Alawi Alqushaibi^1,2,*, Mohd Hilmi Hasan^1,2, Said Jadid Abdulkadir^1,2, Amgad Muneer^1,2, Mohammed Gamal^1,2, Qasem Al-Tashi³, Shakirah Mohd Taib^1,2, Hitham Alhussian^1,2

CMC-Computers, Materials & Continua, Vol.75, No.2, pp. 3223-3238, 2023, DOI:10.32604/cmc.2023.035655

Abstract Diabetes mellitus is a long-term condition characterized by hyperglycemia. It could lead to plenty of difficulties. According to rising morbidity in recent years, the world’s diabetic patients will exceed 642 million by 2040, implying that one out of every ten persons will be diabetic. There is no doubt that this startling figure requires immediate attention from industry and academia to promote innovation and growth in diabetes risk prediction to save individuals’ lives. Due to its rapid development, deep learning (DL) was used to predict numerous diseases. However, DL methods still suffer from their limited prediction performance due to the hyperparameters… More >

K. Ishwarya, A. Alice Nithya^*

CMC-Computers, Materials & Continua, Vol.74, No.3, pp. 6081-6099, 2023, DOI:10.32604/cmc.2023.034654

Abstract Human pose estimation (HPE) is a procedure for determining the structure of the body pose and it is considered a challenging issue in the computer vision (CV) communities. HPE finds its applications in several fields namely activity recognition and human-computer interface. Despite the benefits of HPE, it is still a challenging process due to the variations in visual appearances, lighting, occlusions, dimensionality, etc. To resolve these issues, this paper presents a squirrel search optimization with a deep convolutional neural network for HPE (SSDCNN-HPE) technique. The major intention of the SSDCNN-HPE technique is to identify the human pose accurately and efficiently.… More >

Saeed Masoud Alshahrani¹, Saud S. Alotaibi², Shaha Al-Otaibi³, Mohamed Mousa⁴, Anwer Mustafa Hilal^5,*, Amgad Atta Abdelmageed⁵, Abdelwahed Motwakel⁵, Mohamed I. Eldesouki⁶

CMC-Computers, Materials & Continua, Vol.74, No.2, pp. 3117-3131, 2023, DOI:10.32604/cmc.2023.033038

Abstract Object detection (OD) in remote sensing images (RSI) acts as a vital part in numerous civilian and military application areas, like urban planning, geographic information system (GIS), and search and rescue functions. Vehicle recognition from RSIs remained a challenging process because of the difficulty of background data and the redundancy of recognition regions. The latest advancements in deep learning (DL) approaches permit the design of effectual OD approaches. This study develops an Artificial Ecosystem Optimizer with Deep Convolutional Neural Network for Vehicle Detection (AEODCNN-VD) model on Remote Sensing Images. The proposed AEODCNN-VD model focuses on the identification of vehicles accurately… More >

Hussain Alaaedi^*, Masoud Sabaei

Intelligent Automation & Soft Computing, Vol.36, No.1, pp. 633-646, 2023, DOI:10.32604/iasc.2023.032462

Abstract Enabling high mobility applications in millimeter wave (mmWave) based systems opens up a slew of new possibilities, including vehicle communications in addition to wireless virtual/augmented reality. The narrow beam usage in addition to the millimeter waves sensitivity might block the coverage along with the reliability of the mobile links. In this research work, the improvement in the quality of experience faced by the user for multimedia-related applications over the millimeter-wave band is investigated. The high attenuation loss in high frequencies is compensated with a massive array structure named Multiple Input and Multiple Output (MIMO) which is utilized in a hyperdense… More >

Ji Wang¹, Liming Li^1,2,3, Shubin Zheng^1,3, Shuguang Zhao², Xiaodong Chai^1,3, Lele Peng^1,3, Weiwei Qi^1,3, Qianqian Tong¹

CMES-Computer Modeling in Engineering & Sciences, Vol.134, No.3, pp. 1671-1706, 2023, DOI:10.32604/cmes.2022.022143

Abstract Loosening detection; cascade deep convolutional neural network; object localization; saliency detection problem of bolts on axlebox covers. Firstly, an SSD network based on ResNet50 and CBAM module by improving bolt image features is proposed for locating bolts on axlebox covers. And then, the A²-PFN is proposed according to the slender features of the marker lines for extracting more accurate marker lines regions of the bolts. Finally, a rectangular approximation method is proposed to regularize the marker line regions as a way to calculate the angle of the marker line and plot all the angle values into an angle table, according… More > Graphic Abstract

