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

World University root node

Engineering and Technology

4th International Conference on Inventive Research in Computing Applications, ICIRCA 2022 - Proceedings

Impact of the Intelligent Control System in Assisting Humans Regarding Complex Information Processing

Documents containing biomedical information come in a variety of formats. Clinical, research doctors and researchers can use biomedical literature to assess the most recent achievements in a field of study to generate and validate new theories, conduct experiments, and analyze the results. Medical records are difficult to analyze since they are normally presented in plain text, have a very particular technical language, and are almost always unstructured. Due to the ongoing increase of online text resources Document summarization offers a plethora of applications in data extraction and web search. We have done a survey of automatic text summarization techniques. Considering the existing research gaps, we have proposed a model which utilizes BERT and Group Average Linkage clustering on articles taken from PubMed. This work would be helpful for people in the biomedical domain by summarizing huge biomedical texts. It would provide a brief summary by condensing the information in a nutshell, thus saving time. © 2022 IEEE.

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

Extensive Study of Automatic Text Summarization on Biomedical Texts

Blockchain is incredibly emerging technologies in today's world. Now a days major issue faced by educational system is regarding fake documents, certificates. There are so many cases registered for the duplication of documents and all those cases go unnoticed. In this blockchain based system information stored is immutable it means it cannot be manipulated. Even if some attacker tries to manipulate the information present in the blockchain then hash value of the block gets changed. So, for this attacker has to recalculate the hash for the all the blocks in network, which is next to impossible. Thus, documents storage in blockchain provides high level security for the same. In this paper we have included various existing approaches for document security using blockchain platform and its benefits and limitations are also included so that one can get proper idea to rework or enhance the existing work with more advance technique. Also, we have highlighted problem of document storage and security enhancement issue in blockchain network. © 2022 IEEE.

2022 International Conference for Advancement in Technology, ICONAT 2022

Techniques of Securing Educational Document using Blockchain and IPFS based System: A Review

One of the most significant and challenging areas of computer vision is object recognition and tracking, which is extensively utilised in many industries including health care monitoring, autonomous driving, anomaly detection, etc. The tracking of moving objects in videos is actively researched over the past two decades due to its practical applications in many fields such as event analysis, human-computer interaction, crowd analysis, video surveillance, behaviour analysis, etc. The effectiveness of object trackers and detectors has significantly increased with the rapid advancement of deep learning (DL) networks and GPU processing capability. New methods have been presented for object recognition and tracking in video as a result of extensive study in this field. This article addressed the several processes of object tracking in video sequences: object detection, object classification, and object tracking, in order to comprehensively comprehend the key advancements in the object detection and tracking pipeline. Additionally, we thoroughly examine the various approaches available for object recognition, categorization, and tracking. © 2022 IEEE.

Proceedings - International Conference on Augmented Intelligence and Sustainable Systems, ICAISS 2022

DeepSORT: Real Time &amp; Multi-Object Detection and Tracking with YOLO and TensorFlow

In the era of Big Data Analytics, information dissemination, data integrity, and identifying unique records from large pool of data poses a big challenge for analysts in entity matching and linking scenarios. Data ingestion from multiple sources of same real-world entity exhibits several data quality issues like redundancy, incorrectness, variations, etc. Also, there are data input errors like typographical/spelling mistakes as well as missing fields. In order to achieve entity resolution, uniqueness and eradicate data redundancy and improve the data quality issues, deduplication is the solution. India being a multi-lingual and multi-cultural country with vast demographic variations, there is a need to develop India-centric model for handling deduplication on various Indian structured data held by various authorities. This research proposes a novel approach catering to India-centric demographic variations, region-specific naming conventions, address standardization using a highly customizable and scalable deep learning approach, by customizing DeepMatcher algorithm along with a synthetic data generation tool reckoning Indian variations of names and addresses in a region-specific manner. © 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

Lecture Notes in Networks and Systems

A Novel Approach of Deduplication on Indian Demographic Variation for Large Structured Data

The research of brain tumor classification by hand is a time-consuming operation with the potential for human mistakes. An accurate and automatic brain tumor classification technique is provided in this approach. To segment the brain part first, a thresholding approach is performed, followed by a morphological operation. Because the brain Magnetic Resonance Imaging(MRI) dataset is limited, the Convolutional Neural Network's (CNN) 'VGG19' transfer learning network is used. With little weights and photos of benign, glioma, and meningioma, VGG19 classification layer is substituted by the softmax layer. After the model is created, it is saved and using Flask, a WebApp is created that will help us to detect the brain tumor using Hybrid Multi-Cloud and Machine Learning Operations(MLOps) tools. Identity and Access Management (IAM) is used to keep the data secure. This end-to-end system can be used for object detection applications in hospitals and local medical clinics. © 2022 IEEE.

