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.

Multi-Objective Optimization of WEDM of Aluminum Hybrid Composites Using AHP and Genetic Algorithm

Abstract: Aluminum hybrid composites have the potential to satisfy emerging demands of lightweight materials with enhanced mechanical properties and lower manufacturing costs. There is an inclusion of reinforcing materials with variable concentrations for the preparation of hybrid metal matrix composites to attain customized properties. Hence, it is obligatory to investigate the impact of different machining conditions for the selection of optimum parameter settings for aluminum-based hybrid metal matrix composite material. The present study aims to identify the optimum machining parameters during wire electrical discharge machining of samples prepared with graphite, ferrous oxide, and silicon carbide. In the present research work, five different process parameters and three response parameters such as material removal rate, surface roughness, and spark Gap are considered for process optimization. Energy-dispersive spectroscopy and scanning electron microscopy analysis reported the manifestation of the recast layer. Analytical hierarchy process and genetic algorithm have been successfully implemented to identify the best machining conditions for hybrid composites.

Sorption of Pb(II), Cu(II), Fe(II) and Cr(VI) metal ions onto cross-linked graft copolymers of chitosan with binary vinyl monomer mixtures

Abstract: Novel adsorbents for sorption of Pb(II), Cu(II), Fe(II) and Cr(VI) metal ions from waste water were synthesized by simultaneous cross-linking and graft copolymerization of chitosan (Ch) with N-isopropyl acrylamide (NIPAM) and its binary comonomers acrylic acid, acrylamide and acrylonitrile in presence of azo-bis(isobutyronitrile) (AIBN) as free radical initiator and N,N′-methylene bisacrylamide (MBA) cross-linker. FTIR, XRD, SEM and TGA/DTA techniques were used to describe the structural aspects of cross-linked graft copolymers. The improvement in swelling properties of the synthesized hydrogels was utilized to explore their potential for sorption of toxic metal ions from aqueous solutions. Metal ion sorption by synthesized cross-linked adsorbents was studied as a function of the change in contact time, pH, temperature and concentration of metal ions from a solution of individual metal ions. Selective sorption of metal ions was performed by immersing polymeric samples in a solution containing four metal ions in the same concentration. Candidate polymers showed preferential sorption of metal ions in order of Cu(II) > Pb(II) > Fe(II) > Cr(VI) ions. Cross-Ch-g-poly(NIPAM-co-AAc), Cross-Ch-g-poly(NIPAM-co-AAm) and Cross-Ch-g-poly(NIPAM), showed best results for sorption of all ions from single and four component solutions.

FedLearnSP: Preserving Privacy and Security using Federated Learning and Edge Computing

Abstract: Enormous amount of information is processed at different web sites, on a number of different AI tools and in multiple data silos. Sharing data between various sources, this is a significant obstacle, due to administrative, organizational and security considerations. One possible solution is federated machine learning (FML), a system that simultaneously sends machine learning algorithms to all data sources, trains models at each source and aggregates the learned models. This technique ensures consumer influenced solution by processing the data locally. This work is the first to investigate the applicability of Internet attack detection through FML, to the best of our knowledge. Our primary contributions include the application of federated learning to satisfy customer search queries by detecting malicious spam images, which may lead these AI systems to retrieve irrelevant information. We assess and analyze the FML-entangled learning output comprehensively in different ways adjustments including balanced and imbalanced customer data distribution, scalability, and overhead communication. Our measuring results show that FML suits practical scenarios, where variable image size, including the animation ratio to legitimate samples of images present among the advertisements that may distract consumer from fetching relevant results. With the evaluated results, the state-of-the-art FedLearnSP proved significant image spam detection.