Dr Nellutla Sasikala

@kitss.edu.in

Professor Electronics & Communication Engineering
Kamala Institute of Technology & Science

Dr Nellutla Sasikala


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https://researchid.co/nsasikala

RESEARCH, TEACHING, or OTHER INTERESTS

Signal Processing, Computer Vision and Pattern Recognition, Artificial Intelligence
10

Scopus Publications

97

Scholar Citations

5

Scholar h-index

4

Scholar i10-index

Scopus Publications

  • Automated Cardiovascular Lesion Segmentation in Coronary CT Angiography Using Trans U Net: A Transformer-Based Deep Learning Approach
    International Journal of Intelligent Engineering and Systems, 2025
    Accurate and automated segmentation of cardiovascular lesions in Coronary CT Angiography (CCTA) is critical for early diagnosis and treatment planning of coronary artery disease (CAD).This study proposes a two-stage hybrid framework that integrates feature-based classification with deep learning-based segmentation for improved medical image interpretation.Initially, images undergo preprocessing, including resizing, contrast enhancement, and normalization, to enhance visual quality.Feature extraction is performed using Local Binary Patterns (LBP) and Adaptive Weighted Multi-Resolution Gray-Level Co-Occurrence Matrix (AWMR-GLCM), capturing texture and spatial characteristics.The extracted features are cascaded and classified using a Long Short-Term Memory (LSTM) network to learn temporal dependencies.Simultaneously, a deep learning-based segmentation model, Trans U Net, is trained to precisely delineate affected regions.The proposed method achieved superior performance, attaining 98.54% accuracy, 99.64% F1-score, and 99.04% precision for classification, while the segmentation stage obtained 98.25% DSC, 98.31% IoU, and 99.47% HD, demonstrating the robustness and reliability of the approach.Experimental results demonstrate the effectiveness of combining feature-based classification with deep learning-based segmentation, enhancing robustness and reliability in medical image analysis.
  • An Entropic EOQ Inventory Control using Dynamic Programming
    16th International Conference on Advances in Computing Control and Telecommunication Technologies Act 2025, 2025
  • Highly Accurate Skin Cancer Diagnosis Using HMT-NET and Vision Transformer Models
    Sasikala N., Vaka A. R., Suguna R., Kumar N., Parvez M. M., Jamal K.
    Journal of Biomedical Photonics and Engineering, 2025
    Skin cancer, especially melanoma, is one of the most aggressive and fatal cancers, with rising global incidence driven largely by ultraviolet exposure. Early detection is critical, as visual similarities among lesions complicate diagnosis. Clinical methods include self-examination, dermoscopic, and biopsy, while computational approaches range from traditional Computer-aided design systems to deep learning models like convolutional neural network and Vision Transformers. Although these models enhance accuracy, they face limitations such as high data requirements, limited interpretability, and inconsistent image quality, emphasizing the need for scalable and explainable diagnostic systems in clinical settings. A Hybrid Multi-layer Transformer Network (HMT-NET) and Vision Transformer (ViT)-based model is proposed for accurate skin lesion segmentation and multiclass skin cancer classification. This study presents a hybrid HMT-NET and ViT framework for accurate skin lesion segmentation and classification. Evaluated on HAM10000 and ISIC2019 datasets, the model achieved high Dice score and Jaccard index and 98.75% classification accuracy, demonstrating superior performance and reliability for integration into automated dermatological diagnostic systems.
  • Machine Interpretation of Ballet Dance: Alternating Wavelet Spatial and Channel Attention Based Learning Model
    P. V. V. Kishore, D. Anil Kumar, P. Praveen Kumar, D. Srihari, N. Sasikala, L. Divyasree
    IEEE Access, 2024
    ‘Ballet’ is a 15 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><i>th</i></sup> - century concert performing dance form that originated in Italy. Current AI models for ballet dance pose identification in live performance videos is challenging due to variational pixel distribution of human actions across backgrounds. Notably, their performance on online video datasets improved with both channel (CA) and spatial attention (SA) models but tend to generate over-smoothed Convolutional features due to feature averaging in the attention network. Alternatively, wavelet attention preserves both high and low frequency components in the features which improves the test accuracy. Applying CA and SA on wavelet features simultaneously resulted in hyper-refined features due to double averaging. To overcome this drawback, Alternating Wavelet Channel and Spatial Attention (AWCSA) across any learning network as backbone architecture is proposed. The global features across the residual connections in the backbone (ResNet50) are amplified exclusively with low and high-frequency local features across the channel and spatial dimensions alternatively one after the other. The Ballet online dance video dataset (BOVD23) evaluates the performance of the proposed AWCSA along with baseline action datasets. The end-to-end trained AWCSA has recorded a 6-8% higher performance metrics on BOVD23 dataset over the counterparts.
