Segmentation and Classification of Skin Lesions for Disease Diagnosis

In this paper, a novel approach for automatic segmentation and classification of skin lesions is proposed. Initially, skin images are filtered to remove unwanted hairs and noise and then the segmentation process is carried out to extract lesion areas. For segmentation, a region growing method is applied by automatic initialization of seed points. The segmentation performance is measured with different well known measures and the results are appreciable. Subsequently, the extracted lesion areas are represented by color and texture features. SVM and k-NN classifiers are used along with their fusion for the classification using the extracted features. The performance of the system is tested on our own dataset of 726 samples from 141 images consisting of 5 different classes of diseases. The results are very promising with 46.71% and 34% of F-measure using SVM and k-NN classifier respectively and with 61% of F-measure for fusion of SVM and k-NN.Comment: 10 pages, 6 figures, 2 Tables in Elsevier, Proceedia Computer Science, International Conference on Advanced Computing Technologies and Applications (ICACTA-2015

Microwave photovoltage and photoresistance effects in ferromagnetic microstrips

We investigate the dc electric response induced by ferromagnetic resonance in ferromagnetic Permalloy (Ni80Fe20) microstrips. The resulting magnetization precession alters the angle of the magnetization with respect to both dc and rf current. Consequently the time averaged anisotropic magnetoresistance (AMR) changes (photoresistance). At the same time the time-dependent AMR oscillation rectifies a part of the rf current and induces a dc voltage (photovoltage). A phenomenological approach to magnetoresistance is used to describe the distinct characteristics of the photoresistance and photovoltage with a consistent formalism, which is found in excellent agreement with experiments performed on in-plane magnetized ferromagnetic microstrips. Application of the microwave photovoltage effect for rf magnetic field sensing is discussed.Comment: 16 pages, 15 figure

On the line shape of the electrically detected ferromagnetic resonance

This work reviews and examines two particular issues related with the new technique of electrical detection of ferromagnetic resonance (FMR). This powerful technique has been broadly applied for studying magnetization and spin dynamics over the past few years. The first issue is the relation and distinction between different mechanisms that give rise to a photovoltage via FMR in composite magnetic structures, and the second is the proper analysis of the FMR line shape, which remains the "Achilles heel" in interpreting experimental results, especially for either studying the spin pumping effect or quantifying the spin Hall angles via the electrically detected FMR.Comment: 14 pages, 9 figure

An Ensemble Learning Approach for Fast Disaster Response using Social Media Analytics

Natural disaster happens, as a result of natural hazards that cause financial, environmental or human losses. Natural disasters strike unexpectedly, affecting the lives of tens of thousands of people. During the flood, social media sites were also heavily used to disseminate information about flooded areas, rescue agencies, food and relief centres. This work proposes an ensemble learning strategy for combining and analysing social media data in order to close the gap and progress in catastrophic situation. To enable scalability and broad accessibility of the dynamic streaming of multimodal data namely text, image, audio and video, this work is designed around social media data. A fusion technique was employed at the decision level, based on a database of 15 characteristics for more than 300 disasters around the world (Trained with MNIST dataset 60000 training images and 10000 testing images).  This work allows the collected multimodal social media data to share a common semantic space, making individual variable prediction easier. Each  merged numerical vector(tensors) of text and audio  is sent into the K-CNN algorithm, which is an  unsupervised learning algorithm (K-CNN), and the  image and video data is given to a deep learning  based Progressive Neural Artificial Search (PNAS).  The trained data acts as a predictor for future  incidents, allowing for the estimation of total  deaths, total individuals impacted, and total  damage, as well as specific suggestions for food,  shelter and housing inspections. To make such a prediction, the trained model is presented a satellite image from before the accident as well as the geographic and demographic conditions, which is expected to result in a prediction accuracy of more than 85%

Textural features in flower classification

In this work, we investigate the effect of texture features for the classification of flower images. A flower image is segmented by eliminating the background using a threshold-based method. The texture features, namely the color texture moments, gray-level co-occurrence matrix, and Gabor responses, are extracted, and combinations of these three are considered in the classification of flowers. In this work, a probabilistic neural network is used as a classifier. To corroborate the efficacy of the proposed method, an experiment was conducted on our own data set of 35 classes of flowers, each with 50 samples. The data set has different flower species with similar appearance (small inter-class variations) across different classes and varying appearance (large intra-class variations) within a class. Also, the images of flowers are of different pose, with cluttered background under various lighting conditions and climatic conditions. The experiment was conducted for various sizes of the datasets, to study the effect of classification accuracy, and the results show that the combination of multiple features vastly improves the performance, from 35% for the best single feature to 79% for the combination of all features. A qualitative comparative analysis of the proposed method with other well-known existing state of the art flower classification methods is also given in this paper to highlight the superiority of the proposed method. (c) 2010 Elsevier Ltd. All rights reserved

Acyclic pyrazolo[3,4-d]pyrimidine nucleoside as potential leishmaniostatic agent

A new synthesis of 6-amino-1-hydroxyethoxymethyl-4 (5H)-oxopyrazolo[3, 4]pyrimidine (4) has been mentioned. Compound 4 exhibited inhibition of amastigotes of Leishmania donovani to the extent of 89% at 30 μg/mL, whereas iso-guanine analogue 5 had the inhibition only to the extent of 52.8% at 100 μg/mL in vitro. In hamster model the maximum inhibitory response for compound 4 against amastigotes multiplication was observed to be 94% at 50 Mug/kg single dose for 5 consecutive days