Kirti Aggarwal

@jiit.ac.in

Assistant Professor (Senior Grade) / Department of Computer Science & Engineering and Information Technology
Jaypee Institute of Information Technology

Kirti Aggarwal


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https://researchid.co/aggarwalkirti25

RESEARCH, TEACHING, or OTHER INTERESTS

Computer Engineering, Computer Science Applications
19

Scopus Publications

137

Scholar Citations

7

Scholar h-index

5

Scholar i10-index

Scopus Publications

  • Leveraging DCGANs and Cyclic GANs for Synthetic MRI Image Generation and Neural Network Optimization
    Kirti Aggarwal, Vikalp Srivastava, Kunal Kartikeya
    Generative Artificial Intelligence Technology and Applications, 2025
    In the field of healthcare imaging, the utilization of synthetic data shows considerable potential for addressing challenges associated with data scarcity and privacy concerns. This paper delves into the implementation details and challenges encountered in a Magnetic Resonance Imaging (MRI) project aiming at generating synthetic MRI images for neural network training. The process begins with data collection from Google API datasets, followed by meticulous preprocessing to ensure data quality and consistency. The generation of synthetic MRI images is facilitated through the deployment of Deep Convolutional Generative Adversarial Networks (DCGANs), which are trained to understand the fundamental pattern of MRI images and generate high-fidelity counterparts. Subsequently, a Cycle-Consistent Generative Adversarial Network (Cyclic GAN) is employed to further refine the quality of synthetic images through iterative translation and reconstruction processes. Concurrently, a Convolutional Neural Network (CNN) model is developed and trained on both real and synthetic MRI datasets to perform neuroimaging tasks such as brain tumor detection. The efficacy of the CNN model on artificial data is thoroughly evaluated, and optimization techniques including fine-tuning of GAN parameters and CNN architecture adjustments are explored to enhance performance. The paper concludes with a comprehensive analysis of the project’s input and output, highlighting the close resemblance between real and synthetic MRI images and affirming the usefulness of artificial data in augmenting neural network training for medical imaging applications.
  • Genetic algorithm-based framework for optimizing medical image enhancement
    Kirti Aggarwal, Meenal Jain
    Computational Intelligence for Connective Cognition Networks Advances and Applications, 2025
    Enhancing the quality of medical images is critical for efficient decision-making and correct diagnosis. This research work represents a framework that uses a genetic algorithm (GA) to optimize the process of image enhancement. It addresses various challenges related to the generation of high-quality images from initial data that might contain blurriness, noise, and poor contrast. The proposed method involves producing an enhanced image from an initial image evolved through multiple generations of GA. A fitness function is used through each iteration to assess the quality of produced image. The algorithm proposed in this work continuously evolves the image by applying selection, crossover, and mutation process, until a better outcome is achieved. This approach helps in increasing the visual clarity of regions of interest (ROI) in medical Images. This helps in identifying anomalies and making accurate clinical diagnosis. This work explores the implications of the proposed approach in other domains as well. This work bridges the gap between computational intelligence and practical medical applications by integrating GA within a connective cognition framework.
  • Machine learning security on drones or UAV
    Meenal Jain, Kirti Aggarwal
    Computational Intelligence for Connective Cognition Networks Advances and Applications, 2025
