MIT-World Peace University (MIT-WPU) is a top educational institute located in Pune, India. It is recognized within&nbsp;UGC under Government of Maharashtra Act No. XXXV 2017.&nbsp;

It is part of the MIT group of institutions (established in 1983) which covers 10+ campuses across India and has more than 50,000+ students enrolled across various disciplines at a given point of time. The institute houses 15 schools ranging from Engineering &amp; Technology, Design, Pharmacy, Commerce, Governance among others. It offers undergraduate, postgraduate and PhD programs among different disciplines.&nbsp;

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MIT World Peace University

Engineering and Technology

World University root node

School of Computer Engineering & Technology

INTERNATIONAL JOURNAL OF ENGINEERING RESEARCH \& TECHNOLOGY (IJERT)

Face Grouping using Haar Cascade Classifier

Business concept on Renting Vehicle

Segmentation of symbols or characters in the OCR process is a very critical and important phase, as it directly affects the recognition system. If objects in the image are not accurately segmented, then the recognition will also be false and this ultimately affects the performance of the system. Brahmi script contains certain alphabets which comprise some isolated symbols. In this paper, the authors have proposed an innovative approach to segment the lines from the digital Brahmi estampage document and further segment the alphabets from the line, which are regular characters as well as special characters comprising isolated symbols. The authors have developed an algorithm that effectively segments the lines, regular characters and special characters. Advancements in the algorithm can be made to improve the results for accuracy.

Advances in Intelligent Systems and Computing

Segmentation of characters from degraded Brahmi script images

Rotary friction welding is a solid state metal joining process and it has many applications in future upcoming days because of its advantages like without fumes and smokeless operation. In this process heat generates through mechanical friction between one continuously rotating component and one stationary component. After heat generation phase an additional forging force is applied which completes coalescence. Since no melting of material occurs during friction welding, but metal gets deform after heat generation at welding zone. The main advantages of friction welding include low distortion, absence of weld related defects, high joint strength, ease of automation, ability to weld alloys and metals which are not weldable by conventional welding methods. In this research work weld specimens were prepared on all geared lathe conventional machine by consideration of parameters like rpm of rotating specimen, friction and forging force applied. This study includes that joining of metal initially carried out under normal atmospheric conditions i.e. air environment and secondly, continuous stream of water under the wet environment. The materials were used EN1A bright mild steel and only similar combinations were used for study purpose. An experimental setup was prepared for force measurement on all geared lathe machine. Mechanical properties of joints were evaluated utilizing tensile test and hardness at heat affected zone. © 2020 Elsevier Ltd. All rights reserved.

School of Mechanical Engineering

Materials Today: Proceedings

Experimental analysis of rotary friction joining process by EN1A bright mild steel under normal air and wet environment condition

International Journal of Engineering and Advanced Technology (IJEAT)

Security Implications for Json web Token Used in MERN Stack for Developing E-Commerce Web Application

Machine learning is defined as an application of artificial intelligence where available information is used through algorithms to process or assist the processing of any set of data. There is growing evidence of machine learning being used in many oil and gas industry operations for better predictions, right from facies identification to pipeline maintenance, thereby enhancing safety significantly. The rate of a production well decreases with time and this relationship is known as the decline curve. Decline curve analysis is a graphical method used for analyzing oil and gas production rates and forecasting future performance, thereby assisting in overall field performance management. Arp’s decline curve analysis is usually the most common method which includes a comprehensive set of equations defining the exponential, harmonic and hyperbolic declines which can only be applied in case of a stabilized production trend. This paper focuses on predicting the decline curve by using appropriately designed neural networks and machine learning algorithms irrespective of production trend. The feature vectors are geological location of the well, production data, pressure data, and operating constraints. Fine tuning these parameters lead us to predict the performance of a well with reasonable certainty. © 2021, Springer Nature Singapore Pte Ltd.

