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

Visual Pattern Recognition Networks (VPRN) delivers high performance using deep neural networks. With the advancements in deep neural networks VPR network has gained wide popularity. Continuous advancements will be nurtured with the availability of big data and enormous computing powers. However, such DNN based VPRN models are plunged with computational complexities, intense memory requirements, huge energy expenses which impedes its deployment in resource constrained, strict latency required environments such as edgeAI. For instance, the VGG-16 model needs 500 MB of storage space, has 138 million parameters and involves 15.5 billion Floating Point Operations (FLOPs) to classify a single image with a 32 bit floating point addition that consumes 0.9 pJ. Such overheads demand for compression of VPRN models without impairing its performance. Various compression methods are reported in literature. This survey paper presents a survey on pruning, a popular compression technique for DNN as applied for VPRN. This paper presents comprehensive survey, comparison and points further research direction. © 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

World University root node

School of Electronics & Communication Engineering

Lecture Notes in Electrical Engineering

Pruning for Compression of Visual Pattern Recognition Networks: A Survey from Deep Neural Networks Perspective

The advent of cloud computing has enabled many users and organizations to make better use of system and business resources. Cloud computing's primary benefits are its cheaper service costs and the absence of the requirement for users to invest in expensive computing hardware. Because of the accessibility of its resources and flexibility for user computing operations, individuals and organizations migrate their programs, data, and resources to cloud storage services. A transition from localized to remote computing has brought with it a plethora of security risks and challenges for both users and service providers. However, there is one significant disadvantage to cloud computing: the storage of your data is in the hands of a third party. Amongst the most daunting concerns in providing powerful storage and processing as on-demand Services is the security threat posed by resource sharing in Cloud computing. Increasing efficiency and better performance are driving governments and organizations around the world to use cloud computing, either from scratch or as part of existing infrastructure. Risk and challenges associated with cloud computing, as well as the measures that can be taken to help shield the cloud from security threats, are discussed in this paper. © 2022 IEEE.

School of Computer Engineering & Technology

Proceedings - 2022 6th International Conference on Intelligent Computing and Control Systems, ICICCS 2022

A Survey on Security Threats in Cloud Computing Service Models

Background: Analysis of tertiary mixtures of analytes along with their impurities with simple and cost effective manner is always of interest. Utility of chemometric techniques are growing in pharmaceuticals, it improve speediness in the analysis and also provide analytical solutions with reduce the number of steps in the analytical method. In this study UV-Visible spectrophotometry coupled with principle component regression (PCR) and partial least square (PLS) multivariate methods was applied for estimation of three drugs in their formulation. Method: The calibration and validation sets were prepared in linear concentration range of three drugs and major impurities of paracetamol and aceclofenac. The series of sets were prepared using multilevel multifactorial design. Leave-One-Out (LOO) cross validation technique was employed to get essential number of Latent variables (LVs) that provides the greatest predictive ability. The developed method was studied for qualitative and quantitative analysis of titled drugs and validated as per regulatory guidelines. Results: The results showed the values of coefficient of determination (R2) for all drugs and impurities was higher than 0.99 indicating high acceptability. The obtained RMSE values were relatively low. Coefficient of determination and RMSE values indicate good accuracy and precision, respectively. Conclusion: Proposed method was successfully used for analysis of aceclofenac, paracetamol and chlorzoxazone in tablet dosage form and major impurities of aceclofenac, paracetamol in bulk. © 2022 American Society of Civil Engineers (ASCE). All rights reserved.

