Le Zou¹, Linsong Hu¹, Yifan Wang¹, Zhize Wu², Xiaofeng Wang^1,*

CMC-Computers, Materials & Continua, Vol.79, No.1, pp. 927-941, 2024, DOI:10.32604/cmc.2024.048889

Abstract Depth estimation is an important task in computer vision. Collecting data at scale for monocular depth estimation is challenging, as this task requires simultaneously capturing RGB images and depth information. Therefore, data augmentation is crucial for this task. Existing data augmentation methods often employ pixel-wise transformations, which may inadvertently disrupt edge features. In this paper, we propose a data augmentation method for monocular depth estimation, which we refer to as the Perpendicular-Cutdepth method. This method involves cutting real-world depth maps along perpendicular directions and pasting them onto input images, thereby diversifying the data without compromising edge features. To validate the… More >

Parth Khandelwal¹, Harshit², Indranil Manna^1,3,*

CMC-Computers, Materials & Continua, Vol.79, No.1, pp. 1727-1755, 2024, DOI:10.32604/cmc.2024.042752

Abstract Metallic alloys for a given application are usually designed to achieve the desired properties by devising experiments based on experience, thermodynamic and kinetic principles, and various modeling and simulation exercises. However, the influence of process parameters and material properties is often non-linear and non-colligative. In recent years, machine learning (ML) has emerged as a promising tool to deal with the complex interrelation between composition, properties, and process parameters to facilitate accelerated discovery and development of new alloys and functionalities. In this study, we adopt an ML-based approach, coupled with genetic algorithm (GA) principles, to design novel copper alloys for achieving… More > Graphic Abstract

Bo Liu^1,2, Li Feng^1,*, Yufeng Chen³

CMES-Computer Modeling in Engineering & Sciences, Vol.140, No.1, pp. 845-862, 2024, DOI:10.32604/cmes.2024.047695

Abstract This paper focuses on the effective utilization of data augmentation techniques for 3D lidar point clouds to enhance the performance of neural network models. These point clouds, which represent spatial information through a collection of 3D coordinates, have found wide-ranging applications. Data augmentation has emerged as a potent solution to the challenges posed by limited labeled data and the need to enhance model generalization capabilities. Much of the existing research is devoted to crafting novel data augmentation methods specifically for 3D lidar point clouds. However, there has been a lack of focus on making the most of the numerous existing… More >

Gyu-Il Kim¹, Hyun Yoo², Han-Jin Cho³, Kyungyong Chung^4,*

CMC-Computers, Materials & Continua, Vol.78, No.2, pp. 1713-1730, 2024, DOI:10.32604/cmc.2023.046324

Abstract Time-series data provide important information in many fields, and their processing and analysis have been the focus of much research. However, detecting anomalies is very difficult due to data imbalance, temporal dependence, and noise. Therefore, methodologies for data augmentation and conversion of time series data into images for analysis have been studied. This paper proposes a fault detection model that uses time series data augmentation and transformation to address the problems of data imbalance, temporal dependence, and robustness to noise. The method of data augmentation is set as the addition of noise. It involves adding Gaussian noise, with the noise… More >

Derwin Suhartono^1,*, Alif Tri Handoyo¹, Franz Adeta Junior²

CMC-Computers, Materials & Continua, Vol.77, No.3, pp. 3637-3657, 2023, DOI:10.32604/cmc.2023.045301

Abstract Sarcasm detection in text data is an increasingly vital area of research due to the prevalence of sarcastic content in online communication. This study addresses challenges associated with small datasets and class imbalances in sarcasm detection by employing comprehensive data pre-processing and Generative Adversial Network (GAN) based augmentation on diverse datasets, including iSarcasm, SemEval-18, and Ghosh. This research offers a novel pipeline for augmenting sarcasm data with Reverse Generative Adversarial Network (RGAN). The proposed RGAN method works by inverting labels between original and synthetic data during the training process. This inversion of labels provides feedback to the generator for generating… More >

Chia-Wei Jan¹, Yu-Jhih Chiu¹, Kuan-Lin Chen², Ting-Chun Yao³, Ping-Huan Kuo^1,4,*

