Emad Sami Jaha^*

CMC-Computers, Materials & Continua, Vol.75, No.2, pp. 2805-2831, 2023, DOI:10.32604/cmc.2023.036169

Abstract A document layout can be more informative than merely a document’s visual and structural appearance. Thus, document layout analysis (DLA) is considered a necessary prerequisite for advanced processing and detailed document image analysis to be further used in several applications and different objectives. This research extends the traditional approaches of DLA and introduces the concept of semantic document layout analysis (SDLA) by proposing a novel framework for semantic layout analysis and characterization of handwritten manuscripts. The proposed SDLA approach enables the derivation of implicit information and semantic characteristics, which can be effectively utilized in dozens of practical applications for various… More >

Fadwa Alrowais¹, Radwa Marzouk^2,3, Fahd N. Al-Wesabi^4,*, Anwer Mustafa Hilal⁵

Intelligent Automation & Soft Computing, Vol.36, No.3, pp. 3325-3342, 2023, DOI:10.32604/iasc.2023.036354

Abstract Sign language recognition can be treated as one of the efficient solutions for disabled people to communicate with others. It helps them to convey the required data by the use of sign language with no issues. The latest developments in computer vision and image processing techniques can be accurately utilized for the sign recognition process by disabled people. American Sign Language (ASL) detection was challenging because of the enhancing intraclass similarity and higher complexity. This article develops a new Bayesian Optimization with Deep Learning-Driven Hand Gesture Recognition Based Sign Language Communication (BODL-HGRSLC) for Disabled People. The BODL-HGRSLC technique aims to… More >

Onder Alparslan^*, Omer Cetin

Intelligent Automation & Soft Computing, Vol.36, No.3, pp. 3355-3370, 2023, DOI:10.32604/iasc.2023.035689

Abstract Unknown closed spaces are a big challenge for the navigation of robots since there are no global and pre-defined positioning options in the area. One of the simplest and most efficient algorithms, the artificial potential field algorithm (APF), may provide real-time navigation in those places but fall into local minimum in some cases. To overcome this problem and to present alternative escape routes for a robot, possible crossing points in buildings may be detected by using object detection and included in the path planning algorithm. This study utilized a proposed sensor fusion method and an improved object classification method for… More >

Mesfer Al Duhayyim^*

Intelligent Automation & Soft Computing, Vol.36, No.3, pp. 2473-2489, 2023, DOI:10.32604/iasc.2023.033585

Abstract Falls are the most common concern among older adults or disabled people who use scooters and wheelchairs. The early detection of disabled persons’ falls is required to increase the living rate of an individual or provide support to them whenever required. In recent times, the arrival of the Internet of Things (IoT), smartphones, Artificial Intelligence (AI), wearables and so on make it easy to design fall detection mechanisms for smart homecare. The current study develops an Automated Disabled People Fall Detection using Cuckoo Search Optimization with Mobile Networks (ADPFD-CSOMN) model. The proposed model’s major aim is to detect and distinguish… More >

Madhuri Agrawal^*, Shikha Agrawal

Intelligent Automation & Soft Computing, Vol.36, No.3, pp. 2653-2667, 2023, DOI:10.32604/iasc.2023.033493

Abstract

Suspicious fall events are particularly significant hazards for the safety of patients and elders. Recently, suspicious fall event detection has become a robust research case in real-time monitoring. This paper aims to detect suspicious fall events during video monitoring of multiple people in different moving backgrounds in an indoor environment; it is further proposed to use a deep learning method known as Long Short Term Memory (LSTM) by introducing visual attention-guided mechanism along with a bi-directional LSTM model. This method contributes essential information on the temporal and spatial locations of ‘suspicious fall’ events in learning the video frame in both… More >

Amr Abozeid^1,*, Ahmed I. Taloba^1,2, Rasha M. Abd El-Aziz^1,3, Alhanoof Faiz Alwaghid¹, Mostafa Salem³, Ahmed Elhadad^1,4

