Kishor Datta Gupta

Kishor Datta Gupta
Clark Atlanta University | CAU · Department of Computer and Information Science

Doctor of Philosophy
Assistant Professor, Clark Atlanta University | Senior Member, IEEE

About

66
Publications
37,666
Reads
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337
Citations
Introduction
Dr. Kishor Datta Gupta (Senior Member, IEEE) is an assistant professor at Clark Atlanta University. He obtained his Ph.D. degree from the Department of Computer Science, The University of Memphis. He is the co-inventor of a patent on adversarial defense and published several journals and peer-reviews conference papers on research related to adversarial machine learning
Education
September 2017 - June 2021
The University of Memphis
Field of study
  • Computer Science
August 2016 - August 2017
Lamar University
Field of study
  • Computer Science
February 2007 - August 2011

Publications

Publications (66)
Conference Paper
Full-text available
Realtime estimation of travel time is a key traffic parameter for designing and planning for transportation systems, particularly when providing mobility-on-demand (MOD)services. However, the analysis and prediction of travel time can be delayed significantly due to the complexity and huge computational requirements of microsimulation models. Thus,...
Preprint
p>In this paper, we develop a data-driven performance-based evaluation framework for a novel electric vertical takeoff and landing aircraft (eVTOL) in the context of Urban Air Mobility (UAM) applications. First, a two-stage comprehensive simulation framework is developed to generate a benchmark database for the performance evaluation of both UAS Tr...
Preprint
p>In this paper, we develop a data-driven performance-based evaluation framework for a novel electric vertical takeoff and landing aircraft (eVTOL) in the context of Urban Air Mobility (UAM) applications. First, a two-stage comprehensive simulation framework is developed to generate a benchmark database for the performance evaluation of both UAS Tr...
Conference Paper
Full-text available
The purpose of this paper is to devise an inter-pretable hybrid classification model of Convolutional Neural Networks (CNN) and a Learning Classifier System (LCS). The presented hybrid system integrates the fundamental attributes from both types of these classifiers. In the proposed hybrid model CNN works as an automatic feature extractor, and the...
Article
Full-text available
In this paper, a numerical study has been undertaken on the susceptible-infected-recovered (SIR) epidemic model that encompasses the mechanisms of the evolution of disease transmission; a prophylactic vaccination strategy in the susceptible populations, depending on the infective individuals. We furnish numerical and graphical simulation combined w...
Preprint
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In this paper, we proposed a new clustering-based active learning framework, namely Active Learning using a Clustering-based Sampling (ALCS), to address the shortage of labeled data. ALCS employs a density-based clustering approach to explore the cluster structure from the data without requiring exhaustive parameter tuning. A bi-cluster boundary-ba...
Article
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The human immunodeficiency virus (HIV) mainly attacks CD4+ T cells in the host. Chronic HIV infection gradually depletes the CD4+ T cell pool, compromising the host’s immunological reaction to invasive infections and ultimately leading to acquired immunodeficiency syndrome (AIDS). The goal of this study is not to provide a qualitative description o...
Conference Paper
Full-text available
Increased traffic density with a greater degree of increased automation in aviation is expected within the next decade. Therefore, airspace capacity will become more congested and result in increasing challenges for detecting conflicts between aerial vehicles. Furthermore, because these vehicles rely on surrounding vehicles following a planned path...
Article
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Decentralized Finance (DeFi) is an emerging and revolutionizing field with notable uncertainties of reliability to be used on a mass scale. On the other hand, Artificial Intelligence (AI) has proved to be a crucial helping tool in numerous domains. In this study, we present a systematic review of the utility of AI in DeFi in terms of impact, reliab...
Article
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Active learning (AL) has widely been used to address the shortage of labeled datasets. Yet, most AL techniques require an initial set of labeled data as the knowledge base to perform active querying. The informativeness of the initial labeled set significantly affects the subsequent active query; hence the performance of active learning. In this pa...
Conference Paper
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In recent years, data security has been one of the biggest concerns, and individuals have grown increasingly worried about the security of their personal information. Personalization typically necessitates the collection of individual data for analysis, exposing customers to privacy concerns. Companies create an illusion of safety to make people fe...
