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

Dental implants are the first choice in replacing any lost teeth. The material used for dental implants should have high mechanical strength, superior biocompatibility, good osseointegration ability, high resistance to corrosion and wear and better aesthetic appeal. Titanium (Ti) is popularly used in dentistry for implant purpose as it exhibits excellent mechanical and biological properties. The hypersensitivity and grey colour of the implant has raised demands for metal-free implant materials with more aesthetic value. With the advancements in biosciences and technology there are various materials options for implants in dentistry. The objective of this review article is to study different materials properties which can be a viable alternative to titanium. Ceramics (zirconia) and polymers (PEEK, PMMA) are emerging as promising metal-free alternative to titanium implants. Nevertheless, further long-term clinical rates and studies are needed to validate total replacement of titanium by the aforementioned materials.

World University root node

School of Mechanical Engineering

AIP Conference Proceedings

Biomaterials used in dental applications to improve success rate of implantation: A review

In recent times, there has been significant research in the area of vibration in aircraft wings. Vibration can cause some serious failure in the structure. The increase in research in vibration has led to taking design considerations in the wings. This paper specifically focuses on the vibration in wings. Modal analysis of wing with the rectangular cross section is done with the use of Finite Element Analysis. Three different materials (Aluminum, Carbon Fiber and E-glass Fiber) have been incorporated which describe the effect of free vibration. The results show that Aluminum 2024 is best suited for the design of aircraft wings compared to other two materials. © 2021 Elsevier Ltd. All rights reserved.

Materials Today: Proceedings

Comparative study on vibration characteristics of aircraft wings using finite element method

Purpose Dental implants are placed in the jaw of a patient to perform the function of a missing tooth. However, before the implant is subjected to masticatory loading, sufficient osseointegration should take place to prevent its failure. Resonance Frequency Analysis is one of the methods to measure the primary stability of an implant system in terms of micromotion. This paper aims to formulate a mathematical model of the dental prosthetic for the vibroacoustic RFA technique. Methods Vibroacoustic RFA is a novel method that excites the implant using either a loudspeaker or a buzzer and captures the subsequent displacement. In this study, a single degree of freedom mathematical model is excited using a sinusoidal force. The subsequent displacement or micro-motion of the implant system is found out by basic equations of vibrations while varying the input frequency between 1 – 50 Hz. Results The resonant frequency corresponding to the maximum micro-motion which lies between 1.2 – 2 μm is plotted using MATLAB. The resonant frequency values lie between 8975 – 8995 Hz with an average value of 8985 Hz. When the input frequency increases beyond 40 Hz, the micromotion shows a percentage increase of 40.62%. Conclusion A higher value of resonance frequency indicates better osseointegration with decrease in micromotion. A Mathematical model and subsequent signal analysis using RFA is advantageous for implant stability measurement. Further research is needed to develop multiple degree of freedom systems to simulate the dental implant model.

Journal of Medical and Biological Engineering

A Mathematical Model for Biomechanical Evaluation of Micro-motion in Dental Prosthetics using Vibroacoustic RFA

Until recently, synthetic filler materials have been the preferred choice for reinforcement of polymers to improve its toughness. However, natural filler and fiber materials are emerging as suitable alternatives to synthetic materials for reinforcing polymers due to their environmental friendliness, high abundance, renewability, and cost-effectiveness. In the present study, the waste portion of the fruit Citrus Maxima (CM) commonly known as Pomelo, in the form of fine powder was blended with Low-Density <a href="https://www.sciencedirect.com/topics/materials-science/polyethylene">Polyethylene</a> (LDPE) for making the composite using the injection molding method. The peel of CM consists of a foamy portion which was separated, thoroughly dried at 50–70&nbsp;°C and ground into a fine powder and was used as an eco-friendly <a href="https://www.sciencedirect.com/topics/materials-science/particulate-filler">particulate filler</a> with the LDPE matrix in different proportions (0, 5, 10, 15, and 20&nbsp;wt%) in treated and untreated form. The filler was treated with 2&nbsp;wt% of <a href="https://www.sciencedirect.com/topics/materials-science/silane">Silane</a> coupling agent. <a href="https://www.sciencedirect.com/topics/materials-science/x-ray-diffraction-analysis">XRD</a> and <a href="https://www.sciencedirect.com/topics/materials-science/infrared-spectroscopy">FTIR</a> confirmed the cellulosic behavior of the CM foam. The effect of filler content on the mechanical properties of the untreated and treated composites was investigated. The morphology and the microstructure of the composites were observed by XRD, FTIR, and SEM. The surface treatment showed significant improvement in the <a href="https://www.sciencedirect.com/topics/materials-science/tensile-strength">tensile strength</a>, % elongation and impact strength for the treated composite at higher loading of filler. The mechanical behaviour of the composites led to the successful utilization of CM waste portion for making an eco-friendly reinforcing additive to form LDPE composites.