  • EAgri: Smart Agriculture Monitoring Scheme using Machine Learning Strategies
    J. Venkatesh, K. K. Ramasamy, M. Aruna, K Praveen Kumar Rao, Nellutla Sasikala, Karthik Nasani
    Proceedings of the 2022 International Conference on Innovative Computing Intelligent Communication and Smart Electrical Systems Icses 2022, 2022
    The logic of Machine Learning and its predictive strategies are applied to many different applications to attain good benefits over now-a-days. This paper associates the machine learning concept to improve the production on agricultural field as well as the novel adaptive technologies are associated into this learning concept to make a proper agricultural monitoring system in fine manner. This paper is intended to design a new Agricultural Monitoring robot called eAgriBot, in which it integrates the logic of Machine Learning and produce an intelligent predictions to prevent the crops from affections including weather conditions, rainfall and soil water level. In parallel, the eAgriBot contains a high resolution digital camera to capture the pictures of the crops and maintains that into the server unit in proper manner. In literature, there are many approaches designed to provide an automated watering system, systematic pesticide spraying and so on. But all are dependent on the human operations, in which the automatic watering system requires the manual trigger from either SMS or other internet associated operations; similarly the systematic pesticide mechanism requires the same kind of trigger to perform the action. These cases are critical in terms of monitoring the agricultural field from remote environment. The concept of Internet of Things (IoT) is associated over this approach to push and update the agricultural data collected by the eAgriBot to the Cloud Server. This entire process is controlled and manipulated by the novel machine learning strategy called Smart Learning Assisted Data Manipulation (SLADM), in which it is derived from the traditional Random Forest Classification logic with specific parameter modification called dynamic threshold fixation. In general the Random Forest logic uses the constant threshold for data processing, but in this approach dynamic principles are applied to improve the prediction accuracy. With the help of this system plants leaf disease can easily be monitored with the help of digital camera associated with the eAgriBot. It captures the crop field images and pass it to the server end for processing, in which the server end accumulates that and process that by using proposed machine learning called Smart Learning Assisted Data Manipulation. The proposed SLADM scheme is designed robustly for analyzing the both the image and data content, so that the logic identifies the severity in leaves and the data as well. In case of any severity level mismatching found by the algorithm, it immediately notifies that to the respective farmer to take an appropriate action to save the plants from the disease spread. The data available into the cloud server can easily monitored by the farmers from anywhere in the globe as well as this approach of SLADM provides an accurate agricultural field predictions, so that the farmers can easily monitor the field without any complexities and attain good production level easily
  • Train bogie part recognition with multi-object multi-template matching adaptive algorithm
    N. Sasikala, P.V.V. Kishore
    Journal of King Saud University Computer and Information Sciences, 2020
    Automation of train rolling stock monitoring system by recognizing the bogie parts is a process of identifying defects in the train undercarriage moving at >30 Kmph. Recognizing the parts of a moving train using computer vision models based on color and texture with deformable curve segmentation models is a challenging and computationally intensive. A multi object multi-template model is proposed to solve this problem in a computationally less intensive process. A multi object multi template library with 26 objects in 40 bogie frames was created based on statistical parameters of the bogie part in the center of the frame through template extraction model. Fifty templates were designed from 250 frames of the first bogie movement through the camera plane. Maximum normalized cross correlation coefficient calculated on each frame with a 26 – by – 40 template matrices identifies the bogie parts in the frame in a single computation. High speed recording of the train bogies at 240 fps establishes the datasets for experimentation having 2 trains with 20 coaches each capturing 15,000 frames per train. The correct recognition accuracy is 91% with a false recognition rate of 15%.