Conference Paper
Full-text available
Detecting brown spot in rice leaf is an urgent complication in the agricultural field as Brown Spot disease lessen the rice yield remarkably. Several segmentation techniques have been applied to identify and extract the infected portion of the rice-leaf and machine learning algorithms such as decision trees, support vector machines are applied to d...
Article
Defenses against adversarial attacks are essential to ensure the reliability of machine-learning models as their applications are expanding in different domains. Existing ML defense techniques have several limitations in practical use. We proposed a trustworthy framework that employs an adaptive strategy to inspect both inputs and decisions. In par...
Article
Streaming feature selection (SFS) is emerging as a key research direction which addresses the non-stationary property of feature streams when the sample size is fixed. Most existing SFS techniques are supervised methods, and ignore the label scarcity. Real-world datasets are typically unlabeled and the labeling costs are expensive. Although some un...
Preprint
Full-text available
Decentralized Finance (DeFi) is an emerging and revolutionizing field with notable uncertainties of reliability to be used on a mass scale. On the other hand, Artificial Intelligence (AI) has proved to be a crucial helping tool in numerous domains. In this study, we present a systematic review of the utility of AI in Defi in terms of impact, reliab...
Article
Full-text available
Digital currency is primarily designed on problems that are computationally hard to solve using traditional computing techniques. However, these problems are now vulnerable due to the computational power of quantum computing. For the post-quantum computing era, there is an immense need to re-invent the existing digital security measures. Problems t...
Article
Full-text available
The Negative selection Algorithm (NSA) is one of the important methods in the field of Immunological Computation (or Artificial Immune Systems). Over the years, some progress was made which turns this algorithm (NSA) into an efficient approach to solve problems in different domain. This review takes into account these signs of progress during the l...
Article
Full-text available
Various adversarial attack methods pose a threat to secure machine learning models. Pre-processing-based defense against adversarial input was not adequate, and they are vulnerable to adaptive attacks. Our study proposed a dynamic pre-process-based defense technique leveraging a Genetic Algorithm that can defend against traditional adaptive attacks...
Thesis
Defenses against adversarial attacks are essential to ensure the reliability of machine learning models as their applications are expanding in different domains. Existing ML defense techniques have several limitations in practical use. I proposed a trustworthy framework that employs an adaptive strategy to inspect both inputs and decisions. In part...
Article
Full-text available
Heart disease, one of the main reasons behind the high mortality rate around the world, requires a sophisticated and expensive diagnosis process. In the recent past, much literature has demonstrated machine learning approaches as an opportunity to efficiently diagnose heart disease patients. However, challenges associated with datasets such as miss...
Preprint
Full-text available
We have seen a surge in research aims toward adversarial attacks and defenses in AI/ML systems. While it is crucial to formulate new attack methods and devise novel defense strategies for robustness, it is also imperative to recognize who is responsible for implementing, validating, and justifying the necessity of these defenses. In particular, whi...
Conference Paper
Full-text available
In this research, we propose novel reinforcement learning-based algorithms to recommend users without collecting identifiable data. With just only user activity on a session, our algorithm can model and track user behavior and formulate a recommendation system. We conclude our algorithms demonstrate positive results in capturing user behavior witho...
Article
Full-text available
The quadrotor is an ideal platform for testing control strategies because of its non-linearity and under-actuated configuration, allowing researchers to evaluate and verify control strategies. Several control strategies are used, including Proportional-Integral-Derivative (PID), Linear Quadratic Regulator (LQR), Backstepping, Feedback Linearization...
Preprint
Full-text available
The Negative selection Algorithm (NSA) is one of the important methods in the field of Immunological Computation (or Artificial Immune Systems). Over the years, some progress was made which turns this algorithm (NSA) into an efficient approach to solve problems in different domain. This review takes into account these signs of progress during the l...
Article
Full-text available
Facial recognition (FR) in unconstrained weather is still challenging and surprisingly ignored by many researchers and practitioners over the past few decades. Therefore, this paper aims to evaluate the performance of three existing popular facial recognition methods considering different weather conditions. As a result, a new face dataset (Lamar U...
