Atif Mehmood^1,2, Zhonglong Zheng^1,*, Rizwan Khan¹, Ahmad Al Smadi³, Farah Shahid^1,2, Shahid Iqbal⁴, Mutasem K. Alsmadi⁵, Yazeed Yasin Ghadi⁶, Syed Aziz Shah⁸, Mostafa M. Ibrahim⁷

CMC-Computers, Materials & Continua, Vol.80, No.2, pp. 2081-2098, 2024, DOI:10.32604/cmc.2024.046869 - 15 August 2024

Abstract Neuroimaging has emerged over the last few decades as a crucial tool in diagnosing Alzheimer’s disease (AD). Mild cognitive impairment (MCI) is a condition that falls between the spectrum of normal cognitive function and AD. However, previous studies have mainly used handcrafted features to classify MCI, AD, and normal control (NC) individuals. This paper focuses on using gray matter (GM) scans obtained through magnetic resonance imaging (MRI) for the diagnosis of individuals with MCI, AD, and NC. To improve classification performance, we developed two transfer learning strategies with data augmentation (i.e., shear range, rotation, zoom… More >

Seolhee Kim¹, Sang-Duck Lee^2,*

CMC-Computers, Materials & Continua, Vol.80, No.1, pp. 195-215, 2024, DOI:10.32604/cmc.2024.052070 - 18 July 2024

Abstract Damage to parcels reduces customer satisfaction with delivery services and increases return-logistics costs. This can be prevented by detecting and addressing the damage before the parcels reach the customer. Consequently, various studies have been conducted on deep learning techniques related to the detection of parcel damage. This study proposes a deep learning-based damage detection method for various types of parcels. The method is intended to be part of a parcel information-recognition system that identifies the volume and shipping information of parcels, and determines whether they are damaged; this method is intended for use in the… More >

Ghobad Shafiei Sabet^1,*, Ali Sari¹, Ahmad Fakhari^2,*, Nasrin Afsarimanesh³, Dominic Organ⁴, Seyed Mehran Hoseini¹

Frontiers in Heat and Mass Transfer, Vol.22, No.2, pp. 491-509, 2024, DOI:10.32604/fhmt.2024.048290 - 20 May 2024

Abstract This study conducts both numerical and empirical assessments of thermal transfer and fluid flow characteristics in a Solar Air Collector (SAC) using a Delta Wing Vortex Generator (DWVG), and the effects of different height ratios (R = 0.6, 0.8, 1, 1.2 and 1.4) in delta wing vortex generators, which were not considered in the earlier studies, are investigated. Energy and exergy analyses are performed to gain maximum efficiency. The Reynolds number based on the outlet velocity and hydraulic diameter falls between 4400 and 22000, corresponding to the volume flow rate of 5.21–26.07 m/h. It is More >

Do Nang Toan^1,*, Lam Thanh Hien², Ha Manh Toan¹, Nguyen Trong Vinh², Pham Trung Hieu¹

Intelligent Automation & Soft Computing, Vol.39, No.2, pp. 319-335, 2024, DOI:10.32604/iasc.2024.046925 - 21 May 2024

Abstract Cancer is one of the most dangerous diseases with high mortality. One of the principal treatments is radiotherapy by using radiation beams to destroy cancer cells and this workflow requires a lot of experience and skill from doctors and technicians. In our study, we focused on the 3D dose prediction problem in radiotherapy by applying the deep-learning approach to computed tomography (CT) images of cancer patients. Medical image data has more complex characteristics than normal image data, and this research aims to explore the effectiveness of data preprocessing and augmentation in the context of the… More >

Jiakai Li, Jianpeng Hu^*, Geng Zhang

CMC-Computers, Materials & Continua, Vol.79, No.2, pp. 2481-2503, 2024, DOI:10.32604/cmc.2024.050005 - 15 May 2024

Abstract In the process of constructing domain-specific knowledge graphs, the task of relational triple extraction plays a critical role in transforming unstructured text into structured information. Existing relational triple extraction models face multiple challenges when processing domain-specific data, including insufficient utilization of semantic interaction information between entities and relations, difficulties in handling challenging samples, and the scarcity of domain-specific datasets. To address these issues, our study introduces three innovative components: Relation semantic enhancement, data augmentation, and a voting strategy, all designed to significantly improve the model’s performance in tackling domain-specific relational triple extraction tasks. We first… More >

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 - 25 April 2024

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… 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 - 25 April 2024

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… 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 - 16 April 2024

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… More >

Irfan Haider¹, Muhammad Attique Khan^1,*, Muhammad Nazir¹, Taerang Kim², Jae-Hyuk Cha²

Computer Systems Science and Engineering, Vol.48, No.2, pp. 529-554, 2024, DOI:10.32604/csse.2023.042080 - 19 March 2024

Abstract Fruit infections have an impact on both the yield and the quality of the crop. As a result, an automated recognition system for fruit leaf diseases is important. In artificial intelligence (AI) applications, especially in agriculture, deep learning shows promising disease detection and classification results. The recent AI-based techniques have a few challenges for fruit disease recognition, such as low-resolution images, small datasets for learning models, and irrelevant feature extraction. This work proposed a new fruit leaf leaf leaf disease recognition framework using deep learning features and improved pathfinder optimization. Three fruit types have been… 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 - 27 February 2024

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… More >