Artificial Intelligence (AI) is the next society transformation builder. Massive AI-based applications include cloud servers, cell phones, cars, and pandemic...

Training large-scale deep neural networks is a long, time-consuming operation, often requiring many GPUs to accelerate. In large models, the time spent loadi...

Binary neural networks improve computationally efficiency of deep models with a large margin. However, there is still a performance gap between a successful...

Implementation of quantized neural networks on computing hardware leads to considerable speed up and memory saving. However, quantized deep networks are diff...

Many biological datasets such as microarrays, metabolomics, and proteomics involve observations (or subjects) in rows, and attributes (or genes, metabolites,...

Visualization of high-dimensional data is counter-intuitive using conventional graphs. Parallel coordinates is proposed, as an alternative, to explore multiv...

Simple, intuitive, and scalable to large problems, \(k\)-means clustering is perhaps the most frequently-used technique for unsupervised learning. However...

An electronic nose (e-nose), or artificial olfactometer, is a device that analyzes the air to quantify odor concentration using an array of gas sensors. Thi...

An electronic nose (e-nose) is a device that analyzes the chemical components of an odour. The e-nose consists of an array of gas sensors for chemical detect...

Airline companies are subject to a considerable amount of disruptions during their operations. It is vital for many industries including the airline industry...

An artificial olfaction called electronic nose (e-nose) relies on an array of gas sensors with the capability of mimicking the human sense of smell. Applying...

Nowadays, tremendous data are continuously gathering from the smart card in public transport domain. Such data, conveying two viable distinct information, ca...

Intelligent transportation has been emerged as one of the data mining and machine learning applications. The smart card data nowadays are continuously gather...

Clustering and classification of replicated biological data is often performed using classical techniques that inappropriately treat the data as unreplicated...

Virtual metrology in quality control deals with drifts in product quality that occur during non-sampling periods. This approach enables a hundred percent con...

While there has been a surge of articles on convergence diagnostic tools for MCMC on continuous stationary distributions and ordinal state spaces, Bayesian c...

In many applications, such as metabolomics, data are composed of several continuous measurements of subjects (tissues) over multiple variables (metabolites)....

Measurement systems studies are an integral part of most quality improvement processes. A desirable measurement system must produce repeatable and reprodu...

This work presents a variation of the elastic net penalization method. We propose applying a combined \(l\)₁ and \(l\)₂ norm pena...

The frontier of the Production Possibility Set (PPS) consists of two types of full dimensional facets; efficient and weak facets. Identification of all facet...

Since its inception, stochastic Data Envelopment Analysis (DEA) has found many applications. The approach commonly taken in stochastic DEA is via chance cons...