Conference Paper
Full-text available
With the increased popularity of Machine Learning (ML) in real-world applications, adversarial attacks are emerging to subvert the ML-based systems. Existing adversarial defense techniques are ineffective against adaptive adversarial attacks since these are highly depend on prior knowledge of adversarial attacks and ML model's specificity. We propo...
Conference Paper
Full-text available
Newspaper reports are a daily information tank for the majority of the world. We rely on newspapers as a primary source of information. In this research, we introduce a collection of 1050 news report dataset on COVID-19 from two different countries and used Natural Language Processing techniques to extract knowledge about the virus, including the n...
Conference Paper
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The primary care doctor suggests a medical specialty doctor after careful evaluations of all symptoms and diagnostics reported from the patient. These symptoms and diagnostics reports can now be gathered using several automated patient content management systems. Identifying which specialty doctor is needed for a patient still depends on the primar...
Conference Paper
Full-text available
Adversarial attacks are considered security risks for Artificial Intelligence-based systems. Researchers have been studying different defense techniques appropriate for adversarial attacks. Evaluation strategies of these attacks and corresponding defenses are primarily conducted on trivial benchmark analysis. We have observed that most of these ana...
Conference Paper
Full-text available
Artificial intelligence persists on being a right-hand tool for many branches of biology. From preliminary advices and treatments, such as understanding if symptoms related to fever or cold, to critical detection of cancerous cell or classification of X-rays, traditional machine learning and deep learning techniques achieved remarkable feats. Howev...
Article
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The outbreak of COVID-19 has caused more than 200,000 deaths so far in the USA alone, which instigates the necessity of initial screening to control the spread of the onset of COVID-19. However, screening for the disease becomes laborious with the available testing kits as the number of patients increases rapidly. Therefore, to reduce the dependenc...
Conference Paper
Full-text available
Modern deep learning models for the computer vision domain are vulnerable against adversarial attacks. Image prepossessing technique based defense against malicious input is currently considered obsolete as this defense is not effective against all types of attacks. The advanced adaptive attack can easily defeat pre-processing based defenses. In th...
Article
Full-text available
Cloud computing gained much popularity in the recent past due to its many internet-based services related to data, application, operating system, and eliminating the need for central hardware access. Many of the challenges associated with cloud computing can be specified as network load, security intrusion, authentication, biometric identification,...
Preprint
Full-text available
From a medical transcript, our tool detects what type of doctor is required by the patient in this demo. We used multiple machine learning methods with natural language processing algorithms to provide the best possible results. Our empirical experiments show promising results using the ensemble of different machine learning technique.
Preprint
Full-text available
In this paper, we introduce a collection of 1000 news report dataset on COVID-19 from two different countries and used Natural Language Processing techniques to extract knowledge about the virus, including the number of COVID-cases, trending topics month, sentiment analysis, etc. Moreover, we compared how the virus spreads and impacts a developed c...
Preprint
The outbreak of COVID-19 disease caused more than 100,000 deaths so far in the USA alone. It is necessary to conduct an initial screening of patients with the symptoms of COVID-19 disease to control the spread of the disease. However, it is becoming laborious to conduct the tests with the available testing kits due to the growing number of patients...
Preprint
Predicting if red blood cells (RBC) are infected with the malaria parasite is an important problem in Pathology. Recently, supervised machine learning approaches have been used for this problem, and they have had reasonable success. In particular, state-of-the-art methods such as Convolutional Neural Networks automatically extract increasingly comp...
Preprint
Developing secure machine learning models from adversarial examples is challenging as various methods are continually being developed to generate adversarial attacks. In this work, we propose an evolutionary approach to automatically determine Image Processing Techniques Sequence (IPTS) for detecting malicious inputs. Accordingly, we first used a d...
Conference Paper
Full-text available
Over the decades with advancement in artificial intelligent systems, stylometry has proven to be crucial in authorship attribution. It is an evident that by natural development , writing styles between individuals are unique and cannot be fabricated. Stylometric analysis research has been done for author identification and there is significant prog...
Conference Paper
Full-text available
Predicting if red blood cells (RBC) are infected with the malaria parasite is an important problem in Pathology. Recently, supervised machine learning approaches have been used for this problem, and they have had reasonable success. In particular, state-of-the-art methods such as Convolutional Neural Networks automatically extract increasingly comp...
