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Results for "SnowHeap"

Alumnus innovator builds AI venture

MBZUAI ·

MBZUAI alumnus Abdulwahab Sahyoun launched SnowHeap LLC, an AI-powered data analytics company. Sahyoun, a machine learning engineer with roots in Lebanon, aims to provide strategic tech consulting and develop in-house AI products. He was inspired by MBZUAI and the UAE's startup ecosystem to pursue his entrepreneurial ambitions. Why it matters: The story highlights MBZUAI's role in fostering AI entrepreneurship and the UAE's attractiveness for AI ventures.

Smart Waste Management System for Makkah City using Artificial Intelligence and Internet of Things

arXiv ·

A research paper proposes a smart waste management system called TUHR for Makkah, Saudi Arabia, leveraging IoT and AI to handle waste accumulation during the annual pilgrimage. The system uses ultrasonic sensors to monitor waste levels and gas detectors to identify harmful substances, alerting authorities when containers are full or hazards are detected. The proposed system aligns with Saudi Vision 2030 by promoting sustainability and improving public health through optimized waste management.

WISE Summit - IIE

Qatar Foundation ·

The title refers to a WISE Summit, an initiative often associated with the Qatar Foundation, potentially involving the Institute of International Education (IIE). No specific details regarding the summit's agenda, discussions, or outcomes were provided in the content. Why it matters: WISE Summits are significant platforms for global education policy and innovation, often shaping educational discourse within the Middle East, though its direct relevance to AI cannot be determined from the title alone.

Hybrid Deep Feature Extraction and ML for Construction and Demolition Debris Classification

arXiv ·

This paper introduces a hybrid deep learning and machine learning pipeline for classifying construction and demolition waste. A dataset of 1,800 images from UAE construction sites was created, and deep features were extracted using a pre-trained Xception network. The combination of Xception features with machine learning classifiers achieved up to 99.5% accuracy, demonstrating state-of-the-art performance for debris identification.