Kunal Thakur¹, Ashu Taneja^1,*, Ali Alqahtani², Nayef Alqahtani³

CMC-Computers, Materials & Continua, Vol.85, No.3, pp. 4827-4848, 2025, DOI:10.32604/cmc.2025.067901 - 23 October 2025

Abstract Accident detection plays a critical role in improving traffic safety by enabling timely emergency response and reducing the impact of road incidents. The main challenge lies in achieving real-time, reliable and highly accurate detection across diverse Internet-of-vehicles (IoV) environments. To overcome this challenge, this paper leverages deep learning to automatically learn patterns from visual data to detect accidents with high accuracy. A visual classification model based on the ResNet-50 architecture is presented for distinguishing between accident and non-accident images. The model is trained and tested on a labeled dataset and achieves an overall accuracy of… More >

Buddhini Chaturika Jayasinghe¹, Neel Chaminda Withanage¹, Prabuddh Kumar Mishra^2,*

Revue Internationale de Géomatique, Vol.34, pp. 707-729, 2025, DOI:10.32604/rig.2025.067395 - 12 September 2025

Abstract Road Traffic Accidents (RTAs) pose significant threats to public safety and urban infrastructure. While numerous studies have addressed this issue in other countries, there remains a notable gap in localized RTA research in Sri Lanka. In this context, the present study investigates the spatial and temporal patterns of RTAs in the Matara urban area in 2023, with the goal of supporting evidence-based policy interventions. A suite of GIS-based spatial analysis techniques including hotspot analysis, kernel density estimation, GiZscore mapping, and spatial autocorrelation (Moran’s I = 0.36, p < 0.01) was applied to examine the distribution and… More >

Habib Hadj-Mabrouk^*

CMC-Computers, Materials & Continua, Vol.84, No.3, pp. 4399-4430, 2025, DOI:10.32604/cmc.2025.067143 - 30 July 2025

Abstract A literature review on AI applications in the field of railway safety shows that the implemented approaches mainly concern the operational, maintenance, and feedback phases following railway incidents or accidents. These approaches exploit railway safety data once the transport system has received authorization for commissioning. However, railway standards and regulations require the development of a safety management system (SMS) from the specification and design phases of the railway system. This article proposes a new AI approach for analyzing and assessing safety from the specification and design phases of the railway system with a view to… More >

Haoyu Cui¹, Congxin Yang^1,2,*, Yanlei Guo¹, Tianzhi Lv¹, Sen Zhao¹

FDMP-Fluid Dynamics & Materials Processing, Vol.20, No.12, pp. 2907-2926, 2024, DOI:10.32604/fdmp.2024.055301 - 23 December 2024

Abstract Most of existing methods for the safety assessment of the primary cooling loop of nuclear reactors in conditions of reactor coolant pump (RCP) failure (rotor seizure accident) essentially rely on the combination of one-dimensional theory and experience. This study introduces a novel three-dimensional model of the ‘Hualong-1’ (HPR1000) primary loop and uses the method of matching the resistance characteristics of the tube to ensure that the main pump operates at the rated operating condition. In particular, the three-dimensional unsteady numerical calculation of the RCP behavior in the rotor-seizure accident condition is carried out in the More >

Mengdong An¹, Weiyuan Zhong¹, Wei Xu², Xiuli Wang^1,*

FDMP-Fluid Dynamics & Materials Processing, Vol.20, No.6, pp. 1331-1349, 2024, DOI:10.32604/fdmp.2023.046604 - 27 June 2024

Abstract The reactor coolant pump (RCP) rotor seizure accident is defined as a short-time seizure of the RCP rotor. This event typically leads to an abrupt flow decrease in the corresponding loop and an ensuing reactor and turbine trip. The significant reduction of core coolant flow while the reactor is being operated at full load can have very negative consequences. This potentially dangerous event is typically characterized by a complex transient behavior in terms of flow conditions and energy transformation, which need to be analyzed and understood. This study constructed transient flow and rotational speed mathematical More > Graphic Abstract

