Road safety is a critical global concern, with manual enforcement of helmet laws and vehicle safety standards (e.g., rear-view mirror presence) being resource-intensive and inconsistent. This paper presents an AI-powered system to automate traffic violation detection, significantly enhancing enforcement efficiency and road safety. The system leverages YOLOv8 for robust object detection and EasyOCR for license plate recognition. Trained on a custom dataset of annotated images (augmented for diversity), it identifies helmet non-compliance, the absence of rear-view mirrors on motorcycles, an innovative contribution to automated checks, and extracts vehicle registration numbers. A Streamlit-based interface facilitates real-time monitoring and violation logging. Advanced image preprocessing enhances license plate recognition, particularly under challenging conditions. Based on evaluation results, the model achieves an overall precision of 0.9147, a recall of 0.886, and a mean Average Precision (mAP@50) of 0.843. The mAP@50 95 of 0.503 further indicates strong detection capability under stricter IoU thresholds. This work demonstrates a practical and effective solution for automated traffic rule enforcement, with considerations for real-world deployment discussed.

What are the paths to MLOps Success? What is the data-centric approach to MLOps? Why does MLOps need open-source software development? How does AWS support open-source machine learning? Why does MLOps need vendors as well as open-source software?

For as long as we can remember, creative decision-making has been largely subjective. In the past decade, however, the subjectivity of creativity has begun to change. More than ever, decisions within major companies and even the arts have been straying out of the realm of subjectivity and into the realm of objectivity and data. When a record label signs a new artist, it wants to see Spotify data signaling this artist's fanbase is trending up and to the right. When a car manufacturer hires an advertising agency and asks for creative campaign ideas, it wants those ideas to be backed up by data that signals emerging demand.

Robots are increasingly being deployed in retail environments. The reasons for this include: to relieve staff from the performance of repetitive and mundane tasks; to reallocate staff to more value-added, customer-facing activities; to realize operational improvements; and, to utilize real-time in-store generated data. Due to the impact of the 2020 Coronavirus outbreak, we can now add a new reason to use robots in retail: to assist with customer and employee safety. In this Research Article, the Retail Analytics Council at NWU presents information on the benefits associated with deploying robots in stores. Estimates of the size of the global retail robot market are advanced.

In this survey, we study how recent advances in machine intelligence are disrupting the world of business processes. Over the last decade, there has been steady progress towards the automation of business processes under the umbrella of "robotic process automation" (RPA). However, we are currently at an inflection point in this evolution, as a new paradigm called "Intelligent Process Automation" (IPA) emerges, bringing machine learning (ML) and artificial intelligence (AI) technologies to bear in order to improve business process outcomes. The purpose of this paper is to provide a survey of this emerging theme and identify key open research challenges at the intersection of AI and business processes. We hope that this emerging theme will spark engaging conversations at the RPA Forum.

The Global Automotive Artificial Intelligence Market 2020 Report Covers the in-depth valuable … International Business Machines Corporation

New Jersey, United States –The report on the global Artificial Intelligence in Manufacturing market is a compilation of intelligent, broad research studies that will help players and stakeholders to make informed business decisions in future. It offers specific and reliable recommendations for players to better tackle challenges in the global Artificial Intelligence in Manufacturing market. Furthermore, it comes out as a powerful resource providing up to date and verified information and data on various aspects of the global Artificial Intelligence in Manufacturing market. Readers will be able to gain deeper understanding of the competitive landscape and its future scenarios, crucial dynamics, and leading segments of the global Artificial Intelligence in Manufacturing market. Buyers of the report will have access to accurate PESTLE, SWOT, and other types of analysis on the global Artificial Intelligence in Manufacturing market.

Using virtual technology to take on the work of humans when interacting with applications will transform the procure-to-pay function. Automating process through robotics will allow you to perform repetitive tasks continuously and flawlessly all the time with minimal set up. Technology ensures error-prone payable processes and eliminates much of the mundane and time-consuming tasks such as data-entry. Also, robotics in AP will accelerate cycle times, easily manage tasks in volume, ensure consistency in operating procedures and streamlines audit & compliance activities. Human work can be combined with robotic process automation to streamline the supplier management and to tightly align accounts payable with other departments.