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

Aesthetic assessment of images has been getting a lot of attention for the past decade in the field of computer vision. Large amounts of social media and advertising data in the form of images is continuously analyzed to assign it an aesthetic quality value to improve businesses as well as for gaining more popularity across the web. Visual perception by humans cannot be fully replicated by a machine and continuously more work is being published on aesthetic classification of images. In this paper, we have presented a convolutional neural network model which automatically extracts high level features and distinguishes a set of images into pleasing and non-pleasing categories. Our dataset has been compiled from a variety of sources on the web to make it as diverse as possible. Compared to the traditional handcrafted methods and other machine learning models, our CNN model has provided a better classification accuracy of 68% on our dataset.

School of Computer Engineering & Technology

International Journal of Engineering Research & Technology (IJERT)

Image Aesthetics Assessment using DeepLearning (based on High Level ImageAttributes)

In the present work, the adsorption of nickel ions from leachate sample by activated carbon prepared from seeds of DelonixRegia was studied. The activated carbon prepared by pretreatment with KOH was subjected to SEM, XRD and FTIR analysis. Theinfluences of different experimental parameters on nickel uptake were investigated such as sorption time, final solution pH,adsorbent dosage and temperature. The experimental data were correlated to different kinetic and adsorption models andthe corresponding kinetic parameters were determined. The % removal was found to be 75.03% and follows pseudofirstorder kinetic model for Ni. These parameters are considered fundamental for further studies involving the scale-up of theprocess for continuous studies

School of Chemical Engineering

World University root node

International Journal of Science Technology & Engineering

Removal of Heavy Metal Ions from Leachate using Natural Adsorbent Delonix Regia (DRAC)

The basic objective of the paper is to identify the desired circulation ratio for the natural circulation of water tube boilers in different operating conditions. This requires the basic study of heat flux and the mode of the boiling heat transfer, and the phenomenon like departure from nucleate boiling and tube overheating. The parameters, which need to be studied are heat flux, pressure, dryness fraction, void fraction, liquid velocity and their impact on the required circulation ratio. For a natural circulation boiler, the circulation ratio is one of the most important design parameters as the other design parameter like critical heat flux and skin temperature are mainly derived from the circulation ratio. The required circulation ratio can vary with the boiler pressure, liquid velocity and maximum heat flux. This study is intended to provide input for the safe and optimum design of a natural circulation boiler.

School of Mechanical Engineering

Fulltext

Journal of Physics: Conference Series

Study of circulation ratio for natural circulation in water-tube boiler at different operating conditions.

Recently few countries have tested the scramjet engine for aerospace application. The challenge was to achieve complete combustion atsupersonic flow and to generate the required thrust for given inlet flow conditions. Research is still going on so as to attain the necessary mixing andefficient combustion inside a scramjet combustor. Performance parameter like total pressure loss across the scramjet combustor is one of the crucialparameters and has to be minimized. Factors such as equivalence ratio, inlet flow conditions affect the combustion process and hence the useful thrust.Also, the fuel injection configuration plays a vital role in mixing and thus on the performance of a scramjet combustor. Over the recent years numericalsimulation has been acknowledged as a promising tool for investigation of Scramjet flow. In this work, the investigation of effect of injector configuration hasbeen carried out using a commercial CFD tool. Validation of initial numerical results is done with existing experimental results in the available literature.Modified configurations were obtained by varying number of injection holes for same equivalence ratio. These cases were simulated to observe the rightbargain between combustion efficiency and total pressure loss. Conclusions are based on performance parameters like total pressure loss, combustionefficiency, top wall static pressure etc. Mass fraction contour plots of species are obtained to comment further on mixing and combustion performance.

International Journal Of Scientific & Technology Research

Numerical Investigation of Effect of Fuel Jet Configuration on Scramjet Combustor Performance

Big data is progressively being used in various areas, such as industry, financial dealing, medicine, and so on, as it can handle the challenges in processing large amounts of data. One of the data mining techniques used widely and effectively to classify big data is the MapReduce model. In this paper, an approach for the classification of big data is developed using Cuckoo–Grey wolf based Correlative Naive Bayes classifier and MapReduce Model (CGCNB-MRM). Accordingly, a novel classifier, named Cuckoo–Grey wolf based Correlative Naive Bayes classifier (CG-CNB), is designed by modifying CNB classifier with a newly developed optimization algorithm, Cuckoo–Grey Wolf based Optimization (CGWO). CGWO algorithm is designed by the effective integration of Cuckoo Search (CS) Algorithm into Grey Wolf Optimizer (GWO), to optimize the CNB model by the optimal selection of the model parameters. Finally, the proposed CGCNB-MRM approach performs the classification for each data samples based on the probability index table and the posterior probability of the data. Three metrics, such as accuracy, sensitivity, and specificity, are utilized for the performance evaluation of the proposed CGCNB-MRM approach, where it could achieve 80.7% accuracy with 84.5% sensitivity and 76.9% specificity and thus, prove its effectiveness in big data classification.

