arXiv subjects

Kishor Datta Gupta

Publications and source records attributed to Kishor Datta Gupta.

At least 37 records · Page 2Linked to original sources

arXiv2025

An Extensive and Methodical Review of Smart Grids for Sustainable Energy Management-Addressing Challenges with AI, Renewable Energy Integration and Leading-edge Technologies

Energy management decreases energy expenditures and consumption while simultaneously increasing energy efficiency, reducing carbon emissions, and enhancing operational performance. Smart grids are a type of sophisticated energy infrastructure that increase the generation and distribution of electricity's sustainability, dependability, and efficiency by utilizing digital communication technologies. They combine a number of cutting-edge techniques and technology to improve energy resource management. A large amount of research study on the topic of smart grids for energy management has been completed in the last several years. The authors of the present study want to cover a number of topics, including smart grid benefits and components, technical developments, integrating renewable energy sources, using artificial intelligence and data analytics, cybersecurity, and privacy. Smart Grids for Energy Management are an innovative field of study aiming at tackling various difficulties and magnifying the efficiency, dependability, and sustainability of energy systems, including: 1) Renewable sources of power like solar and wind are intermittent and unpredictable 2) Defending smart grid system from various cyber-attacks 3) Incorporating an increasing number of electric vehicles into the system of power grid without overwhelming it. Additionally, it is proposed to use AI and data analytics for better performance on the grid, reliability, and energy management. It also looks into how AI and data analytics can be used to optimize grid performance, enhance reliability, and improve energy management. The authors will explore these significant challenges and ongoing research. Lastly, significant issues in this field are noted, and recommendations for further work are provided.

cs.LG↗

arXiv2024

Power Plays: Unleashing Machine Learning Magic in Smart Grids

The integration of machine learning into smart grid systems represents a transformative step in enhancing the efficiency, reliability, and sustainability of modern energy networks. By adding advanced data analytics, these systems can better manage the complexities of renewable energy integration, demand response, and predictive maintenance. Machine learning algorithms analyze vast amounts of data from smart meters, sensors, and other grid components to optimize energy distribution, forecast demand, and detect irregularities that could indicate potential failures. This enables more precise load balancing, reduces operational costs, and enhances the resilience of the grid against disturbances. Furthermore, the use of predictive models helps in anticipating equipment failures, thereby improving the reliability of the energy supply. As smart grids continue to evolve, the role of machine learning in managing decentralized energy sources and enabling real-time decision-making will become increasingly critical. However, the deployment of these technologies also raises challenges related to data privacy, security, and the need for robust infrastructure. Addressing these issues in this research authors will focus on realizing the full potential of smart grids, ensuring they meet the growing energy demands while maintaining a focus on sustainability and efficiency using Machine Learning techniques. Furthermore, this research will help determine the smart grid's essentiality with the aid of Machine Learning. Multiple ML algorithms have been integrated along with their pros and cons. The future scope of these algorithms are also integrated.

cs.AI↗

arXiv2024

Present and Future of AI in Renewable Energy Domain : A Comprehensive Survey

Artificial intelligence (AI) has become a crucial instrument for streamlining processes in various industries, including electrical power systems, as a result of recent digitalization. Algorithms for artificial intelligence are data-driven models that are based on statistical learning theory and are used as a tool to take use of the data that the power system and its users generate. Initially, we perform a thorough literature analysis of artificial intelligence (AI) applications related to renewable energy (RE). Next, we present a thorough analysis of renewable energy factories and assess their suitability, along with a list of the most widely used and appropriate AI algorithms. Nine AI-based strategies are identified here to assist Renewable Energy (RE) in contemporary power systems. This survey paper comprises an extensive review of the several AI techniques used for renewable energy as well as a methodical analysis of the literature for the study of various intelligent system application domains across different disciplines of renewable energy. This literature review identifies the performance and outcomes of nine different research methods by assessing them, and it aims to distill valuable insights into their strengths and limitations. This study also addressed three main topics: using AI technology for renewable power generation, utilizing AI for renewable energy forecasting, and optimizing energy systems. Additionally, it explored AI's superiority over conventional models in controllability, data handling, cyberattack prevention, smart grid implementation, robotics- AI's significance in shaping the future of the energy industry. Furthermore, this article outlines future directions in the integration of AI for renewable energy.

cs.LG↗

arXiv2024

Securing the Diagnosis of Medical Imaging: An In-depth Analysis of AI-Resistant Attacks

