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 Mechanical Engineering

Materials Today: Proceedings

Structural acoustics analysis of automobile parts by using finite element method

Low salinity Waterflooding (LSWF) is a proven EOR technique which has emerged in the last few decades. In this, the salinity of the injected water is reduced. Studies have unveiled that the LSWF can improve about 5–38% of oil recovery. This work aims to study the effectiveness of LSWF by modeling Sea waterflooding (SWF) and LSWF using a commercial reservoir simulator and justifying the proposed mechanisms responsible for the incremental oil recovery due to LSWF. A critical parametric study has been undertaken, which led us to conclude that LSWF is a cost-effective, and environment-friendly method that results in additional oil recovery for sandstone reservoirs. © 2023 Taylor & Francis Group, LLC.

School of Petroleum Engineering

Petroleum Science and Technology

Investigation of impact of low salinity water flooding on oil recovery for sandstone reservoirs–a simulation approach

Seepage is the phenomenon of water infiltrating through a gravity dam's foundation, causing erosion and weakening the dam's construction over time. If not properly managed, this can eventually lead to the dam's catastrophic failure, posing a significant danger to public safety and the environment. As a result, precise seepage prediction in gravity dams is essential for ensuring their safety and stability. This review paper looks at the use of artificial intelligence (AI) techniques for predicting seepage in gravity dams, as well as the challenges and possible solutions. The paper identifies and suggests potential solutions to the challenges connected with using AI for seepage prediction, such as data quality and model interpretability. The paper also covers future research paths, such as the creation of advanced machine learning algorithms and the improvement of data collection and processing. Overall, this review gives insight on the current state of the art in using AI to predict gravity dam seepage and recommends methods to improve the accuracy and reliability of such models. © 2023 The Authors.

School of Civil Engineering

Aqua Water Infrastructure, Ecosystems and Society

A review of artificial intelligence methods for predicting gravity dam seepage, challenges and way-out

Electric Vehicles (EVs) are gaining interest because of the sky rocketing oil prices and increased pollution level in major cities across the world. Almost all EV motors need rare earth magnet which are in limited supply. In order to reduce this dependency, EVs are looking for magnet free motors. One of the alternatives gaining significant mindshare across industry is Switched Reluctance motor (SRM). SRM largely satisfies all the requirements of EV. However, its performance is mainly dependent on converter and controller topology. This paper critically analyses the various parameter of SRM during dynamic loading condition like speed, torque ripple and current, using various converter topologies. Analysis of 60 kW, 4 phase SRM for EV application has been presented with mid-point converter and asymmetric bridge converters along with the proportional plus integral (PI) hysteresis controller. Results shows that asymmetric bridge converter provides enhanced performance for EV application. © 2022

School of Electrical Engineering

Comparative analysis of converter topologies used in switched reluctance motor for high torque electric vehicle application

Learning is a lifelong process that allows individuals to acquire knowledge, skills, and attitudes through various experiences. In the recent pandemic times, there was a transformation in ways the society was acquiring knowledge and taking up education. There has been an exponential growth in number of e-learners attending classes in synchronous and asynchronous e-learning platform. The launch of e-university will benefit the e-learners to continue learning. This has created a need for evaluating the ecosystem of e-learning, the learning platform, learning analytics, e-learners satisfaction, quality of academic programs offered by the university and its reputation. The attention of e-learners based on reviews in terms of desired field of study, faculty expertise, research opportunities, and the curriculum structure. The key attributes of e-learning are engagement, assessment, relevance, reflection, personalized learning recommendations and continual learning. This has opened an avenue for research to analyze the reviews of students to evaluate the e-learning platforms based on learning outcomes achieved. Most Challenging task is to find the perspective of the e-learners’ emotions from the huge data of the elearners reviews. Text data gives qualitative information and this actionable knowledge can be quantified. The reviews on all e-learning platforms are mostly textual and this qualitative data needs to be quantified for analysis. There is a necessity to propose contextual emotion detection of e-learners by extracting the relevant information. Machine learning algorithms have revolutionized the text mining to get insights from diverse and huge dataset. This paper leverages machine learning techniques Multilayer Perceptron (MLP), Logistic Regression (LR), Random Forest (RF), Decision Tree (DT) used in emotion detection and analysis of e-learners to correlate the student satisfaction index are evaluated using E-Learners Academic Reviews (ELAR) dataset. The DT and RF models consistently had high precision and accuracy scores of more than 90% in all academic emotion categories of excitement, happy, satisfied, not satisfied and frustration. This research also highlights the advantages of estimating the emotions of e-learners to evolve an e-learning platform that is conducive to their retention and satisfaction. © 2023, Ismail Saritas. All rights reserved.

School of Electronics & Communication Engineering

International Journal of Intelligent Systems and Applications in Engineering

Computation of E-learners Textual Emotion to Enhance learning Experience

Cardiovascular disease has become more concern in the hectic and stressful life of modern era. Machine learning techniques are becoming reliable in medical treatment to help the doctors. But the ML algorithms are sensitive to data sets. Hence a Smart Robust Predictive System is almost essential which can work efficiently on all data sets. The study proposes ensembled classifier validating its performance on five different data sets-Cleveland, Hungarian, Long Beach, Statlog and Combined datasets. The developed model deals with missing values and outliers. Synthetic Minority Oversampling Technique (SMOTE) was used to resolve the class imbalance issue. In this study, performance of five individual classifiers - Support Vector Machine Radial (SVM-R), Logistic Regression (LR), Naïve Bayes (NB), Random Forest (RF) and XGBoost, was compared with five ensembled classifiers on five different data sets. On each data set the top three performers were identified and were combined to give ensemble classifiers. Thus, in all total 25 experimentation were done. The results have shown that out of all classifiers implemented, the proposed system outperforms on all the data sets. The performance was validated by 10-fold cross validation The proposed system gives the highest accuracy and sensitivity of 87% and 86% respectively. © (2023), All Rights Reserved.

