Expert System
Artificial Intelligence
Cryptography and Network Security
41
Scopus Publications
1420
Scholar Citations
23
Scholar h-index
32
Scholar i10-index
Scopus Publications
Pipelined Reversible Logic with Clock Gating for Secure and Energyefficient Visual Data Processing Sairam Nam, Niranjan ReddyKallem, Vijender Solanki, RadhaKrishna Karne, V K Gunjan, Ninni Singh 2025 10th International Conference on Research in Intelligent Computing in Engineering Rice 2025, 2025 This paper presents an energy-efficient framework for secure visual data processing using reversible logic synthesis in VLSI systems. The proposed Reversible Logic Gate Cryptography Design (RLGCD) integrates Linear Feedback Shift Registers (LFSR) for dynamic key generation and Least Significant Bit (LSB) watermarking to strengthen data integrity. To enhance performance, a pipelined architecture is employed to increase throughput and reduce latency, while synthesisbased clock gating is incorporated to minimize dynamic power consumption by suppressing unnecessary switching activity. The design is implemented and verified on FPGA, demonstrating notable improvements in power efficiency, timing performance, and clock utilization compared with conventional irreversible logic architectures. The integration of reversible logic, pipelining, and clock gating offers a scalable and secure approach for low-power cryptographic systems, providing a viable pathway toward next-generation energy-aware encryption and decryption hardware.
Sentiment Analysis Unleashed: Leveraging VADER for Evaluation of Public Opinion Suhawni Arora, Ashmit Malhotra, Amit Kumar, Ninni Singh 2024 11th International Conference on Reliability Infocom Technologies and Optimization Trends and Future Directions Icrito 2024, 2024 Digital reviews provide real-world feedback on products and services in an era of online commerce and access to information. Providing feedback fosters trust and credibility among potential customers, enabling them to make informed purchasing decisions. In review classification, sentiment analysis is a key component that assesses the emotional tone of the reviews. Businesses can prioritize responses, manage their reputation, and extract actionable insights by categorizing reviews as positive, negative, or neutral. The study also includes neutral feedback, which includes suggestions for incremental improvement, which assists in continuous product refinement. In the proposed work, more than 5,68,455 reviews are divided into positive negative, and neutral sentiments by sentiment analysis. The results demonstrate VADER's robustness and versatility, showing its capacity to accurately gauge public sentiment across different contexts and types of language, including slang, emojis, and colloquial expressions. We also compare VADER's performance with other sentiment analysis algorithms, underscoring its advantages in handling informal online communication.
Understanding Audience Engagement in Award Winning Politoons- A Regression Analysis of So Sorry Series Piyush Chauhan, Ninni Singh, Vinit Kumar Gunjan, Ved Prakash Bhardwaj Icccmla 2024 6th International Conference on Cybernetics Cognition and Machine Learning Applications, 2024 This study investigates the factors influencing viewer engagement in the award-winning So Sorry Politoons series by India Today Group. Using simple and multiple linear regression analyses, we examine the impact of release date, video length, and number of likes on viewership. The results of this study help to explain the dynamics of political cartoons in relation to audience involvement, with practical implications for content creators and marketers aiming to optimize viewer reach. By understanding these key drivers, stakeholders can strategically enhance the impact of digital content in the competitive landscape of online media.
Exploring Transfer Learning in Brain Tumor Classification with Deep Neural Networks Vikas Singh Rawat, Sakshi Johar, Amit Kumar, Ninni Singh 2024 11th International Conference on Reliability Infocom Technologies and Optimization Trends and Future Directions Icrito 2024, 2024 In today's era, medical advancements have reached their peak but still, some bugs led to the emergence and involvement of advanced techniques like Machine learning, Artificial Intelligence, and Deep Learning. These three are interconnected and have strong relationships that work as such, to increase the growth of work in any domain with rapid accuracy and without any error. This paper deals with the identification and categorization of tumors. Brain tumors are a result of abnormal behavior of cell division which leads to uncontrolled growth of cells and results in the formation of masses and lumps in the brain. The detection of tumors is done using radiology with the help of MRI techniques or MRI scans. Traditional scans sometimes lead to errors causing misdiagnoses. This process is also timeconsuming and as such requires high expertise. To elevate this process, in this paper, we used a multi-layered CNN model which takes the MRI images as input and classifies it into one of the four major categories i.e., ‘Glioma’, ‘Meningioma’, ‘Pituitary’ or ‘No Tumor’. The publicly available dataset used to train this model contains over 7000 images which have been segregated into testing and training where both classes contain 4 different segmentations depicting the different brain tumors. The training folder contains nearly 5200 images, and the testing folder has over 1500 images. The accuracy of the model has been tested to over 96 percent. Then we converted the above model to the VGG16 model by using the study of Transfer Learning and the model gave up to 98.04% accuracy and validation accuracy was approximately 94.73%.
