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In the United States, banking desert status is classified annually from that same year’s branch data, producing a contemporaneous snapshot of current access rather than a forecast of which communities are likely to change status next. This …

We developed a series of financial social media sentiment analysis models starting with PyFin-Sentiment, a recent state-of-the-art domain-specific large language model for the financial domain. We focused on label accuracy and improvements …

Healthcare fraud, waste, and abuse (FWA) are among the most significant drivers of financial inefficiency in healthcare systems worldwide, with estimates suggesting they contribute to as much as 10% of total healthcare expenditures. In the …

Solar photovoltaic (PV) systems are widely adopted for sustainable energy production; however, physical defects such as glass breakage significantly reduce panel efficiency and system output. In this study, a real-world case analysis was co…

Early detection of lung cancer remains a critical challenge in medical imaging, where accurate classification between benign and malignant pulmonary nodules can significantly impact patient outcomes. This study presents a comprehensive comp…

Differentiated thyroid cancer (DTC) is the most frequent form of thyroid cancer. Although this type of cancer shows a favourable prognosis, the risk of recurrence remains a critical concern. Early and accurate prediction of the risk of recu…

The rapid expansion of the mobile gaming industry has underscored the need to understand user sentiment, with social media platforms like X (Twitter) providing key insights. This study applied sentiment analysis to English and Turkish tweet…

E-commerce is evolving rapidly, creating a more competitive market environment. With this development, gaining a competitive advantage has become even more crucial. Companies implement various strategic moves to maintain their position in t…

Forecasting earthquakes is challenging due to the difficulty in detecting underground phenomena. However, statistically, 30% of large earthquake foreshocks occur before the main shock, which indicates that, theoretically, it is possible to …

The study will utilize machine learning algorithms such as Random Forest, Gradient Boosting, and Support Vector Regression (SVR) on data collected from the districts of Nalgonda, Yadadri Bhuvanagiri, and Suryapet over the past two years. Th…

The Internet of Vehicles (IoV) is a branch of the Internet of Things that deals with vehicle-to-vehicle communication and intelligent transport systems (ITS). But this connection is not without consequences, because the more a system exchan…

Three-phase asynchronous motors are widely used in many areas of industry. Unplanned malfunctions in the motor can cause the entire system to become inefficient. Detecting and intervening the malfunctions before they grow has a direct impac…

Deep learning is a machine learning sub-field that uses deep neural networks. Instead of customized algorithms for each study in this field, it is aimed to cover the wider data set of solutions based on learning the data. Deep learning is a…