Satish Kumar Satti

@vignan.ac.in

Assistant Professor
VIGNAN FOUNDATION FOR SCIENCE TECHNOLOGY AND RESEARCH

Satish Kumar Satti


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https://researchid.co/sskumar789

RESEARCH, TEACHING, or OTHER INTERESTS

Computer Vision and Pattern Recognition, Computer Science
28

Scopus Publications

336

Scholar Citations

11

Scholar h-index

12

Scholar i10-index

Scopus Publications

  • Unfolding the diagnostic pipeline of diabetic retinopathy with artificial intelligence: A systematic review
    K Suganya Devi, Hemanth Kumar Vasireddi, GNV Raja Reddy, Satish Kumar Satti
    Survey of Ophthalmology, 2026
  • Evaluating Yolo Models for Detecting Crowds in Sparse Regions
    Satish Kumar Satti, Vyshnavi Kagga
    Lecture Notes in Networks and Systems, 2026
  • Comparative Performance Analysis of YOLO-Based Models for Gunny Bag Detection and Counting in Warehouse Environments
    Kasukurthi Lakshmi Prasanna, Satish Kumar Satti
    2026 IEEE 15th International Conference on Communication Systems and Network Technologies Csnt 2026, 2026
    Computer vision is an emerging technology, and it is widely used for object detection in different industrial and warehouse settings. In this work, eight different YOLO-based object detection models, namely YOLOv5, YOLOv7, YOLOv8 (Nano and Small), YOLOv9, YOLOv11 (Nano and Small), and YOLOv12, are adopted to assess the performance of gunny bag object detection and counting. All these adopted models are trained and evaluated on a custom-built dataset. This dataset consists of gunny bag objects that are captured in different warehouses and godowns. These models are evaluated using the metrics precision, recall, and mean Average Precision(mAP). The experimental results indicated that YOLOv8 nano performed better than other techniques in the context of gunny bag object detection and counting with <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$93.8 \% m A P^{50}$</tex>. It is also observed that YOLOv8 Small and YOLOv11 techniques had a high precision and recall value in detecting gunny bags.
  • Air-Written Multicharacter Detection and Classification Using Vision-Based Hand Gestures and an Optimized ResYOLO-Transformer
    Satish Kumar Satti, M Prasad
    IEEE Sensors Journal, 2026
    Air writing is a cutting-edge method of contactless human-machine interaction. It involves writing characters or words in the air with fingertip gestures. This method replaces keyboards and touchscreens, making it particularly useful for smart devices, healthcare applications, and hands-free text input. Predicting a single character in air writing is simple. However, detecting and classifying multiple or overlapping characters remains difficult. To address this issue, we proposed a vision-sensor-based approach that includes a Hand Tracking Algorithm and a ResYOLO-Transformer model. We also use the chaotic honey badge algorithm to optimise hyperparameters. This ensures an ideal balance across parameters. It helps avoid local optima and enhances the exploration-exploitation balance, improving prediction accuracy. A custom dataset with 26 classes was created. We used specific hand gestures to ensure that each character’s coordinates were recorded separately, even if they overlapped. The proposed model was trained and evaluated on custom and ISI datasets. It achieved an accuracy of 97.49%, demonstrating its effectiveness in robust air-written character detection and classification. Compared to other cutting-edge models such as YOLOV<sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">5</sub>, YOLOV<sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">7</sub>, YOLOV<sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">9</sub>, YOLOV<sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">11</sub>, and Vision Transformer, the proposed ResYOLO-Transformer model performs better. Furthermore, when integrated with the CHBA, the proposed model outperformed other optimisation techniques like CSO, PSO, BSO, and CJAYA. It achieved an improved prediction accuracy of 98.89%.
  • Time-and-Traffic-aware collaborative task offloading with service caching-replacement in cloud-assisted mobile edge computing
