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1st International Conference on Smart Sustainable Intelligent Computing and Applications, ICITETM 2020

作者： Gupta, Sangeeta Gautam, Gaurava Deepak Vats, Divyanshu Varshney, Pragya Srivastava, Smriti Department of Instrumentation and Control Engineering Netaji Subhas Institute of Technology Dwarka Sec-3 New Delhi110078 India

Chaotic system parameter modeling is one of the significant topics in the field of non-linear science and therefore has attracted the attention of many researchers in this field. This paper discusses parameter recognition of Fractional Order Financial Chaotic System by employing different metaheuristic algorithms. The implemented algorithms include Artificial bee colony, Grey wolf optimizer, Whale optimization algorithm and Ant colony optimizer. Mean Square Error is employed as objective function to estimate parameters of system under consideration. Results of optimized parameters using the above-mentioned optimization are compared with each other and discussed in the paper. The overall outcome shows that Whale optimization algorithm gives more accurate and robust results with higher convergence rate as compared with other algorithms. © 2020 The Authors. Published by Elsevier B.V.

关键词： Ant colony optimization

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Physical, Mechanical, and Tribological Properties of Industrial Waste Fly Ash Reinforced AA5083 Composites Fabricated by Stir Casting Process

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Journal of Bio- and Tribo-Corrosion 2021年第3期7卷 1-14页

作者： Gangwar, Swati Singh, Ranjeet Kumar Yadav, Prabhat Chand Sahu, Sandeep Department of Mechanical Engineering Madan Mohan Malaviya University of Technology Gorakhpur273016 India Department of Mechanical Engineering Netaji Subhas University of Technology New Delhi110078 India Department of Materials Science and Engineering Indian Institute of Technology Kanpur Kanpur208016 India Directorate of Nanomaterials Defence Materials and Stores Research and Development Establishment Defence Research and Development Organisation Kanpur208013 India Department of Mechanical Engineering University of Southampton Highfield Campus SouthamptonSO17 1BJ United Kingdom

Reinforcement of second phase particle in metal/alloy to develop metal matrix composite is a standard practice to enhance various properties required for commercial applications. In this work, an industrial waste, fly ash, has been used as reinforcement in aluminum alloy AA5083 to improve its physical, mechanical, and tribological properties. The metal matrix composites were fabricated using high temperature vacuum stir casting technique with varying fraction of fly ash (viz. 0, 4, and 8 wt.%). Reinforcement of fly ash in Al alloy improved its hardness from ~ 50 BHN (for initial alloy) to ~ 70 BHN for 8 wt.% fly ash. Meanwhile, impact strength indicated a reverse trend in comparison to that of hardness, with a marginal change in values from 258 to 248 J. Wear behavior of fabricated composites was investigated against EN31 hardened steel disc at different process parameters, viz. fly ash content, sliding velocity, and normal load. Worn surface of samples were examined under scanning electron microscope to find the wear mechanism. It showed severe abrasive grooves along with brittle fracture for lower fraction of fly ash (i.e., 0 and 4 wt.%), while mild abrasion followed by fragmentation of protective tribolayer was observed for the larger fraction of fly ash (i.e., 8 wt.%). The optimum condition among various process parameters for minimum sliding wear was obtained by Taguchi design of experiments. The minimum wear rate was obtained for 8 wt.% fly ash reinforced composite at sliding velocity 1.047 m/s and normal load 15 N. © 2021, The Author(s), under exclusive licence to Springer Nature Switzerland AG.

关键词： Fly ash

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The Effect of Human v/s Synthetic Test Data and Round-Tripping on Assessment of Sentiment Analysis Systems for Bias

The Effect of Human v/s Synthetic Test Data and Round-Trippi...