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

Brain Tumor Detection using MLops and Hybrid Multi-Cloud

Many people developed serious respiratory issues as a result of the Coronavirus illness outbreak, which was recognised as a global health threat. Because the virus initially targets the lungs in the human body, chest x-ray imaging features were thought to be effective for early identification of the illness.The chest x-ray data of 141 infected patients and 142 non infected patients (112 images in training dataset and 30 in test dataset each) was utilised to develop a CNN (Convolutional Neural-Network) model for early disease identification in this study, which was based on open data from Cohen J. Morrison P. Dao L., 2020. The model was trained with 100 epochs of both infected and non-infected people's chest x-ray pictures, resulting in a final accuracy of 0.97. In order to use this model as a professional diagnosis element, it is highly recommended it be improved with more images and the model can be restructured to get a better accuracy. © The Electrochemical Society

ECS Transactions

CoviDet: COVID-19 Case Detection Utilising Deep Neural Networks Using X-Ray Images

Automatically describing the information of an image using properly constructed sentences is a tricky task in any language. However, it has the potential to have a significant effect by enabling visually challenged individuals to better understand their surroundings. This paper proposes an image captioning system that generates detailed captions and extracts text from an image, if any, and uses it as a part of the caption to provide a more precise description of the image. To extract the image features, the proposed model uses Convolutional Neural Networks (CNNs) followed by Long Short-Term Memory (LSTM) that generates corresponding sentences based on the learned image features. Further, using the text extraction module, the extracted text (if any) is included in the image description and the captions are presented in audio form. Publicly available benchmark datasets for image captioning like MS COCO, Flickr-8k, Flickr-30k have a variety of images, but they hardly have images that contain textual information. These datasets are not sufficient for the proposed model and this has resulted in the creation of a new image caption dataset that contains images with textual content. With the newly created dataset, comparative analysis of the experimental results is performed on the proposed model and the existing pre-trained model. The obtained experimental results show that the proposed model is equally effective as the existing one in subtitle image captioning models and provides more insights about the image by performing text extraction.

Engineering, Technology & Applied Science Research

D-CNN: A New model for Generating Image Captions with Text Extraction Using Deep Learning for Visually Challenged Individuals

Data can be turned into easily understandable format and relevant business information with data visualization. Nowadays, there is more use of data visualization in business intelligence. Datasets can be visualized in a variety of visual insights using a different dynamic or interactive approach. This paper focuses on Microsoft Power BI operations and interactive display of educational institution databases with multiple modules. It uses live dashboards, interactive reports, charts, graphs, etc. to help users build significant business insight quickly and effectively. Selecting categories, values and fields make visualizations more accurate and gives desired results. Features like slicer and Q&A help to analyze, find out results quickly on larger datasets. Applying filters on visuals based on data features and set range to get insights from large datasets makes it easier. To determine relationship between features, OLAP visuals are created and compared to Power BI based on field values. © 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

Effective Use of Visualization Techniques for Educational Institutional Data Analysis

In healthcare machine learning is used mainly for disease diagnosis or acute condition detection based on patient data analysis. In the proposed work diabetic patient dataset analysis is done for hypoglycemia detection which means the lowering of blood glucose level (BGL). Often in healthcare it is observed that the dataset is imbalanced. Therefore, an Ensemble Approach using imbalanced dataset techniques synthetic minority over-sampling technique and adaptive synthetic oversampling methods with different evaluation methods like train-test, K-fold, stratified K-Fold and repeat traintest were used. This ensemble approach was implemented on diabetic dataset using K-nearest neighbor (KNN), support vector machine (SVM), random forest (RF), Naïve Bayes (NB) and logistic regression classifiers with average Stacking-C method thereafter to conclude. Comparative analysis was done using three different considerations. The results showed that KNN and random forest gives more stable metric values both on balanced and imbalanced dataset. The confusion matrix consideration concluded that KNN and random forest were found to be better with least false negative and maximum true positive count. But if average train and test time is taken into consideration then Naïve Bayes and random forest had least average traintest time. Thus, the three different considerations concluded that the proposed ensemble approach gives better clarity for different classifier implementation using machine learning.