Institution
Chandigarh University
Education•Mohali, India•
About: Chandigarh University is a education organization based out in Mohali, India. It is known for research contribution in the topics: Materials science & Computer science. The organization has 1358 authors who have published 2104 publications receiving 10050 citations.
Papers
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TL;DR: In this paper , the integration of several biorefinery processes for the production of multifaceted products is discussed to pave the path towards circular economy through the closed-loop approach.
72 citations
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TL;DR: In this paper, the integration of several biorefinery processes for the production of multifaceted products is discussed to pave the path towards circular economy through the closed-loop approach.
72 citations
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TL;DR: It was found that MQL outperformed the dry as well as wet condition in surface grinding due to its effective penetration ability and improved heat dissipation property.
Abstract: In the present study, the machinability indices of surface grinding of AISI D2 steel under dry, flood cooling, and minimum quantity lubrication (MQL) conditions are compared. The comparison was confined within three responses, namely, the surface quality, surface temperature, and normal force. For deeper insight, the surface topography of MQL-assisted ground surface was analyzed too. Furthermore, the statistical analysis of variance (ANOVA) was employed to extract the major influencing factors on the above-mentioned responses. Apart from this, the multi-objective optimization by Grey⁻Taguchi method was performed to suggest the best parameter settings for system-wide optimal performance. The central composite experimental design plan was adopted to orient the inputs wherein the inclusion of MQL flow rate as an input adds addition novelty to this study. The mathematical models were formulated using Response Surface Methodology (RSM). It was found that the developed models are statistically significant, with optimum conditions of depth of cut of 15 µm, table speed of 3 m/min, cutting speed 25 m/min, and MQL flow rate 250 mL/h. It was also found that MQL outperformed the dry as well as wet condition in surface grinding due to its effective penetration ability and improved heat dissipation property.
72 citations
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TL;DR: This study provided in-depth insights on the effects of amino acid substitutions on IL-8 protein structure, function and disease association and also appeared to be an important prognostic marker for detection of patients with gastric and lung cancer.
Abstract: Here we report an in-silico approach for identification, characterization and validation of deleterious non-synonymous SNPs (nsSNPs) in the interleukin-8 gene using three steps. In first step, sequence homology-based genetic analysis of a set of 50 coding SNPs associated with 41 rsIDs using SIFT (Sorting Intolerant from Tolerant) and PROVEAN (Protein Variation Effect Analyzer) identified 23 nsSNPs to be putatively damaging/deleterious in at least one of the two tools used. Subsequently, structure-homology based PolyPhen-2 (Polymorphism Phenotyping) analysis predicted 9 of 23 nsSNPs (K4T, E31A, E31K, S41Y, I55N, P59L, P59S, L70P and V88D) to be damaging. According to the conditional hypothesis for the study, only nsSNPs that score damaging/deleterious prediction in both sequence and structural homology-based approach will be considered as 'high-confidence' nsSNPs. In step 2, based on conservation of amino acid residues, stability analysis, structural superimposition, RSMD and docking analysis, the possible structural-functional relationship was ascertained for high-confidence nsSNPs. Finally, in a separate analysis (step 3), the IL-8 deregulation has also appeared to be an important prognostic marker for detection of patients with gastric and lung cancer. This study, for the first time, provided in-depth insights on the effects of amino acid substitutions on IL-8 protein structure, function and disease association.
72 citations
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TL;DR: The security of the IoT devices by detecting spam using ML is proposed using a framework of five ML models evaluated using various metrics with a large collection of inputs features sets to achieve this objective.
Abstract: The Internet of Things (IoT) is a group of millions of devices having sensors and actuators linked over wired or wireless channel for data transmission. IoT has grown rapidly over the past decade with more than 25 billion devices expected to be connected by 2020. The volume of data released from these devices will increase many-fold in the years to come. In addition to an increased volume, the IoT devices produces a large amount of data with a number of different modalities having varying data quality defined by its speed in terms of time and position dependency. In such an environment, machine learning (ML) algorithms can play an important role in ensuring security and authorization based on biotechnology, anomalous detection to improve the usability, and security of IoT systems. On the other hand, attackers often view learning algorithms to exploit the vulnerabilities in smart IoT-based systems. Motivated from these, in this article, we propose the security of the IoT devices by detecting spam using ML. To achieve this objective, Spam Detection in IoT using Machine Learning framework is proposed. In this framework, five ML models are evaluated using various metrics with a large collection of inputs features sets. Each model computes a spam score by considering the refined input features. This score depicts the trustworthiness of IoT device under various parameters. REFIT Smart Home data set is used for the validation of proposed technique. The results obtained proves the effectiveness of the proposed scheme in comparison to the other existing schemes.
70 citations
Authors
Showing all 1533 results
Name | H-index | Papers | Citations |
---|---|---|---|
Neeraj Kumar | 76 | 587 | 18575 |
Rupinder Singh | 42 | 458 | 7452 |
Vijay Kumar | 33 | 147 | 3811 |
Radha V. Jayaram | 32 | 114 | 3100 |
Suneel Kumar | 32 | 180 | 5358 |
Amanpreet Kaur | 32 | 367 | 5713 |
Vikas Sharma | 31 | 145 | 3720 |
Munish Kumar Gupta | 31 | 192 | 3462 |
Vijay Kumar | 30 | 113 | 2870 |
Shashi Kant | 29 | 160 | 2990 |
Sunpreet Singh | 29 | 153 | 2894 |
Gagangeet Singh Aujla | 28 | 109 | 2437 |
Deepak Kumar | 28 | 273 | 2957 |
Dilbag Singh | 27 | 77 | 1723 |
Tejinder Singh | 27 | 162 | 2931 |