Abstract: Machine learning methods are becoming more and more popular in the field of computer-aided drug design. The specific data characteristic, including sparse, binary representation as well as ...

Abstract: We consider the binary classification problem of static and dynamic mixed data in this paper. Different from mixed categorical and numerical data, the dynamic variables in the new type of ...

CLAP (Contrastive Language-Assembly Pre-training) is a framework that learns binary code representations through natural language supervision. By aligning binary code with natural language ...

The bias problem in classification tasks and the different strategies used for bias mitigation. How these strategies are grouped into categories and a brief introduction of the most representative ...

Optical neural networks implemented with Mach-Zehnder Interferometer (MZI) arrays are a promising solution to enable fast and energy-efficient machine learning inference, yet finding a practical ...

In veterinary medicine, attempts to apply artificial intelligence (AI) to ultrasonography have rarely been reported, and few studies have investigated the value of AI in ultrasonographic diagnosis.

1 Department of Information and Communication Technology, Comilla University, Cumilla, Bangladesh. 2 Department of Computer Science and Engineering, Bangladesh Army International University of Science ...

Because machine learning with deep neural techniques has advanced quickly, our resident data scientist updates binary classification techniques and best practices based on experience over the past two ...

While it would be desirable that the output of binary classification algorithms be the probability that the classification is correct, most algorithms do not provide a method to calculate such a ...