Effectiveness of deep learning in early-stage oral cancer detections and classification using histogram of oriented gradients Chiranjit Dutta, Prasad Sandhya, Kandasamy Vidhya, Ramanathan Rajalakshmi, Devasahayam Ramya, Kotakonda Madhubabu Expert Systems, 2024 Early detection of oral cancer (OC) improves survival prospects. Artificial intelligence (AI) is gaining popularity in diagnostic medicine. Oral cancer is a primary global health concern, accounting for 177,384 deaths in 2018; most cases occur in low‐ and middle‐income countries. Automated disease identification in the oral cavity may be facilitated by the ability to identify both possibly and definite malignant lesions. This study aimed to examine the evidence currently available on the effectiveness of AI in diagnosing OC. They highlighted the ability of AI to analyse and identify the early stages of OC. Furthermore, radial basis function networks (RBFN) were employed to develop automated systems to generate intricate patterns for this challenging operation. The stochastic gradient descent algorithm (SGDA) selected the model parameters that best matched the predicted and observed results. It can be used. The initial data was collected for this study to evaluate. Two deep learning‐based computer vision algorithms have been developed to recognize and categorize oral lesions, which is necessary for the early detection of oral cancer. Several examples of HoG include the Canny edge detector, SIFT (scale invariant and feature transform), and SIFT (scale invariant and feature transform). In computer vision and image processing, it is used to find objects. We investigated the potential uses of deep learning‐based computer vision techniques in oral cancer and the viability of an automated system for OC recognition based on photographic images. That made calculations to determine the accuracy, sensitivity, specificity, and receiver operating characteristic curve areas across all validation datasets, including internal, external, and clinical validation (AUC). The RBFN‐SDC model outperformed all others. For 1000 data points, the accuracy of the RBFN‐SDC model is 99.99%, while the accuracy of the R‐CNN, CNN, DCNN, and SVM models is 91.54%, 90.14%, 93.89%, and 94.87%, respectively.
AI-Driven Smart Irrigation: Enhancing Agricultural Water Efficiency Through Intelligent Valve Regulation in Piped and Micro Irrigation Networks B. Alex, G. Jignasa, K. Madhubabu, A. Gopi 1st International Conference on Pioneering Developments in Computer Science and Digital Technologies Ic2sdt 2024 Proceedings, 2024 This paper presents a revolutionary solution to optimize water use in agriculture, using an AI-based smart irrigation system. Our approach focuses on automatic adjustment of water release control valves, guided by artificial intelligence (AI) that takes into account the complex relationship between soil moisture and ambient temperature in the plant root zone. The decision-making process in our system is powered by a deep neural network meticulously trained on a comprehensive dataset, including the dynamic interaction of soil moisture, temperature changes of the crop. The reason for using an AI model that focuses specifically on the relationship between soil moisture and temperature lies in its ability to recognize small details, complex patterns and correlations that impact irrigation need. Traditional methods are often unable to adapt to the complex and dynamic nature of agricultural ecosystems. By harnessing the power of AI, our system not only captures, but also learns and predicts, optimal irrigation conditions, thereby promoting resource-efficient agricultural practices. The proposed system seamlessly integrates with micro-irrigation and pipeline networks, ensuring flexibility in different agricultural environments. Using MQTT communication powered by Node-RED, collected data is efficiently transmitted to the cloud, allowing real-time monitoring and analysis. This combination of AI, sensor technology and cloud connectivity aims to provide farmers with accurate information, promote water conservation, optimize crop yields and reduce environmental impact. Ultimately, our research contributes to advancing sustainable agriculture by addressing important challenges and paving the way for a new era of smart irrigation practices.