Embedding learning models with pedagogy recommendation in adaptive intelligent tutoring system Amit Kumar, Devesh Pratap Singh, Ninni Singh, Neeraj Kumar Pandey Embedded Devices and Internet of Things Technologies and Applications, 2024 A learning style is an active trait of a learner that contributes to the identification of individual differences in the design of an adaptive intelligent tutoring system. The learner's learning style greatly impacts an intelligent tutoring system's adaptability. The learner's characteristics, needs, cognitive ability, and meta-cognitive skills play an important role and have been extensively studied for determining a learner's learning style. Based on various learning and cognitive behavior characteristics, there are over 70 learning style models. The aim of this study is to curtly review 13 learning style models in adaptive hypermedia, along with the model's strengths, weaknesses, and applications. This study contributes to formulating a suitable learning style model for enhancing learning gains and making learning more efficient. As a result, it is suggested that an intelligent tutoring system can be developed to identify learners’ learning styles and tailor their education accordingly.
Multi-Controller Model for Improving the Performance of IoT Networks Ganesh Davanam, Suresh Kallam, Ninni Singh, Vinit Kumar Gunjan, Sudipta Roy, Javad Rahebi, Ali Farzamnia, Ismail Saad Energies, 2022 Internet of Things (IoT), a strong integration of radio frequency identifier (RFID), wireless devices, and sensors, has provided a difficult yet strong chance to shape existing systems into intelligent ones. Many new applications have been created in the last few years. As many as a million objects are anticipated to be linked together to form a network that can infer meaningful conclusions based on raw data. This means any IoT system is heterogeneous when it comes to the types of devices that are used in the system and how they communicate with each other. In most cases, an IoT network can be described as a layered network, with multiple tiers stacked on top of each other. IoT network performance improvement typically focuses on a single layer. As a result, effectiveness in one layer may rise while that of another may fall. Ultimately, the achievement issue must be addressed by considering improvements in all layers of an IoT network, or at the very least, by considering contiguous hierarchical levels. Using a parallel and clustered architecture in the device layer, this paper examines how to improve the performance of an IoT network’s controller layer. A particular clustered architecture at the device level has been shown to increase the performance of an IoT network by 16% percent. Using a clustered architecture at the device layer in conjunction with a parallel architecture at the controller layer boosts performance by 24% overall.
Comparison of various dos algorithm Mainul Hasan, Amogh Venkatanarayan, Inder Mohan, Ninni Singh, Gunjan Chhabra International Journal of Information Security and Privacy, 2020
Implementation and evaluation of personalized intelligent tutoring system International Journal of Innovative Technology and Exploring Engineering, 2019
Understanding Audience Engagement in Award Winning Politoons-A Regression Analysis of So Sorry Series P Chauhan, N Singh, VK Gunjan, VP Bhardwaj 2024 IEEE 6th International Conference on Cybernetics, Cognition and Machine … , 2024 2024
Embedding learning models with pedagogy recommendation in adaptive intelligent tutoring system A Kumar, DP Singh, N Singh, NK Pandey Embedded Devices and Internet of Things, 307-320 , 2024 2024
