Recently, data science techniques utilize artificial intelligence (AI) techniques who start and run small and medium-sized enterprises (SMEs) to take an influence and grow their businesses. For SMEs, owing to the inexistence of consistent data and other features, evaluating credit risks is difficult and costly. On the other hand, it becomes necessary to design efficient models for predicting business failures or financial crises of SMEs. Various data classification approaches for financial crisis prediction (FCP) have been presented for predicting the financial status of the organization by the use of past data. A major process involved in the design of FCP is the choice of required features for enhanced classifier outcomes. With this motivation, this paper focuses on the design of an optimal deep learning-based financial crisis prediction (ODL-FCP) model for SMEs. The proposed ODL-FCP technique incorporates two phases: Archimedes optimization algorithm based feature selection (AOA-FS) algorithm and optimal deep convolution neural network with long short term memory (CNN-LSTM) based data classification. The ODL-FCP technique involves a sailfish optimization (SFO) algorithm for the hyperparameter optimization of the CNN-LSTM method. The performance validation of the ODL-FCP technique takes place using a benchmark financial dataset and the outcomes are inspected in terms of various metrics. The experimental results highlighted that the proposed ODL-FCP technique has outperformed the other techniques.
Customer retention is one of the challenging issues in different business sectors, and various firms utilize customer churn prediction (CCP) process to retain existing customers. Because of the direct impact on the company revenues, particularly in the telecommunication sector, firms are needed to design effective CCP models. The recent advances in machine learning (ML) and deep learning (DL) models enable researchers to introduce accurate CCP models in the telecommunication sector. CCP can be considered as a classification problem, which aims to classify the customer into churners and non-churners. With this motivation, this article focuses on designing an arithmetic optimization algorithm (AOA) with stacked bidirectional long short-term memory (SBLSTM) model for CCP. The proposed AOA-SBLSTM model intends to proficiently forecast the occurrence of CC in the telecommunication industry. Initially, the AOA-SBLSTM model performs pre-processing to transform the original data into a useful format. Besides, the SBLSTM model is employed to categorize data into churners and non-churners. To improve the CCP outcomes of the SBLSTM model, an optimal hyperparameter tuning process using AOA is developed. A widespread simulation analysis of the AOA-SBLSTM model is tested using a benchmark dataset with 3333 samples and 21 features. The experimental outcomes reported the promising performance of the AOA-SBLSTM model over the recent approaches.
Financial crisis prediction (FCP) received significant attention in the financial sector for decision-making. Proper forecasting of the number of firms possible to fail is important to determine the growth index and strength of a nation's economy. Conventionally, numerous approaches have been developed in the design of accurate FCP processes. At the same time, classifier efficacy and predictive accuracy are inadequate for real-time applications. In addition, several established techniques carry out well to any of the specific datasets but are not adjustable to distinct datasets. Thus, there is a necessity for developing an effectual prediction technique for optimum classifier performance and adjustable to various datasets. This paper presents a novel multi-vs. optimization (MVO) based feature selection (FS) with an optimal variational auto encoder (OVAE) model for FCP. The proposed multi-vs. optimization based feature selection with optimal variational auto encoder (MVOFS-OVAE) model mainly aims to accomplish forecasting the financial crisis. For achieving this, the proposed MVOFS-OVAE model primarily pre-processes the financial data using min-max normalization. In addition, the MVOFS-OVAE model designs a feature subset selection process using the MVOFS approach. Followed by, the variational auto encoder (VAE) model is applied for the categorization of financial data into financial crisis or non-financial crisis. Finally, the differential evolution (DE) algorithm is utilized for the parameter tuning of the VAE model. A series of simulations on the benchmark dataset reported the betterment of the MVOFS-OVAE approach over the recent state of art approaches.
Due to the COVID-19 epidemic, the term "change in children's lifestyle behavior" is currently a hot topic in society. Children's lifestyle behaviors may have been altered by home confinement during the epidemic, although evidence is still emerging and limited, which talks about SDG 3: Good Health and Well-Being. The present study focuses on the important factors that lead to change in lifestyle of children, to examine the effects of COVID-19 confinement, and to suggest preventive measures. The design used for this study is descriptive and an online survey was conducted within Tamilnadu with a sample size of 105. Correlation, chi-square are the tools used for testing the variables. This study identifies an adverse impact on mental health, such as decreased well-being and increased post-traumatic stress disorders (PTSD) and anger. These findings should be taken into account to design and implement health strategies for preserving children’s health during pandemics.
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