Heritage Institute of Technology

About: Heritage Institute of Technology is a based out in . It is known for research contribution in the topics: Support vector machine & Transconductance. The organization has 581 authors who have published 1045 publications receiving 8345 citations.

Building resilient community using social technologies: a precursory measure for effective disaster management

Abstract: Natural disasters like cyclones are a common phenomenon in India, especially in coastal regions. During disaster, victims of a village/ locality normally take shelters in groups in some nearby safe areas (for example, Multi Purpose Cyclone Shelters). Despite of having access to physical infrastructure with basic facilities, disaster victims at cyclone shelters often find it difficult to communicate their exact situational information (like, resource needs and damage information) directly to the local/central authorities. Due to lack of coordination among relief workers and the remote central office, conflicting demands are often generated, causing mismanagement and delay in relief operation. This paper proposes innovative usage of social technology to build resilient community, a mandatory prerequisite to enhance disaster preparedness and coping up mechanisms among collectives inhabited in vulnerable areas. This paper presents an architecture of a Knowledge Informatics Hubs using social technologies at local multipurpose cyclone shelters (MPCS) in coastal areas that will empower the local community members to interact and collaborate with each other, establish communication with various supporting agencies during disaster, establish links with experts and participate in local governance. The aim of creating such a hub would be to create digitally empowered resilient communities who are not only prepared to prevent or minimize the risk of damage to life, property, and the environment due to disaster, but also have the ability to quickly reinstate their normal life and livelihood after a crisis, reopen businesses, and restore other essential services needed for the full economic recovery.

Cell-graph coloring for cancerous tissue modelling and classification

Abstract: In traditional cancer diagnosis process, pathologists manually examine biopsies to make diagnostic assessments. The assessments are largely based on visual interpretation of cell morphology and tissue distribution, lacking of quantitative measures. Therefore, they are subject to considerable inter-observer variability. To circumvent this problem, numerous studies aim at quantifying the characteristics of cancerous cells and tissues that distinguish them from their counterparts. Such quantification facilitates to design automated systems that operate on quantitative measures, and in turn, to reduce the inter-observer variability. There is a computational model available that relies solely on the topological features of cancerous cells in a tumor. Despite their complex dynamic nature, the self-organizing clusters of cancerous cells exhibit distinctive graph properties that distinguish the cancerous tissue from non-cancerous tissues; e.g. from a healthy tissue or an inflamed tissue. It is difficult to distinguish a cancerous tissue sample visually from an inflamed one. It is possible to construct a graph of the cells (cell graph) based on the location of the cells in the low-magnification image of a tissue sample surgically removed from a human patient. Assuming the cells present in a sample as the vertices of the cell graphs and the edges connecting those vertices/cells we can construct the cell graphs. There is a possibility of implementing the technique of using cell graphs to detect cancerous sample biopsies using some simple or a little bit complex computational techniques. Here possibly a new way is going to be introduced in this field, which is an application of graph coloring using the cell graphs to classify the normal, cancerous and inflamed sample biopsies. This work intends to automate the solution to the problem of identifying cancerous sample biopsies applying customized graph Coloring method solving by Genetic Algorithm on the cell graphs.

Free vibration analysis of axially functionally graded linearly taper beam on elastic foundation

Abstract: In the present study non-linear free vibration analysis is performed on a linearly tapered Axially Functionally Graded (AFG) beam resting on an elastic foundation with different boundary conditions. Firstly the static problem is carried out through an iterative scheme using a relaxation parameter and later on the subsequent dynamic problem is solved as a standard eigen value problem. Minimum potential energy principle is used for the formulation of the static problem and for the dynamic problem Hamilton's principle is utilized. The free vibrational frequencies are tabulated for different taper parameter and different foundation stiffness. The dynamic behaviour of the system is presented in the form of backbone curves in dimensionless frequency-amplitude plane.

Analysis of size distribution of red blood corpuscles using surface Plasmon resonance sensor for medical diagnosis

Abstract: The paper provides an analysis of different sizes red blood corpuscles (RBC) using surface Plasmon resonance (SPR) sensor. Change of RBC size affects the change of refractive index. SPR sensor is sensitive towards this small change of refractive index. A general formula is given in this paper such that just observing the output intensity of SPR sensor, the size of the RBC present as dielectric at the interface of metal in SPR sensor can be determined.