Breast cancer is the second most common cancer in the world and is difficult to accurately identify and treat. Diagnostic computational tools can be used effectively, with high degree of accuracy, to recognize and differentiate between the two known types of breast lesion, namely benign and malignant. These modelling tools include artificial intelligence techniques such as Artificial Neural Net...

Previous studies of the effect of word alignment on translation quality in SMT generally explore link level metrics only and mostly do not show any clear connections between alignment and SMT quality. In this paper, we specifically investigate the impact of word alignment on two pre-reordering tasks in translation, using a wider range of quality indicators than previously done. Experiments on G...

The development of convolution neural networks (CNNs) has become a significant means to solve the problem remote sensing scene image classification. However, well-performing CNNs generally have high complexity and are prone overfitting. To handle above problem, we present new classification approach using an mmsCNN–HMM combined model with stacking ensemble mechanism in this paper. First all, mo...

Designing a practical watermarking scheme with high robustness, feasible imperceptibility, and large capacity remains one of the most important research topics in robust watermarking. This paper presents a posterior hidden Markov model (HMM-) based informed image watermarking scheme, which well enhances the practicability of the prior-HMM-based informed watermarking with favorable robustness, i...

1. The dependence on ATP concentration of ATPase activity and light scattering decrease of acto-HMM could be described at very low ionic strength by one hyperbolic adsorption isotherm with a dissociation constant of 3 X 10(-6)M. Hence the increase of ATP ase activity was paralleled by a decrease in light scattering. At higher values of ionic strength ATPase activity stopped rising before HMM wa...

This paper describes hybrid Hidden Markov Models / Artificial Neural Networks (HMM/ANN) models devoted to speech recognition, and in particular Loquendo HMM/ANN, that is the core of Loquendo ASR. While Hidden Markov Models (HMM) is a dominant approach in most state-of-the-art speaker-independent, continuous speech recognition systems (and commercial products), Artificial Neural Networks (ANN) a...

Handwritten character recognition of Urdu script based languages is one of the most difficult task due to complexities of the script. Urdu script based languages has not received much attestation even this script is used more than 1/6th of the population. The complexities in the script makes more complicated the recognition process. The problem in handwritten numeral recognition is the shape si...

In the world of sign language, and gestures, a lot of research work has been done over the past three decades. This has brought about a gradual transition from isolated to continuous, and static to dynamic gesture recognition for operations on a limited vocabulary. In present scenario, human machine interactive systems facilitate communication between the deaf, and hearing people in real world ...

When applied to interactive seminars, the detection of acoustic events from only audio information shows a large amount of errors, which are mostly due to the temporal overlaps of sounds. Video signals may be a useful additional source of information to cope with that problem for particular events. In this work, we aim at improving the detection of steps by using two audiobased AED systems, wit...

The work presented in this paper describes a Hidden Markov Model (HMM)-based framework for face recognition and face detection. The observation vectors used to characterize the states of the HMM are obtained using the coeecients of the Karhunen-Loeve Transform (KLT). The face recognition method presented in this paper reduces signiicantly the computational complexity of previous HMM-based face ...