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

Engineering and Technology

World University root node

International Research Journal of Engineering and Technology

Survey paper on Traffic Management System using IOT for Emergency Vehicles

Nowadays, the authenticity of the RFID tags cannot be assured in the supply chain since these can be easily duplicated in the public space. We propose a novel Product Ownership Management System (POMS) of products for anti-counterfeits that can be used in the post supply chain by using the QR code. With the projected POMS, a consumer can reject the buying of counterfeits by scanning a QR code, if the seller does not have their proprietorship. This paper gives an application of the system that will help to overcome the problems related with the presently functioning supply chain management system and runs the mechanism to show the ownership of the products.

International Journal of Computer Applications Technology and Researc

Product Ownership Management System (POMS) in the Post Supply Chain Using BlockChain

This study proposes a Convolutional neural networkmodel trained from scratch to classify and detect the presence ofpneumonia from a collection of chest X-ray image samples. Unlikeother methods that rely solely on transfer learning approaches ortraditional handcrafted techniques to achieve a remarkableclassification performance, we constructed a Convolutional neuralnetwork model from scratch to extract features from a given chest Xrayimage and classify it to determine if a person is infected withpneumonia. This model could help mitigate the reliability andinterpretability challenges often faced when dealing with medicalimagery. Unlike other deep learning classification tasks withsufficient image repository, it is difficult to obtain a large amount ofpneumonia dataset for this classification task; therefore, we deployedseveral data augmentation algorithms to improve the validation andclassification accuracy of the CNN model and achieved remarkablevalidation accuracy. Our classification method uses convolutionalneural networks for classifying the images and early diagnosis ofPneumonia. Our findings yield an accuracy of 85.73%, surpassing thepreviously top scoring accuracy of 78.73%

International Journal of Computer Applications Technology and Research(IJCATR)

Pneumonia Detection using X-Ray Images with Deep Learning

Underwater imaging is one of the most interesting and popular field for researchers and scientists now a day. Generally, underwater images are suffered from poor visibility and color shift because of absorption of different wavelength lights and scattering of water droplets. To address these problems, this paper proposed a novel framework based on fusion principle for enhancing of underwater images. Our strategy consists of four main steps. In first step, we perform color correction using white balancing based on gray world algorithm to remove the unwanted color cast. Then we processed the white balanced image using contrast enhancement techniques i.e. gamma correction of optimal value is 1.5 and sharpening. After second step, both versions of images of contrast enhancement techniques are processed through weight mapping of three categories i.e. luminance, chromatic and saliency. Finally, both versions of images are fused through stationary wavelet based fusion rule to get better quality final enhanced image. Our quantitative and qualitative analysis reveals that our approach outperforms better as compared to state-of-the-art algorithms. Our corroboration also demonstrates that the enhanced images are characterized by improved edge sharpness, global contrast and clear visible dark regions.