Air pollution, a silent public health crisis, demands smarter solutions. This research explores a novel approach: using machine learning and the Internet of Things (IoT) to predict hyper-local Air Quality Index (AQI). We integrate a Random Forest algorithm with NodeMCU for pinpoint-accurate AQI forecasts. The core lies in a robust machine learning model trained on historical data rich in environmental parameters-air quality indicators like PM2.5 and ozone, coupled with meteorological factors. This empowers the model to predict future AQI levels with exceptional accuracy. Real-time data acquisition is handled by NodeMCU, an IoT platform acting as our environmental guardian. It gathers live data and feeds it to the model, enabling real-time, location-specific AQI predictions. The model's effectiveness is rigorously evaluated, achieving a promising 99% accuracy-a testament to its potential to revolutionize air quality management. By harnessing the power of machine learning and IoT, this research paves the way for a future where proactive air quality management fosters healthier cities and a more sustainable future.

In today's era of digitalization, streamlining traffic management and ensuring public safety are paramount. Traditional methods of driver license verification often face challenges in terms of efficiency, accuracy, and real-time capabilities. To address these limitations, we propose a novel Driving License Verification System (DLVS) that leverages advanced facial recognition and optical flow techniques to revolutionize driver license verification. The DLVS seamlessly integrates with traffic management systems, enabling real-time verification of driver licenses, enhancing traffic communication, and promoting overall public safety.

Expressive Imagine an art installation that transcends the static canvas, transforming in real-time based on the collective emotional pulse of the room. This isn't a scene from science fiction – it's the groundbreaking reality of Expressive Canvas. This innovative artwork harnesses the power of cutting-edge technology to create a truly immersive experience, blurring the lines between art and audience.At the heart of Expressive Canvas lies advanced emotion recognition software. With an accuracy of approximately 63%, this sophisticated system analyzes the subtle expressions on viewers' faces, capturing the nuances of joy, sadness, surprise, and everything in between. These emotional cues are then translated into a mesmerizing dance of color, light, and form. A collective sigh of contentment might bathe the scene in warm hues, while a ripple of anxiety could send shivers of cool blues across the canvas. Every laugh, tear, and flicker of emotion becomes a co-creator, shaping the artwork in a perpetual metamorphosis.Expressive Canvas isn't just a spectacle; it's a catalyst for profound experiences. By witnessing the impact of their own emotions on the artwork, viewers embark on a journey of self-discovery. They gain a heightened awareness of their inner landscape, their feelings reflected and amplified on the ever-changing canvas. As the collective mood paints the scene, a sense of shared humanity emerges. Individual emotions become interwoven, creating a vibrant tapestry that celebrates the beautiful complexity of human experience.This groundbreaking installation leverages the power of machine learning and image processing to redefine the future of art. It is a living, breathing entity – a conversation between audience and technology that fosters deeper connections than ever before. Expressive Canvas is not merely observed; it's actively participated in, inviting viewers to become co-creators in a world where art is a tool for emotional exploration, social connection, and a gateway to a richer understanding of ourselves and others.

In today's era websites have emerged as a means for businesses and organizations to connect with their customers. To ensure a user experience, across devices such as desktops, laptops, smartphones and tablets it is essential for a website to be both responsive and visually appealing. This survey paper aims to provide an overview of the methods recommended approaches and current patterns in designing and developing websites that're both responsive and visually captivating. The objective of this paper is to equip web developers, designers and researchers, with an understanding of the process involved in creating websites that're both responsive and visually appealing. As digital technologies continue to advance it is becoming increasingly vital to create engaging web experiences that cater to the user's needs.

This study tackles the limitations of traditional distance metrics in machine learning, particularly their inadequacy in managing missing data and varying feature importance. We introduce a novel custom distance metric that offers a more nuanced and robust measure of similarity, addressing the complexities and imperfections of real-world data. Our extensive evaluation of the CustomKNN algorithm, leveraging this custom distance metric, reveals exceptional performance and robustness across diverse datasets, consistently outperforming traditional metrics in various classification tasks. The custom distance metric shows superior robustness to missing data and scalability, despite its higher computational complexity and memory requirements. Our findings highlight the versatility and effectiveness of the CustomKNN algorithm, establishing it as a formidable option for classification tasks. This research advances the development of more adaptable and context-aware distance metrics, significantly enhancing the accuracy and reliability of machine learning models in practical applications.