    Gurpreet Singh Chhabra, Satish Kumar Satti, Goluguri N. V. Rajareddy, Abhijeet Mahapatra, Gondi Lakshmeeswari, Kaushik Mishra
    Cluster Computing, 2025
    The rapid growth of Internet of Things (IoT) applications has increased the demand for ultra-low-latency and energy-efficient computing. While Mobile Edge Computing (MEC) addresses these demands by shifting computation from the centralized cloud to edge servers, its limited resources pose a major challenge. In particular, making optimal decisions for service caching and task offloading under dynamic network conditions and energy constraints remains a critical issue. Efficient caching is essential for latency-sensitive IoT tasks, yet only a subset of services can be stored at MEC-enabled base stations (BSs) due to storage limitations. This paper proposes a Cloud-assisted MEC framework that jointly optimizes service caching, service replacement, and task offloading to enhance long-term system performance. A two-phase solution is developed: first, an Irregular Cellular Learning Automata (ICLA)-based algorithm classifies traffic patterns and timescales, and a Distributed Deep Reinforcement Learning (DDRL) algorithm performs adaptive, decentralized task offloading. To address caching constraints, a dynamic 0–1 knapsack approach selects services based on popularity, while a Q-learning-based policy handles service replacement. Simulation results validate the framework’s effectiveness, showing significant reductions in service latency and energy usage, with improved scalability and adaptability over traditional centralized approaches. The proposed method offers a robust and practical solution for next-generation MEC systems supporting real-time IoT services.
  • A digital twin-enabled fog-edge-assisted IoAT framework for Oryza Sativa disease identification and classification
    Goluguri N.V. Rajareddy, Kaushik Mishra, Satish Kumar Satti, Gurpreet Singh Chhabra, Kshira Sagar Sahoo, Amir H. Gandomi
    Ecological Informatics, 2025
    The integration of agri-technology with the Internet of Agricultural Things (IoAT) is revolutionizing the field of smart agriculture, particularly in diagnosing and treating Oryza sativa (rice) diseases. Given that rice serves as a staple food for over half of the global population, ensuring its healthy cultivation is crucial, particularly with the growing global population. Accurate and timely identification of rice diseases, such as Brown Leaf Spot (BS), Bacterial Leaf Blight (BLB), and Leaf Blast (LB), is therefore essential to maintaining and enhancing rice production. In response to this critical need, the research introduces a timely detection system that leverages the power of Digital Twin (DT)-enabled Fog computing, integrated with Edge and Cloud Computing (CC), and supported by sensors and advanced technologies. At the heart of this system lies a sophisticated deep-learning model built on the robust AlexNet neural network architecture. This model is further refined by including Quaternion convolution layers, which enhance colour information processing, and Atrous convolution layers, which improve depth perception, particularly in extracting disease patterns. To boost the model's predictive accuracy, the Chaotic Honey Badger Algorithm (CHBA) is employed to optimize the CNN hyperparameters, resulting in an impressive average accuracy of 93.5 %. This performance significantly surpasses that of other models, including AlexNet, AlexNet-Atrous, QAlexNet, and QAlexNet-Atrous, which achieved respective accuracies of 75 %, 84 %, 89 %, and 91 %. Moreover, the CHBA optimization algorithm outperforms other techniques like CSO, BSO, PSO, and CJAYA and demonstrates optimal results with an 80–20 % training-testing parameter split. Service latency analysis further reveals that the Fog-Edge-assisted environment is more efficient than the Cloud-assisted model for latency reduction. Additionally, the DT-enabled QAlexNet-Atrous-CHBA model proves to be far superior to its non-DT counterpart, showing substantial improvements in 18.7 % in Accuracy, 17 % in recall, 19 % in Fβ-measure, 17.3 % in specificity, and 13.4 % in precision, respectively. These enhancements are supported