PLANE LANGUAGES AND THEIR PROPERTIES

In this paper we study the Generalized Parikh Vector of words over three letter alphabet. For S = {a, b, c} the GPVs of words lie in the tetrahedron whose vertices are (1, 0, 0), (0, 1, 0), (0, 0, 1) and (0, 0, 0). All GPVs of words of equal length lie on the same plane. Plane languages and their language theoretical properties are studied. Further, the GPVs of words lying on surfaces are discussed. The concepts of surface language, language surface and their properties are also studied in this paper

SHUFFLE ON ARRAY LANGUAGES GENERATED BY ARRAY GRAMMARS

Abstract. Motivated by the studies done by G

インド ニ オケル シカク ニ ショウガイ オ モツ ヒトビト ノ タメノ コウトウ キョウイク エノ ミチ

Visual impairment is one of the major socio-economic issues in India. There is an urgent need for awareness among educators, policy makers, parents and the disabled people among visual impairment. This paper presents a brief summary about the prevailing general situation of the visually impaired people and their training and job placement prospects in the country. Emerging needs concerning educational facilities for the visually impaired and focus on the issues related to cost effective integrated education have been discussed. Developing assistive technologies and the role of institutions in making them accessible to the disabled in the new information age are also discussed

AUTOMATED DETECTION OF MITOTIC FIGURES IN BREAST CANCER HISTOPATHOLOGY IMAGES USING GABOR FEATURES AND DEEP NEURAL NETWORKS

The count of mitotic figures in Breast cancer histopathology slides is the most significant independent prognostic factor enabling determination of the proliferative activity of the tumor. In spite of the strict protocols followed, the mitotic counting activity suffers from subjectivity and considerable amount of observer variability despite being a laborious task. Interest in automated detection of mitotic figures has been rekindled with the advent of Whole Slide Scanners. Subsequently mitotic detection grand challenge contests have been held in recent years and several research methodologies developed by their participants. This paper proposes an efficient mitotic detection methodology for Hematoxylin and Eosin stained Breast cancer Histopathology Images using Gabor features and a Deep Belief Network- Deep Neural Network architecture (DBN-DNN). The proposed method has been evaluated on breast histopathology images from the publicly available dataset from MITOS contest held at the ICPR 2012 conference. It contains 226 mitoses annotated on 35 HPFs by several pathologists and 15 testing HPFs, yielding an F-measure of 0.74. In addition the said methodology was also tested on 3 slides from the MITOSIS- ATYPIA grand challenge held at the ICPR 2014 conference, an extension of MITOS containing 749 mitoses annotated on 1200 HPFs, by pathologists worldwide. This study has employed 3 slides (294 HPFs) from the MITOS-ATYPIA training dataset in its evaluation and the results showed F-measures 0.65, 0.72and 0.74 for each slide. The proposed method is fast and computationally simple yet its accuracy and specificity is comparable to the best winning methods of the aforementioned grand challenge