Data & Knowledge Engineering

Integrating Cuckoo search-Grey wolf optimization and Correlative Naive Bayes classifier with Map Reduce model for big data classification

The brain tumor is a cluster of the abnormal tissues, and it is essential to categorize brain tumors for treatment using Magnetic Resonance Imaging (MRI). The segmentation of tumors from brain MRI is understood to be complicated and also crucial tasks. It can be further use in surgery, medical preparation, and assessments. In addition to this, the brain MRI classification is also essential. The enhancement of machine learning and technology will aid radiologists in diagnosing tumors without taking invasive steps. In this paper, the method to detect a brain tumor and classification has been present. Brain tumor detection processes through pre-processing, skull stripping, and tumor segmentation. It is employing a thresholding method followed by morphological operations. The number of training image influences the feature extracted by the CNN, then CNN models overfit after some epoch. Hence, deep learning CNN with transfer learning techniques has evolved. The tumorous brain MRI is classified using CNN based AlexNet architecture. Further, the malignant brain tumor is classified using GooLeNet transfer learning architecture. The performance of this approach is evaluated by precision, recall, F-measure, and accuracy metrics.

School of Electronics & Communication Engineering

International Journal of Advanced Computer Science and Applications(IJACSA)

A framework for Brain Tumor Segmentation and Classification using Deep Learning Algorithm

Selection of solvent is the crucial factor in development of a successful solvent extraction process. The equilibrium data needed pertain to selectivity and distribution coefficients for each individual components present in the system. In continuation with earlier work, the results of an attempt to provide a theoretical basis for predicting a suitable solvent for extracting ethanol from its aqueous solution are presented in this work. The approach used is based on the 3-D Hansen Solubility Parameters. The results obtained are compared for prediction of better solvent for separation with maximum error obtained 9.37%.

Separation Science and Technology

Prediction of effectiveness of solvent using distribution coefficient calculation in solvent extraction with the help of Hansen solubility parameters

Laser beam welding is popular in microsystems technology by its specific parameters. Mostly, it is categorized by momentary nature and really short period of the method. The laser beam is concentrated on the materials to be welded and therefore, the process is usually autogenous, does not require any additional filler material. Due to, laser welding could be a fast-growing application area for industrial lasers. Many authors conducted experiments on measurement of penetration depth using large thickness of sheet by varying process parameters and developed thermal model referring to temperature distribution within work piece. The present work selected less than 1 mm stainless steel sheet and trials taken by varying combination of power, speed and sheet thickness to predict weld bead geometry and validated by image processing technique. Furthermore, the experimental setup is developed to measure online bottom surface temperature of work piece during welding by temperature data logger. Finally, found that the net measured temperature by data logger has shown sensitive indicator of penetration depth and measured value of depth of penetration by experimentation, optimization and image processing technique satisfactorily.

Lecture Notes of Mechanical Engineering

Correlation Assessment of Weld Bead Geometry and Temperature Circulation by Online Measurement in Nd: YAG Laser Welding

Automotive requirement focuses on reducing misalignment that occurs in transmission systems. Due attention is given to the final gear drive including hypoid gear as it is easily susceptible to misalignment. It is significant to consider deviation in mounting position of gears along axial and angular directions to have reduced dynamic issues and smooth transmission. An alignment error between the gears contributes towards unbalanced force distribution. It is the major source of deviation from perfect conjugate gear drive. The paper focuses on identifying deflection that occur in Hypoid Gear having major application in Differential gear of automobile by considering the misalignment between them. Work done in this paper proposes new way to identify misalignment in hypoid gear using beam element modeling. With beams, it becomes easier to identify exact nature and deflection of gears from designed position. ANSA software is used as pre-processor. Maximum misalignment of 0.763 mm is found in Hypoid offset direction. The method is found to be in good agreement with analytical results for misalignments.

International Journal of Scientific & Technology Research

Investigation Of Misalignments In Hypoid Gear Using Element Based Modeling.

In recent years due to environmental issues, have a great demand to search for alternative energy sources. The high demand of fuels in transportation and industrial sectors strengthen the identification of alternative fuel sources. In this regards biodiesel and its different blends (corrosive environments) with petro diesel, can be considered a prompt substitute for petro diesel. But the use of biodiesel possesses several limitations, such as metal surface degradation, auto oxidation and instability of fuel properties. Automobile industries are also facing a major problem related to compatibility of automotive components with biodiesel and its blends. The majority of engine components manufactured by different metals and non-metals which gets affected due to contact with biodiesel. Current research used nodular (spheroidal graphite) cast iron which is widely used for manufacturing of engine parts like piston and piston rings. For automotive components palm biodiesel (B100) is more corrosive as compared with petro diesel (B0). Therefore, this research was carried out on nodular cast iron used for piston ring in different corrosive environment of fuel such as petro diesel (B0), palm biodiesel (B100) and their respective blends (B20, B10 & B5). The corrosiveness behavior of nodular C.I. with different blends was tested using static immersion test followed by Open Circuit Potential (OCP), Electrochemical Impedance Spectroscopy (EIS) & Tafel plot. Electro chemical degradation studies were characterize a nodular cast iron metal surface by Scanning Electron Microscopy (SEM) and Energy Dispersion Spectrography (EDS) after immersion test. Results conclude palm biodiesel is more corrosive to nodular C.I. than petro diesel and with different blends under above testing methods.