Machine learning applications in internet-of-drones: Systematic review, recent deployments, and open issues
Deep Learning (DL) and Machine Learning (ML) are effectively utilized in various
complicated challenges in healthcare, industry, and academia. The Internet of Drones (IoD) …
complicated challenges in healthcare, industry, and academia. The Internet of Drones (IoD) …
Revolutionizing municipal solid waste management (MSWM) with machine learning as a clean resource: Opportunities, challenges and solutions
Effective municipal solid waste management is essential for public health, environmental
protection, economic benefits, and clean energy generation for future commercial …
protection, economic benefits, and clean energy generation for future commercial …
[HTML][HTML] Role of machine learning in attaining environmental sustainability
Climate change and sustainable development are significant challenges that must be
addressed as soon as possible. As a result of this vision, the renewable energy (RE) …
addressed as soon as possible. As a result of this vision, the renewable energy (RE) …
Machine learning and internet of things applications in enterprise architectures: Solutions, challenges, and open issues
The rapid growth of the Internet of Things (IoT) has led to its widespread adoption in various
industries, enabling enhanced productivity and efficient services. Integrating IoT systems …
industries, enabling enhanced productivity and efficient services. Integrating IoT systems …
Predictive modelling of cohesion and friction angle of soil using gene expression programming: a step towards smart and sustainable construction
To achieve smart and sustainable construction goals, machine learning (ML) techniques can
serve as a cost-effective and efficient substitute for labour-intensive, laboratory, or in situ …
serve as a cost-effective and efficient substitute for labour-intensive, laboratory, or in situ …
Smart City Middleware: A Survey and a Conceptual Framework
C Goumopoulos - IEEE Access, 2024 - ieeexplore.ieee.org
Smart city middleware serves as a foundational tool in the evolution of urban digitalization,
acting as an intermediary software layer that simplifies the development, deployment, and …
acting as an intermediary software layer that simplifies the development, deployment, and …
Adaptive method for machine learning model selection in data science projects
C Tavares, N Nascimento, P Alencar… - … Conference on Big …, 2022 - ieeexplore.ieee.org
Data science projects involve a machine learning (ML) process based on data, code, and
models that change over time. For example, the datasets may increase in size and allow an …
models that change over time. For example, the datasets may increase in size and allow an …
Toward Greener Smart Cities: A Critical Review of Classic and Machine-Learning-Based Algorithms for Smart Bin Collection
This study critically reviews the scientific literature regarding machine-learning approaches
for optimizing smart bin collection in urban environments. Usually, the problem is modeled …
for optimizing smart bin collection in urban environments. Usually, the problem is modeled …
[图书][B] Cognitive Analytics and Reinforcement Learning: Theories, Techniques and Applications
R Elakkiya, V Subramaniyaswamy - 2024 - books.google.com
COGNITIVE ANALYTICS AND REINFORCEMENT LEARNING The combination of cognitive
analytics and reinforcement learning is a transformational force in the field of modern …
analytics and reinforcement learning is a transformational force in the field of modern …
Smart cities ecosystem in the modern digital age: an introduction
Smart cities are those that use science, engineering, artificial intelligence, digital knowledge,
and other technologies to progress the well-being of residents, boost economic …
and other technologies to progress the well-being of residents, boost economic …