International Journal of Advanced Computer Science and Applications

A Knowledge Based Framework for Cardiovascular Disease Prediction

A microplate heat exchanger is one of the most compact types of heat exchanger used for cooling systems, and not much research was carried out to study the performance of this type of heat exchanger with hybrid nanofluids. In this regard, the performance analysis of the microplate heat exchanger is carried out by estimating the convective heat transfer coefficient in terms of Nusselt number using a hybrid nanofluid. In current research work, Microplate heat exchangers tested using TiO2/ethylene glycol, ZnO/ethylene glycol nanofluids, and a hybrid nanofluid with varied nanoparticle volume fractions. Based on the results, it was found that the thermal conductivity of hybrid nanofluids and the overall heat transfer coefficient by applying hybrid nanofluids show better enhancement than nanofluids. The maximum thermal conductivity ratio between the hybrid nanofluid and the base fluid is 2.10. The maximum Nusselt number of 35.8 was observed for hybrid (TiO2–ZnO/ethylene glycol) at 50 °C and a volume fraction of 4%. © 2023 The Authors

Case Studies in Thermal Engineering

A case study on analyzing the performance of microplate heat exchanger using nanofluids at different flow rates and temperatures

In today's scenario, congestion has become a major issue. Mainly the urban cities hit the hardest and its ever- increasing nature, real time knowledge of automobile road traffic density is essential for improved control of traffic signals and efficient management of traffic. The factors that contribute to traffic congestion are inadequate capacity, constrained demand, significant delays at red lights, etc. Capacity constraints and demand constraints are somehow related, but the delay for each signal is hard coded and traffic independent. So, to better meet this growing demand, the traffic control needs to be simulated and optimized. In recent times, image processing and surveillance systems are established in passenger information, access measurement and traffic control for real- time updates. Estimation of traffic bulkiness can also be performed using image processing. Tracing images of the moving automobiles can provide a quantitative explanation of traffic flow. This paper demonstrates how to utilize real-time live video feeds from cameras at intersections to perform instantaneous traffic bulkiness calculations using image processing and vehicle detection using EfficientDet architecture and TensorFlow lite. It also aims to reduce traffic jams and accidents by focusing on algorithms that switch signal lights on the basis of density of vehicles on the road and the priority set for specific emergency vehicles. In turn, it provides people with safe transportation, reduces fuel consumption and waiting times. Vehicle recognition is done by the system from images and not by using electronic sensors mounted on the roadway. Installation of camera is done next to the traffic light, which captures the video feed sent to the Raspberry Pi. The proposed algorithm aids to manage the timing of the traffic lights which in turn helps to achieve the goal of automatic control of traffic situations. © 2023 The Author(s)

e-Prime - Advances in Electrical Engineering, Electronics and Energy

Real-time traffic control and monitoring

The repetitive modelling required for traditional structural design work is a laborious and time-consuming task. Yet, the cost significantly increases due to the wide variety of profiles. It is suggested to use grouping technique along with algorithms to optimize structural elements in order to address these issues. In this paper, two meta-heuristic algorithms viz; Cuckoo Search algorithm and Thermal Exchange Optimization algorithm are employed for weight optimization of FPSO supporting structure. These algorithms identify the structure’s least weight for the offshore floating platform design. The current work suggests a method for FPSO structure design that is reliability-based and optimal. The current study discusses the optimized design of the topside FPSO structural assembly for various load conditions, such as production tank pressure loads, equipment loads, piping loads, electrical and instrumentation loads, girder deformation loads, wind load, and so on. The effects of hull flexibility, as well as wind and motion-induced accelerations, are considered for both Normal Operating and Extreme Operating conditions. The analysis and design optimization are carried out in the STAAD Pro and analytical tool by selecting the least weight as an objective function while adhering to the API design constraints on displacement and stress limits. Module primary structure design can be iteratively “optimized” using the algorithm and stress ratio as described above. By implication, the algorithms, FPSO supporting structure translates into optimal design, resulting in cost savings. © 2023, The Author(s), under exclusive licence to Springer Nature Switzerland AG.

Asian Journal of Civil Engineering

Optimal design of FPSO topside module for in-place, lift and weighing conditions utilizing meta-heuristic optimization algorithms

Potholes are a threat on roads, and their presence compromises driver, vehicle, and pedestrian safety. In developing countries, the primary reason for road accidents is bad road conditions, resulting in human life and property loss. In countries like India, Road maintenance is a challenging activity. Accidents rates are increasing year by year due to the up-surging potholes count. This paper presents the system as ``Forward View Guidance for pothole detection for Indian passenger Car.{''} The camera captures video images, and a deep learning algorithm is used to classify the images as potholes and regular roads. The camera will also provide a view of the vehicle's front, highlighting the pothole. Deep learning YOLOv3 and YOLOv5 algorithms are used to train the model and tested with the Kaggle pothole detection datasets to predict the model's accuracy for detection. The proposed system will help monitor the road's condition, count the number of potholes on the road, and generate an alert signal. The performance of the proposed system is evaluated using precision, recall, and average precision (AP). The experimentation results show that the YOLOv5 algorithm performs best than the YOLOv3 algorithm. The precision, recall, and average precision (AP) for YOLOv5 are obtained as 0.763, 0.548, and 0.635, respectively. The system algorithm is implemented on the Raspberry Pi 4B model, which can be easily fitted as an addon system in the vehicle.