Smart Duster for Schools S. Mohith Sai Teja, S. Preetham, Ch. Jayalakshmi, K. Madhubabu International Interdisciplinary Humanitarian Conference for Sustainability Iihc 2022 Proceedings, 2022 Teaching and learning in schools and colleges are done by writing on boards over the years. Cleaning the board using a duster increases the chalk dust particles in the classroom atmosphere. When concentration levels of the chalk dust suspension exceed a safe limit, occupants of the classroom, especially the teacher, are exposed to severe health hazards. Accumulation of chalk dust in the lungs causes a disease called interstitial pneumonia and multiple bullae, and it also leads to irritation of eyes, skin, and many more health issues. The main aim of implementing the chalk dust collecting system is to reduce the levels of chalk dust in the classroom atmosphere produced while erasing with the help of a vacuum. This system erases the board with the help of dusters placed in the vertical frame controlled by bluetooth controller/push buttons.
The prediction of diseases using rough set theory with recurrent neural network in big data analytics Vamsidhar Talasila, , Kotakonda Madhubabu, Meghana Mahadasyam, Naga Atchala, Lakshmi Kande, , , , and International Journal of Intelligent Engineering and Systems, 2020 In a modern life, early healthcare prediction plays an important role to prevent the loss of life caused by prediction delays in treatment. Nowadays, the researchers focused on the Big data analysis, which is used to identify the future health status and provides an efficient way to overcome the issues in early prediction. Many researches are going on predictive analytics using machine learning techniques to provide a better decision making. Big data analysis provides great opportunities to predict future health status from health parameters and provide best outcomes. However, the data classification is one of the major challenging tasks due to noisy data or missing data in the dataset. Feature selection techniques play an important role in the classification process by removing irrelevant features from the extracted data. In this research work, the Rough Set Theory (RST) technique is used to select the most relevant features, which helps to provide the efficient classification of medical data and disease detection. The selected features are given as input to the Recurrent Neural Network (RNN) technique for disease prediction. The proposed method is also called as RST-RNN, where the experiments are carried out on the UCI machine learning repository dataset in terms of accuracy, f-measure, sensitivity and specificity. The results showed that the RST-RNN method achieved accuracy of 98.57%, where the existing Support Vector Machine (SVM) achieved 90.57% accuracy and Naive Bayes (NB) achieved 97.36% accuracy for heart disease dataset.
Efficient Path Reconstruction Algorithms for Multi Hop Wireless Networks - A Survey K. Madhubabu, N. Snehalatha Proceedings of the 5th International Conference on Inventive Computation Technologies Icict 2020, 2020 Low strength and multi host Wi-Fi networks are estimated as an interesting and present growing technology that remains mostly useful in various Internet of Things applications. Considering packet level path is required for dealing with large sized multi node wireless networks. A packet type routing varies the route to take a packet through a network. The packet level routing is used for handling many network management things. Packet level route recreation is difficult and usage has some complexity in large sized networks. There are many number of route recreation algorithms but their scenarios are different from network to network. Security remains as the major concern in Wireless networks applications. The proposed idea provides the quality of services to network users in order to achieve this by exploring the paths from source to destination and good path selection are considered as the very important aspects for qualitative data transmission services. The idea is simple to logically transfer the sensitive information from source to destination node in the network and also stabilizes the route reconstruction performance ratios to all the networks.
