Unplanned downtime in industrial sectors presents significant challenges, impacting both production efficiency and profitability. To tackle this issue, companies are actively working towards optimizing their operations and reducing disruptions that hinder their ability to meet customer demands and financial goals. Predictive maintenance, utilizing advanced technologies like data analytics, machine learning, and IoT devices, offers real-time equipment data monitoring and analysis. This research study centers on the development of a versatile machine-learning model for predicting faults in induction motors within industrial environments. Implementing such a model can enable proactive maintenance, ultimately leading to decreased downtime in industrial operations. The study involved the acquisition of a dataset comprising healthy and faulty conditions of four 3-phase induction motors, along with relevant features for fault prediction. Multiple machine learning algorithms were trained using this dataset, exhibiting promising performance. The Random Forest (RF) model achieved the highest accuracy at 0.91, closely followed by the Artificial Neural Network (ANN) and k-nearest Neighbors (k-NN) models, both achieving an accuracy of 0.9. Meanwhile, the Decision Tree (DT) model showed the lowest accuracy at 0.89. Further model evaluation was carried out using a confusion matrix, which provided a detailed breakdown of the models' performance for each class, revealing the number of correctly and incorrectly classified induction motor conditions. The results from the confusion matrix indicate that the models effectively classified the various states and conditions of the induction motors. To enhance model performance in future work, potential avenues include refining the ANN and RF models, exploring transfer learning or ensemble methods, and incorporating diverse datasets to improve generalization.

Addressing the increasing demand for accessible sign language learning tools, this paper introduces an innovative Machine Learning-Driven Web Application dedicated to Sign Language Learning. This web application represents a significant advancement in sign language education. Unlike traditional approaches, the application’s unique methodology involves assigning users different words to spell. Users are tasked with signing each letter of the word, earning a point upon correctly signing the entire word. The paper delves into the development, features, and the machine learning framework underlying the application. Developed using HTML, CSS, JavaScript, and Flask, the web application seamlessly accesses the user’s webcam for a live video feed, displaying the model’s predictions on-screen to facilitate interactive practice sessions. The primary aim is to provide a learning platform for those who are not familiar with sign language, offering them the opportunity to acquire this essential skill and fostering inclusivity in the digital age.

The rapid increase in global electricity demand, driven by technological advancements and the electrification of various sectors, demands effective energy forecasting to ensure sustainable and proactive energy management. This study marks the first stage in the development of a smarter energy management solution, which is based on cooperative intelligence distributed at the smart grid levels, involving the entire chain from producers to consumers. Consequently, we conducted a comparative analysis of three advanced forecasting models to determine their effectiveness in predicting energy consumption. Using a real dataset, collected in a student residency in Bologna, Italy, which includes weather and energy consumption data, the models were evaluated using metrics such as mean absolute error (MAE), mean squared error (MSE), root mean squared error (RMSE) and R². The results indicated that the LSTM model outperformed the others, demonstrating superior accuracy in capturing temporal dependencies. The Transformer model also showed robust performance, highlighting its potential in scenarios that require the capture of long-range dependencies. This study sets the stage for further research and development to leverage advanced forecasting techniques to optimize energy distribution and ensure the sustainability of energy systems.

The global imperative to transition towards sustainable energy sources has sparked innovative solutions for energy generation and environmental conservation challenges. As fossil fuel usage for power generation continues to raise environmental concerns, converting kinetic energy from vehicular motion via speed breakers presents a unique avenue for renewable power production. This study explores the concept of utilizing speed breakers as a means of electricity generation to power little power-consuming but critical load, with Covenant University serving as a pertinent case study. This research investigates the technical, economic, and environmental implications of implementing speed breaker-based electricity generation within Covenant University. Analyzing the university's energy consumption patterns showed that some loads do not require much power but are critical. Street lighting is one of such loads. This study discerns the potential contribution of speed breaker-generated electricity to address energy demands by simulation and constructing a prototype. Advanced engineering tools, such as simulation software Fusion 360 and Proteus 8.0, were employed to model and integrate the roller speed breaker mechanism with the electrical infrastructure. The findings offer valuable insights into the viability of speed breaker-generated electricity as an alternative energy source, paving the way for sustainable energy practices in educational institutions and beyond.