Computer Science and Engineering

Shiv Nadar University is a comprehensive, multidisciplinary, research-focused, and student-centric University offering a full range of academic programs at the undergraduate, postgraduate and doctoral level. With state-of-the-art infrastructure, the University comprises of academic wings, sophisticated labs, international standard sports facilities, amphitheaters, auditorium, conference rooms and smart classrooms. The University&rsquo;s goal is to become internationally recognized for the quality of its research and creative endeavors and their applicability to improving quality of life, generating new insights and expanding the boundaries of human knowledge creativity. Committed to excellence in teaching, research and service, the University aims to serve the higher education needs of India and the world beyond.

Shiv Nadar University

The crop diseases can’t detected accurately by only analysing separate disease basis. Only with the help of making comprehensive analysis framework, users can get the predictions of most expected diseases. In this research, IOT and machine learning based technique capable of processing acquisition, analysis and detection of crop health information in the same platform is introduced. The proposed system supports distinguished services by monitoring crop and also managed its data, devices and models. This system also supports data sharing and communication with the help of IOT using unmanned aerial vehicle (UAV) and maintains high communication standards even in bad communication environment. Therefore, IOT and machine learning ensures the high accuracy of disease prediction in crop. The proposed integrated system is capable of detecting health of crop through analysis of multi-spectral images captured through the IOT associated UAV. The various machine learning is also applied to test the performance of our system and compared with the existing disease detection methods.&nbsp;

World University root node

(SoE) School of Engineering

Journal of Scientific and Industrial Research

Detecting Crop Health using Machine Learning Techniques in Smart Agriculture System

Early action prediction in video is a challenging task where the action of a human performer is expected to be predicted using only the initial few frames. We propose a novel technique for action prediction based on Deep Reinforcement learning, employing a Deep Q-Network (DQN) and the ResNext as the basic CNN architecture. The proposed DQN can predict the actions in videos from features extracted from the first few frames of the video, and the basic CNN model is adjusted by tuning the hyperparameters of the CNN network. The ResNext model is adjusted based on the reward provided by the DQN, and the hyperparameters are updated to predict actions. The agent's stopping criteria is higher or equal to the validation accuracy value. The DQN is rewarded based on the sequential input frames and the transition of action states (i.e., prediction of action class for an incremental 10 percent of the video). The visual features extracted from the first 10 percent of the video is forwarded to the next 10 percent of the video for each action state. The proposed method is tested on the UCF101 dataset and has outperformed the state-of-the-art in action prediction.

Early Prediction of Human Action by Deep Reinforcement Learning

Social Media these days has become the prime medium for religious debates. While some people utilise the medium wisely to talk about the rational thoughts associated with different religions, others might use it to marginalize or dehumanize certain religions. Therefore, there is a dire need to analyse content having a religious context so that appropriate actions can be taken by the social media or blogging platforms against people attempting to propagate unhealthy religious messages. Therefore, in this paper, a multi-module machine learning based algorithm is proposed to analyse the religious context in online social media content. To do that, data has been gathered from popular social media sites Twitter. A methodology is then developed to identify tweets that talk about religion. Once, it is detected if the tweet is religion themed or not, sentiment analysis is performed and then the nature of the tweets (offensive or not) is detected to get a deeper understanding of the content that is being posted online. Based on the evaluation of various classification models, it is found that Naive Bayes performs the best at detecting a religious theme in tweets, achieving an accuracy of 84%. On the other hand, XGBoost performs the best at detection of offensive content in the domain of religion. Our results identify online religious offensive content and offer a broader understanding of the phenomenon, providing directions for prevention and detection approaches.&nbsp;

Machine Learning and Data Mining in Pattern Recognition

Monitoring Social Media Content for Religious Context

Today, everything is progressing to ‘Smart' to enhance the environment via technological progress including IoT, big data, AI, ICT, and so on. But, in this whole process, the sustainability of being ‘smart' is implemented by the cloud-based technology, which also acts as an engine. Smart society is another live example of this era that makes potential use of digital technology and sensor devices to improve people's lives through the internet. It also incorporates cloud computing, which significantly benefits them by offering a sustainable environment to access the computing power at large scale, which they could not previously access due to lack of resources. This chapter provide a broad overview of smart society that covers the scope and services, technological pillars, features necessities to be titled as ‘smart', and a sustainable development. Also, it broadly covers the role of cloud computing, related technologies, and generic architecture for the sustainability of smart societies followed by applications with a case study and challenges.