School of Pharmacy

Pharmacy

School of Petroleum Engineering

Biomedical and Biotechnology Research Journal

Development and validation of chemometric-assisted spectrophotometric method for the simultaneous estimation of aceclofenac, paracetamol, and chlorzoxazone with impurities

There is a significant increase in tunneling operations in most of the metropolitan cities of India for developing rapid transit systems. Provision of tunnels proves to be beneficial in densely crowded areas where availability of space is a major concern. Tunneling is a costly affair owing to the high number of uncertainties involved as compared to other infrastructure projects. The delay and cost escalations during construction of tunnel can be mitigated by identifying the uncertainties in the planning stage and taking appropriate steps during execution. This requires a robust model incorporating the most significant risk factors affecting the projects. In the present work, an exhaustive list of risk factors affecting tunnel projects is extracted from comprehensive literature review on tunnel construction. The list is augmented through interaction with stakeholders and collection of field data. This exercise provided an extensive pool of factors, which can't be completely incorporated while formulating the model to reduce delays. This work attempts to identify most critical risk factors from the pool. Prioritization of risk factors from the pool is done with a framework using RII [Relative Importance Index]. The pool is classified in a 3-level hierarchical structure. Expert opinion regarding relative importance of each factor is captured through a questionnaire survey and relative weights are assigned. This work is an attempt to compile and prioritize predictable risk factor database by merging a comprehensive literature review of scientific papers on tunnel construction projects and opinion of experts working in tunnel construction. The identification of critical factors in planning phase, may reduce complexity during execution. The proposed framework is expected to help researchers and decision makers to rank the risk factors and focus only on the most critical ones for minimizing the risks and reducing delays in project completion. © 2022

School of Civil Engineering

Materials Today: Proceedings

An integrated approach for identification and prioritization of risk factors in tunnel construction

The Internet of Vehicles is considered an adaptation to the current transportation system's severe position, thanks to advancements in wireless communication systems and intelligent mobility technologies. The autos in the Network of Vehicles system generate Vehicular Networks and constantly deliver additional functionality inspired by real driving data transmitted by the vehicles using wireless communication technology. Drivers may modify the distance between automobiles while planning their route depending on current traffic conditions, positively affecting the efficiency and safety of the transport network. Because of the unique capabilities of a Technology of Devices service platform, automobiles must transmit their position information often. Assailants can gather and analyze automobile transmit data to acquire personal information and perhaps even track the customer's vehicle path, creating new security challenges. This article offers a Blockchain-based confidentiality solution for such Vehicular networks. This same solution incorporates blockchain technology with the Website of Vehicles network to provide a secure and convenient two-way authentication mechanism procedure based on encryption, as well as removing the conventional Internet of Vehicles system's centralized dependency issue. © 2022 IEEE.

2022 IEEE International Conference on Blockchain and Distributed Systems Security, ICBDS 2022

Internet of Vehicles System Based on Blockchain

Lung diseases are a major concern nowadays. The human body consists of diverse fundamental organs, out of which Lung is one of them. The Lungs are made up of two sections, right lung and left lung and the main function of lungs is to facilitate exchange of gas which is called as breathing or respiration. Our present modern lifestyle, ecological contamination, adulteration is helping in expanding the Human Lungs issue. What's more, simultaneously there are various Image processing techniques that are blending the tremendous answer for the medical field to identify and analyze the Lung's illnesses. A system development which is based on Deep learning is proposed to get better and accurate result which includes Image Processing techniques like image pre-processing, segmentation, thresholding, image enhancement and classification. Computer Tomography (CT) scan images takes from LIDC database as an input to examine the Lungs disorders in human body. Convolution Neural Network is practiced to perform classification of input lung images into cancerous and noncancerous classes. This technique will assist to oncologists as second opinion and will develop the accurate quality of lung cancer detection. With the proposed system, lung cancer detection is done with 98.44% of accuracy. © 2022 IEEE.

