Maha Farouk Sabir¹, Mahmoud Ragab^2,3,*, Adil O. Khadidos², Khaled H. Alyoubi¹, Alaa O. Khadidos^1,4

Computer Systems Science and Engineering, Vol.48, No.3, pp. 627-643, 2024, DOI:10.32604/csse.2023.041551

Abstract Big data and information and communication technologies can be important to the effectiveness of smart cities. Based on the maximal attention on smart city sustainability, developing data-driven smart cities is newly obtained attention as a vital technology for addressing sustainability problems. Real-time monitoring of pollution allows local authorities to analyze the present traffic condition of cities and make decisions. Relating to air pollution occurs a main environmental problem in smart city environments. The effect of the deep learning (DL) approach quickly increased and penetrated almost every domain, comprising air pollution forecast. Therefore, this article develops… More >

Ali Sheraz Akram¹, Sagheer Abbas¹, Muhammad Adnan Khan^2,3,5, Atifa Athar⁴, Taher M. Ghazal^5,6, Hussam Al Hamadi^7,*

CMC-Computers, Materials & Continua, Vol.78, No.1, pp. 959-973, 2024, DOI:10.32604/cmc.2023.032216

Abstract Energy management is an inspiring domain in developing of renewable energy sources. However, the growth of decentralized energy production is revealing an increased complexity for power grid managers, inferring more quality and reliability to regulate electricity flows and less imbalance between electricity production and demand. The major objective of an energy management system is to achieve optimum energy procurement and utilization throughout the organization, minimize energy costs without affecting production, and minimize environmental effects. Modern energy management is an essential and complex subject because of the excessive consumption in residential buildings, which necessitates energy optimization… More >

Wenbing Zhao^1,*, Chenxi Huang², Yizhang Jiang³

CMES-Computer Modeling in Engineering & Sciences, Vol.138, No.2, pp. 1015-1017, 2024, DOI:10.32604/cmes.2023.031566

Abstract This article has no abstract. More >

Farhan Amin, Gyu Sang Choi^*

CMC-Computers, Materials & Continua, Vol.77, No.1, pp. 1165-1181, 2023, DOI:10.32604/cmc.2023.040693

Abstract In recent years, the Internet of Things (IoT) has played a vital role in providing various services to users in a smart city. However, searching for services, objects, data, and frameworks remains a concern. The technological advancements in Cyber-Physical Systems (CPSs) and the Social Internet of Things (SIoT) open a new era of research. Thus, we propose a Cyber-Physical-Social Systems (CPSs) for service search. Herein, service search and object discovery operation carries with the suitable selection of friends in the network. Our proposed model constructs a graph and performs social network analysis (SNA). We suggest More >

Gagan Deep^*, Jyoti Verma

Journal on Artificial Intelligence, Vol.5, pp. 57-73, 2023, DOI:10.32604/jai.2023.043329

Abstract This research explores the increasing importance of Artificial Intelligence (AI) and Machine Learning (ML) with relation to smart cities. It discusses the AI and ML’s ability to revolutionize various aspects of urban environments, including infrastructure, governance, public safety, and sustainability. The research presents the definition and characteristics of smart cities, highlighting the key components and technologies driving initiatives for smart cities. The methodology employed in this study involved a comprehensive review of relevant literature, research papers, and reports on the subject of AI and ML in smart cities. Various sources were consulted to gather information… More >

Jiaxing Wang, Lanlan Rui, Yang Yang^*, Zhipeng Gao, Xuesong Qiu

CMC-Computers, Materials & Continua, Vol.76, No.2, pp. 2355-2384, 2023, DOI:10.32604/cmc.2023.034993

