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2021 IEEE International Conference on Robotics, Automation and Artificial Intelligence, RAAI 2021

作者： Patel, Shaswat Lohakare, Maithili Prajapati, Samyak Singh, Shaanya Patel, Nancy Department of Computer Science and Engineering Netaji Subhas Institute of Technology New Delhi India Department of Computer Science and Engineering Pandit Deendayal Energy University Gandhinagar India Department of Computer Science and Engineering National Institute of Technology Delhi New Delhi India Bachelor of Medicine Bachelor of Surgery AMC MET Medical College Ahmedabad India

ISBN: (纸本)9781665425322

Patients with long-standing diabetes often fall prey to Diabetic Retinopathy (DR) resulting in changes in the retina of the human eye, which may lead to loss of vision in extreme cases. The aim of this study is two-fold: (a) create deep learning models that were trained to grade degraded retinal fundus images and (b) to create a browser-based application that will aid in diagnostic procedures by highlighting the key features of the fundus image. In this research work, we have emulated the images plagued by distortions by degrading the images based on multiple different combinations of Light Transmission Disturbance, Image Blurring and insertion of Retinal Artifacts. InceptionV3, ResNet-50 and InceptionResNetV2 were trained and used to classify retinal fundus images based on their severity level and then further used in the creation of a browser-based application, which implements the Integration Gradient (IG) Attribution Mask on the input image and demonstrates the predictions made by the model and the probability associated with each class. ©2021 IEEE

关键词： Grading

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Multi-attributed and structured text-to-face synthesis

arXiv

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arXiv 2021年

作者： Wadhawan, Rohan Drall, Tanuj Singh, Shubham Chakraverty, Shampa Department of Computer Science and Engineering Netaji Subhas Institute of Technology New Delhi India

Generative Adversarial Networks (GANs) have revolutionized image synthesis through many applications like face generation, photograph editing, and image super-resolution. Image synthesis using GANs has predominantly been uni-modal, with few approaches that can synthesize images from text or other data modes. Text-to-image synthesis, especially text-to-face synthesis, has promising use cases of robust face-generation from eye witness accounts and augmentation of the reading experience with visual cues. However, only a couple of datasets provide consolidated face data and textual descriptions for text-to-face synthesis. Moreover, these textual annotations are less extensive and descriptive, which reduces the diversity of faces generated from it. This paper empirically proves that increasing the number of facial attributes in each textual description helps GANs generate more diverse and real-looking faces. To prove this, we propose a new methodology that focuses on using structured textual descriptions. We also consolidate a Multi-Attributed and Structured Text-to-face (MAST) dataset consisting of high-quality images with structured textual annotations and make it available to researchers to experiment and build upon. Lastly, we report benchmark Fréchet's Inception Distance (FID), Facial Semantic Similarity (FSS), and Facial Semantic Distance (FSD) scores for the MAST dataset. Copyright © 2021, The Authors. All rights reserved.

关键词： Generative adversarial networks

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

arXiv

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arXiv 2024年

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

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. © 2024, CC BY-NC-ND.

关键词： Sentiment analysis

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A Novel Computational Model for Hardware-Level Security of Cloud Databases in Public Clouds

A Novel Computational Model for Hardware-Level Security of C...

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International Conference on Information Systems and computer Networks (ISCON)

作者： Arun Kumar Yadav Rajendra Kumar Bharti Ram Shringar Raw VMSB-Uttarakhand Technical University Dehradun Uttarakhand India Department of CSA ITM University Gwalior MP India Department of Computer Science & Engineering Bipin Tripathi Kumaon Institute of Technology Dwarahat Uttarakhand India Department of Computer Science & Engineering Netaji Subhas University of Technology East Campus Delhi India