  • 3D hand gesture representation and recognition through deep joint distance measurements
    P. Vasavi, Suman Maloji, E. Kiran, D. Anil, N. Sasikala
    International Journal of Advanced Computer Science and Applications, 2020
    Hand gestures with finger relationships are among the toughest features to extract for machine recognition. In this paper, this particular research challenge is addressed with 3D hand joint features extracted from distance measurements which are then colour mapped as spatio temporal features. Further patterns are learned using an 8-layer convolutional neural network (CNN) to estimate the hand gesture. The results showed a higher degree of recognition accuracy when compared to similar 3D hand gesture methods. The recognition accuracy for our dataset KL 3DHG with 220 classes was around 94.32%. Robustness of the proposed method was validated with only available benchmark 3D skeletal hand gesture dataset DGH 14/28.
  • Localized region based active contours with a weakly supervised shape image for inhomogeneous video segmentation of train bogie parts in building an automated train rolling examination
    N. Sasikala, P. V. V. Kishore, D. Anil Kumar, Ch. Raghava Prasad
    Multimedia Tools and Applications, 2019
    Active Contours have been widely acknowledged for providing a dependable solution to complex image segmentation problems which are represented as a functional optimization problem. Inhomogeneous image intensity regions present difficulties for the evolving curve, which are driven by nonlinear variations in a region’s intensity pixels. The problem was approached using the local region’s statistical information for improving the segmentation accuracies. However, the local information of the region was calculated using the inhomogeneous pixel intensities, which in turn degrade the segmentation outputs. In this paper, we approach this problem by introducing a weakly supervised shape image (WSSI), which form a pre-defied shape term within a local window and a local region difference term to improve the segmentation results. The proposed model is robust to image intensity variations and are computationally efficient on high-resolution images. To test and validate the proposed active contour model, we choose a real-time application, Automated Rolling Stock Examination using computer vision. The problem here is to extract the bogie parts using the proposed method to monitor their conditional health on a moving train from the captured high speed video sequence. We have created the bogie dataset with five trains at different time zones using a 240fps sports action camera in full HD mode. We apply our proposed method on these video frames to segment 10 vital parts of the train bogie on a 10000-frame video sequence per train. The proposed method has been compared with similar state-of-the-art localized region based active contour methods for segmentation accuracy.
  • Flower image segmentation using watershed and marker controlled watershed transform domain algorithms
    Arpn Journal of Engineering and Applied Sciences, 2018
  • Unifying Boundary, Region, Shape into Level Sets for Touching Object Segmentation in Train Rolling Stock High Speed Video
    N. Sasikala, P.V.V. Kishore, Ch. Raghava Prasad, E. Kiran Kumar, D. Anil Kumar, M. Teja Kiran Kumar, M.V.D. Prasad
    IEEE Access, 2018
    Traditional level sets suffer from two major limitations: 1) unable to detect touching object boundaries and 2) segment partially occluded objects. In this paper, we present a model and simulation of a level set functional with unified knowledge of objects region, boundary, and shape models. The simulations of the proposed model were tested on high-speed videos of the train rolling stock for bogie part segmentation. The proposed model will resolve single- and multi-object segmentation of touching boundaries and partially occulted mechanical parts on a train bogie. Simulations on high-speed videos of four trains with 1 0720 frames have resulted in near perfect segmentation of 10 touching and occluded bogie parts. The proposed model performed better than the state-of-the-art level set segmentation models, showing faster and more accurate segmentations of moving mechanical parts in high-speed videos.

