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

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 have been configured for the… 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

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. The proffered ISSA empowers smart… 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

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 detection was applied to mark… 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

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 HSAR in video frames. The… 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

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 of subjects becomes difficult. To… More >

Qurat-ul-Ain Arshad¹, Mudassar Raza¹, Wazir Zada Khan², Ayesha Siddiqa², Abdul Muiz², Muhammad Attique Khan^3,*, Usman Tariq⁴, Taerang Kim⁵, Jae-Hyuk Cha^5,*

CMC-Computers, Materials & Continua, Vol.76, No.1, pp. 1103-1125, 2023, DOI:10.32604/cmc.2023.039752

Abstract Anomalous situations in surveillance videos or images that may result in security issues, such as disasters, accidents, crime, violence, or terrorism, can be identified through video anomaly detection. However, differentiating anomalous situations from normal can be challenging due to variations in human activity in complex environments such as train stations, busy sporting fields, airports, shopping areas, military bases, care centers, etc. Deep learning models’ learning capability is leveraged to identify abnormal situations with improved accuracy. This work proposes a deep learning architecture called Anomalous Situation Recognition Network (ASRNet) for deep feature extraction to improve the detection accuracy of various anomalous… More >

A. Manju¹, A. Revathi², M. Arivukarasi¹, S. Hariharan³, V. Umarani⁴, Shih-Yu Chen^5,*, Jin Wang⁶

Intelligent Automation & Soft Computing, Vol.37, No.1, pp. 905-920, 2023, DOI:10.32604/iasc.2023.038444

Abstract With the proliferation of the internet, big data continues to grow exponentially, and video has become the largest source. Video big data introduces many technological challenges, including compression, storage, transmission, analysis, and recognition. The increase in the number of multimedia resources has brought an urgent need to develop intelligent methods to organize and process them. The integration between Semantic link Networks and multimedia resources provides a new prospect for organizing them with their semantics. The tags and surrounding texts of multimedia resources are used to measure their semantic association. Two evaluation methods including clustering and retrieval are performed to measure… More >

Latifah Almuqren¹, Manar Ahmed Hamza^2,*, Abdullah Mohamed³, Amgad Atta Abdelmageed²

CMC-Computers, Materials & Continua, Vol.75, No.3, pp. 4917-4933, 2023, DOI:10.32604/cmc.2023.037738

Abstract Face recognition technology automatically identifies an individual from image or video sources. The detection process can be done by attaining facial characteristics from the image of a subject face. Recent developments in deep learning (DL) and computer vision (CV) techniques enable the design of automated face recognition and tracking methods. This study presents a novel Harris Hawks Optimization with deep learning-empowered automated face detection and tracking (HHODL-AFDT) method. The proposed HHODL-AFDT model involves a Faster region based convolution neural network (RCNN)-based face detection model and HHO-based hyperparameter optimization process. The presented optimal Faster RCNN model precisely recognizes the face and… More >

Wu Song¹, Yayuan Tang^2,3,*, Wenxue Tan¹, Sheng Ren¹

CMES-Computer Modeling in Engineering & Sciences, Vol.137, No.1, pp. 509-526, 2023, DOI:10.32604/cmes.2023.026933

Abstract In the environment of smart examination rooms, it is important to quickly and accurately detect abnormal behavior (human standing) for the construction of a smart campus. Based on deep learning, we propose an intelligent standing human detection (ISHD) method based on an improved single shot multibox detector to detect the target of standing human posture in the scene frame of exam room video surveillance at a specific examination stage. ISHD combines the MobileNet network in a single shot multibox detector network, improves the posture feature extractor of a standing person, merges prior knowledge, and introduces transfer learning in the training… More >

Sareer Ul Amin¹, Yongjun Kim², Irfan Sami³, Sangoh Park^1,*, Sanghyun Seo^4,*

Computer Systems Science and Engineering, Vol.46, No.3, pp. 3939-3958, 2023, DOI:10.32604/csse.2023.034805

Abstract In the present technological world, surveillance cameras generate an immense amount of video data from various sources, making its scrutiny tough for computer vision specialists. It is difficult to search for anomalous events manually in these massive video records since they happen infrequently and with a low probability in real-world monitoring systems. Therefore, intelligent surveillance is a requirement of the modern day, as it enables the automatic identification of normal and aberrant behavior using artificial intelligence and computer vision technologies. In this article, we introduce an efficient Attention-based deep-learning approach for anomaly detection in surveillance video (ADSV). At the input… More >