Chandigarh University

About: Chandigarh University is a education organization based out in Mohali, India. It is known for research contribution in the topics: Computer science & Chemistry. The organization has 1358 authors who have published 2104 publications receiving 10050 citations.

The Performance of Brain Tumor Diagnosis Based on Machine Learning Techniques Evaluation - A Systematic Review

Abstract: This research aims to investigate the performance of brain tumor diagnosis and treatment using machine learning algorithms. This study provides systematic review of papers on the improvement of human life. The papers reviewed are taken from relevant articles published between October 2012 and December 2019. The investigation is done against the algorithm type, dataset, the proposed model, and the performance in each of the papers. The accuracy result among the papers papers studied is ranged between 79% - 97.7%. The algorithms they used are CNN, KNN, C-means, RF, respectively, ordered from the highest frequency of use to the lowest. In the papers studied, it was shown that various methods had been used with good results. However, the confidence in the research results in term of accuracy for the detection of brain tumors still needs to be increased. Furthermore, building a software applications can be very useful to solve real cases.

Recent Advances of Efficient Design of Terahertz Quantum-Cascade Lasers

Abstract: Terahertz (THz) quantum cascade lasers (QCLs) are electrically pumped and heterostructure based semiconductor laser sources with intersubband transitions of electrons in different layers of the quantum wells and barriers. The THz QCLs have high output power in THz region which make them important from application point of view. Recently intensive research has been carried out by researchers for obtaining efficient designs of THz sources. Most of the researchers have investigated the THz frequency range between 0.1 and 3 THz; however, the output power of the THz sources in the frequency range 3–5 THz is small because of transit time and resistance-capacitance effects. Nevertheless, the present review is focused for the development of efficient THz QCL sources in the frequency range from 3 to 5 THz where one of the major problem of thermal backfilling of the carriers has to be overcome by engineering the heterostructure.

Infrared and Visible Image Fusion for Concealed Weapon Detection using Transform and Spatial Domain Filters

Abstract: Image fusion is mandatory to enhance the object recognition in images by concatenating many sources of planetoid, aerial and ground-based imaging systems with other related data set. Image fusion is the methodology of overlapping at least a couple of pictures so that their combination enhances the overall information content. In literature, there are many algorithms, whose objective is the fusion of images to improve the characteristics of the final image. This work primarily presents a contrast between thee cosine harmonic transform of wavelet transform, guided filter algorithm and discrete form of Wavelet transform. It can be stated with simple observation that the image fusion techniques based on guided refining or masking retrieve a good visual resultant image. Their performance is evaluated on Standard “Gun” dataset with the help of three most widely used performance evaluation metrics. The experimentation and work done on MATLAB, validates the superiority of DCHWT based fusion algorithm over others.

A Comparative Study of Tree-Based and Apriori-Based Approaches for Incremental Data Mining

Abstract: Association rule mining is an iterative and interactive process of discovering valid, novel, useful, understandable and hidden associations from the massive database. The Colossal databases require powerful and intelligent tools for analysis and discovery of frequent patterns and association rules. Several researchers have proposed the many algorithms for generating item sets and association rules for discovery of frequent patterns, and minning of the association rules. These proposals are validated on static data. A dynamic database may introduce some new association rules, which may be interesting and helpful in taking better business decisions. In association rule mining, the validation of performance and cost of the existing algorithms on incremental data are less explored. Hence, there is a strong need of comprehensive study and in-depth analysis of the existing proposals of association rule mining. In this paper, the existing tree-based algorithms for incremental data mining are presented and compared on the baisis of number of scans, structure, size and type of database. It is concluded that the Can-Tree approach dominates the other algorithms such as FP-Tree, FUFP-Tree, FELINE Alorithm with CATS-Tree etc.This study also highlights some hot issues and future research directions. This study also points out that there is a strong need for devising an efficient and new algorithm for incremental data mining.