset and a short time span, the findings highlight the effectiveness of combining feature engineering with hybrid deep learning architectures for robust AQI prediction and support their potential use in data-driven air quality monitoring systems.