School of Petroleum Engineering

10th International Advanced Computing Conference (Springer)

Enabling Oil Production Forecasting using Machine Learning

Umiat field, located in Alaska North Slope (ANS) poses unique development challenges because of its remote location and permafrost within the reservoir. This hinders the field development, and further leads to a potential low expected oil recovery. The objective of this work is to assess various possible well patterns of the Umiat field development, and perform a detailed parametric study to maximize oil recovery and minimize well costs using statistical methods. Design of Experiments (DoE) is implemented to design simulation runs for characterizing system behavior using the effect of certain critical parameters, such as well type, horizontal well length, well pattern geometry, and injection/production constraints on oil recovery. After carrying out simulation runs using a commercially available simulation software, well cost is estimated for each simulation case. Response Surface Methodology (RSM) is used for optimization of well pattern parameters. The parameters, their interactions and response are modeled into a mathematical equation to maximize oil recovery and minimize well cost. Economics plays a key role in deciding the best well pattern for any field during the field development phase. Hence, while solving the optimization problem, well costs have been incorporated in the analysis. Thus, based on the results of the study performed on selected parameters, using interdependence of the above mentioned methodologies, optimum combinations of variables for maximizing oil recovery and minimizing well cost will be obtained. Additionally, reservoir level optimization assists in providing a much needed platform for solving the integrated production optimization problem involving parameters relevant at different levels, such as reservoir, wells and field. As a result, this optimum well pattern methodology will help ensure optimum oil recovery in the otherwise economically unattractive field, and can provide significant insights into developing the field more efficiently. Computational algorithms are gaining popularity for solving optimization problems, as opposed to manual simulations. DoE is effective, simple to use and saves computational time, when compared to algorithms. Although DoE has been used widely in the oil industry, its application in domains like well pattern optimization is novel. This paper presents a case study for the application of DoE and RSM to well optimization in a real existing field, considering all possible scenarios and variables. Copyright 2019, Society of Petroleum Engineers.

SPE/IATMI Asia Pacific Oil &amp; Gas Conference and Exhibition

Application of Design of Experiments for Well Pattern Optimization in Umiat Oil Field: A Natural Petroleum Reserve of Alaska Case Study

This study investigated the potential of rice straw ash (RSA) (produced from open-air burning of rice straw) and microsilica (MS) as a pozzolan in rigid pavements. Proportions of RSA (5%–30%), MS (2.5%–10%) and combination of RSA (5%, 10%) + MS (5%, 7.5%) by weight of OPC in the paste and mortar were studied. Various tests like normal consistency, setting time, soundness and X-ray diffraction (XRD) analysis were performed on the cementitious paste. It was observed that the admixing of MS did not affect the setting times and soundness of the cement paste but increased the normal consistency while admixing of RSA remarkably increased all of them. It was also found that OPC can be replaced by RSA up to 10% and by MS up to 7.5% in mortar without any loss of compressive strength. In the assessment of mechanical strengths of concrete, it was found that RSA and MS increase various strengths of concrete up to 7% and up to 24%, respectively. Furthermore, these results were validated by XRD and Scanning electron microscope (SEM) analysis. In the evaluation of durability properties of concrete, it was observed that MS and RSA significantly increase the defense mechanism of concrete to water absorption, chloride ion penetration, acid attack and accelerated carbonation curing (ACC). The design thickness of the concrete slab of the selective concrete mixes was computed as per structural design in accordance with IRC 58:2015. The cost comparison was made on the basis of the total cost of 1 m3 of concrete and the total cost of constructing 1 km of 4 lane National Highway (NH) (one-way carriageway). The admixing of 10% RSA reduces the slab thickness by 11%, cost of 1 m3 of concrete by 3% and cost of constructing NH by 14%. © 2020 Elsevier Ltd

School of Civil Engineering

Construction and Building Materials

A comprehensive investigation on application of microsilica and rice straw ash in rigid pavement

New Trends in Computational Vision and Bio-Inspired Computing - Selected Works Presented at the ICCVBIC 2018

Privacy Assurance with Content Based Access Protocol to Secure Cloud Storage

The integration of the machine learning techniques in healthcare can be of huge benefit aimed at curing illness of millions of people. A lot of effort has been taken by researchers to detect and provide early-stage insights into cancer diagnosis. In the machine learning research community, various algorithms-KNN, SVM, Decision Trees and Random Forest have been applied to calculate the presence or decisiveness of cancer in correspondence with the symptoms shown by the patients. This paper aims to analyze the symptoms of the different age groups Youth, Working Class and Elderly. Tree-based algorithms like Decision Trees, Random Forest and XGBoost have been used to identify the underlying data patterns in order to calculate relative feature importances. It has been concluded that Coughing of Blood, Clubbing of Finger Nails, Genetic Risk, Passive Smoking and Snoring are the factors that are responsible for lung cancer in all the age groups in most of the cases. © 2020 IEEE.