Machine learning (ML) is a rapidly developing area of medicine that uses significant resources to apply computer science and statistics to medical issues. ML's proponents laud its capacity to handle vast, complicated, and erratic medical data. It's common knowledge that attackers might cause misclassification by deliberately creating inputs for machine learning classifiers. Research on adversarial examples has been extensively conducted in the field of computer vision applications. Healthcare systems are thought to be highly difficult because of the security and life-or-death considerations they include, and performance accuracy is very important. Recent arguments have suggested that adversarial attacks could be made against medical image analysis (MedIA) technologies because of the accompanying technology infrastructure and powerful financial incentives. Since the diagnosis will be the basis for important decisions, it is essential to assess how strong medical DNN tasks are against adversarial attacks. Simple adversarial attacks have been taken into account in several earlier studies. However, DNNs are susceptible to more risky and realistic attacks. The present paper covers recent proposed adversarial attack strategies against DNNs for medical imaging as well as countermeasures. In this study, we review current techniques for adversarial imaging attacks, detections. It also encompasses various facets of these techniques and offers suggestions for the robustness of neural networks to be improved in the future.

cs.CR↗

arXiv2024

UAV (Unmanned Aerial Vehicles): Diverse Applications of UAV Datasets in Segmentation, Classification, Detection, and Tracking

Unmanned Aerial Vehicles (UAVs), have greatly revolutionized the process of gathering and analyzing data in diverse research domains, providing unmatched adaptability and effectiveness. This paper presents a thorough examination of Unmanned Aerial Vehicle (UAV) datasets, emphasizing their wide range of applications and progress. UAV datasets consist of various types of data, such as satellite imagery, images captured by drones, and videos. These datasets can be categorized as either unimodal or multimodal, offering a wide range of detailed and comprehensive information. These datasets play a crucial role in disaster damage assessment, aerial surveillance, object recognition, and tracking. They facilitate the development of sophisticated models for tasks like semantic segmentation, pose estimation, vehicle re-identification, and gesture recognition. By leveraging UAV datasets, researchers can significantly enhance the capabilities of computer vision models, thereby advancing technology and improving our understanding of complex, dynamic environments from an aerial perspective. This review aims to encapsulate the multifaceted utility of UAV datasets, emphasizing their pivotal role in driving innovation and practical applications in multiple domains.

cs.CV↗

arXiv2024

Physical Rule-Guided Convolutional Neural Network

The black-box nature of Convolutional Neural Networks (CNNs) and their reliance on large datasets limit their use in complex domains with limited labeled data. Physics-Guided Neural Networks (PGNNs) have emerged to address these limitations by integrating scientific principles and real-world knowledge, enhancing model interpretability and efficiency. This paper proposes a novel Physics-Guided CNN (PGCNN) architecture that incorporates dynamic, trainable, and automated LLM-generated, widely recognized rules integrated into the model as custom layers to address challenges like limited data and low confidence scores. The PGCNN is evaluated on multiple datasets, demonstrating superior performance compared to a baseline CNN model. Key improvements include a significant reduction in false positives and enhanced confidence scores for true detection. The results highlight the potential of PGCNNs to improve CNN performance for broader application areas.

cs.CV↗

arXiv2024

Deep Learning for Automated Wound Classification And Segmentation

Wounds, such as foot ulcers, pressure ulcers, leg ulcers, and infected wounds, come up with substantial problems for healthcare professionals. Prompt and accurate segmentation is crucial for effective treatment. However, contemporary methods need an exhaustive model that is qualified for both classification and segmentation, especially lightweight ones. In this work, we tackle this issue by presenting a new architecture that incorporates U-Net, which is optimized for both wound classification and effective segmentation. We curated four extensive and diverse collections of wound images, utilizing the publicly available Medetec Dataset, and supplemented with additional data sourced from the Internet. Our model performed exceptionally well, with an F1 score of 0.929, a Dice score of 0.931 in segmentation, and an accuracy of 0.915 in classification, proving its effectiveness in both classification and segmentation work. This accomplishment highlights the potential of our approach to automating wound care management.

eess.IV↗

arXiv2024

AI-Driven Approaches for Optimizing Power Consumption: A Comprehensive Survey

Reduced environmental effect, lower operating costs, and a stable and sustainable energy supply for current and future generations are the main reasons why power optimization is important. Power optimization makes ensuring that energy is used more effectively, cutting down on waste and optimizing the utilization of resources.In today's world, power optimization and artificial intelligence (AI) integration are essential to changing the way energy is produced, used, and distributed. Real-time monitoring and analysis of power usage trends is made possible by AI-driven algorithms and predictive analytics, which enable dynamic modifications to effectively satisfy demand. Efficiency and sustainability are increased when power consumption is optimized in different sectors thanks to the use of intelligent systems. This survey paper comprises an extensive review of the several AI techniques used for power optimization as well as a methodical analysis of the literature for the study of various intelligent system application domains across different disciplines of power consumption.This literature review identifies the performance and outcomes of 17 different research methods by assessing them, and it aims to distill valuable insights into their strengths and limitations. Furthermore, this article outlines future directions in the integration of AI for power consumption optimization.