    Unmanned aerial vehicles (UAVs) and drone technology have seen significant developments, spurring innovation across several sectors. This chapter summarises a thorough examination of numerous research publications and approaches, emphasising transformational advances and significant challenges in this dynamic topic. Drones offer a range of advantages; however, they can also be used as a means of physical and cyberattack. This chapter examines various applications of drone use in future smart cities with a focus on cybersecurity, self-privacy and safety of the common man. It also provides results on cyberattacks using drones. Finally, the chapter examines the vulnerability of deep learning algorithms used in UAVs to adversarial samples, which could potentially lead to misbehaviour in real-world situations. This chapter thus examines the use of algorithms for the purpose of attacking and defending UAVs, highlighting their importance in maintaining drone security. Simultaneously, the rise of drones, particularly in smaller forms, promises intriguing prospects across industries, with the Internet of Things (IoT) being used for navigational services. However, fundamental design flaws offer significant privacy and security issues within drone networks (NoD), necessitating reinforced infrastructure. The investigation of these problems emphasises the need for better security measures.
  • Computational Intelligence for Connective Cognition Networks: Advances and Applications
    Kirti Aggarwal, Anuja Arora, Zahid Akhtar, Alessandro Bruno
    Computational Intelligence for Connective Cognition Networks Advances and Applications, 2025
    The classification of portable document format (PDF) documents from a collection of documents of different languages is an error-prone, tedious, and time-consuming process. This work utilizes deep learning techniques for the efficient classification of such documents. The document classification engine proposed in this work can assist humans in the separation of documents of different languages into different folders. This prevents the document from getting lost if they are not properly labeled and accelerates the process of usage of these documents for proper application. The present work utilizes the documents of three languages for classification. Multiple deep learning algorithms have been applied in the present work including the use of Bi-LSTMs to classify the documents. The proposed model achieved an accuracy of approx. 99%. The advantage of using Bi-LSTM is that it requires much less computing resources as compared to other heavier models. In the present work the results for the proposed methodology are further compared with state-of-the-art methodologies and the results significantly outperformed the existing models.
  • Neural Network-Driven Simulated Annealing for Trust-Persuasion Optimized Influence Maximization
    Kirti Aggarwal
    2025 17th International Conference on Contemporary Computing Ic3 2025, 2025
    An innovative methodology for maximizing the influence in trust-persuasion network by integrating simulated annealing algorithm with a Multi-Layer Perceptron (MLP) regressor is introduced in this paper. The proposed algorithm known as Neural Network-Driven Simulated Annealing (NNDSA) makes use of the adaptive capabilities of the simulated annealing algorithm, guided by realization from a trained MLP regressor to find the optimized seed set. By including the trust, persuasion, and opinion factors, NNDSA embodies the diverse aspects of influence propagation. Here trust determines information credibility, persuasion determines message impact and the opinion aspect models the growing beliefs of peoples in the network. Experiments are performed on two datasets: Bitcoin and Advogato, where the NNDSA algorithm with MLP regressor outperforms the one with- out MLP regressor and achieves higher influence propagation, while maintain the attractive execution time. The proposed algorithm with MLP regressor achieves average influence spread of 592.2 on the Bitcoin dataset and 991.2 on the Advogato dataset, outperforming the influence spread achieved without MLP regressor by 36.6 and 6.6 respectively. These finding reflects the potential of NNDSA technique with MLP regressor for the advancement of influence maximization strategies.
  • Deep Learning-Driven CNN Models for Enhanced Brain Tumor Classification
