Chan Hyeong Cho, Hyun Min Song^*, Taek-Young Youn^*

CMES-Computer Modeling in Engineering & Sciences, Vol.141, No.3, pp. 1911-1931, 2024, DOI:10.32604/cmes.2024.053430 - 31 October 2024

Abstract With the advancement of video recording devices and network infrastructure, we use surveillance cameras to protect our valuable assets. This paper proposes a novel system for encrypting personal information within recorded surveillance videos to enhance efficiency and security. The proposed method leverages Dlib’s CNN-based facial recognition technology to identify Regions of Interest (ROIs) within the video, linking these ROIs to generate unique IDs. These IDs are then combined with a master key to create entity-specific keys, which are used to encrypt the ROIs within the video. This system supports selective decryption, effectively protecting personal information More >

Yuanyuan Hao¹, Dongye Lyu², Siyu Zhang¹, Bin Guo^3,*, Jin Yan^4,*

International Journal of Mental Health Promotion, Vol.26, No.10, pp. 865-873, 2024, DOI:10.32604/ijmhp.2024.055231 - 31 October 2024

Abstract Background: Youth emerge as an essential period in forming lifelong health and well-being. During adolescence, physical activity (PA) improves cardiovascular health, fitness levels, and the prevention of chronic illnesses, and the positive effect is also extended to the mental realm, as PA reduces symptoms of anxiety and depression. Therefore, the current study aims to enhance our understanding of the correlation between sports participation and depressive symptoms. Additionally, it seeks to explore how demographics such as gender, age, and race can shape these connections. Methods: The research sample was established by utilising the U.S. Youth Risk… More >

Vesal Khean¹, Chomyong Kim², Sunjoo Ryu², Awais Khan¹, Min Kyung Hong³, Eun Young Kim⁴, Joungmin Kim⁵, Yunyoung Nam^3,*

CMC-Computers, Materials & Continua, Vol.81, No.1, pp. 773-787, 2024, DOI:10.32604/cmc.2024.056767 - 15 October 2024

Abstract Human Interaction Recognition (HIR) was one of the challenging issues in computer vision research due to the involvement of multiple individuals and their mutual interactions within video frames generated from their movements. HIR requires more sophisticated analysis than Human Action Recognition (HAR) since HAR focuses solely on individual activities like walking or running, while HIR involves the interactions between people. This research aims to develop a robust system for recognizing five common human interactions, such as hugging, kicking, pushing, pointing, and no interaction, from video sequences using multiple cameras. In this study, a hybrid Deep… More >

Naeem Raza¹, Muhammad Asif Habib¹, Mudassar Ahmad¹, Qaisar Abbas^2,*, Mutlaq B. Aldajani², Muhammad Ahsan Latif³

CMC-Computers, Materials & Continua, Vol.81, No.1, pp. 911-931, 2024, DOI:10.32604/cmc.2024.055049 - 15 October 2024

Abstract Vision-based vehicle detection in adverse weather conditions such as fog, haze, and mist is a challenging research area in the fields of autonomous vehicles, collision avoidance, and Internet of Things (IoT)-enabled edge/fog computing traffic surveillance and monitoring systems. Efficient and cost-effective vehicle detection at high accuracy and speed in foggy weather is essential to avoiding road traffic collisions in real-time. To evaluate vision-based vehicle detection performance in foggy weather conditions, state-of-the-art Vehicle Detection in Adverse Weather Nature (DAWN) and Foggy Driving (FD) datasets are self-annotated using the YOLO LABEL tool and customized to four vehicle… More >

Pakorn Santakij¹, Samai Srisuay^2,*, Pongporn Punpeng¹

Computer Systems Science and Engineering, Vol.48, No.3, pp. 665-689, 2024, DOI:10.32604/csse.2024.045066 - 20 May 2024

Abstract Social media has revolutionized the dissemination of real-life information, serving as a robust platform for sharing life events. Twitter, characterized by its brevity and continuous flow of posts, has emerged as a crucial source for public health surveillance, offering valuable insights into public reactions during the COVID-19 pandemic. This study aims to leverage a range of machine learning techniques to extract pivotal themes and facilitate text classification on a dataset of COVID-19 outbreak-related tweets. Diverse topic modeling approaches have been employed to extract pertinent themes and subsequently form a dataset for training text classification models.… More >