Today, all cloud-based social media applications are data-intensive, and the large amounts of data they generate are managed on the public cloud, which compromises user data security and privacy. Many government and nongovernment organizations that manage users’ sensitive and personal data do not choose to use the public cloud to administer their databases because of the numerous security risks it has. However, these organizations are jeopardizing the performance of the application by handling the user’s data on the private cloud. Without jeopardizing user data security and privacy or application performance, a security solution will encourage these organizations to manage their databases on the public cloud. This paper presents an L-PNR computational model for the hardware-level security of cloud databases in a multi-tenant public cloud environment. The L-PNR model’s performance has been simulated and evaluated using VMware vCenter Simulator. According to experimental findings, the proposed model outperforms the current models THR-RS, LR-MU, DDPA, and DSRBACA in terms of performance and security. The suggested L-PNR model may be used by cloud service providers (CSPs) to offer data security over the public cloud, where organizations can affordably manage their sensitive data.

关键词： Cloud computing Data privacy Databases Social networking (online) Computational modeling Data security Government

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Stability analysis of a dark energy model in Rastall gravity

arXiv

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arXiv 2024年

作者： Shaily Singh, Akanksha Singh, J.K. Hussain, Saddam Myrzakulov, Ratbay Department of Mathematics Netaji Subhas University of Technology New Delhi110 078 India School of Computer Science Engineering and Technology Bennett University Greater Noida India Department of Physics Indian Institute of Technology Uttar Pradesh Kanpur208016 India Ratbay Myrzakulov Eurasian International Centre for Theoretical Physics Astana Kazakhstan

We study a cosmological model in Rastall’s theory of gravity in the framework of the flat FLRW metric. We formulate the value of the Hubble parameter, which contains two model parameters, α and j. Employing the Markov Chain Monte Carlo (MCMC) sampling technique, we determine the values of these model parameters along with their uncertainties. Moreover, we derive the equation of state (EoS) parameter, which converges around the quintessence region. We perform a dynamical system analysis using the linearization technique to validate the results independently. Also, we discuss various physical attributes of the model, highlighting the transition to acceleration and the violation of the strong energy condition (SEC) in the late stages of evolution. In conclusion, our model mimics the behavior of a dark matter fluid during the past epoch and transitions into a quintessence dark energy model in the future epoch. © 2024, CC BY.

关键词： Equations of state

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A modified incremental conductance maximum power point technique for standalone PV system 7

A modified incremental conductance maximum power point techn...

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7th International Conference on Signal Processing and Integrated Networks, SPIN 2020

作者： Chauhan, Urvashi Rani, Asha Singh, Vijander Kumar, Bhavnesh University of Delhi Division of Instrumentation and Control Engineering Netaji Subhas Institute of Technology New Delhi110078 India

ISBN: (纸本)9781728154756

The main aim of this work is to design an effective MPPT algorithm so that the power utilization efficiency of PV array is enhanced. Therefore improved incremental conductance MPPT controller i.e. INC-PID MPPT is proposed which utilizes the concept of traditional incremental conductance algorithm and proportion-integral-derivative controller. The conventional MPPT methods i.e. incremental conductance and perturb & observation have simple structure and may be implemented easily. However these algorithms suffer from low tracking efficiency and steady state oscillations. The proposed technique overcomes these drawbacks and also preserves the simple structure. The controller parameters are optimized using grey wolf optimization (GWO). The proposed algorithm is applied to multi crystal solar panel, KC130GT by M/S Kyocera, and power tracking capability of INC-PID MPPT is evaluated for various patterns of irradiance. The suggested technique proves to be superior than existing techniques in terms of settling time, undershoot and rise time. © 2020 IEEE.

关键词： Controllers

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A robust approach to detect video-based attacks to enhance security

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International Journal of Innovative Computing and Applications 2019年第3-4期10卷 175-184页

作者： Arora, Shefali Bhatia, M.P.S. Department of Computer Engineering Netaji Subhas Institute of Technology Delhi India

Face authentication has become widespread on smart devices and in various applications these days. Real time detection of human faces in video surveillance systems is challenging due to variations in expression and background conditions. Thus, precise detection of spoofed faces is important to make such security systems robust against potential attacks. Several deep learning-based techniques involving the use of convolutional neural networks have proven to be excellent in detection of spoofed faces. In this paper, we have proposed the combined use of spatial and temporal information from facial images using CNN and long short-term memory networks. We have tested the approach on Idiap Replay Attack benchmark and compared the results with the application of pre-trained models like InceptionV3, VGGNet and ResNet models to detect replay attacks during video surveillance. Our approach proves to be robust and more efficient for detection of security breaches in real time situations. © 2019 Inderscience Enterprises Ltd.