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IEEE International Conference on Trust, Privacy and Security in Intelligent Systems and Applications (TPS-ISA)

作者： Kausik Lakkaraju Aniket Gupta Biplav Srivastava Marco Valtorta Dezhi Wu AI Institute University of South Carolina Columbia SC USA Dept. of Computer Science Netaji Subhas University of Technology Delhi India Dept. of Computer Science and Engineering University of South Carolina Columbia SC USA Dept. of Integrated Information Technology University of South Carolina Columbia SC USA

Sentiment Analysis Systems (SASs) are data-driven Artificial Intelligence (AI) systems that output polarity and emotional intensity when given a piece of text as input. Like other AIs, SASs are also known to have unstable behavior when subjected to changes in data which can make them problematic to trust out of concerns like bias when AI works with humans and data has protected attributes like gender, race, and age. Recently, an approach was introduced to assess SASs in a blackbox setting without training data or code, and rating them for bias using synthetic English data. We augment it by introducing two human-generated chatbot datasets and also considering a round-trip setting of translating the data from one language to the same through an intermediate language. We find that these settings show SASs performance in a more realistic light. Specifically, we find that rating SASs on the chatbot data showed more bias compared to the synthetic data, and round-tripping using Spanish and Danish as intermediate languages reduces the bias (up to 68% reduction) in human-generated data while, in synthetic data, it takes a surprising turn by increasing the bias! Our findings will help researchers and practitioners refine their SAS testing strategies and foster trust as SASs are considered part of more mission-critical applications for global use.

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RoadWay

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Multimedia Tools and Applications 2022年第4期82卷 4965-4978页

作者： Singal, Gaurav Singhal, Himanshu Kushwaha, Riti Veeramsetty, Venkataramana Badal, Tapas Lamba, Sonu Netaji Subhas University of Technology Delhi India IIIT Vadodara India Bennett University Greater Noida India Center for Artificial intelligence and deep learning Department of electrical and Electronics Engineering S R Engineering college Warangal India Thapar Institute of Engineering and Technology Patiala India

Locomotion is basic to all human needs. Modern-day transport has come a long way but still far away from perfection and all-around safety. Lane Detection is a concept of demarcating lanes on the roads while the vehicle is moving. Lane detection algorithm is crucial aspect in making intelligent driving systems that can be used in autonomous self-driving vehicles, road safety, and accidents prevention systems, testing and analyzing driving skills, etc. Lane detection systems using hand crafted features fails in complex scenarios like adverse weather condition, low illumination, sharp turns and occlusion. Recently, deep learning models have been used remarkable in driving assistance systems and shows a significant improvement in their performance. Although, deep learning based methods has shown significant success in lane detection using hybrid techniques, that includes FCN, CNN and RNN. But, a safe driving assistance system can be used to save lives by avoiding accidents, it is crucial to have a real-time lane detection method. We have proposed a lightweight model that can detect lane with high accuracy and low execution time. The size of model has been kept short to make it hardware deployable and perform in real-time. We have designed and trained a deep Convolutional Neural Network (CNN) model for lane detection since a CNN based model is known to work best for image classification datasets. We have used multiple networks and optimization criteria as hyper-parameters and proposed the one with higher F1 score and execution time in comparison to other methods. The training part is done on Supercomputer NVIDIA DGX V100.

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Designing Digital Content for Children: Understanding children’s capabilities

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Childhood Education 2021年第1期97卷 75-78页

作者： Yadav, Savita Chakraborty, Pinaki Department of Computer Science and Engineering Netaji Subhas University of Technology New Delhi India

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MeFunX: A novel meta-learning-based deep learning architecture to detect fungal infection directly from microscopic images

Franklin Open

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Franklin Open 2024年 6卷

作者： Rawat, Shubhankar Bisht, Bhanvi Bisht, Virender Rawat, Nitin Rawat, Aditya Department of Instrumentation and Control Engineering Netaji Subhas University of Technology India Department of Electronics and Communication Engineering Indira Gandhi Delhi Technical University For Women India Department of Mathematics Al-Falah University India Department of Industrial Management Indian Institute of Management Mumbai India School of Engineering and the Built Environment Birmingham City University United Kingdom

