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

Garbage disposal is a very important task in a healthy and green environment. With spreading awareness among the citizens of India regarding the importance of a clean environment to decrease the consumption of natural resources and garbage disposal, the recycling industry is booming. The quantity of generated trash in day-to-day life is affecting land, water, and air which causes a serious threat to the aquatic species and their surroundings and ultimately to humans if not managed properly. Conventional garbage disposal systems are on the rise and need accurate and efficient segmentation and recognition mechanisms. This demand coincides with the increase of computational capabilities of modern computer architectures and more effective algorithms for image recognition. To deal with this problem the garbage classification process is automated by building an image classifier using a convolutional neural network (CNN) and thereby decrease the time for the waste segregation and make it cost-effective. The idea behind making the process automated is to decrease human intervention and make this waste segregation process more productive. In this work, three different models are being tested for higher accuracy: Simple CNN, ResNet50, and VGG16 trained on various datasets of images, these are used to extract features from images and feed them into a classifier for dump/trash classification. The trained models are then fed to a mobile application that captures pictures in the real-time using camera. The experimental results conclude the performance of VGG16 using Transfer Learning being significantly higher than all other models for the purpose of Trash Classification and the performance of Simple CNN being better for Dump Classification. © 2021 IEEE.

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School of Computer Engineering & Technology

Engineering and Technology

2021 6th International Conference on Recent Trends on Electronics, Information, Communication and Technology, RTEICT 2021

Garbage Classifying Application Using Deep Learning Techniques

Now a day’s fingerprint-based authentication system is becoming very popular because of its applications. The fingerprint of an individual is a unique and persistent feature that can be used for identification and authentication. There are several different methods that has been implemented by different researchers for the fingerprint recognition. In this paper, we examine writing of various systems and angles utilized for unique finger impression acknowledgment.

National Journal of Android and IOS Applications and Testing

Methodological Analysis for Different Fingerprint Matching Techniques

In this paper, we present a system that automates payment using face recognition. It uses eye blink detection as its prime method. The system keeps a track on the duration of userâ€™s eye blink. Once the blink is detected, the system captures the face from the frame, which can be used as a template for face recognition. Once the face has been recognized, the money is debited from his account (wallet) . The system is more secure and convenient for users than the traditional methods of authentication. It can effectively block spoofing attempts using other peopleâ€™s still images or pre-recorded videos as there are no offline templates required for the functioning of the system.

International Journal of Technical Research and Applications

AUTOMATIC PAYMENT USING FACERECOGNITION

Retinal diseases like glaucoma, diabetic retinopathy, age-related macular degeneration, stargardt’s disease, retinopathy of prematurity that can lead to blindness manifest as artifacts in the retinal image. The term Diabetic retinopathy is the serious issue that is caused by the diabetes, which affects the eyes and results in blindness. It’s due to damage of the arteries and veins that are present in the fundus of the eye (retina) that is the origin of light sensitive tissues. Although DR can be prevalent now days, its prevention remains challenging. Visual analysis of the funds and consideration of color photographs Ophthalmologists directly examine the existence and severity of DR. This process is expensive as well as time consuming as there are huge number of diabetes patients worldwide. The automatic DR system is expanded to predict various related diseases that are analyzed.

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International Journal for Research in Applied Science & Engineering Technology

Detection of Retinal Blood Vessels for Disease Prediction using Machine Learning

Web Heat Maps are used to identify the click patterns and activities visited by the users of the website. Using heat maps, one can make a manual decision based on the user's click activity. This paper proposes a framework using TensorFlow to identify and detect the users click activity in real time. Tensor Flow also suggest or take business decisions predicted through users clicks. This paper models Tensor Flow's machine learning library to take automated decisions like placement of suitable products, placement of advertisements and others based on the highest clicks recorded by the users. The results predict that the future businesses like e-commerce, fashion and retail industry can benefit more if this framework is deployed in such applications. © 2018 IEEE.

Proceedings of the 2018 International Conference on Recent Trends in Advanced Computing, ICRTAC-CPS 2018

TensorFlow based website click through Rate (CTR) prediction using heat maps

In this paper, we have done an assessment to evaluate the efficiency of different feature descriptor algorithms like Local Binary Pattern (LBP), Scale Invariant Feature Transform (SIFT), Speeded-Up Robust Features (SURF) and Oriented FAST and Rotated BRIEF (ORB) on gender classification problem. The evaluation is done with and without using the feature descriptors and the results are compared to get a better understanding of the algorithms. Experiments are carried out on CAS-PEAL FRONTAL database. © 2019 IEEE.

Proceedings - 2019 5th International Conference on Computing, Communication Control and Automation, ICCUBEA 2019

Evaluation of different feature descriptor algorithms on classification task

In recent years, deep artificial neural networks is swiftly emerging in pattern recognition and machine learning. Convolutional Neural Networks (CNNs) is one of the most remarkable forms of Artificial Neural Network (ANN) architecture. This paper focuses on Convolutional neural networks and its visualization technique. Compared to other techniques Convolutional neural networks (CNNs) are mostly used in image and pattern recognition problems since they have a number of advantages compared to other techniques. This paper emphasizes on the fundamental of CNNs, functioning and description of the different layers used in Convolutional neural networks.