2022 6th International Conference on Computing, Communication, Control and Automation, ICCUBEA 2022

Machine learning application for Lung Cancer Detection

Coronavirus (COVID19) is a highly contagious virus which had already killed thousands of people and infected millions more throughout the world. One of the primary challenges that medical practitioners encounter in the realm of healthcare is correctly diagnosing patients conditions and infections. So far, the gold standard screening method RT-PCR test which has been designed to detect covid-19 which only has a positive rate ranging between 30 precent and 60 percent. As a result, a system that can accurately identify images and diagnose or anticipate diseases is needed. As a result, we set out to swiftly create a compact CNN architecture capable of recognizing COVID-19-infected individuals. Different CNN architectures are suggested in this paper to extract information from X-rays which further classified into Covid-19, pneumonia, or healthy. Here, we have used two datasets from publically available repositories that are Kaggle and Mendeley [1] [2]. To see how the size of datasets affects CNN performance, we train the suggested CNNs with both the original and enhanced datasets where datasets are splitted into ratios of 80:20 and 70:30 and the comparison is shown. Also suggested CNN model is compared with the five state-of-Art pre-Trained models (VGG-16, ResNet50, InceptionV3, EfficientNetB2, DenseNet121) with the same datasets and splitting ratios. we have also used Some visualization methods through which we can get an exact idea of how CNN functions and the explanation behind the network's decisions. This study suggests a model for classifying COVID-19 patients but makes no claims about medical diagnostic accuracy. © 2022 IEEE.

Proceedings - 2nd International Conference on Next Generation Intelligent Systems, ICNGIS 2022

Chest Abnormality Detection from X-Rays Using Deep Learning

Energy dissipation capacity and seismic performance of steel moment resisting frame with hybrid damping system along with buckling restrained brace is investigated and an innovative approach of design of energy dissipating system is recommended. The nine storied structure is analyzed by nonlinear time history analyses using seven time histories and scaled to achieve a set of results and then compared the various possibilities. The multi-linear elastic and plastic links are used to model energy dissipating devices with the help of SAP 2000. The various results such as story displacement, story drift, roof drift, base shear and roof acceleration of steel moment resisting frame are compared with the system after installation of energy dissipaters in the same moment resisting frame that means a dual buckling restrained brace with steel moment resisting frame system and a dual hybrid damping system with steel moment resisting frame system. It was expected that the system will work in individual phases as earthquake intensity will go on increasing and in end results, the models have shown the same. It also has got depicted that the energy dissipation of the systems gets improved after installation of hybrid damping systems. © 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.

Structural Integrity

Evaluation of Role of Hybrid Damping System in Seismic Assessment

Biomaterials used in implants or clinical instrumentation are commonly used for the re-alignment, replacement, and treatment of deteriorated tissues, broken structures, or other injuries. The study of biomaterials is primarily involved in the chemical and biology field collectively known as biochemistry, biology, tissue engineering, and material science. Appropriate material selection and their characterization is crucial consideration while manufacturing medical implants. The biomaterial used for different medical implants should possess desirable properties, mechanical strength, biostability, good corrosion resistance, and biocompatibility to get acquainted in the physiologic environments. However, many important researches in biomaterial science have come focusing on improving the physical, mechanical and chemical properties regarding the material of implant and thereby improvising the implant strength. This overview examines metallic and ceramics biomaterials which are commonly used biomaterials used in medical implants while primarily focusing on the composition, mechanical properties, advantages, limitations, and applications of the materials. © 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

School of Mechanical Engineering

Recent Advances in Manufacturing Processes and Systems

An Overview on Metallic and Ceramic Biomaterials

This paper aims to develop a predictive model and optimize the performance of the abrasive water jet machining (AWJM) during machining of carbon fiber-reinforced plastic (CFRP) epoxy laminates composite through a unique approach of artificial neural network (ANN) linked with the nondominated sorting genetic algorithm-II (NSGA-II). Initially, 80 AWJM experimental runs were carried out to generate the data set to train and test the ANN model. During the experimentation, the stand-off distance (SOD), water pressure, traverse speed and abrasive mass flow rate (AMFR) were selected as input AWJM variables and the average surface roughness and kerf width were considered as response variables. The established ANN model predicted the response variable with mean square error of 0.0027. Finally, the ANN coupled NSGA-II algorithm was applied to determine the optimum AWJM input parameters combinations based on multiple objectives. © 2022 World Scientific Publishing Company.