In recent years, machine learning, especially deep learning, has developed rapidly and has shown remarkable performance in many tasks of the smart grid field. The representation ability of machine learning algorithms is greatly improved, but with the...
Smart buildings’ role is crucial for advancing smart cities’ performance in achieving environmental sustainability, resiliency, and efficiency. The integration barriers continue due to technology, infrastructure, and operations misalignme...
The extensive use of Internet of Things (IoT) technology has recently enabled the development of smart cities. Smart cities operate in real-time to improve metropolitan areas’ comfort and efficiency. Sensors in these IoT devices are immediately...
In this paper, we consider the intertwined challenges that arise for process optimisation within smart manufacturing, reviewing fundamental aspects including concept drift in relation to the dynamic nature of manufacturing, the prevalence of heteroge...
Smart cities have been a popular topic for the city stakeholders. A smart city is the next urban lifestyle that citizens expect. Due to the hypercompetitive and globalized economy, many cities have already started or are about to start their smart ci...
With the advent of Industry 5.0, the electrical sector has been endowed with intelligent devices that are propelling high penetration of distributed energy microgeneration, VPP, smart buildings, and smart plants and imposing new challenges on the sec...
This paper offers a review of the artificial intelligence (AI) algorithms and applications presently being used for smart machine tools. These AI methods can be classified as learning algorithms (deep, meta-, unsupervised, supervised, and reinforceme...
With the rise of the Internet of Things (IoT), applications have become smarter and connected devices give rise to their exploitation in all aspects of a modern city. As the volume of the collected data increases, Machine Learning (ML) techniques are...
Developments such as the increasing electrical energy demand, growth of renewable energy sources, cyber–physical security threats, increased penetration of electric vehicles (EVs), and unpredictable behavior of prosumers and EV users pose a ran...
Machine learning can be used to automate a wide range of tasks. Smart buildings, which use the Internet of Things (IoT) to connect building operations, enable activities, such as monitoring temperature, safety, and maintenance, for easier controlling...
As blockchain technology continues to advance, smart contracts, a core component, have increasingly garnered widespread attention. Nevertheless, security concerns associated with smart contracts have become more prominent. Although machine-learning t...
The incorporation of renewable energy sources in power grids has necessitated innovative solutions for effective energy management. Smart grids have emerged as transformative systems which integrate consumer, generator and dual-role entities to deliv...
The management of water resources is becoming increasingly important in several contexts, including agriculture. Recently, innovative agricultural practices, advanced sensors, and Internet of Things (IoT) devices have made it possible to improve the...
Smart city is a collective term for technologies and concepts that are directed toward making cities efficient, technologically more advanced, greener and more socially inclusive. These concepts include technical, economic and social innovations. Thi...
This article investigates the innovative role of machine learning (ML) in the development of smart cities, emphasizing the critical interrelationship between ML and urban environments. While existing studies address ML and urban settings separately,...
The unprecedented urban growth of recent years requires improved urban planning and management to make urban spaces more inclusive, safe, resilient and sustainable. Additionally, humanity faces the COVID pandemic, which especially complicates the man...
Fault detection is critical to the resilience and operational integrity of electrical power grids, particularly smart grids. In addition to requiring a lot of labeled data, traditional fault detection approaches have limited flexibility in handling u...
Smart aquaculture is nowadays one of the sustainable development trends for the aquaculture industry in intelligence and automation. Modern intelligent technologies have brought huge benefits to many fields including aquaculture to reduce labor, enha...
With the rapid advancement of digital technologies, smart construction has emerged as a transformative approach within the construction industry. Central to the success of human-machine collaboration is human-machine trust, which plays a critical rol...
The application of artificial intelligence enhances the ability of sensor and networking technologies to realize smart systems that sense, monitor and automatically control our everyday environments. Intelligent systems and applications often automat...
Smart wearable technologies such as fitness trackers are creating many new opportunities to improve the quality of life for everyone. It is usually impossible for visually impaired people to orientate themselves in large spaces and navigate an unfami...
4 March 2024
Background: Over the past few years, clinical studies have utilized machine learning in telehealth and smart care for disease management, self-management, and managing health issues like pulmonary diseases, heart failure, diabetes screening, and intr...
Industry 4.0 concepts and technologies ensure the ongoing development of micro- and macro-economic entities by focusing on the principles of interconnectivity, digitalization, and automation. In this context, artificial intelligence is seen as one of...
Smart policing refers to the use of advanced technologies such as artificial intelligence to enhance policing activities in terms of crime prevention or crime reduction. Artificial intelligence tools, including machine learning and natural language p...
The integration of machine learning (ML), blockchain, and the Internet of Things (IoT) in smart cities represents a pivotal advancement in urban innovation. This convergence addresses the complexities of modern urban environments by leveraging ML&rsq...