RECENT SCHOLAR PUBLICATIONS

  • AI-Driven Non-Invasive Cardiac Risk Assessment in Women: A Technical Review and Conceptual Framework
    MS Dr Nellutla Sasikala, E. Swapna
    JZU - Engineering Science 26 (4), 277-294 , 2026
    2026
  • AI-Driven Non-Invasive Cardiac Risk Assessment in Women: A Technical Review and Conceptual Framework
    MS Dr Nellutla Sasikala, E. Swapna
    JZU - Engineering Science 26 (4), 277-294 , 2026
    2026
  • An Entropic EOQ Inventory Control using Dynamic Programming.
    K Rao, N Sasikala
    Grenze International Journal of Engineering & Technology (GIJET) 11 , 2025
    2025
  • SA-LEACH
    KPK Rao, N Sasikala, B Kalesh
    IJST 18, 238 , 2025
    2025
  • Effective Transmission of Messages in WANETs Adopting Node Stamping Technique
    DKPKR Dr N Sasikala
    Journal of Electrical Systems 20 (3), 6354-6365 , 2024
    2024
  • The Magic of Pixels: Unravelling the Power of AI in Creating Realistic Images
    DN Sasikala
    International Journal of Innovative Research of Science,Engineering … , 2024
    2024
  • ROI SEGMENTATION AND MORPHOLOGICAL ANALYSIS OF KIDNEY STONE DETECTION
    DKPKR Dr N Sasikala
    International Journal of Current Research 16 (4), 278221-27824 , 2024
    2024
  • Machine Interpretation of Ballet Dance: Alternating Wavelet Spatial and Channel Attention Based Learning Model
    LD P. V. V. KISHORE , (Senior Member, IEEE), D. ANIL KUMAR, (Member, IEEE ...
    IEEE Access 12, 55264 – 55280 , 2024
    2024
    Citations: 4
  • Selecting optimal path in multiple path routing for MANETs using fuzzy cost
    DKPKR Dr N Sasikala
    INDIAN JOURNAL OF SCIENCE AND TECHNOLOGY 17 (7), 598-609 , 2024
    2024
  • CHANDRAYAAN: SURVEY ON THE JOURNEY TO MOON
    DKPKR Dr.N.Sasikala
    Futuristic Trends in Contemporary Mathematics & Applications 3, 1-8 , 2024
    2024
  • Selecting optimal path in multiple path routing for MANETs using fuzzy cost
    DKS Dr.N.Sasikala,Dr.K.praveen Kumar Rao
    INDIAN JOURNAL OF SCIENCE AND TECHNOLOGY 17, 598-609 , 2024
    2024
  • Moiré Patterns in Digital Photos-Detection & More Loss Functions
    DN Sasikala
    National Conference on Futuristic Areas in Computer Engineering and … , 2023
    2023
  • Machine learning algorithms of ECG classification for heart disease prediction: Survey
    DN Sasikala
    National Conference on Futuristic Areas in Computer Engineering and … , 2023
    2023
  • The Challenges and Opportunities for New Technologies in Computer Vision and Image Processing: A Paper Review
    DKPKR Dr N Sasikala
    GIS SCIENCE JOURNAL 10 (4), 785-794 , 2023
    2023
  • Image Enhancement and Adjustment using Profile Compression Algorithm for Histogram Equalization
    DN Sasikala
    NOVYI MIR Research Journal 8 (4), 199-209 , 2023
    2023
  • Machine learning algorithms of ECG classification for heart disease prediction: Survey
    DNS R.Bharathi
    Proceedings of 4th National Conference on Futuristic Areas in Computer … , 2023
    2023
  • Moiré Patterns in Digital Photos-Detection & More Loss Functions
    DN Sasikala
    National Conference on Futuristic Areas in Computer Engineering and … , 2023
    2023
  • The Challenges and Opportunities for New Technologies in Computer Vision and Image Processing: A Paper Review
    PM Dr N Sasikala, Dr K Praveen Kumar Rao
    GIS SCIENCE JOURNAL 10 (6), 305-315 , 2023
    2023
  • Image Enhancement and Adjustment using Profile Compression Algorithm for Histogram Equalization
    KA Dr.N.Sasikala, G.Prathyusha, T.Suchithra, B.Dileep Kumar
    NOVYI MIR Research Journal 8 (4), 199-209 , 2023
    2023
  • DETECTION OF SKIN CANCER BY THE USE OF PHYTON
    ES Dr K Praveen Kumar Rao, Dr N. Sasikala
    Strad Research 10 (6), 305-315 , 2023
    2023