by convergence analysis and the Quade rank test, establishing the model's effectiveness and potential to significantly improve rice disease diagnosis and management. This advancement promises to contribute significantly to the sustainability and productivity of global rice cultivation. • Developing a collaborative digital twin-enabled edge-fog assisted framework for Oryza Sativa disease diagnosis. • The detection of plant diseases is performed using a deep learning model called AlexNet. • A Quaternion-valued model is used with the AlexNet architecture to extract the abundant colour information. • Atrous convolution layers have been included in the convolutional neural network architecture. • The Chaotic Honey Badger Algorithm (CHBA) is utilized to optimize the model's parameters.
  • Efficient detection and partitioning of overlapped red blood cells using image processing approach
    Prasenjit Dhar, K. Suganya Devi, Satish Kumar Satti, P. Srinivasan
    Innovations in Systems and Software Engineering, 2025
  • Deep Learning-Driven Multi-Modal Framework for Robust Traffic Sign and Pothole Detection on Indian Roads
    Yalla S J V Durga Bhavani Devika Rani, Satish Kumar Satti
    2025 IEEE 7th International Conference on Computing Communication and Automation Iccca 2025, 2025
    Accurate identification of traffic signs and road surface defects such as potholes is vital for ensuring road safety and enabling intelligent transportation in India. Traditional vision-based methods that depend only on RGB imagery face significant limitations under poor lighting and adverse environmental conditions. To address these challenges, this paper presents a multi-modal deep learning framework that combines RGB and depth information, with scope for incorporating thermal imaging in future extensions. The proposed architecture integrates modality-specific feature extraction, an adaptive cross-modal attention fusion mechanism, and a spatio-temporal refinement module to achieve reliable and consistent real-time detection. Experimental evaluations show that the framework reaches mean average precisions of about 93% for traffic sign detection and 89% for pothole detection, outperforming unimodal baselines by 6–8%. Despite the additional processing required for depth data, the system sustains an inference speed of nearly 45 FPS, confirming its suitability for real-time deployment. These results highlight the framework’s improved accuracy, adaptability to diverse conditions, and operational efficiency compared to existing state-of-the-art methods.
  • Potholes and traffic signs detection by classifier with vision transformers
    Satish Kumar Satti, Goluguri N. V. Rajareddy, Kaushik Mishra, Amir H. Gandomi
    Scientific Reports, 2024
    Detecting potholes and traffic signs is crucial for driver assistance systems and autonomous vehicles, emphasizing real-time and accurate recognition. In India, approximately 2500 fatalities occur annually due to accidents linked to hidden potholes and overlooked traffic signs. Existing methods often overlook water-filled and illuminated potholes, as well as those shaded by trees. Additionally, they neglect the perspective and illuminated (nighttime) traffic signs. To address these challenges, this study introduces a novel approach employing a cascade classifier along with a vision transformer. A cascade classifier identifies patterns associated with these elements, and Vision Transformers conducts detailed analysis and classification. The proposed approach undergoes training and evaluation on ICTS, GTSRDB, KAGGLE, and CCSAD datasets. Model performance is assessed using precision, recall, and mean Average Precision (mAP) metrics. Compared to state-of-the-art techniques like YOLOv3, YOLOv4, Faster RCNN, and SSD, the method achieves impressive recognition with a mAP of 97.14% for traffic sign detection and 98.27% for pothole detection.
  • Terahertz video-based hidden object detection using YOLOv5m and mutation-enabled salp swarm algorithm for enhanced accuracy and faster recognition
    J. Jayachitra, K. Suganya Devi, S. V. Manisekaran, Satish Kumar Satti
    Journal of Supercomputing, 2024
