Currently, numerous research works have been proposed for diagnosing leaf diseases using state of the art convolutional neural networks. In this work, we propose a novel architecture called “Delta Tributary Network” that is built by stacking microarchitecture blocks called delta blocks specifically designed for leaf disease classification. These delta blocks utilize a novel channel split algorithm to reduce the number of channels given as input to 3 × 3 convolution layers. Unlike the existing bottleneck design which uses 1 × 1 convolution layers to decrease channel dimension space, Delta tributary network utilizes the a novel channel split algorithm to control the number of input channels togiven to 3 × 3 convolution layers, thereby preventing the linear stack of layers and henceforth avoiding over fitting and vanishing gradient problems. Delta tributary network when tested on plant village dataset gives an accuracy of 96% with just 0.3 million parameters on 133 MFLOP (Million Floating Point Operations) calculations. Further, delta tributary network tested on other bench mark datasets like CIFAR 10, CIFAR 100, MNIST and Fashion MNIST delivers higher accuracy than the other state of the art models with lesser trainable parameters, proving that delta blocks extract efficient potential features.

Abstract. Gleeble study of the quenching and partitioning (Q&P) process has been performed on Domex 960 steel (Fe, 0.08 %C, 1.79 %Mn, 0.23 %Si, 0.184 %Ti, and 0.038 %Al). The effect of different Q&P conditions on microstructure and mechanical properties were investigated. The aim of the process is to produce a fine grained microstructure for better ductility and controlled amounts of different micro-constituents to increase the strength and toughness simultaneously. Three different quenching temperatures, three partitioning temperatures and three partitioning times have been selected to process the 27 specimens by Gleeble® 1500. The specimens were characterized by means of OM, SEM, XRD, hardness and impact tests. It was found that, fine lath martensite with retained austenite is achievable without high amount of Si or Al in the composition although lack of these elements may cause the formation of carbides and decrease the available amount of carbon for partitioning into the austenite. The hardness increases as the quenching temperature is decreased, however, at highest partitioning temperature (640 • C) the hardness increases sharply due to extensive precipitate formation.

Human race has overcome numerous pandemic and epidemics like Spanish flu, SARS, cholera, plague, etc since ages and COVID 19 pandemic is one among them. COVID 19 being the recent one, is much different than the others due to the contribution of AI in diagnosis and prediction of COVID 19 patients. Among the various use cases, one widely used area is medical diagnosis. AI and deep learning based algorithms are exploited enormously by data scientist to support radiologist in early prediction and detection of corona patients. Subsequently, in this work, we utilize wavelet based Convolutional Neural Networks for detecting and recognizing of COVID 19 cases from chest X ray images. Currently, previous works utilize existing CNN architectures for classification of healthy and affected chest X rays, however these networks process the image in a single resolution and loss the potential features present in other resolutions of the input image. Wavelets are known to decompose the image into different spatial resolutions based on the high pass and low pass frequency components and extract valuable features from the affected portion of lung X ray efficiently. Henceforth, in this article, we utilize a hybrid CNN model of wavelet and CNN to diagnose the lung X rays. The proposed CNN model is trained and validated on open source COVID 19 chest X ray images and performs better than existing state of the art CNN models with an accuracy of 99.25%, ROC-AUC value of 1.00 and very less false negative values. Further, the performance of our model is validated by Gradient Class Activation Map visualization technique. The visualization of feature maps clearly indicates that our proposed network has perfectly extracted features from the corona virus affected portion of the lung.

Inspection of brake components is very essential to detect the damaged manufactured parts before it is assembled in any vehicle. Manual inspection of brakes is extremely difficult since most of defects are very minute and cannot be identified by human eyes. Therefore, automatic inspection of manufactured brakes is indispensible to prevent failure of brakes and accidents. Previously, various research articles perform inspection of brake through conventional image processing and traditional image processing algorithms. However, these techniques are capable of identifying a single fault only and are less robust to detecting numerous faults. Further, the existing techniques hardly localize the exact location of faults in the surface of brake. In order to over these drawbacks, in this research we utilize deep learning object detection algorithms namely Single Shot Detector and Faster RCNN to identify and localize the exact location of fault on the brake surface. Furthermore, the proposed system is capable to detect different types of faults in a single algorithm and is robust to brake’s material surface, environmental and lightening factors. The deep learning algorithms are trained using transfer learning on custom collected dataset. The proposed algorithms deliver an accuracy of 95.64% and mAP of 73.2% on cylindrical grey shade brakes.

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