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

School of Computer Engineering & Technology

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.

Engineering and Technology

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.

World University root node

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

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

Using Drone Technology for Fruit Orchard Management and Waste Reduction

Drought is a frequent hydrometeorological phenomenon that affects every individual, including animals. It causes significant economic and human losses. Various traditional methods are used to assess droughts in the early stages. However, conventional ways are time-consuming, expensive, and laborious. This has led to exploring remote sensing with recent methods for drought assessment. Therefore, the current paper gives a comprehensive overview of the recent studies on agricultural drought impact and its assessment using remote sensing datasets and various spectral index methods. The available satellite data sources have been provided with their sensors and spectral, spatial, and temporal information. Furthermore, we have also provided the general schema for agricultural drought assessment, essential indices, and drought severity methods with their importance and limitations. The normalized difference vegetation index (NDVI), standardized soil moisture index (SSMI), soil moisture percentile (SMP), normalized soil moisture (NSM), standardized precipitation index (SPI), vegetation health index (VHI), and soil moisture deficit index (SMDI) are the most used indices by researchers for drought detection and its assessment. Lastly, this review examines the challenges that need to be handled for the early detection of agricultural drought episodes. It is concluded that a long-term historical record of satellite images and meteorological data is required to calculate drought severity levels and identify drought-prone areas. This review can detect the hidden risks of agricultural drought and offer a theoretical foundation for decision-makers and mitigation agencies. © 2022, The Author(s), under exclusive licence to Società Italiana di Fotogrammetria e Topografia (SIFET).

School of Management UG

Management

Applied Geomatics

A review of agricultural drought assessment with remote sensing data: methods, issues, challenges and opportunities

Lecture Notes in Electrical Engineering

Detecting Diabetic Retinopathy Using Deep Learning Technique with Resnet-50

Indian Journal of Computer Science and Engineering

Software Restructuring at the Function Level Using Entropy Based Fuzzy C-means Clustering for Object Oriented Programming Language

In the past two years 2020 and 2021, the COVID-19 outburst has had a serious effect on human life. The effects and side effects of COVID-19 are being stroked in almost every discipline relevant to survival and development. The healthcare system was in a problematic situation during this tough time in pandemic situation all over the world. One of the many precautions and protections used to break the chain of spreading of this virus is wearing a mask and keeping safe distance. In a network of smart cities where entirely public spaces are monitored by Closed-Circuit Television (CCTV) cameras, we are offering a strategy in this study that restricts the spread of COVID-19 by identifying people not wearing mask. The network alerts the proper authority whenever a person without a mask is discovered. It is believed that our research will help many countries throughout the world stop the spread of this infectious disease. We've examined this on 200 real people in the present, with a 100% success rate. It is also observed that when more than one person in front of CCTV success rate reduced exponentially © 2022 IEEE.

2022 IEEE Pune Section International Conference, PuneCon 2022

Artificial Intelligence and Deep Learning based Face Mask Detection System using Generic Camera

Hepatitis C is a liver disease whose infection is often silent and can lead to fibrosis or cirrhosis if it becomes chronic and goes undetected. It is generally spread through blood-to-blood contact. Hence, it is important to accurately classify blood donors as healthy blood donor or a person having Hepatitis C infection before blood transfusion happens. Nowadays, machine learning has been used in various domains including health care for accurate and fast results. This paper proposes a framework for accurate classification of blood donors using five machine learning algorithms, namely logistic regression, support vector machine, k-nearest neighbours, decision tree, and neural networks. The backward elimination technique is implemented for feature selection to improve the classification accuracy. The experimental results show that k-nearest neighbours perform better with the testing accuracy of 94.3% than other classifiers. © 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

Lecture Notes on Data Engineering and Communications Technologies

A Comparison of Machine Learning Techniques for Categorization of Blood Donors Having Chronic Hepatitis C Infection

Humans can perform an enormous number of actions like running, walking, pushing, and punching, and can perform them in multiple ways. Hence recognizing a human action from a video is a challenging task. In a supervised learning environment, actions are first represented using robust features and then a classifier is trained for classification. The selection of a classifier does affect the performance of human action recognition. This work focuses on the comparison of two structures of the neural network, namely, feed forward neural network and cascade forward neural network, for human action recognition. Histogram of oriented gradients (HOG) and histogram of optical flow (HOF) are used as features for representing the actions. HOG represents the spatial features of the video while HOF gives motion features of the video. The performance of two neural network architectures is compared based on recognition accuracy. Well-known publically available datasets for action and interaction detection are used for testing. It is seen that, for human action recognition applications, feed forward neural network gives better results in terms of higher recognition accuracy than Cascade forward neural network. © 2022 Institute of Advanced Engineering and Science. All rights reserved.

Indonesian Journal of Electrical Engineering and Computer Science

Comparison of feed forward and cascade forward neural networks for human action recognition

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.