Advancing PCOS Diagnosis: Capsule Network-Based Classification using Ultrasound Images Venkatesh G., Bajulunisha A., Sreenivasa Rao Chappidi, Karthikeyan S., Dhivya K., Murugan S. Journal of Innovative Image Processing, 2025 Capsule Networks have been developed as an alternative to Convolutional Neural Networks (CNN) for encapsulating spatial hierarchies in images or signals. The objective is to improve the precision of Polycystic Ovary Syndrome (PCOS) classification by sophisticated image processing methodologies. The proposed system uses the features of Capsule Networks to examine medical ultrasound images for PCOS classification while improving feature protection. It creates a reliable diagnostic model capable of accurately differentiating between healthy and PCOS. Capsule Networks provide more detailed evaluations of ovarian morphology by preserving the orientation and positioning information of characteristics. Three different capsule networks, such as Dynamic Routing CapsNet (DRCN), Expectation-Maximization (EM) Routing CapsNet (EMRCN), and Deep CapsNet (DCN), are analyzed for PCOS classification using more than 3000 images in the PCOS dataset. Results prove that the proposed Deep Capsule Network achieves better overall accuracy of 99.33 %, sensitivity of 99.27 %, and specificity of 99.4 % compared to other types of capsule networks. The combination of Capsule Networks with medical imaging procedures presents a promising framework for timely and precise diagnosis, thereby diminishing diagnostic delays and enhancing patient outcomes in gynaecological healthcare systems.
Deep learning for infectious disease surveillance integrating internet of things for rapid response Subramanian Sumithra, Moorthy Radhika, Gandavadi Venkatesh, Babu Seetha Lakshmi, Balraj Victoria Jancee, Nagarajan Mohankumar, Subbiah Murugan International Journal of Electrical and Computer Engineering, 2025 Particularly in the case of emerging infectious diseases and worldwide pandemics, infectious disease monitoring is essential for quick identification and efficient response to epidemics. Improving surveillance systems for quick reaction might be possible with the help of new deep learning and internet of things (IoT) technologies. This paper introduces an infectious disease monitoring architecture based on deep learning coupled with IoT devices to facilitate early diagnosis and proactive intervention measures. This approach uses recurrent neural networks (RNNs) to identify temporal patterns suggestive of infectious disease outbreaks by analyzing sequential data retrieved from IoT devices like smart thermometers and wearable sensors. To identify small changes in health markers and forecast the development of diseases, RNN architectures with long short-term memory (LSTM) networks are used to capture long-range relationships in the data. Spatial analysis permits the integration of geographic data from IoT devices, allowing for the identification of infection hotspots and the tracking of afflicted persons' movements. Quick action steps like focused testing, contact tracing, and medical resource deployment are prompted by abnormalities detected early by real-time monitoring and analysis. Preventing or lessening the severity of infectious disease outbreaks is the goal of the planned monitoring system, which would enhance public health readiness and response capacities.
Efficient Congestion Management Through IoT - Driven Road User Charging Systems with Reinforcement Learning A. Sahaya Anselin Nisha, N. Venkatesvara Rao, G. Venkatesh, Sree Southry S, S. Murugan, B. Meenakshi 2nd International Conference on Intelligent Cyber Physical Systems and Internet of Things Icoici 2024 Proceedings, 2024 This research aims to find out how to manage congestion more effectively in cities by combining reinforcement learning (RL) with Internet of Things (IoT)-driven road user charging systems. Unlike typical static pricing models, this approach aims to improve traffic flow and reduce congestion by dynamically adjusting prices depending on real-time traffic circumstances. The system gathers and analyzes data on road use trends using IoT infrastructure, enabling adaptive pricing schemes. RL algorithms are then used to adjust billing policies in real time based on user preferences, congestion levels, and traffic volume. The evaluation of proposed approach is based on simulation studies performed in a genuine city setting. Compared to more traditional methods, the results show that it significantly improves commuters' journey times and reduces congestion. The results of this study highlight the promise of RL approaches in combination with IoT road user charging systems to alleviate traffic in cities and improve transportation efficiency generally.
Smart Energy Management Using IoT-Based Embedded Systems K. Vijayalakshmi, Ramakrishnan Raman, G. Venkatesh, Chandrashekhar J Rawandale, G. Kalaimani, C. Srinivasan International Conference on Sustainable Communication Networks and Application Icscna 2023 Proceedings, 2023 It is possible that embedded systems built on top of the Internet of Things (IoT) may be able to assist us in our pursuit of more energy-efficient practices and renewable power sources. The investigation of non-conventional, environmentally friendly forms of power is picking up steam on a worldwide scale. It is feasible that these technologies might be used to manage, optimize, and minimize both the amount of energy consumed and the expenses associated with that usage in commercial and residential buildings. Embedded technologies that are constructed on the Internet of Things provide an alluring opportunity for intelligent energy management. These systems include sensors that monitor energy use, and the generated data is analyzed to determine cost-cutting opportunities. This article explores the primary components and advantages of IoT-based embedded systems for energy management. Some of the topics covered in this article include the use of sensors, various tools for data processing, and real-time monitoring. In addition, the likelihood that these technologies will significantly reduce the amount of energy used as well as the expenses associated with it is examined. This is done with the goal of contributing to a greener future.