This paper presents an intrusion detection system (IDS) leveraging a hybrid machine learning approach aimed at enhancing the security of IoT devices at the edge, specifically for those utilizing the TCP/IP protocol. Recognizing the critical security...
Smart cities generate vast multimodal data from IoT devices, surveillance systems, health monitors, and environmental monitoring infrastructure. The seamless integration and interpretation of such multimodal data is essential for intelligent decision...
Nowadays, the main grid is facing several challenges related to the integration of renewable energy resources, deployment of grid-level energy storage devices, deployment of new usages such as the electric vehicle, massive usage of power electronic d...
The modern control infrastructure that manages and monitors the communication between the smart machines represents the most effective way to increase the efficiency of the industrial environment, such as smart grids. The cyber-physical systems utili...
This study presents the design and evaluation of a plush smart toy prototype for teaching geometry shapes to young children. The hardware design involves the integration of sensors, microcontrollers, an LCD screen, and a machine learning algorithm to...
Adaptation and innovation are extremely important to the manufacturing industry. This development should lead to sustainable manufacturing using new technologies. To promote sustainability, smart production requires global perspectives of smart produ...
Machine learning (ML) has already gained the attention of the researchers involved in smart city (SC) initiatives, along with other advanced technologies such as IoT, big data, cloud computing, or analytics. In this context, researchers also realized...
With the acceleration of urbanization, the construction of smart cities has become a global focal point, with machine learning technology playing a crucial role in this process. This study aims to conduct a bibliometric analysis of the published rese...
Context: Energy utilization is one of the most closely related factors affecting many areas of the smart farm, plant growth, crop production, device automation, and energy supply to the same degree. Recently, 4th industrial revolution technologies su...
Intelligent transportation and advanced mobility techniques focus on helping operators to efficiently manage navigation tasks in smart cities, enhancing cost efficiency, increasing security, and reducing costs. Although this field has seen significan...
With the assistance of machine learning, difficult tasks can be completed entirely on their own. In a smart grid (SG), computers and mobile devices may make it easier to control the interior temperature, monitor security, and perform routine maintena...
Smart cities require the development of information and communication technology to become a reality (ICT). A “smart city” is built on top of a “smart grid”. The implementation of numerous smart systems that are advantageous t...
Recently, a world-wide trend has been observed that there is widespread adoption across all fields to embrace smart environments and automation. Smart environments include a wide variety of Internet-of-Things (IoT) devices, so many challenges face co...
Security and privacy in the Internet of Things (IoT) other significant challenges, primarily because of the vast scale and deployment of IoT networks. Blockchain-based solutions support decentralized protection and privacy. In this study, a private b...
Precipitation in any form—such as rain, snow, and hail—can affect day-to-day outdoor activities. Rainfall prediction is one of the challenging tasks in weather forecasting process. Accurate rainfall prediction is now more difficult than b...
The smart grid is vulnerable to network attacks, thus requiring a high detection rate and fast detection speed for intrusion detection systems. With a fast training speed and a strong model generalization ability, the extreme learning machine (ELM) p...
The rapid expansion of the Internet of Things (IoT) and Machine Learning (ML) has significantly increased the demand for Location-Based Services (LBS) in today’s world. Among these services, indoor positioning and navigation have emerged as cru...
Smart cities (SC) promote economic development, improve the welfare of their citizens, and help in the ability of people to use technologies to build sustainable services. However, computational methods are necessary to assist in the process of creat...
Electricity theft remains a critical challenge that destabilizes power systems, causes significant financial losses, and disrupts the grid, particularly in developing countries. This study presents a machine learning framework integrating an ANN and...
Background: Smart mining is rapidly evolving through the integration of automation, advanced sensing technologies, and Machine Learning (ML). Methods: This Systematic Literature Review (SLR), based on 99 peer-reviewed studies published between 2021 a...
Electric load management through continuous monitoring and intelligent controlling has become a pressing requirement, particularly in light of rising electrical energy costs. The main purpose of this work is to realize a low-voltage electrical distri...
This paper defines a smart home use case to automatically adjust home temperature and/or hot water. The main objective is to reduce the energy consumption of cooling, heating and hot water systems in smart homes. To this end, the residents set a temp...
The exponential growth of machine learning (ML) applications in smart grid (SG) research over the past decade has generated a vast and fragmented body of literature that lacks systematic synthesis. This study addresses that gap by presenting a compre...
Addressing data anomalies (e.g., garbage data, outliers, redundant data, and missing data) plays a vital role in performing accurate analytics (billing, forecasting, load profiling, etc.) on smart homes’ energy consumption data. From the litera...
Modern greenhouse horticulture under the Agriculture 4.0 paradigm relies heavily on smart, automated, and energy-intensive climate control, making a reliable and sustainable power supply a core operational requirement. This study presents a data-driv...
of 83