Integration and Implementation of the Internet of Things Through Cloud Computing

Sustainability of Cloud Based Smart Society

In many existing solutions of image-to-image rendering problems, the only focus is to find the closest output of the Generative Adversarial Network (GAN). In this research article, authors propose a generative adversarial network, a solution to pixel-to-pixel rendering problems and reduced the loss function to the maximum under all interactions. For achieving the best result, we have considered the mean square loss function in the generator and binary cross for the discriminator. Our proposed model deals with not only images but also read sketches where the edges are not sharp too. We have used a facade dataset to test our proposed model.

IT in Industry

CONDITIONAL GANS AS A SOLUTION TO IMAGE-TO-IMAGE RENDERING PROBLEMS

In this chapter, we propose a technique for the classification of yoga poses/asanas by learning the 3D landmark points in human poses obtained from a single image. We apply an encoder architecture followed by a regression layer to estimate pose parameters like shape, gesture, and camera position, which are later mapped to 3D landmark points by the SMPL (Skinned Multi-Person Linear) model. The 3D landmark points of each image are the features used for the classification of poses. We experiment with different classification models, including k-nearest neighbors (kNN), support vector machine (SVM), and some popular deep neural networks such as AlexNet, VGGNet, and ResNet. Since this is the first attempt to classify yoga asanas, no dataset is available in the literature. We propose an annotated dataset containing images of yoga poses and validate the proposed method on the newly introduced dataset.

Digital Techniques for Heritage Presentation and Preservation

Classification of Yoga Asanas from a Single Image by Learning the 3D View of Human Poses

Multi-view data contains a set of features representing different perspectives associated with the same data and this phenomenon can be commonly observed in real-world applications. Multi-view clustering in terms of text and image data faces substantial challenges such as Structure-preserving and Sparsity. Existing methods do not conserve the structure of data space and the recent improvements have earmarked only the local layout. Preserving the local structure of data space is not sufficient to handle sparsity in these data. In this paper, we propose a novel clustering approach, called Proximity-based Multi-View Non-negative Matrix Factorization (PMVNMF), which utilizes both the local and global structure of data space conjointly to handle sparsity in real-world multimedia (text and image) data. For each view, the 1-step and 2-step transition probability matrices as the first-order and second-order proximity matrices are constructed to uncover their respective latent local and global geometric structures. Then, view-specific proximity matrices as an integration of the above two types of proximity matrices are constructed. Eventually, Non-negative Matrix Factorization (NMF) is explored via graph regularization and consensus regularization, to consider the obtained integrated graph structures as well as to disclose the indistinct common structure shared by all representations. The algorithm can capture elementary structure of data space and is robust to sparse data. We conduct experiments on six real-world datasets including two text and four image datasets; and compare the performance of the proposed algorithm with eight baseline approaches. Six evaluation metrics including accuracy, f-score, precision, recall, NMI, and entropy are employed to evaluate the performance of algorithm. The results show the outperformance of proposed algorithm over baselines. © 2021 Elsevier Ltd

Information Processing and Management

A novel multi-view clustering approach via proximity-based factorization targeting structural maintenance and sparsity challenges for text and image categorization

