Thapar University

About: Thapar University is a education organization based out in Patiāla, Punjab, India. It is known for research contribution in the topics: Computer science & Cloud computing. The organization has 2944 authors who have published 8558 publications receiving 130392 citations.

A survey on brain tumor detection techniques for MR images

Abstract: One of the most crucial tasks in any brain tumor detection system is the isolation of abnormal tissues from normal brain tissues. Interestingly, domain of brain tumor analysis has effectively utilized the concepts of medical image processing, particularly on MR images, to automate the core steps, i.e. extraction, segmentation, classification for proximate detection of tumor. Research is more inclined towards MR for its non-invasive imaging properties. Computer aided diagnosis or detection systems are becoming challenging and are still an open problem due to variability in shapes, areas, and sizes of tumor. The past works of many researchers under medical image processing and soft computing have made noteworthy review analysis on automatic brain tumor detection techniques focusing segmentation as well as classification and their combinations. In the manuscript, various brain tumor detection techniques for MR images are reviewed along with the strengths and difficulties encountered in each to detect various brain tumor types. The current segmentation, classification and detection techniques are also conferred emphasizing on the pros and cons of the medical imaging approaches in each modality. The survey presented here aims to help the researchers to derive the essential characteristics of brain tumor types and identifies various segmentation/classification techniques which are successful for detection of a range of brain diseases. The manuscript covers most relevant strategies, methods, their working rules, preferences, constraints, and their future snags on MR image brain tumor detection. An attempt to summarize the current state-of-art with respect to different tumor types would help researchers in exploring future directions.

Providing Healthcare-as-a-Service Using Fuzzy Rule Based Big Data Analytics in Cloud Computing

Abstract: With advancements in information and communication technology, there is a steep increase in the remote healthcare applications in which patients can get treatment from the remote places also. The data collected about the patients by remote healthcare applications constitute big data because it varies with volume, velocity, variety, veracity, and value. To process such a large collection of heterogeneous data is one of the biggest challenges which requires a specialized approach. To address this challenge, a new fuzzy rule based classifier is presented in this paper with an aim to provide Healthcare-as-a-Service. The proposed scheme is based upon the initial cluster formation, retrieval, and processing of the big data in cloud environment. Then, a fuzzy rule based classifier is designed for efficient decision making for data classification in the proposed scheme. To perform inferencing from the collected data, membership functions are designed for fuzzification and defuzzification processes. The proposed scheme is evaluated on various evaluation metrics, such as average response time, accuracy, computation cost, classification time, and false positive ratio. The results obtained confirm the effectiveness of the proposed scheme with respect to various performance evaluation metrics in cloud computing environment.

Human Arthritis Analysis in Fog Computing Environment Using Bayesian Network Classifier and Thread Protocol

Abstract: Nowadays, many people are facing the problem of arthritis. Regular monitoring and consultation of joint health from a specialist can help patients with this chronicle disease. The ratio of orthopedic doctors to patients with arthritis is low, worldwide. Use of smart devices can support the healthcare industry a lot. Motivated by these facts, here we propose an architecture to track the hand movements of the patient. For regular monitoring of patients with arthritis, fog and cloud gateways for real-time response generation are used. Thread protocol and Bayesian network classifier have been included in the proposed architecture to achieve reliable communication and anomaly detection, respectively. A dataset of 431 patients with arthritis is taken in real time and simulated on OMNet++ simulator. Observations show that the packet delivery ratio is improved by 15–20%, the response time is reduced by 20–30%, and the packet delivery rate is improved by 25–35%, in comparison to not using the fog and thread protocol.

Muscodor tigerii sp. nov.-Volatile antibiotic producing endophytic fungus from the Northeastern Himalayas

Abstract: Genus Muscodor came into existence with the discovery of Muscodor albus, a sterile endophytic fungus that produces a medley of volatile organic moieties possessing strong antimicrobial activity. The current paper reports Muscodor tigerii as a novel endophytic fungus from the stem internal tissue of Cinnamomum camphora growing in the Tiger Hill area of Darjeeling, West Bengal, India. M. tigerii exhibited distinct morphological, molecular and physiological features than previously reported Muscodor species. The fungus possesses all the morphological features described till date in genus Muscodor making it remarkably unique. The strong fruity smell of the fungus is attributed to 22 volatile organic compounds (VOCs), predominantly 4-Octadecylmorpholine, 1-Tetradecanamine,N,N-dimethyl and 1,2-Benzenedicarboxylic acid, mono(2-ethylhexyl) ester. The in vitro VOC stress assay completely suppressed the growth of Alternaria alternata and Cercospora beticola while growth of other fungal species was inhibited in a range of 10 %-70 %. The growth of Candida albicans in the presence of VOC was reduced by 50 %-65 % while in bacteria 50 %-80 % reduction in growth was observed. Thus, M. tigerii stands as a potential candidate to be further developed into a biocontrol agent.