Open Access
ARTICLE
Sonali Das1, Saroja Kumar Rout2, Sujit Kumar Panda1, Pradyumna Kumar Mohapatra3, Abdulaziz S. Almazyad4, Muhammed Basheer Jasser5,6,*, Guojiang Xiong7, Ali Wagdy Mohamed8,9
CMES-Computer Modeling in Engineering & Sciences, DOI:10.32604/cmes.2024.051856
（This article belongs to the Special Issue: Advances in Swarm Intelligence Algorithms)
Abstract In blood or bone marrow, leukemia is a form of cancer. A person with leukemia has an expansion of white blood cells (WBCs). It primarily affects children and rarely affects adults. Treatment depends on the type of leukemia and the extent to which cancer has established throughout the body. Identifying leukemia in the initial stage is vital to providing timely patient care. Medical image-analysis-related approaches grant safer, quicker, and less costly solutions while ignoring the difficulties of these invasive processes. It can be simple to generalize Computer vision (CV)-based and image-processing techniques and eradicate human… More >
Open Access
ARTICLE
Amit Singhal1, Krishna Kant Agrawal2, Angeles Quezada3, Adrian Rodriguez Aguiñaga4, Samantha Jiménez4, Satya Prakash Yadav5,,*
CMES-Computer Modeling in Engineering & Sciences, DOI:10.32604/cmes.2024.051363
（This article belongs to the Special Issue: Intelligent Medical Decision Support Systems: Methods and Applications)
Abstract The use of Explainable Artificial Intelligence (XAI) models becomes increasingly important for making decisions in smart healthcare environments. It is to make sure that decisions are based on trustworthy algorithms and that healthcare workers understand the decisions made by these algorithms. These models can potentially enhance interpretability and explainability in decision-making processes that rely on artificial intelligence. Nevertheless, the intricate nature of the healthcare field necessitates the utilization of sophisticated models to classify cancer images. This research presents an advanced investigation of XAI models to classify cancer images. It describes the different levels of explainability… More >
Open Access
ARTICLE
Khushal Das1, Fazeel Abid2, Jawad Rasheed3,4,*, Kamlish5, Tunc Asuroglu6,*, Shtwai Alsubai7, Safeeullah Soomro8
CMES-Computer Modeling in Engineering & Sciences, DOI:10.32604/cmes.2024.051335
（This article belongs to the Special Issue: Artificial Intelligence Emerging Trends and Sustainable Applications in Image Processing and Computer Vision)
Abstract Deaf people or people facing hearing issues can communicate using sign language (SL), a visual language. Many works based on rich source language have been proposed; however, the work using poor resource language is still lacking. Unlike other SLs, the visuals of the Urdu Language are different. This study presents a novel approach to translating Urdu sign language (UrSL) using the UrSL-CNN model, a convolutional neural network (CNN) architecture specifically designed for this purpose. Unlike existing works that primarily focus on languages with rich resources, this study addresses the challenge of translating a sign language… More >
Open Access
ARTICLE
Sheng Xiang1, Junhao Ma1, Qunli Shang1, Xianbao Wang1,*, Defu Chen1,2
CMES-Computer Modeling in Engineering & Sciences, DOI:10.32604/cmes.2024.052759
（This article belongs to the Special Issue: Artificial Intelligence Emerging Trends and Sustainable Applications in Image Processing and Computer Vision)
Abstract Effective small object detection is crucial in various applications including urban intelligent transportation and
pedestrian detection. However, small objects are difficult to detect accurately because they contain less information.