Saba Awan^1,*, Zahid Mehmood^2,*

CMC-Computers, Materials & Continua, Vol.78, No.2, pp. 2535-2555, 2024, DOI:10.32604/cmc.2024.047337 - 27 February 2024

Abstract Highway safety researchers focus on crash injury severity, utilizing deep learning—specifically, deep neural networks (DNN), deep convolutional neural networks (D-CNN), and deep recurrent neural networks (D-RNN)—as the preferred method for modeling accident severity. Deep learning’s strength lies in handling intricate relationships within extensive datasets, making it popular for accident severity level (ASL) prediction and classification. Despite prior success, there is a need for an efficient system recognizing ASL in diverse road conditions. To address this, we present an innovative Accident Severity Level Prediction Deep Learning (ASLP-DL) framework, incorporating DNN, D-CNN, and D-RNN models fine-tuned through More >

Nadeem Malik^1,*, Saud Altaf¹, Muhammad Usman Tariq², Ashir Ahmed³, Muhammad Babar⁴

CMC-Computers, Materials & Continua, Vol.77, No.2, pp. 1599-1615, 2023, DOI:10.32604/cmc.2023.040455 - 29 November 2023

Abstract The severity of traffic accidents is a serious global concern, particularly in developing nations. Knowing the main causes and contributing circumstances may reduce the severity of traffic accidents. There exist many machine learning models and decision support systems to predict road accidents by using datasets from different social media forums such as Twitter, blogs and Facebook. Although such approaches are popular, there exists an issue of data management and low prediction accuracy. This article presented a deep learning-based sentiment analytic model known as Extra-large Network Bi-directional long short term memory (XLNet-Bi-LSTM) to predict traffic collisions More >

Youngeun An, Jimin Lee, EunSang Bak^*, Sungbum Pan^*

CMC-Computers, Materials & Continua, Vol.76, No.2, pp. 1817-1832, 2023, DOI:10.32604/cmc.2023.039460 - 30 August 2023

Abstract Emotion recognition based on facial expressions is one of the most critical elements of human-machine interfaces. Most conventional methods for emotion recognition using facial expressions use the entire facial image to extract features and then recognize specific emotions through a pre-trained model. In contrast, this paper proposes a novel feature vector extraction method using the Euclidean distance between the landmarks changing their positions according to facial expressions, especially around the eyes, eyebrows, nose, and mouth. Then, we apply a new classifier using an ensemble network to increase emotion recognition accuracy. The emotion recognition performance was More >

Shyla Del-Aguila-Arcentales¹, Aldo Alvarez-Risco², Diego Villalobos-Alvarez³, Mario Carhuapoma-Yance⁴, Jaime A. Yáñez^5,6,*

International Journal of Mental Health Promotion, Vol.24, No.4, pp. 503-509, 2022, DOI:10.32604/ijmhp.2022.020513 - 27 May 2022

Abstract The general objective of this article is to show the relationship that exists in the COVID-19 pandemic, the mental health of people and the propensity for work-related accidents in companies. Various results are shown that detail how COVID-19 has generated and is generating mental alterations in people such as post-traumatic stress disorder, PTSD for its acronym in English. Likewise, data are presented that report the influence of mental health as a precursor to workplace accidents in different industries, with which it can be concluded that COVID-19 needs a comprehensive approach in companies to prevent it More >

Saba Awan¹, Zahid Mehmood^2,*, Hassan Nazeer Chaudhry³, Usman Tariq⁴, Amjad Rehman⁵, Tanzila Saba⁵, Muhammad Rashid⁶

CMC-Computers, Materials & Continua, Vol.71, No.3, pp. 4609-4626, 2022, DOI:10.32604/cmc.2022.019404 - 14 January 2022

Abstract To determine the individual circumstances that account for a road traffic accident, it is crucial to consider the unplanned connections amongst various factors related to a crash that results in high casualty levels. Analysis of the road accident data concentrated mainly on categorizing accidents into different types using individually built classification methods which limit the prediction accuracy and fitness of the model. In this article, we proposed a multi-model hybrid framework of the weighted majority voting (WMV) scheme with parallel structure, which is designed by integrating individually implemented multinomial logistic regression (MLR) and multilayer perceptron… More >