Ahmed Alhussen¹, Arshiya S. Ansari^2,*

CMC-Computers, Materials & Continua, Vol.79, No.2, pp. 1903-1923, 2024, DOI:10.32604/cmc.2024.049260

Abstract Traffic in today’s cities is a serious problem that increases travel times, negatively affects the environment, and drains financial resources. This study presents an Artificial Intelligence (AI) augmented Mobile Ad Hoc Networks (MANETs) based real-time prediction paradigm for urban traffic challenges. MANETs are wireless networks that are based on mobile devices and may self-organize. The distributed nature of MANETs and the power of AI approaches are leveraged in this framework to provide reliable and timely traffic congestion forecasts. This study suggests a unique Chaotic Spatial Fuzzy Polynomial Neural Network (CSFPNN) technique to assess real-time data… More >

Wenshun Sheng^*, Xiongfeng Yu, Jiayan Lin, Xin Chen

Computer Systems Science and Engineering, Vol.47, No.2, pp. 1549-1569, 2023, DOI:10.32604/csse.2023.039410

Abstract Small targets and occluded targets will inevitably appear in the image during the shooting process due to the influence of angle, distance, complex scene, illumination intensity, and other factors. These targets have few effective pixels, few features, and no apparent features, which makes extracting their efficient features difficult and easily leads to false detection, missed detection, and repeated detection, affecting the performance of target detection models. An improved faster region convolutional neural network (RCNN) algorithm (CF-RCNN) integrating convolutional block attention module (CBAM) and feature pyramid networks (FPN) is proposed to improve the detection and recognition… More >

Yingkun Song¹, Shunhe Hong¹, Chentao Hu¹, Pingan He², Lingbing Tao¹, Zhixin Tie^1,3,*, Chengfu Ding⁴

CMC-Computers, Materials & Continua, Vol.75, No.3, pp. 5761-5784, 2023, DOI:10.32604/cmc.2023.038910

Abstract Rapid and precise vehicle recognition and classification are essential for intelligent transportation systems, and road target detection is one of the most difficult tasks in the field of computer vision. The challenge in real-time road target detection is the ability to properly pinpoint relatively small vehicles in complicated environments. However, because road targets are prone to complicated backgrounds and sparse features, it is challenging to detect and identify vehicle kinds fast and reliably. We suggest a new vehicle detection model called MEB-YOLO, which combines Mosaic and MixUp data augmentation, Efficient Channel Attention (ECA) attention mechanism,… More >