MOST CITED SCHOLAR PUBLICATIONS

  • Feature extraction of real-time image using Sift algorithm
    N Sasikala, V Swathipriya, M Ashwini, V Preethi, A Pranavi, M Ranjith
    European Journal of Electrical Engineering and Computer Science 4 (3) , 2020
    2020
    Citations: 32
  • Train bogie part recognition with multi-object multi-template matching adaptive algorithm
    N Sasikala, PVV Kishore
    Journal of King Saud University-Computer and Information Sciences 32 (5 … , 2020
    2020
    Citations: 15
  • Localized region based active contours with a weakly supervised shape image for inhomogeneous video segmentation of train bogie parts in building an automated train rolling …
    N Sasikala, PVV Kishore, DA Kumar, CR Prasad
    Multimedia Tools and Applications 78 (11), 14917-14946 , 2019
    2019
    Citations: 13
  • Unifying boundary, region, shape into level sets for touching object segmentation in train rolling stock high speed video
    N Sasikala, PVV Kishore, CR Prasad, EK Kumar, DA Kumar, MTK Kumar, ...
    IEEE Access 6, 70368-70377 , 2018
    2018
    Citations: 10
  • 3D hand gesture representation and recognition through deep joint distance measurements
    P Vasavi, S Maloji, EK Kumar, DA Kumar, N Sasikala
    International Journal of Advanced Computer Science and Applications 11 (4) , 2020
    2020
    Citations: 9
  • eAgri: Smart Agriculture Monitoring Scheme using Machine Learning Strategies
    J Venkatesh, KK Ramasamy, M Aruna, KPK Rao, N Sasikala, K Nasani
    2022 International Conference on Innovative Computing, Intelligent … , 2022
    2022
    Citations: 5
  • An Adaptive Edge Detecting Method for Satellite Imagery Based on Canny Edge Algorithm
    N Sasikala
    International Journal of Advanced Engineering Research and Science (IJAERS … , 2020
    2020
    Citations: 5
  • Machine Interpretation of Ballet Dance: Alternating Wavelet Spatial and Channel Attention Based Learning Model
    LD P. V. V. KISHORE , (Senior Member, IEEE), D. ANIL KUMAR, (Member, IEEE ...
    IEEE Access 12, 55264 – 55280 , 2024
    2024
    Citations: 4
  • Flower image segmentation using watershed and marker controlled watershed transform domain algorithms
    BKV Prasad, MVD Prasad, CR Prasad, N Sasikala, S Ravi
    ARPN Journal of Engineering and Applied Sciences 13 (21), 51-64 , 2018
    2018
    Citations: 3
  • Texture Analysis of a Color Image Using Traditional and Circular Gabor Filters
    N Sasikala
    International Journal Of Advanced Research And Science 3 (9), 113-123 , 2016
    2016
    Citations: 1
  • AI-Driven Non-Invasive Cardiac Risk Assessment in Women: A Technical Review and Conceptual Framework
    MS Dr Nellutla Sasikala, E. Swapna
    JZU - Engineering Science 26 (4), 277-294 , 2026
    2026
  • AI-Driven Non-Invasive Cardiac Risk Assessment in Women: A Technical Review and Conceptual Framework
    MS Dr Nellutla Sasikala, E. Swapna
    JZU - Engineering Science 26 (4), 277-294 , 2026
    2026
  • An Entropic EOQ Inventory Control using Dynamic Programming.
    K Rao, N Sasikala
    Grenze International Journal of Engineering & Technology (GIJET) 11 , 2025
    2025
  • SA-LEACH
    KPK Rao, N Sasikala, B Kalesh
    IJST 18, 238 , 2025
    2025
  • Effective Transmission of Messages in WANETs Adopting Node Stamping Technique
    DKPKR Dr N Sasikala
    Journal of Electrical Systems 20 (3), 6354-6365 , 2024
    2024
  • The Magic of Pixels: Unravelling the Power of AI in Creating Realistic Images
    DN Sasikala
    International Journal of Innovative Research of Science,Engineering … , 2024
    2024
  • ROI SEGMENTATION AND MORPHOLOGICAL ANALYSIS OF KIDNEY STONE DETECTION
    DKPKR Dr N Sasikala
    International Journal of Current Research 16 (4), 278221-27824 , 2024
    2024
  • Selecting optimal path in multiple path routing for MANETs using fuzzy cost
    DKPKR Dr N Sasikala
    INDIAN JOURNAL OF SCIENCE AND TECHNOLOGY 17 (7), 598-609 , 2024
    2024
  • CHANDRAYAAN: SURVEY ON THE JOURNEY TO MOON
    DKPKR Dr.N.Sasikala
    Futuristic Trends in Contemporary Mathematics & Applications 3, 1-8 , 2024
    2024
  • Selecting optimal path in multiple path routing for MANETs using fuzzy cost
    DKS Dr.N.Sasikala,Dr.K.praveen Kumar Rao
    INDIAN JOURNAL OF SCIENCE AND TECHNOLOGY 17, 598-609 , 2024
    2024