Niveditha Gopigari

@geethanjaliinstitutions.com

Assistant professor, Computer science and engineering
Niveditha G

Niveditha Gopigari


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https://researchid.co/nive.gopigari

RESEARCH, TEACHING, or OTHER INTERESTS

Artificial Intelligence, Engineering, Biomedical Engineering, Computer Vision and Pattern Recognition
9

Scopus Publications

5

Scholar Citations

2

Scholar h-index

Scopus Publications

  • An Improved Edge Detection with K-means and Canny for Skin Lesion in Melanoma
    K. Gnana Mayuri, G. Niveditha
    Cognitive Science and Technology, 2026
  • Acute Lymphoblastic Leukemia Cell Nuclei Segmentation Using U-Net with Ensemble Feature Extractor
    G. Niveditha, B. Uma Maheswari
    Lecture Notes in Electrical Engineering, 2025
  • Elevating Maternal Healthcare: Synergy of Cardiotocography, Machine Learning Models and Interpretive Analysis
    G Niveditha, B Uma Maheswari, Rocío Pérez de Prado
    Procedia Computer Science, 2025
    The global goal of reducing maternal mortality and improving neonatal health, in particular during labor where clinical actions must be precise and timely, is important. Abnormalities such as low or high maternal oxygen levels, inappropriate uterine contraction patterns, or abnormal fetal heart rates during delivery can endanger the well-being of the mother and child. Currently, many essential signs are primarily assessed and recorded using visual means, which are susceptible to delay or even error. However, of late, machine learning has been characterized as a cutting-edge approach that helps improve practice in clinical settings by generating more accurate forecasts based on intricate data patterns This study’s objective is to formulate an appropriate machine-learning model for predicting maternal and fetal health risks at delivery for improved clinical outcomes. Within the freely accessible cardiotocography dataset consisting of the number of uterine contractions more than and the fetal heart rate of 21 features, Recursive feature elimination (RFE) folds the features down into 15. To solve the problem of the class imbalance MSMOTE (Modified Synthetic Minority Over-sampling Technique) is used for this purpose. A number of the machine learning procedures such as Random Forest, LightGBM, CatBoost, AdaBoost, Decision Tree, K-Nearest Neighbor, Support Vector Machine, Logistic Regression, Multi-Layer Perceptron, and Gradient Boosting amongst others are also trained and subjected to evaluation. Of these, CatBoost and LightGBM models recorded the highest level of accuracy at 96.0% and 95.1% respectively. To further interpret as well as validate the model predictions, LIME (Local Interpretable Model-agnostic Explanations) and SHAP (SHapley Additive exPlanations) used.
  • Enhancing Mobility: A Smart Cane with Integrated Navigation System and Voice-Assisted Guidance for the Visually Impaired
    Muktha D S, G Niveditha, Nikhil Anthony Pinto, Somnath Sinha
    Proceedings 2024 13th IEEE International Conference on Communication Systems and Network Technologies Csnt 2024, 2024
    Blindness is a condition which affects many people, and for the affected people, quality of life can take a big hit. Most blind people already use walking sticks to feel the terrain in front of them as they move around and navigate using touch and sound. However, they cannot judge distances to objects until the cane actually hits the object. In some cases, the contact with the cane may damage the object. Hence, it may be better to have some early warning system so that there is less likelihood of causing damage. This paper presents the design and development of a “Smart Cane” aimed at enhancing mobility and safety for visually impaired individuals. The cane incorporates ultrasonic sensors to detect objects in the user's surroundings. When an object is detected within a specified distance range, the cane provides haptic feedback through a bidirectional vibration motor, alerting the user to its presence. The microcontroller-based system processes data from both sensors and efficiently manages power consumption to ensure extended battery life. The device's design includes user-friendly controls and an ergonomic enclosure to offer ease of use and protection for the electronic components. Further, there is built-in navigation via online Map API. With the convenience of navigating oneself without external assistance, the “Smart Cane” demonstrates great potential to improve the independence and confidence of visually impaired individuals in navigating their environments safely.
  • Agricultural Internet of Things (AIoT) Architecture, Applications, and Challenges
    Kavitha Rajamohan, Sangeetha Rangasamy, Libin Baby, G. Niveditha, D. S. Muktha
    Advanced Iot Technologies and Applications in the Industry 4 0 Digital Economy, 2024
