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

Human beings when born are adorned with a presence that helps them to differentiate among other individuals around ie. 'Face'. Alongside speech and fingerprints, A face is significantly used in biometrics as no two individuals have the same face. Myths though say that in this world you may resemble with some facial characteristics to 7 different individuals then these facial detection and recognition algorithms help us to identify between them. Facial expressions detection, face detection, and recognition have been impactful in the research domain especially in fields of image processing. Although it might sound like a cakewalk though undoubtedly it has proven to be a complex task for a computer to detect and pose all the features of the image passed since various other variables impair the accuracy/confidence of the algorithms mainly illumination variation, the background noise of the image, low resolution, occlusion, blurriness of the image, pixel scale of the image and many more components.Considering these myths, this research is based upon face recognition algorithms and a model to identify if there is a face in an image passed, identify the number of faces, and justify with confidence value obtained which algorithm should be suggested, is most optimum for face detection and recognition among (Haar classifiers, default face recognizer, Local Binary Pattern Histogram (LBPH) face recognizers, {Adaboost algorithm, Eigenface methods, etc.). This research particularly focuses on the use of Haar cascades and the LBPH Face recognizer algorithm as a hybrid combination for the smart voting system as an authentication approach. © 2021 IEEE.}, author_keywords={Face detection; Face Recognition; Haar cascade Classifier; Image processing; LBPH Face Recognizer; Smart Voting system}, keywords={Adaptive boosting; Image classification; Voting machines, Face detection and recognition; Face recognition systems; Faces detection; Haar cascade classifiers; Haar classifiers; Images processing; Local binary pattern histogram face recognizer; Local binary patterns; Smart voting system; Voting systems, Face recognition}, publisher={Institute of Electrical and Electronics Engineers Inc.}, isbn={9781665425032}, language={English}, abbrev_source_title={Int. Conf. Smart Gener. Comput., Commun. Netw., SMART GENCON}, document_type={Conference Paper}, source={Scopus},

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School of Computer Engineering & Technology

2021 International Conference on Smart Generation Computing, Communication and Networking, SMART GENCON 2021

Optimal Face Recognition System using Haar Classifier

Integrating technology in education can be advantageous for universities and help engage better results for students. An understanding of students' perceptions will help identify obstacles and promote online tests at universities, it is, therefore, important to understand from students about their perspective for the online test and paper-based test. To achieve this goal, a 22-item questionnaire was prepared to understand and study the perception of online and paper-based test for university students and was administered to 476 students (379 were male, 96 were female, and 1 were other). The set of research questions comprises – demographic information, students' perception for the paper-based test, and students’ perception for the online test were analyzed through statistical measures like one-way ANOVA, and t-Test with p-value. After careful analysis through various responses across various dimensions, the results of the study indicated that the difference between the perception of students on the paper-based test and the online test is statistically significant. Overall, students' perception of the online test was positive with the mean trend of 3.42. In India, the online test doesn't have a positive approach, universities refrain from online test and continue to make a traditional approach which does not achieve the targeted result. The major concerns highlighted were the technological incompetence of students and teachers alongside distrust in the technology infrastructure. Using our study, universities can identify obstacles, and prepare an appropriate result-driven plan of action for implementing the online test and make easy migration from paper-based test to online test. ©2021 IEEE

School of Civil Engineering

Proceedings - International Conference on Communication, Information and Computing Technology, ICCICT 2021

Perception of Students in Online Test in Engineering: Case of NMIMS University

In this paper, fault detection system for the AC side of the microgrid is proposed. The fault detection system is based on the bayesian regularization neural network. The neural network is trained with the electromagnetic field signals instead of using current signal. In conventional way, current transformer is used for sensing the current signal. In the proposed method, magnetic sensor is used for sensing the magnetic field generated due to the flow of current in the conductor. Various fault conditions are taken into account for the analysis. The magnetic field under normal current condition is different than the magnetic field under fault conditions. Hence, this is the application of the pattern recognition artificial intelligence technique. The dataset for training and testing are generated with the help of simulation model of the microgrid. The microgrid is consisting of solar energy source, full bridge dc-ac converter, LC filters and load. The LC filter is used for getting the smooth ac output as there are harmonics present in the signal prior to it. The line to line and line to ground faults are created at the AC side of microgrid. A mathematical model is created for transforming current signal to magnetic field signal. These magnetic field signals are used to train and test the neural network. The testing results shows that the accuracy of the proposed system is 98.5% which is comparable with the existing methodologies. © 2021 IEEE.

