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

In this research, graphene/Al6061 aluminum matrix nanocomposites were fabricated by stir casting, and the influence of graphene nanoplates on microstructure and mechanical properties of the 6061 aluminum alloy were investigated by field emission scanning electron microscopy (FESEM), energy dispersive spectroscopy, tensile and hardness testing analysis methods. The major limitation in the utilization of 6061 aluminum alloy in heavy stress applications such as airplane fuselages, wings, internal panels, and luxury vehicles chassis is low strength and hardness. This deficiency of 6061 aluminum alloy was tackled by successful reinforcement of graphene nanoplates in 2, 4, 6, 8 and 10 wt.%, using the stir casting process. The FESEM micrographs showed that the graphene nanoplates were uniformly distributed in the 6061-aluminum matrix alloy and tensile strength, hardness, and yield strength enhanced remarkably as compared with unreinforced 6061 aluminum alloy. The as-cast tensile strength, hardness, and yield strength of the graphene/Al6061 nanocomposites were improved by 127%, 158%, and 402%, respectively, compared with the unreinforced Al6061 alloy. It is concluded that the nano thickness of graphene, reinforcement quantity, and manufacturing process are the major factors for the enhancement of microstructure and mechanical properties of graphene/Al6061 nanocomposites. © 2022 The Korean Society for Composite Materials and IOP Publishing Limited.

World University root node

School of Mechanical Engineering

Functional Composites and Structures

Enhanced microstructure and mechanical properties of Al6061 alloy via graphene nanoplates reinforcement fabricated by stir casting

The soil is the most fundamental component for the survival of any living thing that can be found on this planet. A little less than 41 percent of Indians are employed in agriculture, which accounts for approximately 19 percent of the country's gross domestic product. As is the case in every other industry, researchers and scientists in this one are exerting a lot of effort to enhance agricultural practices by utilising cutting-edge methods such as machine learning, artificial intelligence, big data, and so on. The findings of the study described in this paper are predicated on the assumption that the method of machine learning results in an improvement in the accuracy of the prediction of soil chemical characteristics. The correlations that were discovered as a result of this research are essential for comprehending the comprehensive approach to predicting the soil attributes using ML/DL models. A number of findings from previous study have been reported and analysed. A state of the art machine learning algorithm, including Logistic Regression, KNN, Support Vector Machine and Random Forest are implemented and compared. Additionally, the innovative Deep Learning Hybrid CNN-RF and VGG-RNN Model for Categorization of Soil Properties is also implemented along with CNN. An investigation into the significance of the selected category for nutritional categorization revealed that a multi-component technique provided the most accurate predictions. Both the CNN-RF and VGG-RNN models that were proposed were successful in classifying the soil with average accuracies of 95.8% and 97.9%, respectively, in the test procedures. A study was carried out in which the CNN-RF model, the VGG-RNN model, and five other machine learning and deep learning models were compared. The suggested VGG-RNN model achieved superior accuracy of classification and real-time durability, respectively. © 2022 The authors.

School of Computer Engineering & Technology

International Journal on Recent and Innovation Trends in Computing and Communication

A Novel Hybrid AI Federated ML/DL Models for Classification of Soil Components

Data mining algorithms has a wide application in banking domain. Classification algorithms are the one of the popularly used algorithms in the banking sector. One of the major applications of classification algorithms in banking sector is predicting the credit worthiness of the borrower. In this research the dataset is taken from prowessIQ and the dataset is highly imbalanced. The researchers have used data sampling techniques to balance the data. Our approach is to use the ensemble classifiers which are performing pretty well even on unbalanced datasets. Three ensemble techniques are studied in this paper namely Random Forest (RF), XGB and LGBM classifiers. Hyper parameter tuning is done to improve the accuracy of the model. With best possible hyperparameters RF has given an accuracy of 90.2%, XGB has given an accuracy of 92.7% and LGBM has given an accuracy of 92.9%. LGBM has better classified borrowers as worthy and non-worthy even when the dataset is unbalanced. © 2022 IEEE.

2022 IEEE 3rd Global Conference for Advancement in Technology, GCAT 2022

Ensemble Approach with Hyperparameter Tuning for Credit Worthiness Prediction

This paper aims at investigating the effect of strain ratio on LCF behavior of dual phase (DP) steel at room temperature. LCF tests are generally performed at strain ratio of -1. In real life scenarios, cyclic loading does not usually correspond to fully reversed condition due to operational circumstances such as preload, duty cycle, vibrations or the environmental effects. Therefore, it becomes necessary to investigate the fatigue properties and LCF behavior for un-symmetric loading conditions as well. Dual Phase (DP) steels are extensively used in automotive industries owing to its large energy absorption capacity in the event of crash. Hence, evaluation of their LCF behavior becomes crucial. In this study, tests are carried out under strain-controlled conditions at two different strain ratios (-1 and 0.1) to investigate the LCF and mean stress relaxation behavior of a DP steel. Fractured surfaces of failed specimens are investigated under SEM to investigate its crack nucleation and propagation sites and to determine its failure mechanism. A correlation is established between fatigue properties at two strain ratios which can be extended for other grades of dual phase steels. © 2022 SAE International. All Rights Reserved.