    The internet of things (IoT) is a system that involves adding sensors, software, and network connectivity to physical devices, enabling them to collect and exchange data. This technology has the potential to bring significant advancements to various sectors, including agriculture. In farming, the agricultural internet of things (AIoT) utilizes IoT to improve efficiency, sustainability, and productivity. Through the real-time collection and analysis of data, AIoT can optimize growing conditions, prevent diseases and pests, and ultimately increase crop yields. By monitoring factors such as soil moisture, temperature, and nutrient levels, AIoT technology can effectively track crop health and detect potential issues in advance. In this way, AIoT technology is helping farmers to make more informed decisions and take more effective actions to improve crop yields, reduce waste, and lower costs. AIoT in agriculture finds practical applications in smart irrigation systems, precision agriculture, livestock monitoring systems, and climate control systems. Smart irrigation systems utilize weather data and soil moisture sensors to efficiently manage water consumption. Precision agriculture employs sensors and data analysis techniques to optimize planting, fertilization, and pest control practices. Livestock monitoring systems aid in monitoring and managing the well-being of farm animals. Climate control systems utilize AIoT to regulate and optimize environmental conditions for crops and livestock. Livestock monitoring systems use sensors to track the health and well-being of animals. Climate control systems for greenhouses and barns use AIoT devices to monitor temperature, humidity, and other environmental factors to optimize growing conditions. Sensors can be used to monitor various environmental factors in a farm, by connecting the sensors to a cloud-based platform for storing and analyzing data. The wireless sensor networks can be used to calculate the dew point on leaves and adjust the greenhouse environment to prevent and control plant diseases. Drones equipped with sensors, cameras, and other imaging technology can also be used to monitor crop conditions, as this allows farmers to take proactive measures to address these issues, preventing crop loss and reducing the need for pesticides and other chemicals. IoT/sensor nodes are vital components in precision agriculture as they gather real-time data. Integrating data analytics and machine learning into the agricultural system improves its practicality and efficiency. Real-time data availability enhances precision in agriculture, and combining data analytics with this information leads to notable progress in the field. However, AIoT technology is gradually advancing in agriculture, but there is a need for a more rigorous research approach in this area. Additionally, the current literature lacks coherence and solid research on the interconnectedness of technology and agriculture.
  • A Hybrid Algorithm for Document Clustering Using Optimized Kernel Matrix and Unsupervised Constraints
    S. Siamala Devi, M. Deva Priya, P. Anitha Rajakumari, R. Kanmani, G. Poorani, S. Padmavathi, G. Niveditha
    Eai Springer Innovations in Communication and Computing, 2022
  • Reliability of fault tolerance in cloud using machine learning algorithm
    , S. Harini Krishna*, G. Niveditha, , K. Gnana Mayuri, and
    International Journal of Innovative Technology and Exploring Engineering, 2019
    The basic fault tolerance issues seen in cloud computing are identification and recovery. To fight with these issues, so many fault tolerance methods have been designed to decrease the faults. However, due to the reliability and web based service giving behavior, fault tolerance in cloud computing will be a huge challenge. The present model is not just on tolerating faults but also to decrease the possibility of future faults as well[4].The fault tolerance deals with the exact and constant operation of the fault segments. The processing on computing nodes can be done remotely in the real time cloud applications, so there could be more possibilities of errors. Hence there lies an immense necessity for fault tolerance to attain consistency to the real time computing on cloud infrastructure. The “fault tolerance” can be explained through fault processing that have two basic stages. The stages are (i) The effective error processing stage which is used to intended for carrying the “effective error” back to inactive state, i.e., before the error occurred (ii) The latent error processing stage intended for guaranteeing that the fault does not get effective once again.
  • Developing fault tolerance in cloud computing based on machine learning approaches
    Journal of Advanced Research in Dynamical and Control Systems, 2018
  • DWT-based face recognition using fast walsh hadamard transform and chiral image superimposition as pre-processing techniques
    G V Niveditha, B P Sharmila, K Manikantan, S Ramachandran
    2nd International Conference on Electronics and Communication Systems Icecs 2015, 2015
    One of the major challenges encountered by current Face Recognition (FR) techniques lies in the difficulties of handling varying poses and illuminations. In this paper we propose three novel techniques, viz., Fast Walsh Hadamard Transform (FWHT), Chiral Image Superimposition (CIS) and Discrete Wavelet Transform (DWT), to improve the performance of a FR system. FWHT is used for illumination normalization, CIS combats pose variances and DWT along with its sub-bands LL and LH are used for efficient feature extraction. A Binary Particle Swarm Optimization (BPSO) based feature selection is used to reduce the number of selected facial features for recognition. Individual stages of the FR system are examined and an attempt is made to improve each stage. Experimental results show the promising performance of the proposed techniques for face recognition on four benchmark face databases, namely, Color FERET, ORL, CMU PIE and Extended Yale B.