    Kirti Aggarwal, Kunal Kartikeya, Vikalp Srivastava
    Studies in Computational Intelligence, 2025
  • Stock Indexes Community Identification Using BAT-Modified Optimization Algorithm
    Kirti Aggarwal, Anuja Arora
    SN Computer Science, 2024
  • Predictive Modeling of Drug-Drug Interactions: A Link Prediction Approach
    Drishika Chauhan, Navjyot Narang, Pratyasha Shukla, Kirti Aggarwal
    ACM International Conference Proceeding Series, 2024
    This research paper presents a novel approach to predicting drug-drug interactions (DDIs) through the integration of computational methodologies and pharmaceutical expertise. The accurate anticipation of potential interactions between medications is crucial for ensuring patient safety and optimizing therapeutic outcomes in an era marked by a growing array of medications and increasing complexities in drug therapy regimens. Drawing inspiration from the pressing need within the pharmaceutical industry and healthcare sector, this research aims to develop reliable predictive models capable of identifying and mitigating the risks associated with DDIs. Leveraging diverse data sources and cutting-edge machine learning techniques, our methodology encompasses matrix perturbation, similarity-based modeling, and ensemble learning algorithms, each offering unique insights into the underlying mechanisms driving drug interactions. Evaluation metrics including accuracy, F1 Score, and recall are utilized to assess the performance of our model, with visualization techniques providing insights into the dynamics of drug interaction networks. Through comprehensive experimentation and analysis, our findings contribute to advancing drug safety and therapeutic decision-making, ultimately benefiting patient care and public health on a global scale.
  • Influence maximization in social networks using discrete BAT-modified (DBATM) optimization algorithm: a computationally intelligent viral marketing approach
    Kirti Aggarwal, Anuja Arora
    Social Network Analysis and Mining, 2023
  • Assessment of Discrete BAT-Modified (DBAT-M) Optimization Algorithm for Community Detection in Complex Network
    Kirti Aggarwal, Anuja Arora
    Arabian Journal for Science and Engineering, 2023
  • An Intelligent Article Knowledge Graph Formation Framework Using BM25 Probabilistic Retrieval Model
    Jasir Mohammad Zaeem, Vibhor Garg, Kirti Aggarwal, Anuja Arora
    Lecture Notes in Computer Science Including Subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics, 2023
  • StressMLIoT: IoT Sensor Features Reduction and Machine Learning driven Stress Identification System
    Samarth Vinayaka, Harsh Dhariwal, Kirti Aggarwal, Anuja Arora
    Proceedings of 2023 2nd International Conference on Informatics Ici 2023, 2023
  • Assessment of Modified BAT Algorithm for MOOC Learner Influence Maximization
    Kirti Aggarwal, Anuja Arora
    ACM International Conference Proceeding Series, 2022
  • Detecting Community Structure in Financial Markets Using the Bat Optimization Algorithm
    Kirti Aggarwal, Anuja Arora
    International Journal of Information Technology Project Management, 2022
  • Hand Gesture Recognition for Real-Time Game Play Using Background Elimination and Deep Convolution Neural Network
    Kirti Aggarwal, Anuja Arora
    Studies in Systems Decision and Control, 2022
  • An Approach to Control the PC with Hand Gesture Recognition using Computer Vision Technique
    Kirti Aggarwal, Anuja Arora
    Proceedings of the 2022 9th International Conference on Computing for Sustainable Global Development Indiacom 2022, 2022
  • Influence Maximization for MOOC Learners Using BAT Optimization Algorithm
    Kirti Aggarwal, Anuja Arora
    International Journal of Fuzzy System Applications, 2022
  • Comparison of RC6, modified RC6 & enhancement of RC6
    Kirti Aggarwal
    Conference Proceeding 2015 International Conference on Advances in Computer Engineering and Applications Icacea 2015, 2015
  • Hash-RC6 - Variable length Hash algorithm using RC6
    Kirti Aggarwal, Harsh K. Verma
    Conference Proceeding 2015 International Conference on Advances in Computer Engineering and Applications Icacea 2015, 2015

