M
Mohammad Farukh Hashmi
Researcher at National Institute of Technology, Warangal
Publications - 63
Citations - 736
Mohammad Farukh Hashmi is an academic researcher from National Institute of Technology, Warangal. The author has contributed to research in topics: Computer science & Pattern recognition (psychology). The author has an hindex of 9, co-authored 41 publications receiving 345 citations. Previous affiliations of Mohammad Farukh Hashmi include Mandsaur Institute of Technology & Visvesvaraya National Institute of Technology.
Papers
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Journal ArticleDOI
Efficient Pneumonia Detection in Chest Xray Images Using Deep Transfer Learning.
Mohammad Farukh Hashmi,Satyarth Katiyar,Avinash G. Keskar,Neeraj Dhanraj Bokde,Zong Woo Geem +4 more
TL;DR: A novel approach based on a weighted classifier is introduced, which combines the weighted predictions from the state-of-the-art deep learning models such as ResNet18, Xception, InceptionV3, DenseNet121, and MobileNetV3 in an optimal way and is able to outperform all the individual models.
Proceedings ArticleDOI
Copy move forgery detection using DWT and SIFT features
TL;DR: An algorithm of image-tamper detection based on the Discrete Wavelet Transform i.e. DWT is developed that allows us to detect whether image forgery has occurred or not and also localizes the forgery i.i. it tells us visually where the copy-move forgeries has occurred.
Journal ArticleDOI
Copy-move Image Forgery Detection Using an Efficient and Robust Method Combining Un-decimated Wavelet Transform and Scale Invariant Feature Transform
TL;DR: A unique method for copy-move forgery detection which can sustained various pre-processing attacks using a combination of Dyadic Wavelet Transform (DyWT) and Scale Invariant Feature Transform (SIFT).
Journal ArticleDOI
Investigations of Object Detection in Images/Videos Using Various Deep Learning Techniques and Embedded Platforms—A Comprehensive Review
TL;DR: This paper shows a detailed survey on recent advancements and achievements in object detection using various deep learning techniques, and identifies promising future directions.
Journal ArticleDOI
LoRa-LBO: An Experimental Analysis of LoRa Link Budget Optimization in Custom Build IoT Test Bed for Agriculture 4.0
TL;DR: This study implemented a custom-based sensor node, gateway, and handheld device for real-time transmission of agricultural data to a cloud server and concludes that hybrid range-based localization algorithms are more reliable and scalable for deployment in the agricultural field.