  • An hybrid soft attention based XGBoost model for classification of poikilocytosis blood cells
    Prasenjit Dhar, K. Suganya Devi, Satish Kumar Satti, P. Srinivasan
    Evolving Systems, 2024
  • Drowsy Alert: A System to Detect and Alert Driver's Drowsiness for Road Safety
    Satish Kumar Satti, Goluguri N V Rajareddy, N V Vishnumurthy Ravipati, S P N L Gayatri Samanvita
    2024 IEEE Students Conference on Engineering and Systems Interdisciplinary Technologies for Sustainable Future Sces 2024, 2024
  • An Ensemble Technique for Predicting the Human Heart Disease
    Uttej Kumar Nannapaneni, Satish Kumar Satti, B. Himaja, K. Naga Poojitha, K. Harshini
    Lecture Notes in Networks and Systems, 2024
  • An optimal deep learning model for recognition of hidden hazardous weapons in terahertz and millimeter wave images
    J. Jayachitra, Suganya Devi K, S. V. Manisekaran, Satish Kumar Satti
    Earth Science Informatics, 2023
  • HPKNN: Hyper-parameter optimized KNN classifier for classification of poikilocytosis
    Prasenjit Dhar, Suganya Devi Kothandapani, Satish Kumar Satti, Srinivasan Padmanabhan
    International Journal of Imaging Systems and Technology, 2023
  • EEEDCS: Enhanced energy efficient distributed compressive sensing based data collection for WSNs
    Sekar K., Suganya Devi K., Satish Kumar Satti, Srinivasan P.
    Sustainable Computing Informatics and Systems, 2023
  • Recognizing the Indian Cautionary Traffic Signs using GAN, Improved Mask R-CNN, and Grab Cut
    Satish Kumar Satti, Suganya Devi K, Srinivasan P
    Concurrency and Computation Practice and Experience, 2023
  • Image Caption Generation using ResNET-50 and LSTM
    Satish Kumar Satti, Goluguri N V Rajareddy, Prasad Maddula, N V Vishnumurthy Ravipati
    Conference Proceedings 2023 IEEE Silchar Subsection Conference Silcon 2023, 2023
  • Unified approach for detecting traffic signs and potholes on Indian roads
    Satish Kumar Satti, Suganya Devi K., Prasad Maddula, N.V.Vishnumurthy Ravipati
    Journal of King Saud University Computer and Information Sciences, 2022
  • Detecting potholes on Indian roads using Haar feature-based cascade classifier, convolutional neural network, and instance segmentation
    Satish Kumar Satti, K. Suganya Devi, Prasenjit Dhar, P. Srinivasan
    Soft Computing, 2022
  • R-ICTS: Recognize the Indian cautionary traffic signs in real-time using an optimized adaptive boosting cascade classifier and a convolutional neural network
    Satish Kumar Satti, Suganya Devi K, Srinivasan P
    Concurrency and Computation Practice and Experience, 2022
  • A Hybrid Biosignal Compression Model for Healthcare Sensor Networks
    T Dheepa, K Sekar, Satish Kumar Satti, Goluguri N V Rajareddy
    4th IEEE International Conference on Artificial Intelligence in Engineering and Technology Iicaiet 2022, 2022
  • Computer Vision-Based System for Locating and Counting Vacant Parking Lot
    N V Vishnumurthy Ravipati, V R Narasimharao Mondreti, Satish Kumar Satti, Chandra Sekhar Gurugunti, V N S Lalitha Jakkampudi
    IEEE International Conference on Data Science and Information System Icdsis 2022, 2022
  • ICTS: Indian Cautionary Traffic Sign Classification Using Deep Learning
    Satish Kumar Satti, K Suganya Devi, K Sekar, Prasenjit Dhar, P Srinivasan
    IEEE International Conference on Distributed Computing and Electrical Circuits and Electronics Icdcece 2022, 2022
  • A machine learning approach for detecting and tracking road boundary lanes
    Satish Kumar Satti, K. Suganya Devi, Prasenjit Dhar, P. Srinivasan
    ICT Express, 2021
  • Detail Study of Different Algorithms for Early Detection of Cancer
    Prasenjit Dhar, K. Suganya Devi, Satish Kumar Satti, P. Srinivasan
    Studies in Computational Intelligence, 2021
  • Enhancing and Classifying Traffic Signs Using Computer Vision and Deep Convolutional Neural Network
    Satish Kumar Satti, K. Suganya Devi, Prasenjit Dhar, P. Srinivasan
    Communications in Computer and Information Science, 2020
  • An efficient noise separation technique for removal of gaussian and mixed noises in monochrome and color images
    Satish Kumar Satti, S. Devi, P. Dhar, P. Srinivasan
    International Journal of Innovative Technology and Exploring Engineering, 2019

RECENT SCHOLAR PUBLICATIONS

  • Comparative Performance Analysis of YOLO-Based Models for Gunny Bag Detection and Counting in Warehouse Environments
    KL Prasanna, SK Satti
    2026 IEEE 15th International Conference on Communication Systems and Network … , 2026
    2026
  • Air-Written Multi-Character Detection and Classification Using Vision-Based Hand Gestures and an Optimized ResYOLO-Transformer
    SK Satti, M Prasad
    IEEE Sensors Journal , 2025
    2025
    Citations: 1
  • Deep Learning-Driven Multi-Modal Framework for Robust Traffic Sign and Pothole Detection on Indian Roads
    YSJVDBD Rani, SK Satti
    2025 IEEE 7th International Conference on Computing, Communication and … , 2025
    2025
  • Determining Rainfall Thresholds for Landslide Prediction: A Case Study on the Hills of Assam
    SK Satti
    2025
  • Time-and-Traffic-aware collaborative task offloading with service caching-replacement in cloud-assisted mobile edge computing
    GS Chhabra, SK Satti, GNV Rajareddy, A Mahapatra, G Lakshmeeswari, ...
    Cluster Computing 28 (14), 900 , 2025
    2025
    Citations: 2
  • Real-Time Surveillance System to Monitor Vehicles and Pedestrians for Road Traffic Management
    SK Satti, K Suganya Devi, NB Muppalaneni, P Maddula
    AI-Driven Transportation Systems: Real-Time Applications and Related … , 2025
    2025
  • Real-Time Surveillance System
    SK Satti, KS Devi, NB Muppalaneni
    AI-Driven Transportation Systems: Real-Time Applications and Related … , 2025
    2025
  • Unfolding the diagnostic pipeline of diabetic retinopathy with artificial intelligence: A systematic review
    KS Devi, HK Vasireddi, GNVR Reddy, SK Satti
    Survey of Ophthalmology , 2025
    2025
    Citations: 4
  • A digital twin-enabled fog-edge-assisted IoAT framework for Oryza Sativa disease identification and classification
    GNV Rajareddy, K Mishra, SK Satti, GS Chhabra, KS Sahoo, AH Gandomi
    Ecological Informatics 87, 103063 , 2025
    2025
    Citations: 4
  • Evaluating Yolo Models for Detecting Crowds in Sparse Regions
    SK Satti, V Kagga
    International Conference on Information and Communication Technology for … , 2025
    2025
  • Efficient detection and partitioning of overlapped red blood cells using image processing approach
    P Dhar, K Suganya Devi, SK Satti, P Srinivasan
    Innovations in Systems and Software Engineering 21 (1), 79-91 , 2025
    2025
    Citations: 15
  • Drowsy alert: A system to detect and alert driver's drowsiness for road safety
    SK Satti, GNV Rajareddy, NVV Ravipati, SG Samanvita
    2024 IEEE Students Conference on Engineering and Systems (SCES), 1-6 , 2024
    2024
    Citations: 2
  • Terahertz video-based hidden object detection using YOLOv5m and mutation-enabled salp swarm algorithm for enhanced accuracy and faster recognition
    J Jayachitra, KS Devi, SV Manisekaran, SK Satti
    The Journal of Supercomputing 80 (6), 8357-8382 , 2024
    2024
    Citations: 9
  • An hybrid soft attention based XGBoost model for classification of poikilocytosis blood cells
    P Dhar, K Suganya Devi, SK Satti, P Srinivasan
    Evolving Systems 15 (2), 523-539 , 2024
    2024
    Citations: 7
  • Potholes and traffic signs detection by classifier with vision transformers
    SK Satti, GNV Rajareddy, K Mishra, AH Gandomi
    Scientific reports 14 (1), 2215 , 2024
    2024
    Citations: 36
  • An Ensemble Technique for Predicting Human Heart Disease
    UK Nannapaneni, SK Satti, B Himaja, KN Poojitha
    Intelligent Computing Systems and Applications: Proceedings of the 2nd … , 2024
    2024
  • Image caption generation using ResNET-50 and LSTM
    SK Satti, GNV Rajareddy, P Maddula, NVV Ravipati
    2023 IEEE Silchar Subsection Conference (SILCON), 1-6 , 2023
    2023
    Citations: 23
  • An optimal deep learning model for recognition of hidden hazardous weapons in terahertz and millimeter wave images
    J Jayachitra, SD K, SV Manisekaran, SK Satti
    Earth Science Informatics 16 (3), 2709-2726 , 2023
    2023
    Citations: 13
  • EEEDCS: Enhanced energy efficient distributed compressive sensing based data collection for WSNs
    K Sekar, SK Satti, P Srinivasan
    Sustainable Computing: Informatics and Systems 38, 100871 , 2023
    2023
    Citations: 5
  • HPKNN: Hyper‐parameter optimized KNN classifier for classification of poikilocytosis
    P Dhar, SD Kothandapani, SK Satti, S Padmanabhan
    International Journal of Imaging Systems and Technology , 2023
    2023
    Citations: 14

MOST CITED SCHOLAR PUBLICATIONS

  • Unified approach for detecting traffic signs and potholes on Indian roads
    SK Satti, K Suganya Devi, P Maddula, NVV Ravipati
    Journal of King Saud University-Computer and Information Sciences , 2021
    2021
    Citations: 52
  • A machine learning approach for detecting and tracking road boundary lanes
    SK Satti, KS Devi, P Dhar, P Srinivasan
    ICT Express 7 (1), 99-103 , 2021
    2021
    Citations: 37
  • Potholes and traffic signs detection by classifier with vision transformers
    SK Satti, GNV Rajareddy, K Mishra, AH Gandomi
    Scientific reports 14 (1), 2215 , 2024
    2024
    Citations: 36
  • Image caption generation using ResNET-50 and LSTM
    SK Satti, GNV Rajareddy, P Maddula, NVV Ravipati
    2023 IEEE Silchar Subsection Conference (SILCON), 1-6 , 2023
    2023
    Citations: 23
  • ICTS: Indian cautionary traffic sign classification using deep learning
    SK Satti, KS Devi, K Sekar, P Dhar, P Srinivasan
    2022 IEEE International Conference on Distributed Computing and Electrical … , 2022
    2022
    Citations: 17
  • Efficient detection and partitioning of overlapped red blood cells using image processing approach
    P Dhar, K Suganya Devi, SK Satti, P Srinivasan
    Innovations in Systems and Software Engineering 21 (1), 79-91 , 2025
    2025
    Citations: 15
  • R‐ICTS: Recognize the Indian cautionary traffic signs in real‐time using an optimized adaptive boosting cascade classifier and a convolutional neural network
    SK Satti
    Concurrency and Computation: Practice and Experience 34 (10), e6796 , 2021
    2021
    Citations: 15
  • Enhancing and Classifying Traffic Signs Using Computer Vision and Deep Convolutional Neural Network
    PS Satish Kumar Satti,K Suganya Devi, Prasenjit Dhar
    Communications in Computer and Information Science 1240, 243-253 , 2020
    2020
    Citations: 15
  • HPKNN: Hyper‐parameter optimized KNN classifier for classification of poikilocytosis
    P Dhar, SD Kothandapani, SK Satti, S Padmanabhan
    International Journal of Imaging Systems and Technology , 2023
    2023
    Citations: 14
  • An Efficient Noise Separation Technique for Removal of Gaussian and Mixed Noises in Monochrome and Color Images
    SK Satti, K Suganya Devi, P Dhar, P Srinivasan
    International Journal of Innovative Technology and Exploring Engineering 8 … , 2019
    2019
    Citations: 14
  • An optimal deep learning model for recognition of hidden hazardous weapons in terahertz and millimeter wave images
    J Jayachitra, SD K, SV Manisekaran, SK Satti
    Earth Science Informatics 16 (3), 2709-2726 , 2023
    2023
    Citations: 13
  • Recognizing the Indian Cautionary Traffic Signs using GAN, Improved Mask R‐CNN, and Grab Cut
    SK Satti
    Concurrency and Computation: Practice and Experience, e7453 , 2023
    2023
    Citations: 10
  • Terahertz video-based hidden object detection using YOLOv5m and mutation-enabled salp swarm algorithm for enhanced accuracy and faster recognition
    J Jayachitra, KS Devi, SV Manisekaran, SK Satti
    The Journal of Supercomputing 80 (6), 8357-8382 , 2024
    2024
    Citations: 9
  • Detecting potholes on Indian roads using Haar feature-based cascade classifier, convolutional neural network, and instance segmentation
    SK Satti, KS Devi, P Dhar, P Srinivasan
    Soft Computing 26 (18), 9141-9153 , 2022
    2022
    Citations: 9
  • Detail Study of Different Algorithms for Early Detection of Cancer
    P Dhar, K Suganya Devi, SK Satti, P Srinivasan
    Health Informatics: A Computational Perspective in Healthcare, 207-232 , 2021
    2021
    Citations: 8
  • An hybrid soft attention based XGBoost model for classification of poikilocytosis blood cells
    P Dhar, K Suganya Devi, SK Satti, P Srinivasan
    Evolving Systems 15 (2), 523-539 , 2024
    2024
    Citations: 7
  • Indian cautionary traffic sign data-set
    SK Satti, K Suganya Devi
    IEEE Dataport, 98-112 , 2020
    2020
    Citations: 7
  • Computer vision-based system for locating and counting vacant parking lot
    NVV Ravipati, VRN Mondreti, SK Satti, CS Gurugunti, VNSL Jakkampudi
    2022 IEEE International Conference on Data Science and Information System … , 2022
    2022
    Citations: 6
  • Efficient technique for removal of white and mixed noises in gray scale images
    SS Kumar, S Devi K, RV Murthy, S P
    International Journal for Innovative Engineering & Management Research 8 (09 … , 2019
    2019
    Citations: 6
  • EEEDCS: Enhanced energy efficient distributed compressive sensing based data collection for WSNs
    K Sekar, SK Satti, P Srinivasan
    Sustainable Computing: Informatics and Systems 38, 100871 , 2023
    2023
    Citations: 5