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.

Thermoluminescence study of ZnS:Cu nanoparticles

Abstract: In this paper, we report on the TL glow curves and kinetic parameters, activation energy, order of kinetics, and the frequency factor of copper-doped zinc sulfide nanophosphor under UV irradiations. The sample was prepared by the chemical precipitation method; thereafter, the TL glow curves were recorded for different doses of UV exposure at a heating rate of 10 °C/s. The synthesized nanophosphor exhibited TL glow peaks at 241, 255, and 281 °C for the heating rate 10 °C/s at different doses of 5, 10, and 15 min of UV exposure. The kinetic parameters activation energy E, the order of kinetics b, and the frequency factor S of synthesized nanophosphor of ZnS:Cu have been calculated by using a peak shape method while the trap depth was determined using different formulae. The sample was characterized by XRD (X-ray diffraction) and SEM (scanning electron microscope).

Automated Essay Grading: An Empirical Analysis of Ensemble Learning Techniques

Abstract: Automated essay grading refers to the application of natural language processing tools for assigning scores to student essays. It is an important research domain as teachers are often required to grade a large amount of student essays in educational settings. Fair grading of essays is a challenging and tedious task. Teachers often consider this as unproductive work. Thus, there is a need for an automated approach for teachers so that they are no longer required to manually grade the student essays. Various automated essay scoring systems using machine learning and information retrieval concepts have been developed in the past studies. In the recent years, ensemble classification techniques have gained popularity. Ensemble techniques use multiple classifiers for making a prediction and have proved to be outperforming classical machine learning. In this paper, we present an empirical study of ensemble learning techniques for classification of student essays. We studied performance of five machine learning and four ensemble learning techniques for conducting experiments. We further utilized feature selection technique to improve the prediction efficiency. The performance results on automated student assessment prize dataset available on Kaggle showed that ensemble techniques outperform the efficiency of traditional machine learning techniques.

Depression discovery in cancer communities using deep learning

Abstract: We are said to be living in the “information age,” and data are the capital of the new economy. With the continuous explosion in the extent of data being created every day on online portals and social networking websites, industries today are collecting and analyzing more data than ever before. Data are readily available, finding valuable insights are the struggle. The easy accessibility of data, new cutting-edge technologies, and a cultural shift toward data-driven decision-making drive the demand for sentiment analysis (SA) and make it relevant in each and every domain such as politics, marketing, healthcare, and so on. In the healthcare domain, cancer is a deadly disease that claims almost 10 million lives every year. The alarming numbers of fatalities are caused due to privation of timely cancer detection, tardy medical attention, or in some cases from patients losing the will to live due to a protracted and unending treatment procedure. Governments across the world are taking steps to ensure timely cancer detection and treatment. However, little attention is being paid to the seemingly unending treatment course taking a toll on the patient's mental health, thus crushing the patient's spirit to continue. In this chapter, we investigate the use of different deep neural network architectures and natural language processing for depression detection in cancer communities. Depression detection using SA can be of great assistance to the doctors treating cancer patients and aid them in deciding whether along with the cancer treatment their patients need help from psychologists or psychiatrists.

Segmentation of Noisy Mammograms Using Hybrid Techniques

Abstract: Breast cancer is the most routinely identified carcinoma among women in India, and it is one of the foremost causes of cancer death in women. Radiologists prefer mammograms for visualizing breast cancer. Different types of noises including Gaussian noise and salt-and-pepper noise affect the mammograms leading to inaccurate classification. Mammograms consist of numerous artifacts too, which depressingly affect the finding of breast cancer. The existence of pectoral muscles makes anomaly finding a cumbersome task. The recognition of glandular tissue in mammograms is vital in assessing asymmetry between left and right breasts and in conjecturing the radiation risk associated with screening. Thus, the proposed method focuses on improving the segmentation accuracy of noisy mammograms. It involves preprocessing which includes denoising using a pretrained convolutional neural network, artifacts removal using thresholding, and modified region growing and enhancement using two-stage adaptive histogram equalization along with segmentation of mammogram images into sections conforming to different densities using K-means clustering. The projected method has been confirmed on the Mini-MIAS database with ground truth provided by expert radiologists. The results illustrate that the proposed method is competent in eradicating noise and pectoral muscles without degrading quality and contrast and in fragmenting different denoised mammograms into different mammographic densities with high accuracy.

Comparitive analysis of different modes of operation for Zigbee mac with variation in network size and traffic

Abstract: In this paper we have presented a brief review of ZigBee technology, the factors that led to its development and the subsequent versions that evolved. This paper presents and briefs different modes of operation for ZigBee devices and the different layers of ZigBee with emphasis on the operation of MAC layer. The Zigbee devices can operate in either beacon or non-beacon mode. We analysed and compared throughput and energy consumption of these operational modes with respect to varying network size and number of packets transmitted.