关键词： Security systems

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Mental arithmetic task load recognition using EEG signal and Bayesian optimized K-nearest neighbor

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International Journal of Information technology (Singapore) 2021年第6期13卷 2363-2369页

作者： Sharma, Lakhan Dev Chhabra, Himanshu Chauhan, Urvashi Saraswat, Ritesh Kumar Sunkaria, Ramesh Kumar School of Electronics Engineering VIT-AP University Amaravati India Electronics and Communication Engineering M.L.V. Textile and Engineering College Bhilwara India Instrumentation and Control Engineering Division Netaji Subhas University of Technology Delhi India Electronics and Communication Engineering Dr. B. R. Ambedkar National Institute of Technology Jalandhar India

Cognitive load recognition during mental arithmetic activity facilitates to observe and identify the brain’s response towards stress stimulus. As a result, an efficient mental load characterization approach using electroencephalogram (EEG) signal and Bayesian optimized K-Nearest Neighbor (BO-KNN) has been proposed in this work. The study has been conducted on a recorded EEG dataset of 30 healthy subjects who were exposed to an arithmetic questioner. To obtain artifacts free EEG signal, the Savitzky–Golay filtering approach has been utilized. Further, the decomposition of the extracted EEG signal has been carried out using stationary wavelength transform. In this work, the entropy based feature extraction has been performed followed by F-score based feature selection. Top 40 features having the highest precedence have been used for classification using BO-KNN. The rigorous experimental analysis has been performed to analyze the effectiveness of the proposed method over other state-of-the-art methods and it shows that the classification accuracy is substantially improved. © 2021, Bharati Vidyapeeth's institute of computer Applications and Management.

关键词： Bayesian optimized K-nearest neighbor (BO-KNN) Cognitive task EEG Stationary wavelet transformation

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Transforming Hospital Visitor Screening with Cloud-Powered Deep Learning for Safety and Security

Transforming Hospital Visitor Screening with Cloud-Powered D...

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IEEE Students' Conference on Electrical, Electronics and computer Science (SCEECS)

作者： D. Antony Joseph Rajan Sivanesan T M N. Mohankumar M. Srinivasan Audithan Sivaraman S. Velmurugan Department of Computer Science and Engineering Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Saveetha University Chennai Tamil Nadu India Department of Computer Science and Engineering Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology Chennai Tamil Nadu India Symbiosis Institute of Technology Symbiosis International (Deemed University) Pune India Department of Information Technology P. S. V College of Engineering and Technology Krishnagiri Tamilnadu Department of Information Technology Netaji Subhas University of Technology (Govt of NCTDelhi) Jafferpur New Delhi Department of Electronics and Communication Engineering T.J.S. Engineering College Tamil Nadu India

ISBN: (数字)9798331529833

ISBN: (纸本)9798331529840

This research describes a novel approach to hospital visitor screening utilizing cloud-powered deep learning using convolutional neural networks (CNNs). Our proposed approach will fill this need by improving threat detection accuracy and decreasing false positives. Our system can successfully detect possible dangers among visitors by using CNNs, renowned for their ability to perform image recognition tasks. Integrating cloud computing also makes scalability, real-time processing, and centralized administration possible across several hospital locations. This rigorous testing and evaluation technique improves hospital security requirements reliably and resiliently. It presents novel methods to transform visitor screening fundamentally, solving the challenge of keeping patients, staff, and facilities secure from evolving dangers. The proposed method greatly improves hospital security since it might increase safety and reduce dangers.

关键词： Deep learning Cloud computing Image recognition Accuracy Hospitals Threat assessment Real-time systems Security Convolutional neural networks Testing

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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

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