Samina Amin¹, Muhammad Ali Zeb¹, Hani Alshahrani^2,*, Mohammed Hamdi², Mohammad Alsulami², Asadullah Shaikh³

CMES-Computer Modeling in Engineering & Sciences, Vol.139, No.2, pp. 1167-1202, 2024, DOI:10.32604/cmes.2023.043921 - 29 January 2024

Abstract Social media (SM) based surveillance systems, combined with machine learning (ML) and deep learning (DL) techniques, have shown potential for early detection of epidemic outbreaks. This review discusses the current state of SM-based surveillance methods for early epidemic outbreaks and the role of ML and DL in enhancing their performance. Since, every year, a large amount of data related to epidemic outbreaks, particularly Twitter data is generated by SM. This paper outlines the theme of SM analysis for tracking health-related issues and detecting epidemic outbreaks in SM, along with the ML and DL techniques that… More >

Abdul Rehman, Dongsun Kim^*, Anand Paul

CMC-Computers, Materials & Continua, Vol.77, No.2, pp. 2289-2307, 2023, DOI:10.32604/cmc.2023.036435 - 29 November 2023

Abstract Disasters such as conflagration, toxic smoke, harmful gas or chemical leakage, and many other catastrophes in the industrial environment caused by hazardous distance from the peril are frequent. The calamities are causing massive fiscal and human life casualties. However, Wireless Sensors Network-based adroit monitoring and early warning of these dangerous incidents will hamper fiscal and social fiasco. The authors have proposed an early fire detection system uses machine and/or deep learning algorithms. The article presents an Intelligent Industrial Monitoring System (IIMS) and introduces an Industrial Smart Social Agent (ISSA) in the Industrial SIoT (ISIoT) paradigm.… More >

Asifa Mehmood Qureshi¹, Nouf Abdullah Almujally², Saud S. Alotaibi³, Mohammed Hamad Alatiyyah⁴, Jeongmin Park^5,*

CMC-Computers, Materials & Continua, Vol.76, No.3, pp. 3707-3725, 2023, DOI:10.32604/cmc.2023.040738 - 08 October 2023

Abstract Road congestion, air pollution, and accident rates have all increased as a result of rising traffic density and worldwide population growth. Over the past ten years, the total number of automobiles has increased significantly over the world. In this paper, a novel method for intelligent traffic surveillance is presented. The proposed model is based on multilabel semantic segmentation using a random forest classifier which classifies the images into five classes. To improve the results, mean-shift clustering was applied to the segmented images. Afterward, the pixels given the label for the vehicle were extracted and blob… More >

Safdar Khan¹, Muhammad Attique Khan², Jamal Hussain Shah^1,*, Faheem Shehzad², Taerang Kim³, Jae-Hyuk Cha³

Computer Systems Science and Engineering, Vol.47, No.2, pp. 2337-2360, 2023, DOI:10.32604/csse.2023.040410 - 28 July 2023

Abstract Human Suspicious Activity Recognition (HSAR) is a critical and active research area in computer vision that relies on artificial intelligence reasoning. Significant advances have been made in this field recently due to important applications such as video surveillance. In video surveillance, humans are monitored through video cameras when doing suspicious activities such as kidnapping, fighting, snatching, and a few more. Although numerous techniques have been introduced in the literature for routine human actions (HAR), very few studies are available for HSAR. This study proposes a deep convolutional neural network (CNN) and optimal featuresbased framework for… More >

Sang-Soo Yeo¹, Seungmin Rho^2,*, Hyungjoon Kim³, Jibran Safdar⁴, Umar Zia⁵, Mehr Yahya Durrani⁵

Computer Systems Science and Engineering, Vol.47, No.2, pp. 2027-2047, 2023, DOI:10.32604/csse.2023.040327 - 28 July 2023

Abstract Intelligent vision-based surveillance systems are designed to deal with the gigantic volume of videos captured in a particular environment to perform the interpretation of scenes in form of detection, tracking, monitoring, behavioral analysis, and retrievals. In addition to that, another evolving way of surveillance systems in a particular environment is human gait-based surveillance. In the existing research, several methodological frameworks are designed to use deep learning and traditional methods, nevertheless, the accuracies of these methods drop substantially when they are subjected to covariate conditions. These covariate variables disrupt the gait features and hence the recognition… More >