Walsh code based numerical mapping method for the identification of protein coding regions in eukaryotes

Tamil is one of the ancient Indian languages which has a vast collection of literature in the form of palm leaf, stones, metal plates, and other materials. Palm leaf manuscript was a broad tool to narrate medicines, literature, drama, and many more. Recognition of the characters written in palm leaf manuscripts is still an open task because of the need for digitization and transcription. In this paper, the recurrent neural network (RNN) is used to train the features of the characters extracted from the palm leaf. This method contains a preprocessing method to eliminate noise; then the character is segmented from the image and trained using the bidirectional long short-term memory (BLSTM) network. A feature vector with nine zones of character strokes is used to train and test the characters. A rich set of characters are used to train the features of the characters. This method provides better recognition accuracy than the other neural network-based character recognition.

Recurrent Neural Network-Based Character Recognition System for Tamil Palm Leaf Manuscript Using Stroke Zoning

We propose a novel generative approach for face recognition, in which sparse facial features are extracted from high resolution color face images using predefined landmark topologies which mark discriminative locations on face images, unlike the appearance-based approach, in which low resolution grayscale face images are used, reducing the computational complexity. By adopting a common landmark topology, the dissimilarity between distinct face images can be scored in terms of the dissimilarities between their corresponding landmarks, which are obtained by proposed geodesic distance approximations between multivariate normal distributions which represent the color intensities in the vicinities of each landmark location. The classification process of new face samples occurs by the determination of the face image sample present in the training set which minimizes the dissimilarity score. The proposed method was compared with representative current state-of-the-art methods using color or grayscale face images and presented the higher recognition rates. Moreover, these results also support a trend in which color information is relevant in face recognition.

/pdf/face-recognition-based-on-geodesic-distance-approximations-4wgthcpbvv.pdf

Face recognition based on geodesic distance approximations between multivariate normal distributions

A new numerical approach for DNA representation using modified Gabor wavelet transform for the identification of protein coding regions

Geodesic distance is a natural dissimilarity measure between
probability distributions of a specific type, and can be used to
discriminate texture in image-based measurements Furthermore, since
there is no known closed-form solution for the geodesic distance
between general multivariate normal distributions, we propose two
efficient approximations to be used as texture dissimilarity metrics
in the context of face recognition A novel face recognition
approach based on texture discrimination in high-resolution color
face images is proposed, unlike the typical appearance-based
approach that relies on low-resolution grayscale face images In our
face recognition approach, sparse facial features are extracted
using predefined landmark topologies, that identify discriminative
image locations on the face images Given this landmark topology,
the dissimilarity between distinct face images are scored in terms
of the dissimilarities between their corresponding face landmarks,
and the texture in each one of these landmarks is represented by
multivariate normal distributions, expressing the color distribution
in the vicinity of each landmark location The classification of new
face image samples occurs by determining the face image sample in
the training set which minimizes the dissimilarity score, using the
nearest neighbor rule The proposed face recognition method was
compared to methods representative of the state-of-the-art, using
color or grayscale face images, and presented higher recognition
rates Moreover, the proposed texture dissimilarity metric also is
efficient in general texture discrimination (eg, texture
recognition of material images), as our experiments suggest

/pdf/face-recognition-based-on-texture-information-and-geodesic-4b4nyhm1by.pdf

Face recognition based on texture information and geodesic distance approximations between multivariate normal distributions

We come across a large volume of handwritten texts in our daily lives and handwritten character recognition has long been an important area of research in pattern recognition. The complexity of the task varies among different languages and it so happens largely due to the similarity between characters, distinct shapes and number of characters which are all language-specific properties. There have been numerous works on character recognition of English alphabets and with laudable success, but regional languages have not been dealt with very frequently and with similar accuracies. In this paper, we explored the performance of Deep Belief Networks in the classification of Handwritten Tamil vowels, and conclusively compared the results obtained. The proposed method has shown satisfactory recognition accuracy in light of difficulties faced with regional languages such as similarity between characters and minute nuances that differentiate them. We can further extend this to all the Tamil characters.

http://iopscience.iop.org/article/10.1088/1757-899X/263/5/052035/pdf

Handwritten recognition of Tamil vowels using deep learning

The exceptional speed in increase of genomic data at public databases requires advanced computational tools to perform quick gene analysis. The tools can be devised with the aid of genomic signal processing. The pivotal task in genomic signal processing is numerical mapping. In numerical mapping, the string of nucleotides is transformed into discrete numerical sequence by assigning optimum mathematical descriptor to a nucleotide. The descriptor must be compatible with the further stages of genomic application in order to achieve high efficiency. In this work, a simple numerical mapping method is proposed in which the optimum descriptor value is obtained by applying Gray code concept. The proposed method is evaluated on benchmark databases HRM195 and ASP67 for an identification of protein coding region application. The proposed method exhibits improved exon prediction efficiency in terms of performance accuracy and equal error rate when compared with similar methods.

A Numerical Representation Method for a DNA Sequence Using Gray Code Method

Review on DSP based dynamic gene encoding schemes for the detection of protein coding region

The measurement of alcohol quality plays a key role in the alcohol industry. The traditional methods to evaluate the quality of alcohol, requires special training, costly equipment, and time-consuming procedure. An electronic nose (E- nose) with the integration of machine learning (ML) algorithms can be a reliable solution for fast and easy evaluation of alcohol quality. In this paper, design and functional aspects of E-nose architecture in alcohol industries are discussed with emphasis on the type of sensing technologies and decision-making systems. An intelligent E-nose for assessing alcohol quality is introduced in this paper. The ML algorithms, artificial neural network (ANN), k-nearest neighbors (KNN), and support vector machine (SVM) are implemented on wine spoilage threshold dataset. The performance of intelligent E-nose is validated by extensive simulations. The simulation results confirm better performance with ML algorithms.

Vaegae Naveen Kumar

Papers

Handwritten recognition of Tamil vowels using deep learning

A Numerical Representation Method for a DNA Sequence Using Gray Code Method

Review on DSP based dynamic gene encoding schemes for the detection of protein coding region

Wine Quality Assessment using Electronic Nose

Implementation of an intelligent temperature to voltage converter using adaptive neuro-fuzzy inference system