School of Electrical Engineering

2021 IEEE Mysore Sub Section International Conference, MysuruCon 2021

Artificial intelligence and magnetic field based fault detection system for microgrid

The most common psychiatric disorder is Clinical Depression. More than fifteen percent of people undergo an incident of major depression sometime during their life. There is an increase in depressive disorders and other-regarding symptoms in past years, which leads to the importance of early detection of depression. Detection of depression can be automated by analyzing a person's behaviour and emotions. The depressive disorder influences auditory speech qualities, facial expressions, thinking, and some cardiovascular activities; hence depression can be recognized by analyzing such factors and also trying to find out which method is more suitable and reliable. Through preprocessing, features of different signals are extracted and further handled to develop a machine learning model. Neural Networks are a faster and efficient way to analyze depression. This survey briefs about different techniques and machine learning models for depression detection. © 2021 IEEE.

2021 12th International Conference on Computing Communication and Networking Technologies, ICCCNT 2021

A Survey: Feature Extraction Techniques and machine learning models for Depression Analysis

Post-earthquake utilities of liquid storage tanks do not need any over emphasis. Liquid storage tanks used in public water distribution system provide water for firefighting and also for drinking and household purposes. Tanks used in industry are equally critical as they contain hazardous liquids and their leakage may lead to catastrophic situation. Thus, seismic safety of liquid storage tanks, which are life line structures, is very important. The prime aim of the present work is to review the sloshing phenomenon studied by various researchers in the past. The study is broadly divided into two parts namely tanks without obstruction and tanks with obstruction and its effects on sloshing parameters. A brief review on sloshing problem in complex tank geometries in form of Intze, elliptical and conical tanks is also presented. The study also presents an overview and development of mathematical formulations to predict the sloshing parameters in form of sloshing frequency, convective mass excited and hydrodynamic pressure profiles which enable present structural engineers in designing earthquake-resistant storage structures. A brief review on modeling circular and rectangular liquid storage tanks using ANSYS software is also presented along with the assumptions and suitable element for modeling. The study also investigates the advantages and disadvantages of the different analysis methods available in the finite element program (ANSYS) to obtain the sloshing parameters. The study concludes that an optimum mesh size as detailed in the present study must be evaluated to determine slosh parameters. An arbitrary fine mesh results in a limited number of modes extracted from the finite element analysis and further gives inaccurate solutions. © 2021, Springer Nature Switzerland AG.

Innovative Infrastructure Solutions

An approach to finite element modeling of liquid storage tanks in ANSYS: A review

With the recent growth of media platforms such as YouTube, Spotify, etc. These Platforms make content easily accessible but at the same time causes many problems like plagiarized content. Especially with audio content we can observe multiple types of plagiarism like unauthorized use of music for making remix music. To deal with this problem we use audio fingerprinting. It is the process of presenting an audio signal in a closed-packed way by extracting relevant characteristics of the audio signal. This in itself can be used to check the song, but it won't give an accurate result if the plagiarized song is copied from multiple different songs. To solve this particular problem, we use segment matching. © 2021 IEEE.

Proceedings - 1st International Conference on Smart Technologies Communication and Robotics, STCR 2021

Music Plagiarism Detection using Audio Fingerprinting and Segment Matching

Garbage disposal is a very important task in a healthy and green environment. With spreading awareness among the citizens of India regarding the importance of a clean environment to decrease the consumption of natural resources and garbage disposal, the recycling industry is booming. The quantity of generated trash in day-to-day life is affecting land, water, and air which causes a serious threat to the aquatic species and their surroundings and ultimately to humans if not managed properly. Conventional garbage disposal systems are on the rise and need accurate and efficient segmentation and recognition mechanisms. This demand coincides with the increase of computational capabilities of modern computer architectures and more effective algorithms for image recognition. To deal with this problem the garbage classification process is automated by building an image classifier using a convolutional neural network (CNN) and thereby decrease the time for the waste segregation and make it cost-effective. The idea behind making the process automated is to decrease human intervention and make this waste segregation process more productive. In this work, three different models are being tested for higher accuracy: Simple CNN, ResNet50, and VGG16 trained on various datasets of images, these are used to extract features from images and feed them into a classifier for dump/trash classification. The trained models are then fed to a mobile application that captures pictures in the real-time using camera. The experimental results conclude the performance of VGG16 using Transfer Learning being significantly higher than all other models for the purpose of Trash Classification and the performance of Simple CNN being better for Dump Classification. © 2021 IEEE.