RECENT SCHOLAR PUBLICATIONS

  • Pattern transfer based photorealistic synthetic fake image generation using cycle generative adversarial networks
    A Arora, JM Zaeem, V Garg, K Aggarwal, XZ Gao
    Discover Artificial Intelligence , 2026
    2026
  • Leveraging DCGANs and Cyclic GANs for Synthetic MRI Image Generation and Neural Network Optimization
    K Aggarwal, V Srivastava, K Kartikeya
    Generative Artificial Intelligence, 319-336 , 2025
    2025
  • Machine learning security on drones or UAV
    M Jain, K Aggarwal
    Computational Intelligence for Connective Cognition Networks, 44-59 , 2025
    2025
  • Genetic algorithm-based framework for optimizing medical image enhancement
    K Aggarwal, M Jain
    Computational Intelligence for Connective Cognition Networks, 27-43 , 2025
    2025
  • Computational Intelligence for Connective Cognition Networks: Advances and Applications
    K Aggarwal, A Arora, Z Akhtar, A Bruno
    CRC Press , 2025
    2025
  • Neural Network-Driven Simulated Annealing for Trust-Persuasion Optimized Influence Maximization
    K Aggarwal
    2025 Seventeenth International Conference on Contemporary Computing (IC3), 1-6 , 2025
    2025
  • Deep Learning-Driven CNN Models for Enhanced Brain Tumor Classification
    K Aggarwal, K Kartikeya, V Srivastava
    Revolutionizing Healthcare: Impact of Artificial Intelligence on Diagnosis … , 2025
    2025
    Citations: 1
  • Stock Indexes Community Identification Using BAT-Modified Optimization Algorithm
    K Aggarwal, A Arora
    SN Computer Science 5 (8), 1013 , 2024
    2024
  • ARBP: antibiotic-resistant bacteria propagation bio-inspired algorithm and its performance on benchmark functions
    K Aggarwal, A Arora
    Advances in Computational Intelligence 4 (3), 10 , 2024
    2024
  • Predictive Modeling of Drug-Drug Interactions: A Link Prediction Approach
    D Chauhan, N Narang, P Shukla, K Aggarwal
    Proceedings of the 2024 Sixteenth International Conference on Contemporary … , 2024
    2024
    Citations: 2
  • StressMLIoT: IoT Sensor Features Reduction and Machine Learning driven Stress Identification System
    S Vinayaka, H Dhariwal, K Aggarwal, A Arora
    2023 Second International Conference on Informatics (ICI), 1-7 , 2023
    2023
  • Influence maximization in social networks using discrete BAT-modified (DBATM) optimization algorithm: a computationally intelligent viral marketing approach
    K Aggarwal, A Arora
    Social Network Analysis and Mining 13 (1), 146 , 2023
    2023
    Citations: 14
  • An intelligent article knowledge graph formation framework using bm25 probabilistic retrieval model
    JM Zaeem, V Garg, K Aggarwal, A Arora
    Iberoamerican Knowledge Graphs and Semantic Web Conference, 32-43 , 2023
    2023
    Citations: 4
  • Breast Cancer Classification and Survival Prediction Using Proteomic Analysis
    K Aggarwal, A Arora, J Azzopardi
    Novel Developments in Futuristic AI-based Technologies, 123-138 , 2023
    2023
    Citations: 1
  • Applications of augmented reality in medical training
    R Garg, K Aggarwal, A Arora
    Mathematical Modeling, Computational Intelligence Techniques and Renewable … , 2023
    2023
    Citations: 6
  • Assessment of discrete BAT-modified (DBAT-M) optimization algorithm for community detection in complex network
    K Aggarwal, A Arora
    Arabian Journal for Science and Engineering 48 (2), 2277-2296 , 2023
    2023
    Citations: 8
  • Assessment of modified BAT algorithm for MOOC learner influence maximization
    K Aggarwal, A Arora
    Proceedings of the 2022 Fourteenth International Conference on Contemporary … , 2022
    2022
    Citations: 2
  • Detecting community structure in financial markets using the bat optimization algorithm
    K Aggarwal, A Arora
    International Journal of Information Technology Project Management (IJITPM … , 2022
    2022
    Citations: 2
  • Influence maximization for MOOC learners using BAT optimization algorithm
    K Aggarwal, A Arora
    International Journal of Fuzzy System Applications (IJFSA) 11 (2), 1-19 , 2022
    2022
    Citations: 7
  • An approach to control the PC with hand gesture recognition using computer vision technique
    K Aggarwal, A Arora
    2022 9th International Conference on Computing for Sustainable Global … , 2022
    2022
    Citations: 11