Abstract Crowdsensing, as a data collection method that uses the mobile sensing ability of many users to help the public collect and extract useful information, has received extensive attention in data collection. Since crowdsensing relies on user equipment to consume resources to obtain information, and the quality and distribution of user equipment are uneven, crowdsensing has problems such as low participation enthusiasm of participants and low quality of collected data, which affects the widespread use of crowdsensing. This paper proposes to apply the blockchain to crowdsensing and solve the above challenges by utilizing the characteristics of… More >

Abdulaziz Attaallah¹, Khalil al-Sulbi², Areej Alasiry³, Mehrez Marzougui³, Neha Yadav⁴, Syed Anas Ansar^5,*, Pawan Kumar Chaurasia⁴, Alka Agrawal⁴

Intelligent Automation & Soft Computing, Vol.37, No.2, pp. 1853-1875, 2023, DOI:10.32604/iasc.2023.039761

Abstract Connected and autonomous vehicles are seeing their dawn at this moment. They provide numerous benefits to vehicle owners, manufacturers, vehicle service providers, insurance companies, etc. These vehicles generate a large amount of data, which makes privacy and security a major challenge to their success. The complicated machine-led mechanics of connected and autonomous vehicles increase the risks of privacy invasion and cyber security violations for their users by making them more susceptible to data exploitation and vulnerable to cyber-attacks than any of their predecessors. This could have a negative impact on how well-liked CAVs are with… More >

Zeyu Wu¹, Bo Sun^1,2, Qiang Feng^2,*, Zili Wang¹, Junlin Pan¹

CMES-Computer Modeling in Engineering & Sciences, Vol.137, No.1, pp. 527-554, 2023, DOI:10.32604/cmes.2023.027124

Abstract Due to the high inherent uncertainty of renewable energy, probabilistic day-ahead wind power forecasting is crucial for modeling and controlling the uncertainty of renewable energy smart grids in smart cities. However, the accuracy and reliability of high-resolution day-ahead wind power forecasting are constrained by unreliable local weather prediction and incomplete power generation data. This article proposes a physics-informed artificial intelligence (AI) surrogates method to augment the incomplete dataset and quantify its uncertainty to improve wind power forecasting performance. The incomplete dataset, built with numerical weather prediction data, historical wind power generation, and weather factors data,… More > Graphic Abstract

Sarkar Hasan Ahmed¹, Adel Al-Zebari², Rizgar R. Zebari³, Subhi R. M. Zeebaree^4,*

CMC-Computers, Materials & Continua, Vol.75, No.2, pp. 3993-4008, 2023, DOI:10.32604/cmc.2023.037464

Abstract Remote sensing (RS) presents laser scanning measurements, aerial photos, and high-resolution satellite images, which are utilized for extracting a range of traffic-related and road-related features. RS has a weakness, such as traffic fluctuations on small time scales that could distort the accuracy of predicted road and traffic features. This article introduces an Optimal Deep Learning for Traffic Critical Prediction Model on High-Resolution Remote Sensing Images (ODLTCP-HRRSI) to resolve these issues. The presented ODLTCP-HRRSI technique majorly aims to forecast the critical traffic in smart cities. To attain this, the presented ODLTCP-HRRSI model performs two major processes. More >

Manar Ahmed Hamza^1,*, Hanan Abdullah Mengash², Noha Negm³, Radwa Marzouk², Abdelwahed Motwakel¹, Abu Sarwar Zamani¹

CMC-Computers, Materials & Continua, Vol.75, No.2, pp. 4191-4205, 2023, DOI:10.32604/cmc.2023.027050

Abstract Recently, urbanization becomes a major concern for developing as well as developed countries. Owing to the increased urbanization, one of the important challenging issues in smart cities is waste management. So, automated waste detection and classification model becomes necessary for the smart city and to accomplish better recyclable waste management. Effective recycling of waste offers the chance of reducing the quantity of waste disposed to the land fill by minimizing the requirement of collecting raw materials. This study develops a novel Deep Consensus Network with Whale Optimization Algorithm for Recycling Waste Object Detection (DCNWO-RWOD) in… More >