Efficient utilization of Big data in smart cities is crucial for smooth operation of urban environments. Machine learning-enabled big data analytics is essential for optimizing city operations, improving resource management, and enhancing the quality of urban life. By leveraging machine learning (ML) algorithms to process and analyze the vast amounts of data generated in smart cities, authorities can gain insights and make real-time data-driven decisions. This article provides a comprehensive survey of how ML techniques are applied to analyze the large volumes of data generated by smart cities to improve urban living. Various ML algorithms, such as supervised, unsupervised, and reinforcement learning (RL) are discussed by highlighting their roles in numerous applications. Moreover, their distinguishing features are examined, enabling the selection of the most suitable algorithms for various applications in smart cities. Finally, the survey explores various challenges and suggests numerous research directions.

Alzheimer’s disease (AD) is a progressive neurodegenerative disorder causing cognitive decline and memory loss. With its significant impact on individuals’ lives, AD is the most prevalent form of dementia, contributing to 60-80% of all dementia cases. At the same time, symptoms may not surface until years later, making early detection vital for effective intervention. Thus, this work presents an approach to early AD detection by integrating Genome-Wide Association Studies (GWAS) with deep learning models and Explainable Artificial Intelligence (XAI). First, different classical machine learning models are developed for AD, and a Convolutional Neural Network (CNN) model is trained using the AD GWAS dataset obtained from the AD neuroimaging initiative. We then employ transfer learning to train our CNN model as a base model over the ADNI dataset. In addition, XAI methods are used to interpret the transfer learning model decision. Acknowledging the well-known limitation that classical machine learning is not inherently a generic model. The results from this study will help determine the most critical genetic markers associated with AD and provide transparency in understanding the deep learning model decisions.

Manufacturing generates a vast amount of data both from operations and simulation. Extracting appropriate information from this data can provide insights to increase a manufacturer's competitive advantage through improved sustainability, productivity, and flexibility of their operations. Manufacturers, as well as other industries, have successfully applied a promising statistical learning technique, called neural networks (NNs), to extract meaningful information from large data sets, so called big data. However, the application of NN to manufacturing problems remains limited because it involves the specialized skills of a data scientist. This paper introduces an approach to automate the application of analytical models to manufacturing problems. We present an NN meta-model (MM), which defines a set of concepts, rules, and constraints to represent NNs. An NN model can be automatically generated and manipulated based on the specifications of the NN MM. In addition, we present an algorithm to generate a predictive model from an NN and available data. The predictive model is represented in either Predictive Model Markup Language (PMML) or Portable Format for Analytics (PFA). Then we illustrate the approach in the context of a specific manufacturing system. Finally, we identify future steps planned towards later implementation of the proposed approach.