Fungal infections are a growing threat to human health. They can lead to a range of health problems and can be life-threatening. There are many impediments to the traditional diagnosis of fungal infections, such as a diminishing number of clinical mycologists, expensive procedures, high time consumption, and requirements for sensitivity and specificity. Therefore, early diagnosis of fungal infection is critical to effective treatment. In this paper, a novel meta-learning-based deep learning architecture, termed MeFunX, is proposed for the early detection of fungal infections from microscopic images. MeFunX architecture consisted of two convolutional neural network-based models as base learners and XGBoost as the meta-learner. To assess the proposed approach, standard metrics namely, accuracy, precision, recall and f1-score, were used. The fungal disease identification performance of MeFunX was compared with state-of-the-art architectures like VGG16, InceptionV3, ResNet, AlexNet, DenseNet, and EfficientNet. In addition, MeFunX was also benchmarked against its base learners and other meta-learning model with EfficientNet and ResNet as the base learners to demonstrate the effectiveness of the meta-learning architecture. Rigorous experimentation clearly signifies the superior performance of MeFunX, which achieved an overall accuracy of 92.49 % for the early diagnoses of fungal infections in microscopic images. © 2023 The Authors

关键词： Deep learning Early detection Fungal infection Meta-learning XGBoost

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Cosmic observation of a model in the horizon of f(Q,C)-gravity

arXiv

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arXiv 2024年

作者： Shaily Singh, J.K. Tyagi, Mohit Santos, Joao R.L. School of Computer Science Engineering and Technology Bennett University Greater Noida India Department of Mathematics Netaji Subhas University of Technology New Delhi India KIET Group of Institutions Delhi-NCR Ghaziabad India UFCG-Universidade Federal de Campina Grande-Unidade Academica de Fisica PB Campina Grande58429-900 Brazil

We develop a cosmological model in f(Q, C) gravity within the framework of symmetric teleparallel geometry. In addition to the non-metricity scalar Q, our formulation includes the boundary term C, which accounts for its deviation from the standard Levi-Civita Ricci scalar R∗ in the Lagrangian. We derive the field equations for the metric and affine connection, employ them within a cosmological setting, and a vanishing affine connection to derive modified Friedmann equations. We use the latest observational dataset OHD in the redshift range z ∈ [0, 2.36], Pantheon + SH0ES in the redshift range z ∈ (0.01, 2.26), BAO, and the joint datasets OHD + Pantheon + SH0ES and OHD + Pantheon + SH0ES + BAO to constrain the model parameters of our model by employing Markov Chain Monte Carlo (MCMC) method to minimize the χ2 term. Using the constrained free model parameters we examine the behaviour of different physical parameters of the model and also, we notice that the model transits from deceleration to acceleration. Finally, we observe that the model demonstrates an expanding quintessence dark energy model and converges to the ΛCDM in later times. © 2024, CC BY.

关键词： Markov chains

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Centrality Based Geocasting for Opportunistic Networks 33rd

Centrality Based Geocasting for Opportunistic Networks

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33rd International Conference on Advanced Information Networking and Applications, AINA-2019

作者： Singh, Jagdeep Dhurandher, Sanjay K. Woungang, Isaac Takizawa, Makoto CAITFS Division of Information Technology Netaji Subhas Institute of Technology University of Delhi New Delhi India CAITFS Department of Information Technology Netaji Subhas University of Technology New Delhi India Department of Computer Science Ryerson University TorontoON Canada Department of Advanced Sciences Faculty of Science and Engineering Hosei University Tokyo Japan

ISBN: (纸本)9783030150310

Opportunistic networks (OppNets) are a subclass of delay tolerant networks characterized by unstable topology, intermittent connectivity, and no guarantee of the existence of an end-to-end path to carry the messages from source to destination because of node’s mobility. For these reasons, the routing of data in such networks occurs in a store-carry-and-forward approach. Following this approach combined with geocasting as message transmission strategy, this paper proposes a geo-casting protocol for OppNets named Centrality based Geocasting for OPPortunistic networks (CGOPP for short). The proposed scheme combines uni-casting and flooding by first transferring a message to an individual geographic location, then intelligently flood the message to all nodes inside the area by using a degree centrality function. Simulation results are provided, demonstrating the efficiency of the proposed protocol against traditional geo-casting protocols for OppNets, in terms of average latency, delivery ratio, and number of messages forwarded, under varying number of nodes, buffer sizes, and TTL. © 2020, Springer Nature Switzerland AG.

