Application of Artificial Intelligence in predicting earthquakes: state-of-the-art and future challenges

Predicting the time, location and magnitude of an earthquake is a challenging job as an earthquake does not show specific patterns resulting in inaccurate predictions. Techniques based on Artificial Intelligence (AI) are well known for their capability to find hidden patterns in data. In the case of earthquake prediction, these models also produce a promising outcome. This work systematically explores the contributions made to date in earthquake prediction using AI-based techniques. A total of 84 scientific research papers, which reported the use of AI-based techniques in earthquake prediction, have been selected from different academic databases. These studies include a range of AI techniques including rule-based methods, shallow machine learning and deep learning algorithms. Covering all existing AI-based techniques in earthquake prediction, this paper provides an account of the available methodologies and a comparative analysis of their performances. The performance comparison has been reported from the perspective of used datasets and evaluation metrics. Furthermore, using comparative analysis of performances the paper aims to facilitate the selection of appropriate techniques for earthquake prediction. Towards the end, it outlines some open challenges and potential research directions in the field

Attention-based bi-directional long-short term memory network for earthquake prediction

An earthquake is a tremor felt on the surface of the earth created by the movement of the major pieces of its outer shell. Till now, many attempts have been made to forecast earthquakes, which saw some success, but these attempted models are specific to a region. In this paper, an earthquake occurrence and location prediction model is proposed. After reviewing the literature, long short-term memory (LSTM) is found to be a good option for building the model because of its memory-keeping ability. Using the Keras tuner, the best model was selected from candidate models, which are composed of combinations of various LSTM architectures and dense layers. This selected model used seismic indicators from the earthquake catalog of Bangladesh as features to predict earthquakes of the following month. and Attention mechanism was added to the LSTM architecture to improve the model’s earthquake occurrence prediction accuracy, which was 74.67%. Additionally, a regression model was built using LSTM and dense layers to predict the earthquake epicenter as a distance from a predefined location, which provided a root mean square error of 1.25

An attention-based hybrid architecture with explainability for depressive social media text detection in Bangla

Mental health has become a major concern in recent years. Social media have been increasingly used as platforms to gain insight into a person’s mental health condition by analysing the posts and comments, which are textual in nature. By analysing these texts, depressive posts can be detected. To facilitate this process, this work presents an attention-based bidirectional Long Short-Term Memory (LSTM)- Convolutional Neural Network (CNN) based model to detect depressive Bangla social media texts, which is lighter and more robust than the conventional models and provides better performance. A dataset containing such Bangla texts was also developed in this work to mitigate the scarcity. Different preprocessing stages were followed, and three embeddings were used in this task. Thanks to the attention mechanism, the proposed model achieved an accuracy of 94.3% with 92.63% of sensitivity and 95.12% of specificity. When tested on other languages, such as English, the proposed model performed remarkably. The robustness and explainability of the proposed model were also discussed in this paper. Additionally, when compared with classical machine learning models, ensemble approaches, transformers, other similar models, and existing architectures, the proposed model outperformed them

Design and methods for evaluating an early childhood obesity prevention program in the childcare center setting

BACKGROUND: Many unhealthy dietary and physical activity habits that foster the development of obesity are established by the age of five. Presently, approximately 70 percent of children in the United States are currently enrolled in early childcare facilities, making this an ideal setting to implement and evaluate childhood obesity prevention efforts. We describe here the methods for conducting an obesity prevention randomized trial in the child care setting. METHODS/DESIGN: A randomized, controlled obesity prevention trial is currently being conducted over a three year period (2010-present). The sample consists of 28 low-income, ethnically diverse child care centers with 1105 children (sample is 60% Hispanic, 15% Haitian, 12% Black, 2% non-Hispanic White and 71% of caregivers were born outside of the US). The purpose is to test the efficacy of a parent and teacher role-modeling intervention on children’s nutrition and physical activity behaviors. . The Healthy Caregivers-Healthy Children (HC2) intervention arm schools received a combination of (1) implementing a daily curricula for teachers/parents (the nutritional gatekeepers); (2) implementing a daily curricula for children; (3) technical assistance with meal and snack menu modifications such as including more fresh and less canned produce; and (4) creation of a center policy for dietary requirements for meals and snacks, physical activity and screen time. Control arm schools received an attention control safety curriculum. Major outcome measures include pre-post changes in child body mass index percentile and z score, fruit and vegetable and other nutritious food intake, amount of physical activity, and parental nutrition and physical activity knowledge, attitudes, and beliefs, defined by intentions and behaviors. All measures were administered at the beginning and end of the school year for year one and year two of the study for a total of 4 longitudinal time points for assessment. DISCUSSION: Although few attempts have been made to prevent obesity during the first years of life, this period may represent the best opportunity for obesity prevention. Findings from this investigation will inform both the fields of childhood obesity prevention and early childhood research about the effects of an obesity prevention program housed in the childcare setting. TRIAL REGISTRATION: Trial registration number: NCT0172203

Prospect and potential of Burkholderia sp. against Phytophthora capsici Leonian: a causative agent for foot rot disease of black pepper

Foot rot disease is a very destructive disease in black pepper in Malaysia. It is caused by Phytophthora capsici Leonian, which is a soilborne pathogenic protist (phylum, Oomycota) that infects aerial and subterranean structures of many host plants. This pathogen is a polycyclic, such that multiple cycles of infection and inoculum production occur in a single growing season. It is more prevalent in the tropics because of the favourable environmental conditions. The utilization of plant growth-promoting rhizobacteria (PGPR) as a biological control agent has been successfully implemented in controlling many plant pathogens. Many studies on the exploration of beneficial organisms have been carried out such as Pseudomonas fluorescens, which is one of the best examples used for the control of Fusarium wilt in tomato. Similarly, P. fluorescens is found to be an effective biocontrol agent against the foot rot disease in black pepper. Nowadays there is tremendous novel increase in the species of Burkholderia with either mutualistic or antagonistic interactions in the environment. Burkholderia sp. is an indigenous PGPR capable of producing a large number of commercially important hydrolytic enzymes and bioactive substances that promote plant growth and health; are eco-friendly, biodegradable and specific in their actions; and have a broad spectrum of antimicrobial activity in keeping down the population of phytopathogens, thus playing a great role in promoting sustainable agriculture today. Hence, in this book chapter, the potential applications of Burkholderia sp. to control foot rot disease of black pepper in Malaysia, their control mechanisms, plant growth promotion, commercial potentials and the future prospects as indigenous PGPR were discussed in relation to sustainable agriculture

Factors affecting borrowers’ turnover in microfinance institutions: A panel evidence

In the era of competitiveness, clients or borrowers remain an important asset for financial institutions, as they are the ultimate source of revenue. Although the departure of clients from one microfinance institution (MFI) to another is a common phenomenon, the manner in which organizational characteristics affect turnover is largely unknown in the context of the microfinance industry. Hence, by utilizing recent (2010–18) data on 235 MFIs from the global microfinance industry, this study investigates the factors affecting the borrower turnover rates (BTR) of MFIs by employing conventional panel regression techniques. To overcome endogeneity and ensure robust and dynamic results, the generalized method of moments (GMM) has also been used in this study. The findings reveal that the efficiency-wage and financial self-sufficiency of MFIs reduce BTR, while staff turnover rate, write-off ratio and average loan size increase BTR. Our results remain robust even after controlling for several market and macro-economic factors. The findings could be utilized to generate several policy implications to reduce borrowers’ turnover