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

One of the major problems faced by the oil industry is the problem of unwanted water production. High rates of unwanted water production in a well can make the well uneconomical and reduces the good lifespan. The paper studies the problems faced by a field experiencing a large amount of unwanted water production in the majority of its wells. The data gathered from one of the Indian Western Offshore Oil Fields have been analyzed to identify the problems faced in several wells. Also, the initiatives taken by the company to control high water cut has been discussed. Understanding the water shut-off methods used for mitigating the problem of high water cut and their efficiency, the availability of various Inflow Control Systems for well completion to prevent unwanted water production is studied. Studying the performance of these systems from numerous case studies and literature surveys for mitigating unwanted water production, the paper provides a complete strategy for water control in horizontal wells for different reservoir properties and for future redevelopment plan of the Indian Western Offshore Field followed by the conclusion. © 2022 The authors and IOS Press.

World University root node

School of Petroleum Engineering

School of Chemical Engineering

Advances in Transdisciplinary Engineering

Completion Strategy for Water Control in Horizontal Wells of an Indian Western Offshore Oilfield: A Case Study

A systematic multi-disciplinary design approach to the optimum tailoring of composite materials is a challenging design optimization problem. It involves the tailoring of composite material properties as per the design requirements. While designing any component, typical cost or weight reduction involves the use of optimization together with corresponding analysis. Depending upon various performance requirements, different types of analyses become the design drivers. Often in practice, such design drivers are dealt with separately, allowing the design to iterate until all the constraints are satisfied. However, these all-design drivers act as separate black box units with very tight interfaces, which could lead to quite inefficient designs. Multidisciplinary Design Optimization (MDO) establishes a link between numerous parameters concerning various disciplines. The main objective of this study is to review the different MDO frameworks applied to the design optimization of composite materials and structures by various researchers and to compare their capabilities and constraints. © 2021 Elsevier Ltd

School of Mechanical Engineering

Composite Structures

Multi-disciplinary design optimization of composite structures: A review

The primary goal of the pavement is to provide a safe and smooth riding surface. Few research studies have explored the influence of geopathic stress in road accidents. Still, its effect on the pavement distresses and, as a result, on-road accidents are yet to be explored. This research aims to formulate regression models between the average number of accidents with geopathic stress and Pavement Condition Index (PCI) values. For Pune city, 36 accident black spots on the flexible pavement are considered, and accident data from 2016 to 2020 has been used. On each spot, the Pavement Condition Index (PCI) is determined using the traditional, precise manual method as per IRC 82:2015. Geopathic stress is measured using the innovative NAAV meter instrument. In the NAAV meter due to the influence of geopathic stress, the laser beam deviates, which can be visually observed; which is not possible with other devices such as Esmog Spion and magnetometer. Three linear regression models are developed. The first one relates the average number of road accidents (Ā) with the Pavement Condition Index (PCI). In contrast, the second one relates (Ā) with the electric field output on account of the interference of geopathic stress with a laser beam emitted from the source in the NAAV meter, (NR) in microamperes. Equation three relates the pavement condition index (PCI) with (NR) as well. It is found that the number of accidents increases on distressed spots with the increase in geopathic stress. The mathematical models developed would effectively establish a relationship between road accidents, geopathic stress, and pavement surface condition. This would further enable transportation authorities to predict the number of road accidents at specific locations on existing pavement. In addition, transport authorities can economize at the road maintenance cost by identifying critically distressed sections at black spots which are severely impacted by weak electromagnetic fields emanating from the subgrade on account of the geopathic stress. © 2022

School of Civil Engineering

Materials Today: Proceedings

Effect of geopathic stress on flexible pavement distresses and accidents

Music enthusiasts are growing exponentially and based on this, many songs are being introduced to the market and stored in signal music libraries. Due to this development emotion recognition model from music contents has received increasing attention in today’s world. Of these technologies, a novel Music Emotion Recognition (MER) system is introduced to meet the ever-increasing demand for easy and efficient access to music information. Even though this system was well-developed it lacks in maintaining accuracy of the system and finds difficulty in predicting multi-label emotion type. To address these shortcomings, in this research article, a novel MER system is developed by inter-linking the pre-processing, feature extraction and classification steps. Initially, pre-processing step is employed to convert larger audio files into smaller audio frames. Afterwards, music related temporal, spectral and energy features are extracted for those pre-processed frames which are subjected to the proposed gradient descent based Spiking Neural Network (SNN) classifier. While learning SNN, it is important to determine the optimal weight values for reducing the training error so that gradient descent optimization approach is adopted. To prove the effectiveness of proposed research, proposed model is compared with conventional classification algorithms. The proposed methodology was experimentally tested using various evaluation metrics and it achieves 94.55% accuracy. Hence the proposed methodology attains a good accuracy measure and outperforms well than other algorithms. © 2022, The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature.

School of Computer Engineering & Technology

Multimedia Tools and Applications

Multi-label emotion recognition from Indian classical music using gradient descent SNN model

Covid19 has had a widespread influence on health services and the way of life. A prompt diagnosis is crucial for curbing the development of the disease and lowering the number of fatalities. It is customary and standard routine to employ blood tests to detect presence of pathogen, but because of the time and expense involved, it is often necessary to turn to other rapid and affordable options. We implemented two distinct transference based deep layered architectures in this study i.e., ResidualNet50 along with VGG16, to classify X-rays as COVID19, pneumonia, or normal. ResidualNet50 trained with transference approach outperformed the other deep-learning model i.e., VGG16, in the planned execution. Our proposed transfer deep-learning based model obtained an overall high classification accuracy of 98.5 percent. Result analysis and interpretation via performance curves have been comprehensively discussed in this paper. © 2022 IEEE.