School of Polymer Engineering

Mechanical and morphological properties of Citrus Maxima waste powder filled Low-Density polyethylene composites

The immense pressure and tension has created in the worldwide healthcare systems by disease. Various existing system has defined drug prediction system based on current patient evaluation. In this research we proposed a drug prediction for COVID-19 patient based on protein to protein reactions and availability. In order to evaluate the protein-protein interactions (PPIs) between some of the virus and individual receptors that are also confirmed utilizing biomedical simulations, the framework also defines machine learning models. The classification techniques are consistent with the predictions of separate physical material sequence-based characteristics such as classification of amino acids, distribution of pseudo amino acids and conjoint triads. Finally we will evaluate the system with numerous machine learning algorithm and show the effectiveness of propose systems.

School of Computer Engineering & Technology

COMPUTING COMMUNICATION AND GREEN ENGINEERING-2021

Aspect-Level Drug Reviews Sentiment Analysis and COVID-19 Drug prediction using PPI &amp; Deep Learning

Over a past decade it has been observed that a good research is done in healthcare using Machine Learning(ML) and Deep Learning(DL). To carry on the research or implementation using ML or DL, the main concern is database fields or feature selection for the implementation to be done based on the disease or diagnosis method. This paper mainly focus on the two methodologies for feature selection, the first method is a survey based method with Modified Weighted Sum Method (MWSM) implemented on the data collected and second is the feature selection method using machine learning. The said methods are applied to the data collected from diabetic patients and doctors for finding the features useful for diagnosing Hypoglycemia state (lowering of blood glucose level(BGL) in diabetic patients. Comparative analysis is shown for these method and thus concluding for the features selected. Features considered for this work were superficial body features for hypoglycemia detection. The features which we selected from modified WSM were used for feature selection algorithm implementation using machine learning. Univariate Statistical Tests(UST), Recursive Feature Elimination(RFE), Principle Component Analysis(PCA) and Feature Importance(FI) algorithm were implemented on data thus obtained resulting in omitting or rethinking for features.

2021 Third International Conference on Inventive Research in Computing Applications (ICIRCA)

Feature selection for healthcare study: Modified WSM and Machine Learning approach

In recent years, Crowd Anomaly detection has become a field of critical importance, especially so after the recent surge in technological development in the modern era. An increasing number of situations or places require surveillance which is also required to detect anomalies so as to prevent unforeseen accidents. Crowd Anomaly detection usually works by analyzing the surveillance scene in real time using various concepts and techniques such as AI, ML, optical flow analysis, streak flow analysis etc. to identify anomalies in a crowd of people. If an anomaly is found, the relevant authorities are immediately alerted. In this survey paper, few of these methodologies are analyzed by comparing their merits and demerits. © 2021 IEEE.

