In image classification, various techniques have been developed to enhance the performance of principal component analysis (PCA) dimension reduction techniques with guiding weighting features to remove redundant and irrelevant features. This study proposes the statistically weighted dimension technique based on three distribution-related class behaviors; collection-class, inter-class, and intra-class to enhance the feature-extraction ability before using PCA for feature selection. The data from the statistics-weighted dimension spaces is utilized to reduce dimensionality by reducing the large index data into smaller index data using PCA. The new principal component from the weighted training part by an unlabeled dataset is constructed and then the image is classified efficiently. Additionally, the weighting direction investigates the pros and cons of promoting and demoting to determine the worst or best option utilizing the exponents of three proposed weighted scheme. The experiment is conducted using three datasets, MNIST, E-MNIST, and F-MNIST, along with three image classification algorithms, logistic Regression, KNN, and SVM (RBF). The results clearly demonstrate that the statistically weighted dimension feature can improve the conventional classification accuracy in lower dimensions with an appropriate combination of weighting nearly 3% for the best solution on dimensionality reduction by more than 50%.

In an age characterized by the persistent advancement of internet networks, the widespread reliance on electronic devices emphasizes the crucial role of electrical systems in facilitating everyday activities. However, manual data tracking poses issues for experts and inconveniences users attempting to interpret the raw data. To overcome this issue, the proposed study aims to develop a digital energy monitoring system designed to mitigate human error. The proposed system is precisely designed to measure the various forms of electricity generated by portable sources. Furthermore, integral to its functionality is a user-friendly dashboard interface, enabling real-time monitoring and also analyze the electricity consumption while providing seamless access to historical usage data in graphical formats. Moreover, individual user accounts enhance the system’s usability and accessibility. Beyond its immediate application, the proposed digital energy monitoring solution holds the potential to enhance educational practices in electrical engineering, boost consumer confidence, and serve as a catalyst for future innovations in diverse domains.

The global transition away from fossil fuels is gaining momentum to prevent environmental degradation, and as the supply gap for electricity deepens, so does the quest for alternative power generation technologies. Solar energy utilizing PV cells is one potential approach in this endeavor. This technology, which uses renewable resources, represents a massive step towards sustainability. In light of issues like insufficient energy production and unstable power distribution, there is an urgent need to control and regulate electricity use properly. As the demand for sustainable power in homes and businesses is growing, existing utility systems strive to meet it, prompting a search for more intelligent methods to consume energy. This study proposes a grid-tied photovoltaic (PV) inverter with net metering as an effective way to integrate solar energy generation with existing power infrastructures. The analysis for the proposed scheme is carried out using Proteus software, which provides information about the system’s functionality and how it might help alleviate power shortages and reduce the stress on utility networks. Additionally, this technology encourages a more sustainable approach to electricity usage by enabling distributed generation (DG), which improves energy self-sufficiency. The results demonstrate that grid-tied PV net metering that utilizes IOT significantly reduces energy shortages and ensures a longerlasting, environmentally responsible infrastructure. Finally, the proposed study plays a critical role in realizing a future powered by renewable resources.