ITM University, Gurgaon, Haryana

About: ITM University, Gurgaon, Haryana is a education organization based out in Gurgaon, India. It is known for research contribution in the topics: Encryption & Cryptosystem. The organization has 749 authors who have published 1159 publications receiving 12997 citations.

Performance analysis of LED based indoor VLC system under receiver mobility

Abstract: Existing indoor lighting systems consisting of incandescent bulbs and compact fluorescent lamps(CFLs) are being replaced by light emitting diodes(LEDs) at a much faster pace than envisaged. Such lighting systems are highly energy efficient, cost effective as well as environment-friendly. LEDs can also be used simultaneously for establishing indoor communication infrastructure known as visible light communication(VLC) system. In this paper, the performance of a VLC system is investigated for various communication parameters including the outage area ratio, root mean square (RMS) delay, signal to noise ratio(SNR) and minimum illumination requirement under spatial receiver mobility within the indoor environment.

Bio Inspired Managerial Insights for a Flexible and Efficient Organization: A SAP–LAP Analysis

Abstract: This paper aims at bringing out an analogy between human body and corporate organizations for classical managerial functions, viz. Planning, Organizing, Directing, and Controlling. In human body, every action and response to each stimulus are well planned. It has eleven subsystems, organized into a well structured design and a central directing brain that influences and guides these subsystems to perform in accordance with the plan. A control mechanism in human body minimizes deviations and maintains dynamic equilibrium. This way present analogy visualizes human body as a perfectly planned, organized, directed and controlled system created and sustained by Nature. A SAP–LAP framework of human body organization is also developed to understand the principles and processes behind its perfections. Based on this SAP–LAP model, a number of actions are proposed in this paper that can provide valuable insights for organizations. These insights perhaps can help them in significantly improving their structure and operating procedures, which will enhance the performance of individuals and organizations as a whole. Perhaps the human body is also a perfect example of optimal degree of flexibility in its sub-systems contingent upon their roles and functions.

Classification of brain tumor from magnetic resonance images using probabilistic features and possibilistic Hanman–Shannon transform classifier

Abstract: A brain tumor is considered one of the deadliest forms among all types of cancer due to its aggressive nature leading to patients’ low survival rate. Detection and classification of brain tumors have a significant impact on treatment planning and patient survival. The significance and importance of this work lie in the formulation of several probabilistic features that represent higher‐level probabilistic uncertainty. To create these features, the gain function of probabilistic Hanman transform is replaced with the gain functions of Shannon, Renyi, and Tsallis entropy functions thus paving a way to the corresponding hybrid transforms, Hanman‐Shannon, Hanman‐Renyi, and Hanman‐Tsallis transforms. The new features are extracted from brain MR images to detect and classify tumor by developing the possibilistic Hanman‐Shannon transform classifier. This uses the t‐normed errors between the training and testing features. The proposed system when evaluated on the two Brain MRI datasets yields the highest accuracy of around 99%.

Literature review: continuous improvement through Lean Six Sigma

Abstract: Six Sigma is a business metric used in manufacturing, service and healthcare sectors. The purpose of this paper is to uncovering existing literature gaps in the field of Lean Six Sigma. From the literature review, 2,461 papers were identified based on publication period of 2003 to 2018. It is concluded from the analysis of the results that no framework available in the literature for successful implementation of LSS. There is a need for empirical study that how to create a continuous improvement culture in the organisation and specific barriers for conducting improvements. Lean Six Sigma can be integrated with other improvement philosophies to achieve overall excellence. The thorough review of existing literature reveals that adoption of LSS plays a vital role in process improvement, variation reduction and defect reduction.

DCPM: an effective and robust approach for diabetes classification and prediction

Abstract: Diabetes is the most common medical disorders that occur due to the malfunctioning of the pancreas. It increases the level of sugar in the body and poses a severe concern to human health by adversely affecting almost all major organs of the body, including kidney, heart, eyes, etc. The number of research works in the literature proves that machine learning techniques can increase the early detection of disease and decrease medical error rates to save human life. Developing an accurate and effective diabetes prediction model is always a challenge, as the medical dataset suffers from outliers and missing values. The aim of this study is to build an accurate and robust Diabetes Classification and Prediction Model (DCPM) on a dataset that suffers from the class imbalance problem and contains outliers and missing values. The proposed work devises an effective pre-processing technique to remove outliers, fill missing values, standardize data and select relevant features for model learning in a pipelined manner. The proposed pre-processing techniques were applied on the Pima Indian Diabetes (PID) dataset obtained from the University of California at Irvine (UCI) Repository. The K-NN classifier is optimized to find the optimum value of k and is then trained and evaluated on the most predictive set of features of the pre-processed PID dataset. The performance of the proposed model is assessed using classification accuracy, precision, recall and F1-score. The proposed approach is able to attain statistically good classification accuracy, recall, precision and F1-score as 92.28%, 92.36%, 92.38% and 92.31%, respectively. The proposed model outperforms existing state-of-the-art approaches in terms of accuracy. Therefore, the proposed DCPM can assist the medical experts by providing a quick, precise and reliable recommendation that can be considered while making a crucial decision about the health of a patient in the healthcare sector.