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

The quick deployment of cloud with computing platforms has driven novel tendencies which shifted operations of networks. However, the cloud is facing several security issues and is susceptible because of suspicious tasks and attacks. This paper devises a new method to detect malicious activities in cloud. Here, first step is the simulation of cloud patterns, wherein the data outsourced by the users are utilized for detecting malicious behaviors. The data pre-processing is done to eradicate unnecessary data and noise contained in the data and is performed using a min–max normalization process. The selection of imperative features is done using distance measure, namely Hellinger distance for mining the essential features. The augmentation of data is performed to make the data appropriate for improved processing. The malicious behavior detection is performed by exploiting the Deep Q network wherein training is performed with Autoregressive chimp optimization algorithm (AChOA), which is developed by integrating chimp optimization algorithm (ChOA) and Conditional Autoregressive Value at risk (CAViaR). The proposed AChOA-based Deep Q network outperformed with the highest testing accuracy of 94%, sensitivity of 94.1%, and specificity of 91.4%. © 2022, The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd.

School of Computer Engineering & Technology

World University root node

International Journal of Intelligent Robotics and Applications

An effective optimization enabled deep learning based Malicious behaviour detection in cloud computing

The cloud market is growing every day. So are cloud crimes. To investigate crimes that happen in a cloud environment, an investigation is carried out adhering to the court of law. Forensics investigations require evidence from the cloud. Evidence acquisition in the cloud requires formidable efforts because of physical inaccessibility and the lack of cloud forensics tools. Time is very crucial in any forensic investigation. If the evidence is preserved before the cloud forensic investigation, it can give the investigators a head start. To identify and preserve such potential evidence in the cloud, we propose a system with an artificial intelligence (AI)-based agent, equipped for binary classification that monitors and profiles the virtual machine (VM) from hypervisor level activities. The proposed system classifies and preserves evidence data generated in the cloud. The evidence repository module of the system uses a novel blockchain model approach to maintain the data provenance. The proposed system works at the hypervisor level, which makes it robust for anti-forensics techniques in the cloud. The proposed system identifies potential evidence reducing the effective storage space requirement of the evidence repository. Data provenance incorporated in the proposed system reduces trust dependencies on the cloud service provider (CSP). © 2022 Prasad Purnaye and Vrushali Kulkarni, published by De Gruyter.

Open Computer Science

BiSHM: Evidence detection and preservation model for cloud forensics

School of Electronics & Communication Engineering

International Journal of Ultra Wideband Communications and Systems

Compact 4 × 4 MIMO antenna for WLAN applications

Wood plastic composites (WPC) were made from polypropylene (PP), impact copolymer (ICP) and wood flour (WF) by varying the WF content from 10 to 40% with PP grafted maleic anhydride as a coupling agent. The effect of varying WF content was studied on rheological, thermal, mechanical properties and dynamic mechanical properties. Experimental small amplitude oscillatory shear (SAOS) data was compared with the Einstein-Batchelor and empirical Krieger-Dougherty relations. Significant dependence of mechanical and rheological properties on WF content was observed. Young's modulus, flexural modulus and dynamic shear viscosity increased with WF content. Results of dynamic mechanical analysis (DMA) showed increase in storage modulus with WF content. Three millimeter thick compression molded composites sheets were thermoformed using axisymmetric molds with two draw depths. Sag observed visually during thermoforming decreased with increasing WF content. Components made from the composites showed close to uniform thickness distribution as compared to those from ICP. © 2021 Walter de Gruyter GmbH, Berlin/Boston.

School of Polymer Engineering

Journal of Polymer Engineering

Study of effect of wood-flour content on mechanical, thermal, rheological properties and thermoformability of wood-polypropylene composites

Machine learning is an artificial intelligence application in which accessible data is processed or assisted in the analysis of any given dataset using algorithms. Machine learning is increasingly being employed in numerous oil and gas sector activities for better forecasts, ranging from litho-stratigraphic identification to pipeline network maintenance and service, and hence greatly improving safety. Using properly designed Neural Network algorithms, various parameters of a well under production has been predicted with reasonable accuracy. After a well is drilled and completed with casing, perforations are done to establish communication between the well and reservoir followed by production from the well. There are various production parameters, some of which are critical as they determine the life and health of a well, while others are general parameters which give details of the features and characteristics of the well as well as the reservoir. These parameters are obtained either directly from field measurements in real time with specific equipment or some are calculated with complex empirical formulas. In this paper, we have taken a machine learning methodology by designing neural networks models to predict the decline curve and further determine some of production parameters. © The Electrochemical Society

School of Petroleum Engineering

ECS Transactions

Predicting Critical Parameters of a Declining Oil Well - A Data Driven Approach

Despite extensive research and efforts in handling and mitigation of dust explosion, accidents involving dust explosion take place. The mention of dust explosion in the literature dates to 1785, but industry professionals from oil and gas are unaware of this hazard. An incident is reported leading to the demise of two workers while attempting to unload barite powders. The process industry is highly dependent on powdered chemicals which are used as an additive in drilling fluid and cements such as barite, bentonite, calcium carbonate, lead sulphides, NaOH, etc. The explosive behaviour of these dusts can be determined with the help of the Kst values. The Kst value is dependent on the confinement, dispersion of particles, size of the dust particle, the source of ignition, and presence of free oxygen. Usually, an experimental setup of 20L sphere with two different types of nozzles i.e., perforated annular nozzle or rebound nozzles are majorly used to calculate the value of Kst. While the setup is being prepared at our end, this work presents a comparison of the performance of these nozzles using Computational Fluid Dynamics (CFD) as a tool, to understand the different parameters of dispersion, and the impact they have on the dust explosion. © The Electrochemical Society