RECENT SCHOLAR PUBLICATIONS
Effective Unauthorized Vehicle Detection System for Intelligent Transportation K Madhubabu, N Snehalatha Disruptive technologies in Computing and Communication Systems, 174-179 , 2024 2024
Optimal path selection in vehicular adhoc network using hybrid optimization K Madhubabu, N Snehalatha Multimedia Tools and Applications 83 (6), 18261-18280 , 2024 2024 Citations: 7
Congestion avoidance for electrically charged autonomous vehicles in vehicular Ad hoc network K Madhubabu, N Snehalatha International Journal of System Assurance Engineering and Management 14 (6 … , 2023 2023 Citations: 3
Advanced Link Prediction Technique for Social Networking Websites DS Madhubabu Kotakonda1 , Srujan. S2 , R V V N Bheema Rao3 , Venkateswara ... Turkish Journal of Computer and Mathematics Education 12 (12), 4169-4175 , 2021 2021
Software Metric and Fault Prediction Using Hybrid FISHER Filter- ANNIGMA Framework KM Peddada Venkateswara Rao1 , Dorababu Sudarsa 2 , Ravi Kumar Tata 3 International Journal of Emerging Trends in Engineering Research 8 (9), 5357 … , 2020 2020
The Prediction of Diseases Using Rough Set Theory with Recurrent Neural Network in Big Data Analytics. V Talasila, K Madhubabu, MC Mahadasyam, NJ Atchala, LS Kande International Journal of Intelligent Engineering & Systems 13 (5) , 2020 2020 Citations: 68
Ensemble Face Recognition Using IOT for Universal Purpose CHNGC K Madhu Babu, K Pandu Rang sai reddy, D Rahul International Journal of Advanced Science and Technology 29 (6), 4069 - 4073 , 2020 2020
Efficient Path Reconstruction Algorithms for Multi Hop Wireless Networks-A Survey K Madhubabu, N Snehalatha 2020 International Conference on Inventive Computation Technologies (ICICT … , 2020 2020
Individual-Adaptive Position Update in MANETS KMB SHAIK MASTAN SAHEB1 International Journal of Scientific Engineering and Technology Research 3 … , 2014 2014
Data Security for Multi Users in Cloud by Using Cryptography Technique KMB S.Santhosh1 International Journal of Computer Science Trends and Technology 2 (4), 80-83 , 2014 2014
Improving Data Quality in Applications of Dynamic Forms CRJ 1Kotakonda. Madhu Babu, 2G. Subba Lakshmi International Journal of Computer Science And Technology 3 (3), 660-664 , 2012 2012
MOST CITED SCHOLAR PUBLICATIONS
The Prediction of Diseases Using Rough Set Theory with Recurrent Neural Network in Big Data Analytics. V Talasila, K Madhubabu, MC Mahadasyam, NJ Atchala, LS Kande International Journal of Intelligent Engineering & Systems 13 (5) , 2020 2020 Citations: 68
Optimal path selection in vehicular adhoc network using hybrid optimization K Madhubabu, N Snehalatha Multimedia Tools and Applications 83 (6), 18261-18280 , 2024 2024 Citations: 7
Congestion avoidance for electrically charged autonomous vehicles in vehicular Ad hoc network K Madhubabu, N Snehalatha International Journal of System Assurance Engineering and Management 14 (6 … , 2023 2023 Citations: 3
Effective Unauthorized Vehicle Detection System for Intelligent Transportation K Madhubabu, N Snehalatha Disruptive technologies in Computing and Communication Systems, 174-179 , 2024 2024
Advanced Link Prediction Technique for Social Networking Websites DS Madhubabu Kotakonda1 , Srujan. S2 , R V V N Bheema Rao3 , Venkateswara ... Turkish Journal of Computer and Mathematics Education 12 (12), 4169-4175 , 2021 2021
Software Metric and Fault Prediction Using Hybrid FISHER Filter- ANNIGMA Framework KM Peddada Venkateswara Rao1 , Dorababu Sudarsa 2 , Ravi Kumar Tata 3 International Journal of Emerging Trends in Engineering Research 8 (9), 5357 … , 2020 2020
Ensemble Face Recognition Using IOT for Universal Purpose CHNGC K Madhu Babu, K Pandu Rang sai reddy, D Rahul International Journal of Advanced Science and Technology 29 (6), 4069 - 4073 , 2020 2020
Efficient Path Reconstruction Algorithms for Multi Hop Wireless Networks-A Survey K Madhubabu, N Snehalatha 2020 International Conference on Inventive Computation Technologies (ICICT … , 2020 2020
Individual-Adaptive Position Update in MANETS KMB SHAIK MASTAN SAHEB1 International Journal of Scientific Engineering and Technology Research 3 … , 2014 2014
Data Security for Multi Users in Cloud by Using Cryptography Technique KMB S.Santhosh1 International Journal of Computer Science Trends and Technology 2 (4), 80-83 , 2014 2014
Improving Data Quality in Applications of Dynamic Forms CRJ 1Kotakonda. Madhu Babu, 2G. Subba Lakshmi International Journal of Computer Science And Technology 3 (3), 660-664 , 2012 2012
