[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"project-81995":3},{"id":4,"name":5,"fullName":6,"owner":7,"repo":5,"description":8,"homepage":9,"htmlUrl":9,"language":10,"languages":9,"totalLinesOfCode":9,"stars":11,"forks":12,"watchers":13,"openIssues":12,"contributorsCount":12,"subscribersCount":12,"size":12,"stars1d":12,"stars7d":12,"stars30d":12,"stars90d":12,"forks30d":12,"starsTrendScore":12,"compositeScore":14,"rankGlobal":9,"rankLanguage":9,"license":9,"archived":15,"fork":15,"defaultBranch":16,"hasWiki":17,"hasPages":15,"topics":18,"createdAt":9,"pushedAt":9,"updatedAt":19,"readmeContent":20,"aiSummary":21,"trendingCount":12,"starSnapshotCount":12,"syncStatus":22,"lastSyncTime":23,"discoverSource":24},81995,"SmartQueue","ARULSEBASTIN71\u002FSmartQueue","ARULSEBASTIN71","SmartQueue is an AI-powered smart queue management system designed to reduce waiting time and improve customer experience. It allows users to book appointments, generate QR-based tokens, track live queue status, and receive real-time updates. The system also provides admin dashboards and AI-based crowd prediction for efficient queue handling.",null,"JavaScript",25,0,23,37,false,"main",true,[],"2026-06-12 04:01:36","# SmartQueue — IEEE Final Project (v3)\n\n## Architecture\n\n```\nsmartqueue-final\u002F\n├── ml-service\u002F     ← Python Flask + GradientBoosting ML (port 8000)\n├── backend\u002F        ← Node.js + Express + MongoDB (port 5000)\n└── frontend\u002F       ← React.js (port 3000)\n```\n\n---\n\n## Step 1 — Start ML Service (Python)\n\n```bash\ncd ml-service\npip install flask scikit-learn numpy pandas joblib\npython app.py\n```\n\n**Model auto-loads** from `model.pkl`. Opens at http:\u002F\u002Flocalhost:8000\n\n- `\u002Fpredict-slots` → ML wait time for all 6 time slots\n- `\u002Fpredict`       → Single slot prediction\n- `\u002Fhealth`        → Model accuracy stats (R²=0.9985, MAE=2.32 min)\n- `\u002Fmodel-report`  → Full training report\n\n---\n\n## Step 2 — Start Backend (Node.js)\n\n```bash\ncd backend\nnpm install\nnpm run dev\n```\n\nOpens at http:\u002F\u002Flocalhost:5000. Requires MongoDB running locally.\n\n**To make admin account:**\nAfter registering, open MongoDB Compass:\n```\ndb.users.updateOne({ email: \"admin@gmail.com\" }, { $set: { role: \"admin\" } })\n```\n\n---\n\n## Step 3 — Start Frontend (React)\n\n```bash\ncd frontend\nnpm install\nnpm start\n```\n\nOpens at http:\u002F\u002Flocalhost:3000\n\n---\n\n## AI\u002FML Features (IEEE Paper)\n\n| Feature | Implementation | File |\n|---------|---------------|------|\n| Wait time prediction | GradientBoostingRegressor (n=200, R²=0.9985) | ml-service\u002Ftrain.py |\n| Peak hour heatmap | 30-day booking aggregation → 5×6 matrix | backend\u002Futils\u002Fhelpers.js |\n| AI visit suggestions | ML ranks all 6 slots by predicted wait | ml-service\u002Fapp.py |\n| Tribonacci priority | Dynamic priority score, grows with wait time | backend\u002Futils\u002Fhelpers.js |\n| QR token | Unique tokenId per booking, scannable | backend\u002Froutes\u002Fbookings.js |\n| QR scanner | html5-qrcode camera scanner | frontend\u002Fsrc\u002Fcomponents\u002FQRScanner.js |\n| Nearby alternatives | MongoDB query same-category lower queue | backend\u002Froutes\u002Fbusinesses.js |\n\n## ML Model Details\n\n- Algorithm: GradientBoostingRegressor (scikit-learn)\n- Training samples: 8000\n- Features: slot_hour, day_of_week, queue_length, avg_service_time, is_peak_hour, bookings_30d_slot, elderly_frac\n- MAE: 2.32 minutes\n- R²: 0.9985\n- CV R²: 0.9984\n","SmartQueue是一个基于AI的智能排队管理系统，旨在减少等待时间并提升客户体验。它允许用户预约、生成基于QR码的令牌、实时跟踪队列状态，并接收即时更新。系统还提供了管理员仪表板和基于AI的人流预测功能，以实现高效的队列管理。其核心技术包括使用GradientBoostingRegressor进行等待时间预测（平均绝对误差仅为2.32分钟），以及Tribonacci动态优先级算法来优化排队逻辑。前端采用React.js构建，后端使用Node.js与Express框架配合MongoDB数据库，机器学习服务则通过Python Flask提供。此项目特别适合需要高效管理人流和服务流程的各种商业场景，如银行、医院或零售店等。",2,"2026-06-11 04:07:23","CREATED_QUERY"]