Many current methods, particularly those based on Feature Pyramid Network (FPN), address this challenge by
leveraging multi-scale feature fusion. However, existing FPN-based methods often suffer from inadequate feature
fusion due to varying resolutions across different layers, leading to suboptimal small object detection. To address
this problem, we propose the Two-layer Attention Feature Pyramid Network (TA-FPN), featuring two key modules:
the Two-layer Attention Module (TAM) and the… More >
Graphic Abstract
Open Access
ARTICLE
Shengdong Cheng1, Juncheng Gao1,*, Hongning Qi2,*
CMES-Computer Modeling in Engineering & Sciences, DOI:10.32604/cmes.2024.052830
（This article belongs to the Special Issue: Computational Intelligent Systems for Solving Complex Engineering Problems: Principles and Applications-II)
Abstract Driven piles are used in many geological environments as a practical and convenient structural component. Hence, the determination of the drivability of piles is actually of great importance in complex geotechnical applications. Conventional methods of predicting pile drivability often rely on simplified physical models or empirical formulas, which may lack accuracy or applicability in complex geological conditions. Therefore, this study presents a practical machine learning approach, namely a Random Forest (RF) optimized by Bayesian Optimization (BO) and Particle Swarm Optimization (PSO), which not only enhances prediction accuracy but also better adapts to varying geological environments… More >
Graphic Abstract
Open Access
ARTICLE
Muaadh A. Alsoufi1,*, Maheyzah Md Siraj1, Fuad A. Ghaleb2, Muna Al-Razgan3, Mahfoudh Saeed Al-Asaly3, Taha Alfakih3, Faisal Saeed2
CMES-Computer Modeling in Engineering & Sciences, DOI:10.32604/cmes.2024.052112
（This article belongs to the Special Issue: Emerging Technologies in Information Security )
Abstract The rapid growth of Internet of Things (IoT) devices has brought numerous benefits to the interconnected world. However, the ubiquitous nature of IoT networks exposes them to various security threats, including anomaly intrusion attacks. In addition, IoT devices generate a high volume of unstructured data. Traditional intrusion detection systems often struggle to cope with the unique characteristics of IoT networks, such as resource constraints and heterogeneous data sources. Given the unpredictable nature of network technologies and diverse intrusion methods, conventional machine-learning approaches seem to lack efficiency. Across numerous research domains, deep learning techniques have demonstrated… More >
Open Access
ARTICLE
Dijun Rao1,2,3,4, Min Huang1,2,3,5, Xiuzhi Shi4, Zhi Yu6,*, Zhengxiang He7
CMES-Computer Modeling in Engineering & Sciences, DOI:10.32604/cmes.2024.051402
（This article belongs to the Special Issue: Bio-inspired Optimization in Engineering and Sciences)
Abstract The denoising of microseismic signals is a prerequisite for subsequent analysis and research. In this research, a new microseismic signal denoising algorithm called the Black Widow Optimization Algorithm (BWOA) optimized Variational Mode Decomposition (VMD) joint Wavelet Threshold Denoising (WTD) algorithm (BVW) is proposed. The BVW algorithm integrates VMD and WTD, both of which are optimized by BWOA. Specifically, this algorithm utilizes VMD to decompose the microseismic signal to be denoised into several Band-Limited Intrinsic Mode Functions (BLIMFs). Subsequently, these BLIMFs whose correlation coefficients with the microseismic signal to be denoised are higher than a threshold… More >
Open Access
REVIEW
Ebtesam Ahmad Alomari*
CMES-Computer Modeling in Engineering & Sciences, DOI:10.32604/cmes.2024.052256
（This article belongs to the Special Issue: Emerging Artificial Intelligence Technologies and Applications)
Abstract As Natural Language Processing (NLP) continues to advance, driven by the emergence of sophisticated large language models such as ChatGPT, there has been a notable growth in research activity. This rapid uptake reflects increasing interest in the field and induces critical inquiries into ChatGPT’s applicability in the NLP domain. This review paper systematically investigates the role of ChatGPT in diverse NLP tasks, including information extraction, Name Entity Recognition (NER), event extraction, relation extraction, Part of Speech (PoS) tagging, text classification, sentiment analysis, emotion recognition and text annotation. The novelty of this work lies in its… More >
Open Access
ARTICLE
Lirong Yin1, Lei Wang1, Siyu Lu2,*, Ruiyang Wang2, Youshuai Yang2, Bo Yang2, Shan Liu2, Ahmed AlSanad3, Salman A. AlQahtani3, Zhengtong Yin4, Xiaolu Li5, Xiaobing Chen6, Wenfeng Zheng3,*
CMES-Computer Modeling in Engineering & Sciences, DOI:10.32604/cmes.2024.051083
Abstract This study addresses the limitations of Transformer models in image feature extraction, particularly their lack of inductive bias for visual structures. Compared to Convolutional Neural Networks (CNNs), the Transformers are more sensitive to different hyperparameters of optimizers, which leads to a lack of stability and slow convergence. To tackle these challenges, we propose the Convolution-based Efficient Transformer Image Feature Extraction Network (CEFormer) as an enhancement of the Transformer architecture. Our model incorporates E-Attention, depthwise separable convolution, and dilated convolution to introduce crucial inductive biases, such as translation invariance, locality, and scale invariance, into the Transformer… More >
Login
Register
Submit a Paper
Propose a Special lssue