Background: Rail Madad is a vital platform for passengers to report issues and seek assistance during their train journeys. However, the current process relies heavily on manual complaint registration, leading to delays and inefficiencies. After a grievance is registered on Rail Madad, it is acknowledged with a unique ID, categorized into type of complaints, and assigned to the relevant department. Responsible officials investigate and take corrective actions, updating the system with the status. Categorizing the complaints needs some improvements and can be made to enhance the Rail Madad complaint resolution process using Artificial Intelligence (AI). especially when complainants share photos, videos and audios only. Description: According to the problem statement above, it is imperative that complaints are correctly assigned and classified, and that departmental transitions be seamless. The aforementioned problem necessitates an Al-based solution. Expected Solution: 1. Automated Categorization and Prioritization: Integrate the system with the existing Rail Madad platform to streamline the complaint management process. ? Image and Video Analysis: Use Al-powered image and video recognition to automatically categorize the nature of the complaint (e.g., coach cleanliness, damage, staff behaviour). ? Urgency Detection: Implement Al to assess the severity of the issue from visual content and prioritize complaints that need immediate attention. 2. Enhanced Data Extraction: ? Text Recognition (OCR): Utilize Optical Character Recognition (OCR) to extract textual information from images or videos, such as signage or documents, to gather more contexts about the complaint. ? Metadata Analysis: Extract metadata from images and videos (e.g., timestamp, location) to provide additional information for more accurate and quicker resolution. 3. Automated Response and Routing: ? AI Chatbots: Deploy Al chatbots to provide instant acknowledgment and preliminary responses, collecting additional necessary information from the complainant. ? Smart Routing: Use Al algorithms to route complaints to the most appropriate department or official based on the content of the photos or videos. 4. Predictive Maintenance: ? Issue Prediction: Implement machine learning models to predict recurring issues from the analysis of visual data trends, helping in proactive maintenance and reducing future complaints. 5. Feedback and Continuous Improvement: ? Sentiment Analysis: Use Al to analyze the sentiment of feedback provided by complainants, identifying areas needing improvement. ? Performance Monitoring: Continuously monitor the effectiveness of the complaint resolution process through Al-driven analytics, making data-driven adjustments for process enhancement. ? Accuracy detection: Accuracy of Al-powered complaint detection can be analysed. ? Speed Analysis: Speed of complaint registration and resolution. ? User experience and satisfaction. ? Scalability and integrability with existing system. 6. Training and Support: ? Al-Assisted Training: Provide Al-based training tools for staff to help them better analyze and resolve complaints efficiently using visual data. ? Resource Allocation: Use Al to analyze complaint patterns and allocates resources dynamically, ensuring that high-priority issues receive prompt attention. 7. Evaluation Criteria: ? Accuracy of Al-powered complaint detection ? Speed of complaint registration and resolution ? User experience and satisfaction ? Scalability and integrability with existing systems
Here we have developed a solution for the problem of "Complaint Management System in the Rail Madad Platform".
HOPE YOU WILL LIKE THE VIDEO!!!
Cheers
#technology #artificialintelligence #pythonprogramming #flasktutorial #