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.

Microbes mediated plant stress tolerance in saline agricultural ecosystem

Abstract: The accumulation of salts leads to assimilation of dissolved salts in the soil which results in soil salinity. Salinity affects the crop yield by reducing the levels of minerals availability, inducing ions mediated toxicity, osmotic stress, growth regulators level and reactive oxygen species production which ultimately lead to the inhibition of seed germination, seedling growth, onset of flowering, and fruit set. The beneficial microorganisms are attractive candidate to increase the agricultural productivity in saline ecosystem. The plant beneficial microbiome offers significant prospective to magnify the plant resilience and crop yields in saline agriculture systems either by modulating the uptake of ions, regulation of plant growth regulators and by the production of exopolysaccharides and alleviate the salinity stress. Salt tolerance is a complex manifestation of different physiological and biochemical events. The microbes mediated mechanisms underlying regulation of salinity responses involved in ion transport and homeostasis, osmolytes regulation, hormonal balance, antioxidant machinery and other stress signaling are critical in developing plant adaptation strategies to salinity stress. Therefore, plant beneficial microbes are attractive choice in alleviating plant stresses saline soil.

Deep neuro‐fuzzy approach for risk and severity prediction using recommendation systems in connected health care

Abstract: Internet of Things (IoT) and Data science have revolutionized the entire technological landscape across the globe. Because of it, the health care ecosystems are adopting the cutting‐edge t...

Machine intelligence-based algorithms for spam filtering on document labeling

Abstract: The internet has provided numerous modes for secure data transmission from one end station to another, and email is one of those. The reason behind its popular usage is its cost-effectiveness and facility for fast communication. In the meantime, many undesirable emails are generated in a bulk format for a monetary benefit called spam. Despite the fact that people have the ability to promptly recognize an email as spam, performing such task may waste time. To simplify the classification task of a computer in an automated way, a machine learning method is used. Due to limited availability of datasets for email spam, constrained data and the text written in an informal way are the most feasible issues that forced the current algorithms to fail to meet the expectations during classification. This paper proposed a novel, spam mail detection method based on the document labeling concept which classifies the new ones into ham or spam. Moreover, algorithms like Naive Bayes, Decision Tree and Random Forest (RF) are used in the classification process. Three datasets are used to evaluate how the proposed algorithm works. Experimental results illustrate that RF has higher accuracy when compared with other methods.