Proceedings of the 3rd International Conference on Intelligent Communication Technologies and Virtual Mobile Networks, ICICV 2021

A survey and analysis of crowd anomaly detection techniques

This paper introduces an algorithm, termed self-adaptive genetic grey wolf optimizer (self-adaptive genetic GWO), for privacy preservation using a C-mixture factor. The C-mixture factor improves the privacy of data, in which the data does not satisfy the privacy constraints, such as l-diversity, m-privacy and k-anonymity. Experimentation is carried out using the adult dataset and the effectiveness of the proposed self-adaptive genetic GWO is checked depending on the information loss and the average equivalence class metric values and is evaluated to be the best when compared to other existing techniques with low information loss value as 0.1724 and average equivalence class value as 0.71, respectively.

Advances in Intelligent Systems and Computing

An approach for collaborative data publishing using self-adaptive genetic grey wolf optimizer

The security of privacy is currently a field of critical importance in data mining. Organizations and corporations in this dynamic environment are constantly trying to somehow get the database of their competitors. A detailed review of such databases can be done to retrieve a variety of confidential and sensitive information, links, connections, inferences, and findings. It can implicitly or explicitly cause a major loss to the database owner. The owners of the database sell their data to third parties for money. If a database is not held until it is revealed to a third party, then the data owner can suffer disasters. We consider the issue of collective publication of data to anonymize horizontally separated data on multiple data providers. The proposed work discusses and contributes to this new challenge of data publishing. First, we introduce the notion of group-based classification, which guarantees that the anonymized data satisfies a given privacy constraint against any group of data providers. We present a collaborative data publishing model with privacy preservation for efficiently checking in a group of records. Privacy preservation can be used to identify abnormal behavior in data sharing. Many privacy-preserving data publishing techniques were developed but failed to consider the data set because of its complexity and domain specific nature. Various organizations release information about persons in public for resource sharing. But maintaining the confidentiality of individual information is a difficult task with various data releases from multiple organizations where coordinating before data publication. The proposed model is compared with traditional models and the results depicts that proposed model exhibits better performance. © 2022 Scrivener Publishing LLC.

Machine Learning Paradigm for Internet of Things Applications

A collaborative data publishing model with privacy preservation using group-based classification and anonymity

The rapid development of wireless communication technology makes Orthogonal Frequency Division Multiplexing (OFDM) technology and Multiple Input Multiple Output (MIMO) technology, key candidates for the next generation mobile communication system. OFDM system has high frequency utilization and effectively resists multipath and narrowband interference. MIMO technology can effectively improve system capacity and system separation. This paper presents a hybrid Linear-Quadratic Estimation (LQE) technique for Channel Estimation in MIMO-OFDM system. In channel estimation, the noise covariance matrix (Q) plays a key role. In general, the value of Q depends on the previous state values. In a dynamic system, the previous values are not always known. The proposed hybrid model uses two techniques to address this issue. When the previous state values are known, Fast Adaptive Hybrid - Linear Quadratic Estimation (FAH-LQE) is used and when the previous state values are unknown Discrete Multivariate Hybrid - Linear Quadratic Estimation (DMH-LQE) is used. This enables the channel estimation to perform effectively in both scenarios. The FAH-LQE method uses covariance channel noise estimation equations and updates the threshold limit. DMH-LQE finds previous values using discrete generate matrix. The switching between FAH-LQE and DMH-LQE, based on the availability of previous state values, has increased the performance of the system effectively. The proposed algorithm produced lower BER values. For a mobility of 55km/h at SNR 15, BER values of proposed algorithm are 22%, 10%, 7%, 6%, and 3% lower than existing methods namely CE with and without EEP-Kalman tracking, perfect channel estimation, FAH-LQE, and DMH-LQE respectively. Similarly for a mobility of 115km/h at SNR 15, BER values of the proposed algorithm are 45%, 35%, 20%, 15% and 5% lower than above mentioned existing methods namely CE with and without EEP-Kalman tracking, perfect channel estimation, FAH-LQE, and DMH-LQE respectively. The SER has been reduced by 5% when no compensation method is applied. By using the proposed method, the Peak to Average Power Ratio (PAPR) has also reduced. © 2021, International Journal of Intelligent Engineering and Systems. All Rights Reserved.