Comparative analysis of critical factors for nozzle consideration in dust explosion testing

Industry professionals predict that the use of artificial intelligence and machine learning will significantly impact people's lives in the near future. They believe that it will allow individuals and organizations to perform more efficiently by improving their processes and making them more effective at their jobs. Machine learning could help improve employees' work-life balance by allowing them to perform more efficiently at their jobs. It could also increase the general quality of their lives. What would be the outcome of an experiment involving machine learning to find ways to improve the balance between work and life? The study aimed to analyze individuals' subjective feelings about work-life balance. Through a machine learning method, the researchers could identify the characteristics influencing the balance between work and life. They then used a forecasting model to analyze the data and determine how much women in the IT industry observe a work-life balance. The study was conducted on 150 female IT professionals. The researchers used a questionnaire to gather information about the participants. They then used various deep-learning models to analyze the data. The researchers analyzed the models' performance by calculating the mean absolute and square errors. They found that the models known as the Multilayer Perceptron and the Long Short-term Memory performed well in terms of their accuracy when forecasting future business conditions. Comparing the two models revealed that the Short-Term Long Memory performed better than the MLP in terms of accuracy. This finding could be beneficial for studies related to the behavior of organizations. © 2022 ACM.

ACM International Conference Proceeding Series

Deep Learning Model for Work-Life Balance Prediction for Working Women in IT Industry

This paper aims to present the survey of cloud computing security. A cloud computing security is defined so that it can help to clarify cloud security. Cloud computing has evolved to become an essential component of both current and future information technologies. This technology was created to be used in conjunction with the internet, and it includes features such as data storage, remote access, and so on. Various cryptographic encryption algorithms are compared in terms of their various key features. The primary goal is to highlight such attacks and provide recommendations for resolving data breaching issues using cryptographic algorithms such as Symmetric Key Cryptographic Algorithms (DES, 3DES, AES), Asymmetric Key Cryptographic Algorithms (RSA, DSA, Diffie-Hellman Algorithm), and Hashing Algorithms (MD5, SHA). So, in the paper cloud security challenges and the cryptographic algorithms used to prevent them are discussed. © 2022 IEEE.

Proceedings - 6th International Conference on Computing Methodologies and Communication, ICCMC 2022

Cloud Computing Security using Cryptographic Algorithms

In cloud computing, there are several security concerns. The security concern may increase due to one of the important techniques of virtualization. Cloud computing is also having several important characteristics which are used for its improvement and to provide several services to its end user. Some of the essential and important characteristics are on-demand self-service, broad network access, rapid elasticity, resource pooling, and measured services. By using these characteristics, cloud computing satisfies the needs of its end users, but the base to these characteristics is the virtualization technique. These observations depict the reason for several attacks and especially DoS and DDoS attacks or brute-force attacks. This article provides detailed information on these characteristics and also provides information about flaws and breaches. The reason is virtualization, which makes cloud computing insecure to use. Several techniques and algorithms are used to improve the cloud computing system, but flaws in their application have been discovered. These algorithms may degrade the quality of cloud computing system and may also makes cloud computing insecure to its end users. © 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

Lecture Notes in Networks and Systems

A Survey Paper on Characteristics and Technique Used for Enhancement of Cloud Computıng and Their Security Issues

Deep overbite problem leads to gummy smile in orthodontic patients. The simultaneous intrusion of maxillary (upper) six anterior teeth with mini-implants is a common treatment method for this purpose. Till date, no study has stated desired position of attachment of mini-implant. This is an important parameter as intrusive force vector must pass through centre of resistance (Cres) of maxillary six anterior teeth. Thus, the aim of this study is to obtain the co-ordinates of desired positions of mini-implant with reference to incisal edge of first anterior tooth (maxillary central incisor) from the side view of maxillary six anterior teeth. The purpose is to achieve the effective simultaneous intrusion of maxillary (upper) six anterior teeth. For this purpose, co-ordinates of incisal edge of first anterior tooth (maxillary central incisor), point of attachment of elastomeric chain on the archwire and Cres of maxillary anterior teeth were considered. From these points, the equation to compute co-ordinates of attachment of mini-implants was formulated. Additionally, the required angle of intrusive force was estimated to be 45°. To verify this computation, finite element analysis was performed on the computer aided design (CAD) structures of maxillary six anterior teeth, surrounding tissues and appliance structures. From the formulated equation, four possible positions of mini-implant placements were considered with reference to incisal edge of first anterior tooth (maxillary central incisor). The co-ordinates of these positions were (11, 10.5) - Position {1}, (12, 11.5) - Position {2}, (13, 12.5) - Position {3}, (14, 13.5) - Position {4} with units in mm. From the photographic results of these positions, different statements of recommendations were given in this study from feasibility point of view. Positions {2} and {3} were highly recommended. Position {4} was moderately recommended. Position {1} was least recommended. © 2022