SAE Technical Papers

Study of Strain Ratio effects on Low Cycle Fatigue (LCF) Behavior of Dual Phase (DP) Steel

In today's world, agriculture is the most important source of growth. It is an essential component of both economic and social life. Plant disease is becoming an important field in India, in the literal sense of the word. In the past, disease detection systems were designed for monocot or dicot plant families. Gradually, as scientific and special to some science or trade progress, more safe, good, ready, and working well methods are offered and have undergone growth for early detection of plant disease through the smallest wide space for turning time. We have instrumented a careful way to show monocot and dicot disease in this paper. Our methodical approach aids in the treatment of the disease by reducing the number of errors. To detect plant leaf disease, we took three steps. It primarily takes three forms: first, breaking down the leaf into parts, then removing the points, and finally, ordering. It has come to be part of the acted-on part of plant leaf using the k-means quince into group's expert way in the breaking down into parts process. The material's feel, appearance, and color are revealed after it is broken down into primarily form parts. Finally, the information gleaned from the features is applied to the order using the Support Guide Machine to detect plant disease. © 2022 IEEE.

ICPC2T 2022 - 2nd International Conference on Power, Control and Computing Technologies, Proceedings

Apperception of Plant Disease with avail of algorithm

Energy usage increased in tandem with the growth of cloud computing. This proliferation in energy usage has consequently resulted in a humongous increase in the surrounding carbon footprint. Furthermore, the exponential rise of 'data servers' and other infrastructure is driving up energy consumption. As a result, a reduction in energy use will result in a lesser carbon footprint in the surrounding environment. Green-computing in "cloud computing"is the finding and development of energy-saving digital solutions for reducing carbon pollution in the atmosphere. Green Cloud computing intends to accomplish effective processing and computing infrastructure use and reduce energy usage. In this paper, the carbon footprint produced by cloud infrastructures is analyzed and the compelling need for Green cloud computingis suggested. It is critical to ensure that the foreseeable growth of cloud computing is economically sound. © 2022 IEEE.

Proceedings of the International Conference on Electronics and Renewable Systems, ICEARS 2022

Carbon Footprint Analysis: Need for Green Cloud Computing

Data Storage security is one of the highest concerns for any organisation. Even if the organisation maintains its own infrastructure or hires cloud storage to store its data, privacy of the data and its protection in any mode is always the highest priority for any organisation. The cloud provides with a huge amount of storage space and also with various resources to the different organisations as per their requirements. In this paper, four different techniques that are proposed for data protection and recovery of the data in case of loss due to any eventuality or failure of the functioning of cloud are reviewed. The first technique to be studied is the Seed based algorithm that is used for recovery of data which is remotely stored at any geographical location in a cloud. The technique can restore the data which even if unfortunately is lost can be still recovered. The second technique reviewed is the attribute based access control algorithm which is used to secure the data thereby maintaining the safety and sanctity of the stored data in the cloud. Another technique which is quite novel is a hybrid technique of using MRADO data obfuscation method along with LSB. This provides with data masking wherein the original data remains totally camouflaged under the image that get obfuscated without the attacker coming to know about its existence. Another data protection model mentioned in the paper is the model that is based on an encryption attribute access control. This model proposes a data detection algorithm which detects the access record of the data. Post analysis of the four different algorithms, the use of two of the techniques is proposed for the protection of the data in cloud. © 2022 IEEE.

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

Protection of Data in Cloud based on Seed based Algorithm and Encrypted Access Control

Breast Cancer is a common cancer occuring among women throughout the entire world. This attributes to a majority of deaths and cases in the global statistics, which makes it a significant health problem in our society. This paper showcases the classification of breast cancer tumors and the different boosting/ensemble techniques which are used for a more accurate classification. This research presents the working and comparison between classifiers such as Bagging Meta-Estimator, ADA-Boost and XGBM. We will also be showcasing the comparison between the results of these classifiers/techniques and conclude which one is the most accurate in the classification process. © 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

Lecture Notes on Data Engineering and Communications Technologies

Classification of Breast Tumor Using Ensemble Learning

Visual cryptography (VC) is a secret sharing cryptographic technique, which permits visual information that is encrypted in such a way that the decrypted information shows the visual image. Security is the common problem that arises in the general VC technique. To secure the visual information, an effective method is proposed using a developed sailfish invasive weed optimization (SIWO) algorithm. At first, the grayscale image is shared with multiple parties, and here the shared images are encrypted and decrypted to reconstruct the original image back. The key generation is done using the developed SIWO-based data sharing approach. Here, the developed approach offered the peak signal-to-noise ratiof 34.870 and 33.711 dB for Lena and Cameraman images, respectively, and it attains better performance in terms of secret image sharing. © 2021 Wiley Periodicals LLC.