2021 6th International Conference on Recent Trends on Electronics, Information, Communication and Technology, RTEICT 2021

Garbage Classifying Application Using Deep Learning Techniques

The shift over of the automobile sector from the ICE to the electric drives is imminent due to arising global issues of pollution and ever rising pressure on the demand of the natural resources due to lower efficiency of the ICE drives. This has led to uprising of the Lithium-ion batteries, with addition of the burden of living to expectation of clean energy and higher efficiencies. Alongside, with limitation in the availability of the lithium-ion batteries they carry a hefty price tag with them, hence causing huddles in the research. Lack of research leads to failure of batteries and may cause life threatening situations when operating in the vehicle. In order to insight the working of the lithium-ion batteries under different driving and environmental conditions an analytical model is developed for the coupled electro-chemical and thermal phenomenon. This allows anticipating the behaviour of the battery under different conditions that influence its performance. The 18650 cylindrical batteries with three different chemistries i.e. Lithium Nickel Cobalt Aluminium Oxide (NCA), Lithium Nickel Manganese Cobalt Oxide (NMC) and Lithium Iron Phosphate (LFP) are used to validate the analytical methodology. The experimental measurements of these batteries are carried out initially to predict the surface temperature of the batteries under defined load cycles. Further the surface temperatures of the batteries are analytically determined with the aid of developed model in this research. The analytical analysis includes calculating the SOC, followed by evaluating the internal resistance of the battery at that particular instance and then determining the temperature by solving the energy balance equation using suitable numerical technique. The virtual simulations of the batteries are also carried out using the commercially available Multiphysics software. The developed analytical methodology is validated using the experimental and virtual approach. The developed methodology will assist in predicting the performance of the battery in the battery pack and also to ensure the application of efficient thermal management system. © 2021 SAE International. All rights reserved.

School of Mechanical Engineering

SAE Technical Papers

Coupled Electro-Chemical and Thermal Modeling for Cylindrical Lithium-Ion Batteries

The paper aims at implementing new pedagogy and assessment practices from rigorous literature survey perusing quality papers and articles. The appropriate pedagogy and relevant assessment always go hand in hand. One cannot achieve effective teaching by compromising the other component. In engineering, pedagogy and assessment play extremely important roles. In recent years, engineering education has lost track of the big picture of what the curriculum has to be. In Computer Science Engineering, the course contents often change according to the demands for new technology in the market. Adhering to this fact, the courses must be designed either practical-based or case study based. Rote teaching-learning methods are not as effective as far as the curriculum design in the computing field is concerned. Cyber Security is one such course where students are expected to learn how to create a protective environment for computing and computing resources. The course must be designed in such a way that, students must learn how to identify the vulnerabilities in computing resources and methodologies to mitigate them. This course aims such that students must learn some popular attacks which help them to identify what are the possible ways from where the attacks could happen. The paper makes use of strategic assessment tools for the Cyber Security course (Taught to post-graduation students) and discusses outcomes through course outcome attainment analysis as per the threshold value of attainment set by the Computer Engineering Department to adhere to the accreditation standards. Through course attainment analysis it is observed that, Viva voce assessment tool is not suitable for evaluation of the course as it does not impose the technical details and working of Cyber Security concepts. The overall attainment was 55.55% which is 15% less than the threshold set by Computer Engineering Department. © 2021 MECS.

International Journal of Modern Education and Computer Science

Teaching cyber security course in the classrooms of nmims university

Enterprise optimization has become a major target in the pharmaceutical industry because of the increasing pressure for staying relevant in the worldwide commercial center. The ever-increasing competition, changing regulatory requirements, new emerging technologies along with a lot of other factors are pushing pharmaceutical manufacturers towards Enterprise optimization for the purpose of fast-Tracking innovation to boost their productivity and product quality. The objectives of Enterprise optimization include profit maximization, asset utilization, management of inventory levels, and coordinated decision making. These business exercises are moderately troublesome because of the association of various risk factors. In this paper, applications that have relevance to Enterprise optimization have been reviewed, with special attention to innovative drug development life cycles which are prone to substantial risks. This paper proposes a decision-making methodology with the identification of uncertainties and risks based on the Fuzzy AHP approach. © 2021 IEEE.