cs.AI↗

arXiv2023

Online learning for X-ray, CT or MRI

Medical imaging plays an important role in the medical sector in identifying diseases. X-ray, computed tomography (CT) scans, and magnetic resonance imaging (MRI) are a few examples of medical imaging. Most of the time, these imaging techniques are utilized to examine and diagnose diseases. Medical professionals identify the problem after analyzing the images. However, manual identification can be challenging because the human eye is not always able to recognize complex patterns in an image. Because of this, it is difficult for any professional to recognize a disease with rapidity and accuracy. In recent years, medical professionals have started adopting Computer-Aided Diagnosis (CAD) systems to evaluate medical images. This system can analyze the image and detect the disease very precisely and quickly. However, this system has certain drawbacks in that it needs to be processed before analysis. Medical research is already entered a new era of research which is called Artificial Intelligence (AI). AI can automatically find complex patterns from an image and identify diseases. Methods for medical imaging that uses AI techniques will be covered in this chapter.

eess.IV↗

arXiv2023

Introduction of Medical Imaging Modalities

The diagnosis and treatment of various diseases had been expedited with the help of medical imaging. Different medical imaging modalities, including X-ray, Computed Tomography (CT), Magnetic Resonance Imaging (MRI), Nuclear Imaging, Ultrasound, Electrical Impedance Tomography (EIT), and Emerging Technologies for in vivo imaging modalities is presented in this chapter, in addition to these modalities, some advanced techniques such as contrast-enhanced MRI, MR approaches for osteoarthritis, Cardiovascular Imaging, and Medical Imaging data mining and search. Despite its important role and potential effectiveness as a diagnostic tool, reading and interpreting medical images by radiologists is often tedious and difficult due to the large heterogeneity of diseases and the limitation of image quality or resolution. Besides the introduction and discussion of the basic principles, typical clinical applications, advantages, and limitations of each modality used in current clinical practice, this chapter also highlights the importance of emerging technologies in medical imaging and the role of data mining and search aiming to support translational clinical research, improve patient care, and increase the efficiency of the healthcare system.

eess.IV↗

arXiv2023

Case Study-Based Approach of Quantum Machine Learning in Cybersecurity: Quantum Support Vector Machine for Malware Classification and Protection

Quantum machine learning (QML) is an emerging field of research that leverages quantum computing to improve the classical machine learning approach to solve complex real world problems. QML has the potential to address cybersecurity related challenges. Considering the novelty and complex architecture of QML, resources are not yet explicitly available that can pave cybersecurity learners to instill efficient knowledge of this emerging technology. In this research, we design and develop QML-based ten learning modules covering various cybersecurity topics by adopting student centering case-study based learning approach. We apply one subtopic of QML on a cybersecurity topic comprised of pre-lab, lab, and post-lab activities towards providing learners with hands-on QML experiences in solving real-world security problems. In order to engage and motivate students in a learning environment that encourages all students to learn, pre-lab offers a brief introduction to both the QML subtopic and cybersecurity problem. In this paper, we utilize quantum support vector machine (QSVM) for malware classification and protection where we use open source Pennylane QML framework on the drebin215 dataset. We demonstrate our QSVM model and achieve an accuracy of 95% in malware classification and protection. We will develop all the modules and introduce them to the cybersecurity community in the coming days.

cs.CR↗

arXiv2022

Transfer learning and Local interpretable model agnostic based visual approach in Monkeypox Disease Detection and Classification: A Deep Learning insights

The recent development of Monkeypox disease among various nations poses a global pandemic threat when the world is still fighting Coronavirus Disease-2019 (COVID-19). At its dawn, the slow and steady transmission of Monkeypox disease among individuals needs to be addressed seriously. Over the years, Deep learning (DL) based disease prediction has demonstrated true potential by providing early, cheap, and affordable diagnosis facilities. Considering this opportunity, we have conducted two studies where we modified and tested six distinct deep learning models-VGG16, InceptionResNetV2, ResNet50, ResNet101, MobileNetV2, and VGG19-using transfer learning approaches. Our preliminary computational results show that the proposed modified InceptionResNetV2 and MobileNetV2 models perform best by achieving an accuracy ranging from 93% to 99%. Our findings are reinforced by recent academic work that demonstrates improved performance in constructing multiple disease diagnosis models using transfer learning approaches. Lastly, we further explain our model prediction using Local Interpretable Model-Agnostic Explanations (LIME), which play an essential role in identifying important features that characterize the onset of Monkeypox disease.

