Advances in recent techniques for scientific data collection in the era of big data allow for the
systematic accumulation of large quantities of data at various data-capturing sites. Similarly …

Clustering is one of the most significant applications in the big data field. However, using the
clustering technique with big data requires an ample amount of processing power and …

Cardiovascular disease is a widespread and potentially fatal condition that requires
proactive preventive measures and efficient screening approaches on a global scale. To …

The k-means algorithm, known for its simplicity and adaptability, faces challenges related to
manual cluster number selection and sensitivity to initial centroid placement. This paper …

Text mining is a rapidly growing field in computer science that is used to extract meaningful
information from text data. This information can be used for various applications, such as …

Studies on natural language processing are mainly conducted in English, with very few
exploring languages that are under-resourced, including the Dravidian languages. We …

This paper presents a comparative analysis of different optimization techniques for the K-
means algorithm in the context of big data. K-means is a widely used clustering algorithm …

Existing secure two-party Euclidean distance computation schemes are mostly performed
based on a semi-honest model, which faces bottlenecks in computation efficiency and …

Increasing the production of the three major food crops (MFCs), maize (Zea mays), rice
(Oryza sativa), and wheat (Triticum aestivum), is essential to fulfilling the food demand for …

With the wide application of a modular multilevel converter in various power conversion
fields, submodule open-circuit fault diagnostics have attracted increasing attention, as some …