الصفحة 1
الصفحة 1
Internet of Things and Machine Learning in Agriculture
Machine Learning (ML) and the Internet of Things (IoT) can play a very promising role in the agricultural industry. Some examples include: an AI-powered drone to monitor the field, an IoT-designed automated crop watering system, sensors embedded in the field to monitor temperature and humidity, etc. The agriculture industry is the largest in the world, but when it comes to innovation there is a lot more to explore. IoT devices can be used to analyze the status of crops. For instance, with soil sensors, farmers can detect any irregular conditions such as high acidity and efficiently tackle these issues to improve their yield. In this book, we will point out the challenges facing the agro-industry that can be addressed by ML and IoT and explore the impacts of these technologies in the agriculture sector.
E-Learning Methodologies : Fundamentals, technologies and applications
Covers state of the art topics including user modeling for e-learning systems and cloud, IOT, and mobile-based frameworks. It also considers security challenges and ethical conduct using Blockchain technology. E-learning has become an important part of our educational life with the development of e-learning systems and platforms and the need for online and remote learning. ICT and computational intelligence techniques are being used to design more intelligent and adaptive systems. However, the art of designing good real-time e-learning systems is difficult as different aspects of learning need to be considered including challenges such as learning rates, involvement, knowledge, qualifications, as well as networking and security issues. The earlier concepts of standalone integrated virtual e-learning systems have been greatly enhanced with emerging technologies such as cloud computing, mobile computing, big data, Internet of Things (IoT), AI and machine learning, and AR/VT technologies.