Utilizing Convolutional Neural Networks for the Detection of Early-Stage Leaf Diseases in Potato Crops R Kumar, A Shrivastava, A Kumar, DP Singh, NK Pandey, N Singh International Conference on Intelligent Communication, Control and Devices … , 2024 2024 Citations: 3
Sentiment Analysis Unleashed: Leveraging VADER for Evaluation of Public Opinion S Arora, A Malhotra, A Kumar, N Singh 2024 11th International Conference on Reliability, Infocom Technologies and … , 2024 2024 Citations: 7
Detection of Cardio Vascular abnormalities using gradient descent optimization and CNN N Singh, VK Gunjan, F Shaik, S Roy Health and Technology 14 (1), 155-168 , 2024 2024 Citations: 26
Futuristic Opportunities and Challenges for Cognitive Tutoring Systems N Singh, VK Gunjan, MA Chaurasia 2022 5th International Conference on Computational Intelligence and Networks … , 2022 2022 Citations: 1
Multi-Controller Model for Improving the Performance of IoT Networks G Davanam, S Kallam, N Singh, VK Gunjan, S Roy, J Rahebi, ... Energies 15 (22), 8738 , 2022 2022 Citations: 12
Security in IoT Mesh Networks based on Trust Similarity A Kavitha, VB Reddy, N Singh, VK Gunjan, K Lakshmanna, AA Khan, ... IEEE Access 10, 121712-121724 , 2022 2022 Citations: 36
Detection of lung cancer in CT scans using grey wolf optimization algorithm and recurrent neural network VK Gunjan, N Singh, F Shaik, S Roy Health and Technology 12 (6), 1197-1210 , 2022 2022 Citations: 65
Analysis of Performance Metrics N Singh, VK Gunjan, JM Zurada Cognitive Tutor: Custom-Tailored Pedagogical Approach, 139-172 , 2022 2022 Citations: 1
Building SeisTutor Intelligent Tutoring System for Experimental Learning Domain N Singh, VK Gunjan, JM Zurada Cognitive Tutor: Custom-Tailored Pedagogical Approach, 61-78 , 2022 2022 Citations: 2
Pedagogy Modeling N Singh, VK Gunjan, JM Zurada Cognitive Tutor: Custom-Tailored Pedagogical Approach, 51-60 , 2022 2022 Citations: 1
Performance Metrics: Intelligent Tutoring System N Singh, VK Gunjan, JM Zurada Cognitive Tutor: Custom-Tailored Pedagogical Approach, 117-137 , 2022 2022 Citations: 1
Pedagogy Modeling for Building SeisTutor Intelligent Tutoring System N Singh, VK Gunjan, JM Zurada Cognitive Tutor: Custom-Tailored Pedagogical Approach, 79-101 , 2022 2022 Citations: 1
Domain Modeling N Singh, VK Gunjan, JM Zurada Cognitive Tutor: Custom-Tailored Pedagogical Approach, 31-50 , 2022 2022
Execution of Developed Intelligent Tutoring System N Singh, VK Gunjan, JM Zurada Cognitive Tutor: Custom-Tailored Pedagogical Approach, 103-116 , 2022 2022 Citations: 1
Cognitive Tutor: Custom-Tailored Pedagogical Approach N Singh, VK Gunjan, JM Zurada Springer Nature , 2022 2022 Citations: 2
IoT enabled HELMET to safeguard the health of mine workers N Singh, VK Gunjan, G Chaudhary, R Kaluri, N Victor, K Lakshmanna Computer Communications 193, 1-9 , 2022 2022 Citations: 113
Blockchain-Based Privacy Access Control Mechanism and Collaborative Analysis for Medical Images SM Puja Sahay Prasad , G N Beena Bethel , Ninni Singh , Vinit Kumar Gunjan ... Security and Communication Networks 2 2022, 1-7 , 2022 2022 Citations: 15
Deep Learning and Transfer Learning for Malaria Detection SK Tayyaba Jameela, Kavitha Athotha,Ninni Singh ,Vinit Kumar Gunjan Computational Intelligence and Neuroscience 2022, 1-14 , 2022 2022 Citations: 77
MOST CITED SCHOLAR PUBLICATIONS
Development of Algorithms for an IoT-Based Smart Agriculture Monitoring System KNEA Siddiquee, MS Islam, N Singh, VK Gunjan, WH Yong, MN Huda, ... Wireless Communications and Mobile Computing 2022, 1-16 , 2022 2022 Citations: 116
IoT enabled HELMET to safeguard the health of mine workers N Singh, VK Gunjan, G Chaudhary, R Kaluri, N Victor, K Lakshmanna Computer Communications 193, 1-9 , 2022 2022 Citations: 113