RECENT SCHOLAR PUBLICATIONS

  • Acute Lymphoblastic Leukemia Cell Nuclei Segmentation Using U-Net
    G Niveditha, BU Maheswari
    Computational Intelligence in Machine Learning: Proceedings of the 3rd … , 2025
    2025.0
  • AI-Driven Predictions: Enhancing Liver Disease Diagnosis through Advanced Machine Learning.
    G Niveditha, KG Mayuri
    Library of Progress-Library Science, Information Technology & Computer 44 (3) , 2024
    2024.0
  • Acute Lymphoblastic Leukemia Cell Nuclei Segmentation Using U-Net with Ensemble Feature Extractor
    G Niveditha, B Uma Maheswari
    International Conference on Computational Intelligence in Machine Learning … , 2023
    2023.0
    Citations: 2
  • Information retrieval under the context of data-driven design (d3) in big-data
    G Niveditha, KG Mayuri, SH Krishna
    Solid State Technology 63 (6), 5111-5117 , 2020
    2020.0
    Citations: 1
  • Elevating Maternal Healthcare: Synergy of Cardiotocography, Machine Learning Models and Interpretive Analysis
    GNBUMRP de Prado
    Procedia Computer Science 258, 2787-2797 , 0
    Citations: 2

MOST CITED SCHOLAR PUBLICATIONS

  • Acute Lymphoblastic Leukemia Cell Nuclei Segmentation Using U-Net with Ensemble Feature Extractor
    G Niveditha, B Uma Maheswari
    International Conference on Computational Intelligence in Machine Learning … , 2023
    2023.0
    Citations: 2
  • Elevating Maternal Healthcare: Synergy of Cardiotocography, Machine Learning Models and Interpretive Analysis
    GNBUMRP de Prado
    Procedia Computer Science 258, 2787-2797 , 0
    Citations: 2
  • Information retrieval under the context of data-driven design (d3) in big-data
    G Niveditha, KG Mayuri, SH Krishna
    Solid State Technology 63 (6), 5111-5117 , 2020
    2020.0
    Citations: 1
  • Acute Lymphoblastic Leukemia Cell Nuclei Segmentation Using U-Net
    G Niveditha, BU Maheswari
    Computational Intelligence in Machine Learning: Proceedings of the 3rd … , 2025
    2025.0
  • AI-Driven Predictions: Enhancing Liver Disease Diagnosis through Advanced Machine Learning.
    G Niveditha, KG Mayuri
    Library of Progress-Library Science, Information Technology & Computer 44 (3) , 2024
    2024.0