The significance of user participation in sustaining drinking water quality and assessing other factors, such as cleanliness, sanitary conditions, preservation, and waste treatment, is essential for preserving groundwater quality. Inadequate water quality spreads disease, causes mortality, and hinders socioeconomic growth. In addition, disinfectants such as chlorine and fluoride are used to remove pathogens, or disease-causing compounds, from water. After a substantial amount of chlorine has been added to water, its residue causes an issue. Since the proposed methodology is intended to offer a steady supply of drinkable water, its chlorine concentration must be checked in real-time. The suggested model continually updates the sensor hub regarding chlorine concentration measurements. In addition, these data are transmitted over a communication system for data analysis to analyze chlorine levels within the drinking water and residual chlorine percentage over time using a fuzzy set specifically using a decision tree algorithm. Additionally, a performance investigation of the proposed framework is undertaken to determine the efficiency of the existing model for predicting the quantity of chlorine substance employing metrics such as recall, accuracy, F-score, and ROC. Henceforth, the proposed model has substantially better precision than the existing techniques.

Blockchain technology is a highly developed database system that shares information within a business web. It stores details in blocks connected chronologically, ensuring information integrity through consensus mechanisms that prevent unauthorised alterations. This decentralised system removes the need for a believable mediator, mitigating vulnerabilities and enhancing transaction security. Blockchain’s application spans the energy, finance, media, entertainment, and retail sectors. However, classical blockchain faces threats from quantum computing advancements, necessitating the development of quantum blockchain technology. Quantum blockchain, leveraging quantum computation and information theory, offers enhanced security and immutability. In this paper, different mathematical foundations, practical implementations and effectiveness of lattice‐based cryptography in securing blockchain applications are discussed. Analysis of how the cryptographic techniques can protect blockchain systems against quantum attacks is being done by using mathematical formulations and examples. Quantum computing strengthens blockchain security with advanced encryption and authentication, which is critical for safeguarding diverse sectors from evolving cyber threats. Further study on quantum‐resistant design is necessary if blockchain networks are to be robust and intact in the face of future technological developments.

Online learning has accelerated with the development of the Internet and communication technology. The widely accessible open online courses are delivered using particular digital environments that allow students to participate at speed and location. Virtual learning environments (VLEs) have developed quickly in recent years, giving students access to high-quality digital resources. Online learning environments have numerous benefits but drawbacks, including poor engagement, high dropout rates, low engagement, and self-regulated behavior, making students define their aims. Forecasting failed students in a VLE can help organizations and teachers improve their pedagogical practices and make data-driven decisions. This work proposes a Hybrid Deep Learning (HDL) approach to predict students’ performance utilizing ECNN (Enhanced Convolution Neural Networks) Resnet model-based classification algorithms. The HDL approach is evaluated using the OULAD (Open University Learning Analytics Dataset), which provides a comprehensive and reliable assessment of the model’s performance. The hybrid DLT approaches, demonstrating superiority, exhibited greater prediction accuracy than the existing classifiers. Additionally, the models’ accuracy increases by about 95.67%, higher than other approaches are DFFNN model (93.9%) and MLP model (71.41%).

Because cardiovascular disease (CVD) is still one of the world’s leading causes of death, sophisticated predictive models are required for early detection and prevention. This study examined how to make and compare different CVD prediction models using a large dataset that included biochemical, clinical, and demographic information about each person. During the preprocessing stage, we took great care to ensure the data’s accuracy and quality. We have utilized a variety of machine learning algorithms such as random forest, logistic regression, support vector machines, and deep learning neural networks. We assessed the performance of these models using the accuracy, sensitivity, specificity, and area under the receiver operating characteristic curve (AUC-ROC). Our findings show that while more sophisticated algorithms—especially deep learning models—perform better at spotting possible instances of CVD, more conventional models—such as regression—offer significant predictive power. We also investigated the role that feature selection has in improving the interpretability and efficiency of the model. This study highlights the potential of machine learning to transform CVD prediction and emphasizes the importance of using many forms of data to provide a thorough risk evaluation. Our research adds to the continuing efforts in personalized medicine by providing information on creating more precise and effective predictive tools for cardiovascular health management.