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Abstract

Medical imaging is an important field of research used for the diagnosis and prediction of diseases. Melanoma is considered as one of the hazardous types of cancers and if detected in early stages, it can be cured easily using simple methods. By using clinical examination, it is difficult to predict melanoma at early stages with high accuracy. This paper proposes a novel strategy for the detection of melanoma by skin malignant growth and also proposes a method for early prediction. The proposed system is based on Deep learning algorithm for the prediction of the affected area and type of melanoma using the metrics precision, accuracy, recall and F1 score. The pre-processing methods are utilized for enhancing the image. The Active contour segmentation process differentiates the infected regions from the healthy skin regions. SOM and CNN classifiers are used for the process of classification of melanoma. A randomly chosen sample of 500 images are taken, 350 images are used as the training dataset and 150 images are used as a testing dataset, for which the proposed system showed high efficiency in the detection of melanoma with a greater accuracy of 90%.

The marine researchers analyze the behaviors of fish in the sea by manually viewing the full video for their research activity. Searching events of interest from a video database is a time consuming and tedious process. Video summary refers to representing the whole video using few frames. The objective of this work is to design and develop a statistical video summarization to perform the automatic detection of events of interest in underwater video. In this proposed work, a video is partitioned into adjacent and non-overlapping datacubes. Then, the video frames are transformed into wavelet subbands and the standard deviation between two consecutive frames is computed. Pixels of interest in frames are identified using threshold values. Key frames are identified using Local Maxima and Local Minima. The proposed work effectively detects even the movement of small water bodies such as crabs which is not detected using the existing methods. Finally, this paper presents the experimental results of proposed method and existing methods in terms of metrics that measure the valid of the work.

Thiacloprid residues were estimated in green tea leaves, processed tea and tea infusion by HPLC-Diode Array detection. The average initial deposits of thiacloprid (Alanto 240 SC) on the green tea leaves were found to be 3.72 and 6.77 μg g(-1) at single and double doses, respectively. The results showed that thiacloprid dissipated faster in green tea leaves following a first order reaction kinetics at both application rates. The amount of dissipation in 14 days was 93.37% and 91.62% for single and double doses respectively. Half life (T(1/2)) for degradation of thiacloprid in green tea leaves were observed to be 3.34 and 3.58 days at single and double doses respectively. Thiacloprid residues in processed tea ranged from 0.16 to 0.63 μg g(-1) on seventh day and no residues could be detected on 14th day at single dose. Infusion study indicated that thiacloprid did not infuse into tea liquor from processed tea. The limit of determination was found to be 0.05 μg g(-1).