8th International Conference on Advanced Computing and Communication Systems, ICACCS 2022

Efficient Diagnosis of Covid19 by Employing Deep Transfer Learning on Pretrained VGG and ResidualNet Architectures

In Sensor Networks (SNs), Network Lifetime (NL) enhancement using Data Aggregation Trees (DATs) is an extensively studied NP complete problem. The study in this paper investigates the existing DAT scheduling heuristics and proposes a metric termed as Brevis Factor ρ to evaluate the merit of the employed heuristics. Then, the ρ metric is applied to the state-of-art DAT scheduling heuristics which are typically based on Residual Energy (RE) parameter of the sensor nodes. Further, this paper proposes a new heuristics parameter for DAT scheduling based on the Number of Neighboring nodes (NN). Performance of NN-based heuristics is evaluated with rigorous simulations, and the results demonstrate improvement in NL as compared with the state-of-art. The values of ρ are observed for the existing RE-based heuristics as well as the proposed NN-based heuristics. The results demonstrate how ρ metric values evaluate the DAT scheduling heuristics. In addition, the results facilitate to understand the trade-off associated with the overheads to reconstruct DATs while improving NL. © 2021 John Wiley & Sons Ltd.

International Journal of Communication Systems

Brevis factor ρ to evaluate data aggregation tree scheduling heuristics in sensor networks

For cancer prediction, the prognostic stage is the main factor that helps medical experts to decide the optimal treatment for a patient. The main objective of this study is to predict prognostic stage from the medical records of various health institutions. Total 465 pathological and clinical reports of people living with breast cancer has been collected from India's reputed treatment institutions. Different anatomic and biologic factors are extracted from unstructured medical records using a novel combination of natural language processing (NLP) and fuzzy decision tree (FDT) for prognostic stage detection. This study has extracted the anatomic and biologic factors from medical reports with high accuracy. The average accuracy of the prognostic stage prediction found 93% and 83% in rural and urban regions, respectively. A generalized method for cancer staging with great accuracy in a different medical institution from dissimilar regional areas suggest that the proposed research improves the prognosis of breast cancer. © 2021 Informa UK Limited, trading as Taylor & Francis Group.

Computer Methods in Biomechanics and Biomedical Engineering

Prognostic elements extraction from documents to detect prognostic stage

The use of cloud computing and cloud-based services has increased sharply in the past decade. Regardless of many advantages, the extensive use of the cloud has also created a large attack platform, frequently exploited by cybercriminals, requiring real-time, automated detection and competent forensics tools for investigations. Evidence identification so far has been limited to performance studies on datasets that were created a long time ago and are not specific to cloud environments. In this paper, we introduce a novel dataset for cloud-specific evidence detection. The dataset has two categories: the monitoring database and the evidence database. The monitoring database has 43 features and 9610 records, whereas the evidence database has 360 memory dump files of around 280 GB which contain memory dumps of benign virtual machines and a hostile virtual machine. The dataset will be an important resource for evidence identification research in the cloud environment. © 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

Smart Innovation, Systems and Technologies

Information Retrieval for Cloud Forensics

The advancement of technology has created a huge scope and requirement for transforming the image into a high-visibility image. Here, transforming an image into a high-visibility image means to convert a low-visibility image into a high-visibility image. Super-resolution has many applications worldwide like in medical industries, surveillance, satellite photography, the study of the galaxy, etc. Also, COVID-19 is a monstrous threat to earth. Doctors are predicting whether the patient is having the coronavirus or not via X-Rays and CT-Scans. These scans sometimes miss little details because of the blurriness/low-visibility of the image. This problem can be overcome by using the Super-resolution convolutional neural network (SRCNN). The purpose of the study is the classification of whether the person has the coronavirus or not becomes very accurate by using the SRCNN model. For the transformation of a low-visibility image into a high-visibility image, SRCNN and for classifying whether the person is having coronavirus or not a convolutional neural network (CNN) is used. Our models are trained and tested on four datasets, which are Set5, Set14, COVID-chest-X-Ray dataset, and chest-X-Ray-pneumonia. Our results depict that after applying super-resolution on the X-Rays or the CT-Scans, the classification of COVID-19 attained an accuracy of 95.31% which is higher if compared to the classification of COVID-19 without image super-resolution that attained an accuracy of 92.19%. These were the results after running the model for 20 epochs. Hence, with the help of the SRCNN model, the classification of COVID-19 is much easier and accurate as compared to without the image super-resolution technique. © 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

Analysis and Prediction of COVID-19 with Image Super-Resolution Using CNN and SRCNN-Based Approach

In engineering practice, mechanical vibration plays a very important role, such vibrations can lead to negative issues. The vibration is caused due to various factors like restrained machine vibration which can lead to wear and tear or damage of the equipment. Vibrating machinery can create unnecessary sound, cause protection issues and lead to degradation. It can also cause machinery to use more power and may damage the condition of the product. In order to reduce the negative effect of vibration a design is proposed such that it will not only reduce the vibration to a certain extent but also support the human body while using equipment like jackhammers, heavy drills, etc. The Scissor Like Structure (SLS) can be applied in many other engineering applications to achieve higher quality vibration control. In this article a SLS model is designed with the help of CATIA. Various parametric influences are considered and after taking suitable dimensions the model analysis is done. The calculations were plotted with the help of MATLAB. And, considering all the negative issues caused by vibration a user-friendly exoskeleton is designed. With help of this innovative design simulation, experiments are performed. The results demonstrate the effectiveness and efficiency of this model and also eliminates the chances of failure. At the end the natural frequency of the SLS is calculated. © 2022