Mohammad Sadegh Barkhordari¹, Danial Jahed Armaghani^2,*, Panagiotis G. Asteris³

CMES-Computer Modeling in Engineering & Sciences, Vol.134, No.2, pp. 835-855, 2023, DOI:10.32604/cmes.2022.020840

Abstract The existing strategy for evaluating the damage condition of structures mostly focuses on feedback supplied by traditional visual methods, which may result in an unreliable damage characterization due to inspector subjectivity or insufficient level of expertise. As a result, a robust, reliable, and repeatable method of damage identification is required. Ensemble learning algorithms for identifying structural damage are evaluated in this article, which use deep convolutional neural networks, including simple averaging, integrated stacking, separate stacking, and hybrid weighted averaging ensemble and differential evolution (WAE-DE) ensemble models. Damage identification is carried out on three types of damage. The proposed algorithms are… More >

A. K. Z Rasel Rahman¹, S. M. Nabil Sakif¹, Niloy Sikder¹, Mehedi Masud², Hanan Aljuaid³, Anupam Kumar Bairagi^1,*

Intelligent Automation & Soft Computing, Vol.35, No.3, pp. 3259-3277, 2023, DOI:10.32604/iasc.2023.030142

Abstract Disasters may occur at any time and place without little to no presage in advance. With the development of surveillance and forecasting systems, it is now possible to forebode the most life-threatening and formidable disasters. However, forest fires are among the ones that are still hard to anticipate beforehand, and the technologies to detect and plot their possible courses are still in development. Unmanned Aerial Vehicle (UAV) image-based fire detection systems can be a viable solution to this problem. However, these automatic systems use advanced deep learning and image processing algorithms at their core and can be tuned to provide… More >

Mustufa Haider Abidi^*, Hisham Alkhalefah, Muneer Khan Mohammed

CMC-Computers, Materials & Continua, Vol.73, No.3, pp. 5367-5383, 2022, DOI:10.32604/cmc.2022.030018

Abstract In modern scenarios, Industry 4.0 entails invention with various advanced technology, and blockchain is one among them. Blockchains are incorporated to enhance privacy, data transparency as well as security for both large and small scale enterprises. Industry 4.0 is considered as a new synthesis fabrication technique that permits the manufacturers to attain their target effectively. However, because numerous devices and machines are involved, data security and privacy are always concerns. To achieve intelligence in Industry 4.0, blockchain technologies can overcome potential cybersecurity constraints. Nowadays, the blockchain and internet of things (IoT) are gaining more attention because of their favorable outcome… More >

Xinheng Yuan, Hao Yu, Houlin Zhang, Li Zheng, Erbao Dong^*, Heng’an Wu^*

CMES-Computer Modeling in Engineering & Sciences, Vol.133, No.2, pp. 281-301, 2022, DOI:10.32604/cmes.2022.021383

Abstract Robotic grasping is an essential problem at both the household and industrial levels, and unstructured objects have always been difficult for grippers. Parallel-plate grippers and algorithms, focusing on partial information of objects, are one of the widely used approaches. However, most works predict single-size grasp rectangles for fixed cameras and gripper sizes. In this paper, a multi-scale grasp detector is proposed to predict grasp rectangles with different sizes on RGB-D or RGB images in real-time for hand-eye cameras and various parallel-plate grippers. The detector extracts feature maps of multiple scales and conducts predictions on each scale independently. To guarantee independence… More >

S. Nandagopal^1,*, G. Karthy², A. Sheryl Oliver³, M. Subha⁴

Computer Systems Science and Engineering, Vol.44, No.2, pp. 1719-1733, 2023, DOI:10.32604/csse.2023.028003

Abstract Human Action Recognition (HAR) and pose estimation from videos have gained significant attention among research communities due to its application in several areas namely intelligent surveillance, human robot interaction, robot vision, etc. Though considerable improvements have been made in recent days, design of an effective and accurate action recognition model is yet a difficult process owing to the existence of different obstacles such as variations in camera angle, occlusion, background, movement speed, and so on. From the literature, it is observed that hard to deal with the temporal dimension in the action recognition process. Convolutional neural network (CNN) models could… More >