Tsunami is one of the deadliest natural disasters which can occur, leading to great loss of life and property. This study focuses on predicting tsunamis, using earthquake dataset from the year 1995 to 2023. The research introduces the Hybrid Quantum Neural Network (HQNN), an innovative model that combines Neural Network (NN) architecture with Parameterized Quantum Circuits (PmQC) to tackle complex machine learning (ML) problems where deep learning (DL) models struggle, aiming for higher accuracy in prediction while maintaining a compact model size. The hybrid model’s performance is compared with the classical model counterpart to investigate the quantum circuit’s effectivity as a layer in a DL model. The model has been implemented using 2-6 features through Principle Component Analysis (PCA) method. HQNN’s quantum circuit is a combination of Pennylane’s embedding (Angle Embedding (AE) and Instantaneous Quantum Polynomial (IQP) Embedding) and layer circuits (Basic Entangler Layers (BEL), Random Layers (RL), and Strongly Entangling Layers (SEL)), along with the classical layers. Results show that the proposed model achieved high performance, with a maximum accuracy up to 96.03% using 4 features with the combination of AE and SEL, superior to the DL model. Future research could explore the scalability and diverse applications of HQNN, as well as its potential to address practical ML challenges.

One of the methods most frequently used to recommend films is collaborative filtering. We examine the potential of collaborative filtering in our paper’s discussion of product suggestions. In addition to utilizing collaborative filtering in a new application, the proposed system will present a better technique that focuses especially on resolving the cold start issue. The suggested system will compute similarity using the Pearson Correlation Coefficient (PCC). Collaborative filtering that uses PCC suffers from the cold start problem or a lack of information on new users to generate useful recommendations. The proposed system solves the issue of cold start by gauging each new user by certain arbitrary parameters and recommending based on the choices of other users in that demographic. The proposed system also solves the issue of users’ reluctance to provide ratings by implementing a keyword-based perception system that will aid users in finding the right product for them.

The world’s median population is projected to reach 8.8 billion by 2050, making water management, especially groundwater, increasingly important. This research article seeks to address the pressing need for an effective method of evaluating and forecasting groundwater quality. Traditionally, water quality testing methods entail significant experimentation, a time-consuming procedure. To overcome this challenge in an alternative way, our proposed methodology involves parameterizing groundwater physicochemical parameters (Q-value) using machine learning algorithms. Focusing on groundwater samples collected from the Russell River of Australia between December 2016 and April 2020 (scaled to approximately 1300 datapoints on average), this research article uses the concept of Q value, a standardized measure facilitating comprehensive water quality assessment. This study significantly contributes to sustainable water resource management by providing a comprehensive examination of groundwater quality through the utilization of deep learning algorithms. The proposed models, namely Conv-LSTM with Attention, Conv-Bi-LSTM with Attention, LSTM with Attention, and Bi-LSTM with Attention, not only offer a distinctive framework for forecasting Q values but also serve as essential tools for timely decision making in water resource distribution. Among these, the Bidirectional LSTM with Attention model achieved the highest predictive accuracy, with a root mean square error of 0.0057, a mean absolute error of 0.0022, a symmetric mean absolute percentage error of 3.8875%, and a coefficient of determination of 0.9910. These results demonstrate its effectiveness in capturing variability and accurately explaining observed trends in groundwater quality. The proposed framework is a reliable, scalable, and timely decision-support tool for water resource management and policy making.