Learning styles based adaptive intelligent tutoring systems: Document analysis of articles published between 2001. and 2016. A Kumar, N Singh, NJ Ahuja International Journal of Cognitive Research in Science, Engineering and … , 2017 2017 Citations: 84
Deep Learning and Transfer Learning for Malaria Detection SK Tayyaba Jameela, Kavitha Athotha,Ninni Singh ,Vinit Kumar Gunjan Computational Intelligence and Neuroscience 2022, 1-14 , 2022 2022 Citations: 77
A parametrized comparative analysis of performance between proposed adaptive and personalized tutoring system “seis tutor” with existing online tutoring system N Singh, VK Gunjan, MM Nasralla IEEE Access 10, 39376-39386 , 2022 2022 Citations: 68
Rating‐Based Recommender System Based on Textual Reviews Using IoT Smart Devices M Ahmed, MD Ansari, N Singh, VK Gunjan, SK BV, M Khan Mobile Information Systems 2022 (1), 2854741 , 2022 2022 Citations: 66
Detection of lung cancer in CT scans using grey wolf optimization algorithm and recurrent neural network VK Gunjan, N Singh, F Shaik, S Roy Health and Technology 12 (6), 1197-1210 , 2022 2022 Citations: 65
SeisTutor: A Custom-Tailored Intelligent Tutoring System and Sustainable Education N Singh, VK Gunjan, AK Mishra, RK Mishra, N Nawaz Sustainability 14 (7), 4167 , 2022 2022 Citations: 65
Perimeter degree technique for the reduction of routing congestion during placement in physical design of VLSI circuits K Lakshmanna, F Shaik, VK Gunjan, N Singh, G Kumar, RM Shafi Complexity 2022 (1), 8658770 , 2022 2022 Citations: 63
Information and decision sciences SC Satapathy, J Tavares, V Bhateja, JR Mohanty Proceedings of the 6th International Conference on FICTA , 2017 2017 Citations: 53
Modern Approaches in Machine Learning & Cognitive Science: A Walkthrough VK Gunjan, JM Zurada, N Singh Springer , 2022 2022 Citations: 44
A Novel Architecture for Learner-Centric Curriculum Sequencing in Adaptive Intelligent Tutoring System N Singh, NJ Ahuja, A Kumar Journal of Cases on Information Technology (JCIT) 20 (3), 1-20 , 2018 2018 Citations: 42
Bug Model Based Intelligent Recommender System with Exclusive Curriculum Sequencing for Learner-Centric Tutoring N Singh, NJ Ahuja International Journal of Web-Based Learning and Teaching Technologies … , 2019 2019 Citations: 40
Future Trends for Healthcare Monitoring System in Smart Cities Using LoRaWAN‐Based WBAN I Bouazzi, M Zaidi, M Usman, MZM Shamim, VK Gunjan, N Singh Mobile Information Systems 2022 (1), 1526021 , 2022 2022 Citations: 39
Master-slave group based model for co-ordinator selection, an improvement of bully algorithm B Mishra, N Singh, R Singh 2014 International Conference on Parallel, Distributed and Grid Computing … , 2014 2014 Citations: 38
Security in IoT Mesh Networks based on Trust Similarity A Kavitha, VB Reddy, N Singh, VK Gunjan, K Lakshmanna, AA Khan, ... IEEE Access 10, 121712-121724 , 2022 2022 Citations: 36
Implementation and evaluation of personalized intelligent tutoring system N Singh, A Kumar, NJ Ahuja Int. J. Innov. Technol. Explor. Eng.(IJITEE) 8, 46-55 , 2019 2019 Citations: 36
Empirical analysis of explicating the tacit knowledge background, challenges and experimental findings N Singh, NJ Ahuja International Journal of Innovative Technology and Exploring Engineering 8 … , 2019 2019 Citations: 36
Image data-deduplication using the block truncation coding technique AJ Zargar, N Singh, G Rathee, AK Singh 2015 International Conference on Futuristic Trends on Computational Analysis … , 2015 2015 Citations: 35
Missing value imputation with unsupervised kohonen self organizing map N Singh, A Javeed, S Chhabra, P Kumar Emerging Research in Computing, Information, Communication and Applications … , 2015 2015 Citations: 33