Computer Systems Science and Engineering, Vol.47, No.3, pp. 2989-3010, 2023, DOI:10.32604/csse.2023.042078

Abstract Pneumonia is a common lung disease that is more prone to affect the elderly and those with weaker respiratory systems. However, hospital medical resources are limited, and sometimes the workload of physicians is too high, which can affect their judgment. Therefore, a good medical assistance system is of great significance for improving the quality of medical care. This study proposed an integrated system by combining transfer learning and gradient-weighted class activation mapping (Grad-CAM). Pneumonia is a common lung disease that is generally diagnosed using X-rays. However, in areas with limited medical resources, a shortage of medical personnel may result in… More >

S M Hasan Mahmud^1,2, Md Mamun Ali³, Mohammad Fahim Shahriar¹, Fahad Ahmed Al-Zahrani⁴, Kawsar Ahmed^5,6,*, Dip Nandi¹, Francis M. Bui⁵

CMC-Computers, Materials & Continua, Vol.76, No.3, pp. 3933-3948, 2023, DOI:10.32604/cmc.2023.039020

Abstract Alzheimer’s disease (AD) is a neurodevelopmental impairment that results in a person’s behavior, thinking, and memory loss. The most common symptoms of AD are losing memory and early aging. In addition to these, there are several serious impacts of AD. However, the impact of AD can be mitigated by early-stage detection though it cannot be cured permanently. Early-stage detection is the most challenging task for controlling and mitigating the impact of AD. The study proposes a predictive model to detect AD in the initial phase based on machine learning and a deep learning approach to address the issue. To build… More >

Huafeng Chen¹, Junxing Xue², Hanyun Wen², Yurong Hu¹, Yudong Zhang^3,*

CMES-Computer Modeling in Engineering & Sciences, Vol.138, No.1, pp. 301-320, 2024, DOI:10.32604/cmes.2023.028738

Abstract Optical image-based ship detection can ensure the safety of ships and promote the orderly management of ships in offshore waters. Current deep learning researches on optical image-based ship detection mainly focus on improving one-stage detectors for real-time ship detection but sacrifices the accuracy of detection. To solve this problem, we present a hybrid ship detection framework which is named EfficientShip in this paper. The core parts of the EfficientShip are DLA-backboned object location (DBOL) and CascadeRCNN-guided object classification (CROC). The DBOL is responsible for finding potential ship objects, and the CROC is used to categorize the potential ship objects. We… More >

Abdulghani M. Abdulghani, Mokhles M. Abdulghani, Wilbur L. Walters, Khalid H. Abed^*

Journal on Artificial Intelligence, Vol.5, pp. 15-30, 2023, DOI:10.32604/jai.2023.041341

Abstract The object detection technique depends on various methods for duplicating the dataset without adding more images. Data augmentation is a popular method that assists deep neural networks in achieving better generalization performance and can be seen as a type of implicit regularization. This method is recommended in the case where the amount of high-quality data is limited, and gaining new examples is costly and time-consuming. In this paper, we trained YOLOv7 with a dataset that is part of the Open Images dataset that has 8,600 images with four classes (Car, Bus, Motorcycle, and Person). We used five different data augmentations… More >

Wei Wu¹, Xiulai Li², Zhuhua Hu¹, Xiaozhang Liu^3,*

CMC-Computers, Materials & Continua, Vol.76, No.1, pp. 489-498, 2023, DOI:10.32604/cmc.2023.039929

Abstract In this paper, an advanced YOLOv7 model is proposed to tackle the challenges associated with ship detection and recognition tasks, such as the irregular shapes and varying sizes of ships. The improved model replaces the fixed anchor boxes utilized in conventional YOLOv7 models with a set of more suitable anchor boxes specifically designed based on the size distribution of ships in the dataset. This paper also introduces a novel multi-scale feature fusion module, which comprises Path Aggregation Network (PAN) modules, enabling the efficient capture of ship features across different scales. Furthermore, data preprocessing is enhanced through the application of data… More >