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Fei Wang^1,2, Li Li¹, Yinxing Wan¹, Zhuorong Li¹, Lixian Luo³, Bangshun Hu¹, Jiahui Pan^1,2, Zhenfu Wen⁴, Haiyun Huang^1,2,*
CMC-Computers, Materials & Continua, Vol.76, No.2, pp. 2301-2316, 2023, DOI:10.32604/cmc.2023.037727
Abstract Sleep spindles are an electroencephalogram (EEG) biomarker of non-rapid eye movement (NREM) sleep and have important implications for clinical diagnosis and prognosis. However, it is challenging to accurately detect sleep spindles due to the complexity of the human brain and the uncertainty of neural mechanisms. To improve the reliability and objectivity of sleep spindle detection and to compensate for the limitations of manual annotation, this study proposes a new automatic detection algorithm based on Matching Pursuit (MP) and Least Squares Boosting (LSBoost), where the automatic sleep spindle detection algorithm can help reduce the visual annotation workload of sleep clinicians. Specifically,… More >

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ARTICLE

Dan Zhang¹, Yu Zhang², Yiwen Liang^1,*
CMC-Computers, Materials & Continua, Vol.76, No.2, pp. 2317-2336, 2023, DOI:10.32604/cmc.2023.038026
Abstract The dendritic cell algorithm (DCA) is an excellent prototype for developing Machine Learning inspired by the function of the powerful natural immune system. Too many parameters increase complexity and lead to plenty of criticism in the signal fusion procedure of DCA. The loss function of DCA is ambiguous due to its complexity. To reduce the uncertainty, several researchers simplified the algorithm program; some introduced gradient descent to optimize parameters; some utilized searching methods to find the optimal parameter combination. However, these studies are either time-consuming or need to be revised in the case of non-convex functions. To overcome the problems,… More >

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150

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ARTICLE

Eugene Tan Boon Hong¹, Yung-Wey Chong^1,*, Tat-Chee Wan¹, Kok-Lim Alvin Yau²
CMC-Computers, Materials & Continua, Vol.76, No.2, pp. 2337-2354, 2023, DOI:10.32604/cmc.2023.038695
Abstract The existing dataset for visual dialog comprises multiple rounds of questions and a diverse range of image contents. However, it faces challenges in overcoming visual semantic limitations, particularly in obtaining sufficient context from visual and textual aspects of images. This paper proposes a new visual dialog dataset called Diverse History-Dialog (DS-Dialog) to address the visual semantic limitations faced by the existing dataset. DS-Dialog groups relevant histories based on their respective Microsoft Common Objects in Context (MSCOCO) image categories and consolidates them for each image. Specifically, each MSCOCO image category consists of top relevant histories extracted based on their semantic relationships… More >

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181

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ARTICLE

Jiaxing Wang, Lanlan Rui, Yang Yang^*, Zhipeng Gao, Xuesong Qiu
CMC-Computers, Materials & Continua, Vol.76, No.2, pp. 2355-2384, 2023, DOI:10.32604/cmc.2023.034993
Abstract Crowdsensing, as a data collection method that uses the mobile sensing ability of many users to help the public collect and extract useful information, has received extensive attention in data collection. Since crowdsensing relies on user equipment to consume resources to obtain information, and the quality and distribution of user equipment are uneven, crowdsensing has problems such as low participation enthusiasm of participants and low quality of collected data, which affects the widespread use of crowdsensing. This paper proposes to apply the blockchain to crowdsensing and solve the above challenges by utilizing the characteristics of the blockchain, such as immutability… More >