Conference Paper
Full-text available
In all goal-oriented selection activities, an existence of certain level of bias is unavoidable and may be desired for efficient artificial intelligence based decision support systems. However, a fair independent comparison of all eligible entities is essential to alleviate explicit bias in competitive marketplace. For example, searching online for...
Conference Paper
Full-text available
Consumers now check reviews and recommendations before consuming any services or products. But traders try to shape reviews and ratings of their merchandise to gain more consumers. Seldom they attempt to manage their competitor’s review and recommendation. These manipulations are hard to detect by standard lookup from an everyday consumer, but by t...
Conference Paper
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Shamir's Secret sharing [1] is a quantum attack proof algorithm and is used heavily for secret sharing. But it can also be used for authentication protocols as a replacement of hashing. In this paper, we propose an authentication protocol which will use Shamir's secret sharing method to authenticate with server. Hashing may not be able to hide data...
Chapter
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A machine learning based Distributed Denial of Service (DDoS) attack detection system, implemented in a virtual SDN environment testbed, has been presented in this paper. This system identifies whether any incoming traffic in a network is a DDoS type or not. To implement this approach, we applied AdaBoosting with decision stump as a weak classifier...
Conference Paper
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The success and popularity of Bitcoin mainly focuses the underlying blockchain technology which is totally immutable distributed ledger, highly secured by its P2P network consensus named Proof of Work (PoW). One of the worst threats to a Proof-of-Work based cryptocurrency is 51% attack. If one or more dishonest network peer gains more than 50% of r...
Data
A Genetic Algorithm Approach to Optimize Dispatching for A Microgrid Energy System with Renewable Energy Sources
Conference Paper
Full-text available
Distributed network reconfiguration techniques are used widely to optimize power distribution systems. As renewable energy generation are very stochastic in nature, network reconfiguration with this stochastic nature does not provide the optimal solution. To address this problem a three-objective genetic algorithm approach has been taken in this pr...
Conference Paper
Full-text available
Ransomware attacks in recent years have proved expensive due to significant damages and obstructions these caused in various sectors such as health, insurance, business, and education. Several malware detection methods have been proposed to uncover different malware families, but the problem remained unsolved due to the continuously evolving malwar...
Article
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Ransomware analysis forms a critical aspect of cybersecurity defense mechanism. The number of ransomware attacks has increased in recent years affecting different sectors which includes health, education, finance and banking, and e-commerce. Various work has been done via static and dynamic analysis of malware to build the distinguishing characteri...
Article
Full-text available
Due to the rapid growth of the internet, malicious websites have become the cornerstone for internet crime activities. There are lots of existing approaches to detect benign and malicious websites-some of them giving near 99% accuracy. However, effective and efficient detection of malicious websites has now seemed reasonable enough in terms of accu...
Article
Full-text available
The knowledge of structure of proteins, protein derived compounds and RNA structures in eukaryotic cell is mandatory to understand the functions of these macromolecules.With recent development of Direct Electron Detector Device (DDD) camera and application of maximum likelihood algorithms in volume reconstruction,cryo Electron Microscopy (cryo-EM)...
Article
Full-text available
Quick Response (QR) codes are getting widely popular as the demand for mobile computing is increasing, too. However, a QR code has apparently a data limit problem when we consider designing color QR codes. Despite having color QR codes decoding from printed material, new problems appear due to different printer's color depth, paper quality, environ...
Article
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The report starts with an overview of the blockchain security system and then highlights the specific security threats and summarizes them. We review with some comments and possible research direction. This survey, we examines the security issues of blockchain model related technologies and their applications. The blockchain is considered a still g...
Article
Full-text available
Small scaled image lost some important bits of information which cannot be recovered when scaled back. Using multi-objective genetic algorithm, we can recover these lost bits. In this paper, we described a genetic algorithm approach to recover lost bits while image resized to the smaller version using the original image data bit counts which are...
Article
Full-text available
Quick Response Code has been widely used in the automatic identification fields (Liu, Ju, & Mingjun, 2008). The present work illustrates an image processing system able to discover, split and decodes the most common 2D symbol used in bar code applications. The different symbol is processed by manipulating their similarities, to achieve an integrate...
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