关键词： Floods

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Network analysis of host-pathogen protein interactions in microbe induced cardiovascular diseases

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In Silico Biology 2020年第3-4期14卷 115-133页

作者： Singh, Nirupma Rai, Sneha Bhatnagar, Rakesh Bhatnagar, Sonika Computational and Structural Biology Laboratory Department of Biotechnology Netaji Subhas Institute of Technology Dwarka New Delhi India Computational and Structural Biology Laboratory Department of Biological Sciences and Engineering Netaji Subhas University of Technology Dwarka New Delhi India Amity University Rajasthan Jaipur India

Large-scale visualization and analysis of HPIs involved in microbial CVDs can provide crucial insights into the mechanisms of pathogenicity. The comparison of CVD associated HPIs with the entire set of HPIs can identify the pathways specific to CVDs. Therefore, topological properties of HPI networks in CVDs and all pathogens was studied using Cytoscape3.5.1. Ontology and pathway analysis were done using KOBAS 3.0. HPIs of Papilloma, Herpes, Influenza A virus as well as Yersinia pestis and Bacillus anthracis among bacteria were predominant in the whole (wHPI) and the CVD specific (cHPI) network. The central viral and secretory bacterial proteins were predicted virulent. The central viral proteins had higher number of interactions with host proteins in comparison with bacteria. Major fraction of central and essential host proteins interacts with central viral proteins. Alpha-synuclein, Ubiquitin ribosomal proteins, TATA-box-binding protein, and Polyubiquitin-C B proteins were the top interacting proteins specific to CVDs. Signaling by NGF, Fc epsilon receptor, EGFR and ubiquitin mediated proteolysis were among the top enriched CVD specific pathways. DEXDc and HELICc were enriched host mimicry domains that may help in hijacking of cellular machinery by pathogens. This study provides a system level understanding of cardiac damage in microbe induced CVDs. © 2020 - The authors. Published by IOS Press.

关键词： Central HP-PPIs Immune Network Pathway

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Sentiment analysis using RNN model with LSTM

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International Journal of Intelligent Systems Technologies and Applications 2023年第3期21卷 229-249页

作者： Zhou, Liang Sharma, Arpit Kumar Kanhaiya, Kishan Nandal, Amita Dhaka, Arvind Jiading District Central Hospital Affiliated Shanghai University of Medicine and Health Sciences Shanghai China Center for Medicine Intelligent and Development China Hospital Development Institute Shanghai Jiao Tong University Shanghai China Department of Computer and Communication Engineering Manipal University Jaipur India Netaji Subhas University of Technology Dwarka Sector-3 Dwarka Delhi110078 India

In today’s digital world with a rapid increase in e-commerce portals, the consumers are more oriented towards seeking out online reviews, feedback, or ratings over a product during the online buying process. In this research work, we tried to investigate the relationship between the review ratings and the sentiment of reviews in the form of their polarity. We have tried to predict the sentiments over the given reviews by implementing various machine learning techniques, i.e., logistic regression, support vector machine (SVM), k-nearest neighbours (KNN), and recurrent neural network (RNN). The machine learning techniques predict the sentiments of provided reviews in two scenarios, i.e., scenario 1 – negative (–) and positive (+) and scenario 2 – negative (–), neutral (0) and positive (+). In this paper, we have proposed the architecture for predicting the sentiments with better accuracy over other techniques. Copyright © 2023 Inderscience Enterprises Ltd.

关键词： Sentiment analysis

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