This work proposes a technique of breast cancer detection from mammogram images. It is a multistage process which classifies the mammogram images into benign or malignant category. During preprocessing, images of Mammographic Image Analysis Society (MIAS) database are passed through a couple of filters for noise removal, thresholding and cropping techniques to extract the region of interest, followed by augmentation process on database to enhance its size. Features from Deep Convolution Neural Network (DCNN) are merged with texture features to form final feature vector. Using transfer learning, deep features are extracted from a modified DCNN, whose training is performed on 69% of randomly selected images of database from both categories. Features of Grey Level Co-Occurrence Matrix (GLCM) and Local Binary Pattern (LBP) are merged to form texture features. Mean and variance of four parameters (contrast, correlation, homogeneity and entropy) of GLCM are computed in four angular directions, at ten distances. Ensemble Boosted Tree classifier using five-fold cross-validation mode, achieved an accuracy, sensitivity, specificity of 98.8%, 100% and 92.55% respectively on this feature vector.
The spread and development of the Indus Valley Civilisation, also known as the Harappan civilisation, one of the oldest civilisations of the world, is still an enigma. Indus Valley Civilisation was spread over modern day India and Pakistan. The civilisation has been divided into three phases, Early or Pre-Harappan, Mature or Urban Harappan and Post- or Late Harappan. The Urban phase is very well studied and understood. However, this phase is the culmination of a process that started much earlier. A lot of effort during recent years has led to new discoveries and clues regarding the interactions during the Early Harappan period between now politically divided areas. Unfortunately, this struggle to understand the spread of Early Harappan cultural traits between these distinct regions is one on-going and far from over.
Explorations and subsequent excavations at the site of Juna Khatiya, situated in Kachchh district of Gujarat, India have brought to light noteworthy evidence of the Early Harappan period in terms of artefacts and burials. Other than the ubiquitous pottery, these indications include a lithic blade industry comprising of various types of blades, various types of scrapers, points and associated lithic debitage. The tools are made out of locally available raw material (mostly chalcedony). However, the discovery of a few blades of chert imported from the Rohri hills (situated about 500 km as-the-crow-flies from Gujarat) in modern Pakistan is important. Rohri chert blades are significant since they are very distinct and easily identifiable. The wide distribution of standardised Rohri chert blades is also often regarded as a testimony to the Harappan efficiency in long distance trade and craft production. The technique used in the manufacturing of these blades is known as the crested guiding ridge, a technique not observed in Gujarat before this contact between Sindh (in modern Pakistan) and Gujarat (in modern India) developed. This paper highlights the contributions of lithic artefacts to understand the Early Harappan interactions between these two politically divided but culturally united regions.
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