eess.IV↗

arXiv2022

Negative Selection Approach to support Formal Verification and Validation of BlackBox Models' Input Constraints

Generating unsafe sub-requirements from a partitioned input space to support verification-guided test cases for formal verification of black-box models is a challenging problem for researchers. The size of the search space makes exhaustive search computationally impractical. This paper investigates a meta-heuristic approach to search for unsafe candidate sub-requirements in partitioned input space. We present a Negative Selection Algorithm (NSA) for identifying the candidates' unsafe regions within given safety properties. The Meta-heuristic capability of the NSA algorithm made it possible to estimate vast unsafe regions while validating a subset of these regions. We utilize a parallel execution of partitioned input space to produce safe areas. The NSA based on the prior knowledge of the safe regions is used to identify candidate unsafe region areas and the Marabou framework is then used to validate the NSA results. Our preliminary experimentation and evaluation show that the procedure finds candidate unsafe sub-requirements when validated with the Marabou framework with high precision.

cs.LG↗

arXiv2022

Mitigating shortage of labeled data using clustering-based active learning with diversity exploration

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-based sample query procedure is introduced to improve the learning performance for classifying highly overlapped classes. Additionally, we developed an effective diversity exploration strategy to address the redundancy among queried samples. Our experimental results justified the efficacy of the ALCS approach.

cs.LG↗

arXiv2021

Who is Responsible for Adversarial Defense?

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, which components of the system are vulnerable to what type of adversarial attacks, and the expertise needed to realize the severity of adversarial attacks. Also how to evaluate and address the adversarial challenges in order to recommend defense strategies for different applications. This paper opened a discussion on who should examine and implement the adversarial defenses and the reason behind such efforts.

cs.CR↗

arXiv2021

Negative Selection Algorithm Research and Applications in the last decade: A Review

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 last decade and categorizes those based on different characteristics and performances. Our study shows that NSA's evolution can be labeled in four ways highlighting the most notable NSA variations and their limitations in different application domains. We also present alternative approaches to NSA for comparison and analysis. It is evident that NSA performs better for nonlinear representation than most of the other methods, and it can outperform neural-based models in computation time. We summarize NSA's development and highlight challenges in NSA research in comparison with other similar models.

cs.NE↗

arXiv2020

Study of Different Deep Learning Approach with Explainable AI for Screening Patients with COVID-19 Symptoms: Using CT Scan and Chest X-ray Image Dataset

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. Some studies proposed CT scan or chest X-ray images as an alternative solution. Therefore, it is essential to use every available resource, instead of either a CT scan or chest X-ray to conduct a large number of tests simultaneously. As a result, this study aims to develop a deep learning-based model that can detect COVID-19 patients with better accuracy both on CT scan and chest X-ray image dataset. In this work, eight different deep learning approaches such as VGG16, InceptionResNetV2, ResNet50, DenseNet201, VGG19, MobilenetV2, NasNetMobile, and ResNet15V2 have been tested on two dataset-one dataset includes 400 CT scan images, and another dataset includes 400 chest X-ray images studied. Besides, Local Interpretable Model-agnostic Explanations (LIME) is used to explain the model's interpretability. Using LIME, test results demonstrate that it is conceivable to interpret top features that should have worked to build a trust AI framework to distinguish between patients with COVID-19 symptoms with other patients.

eess.IV↗

arXiv2020

Determining Sequence of Image Processing Technique (IPT) to Detect Adversarial Attacks

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 diverse set of attack methods including adaptive attack methods (on our defense) to generate adversarial samples from the clean dataset. A detection framework based on a genetic algorithm (GA) is developed to find the optimal IPTS, where the optimality is estimated by different fitness measures such as Euclidean distance, entropy loss, average histogram, local binary pattern and loss functions. The "image difference" between the original and processed images is used to extract the features, which are then fed to a classification scheme in order to determine whether the input sample is adversarial or clean. This paper described our methodology and performed experiments using multiple data-sets tested with several adversarial attacks. For each attack-type and dataset, it generates unique IPTS. A set of IPTS selected dynamically in testing time which works as a filter for the adversarial attack. Our empirical experiments exhibited promising results indicating the approach can efficiently be used as processing for any AI model.

cs.CV↗