Earthquake is one of the most hazardous natural calamity. Many algorithms have been proposed for earthquake prediction using expert systems (ES). We aim to identify and compare methods, models, frameworks, and tools used to forecast earthquakes using different parameters. We have conducted a systematic mapping study based upon 70 systematically selected high quality peer reviewed research articles involving ES for earthquake prediction, published between January 2010 and January 2020.To the best of our knowledge, there is no recent study that provides a comprehensive survey of this research area. The analysis shows that most of the proposed models have attempted long term predictions about time, intensity, and location of future earthquakes. The article discusses different variants of rule-based, fuzzy, and machine learning based expert systems for earthquake prediction. Moreover, the discussion covers regional and global seismic data sets used, tools employed, to predict earth quake for different geographical regions. Bibliometric and meta-information based analysis has been performed by classifying the articles according to research type, empirical type, approach, target area, and system specific parameters. Lastly, it also presents a taxonomy of earthquake prediction approaches, and research evolution during the last decade.

Earthquakes are a natural phenomenon which may cause significant loss of life and infrastructure. Researchers have applied multiple artificial intelligence based techniques to predict earthquakes, but high accuracies could not be achieved due to the huge size of multidimensional data, communication delays, transmission latency, limited processing capacity and data privacy issues. Federated learning (FL) is a machine learning (ML) technique that provides an opportunity to collect and process data onsite without compromising on data privacy and preventing data transmission to the central server. The federated concept of obtaining a global data model by aggregation of local data models inherently ensures data security, data privacy, and data heterogeneity. In this article, a novel earthquake prediction framework using FL has been proposed. The proposed FL framework has given better performance over already developed ML based earthquake predicting models in terms of efficiency, reliability, and precision. We have analyzed three different local datasets to generate multiple ML based local data models. These local data models have been aggregated to generate global data model on the central FL server using FedQuake algorithm. Meta classifier has been trained at the FL server on global data model to generate more accurate earthquake predictions. We have tested the proposed framework by analyzing multidimensional seismic data within 100 km radial area from 34.708° N, 72.5478° E in Western Himalayas. The results of the proposed framework have been validated against instrumentally recorded regional seismic data of last thirty-five years, and 88.87% prediction accuracy has been recorded. These results obtained by the proposed framework can serve as a useful component in the development of earthquake early warning systems.

Software Testing is the core part of computer science & engineering curriculum. It has been observed that software testing has been taught much in Computer science & engineering disciplines at undergraduate level. Software Testing Education (STE) involves time, cost, risk, quality, integration, communication, human resource, and procurement management skills. The STE at undergraduate and graduate level is a challenging task because it also requires knowledge and experience. This article aims to investigate and synthesize the state-of-the-art research in STE for the improvement of STE curriculum, pedagogical tools and techniques, cognitive, empirical and assessments methods. STE research approaches has been categorized into five categories including empirical type, research approaches, software testing education processes, key areas, and curricula. The ninety-seven articles for the area of STE have been chosen after rigorous systematic screening process published in during 2004 to 2021. Furthermore, tools and techniques, testing processes, pedagogy and student performance are the frequently addressed in STE; whereas assessment methods, gamification, curriculum and exemplary program development appeared as mostly ignored areas. Lastly, research gaps and challenges relate to STE has been presented as future directions for the faculty, software industry, and researchers.