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192

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ARTICLE

Sani Salisu^1,2, A. S. A. Mohamed^1,*, M. H. Jaafar³, Ainun S. B. Pauzi¹, Hussain A. Younis^1,4
CMC-Computers, Materials & Continua, Vol.76, No.2, pp. 2385-2400, 2023, DOI:10.32604/cmc.2023.035904
Abstract In this article, a comprehensive survey of deep learning-based (DL-based) human pose estimation (HPE) that can help researchers in the domain of computer vision is presented. HPE is among the fastest-growing research domains of computer vision and is used in solving several problems for human endeavours. After the detailed introduction, three different human body modes followed by the main stages of HPE and two pipelines of two-dimensional (2D) HPE are presented. The details of the four components of HPE are also presented. The keypoints output format of two popular 2D HPE datasets and the most cited DL-based HPE articles from… More >

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ARTICLE

Anas Bilal^1,*, Xiaowen Liu¹, Haixia Long^1,*, Muhammad Shafiq², Muhammad Waqar³
CMC-Computers, Materials & Continua, Vol.76, No.2, pp. 2401-2426, 2023, DOI:10.32604/cmc.2023.037857
Abstract Farming is cultivating the soil, producing crops, and keeping livestock. The agricultural sector plays a crucial role in a country’s economic growth. This research proposes a two-stage machine learning framework for agriculture to improve efficiency and increase crop yield. In the first stage, machine learning algorithms generate data for extensive and far-flung agricultural areas and forecast crops. The recommended crops are based on various factors such as weather conditions, soil analysis, and the amount of fertilizers and pesticides required. In the second stage, a transfer learning-based model for plant seedlings, pests, and plant leaf disease datasets is used to detect… More >

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199

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ARTICLE

Jiahao Zhang, Honglin Wang^*
CMC-Computers, Materials & Continua, Vol.76, No.2, pp. 2427-2441, 2023, DOI:10.32604/cmc.2023.038462
Abstract With the rapid development of technology, processing the explosive growth of meteorological data on traditional standalone computing has become increasingly time-consuming, which cannot meet the demands of scientific research and business. Therefore, this paper proposes the implementation of the parallel Clustering Large Application based upon RANdomized Search (CLARANS) clustering algorithm on the Spark cloud computing platform to cluster China’s climate regions using meteorological data from 1988 to 2018. The aim is to address the challenge of applying clustering algorithms to large datasets. In this paper, the morphological similarity distance is adopted as the similarity measurement standard instead of Euclidean distance,… More >

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224

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ARTICLE

Mingqiang Zhou^*, Mengjiao Li, Zhiyuan Qian, Kunpeng Li
CMC-Computers, Materials & Continua, Vol.76, No.2, pp. 2443-2460, 2023, DOI:10.32604/cmc.2023.038675
Abstract Role-based network embedding aims to embed role-similar nodes into a similar embedding space, which is widely used in graph mining tasks such as role classification and detection. Roles are sets of nodes in graph networks with similar structural patterns and functions. However, the role-similar nodes may be far away or even disconnected from each other. Meanwhile, the neighborhood node features and noise also affect the result of the role-based network embedding, which are also challenges of current network embedding work. In this paper, we propose a Role-based network Embedding via Quantum walk with weighted Features fusion (REQF), which simultaneously considers… More >

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179

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Alaa Alsaheel, Reem Alhassoun, Reema Alrashed, Noura Almatrafi, Noura Almallouhi, Saleh Albahli^*
CMC-Computers, Materials & Continua, Vol.76, No.2, pp. 2461-2482, 2023, DOI:10.32604/cmc.2023.040257
Abstract With the increasing use of deep learning technology, there is a growing concern over creating deep fake images and videos that can potentially be used for fraud. In healthcare, manipulating medical images could lead to misdiagnosis and potentially life-threatening consequences. Therefore, the primary purpose of this study is to explore the use of deep learning algorithms to detect deep fake images by solving the problem of recognizing the handling of samples of cancer and other diseases. Therefore, this research proposes a framework that leverages state-of-the-art deep convolutional neural networks (CNN) and a large dataset of authentic and deep fake medical… More >

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230

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Yongxuan Sang, Jiangpo Wei^*, Zhifeng Zhang, Bo Wang
CMC-Computers, Materials & