Advanced transformer with attention-based neural network framework for precise renal cell carcinoma detection using histological kidney images M. Eliazer, Guntupalli Manoj Kumar, Sibi Amaran, Y. Shasikala, Monalisa Sahu, Bibhuti Bhusan Dash, Kanchan Bala Scientific Reports, 2025 Renal cell carcinoma (RCC) is one of the typical categories of kidney cancer and is a varied group of malignancies arising from epithelial cells of the kidney parenchyma. RCC has more than ten subtypes. Classification of RCC sub-types is mainly according to morphologic features seen on histopathological hematoxylin and eosin (H & E)-stained slides. The histology classification of RCCs is of great significance, considering the important therapeutic and prognostic implications of its histologic subtypes. Imaging models play a prominent role in the diagnosis, follow-up, and staging of RCC. Histopathological images comprise morphological markers of disease development that have both predictive and diagnostic value. Recently, deep learning (DL) has achieved advanced performance in various computer vision tasks, including segmentation, image classification, and object detection. With the provision of sufficient data, the precision of a DL-enabled diagnosis model frequently matches or even exceeds that of qualified doctors. This paper presents an Advanced Transformer and Attention-Based Neural Network Framework for the Intelligent Detection of Renal Cell Carcinoma (ATANNF-IDRCC) model. The aim is to develop an accurate and automated model for detecting and ranking RCC using kidney histopathology images. Initially, the image pre-processing stage utilizes the contrast enhancement method to enhance the image quality. Furthermore, the ATANNF-IDRCC model utilizes the Twins-Spatially Separable Vision Transformer (Twins-SVT) method for feature extraction. For the RCC classification process, a hybrid model of bidirectional temporal convolutional network and long short-term memory with an attention mechanism (BiTCN-BiLSTM-AM) is employed. The performance analysis of the ATANNF-IDRCC technique is examined under the RCCGNet dataset. The comparison study of the ATANNF-IDRCC technique demonstrated a superior accuracy value of 98.26% compared to existing models.
Residual Learning Ensemble with Bi-LSTM for Object Motion Recognition Vaidhehi M, Scaria Alex, Guntupalli Manoj Kumar, Briskilal, Vinoth N A S Proceedings of the 7th International Conference on Intelligent Sustainable Systems Iciss 2025, 2025 In this paper, we propose a novel approach for Object Motion Recognition using a Residual Learning Ensemble of Bi-directional Long Short-Term Memory (Bi-LSTM) networks. The goal of the proposed method is to effectively capture and classify the dynamic behavior of moving objects within video sequences or sensor data. Traditional motion recognition methods often face challenges in handling the complexities of object motion due to occlusions, varying velocities, and non-linear patterns. Our approach leverages the power of residual learning to improve the training process and mitigate the vanishing gradient problem, enhancing the model's ability to capture long-term dependencies in sequential data. By employing an ensemble of Bi-LSTMs, we combine multiple models with diverse perspectives, enabling more robust motion feature extraction and classification. The residual connections facilitate efficient learning by allowing the network to focus on learning the residuals or changes in motion, rather than the entire input, leading to improved recognition accuracy. We evaluate our method on standard motion recognition datasets, demonstrating superior performance compared to existing approaches. The results highlight the efficacy of the residual learning ensemble Bi-LSTM model in recognizing complex object motion patterns, providing a promising solution for real-time object motion tracking and recognition applications in robotics, autonomous systems, and surveillance.
Toward Smart Healthcare in Digital Twin for 6G-Powered Sustainable Ultra-Smart Cities M. VAIDHEHI, C. MALATHY, Pradeep SUDHAKARAN, Aswathy K. CHERIAN, R. GEETHA, Guntupalli Manoj KUMAR Driving Innovation Through AI and Digital Twin for 6g Powered Sustainable Ultra Smart Cities, 2025 This chapter explores current applications of digital twins (DTs) in healthcare, assesses the contributions and challenges of consortium-based research efforts, and identifies emerging opportunities for innovation. The increasing complexities due to the aging population and disease burden on the global healthcare system provide a novel opportunity for sixth-generation (6G)-powered DT technologies about patients, medical science and healthcare management. The chapter examines two technologies emerging in smart city development: the DTs and the 6G cellular networks. Sustainable ultra-smart cities need to lower their medical wastes with the help of predictive analytics and optimized supply chains. DT technologies are advancing healthcare through enhanced patient monitoring, early diagnosis and assistance in therapy plans. 6G will enhance telemedicine, remote surgery and real-time patient monitoring while integrating artificial intelligence for improved diagnosis to transform smart healthcare.
Enhanced Automatic Number Plate Recognition for High-Speed Vehicles: Leveraging YOLO and Haar Cascade K. Kumaran, Tharani. R, G. Saranya, V. Prashanthi, G. Manoj Kumar, K. Manoj Sagar Proceedings 2024 4th International Conference on Pervasive Computing and Social Networking Icpcsn 2024, 2024 The integration of Artificial Intelligence (AI) into advanced Automatic Number Plate Recognition (ANPR) systems offers a state-of-the-art approach for precise identification of license plates on vehicles. This system employs sophisticated techniques including template matching and connected component analysis to efficiently extract characters from input images. Plate extraction, character segmentation, and template matching are integral parts of the process, ensuring reliable operation across diverse outdoor environments with a focus on rapid identification during daylight hours. ANPR systems find wide applications in automated toll collection, parking access control, traffic law enforcement, and road traffic monitoring. Leveraging AI, the approach utilizes multiple templates and character identification methods to enhance accuracy and efficiency. Following character recognition, the identified characters are validated against a license plate database, achieving an accuracy of about 91.8%. Renowned for its simplicity and rapidity in character recognition and plate segmentation across various weather conditions, this model represents a significant advancement in high-speed ANPR technology enabled by AI.
AUTOMATED CRADLE with INCUBATOR for INFANTS N. Nasimsha, G. Manoj Kumar, T. Rajalakshmi, E. Rinzan Gafoor Biomedical Engineering Applications Basis and Communications, 2020 Cradle is a household appliance that carries the baby and aids in the comfortable sleep of the infants. In the current scenario, almost 80% of women are working. They find it tough to manage both the household work and office job. Hence, there is a real need for the design and development of a low-cost automatic oscillating cradle that could monitor the real-time parameters of the infants. Unlike adults, infants cannot regulate their body temperature easily. Children are more prone to develop hypothermia and hyperthermia under extreme temperature conditions. An incubator could maintain appropriate conditions for the infant. This study is focused on developing a low-cost automated baby cradle with an incubator that analyzes baby cry and oscillates automatically. The developed system can also maintain suitable environmental conditions for the infant’s growth. If in case attention of a premature baby were to be sought on a regular basis, the developed system can monitor the temperature and heartbeat along with the cabin temperature and humidity. The developed system has an inbuilt alarm that rings when there is an abnormality in the infant’s heartbeat and body temperature. The alarm also indicates when the mattress is wet. A Bluetooth-based mobile application is also designed which could monitor and control the cradle. The proposed prototype model can be employed both in hospitals and at home.
A secure communication using smart mobile and cloud process Mr Guntupalli, Manoj Kumar, K Hemanth, Akshay Sonkar, A Abduiiah, et al. International Journal of Innovative Technology and Exploring Engineering, 2019 Information storage and security is one of key areas where much research is been done in this digital world where we communicate the data over using third party devices such as cloud by using smart devices such as mobiles so the security is an quite challenging factor where we access our data across the globe and with the social media coming into factor for the storage and accessibility of the data so there are many risk factors coming into process so we need to implement a smart and secure system for the authentication threats so here in this paper we implement a smart system in which face recognition aunthication system is implement between the cloud and mobile activity which give more security in terms of data storage and communication and then we evaluate using different graphs and also analyses the attacks
