More than 60 percent of the global surface is covered by clouds, and they play a vital role in the hydrological circle, climate change, and radiation budgets by modifying shortwaves and longwave. Weather forecast reports are critical to areas such as air and sea transport, energy, agriculture, and the environment. The time has come for artificial intelligence-powered devices to take the place of the current method by which decision-making experts determine cloud types. Convolutional neural network models (CNNs) are starting to be utilized for identifying the types of clouds that are caused by meteorological occurrences. This study uses the publicly available Cirrus Cumulus Stratus Nimbus (CCSN) dataset, which consists of 2543 ground-based cloud images altogether. We propose a model called Cloud-MobiNet for the classification of ground-based clouds. The model is an abridged convolutional neural network based on MobileNet. The architecture of Cloud-MobiNet is divided into two blocks, namely the MobileNet building block and the support MobileNet block (SM block). The MobileNet building block consists of the weights of the depthwise separable convolutions and pointwise separable convolutions of the MobileNet model. The SM block is made up of three dense network layers for feature extraction. This makes the Cloud-MobiNet model very lightweight to be implemented on a smartphone. An overall accuracy success of 97.45% was obtained for the CCSN dataset used for cloud-type classification. Cloud-MobiNet promises to be a significant model in the short term, since automated ground-based cloud classification is anticipated to be a preferred means of cloud observation, not only in meteorological analysis and forecasting but also in the aeronautical and aviation industries.

Scholars have classified soil to understand its complex and diverse characteristics. The current trend of precision agricultural technology demands a change in conventional soil identification methods. For example, soil color observed using Munsell color charts is subjective and lacks consistency among observers. Soil classification is essential for soil management and sustainable land utilization, thereby facilitating communication between different groups, such as farmers and pedologists. Misclassified soil can mislead processes; for example, it can hinder fertilizer delivery, affecting crop yield. On the other hand, deep learning approaches have facilitated computer vision technology, where machine-learning algorithms trained for image recognition, comparison, and pattern identification can classify soil better than or equal to human eyes. Moreover, the learning algorithm can contrast the current observation with previously examined data. In this regard, this study implements a convolutional neural network (CNN) model called Soil-MobiNet to classify soils. The Soil-MobiNet model implements the same pointwise and depthwise convolutions of the MobileNet, except the model uses the weight of the pointwise and depthwise separable convolutions plus an additional three dense layers for feature extraction. The model classified the Vellore Institute of Technology Soil (VITSoil) dataset, which is made up of 4864 soil images belonging to nine categories. The VITSoil dataset samples for Soil-MobiNet classification were collected over the Indian states and it is made up of nine major Indian soil types prepared by experts in soil science. With a training and validation accuracy of 98.47% and an average testing accuracy of 93%, Soil-MobiNet showed outstanding performance in categorizing the VITSoil dataset. In particular, the proposed Soil-MobiNet model can be used for real-time soil classification on mobile phones since the proposed system is small and portable.

The city of the future is smart. It is entirely interconnected and will regulate traffic, save energy, fight crime, and it will be assisted by big data and the internet of things (IoT). Cities compose of many systems providing services for us. Systems such as transport, energy, health, and others depend on each other. The connections between these systems seem to have been broken and need to be reconnected to provide efficient services for the citizens. To create a smart city, there is the need to combine internet communication network (5G), application software, database and blockchain application, AI, cloud computing, GPS, maps. For the smart city concept to be instrumental and beneficial to the citizens, there should be a centralized large-scale database center that will tie all these pieces of data generated in the various smart sectors together for it to function as an intelligently coordinated, geographically distributed system. On this score, the authors propose nine main dimensions to the smart city concept. Request access from your librarian to read this chapter's full text.

The world is fascinated to see how technology evolves each passing day. All too soon, there's an emerging technology that is trending around us, and it is no other technology than smart wearable technology. Less attention is paid to the data that our bodies are radiating and communicating to us, but with the timely arrival of wearable sensors, we now have numerous devices that can be tracking and collecting the data that our bodies are radiating. Apart from numerous benefits that we derive from the functions provided by wearable technology such as monitoring of our fitness levels, etc., one other critical importance of wearable technology is helping the advancement of artificial intelligence (AI) and machine learning (ML). Machine learning thrives on the availability of massive data and wearable technology which forms part of the internet of things (IoT) generates megabytes of data every single day. The data generated by these wearable devices are used as a dataset for the training and learning of machine learning models. Through the analysis of the outcome of these machine learning models, scientific conclusions are made.