AB-DAM: attribute-based data access model in blockchain for healthcare applications

Multimedia Tools and Applications - This research introduces a novel attribute-based data access model in Blockchain (AB-DAM) Framework in Healthcare Systems to enhance the authentication of the...     

Attentional networks for music generation

Realistic music generation has always remained as a challenging problem as it may lack structure or rationality. In this work, we propose a deep learning based music generation method in order to produce old style music particularly JAZZ with rehashed melodic structures utilizing a Bi-directional Long Short Term Memory (Bi-LSTM) Neural Network with attention. Owing to the success in modelling long-term temporal dependencies in sequential data and its success in case of videos, Bi-LSTMs with attention serves as a natural choice and early utilization in music generation. We validate in our experiments that Bi-LSTMs with attention are able to preserve the richness and technical nuances of the music performed.

     

Development of a Multi-phase AlCuTaVW High-Entropy Alloy Using Powder Metallurgy Route and its Mechanical Properties

Transactions of the Indian Institute of Metals - Equiatomic AlCuTaVW high-entropy alloy (HEA) composition have resulted in a single-phase solid solution with a bcc crystal structure after...     

Assessing the effect of dynamics on the closed-loop protein-folding hypothesis

The closed-loop (loop-n-lock) hypothesis of protein folding suggests that loops of about 25 residues, closed through interactions between the loop ends (locks), play an important role in protein structure. Coarse-grain elastic network simulations, and examination of loop lengths in a diverse set of proteins, each supports a bias towards loops of close to 25 residues in length between residues of high stability. Previous studies have established a correlation between total contact distance (TCD), a metric of sequence distances between contacting residues (cf. contact order), and the log-folding rate of a protein. In a set of 43 proteins, we identify an improved correlation (r² = 0.76), when the metric is restricted to residues contacting the locks, compared to the equivalent result when all residues are considered (r² = 0.65). This provides qualified support for the hypothesis, albeit with an increased emphasis upon the importance of a much larger set of residues surrounding the locks. Evidence of a similar-sized protein core/extended nucleus (with significant overlap) was obtained from TCD calculations in which residues were successively eliminated according to their hydrophobicity and connectivity, and from molecular dynamics simulations. Our results suggest that while folding is determined by a subset of residues that can be predicted by application of the closed-loop hypothesis, the original hypothesis is too simplistic; efficient protein folding is dependent on a considerably larger subset of residues than those involved in lock formation.     

Higher order spectra analysis of breast thermograms for the automated identification of breast cancer

Breast cancer is a leading cancer affecting women worldwide. Mammography is a scanning procedure involvingX‐rays of the breast. It causes discomfort and may cause high incidence of false negatives. Breast thermography is a new screening method of breast that helps in the early detection of cancer. It is a non‐invasive imaging procedure that captures the infrared heat radiating off from the breast surface using an infrared camera. The main objective of this work is to evaluate the use of higher order spectral features extracted from thermograms in classifying normal and abnormal thermograms. For this purpose, we extracted five higher order spectral features and used them in a feed‐forward artificial neural network (ANN) classifier and a support vector machine (SVM). Fifty thermograms (25 each of normal and abnormal) were used for analysis.SVM presented a good sensitivity of 76% and specificity of 84%, and theANN classifier demonstrated higher values of sensitivity (92%) and specificity (88%). The proposed system, therefore, shows great promise in automatic classification of normal and abnormal breast thermograms without the need for subjective interpretation.     

Microstructural aspects of weld repair in 18 nickel 1800 MPA maraging steel

The study of the microstructural aspects of the maraging steel weld repair has become important as this material finds extensive applications in aerospace and other strategic areas. The details available on these at present are very limited. During one of the fabrication trials of a large pressure vessel made of 18 Ni 1800 MPa maraging steel plates, 7.8 mm thick, defects were observed in the welds which were to be repaired. This called for simulated experiments. The microstructural variations observed in the repaired areas were the result of the repeated thermal cycles they had undergone. Based on this microstructural evidence, a new diagram depicting the newly formed weld zones is constructed from which the mechanical properties of the repair welds can be predicted.     

