Vishwakarma Institute of Information Technology

About: Vishwakarma Institute of Information Technology is a based out in . It is known for research contribution in the topics: Machining & Artificial neural network. The organization has 378 authors who have published 404 publications receiving 2653 citations.

Predicting strength of recycled aggregate concrete using Artificial Neural Network, Adaptive Neuro-Fuzzy Inference System and Multiple Linear Regression

Abstract: Compressive strength of concrete, recognized as one of the most significant mechanical properties of concrete, is identified as one of the most essential factors for the quality assurance of concrete. In the current study, three different data-driven models, i.e., Artificial Neural Network (ANN), Adaptive Neuro-Fuzzy Inference System (ANFIS), and Multiple Linear Regression (MLR) were used to predict the 28 days compressive strength of recycled aggregate concrete (RAC). Recycled aggregate is the current need of the hour owing to its environmental pleasant aspect of re-using the wastes due to construction. 14 different input parameters, including both dimensional and non-dimensional parameters, were used in this study for predicting the 28 days compressive strength of concrete. The present study concluded that estimation of 28 days compressive strength of recycled aggregate concrete was performed better by ANN and ANFIS in comparison to MLR. In other words, comparing the test step of all the three models, it can be concluded that the MLR model is better to be utilized for preliminary mix design of concrete, and ANN and ANFIS models are suggested to be used in the mix design optimization and in the case of higher accuracy necessities. In addition, the performance of data-driven models with and without the non-dimensional parameters is explored. It was observed that the data-driven models show better accuracy when the non-dimensional parameters were used as additional input parameters. Furthermore, the effect of each non-dimensional parameter on the performance of each data-driven model is investigated. Finally, the effect of number of input parameters on 28 days compressive strength of concrete is examined.

Machining of hardened steel—Experimental investigations, performance modeling and cooling techniques: A review

Abstract: The researchers have worked on many facets of machining of hardened steel using different tool materials and came up with their own recommendations. Researchers have tried to investigate the effects of cutting parameters, tool materials, different coatings and tool geometry on different machinability aspects like, the tool life, surface roughness, cutting forces, chip morphology, residual stresses and the tool–chip interface temperature under dry and/or semi-dry and/or flood cooling environment during machining of hardened steels while many of them have ventured to characterize the wear phenomenon. Good amount of research has been performed on an analytical and/or numerical and/or empirical modeling of the cutting forces, tool–chip interface temperature, and tool wear under orthogonal/oblique cutting conditions during machining of hardened steels. This paper presents a comprehensive literature review on machining of hardened steels using coated tools, studies related to hard turning, different cooling methods and attempts made so far to model machining performance(s) so as to give proper attention to the various researcher works.

Image processing techniques for object tracking in video surveillance- A survey

Abstract: Many researchers are getting attracted in the field of object tracking in video surveillance, which is an important application and emerging research area in image processing. Video tracking is the process of locating a moving object or multiple objects over a time using camera. Due to key features of video surveillance, it has a variety of uses like human-computer interactions, security and surveillance, video communication, traffic control, public areas such as airports, underground stations, mass events, etc. Tracking a target in a cluttered premise is still one of the challenging problems of video surveillance. A sequential flow of moving object detection, its classification, tracking and identifying the behavior completes the processing framework of video surveillance. This paper takes insight into tracking methods, their categorization into different types, focuses on important and useful tracking methods. In this paper, we provide a brief overview of tracking strategies like region based, active contour based, etc with their positive and negative aspects. Different tracking methods are mentioned with detailed description. We review general strategies under literature survey on different techniques and finally stating the analysis of possible research directions.

Modeling compressive strength of recycled aggregate concrete by Artificial Neural Network, Model Tree and Non-linear Regression

Abstract: In the recent past Artificial Neural Networks (ANN) have emerged out as a promising technique for predicting compressive strength of concrete. In the present study back propagation was used to predict the 28 day compressive strength of recycled aggregate concrete (RAC) along with two other data driven techniques namely Model Tree (MT) and Non-linear Regression (NLR). Recycled aggregate is the current need of the hour owing to its environmental friendly aspect of re-use of the construction waste. The study observed that, prediction of 28 day compressive strength of RAC was done better by ANN than NLR and MT. The input parameters were cubic meter proportions of Cement, Natural fine aggregate, Natural coarse Aggregates, recycled aggregates, Admixture and Water (also called as raw data). The study also concluded that ANN performs better when non-dimensional parameters like Sand–Aggregate ratio, Water–total materials ratio, Aggregate–Cement ratio, Water–Cement ratio and Replacement ratio of natural aggregates by recycled aggregates, were used as additional input parameters. Study of each network developed using raw data and each non dimensional parameter facilitated in studying the impact of each parameter on the performance of the models developed using ANN, MT and NLR as well as performance of the ANN models developed with limited number of inputs. The results indicate that ANN learn from the examples and grasp the fundamental domain rules governing strength of concrete.