COVID-19 has emerged as a global crisis with unprecedented socio-economic challenges, jeopardizing our lives and livelihoods for years to come. The unavailability of vaccines for COVID-19 has rendered rapid testing of the population instrumental in order to contain the exponential rise in cases of infection. Shortage of RT-PCR test kits and delays in obtaining test results calls for alternative methods of rapid and reliable diagnosis. In this article, we propose a novel deep learning-based solution using chest X-rays which can help in rapid triaging of COVID-19 patients. The proposed solution uses image enhancement, image segmentation, and employs a modified stacked ensemble model consisting of four CNN base-learners along with Naive Bayes as meta-learner to classify chest X-rays into three classes viz. COVID-19, pneumonia, and normal. An effective pruning strategy as introduced in the proposed framework results in increased model performance, generalizability, and decreased model complexity. We incorporate explainability in our article by using Grad-CAM visualization in order to establish trust in the medical AI system. Furthermore, we evaluate multiple state-of-the-art GAN architectures and their ability to generate realistic synthetic samples of COVID-19 chest X-rays to deal with limited numbers of training samples. The proposed solution significantly outperforms existing methods, with 98.67\% accuracy, 0.98&nbsp;Kappa score, and F-1 scores of 100, 98, and 98 for COVID-19, normal, and pneumonia classes, respectively, on standard datasets. The proposed solution can be used as one element of patient evaluation along with gold-standard clinical and laboratory testing. © 2021, The Author(s), under exclusive licence to Springer-Verlag London Ltd. part of Springer Nature.

Fulltext

Neural Computing and Applications

COVIDScreen: explainable deep learning framework for differential diagnosis of COVID-19 using chest X-rays

Recognizing human actions is an active research area, where pose of the performer is an important cue for recognition. However, applying the 3D landmark points of the performer in recognizing action, is relatively less explored area of research due to the challenge involved in the process of extracting 3D landmark points from single view of the performers. With the recent advancements in the area of 3D landmark point detection, exploiting the landmark points in recognizing human action, is a good idea. We propose a technique for Human Action Recognition by learning the 3D landmark points of human pose, obtained from single image. We apply an autoencoder architecture followed by a regression layer to estimate the pose parameters like shape, gesture and camera position, which are later mapped to the 3D landmark points by Skinned Multi Person Linear Model (SMPL model). The proposed method is a novel attempt to apply a CNN based 3D pose reconstruction model (autoencoder) for recognizing action. Further, instead of using the autoencoder as a classifier to classify to 3D poses, we replace the decoder part by a regressor to obtain the landmark points, which are then fed into a classifier. The 3D landmark points of the human performer(s) at each frame, are fed into a neural network classifier as features for recognizing action. © 2021, Springer Nature Singapore Pte Ltd.

Communications in Computer and Information Science

Human Action Recognition from 3D Landmark Points of the Performer

The success of deep learning-based techniques in solving various computer vision problems motivated the researchers to apply deep learning to predict the optical flow of a video in the next frame. However, the problem of predicting the motion of an object in the next few frames remains an unsolved and less explored problem. Given a sequence of frames, predicting the motion in the next few frames of the video becomes difficult in cases where the displacement of optical flow vector across frames is large. Traditional CNNs often fail to learn the dynamics of the objects across frames in case of large displacements of objects in consecutive frames. In this paper, we present an efficient CNN based on the concept of feature pyramid for extracting the spatial features from a few consecutive frames. The spatial features extracted from consecutive frames by a modified PWC-Net architecture are fed into a bidirectional LSTM for obtaining the temporal features. The proposed spatiotemporal feature pyramid is able to capture the abrupt motion of the moving objects in video, especially when displacement of the object is large across the consecutive frames. Further, the proposed spatiotemporal pyramidal feature can effectively predict the optical flow in next few frames, instead of predicting only the next frame. The proposed method of predicting optical flow outperforms the state of the art when applied on challenging datasets such as “MPI Sintel Final Pass,” “Monkaa” and “Flying Chairs” where abrupt and large displacement of the moving objects in consecutive frames is the main challenge. © 2020, Springer-Verlag GmbH Germany, part of Springer Nature.