ThyroScreen system: high resolution ultrasound thyroid image characterization into benign and malignant classes using novel combination of texture and discrete wavelet transform

Using right equipment and well trained personnel, ultrasound of the neck can detect a large number of non-palpable thyroid nodules. However, this technique often suffers from subjective interpretations and poor accuracy in the differential diagnosis of malignant and benign thyroid lesions. Therefore, we developed an automated identification system based on knowledge representation techniques for characterizing the intra-nodular vascularization of thyroid lesions. Twenty nodules (10 benign and 10 malignant), taken from 3-D high resolution ultrasound (HRUS) images were used for this work. Malignancy was confirmed using fine needle aspiration biopsy and subsequent histological studies. A combination of discrete wavelet transformation (DWT) and texture algorithms were used to extract relevant features from the thyroid images. These features were fed to different configurations of AdaBoost classifier. The performance of these configurations was compared using receiver operating characteristic (ROC) curves. Our results show that the combination of texture features and DWT features presented an accuracy value higher than that reported in the literature. Among the different classifier setups, the perceptron based AdaBoost yielded very good result and the area under the ROC curve was 1 and classification accuracy, sensitivity and specificity were 100%. Finally, we have composed an Integrated Index called thyroid malignancy index (TMI) made up of these DWT and texture features, to facilitate distinguishing and diagnosing benign or malignant nodules using just one index or number. This index would help the clinicians in more quantitative assessment of the thyroid nodules.     

Automated classification of patients with coronary artery disease using grayscale features from left ventricle echocardiographic images

Coronary Artery Disease (CAD), caused by the buildup of plaque on the inside of the coronary arteries, has a high mortality rate. To efficiently detect this condition from echocardiography images, with lesser inter-observer variability and visual interpretation errors, computer based data mining techniques may be exploited. We have developed and presented one such technique in this paper for the classification of normal and CAD affected cases. A multitude of grayscale features (fractal dimension, entropies based on the higher order spectra, features based on image texture and local binary patterns, and wavelet based features) were extracted from echocardiography images belonging to a huge database of 400 normal cases and 400 CAD patients. Only the features that had good discriminating capability were selected using t-test. Several combinations of the resultant significant features were used to evaluate many supervised classifiers to find the combination that presents a good accuracy. We observed that the Gaussian Mixture Model (GMM) classifier trained with a feature subset made up of nine significant features presented the highest accuracy, sensitivity, specificity, and positive predictive value of 100%. We have also developed a novel, highly discriminative HeartIndex, which is a single number that is calculated from the combination of the features, in order to objectively classify the images from either of the two classes. Such an index allows for an easier implementation of the technique for automated CAD detection in the computers in hospitals and clinics.     

Rotational hydrodynamic diffusion system to study mass transport across boundaries

The design and operation of a new mass transport technique is presented. Rotational hydrodynamic diffusion system (RHDS) is a method that can be adapted for analytical laboratory analysis as well as industrial-scale separation and purification. Although RHDS is not an electrochemical technique, its concept is derived from hydrodynamic rotating disk electrode voltammetry. A diffusion advantage gained using the RHDS is higher flux of probe molecules across the boundary (e.g., membrane or porous media) with increased rotation rate compared to the static two-half-cell (THC) method. The separation concept of RHDS differs from pressurized, agitated, electrodialysis, and reversed osmosis systems in design and theory. The detection mechanism of the RHDS opens the possibility to study mass transport properties of a large variety of molecules using different types of ultrathin membranes. Therefore, the RHDS is a potential alternative to classical mass transport detection methods such as THC, impedance spectroscopy, and cyclic and rotating disk electrode voltammetry. Theoretical analysis on the rotational hydrodynamic flux is derived and compared to experimental flux measured using HCl, KCl, KNO 3, Ni(NO 3) 2, LiCl, camphor sulfonic acid, and K 3Fe(CN) 6 ionic solutions. Values of effective diffusion coefficients of salts across Nucleopore membranes of thickness 6.0 and 10 mum with pore size 0.1 and 0.2 mum, respectively, are presented and discussed.