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, is augmented based on generative… 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. At the initial stage, the… 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 Smart Cities. The goal of… More >

Mohammad Aldossary^*

CMC-Computers, Materials & Continua, Vol.75, No.1, pp. 633-649, 2023, DOI:10.32604/cmc.2023.035414

Abstract In the smart city paradigm, the deployment of Internet of Things (IoT) services and solutions requires extensive communication and computing resources to place and process IoT applications in real time, which consumes a lot of energy and increases operational costs. Usually, IoT applications are placed in the cloud to provide high-quality services and scalable resources. However, the existing cloud-based approach should consider the above constraints to efficiently place and process IoT applications. In this paper, an efficient optimization approach for placing IoT applications in a multi-layer fog-cloud environment is proposed using a mathematical model (Mixed-Integer Linear Programming (MILP)). This approach… More >

Hong-Hu Chu¹, Muhammad Rizwan Saeed², Javed Rashid^3,4,*, Muhammad Tahir Mehmood⁵, Israr Ahmad⁶, Rao Sohail Iqbal⁴, Ghulam Ali¹

CMC-Computers, Materials & Continua, Vol.75, No.1, pp. 1863-1881, 2023, DOI:10.32604/cmc.2023.035287

Abstract The increasing global population at a rapid pace makes road traffic dense; managing such massive traffic is challenging. In developing countries like Pakistan, road traffic accidents (RTA) have the highest mortality percentage among other Asian countries. The main reasons for RTAs are road cracks and potholes. Understanding the need for an automated system for the detection of cracks and potholes, this study proposes a decision support system (DSS) for an autonomous road information system for smart city development with the use of deep learning. The proposed DSS works in layers where initially the image of roads is captured and coordinates… More >

R. Shanthakumari¹, Yun-Cheol Nam², Yunyoung Nam^3,*, Mohamed Abouhawwash^4,5

Intelligent Automation & Soft Computing, Vol.36, No.2, pp. 1991-2005, 2023, DOI:10.32604/iasc.2023.033696

Abstract Smart cities make use of a variety of smart technology to improve societies in better ways. Such intelligent technologies, on the other hand, pose significant concerns in terms of power usage and emission of carbons. The suggested study is focused on technological networks for big data-driven systems. With the support of software-defined technologies, a transportation-aided multicast routing system is suggested. By using public transportation as another communication platform in a smart city, network communication is enhanced. The primary objective is to use as little energy as possible while delivering as much data as possible. The Attribute Decision Making with Capacitated… More >

Ahmad Naseem Alvi¹, Muhammad Awais Javed^1,*, Mozaherul Hoque Abul Hasanat², Muhammad Badruddin Khan², Abdul Khader Jilani Saudagar², Mohammed Alkhathami²

CMC-Computers, Materials & Continua, Vol.74, No.3, pp. 6321-6334, 2023, DOI:10.32604/cmc.2023.029913

Abstract Wireless nodes are one of the main components in different applications that are offered in a smart city. These wireless nodes are responsible to execute multiple tasks with different priority levels. As the wireless nodes have limited processing capacity, they offload their tasks to cloud servers if the number of tasks exceeds their task processing capacity. Executing these tasks from remotely placed cloud servers causes a significant delay which is not required in sensitive task applications. This execution delay is reduced by placing fog computing nodes near these application nodes. A fog node has limited processing capacity and is sometimes… More >

Hanan Abdullah Mengash¹, Lubna A. Alharbi², Saud S. Alotaibi³, Sarab AlMuhaideb⁴, Nadhem Nemri⁵, Mrim M. Alnfiai⁶, Radwa Marzouk¹, Ahmed S. Salama⁷, Mesfer Al Duhayyim^8,*

CMC-Computers, Materials & Continua, Vol.74, No.2, pp. 4483-4500, 2023, DOI:10.32604/cmc.2023.032588

Abstract In recent times, cities are getting smart and can be managed effectively through diverse architectures and services. Smart cities have the ability to support smart medical systems that can infiltrate distinct events (i.e., smart hospitals, smart homes, and community health centres) and scenarios (e.g., rehabilitation, abnormal behavior monitoring, clinical decision-making, disease prevention and diagnosis postmarking surveillance and prescription recommendation). The integration of Artificial Intelligence (AI) with recent technologies, for instance medical screening gadgets, are significant enough to deliver maximum performance and improved management services to handle chronic diseases. With latest developments in digital data collection, AI techniques can be employed… More >

V. Joseph Raymond^1,2,*, R. Jeberson Retna Raj¹

Computer Systems Science and Engineering, Vol.44, No.3, pp. 2147-2163, 2023, DOI:10.32604/csse.2023.028227

Abstract Android devices are popularly available in the commercial market at different price levels for various levels of customers. The Android stack is more vulnerable compared to other platforms because of its open-source nature. There are many android malware detection techniques available to exploit the source code and find associated components during execution time. To obtain a better result we create a hybrid technique merging static and dynamic processes. In this paper, in the first part, we have proposed a technique to check for correlation between features and classify using a supervised learning approach to avoid Multicollinearity problem is one of… More >

Manar Ahmed Hamza^1,*, Hadeel Alsolai², Jaber S. Alzahrani³, Mohammad Alamgeer^4,5, Mohamed Mahmoud Sayed⁶, Abu Sarwar Zamani¹, Ishfaq Yaseen¹, Abdelwahed Motwakel¹

CMC-Computers, Materials & Continua, Vol.73, No.3, pp. 6563-6577, 2022, DOI:10.32604/cmc.2022.031541

Abstract Intelligent Transportation System (ITS) is one of the revolutionary technologies in smart cities that helps in reducing traffic congestion and enhancing traffic quality. With the help of big data and communication technologies, ITS offers real-time investigation and highly-effective traffic management. Traffic Flow Prediction (TFP) is a vital element in smart city management and is used to forecast the upcoming traffic conditions on transportation network based on past data. Neural Network (NN) and Machine Learning (ML) models are widely utilized in resolving real-time issues since these methods are capable of dealing with adaptive data over a period of time. Deep Learning… More >