This paper presents a novel approach that addresses the challenging task of real-time drivable road region extraction in computer vision. Semantic segmentation, which involves accurately identifying and segmenting objects in real-time data, is a complex problem. However, deep learning has proven to be a powerful technique for achieving semantic segmentation by automatically identifying patterns without the need for explicit programming. To tackle this task, the paper proposes a fusion of the YOLO algorithm and UNET architecture, leveraging their respective strengths. The YOLO algorithm enables high-speed object detection, while the UNET architecture provides advantages in global location utilization, contextual understanding, and performance, even with limited training samples. Importantly, the proposed method is lightweight, making it suitable for deployment on embedded systems with limited computational power. To optimize memory usage and capture context at different scales, the system employs dilated convolutions for efficient feature extraction. The algorithm exhibits exceptional performance in accurately segmenting irregular objects and handles diverse input data types, including images and videos, in real-time. Overall, this paper contributes significantly to the advancement of computer vision technologies and offers a valuable solution for real-time drivable road region extraction. Its potential applications include addressing driving challenges and enhancing safety in autonomous vehicles and intelligent transportation systems.
Now a day’s mostly peoples are attracted towards plastic for their daily needs. It is estimated 55% of global plastic was discarded, 25% was incinerated & only 20% of plastic is recycled. The discarded waste plastics in the land spoil the fertility of the soil, prevents the rain water absorption capacity of the soil. Burning of the waste plastics produces smoke containing carbon-monoxide and some harmful gases in the atmosphere. This can induce air pollution recycling processes; plastic road is one of the best ways for recycling plastics.
Robust and efficient data security measures are of the utmost importance given the growing reliance on digital technologies. I recommend developing a simulated security testing platform using artificial immune algorithms to improve data security in response to the needs of the user. This software provides the capacity to simulate different cyber-attacks, making it easier to assess the efficacy of different safeguards. The platform may adapt to these attacks and learn from them by utilizing the strength of artificial immune mechanisms, increasing its resistance to threats in the future. I acquired beneficial expertise in software development, algorithm design, and data security as a result of my active participation in this project. I'm thrilled to share this study as a proof of my abilities and unwavering commitment to the data security area. The suggested approach makes use of Python-based machine learning techniques, and adds HTML, CSS, and JS for the user interface. The suggested approach also includes a user-friendly interface built using HTML, CSS, and JS, making it easier to integrate with present systems.
Collaborative filtering is a widely used method in Machine Language to discover relationships between data. It facilitates recommendation systems that find similarities between user data and items, recommendation system playing a crucial role in various industries. Multilayer perceptron classifier used in our model, a connection with neural networks that performs well in regression and achieves high accuracy in classification tasks. When compared to other neural network architectures like convolution neural network (CNN), recurrent neural network (RNN), auto encoder (AE), and generative adversarial network (GAN), MLP remains a fundamental approach. Collaborative filtering involves multiple users, viewpoints, and data sources collaborating to classify information or patterns and recommend items that similar users might like. Instead of recommending items based on their features, we group users into neural networks with similar preferences and suggest items based on their classifier's preferences.
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