Classification of skin disease using deep learning neural networks with mobilenet V2 and LSTM

Deep learning models are efficient in learning the features that assist in understanding complex patterns precisely. This study proposed a computerized process of classifying skin disease through deep learning-based MobileNet V2 and Long Short Term Memory (LSTM). The MobileNet V2 model proved to be efficient with a better accuracy that can work on lightweight computational devices. The proposed model is efficient in maintaining stateful information for precise predictions. A grey-level co-occurrence matrix is used for assessing the progress of diseased growth. The performance has been compared against other state-of-the-art models such as Fine-Tuned Neural Networks (FTNN), Convolutional Neural Network (CNN), Very Deep Convolutional Networks for Large-Scale Image Recognition developed by Visual Geometry Group (VGG), and convolutional neural network architecture that expanded with few changes. The HAM10000 dataset is used and the proposed method has outperformed other methods with more than 85% accuracy. Its robustness in recognizing the affected region much faster with almost 2x lesser computations than the conven-tional MobileNet model results in minimal computational efforts. Furthermore, a mobile application is designed for instant and proper action. It helps the patient and dermatologists identify the type of disease from the affected region’s image at the initial stage of the skin disease. These findings suggest that the proposed system can help general practitioners efficiently and effectively diagnose skin conditions, thereby reducing further complications and morbidity

A Unified Metering System Deployed for Water and Energy Monitoring in Smart City

In the context of smart cities in India, accurate meter readings are crucial for managing household water and energy systems efficiently. However, traditional meter reading methods are costly and time-consuming due to the large number of users and the lack of daily usage analysis leading to customer dissatisfaction. The proposed solution to tackle this matter involves implementing an integrated wireless smart energy and water metering system that utilizes smart metering technology. This system can potentially revolutionize how utilities handle energy and water management. The integrated system is designed to replace the mechanical water meters and conventional digital energy meters, whose primary function is to accurately record meter readings for payment purposes, for automatic meter readings that do not require frequent trips to the location where the meters are installed. This article proposes a smart, integrated wireless metering system to revolutionize customer engagement and energy and water utility management. This technology enables the transmission of precise and secure data on water and energy consumption in real-time by employing Low Power Wide Area Networks (LPWAN) technology, known for its low power consumption, cost-effectiveness, long-range coverage, and efficient penetration. The system has a water flow sensor and PZEM-004T for real-time water and energy consumption readings. The interoperable features in the integrated water flow and energy meter are achieved through trial-and-error methods. The trials led to experimental findings that enabled successful communication between the energy and water flow meters and recorded accurate readings. The device provides the utility provider with real-time consumption statistics and the flexibility to turn on and off the system remotely. The system also helps the users by giving them real-time consumption data and preventing overloading situations. The device also notifies the utility company of the theft of electricity. The proposed system overcomes the gaps reported in the traditional systems and design challenges