MOST CITED SCHOLAR PUBLICATIONS

  • Performance evaluation of RC6, blowfish, DES, IDEA, CAST-128 block ciphers
    K Aggarwal, JK Saini, HK Verma
    International Journal of Computer Applications 68 (25) , 2013
    2013
    Citations: 34
  • Hash_RC6—Variable length Hash algorithm using RC6
    K Aggarwal, HK Verma
    2015 International Conference on Advances in Computer Engineering and … , 2015
    2015
    Citations: 21
  • Comparison of RC6, modified RC6 & enhancement of RC6
    K Aggarwal
    2015 International Conference on Advances in Computer Engineering and … , 2015
    2015
    Citations: 19
  • Influence maximization in social networks using discrete BAT-modified (DBATM) optimization algorithm: a computationally intelligent viral marketing approach
    K Aggarwal, A Arora
    Social Network Analysis and Mining 13 (1), 146 , 2023
    2023
    Citations: 14
  • An approach to control the PC with hand gesture recognition using computer vision technique
    K Aggarwal, A Arora
    2022 9th International Conference on Computing for Sustainable Global … , 2022
    2022
    Citations: 11
  • Assessment of discrete BAT-modified (DBAT-M) optimization algorithm for community detection in complex network
    K Aggarwal, A Arora
    Arabian Journal for Science and Engineering 48 (2), 2277-2296 , 2023
    2023
    Citations: 8
  • Influence maximization for MOOC learners using BAT optimization algorithm
    K Aggarwal, A Arora
    International Journal of Fuzzy System Applications (IJFSA) 11 (2), 1-19 , 2022
    2022
    Citations: 7
  • Applications of augmented reality in medical training
    R Garg, K Aggarwal, A Arora
    Mathematical Modeling, Computational Intelligence Techniques and Renewable … , 2023
    2023
    Citations: 6
  • Hand gesture recognition for real-time game play using background elimination and deep convolution neural network
    K Aggarwal, A Arora
    Virtual and Augmented Reality for Automobile Industry: Innovation Vision and … , 2022
    2022
    Citations: 5
  • An intelligent article knowledge graph formation framework using bm25 probabilistic retrieval model
    JM Zaeem, V Garg, K Aggarwal, A Arora
    Iberoamerican Knowledge Graphs and Semantic Web Conference, 32-43 , 2023
    2023
    Citations: 4
  • Predictive Modeling of Drug-Drug Interactions: A Link Prediction Approach
    D Chauhan, N Narang, P Shukla, K Aggarwal
    Proceedings of the 2024 Sixteenth International Conference on Contemporary … , 2024
    2024
    Citations: 2
  • Assessment of modified BAT algorithm for MOOC learner influence maximization
    K Aggarwal, A Arora
    Proceedings of the 2022 Fourteenth International Conference on Contemporary … , 2022
    2022
    Citations: 2
  • Detecting community structure in financial markets using the bat optimization algorithm
    K Aggarwal, A Arora
    International Journal of Information Technology Project Management (IJITPM … , 2022
    2022
    Citations: 2
  • Deep Learning-Driven CNN Models for Enhanced Brain Tumor Classification
    K Aggarwal, K Kartikeya, V Srivastava
    Revolutionizing Healthcare: Impact of Artificial Intelligence on Diagnosis … , 2025
    2025
    Citations: 1
  • Breast Cancer Classification and Survival Prediction Using Proteomic Analysis
    K Aggarwal, A Arora, J Azzopardi
    Novel Developments in Futuristic AI-based Technologies, 123-138 , 2023
    2023
    Citations: 1
  • Pattern transfer based photorealistic synthetic fake image generation using cycle generative adversarial networks
    A Arora, JM Zaeem, V Garg, K Aggarwal, XZ Gao
    Discover Artificial Intelligence , 2026
    2026
  • Leveraging DCGANs and Cyclic GANs for Synthetic MRI Image Generation and Neural Network Optimization
    K Aggarwal, V Srivastava, K Kartikeya
    Generative Artificial Intelligence, 319-336 , 2025
    2025
  • Machine learning security on drones or UAV
    M Jain, K Aggarwal
    Computational Intelligence for Connective Cognition Networks, 44-59 , 2025
    2025
  • Genetic algorithm-based framework for optimizing medical image enhancement
    K Aggarwal, M Jain
    Computational Intelligence for Connective Cognition Networks, 27-43 , 2025
    2025
  • Computational Intelligence for Connective Cognition Networks: Advances and Applications
    K Aggarwal, A Arora, Z Akhtar, A Bruno
    CRC Press , 2025
    2025