Computer Systems Science and Engineering, Vol.46, No.2, pp. 2637-2650, 2023, DOI:10.32604/csse.2023.037761

Abstract Indoor localization methods can help many sectors, such as healthcare centers, smart homes, museums, warehouses, and retail malls, improve their service areas. As a result, it is crucial to look for low-cost methods that can provide exact localization in indoor locations. In this context, image-based localization methods can play an important role in estimating both the position and the orientation of cameras regarding an object. Image-based localization faces many issues, such as image scale and rotation variance. Also, image-based localization’s accuracy and speed (latency) are two critical factors. This paper proposes an efficient 6-DoF deep-learning model for image-based localization. This… More >

N. Sharmili¹, Saud Yonbawi², Sultan Alahmari³, E. Laxmi Lydia⁴, Mohamad Khairi Ishak⁵, Hend Khalid Alkahtani^6,*, Ayman Aljarbouh⁷, Samih M. Mostafa⁸

Computer Systems Science and Engineering, Vol.46, No.2, pp. 2247-2262, 2023, DOI:10.32604/csse.2023.036377

Abstract Facial expression recognition (FER) remains a hot research area among computer vision researchers and still becomes a challenge because of high intra-class variations. Conventional techniques for this problem depend on hand-crafted features, namely, LBP, SIFT, and HOG, along with that a classifier trained on a database of videos or images. Many execute perform well on image datasets captured in a controlled condition; however not perform well in the more challenging dataset, which has partial faces and image variation. Recently, many studies presented an endwise structure for facial expression recognition by utilizing DL methods. Therefore, this study develops an earthworm optimization… More >

Abdullah S. AL-Malaise AL-Ghamdi^1,2,3, Sultanah M. Alshammari^3,4, Mahmoud Ragab^3,5,6,*

Computer Systems Science and Engineering, Vol.46, No.2, pp. 1863-1877, 2023, DOI:10.32604/csse.2023.035869

Abstract Notwithstanding the religious intention of billions of devotees, the religious mass gathering increased major public health concerns since it likely became a huge super spreading event for the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). Most attendees ignored preventive measures, namely maintaining physical distance, practising hand hygiene, and wearing facemasks. Wearing a face mask in public areas protects people from spreading COVID-19. Artificial intelligence (AI) based on deep learning (DL) and machine learning (ML) could assist in fighting covid-19 in several ways. This study introduces a new deep learning-based Face Mask Detection in Religious Mass Gathering (DLFMD-RMG) technique during the… More >

Mi Zhou¹, Rui Liu^1,*, Pengfei Yi¹, Dongsheng Zhou^1,2,*

CMES-Computer Modeling in Engineering & Sciences, Vol.136, No.2, pp. 2093-2109, 2023, DOI:10.32604/cmes.2023.024189

Abstract Multi-view multi-person 3D human pose estimation is a hot topic in the field of human pose estimation due to its wide range of application scenarios. With the introduction of end-to-end direct regression methods, the field has entered a new stage of development. However, the regression results of joints that are more heavily influenced by external factors are not accurate enough even for the optimal method. In this paper, we propose an effective feature recalibration module based on the channel attention mechanism and a relative optimal calibration strategy, which is applied to the multi-view multi-person 3D human pose estimation task to… More > Graphic Abstract

Fengyu Xu^1,2, Masoud Kalantari³, Bangjian Li², Xingsong Wang^2,*

CMC-Computers, Materials & Continua, Vol.75, No.1, pp. 2209-2226, 2023, DOI:10.32604/cmc.2023.027102

Abstract The automatically defect detection method using vision inspection is a promising direction. In this paper, an efficient defect detection method for detecting surface damage to cables on a cable-stayed bridge automatically is developed. A mechanism design method for the protective layer of cables of a bridge based on vision inspection and diameter measurement is proposed by combining computer vision and diameter measurement techniques. A detection system for the surface damages of cables is de-signed. Images of cable surfaces are then enhanced and subjected to threshold segmentation by utilizing the improved local grey contrast enhancement method and the improved maximum correlation… More >