Bankruptcy is one of the biggest threats to a company's reputation, occurring when it is unable to pay back outstanding debts to banks, lenders, and suppliers. Predicting bankruptcy accurately and promptly allows companies to take remedial action in advance and avoid it. To achieve this goal, current research is investigating a combination of techniques that can accurately predict bankruptcy. The proposed method employs various ensemble techniques to combine the best methods for improved accuracy. The proposed ensemble models have been compared using both the original imbalanced dataset and the balanced dataset created by oversampling. The proposed method of using the balanced dataset on the ensemble models outperformed the models using the original dataset in accuracy and other performance metrics. Balance Bagging achieved the highest accuracy at 98.77%, followed by Random Forest at 98.68%, and AdaBoost at 96.9%. These results are a significant achievement compared to state-of-the-art techniques.
Risk management in Nepal has never been a matter of applying textbook formulas to Himalayan data. The country’s management systems—spanning hydropower consortia in Gandaki, microfinance networks in the Terai, tourism supply chains in Solu-Khumbu, and federal bureaucracies still f…
The focus of recent research has been on using advanced computer-aided diagnostic (CAD) techniques and a variety of modalities to identify neurological disorders. Important and possibly deadly conditions, neurological diseases such as Alzheimer's disease (AD), stroke, epilepsy, P…
India’s economy is primarily based on agriculture. Agriculture has significant contribution in nation’s GDP. Food security and employment significantly influenced by agriculture. However factors like uncertain weather conditions, poor quality of seeds and plant diseases impact on…
In the unstable foreign exchange environment that emerged in Nigeria following subsidy removal, exchange rate risk management and financial decision-making have become increasingly difficult. Under such conditions, traditional forecasting methods and static hedging strategies may…
Accurate and prompt prediction of PM2.5 concentration is crucial to reduce the impacts of air pollution on human health and city ecosystems. In this study, a hybrid ensemble learning model for hourly PM2.5 predictions is proposed, combining advanced data preprocessing, temporal f…
Tur dal, renowned as one of the most popular pulses globally, encompasses a wide range of varieties with significant variations in texture, colour, and other attributes. Accurately identifying tur dal varieties, is essential to satisfy consumer demands and uphold consumer rights…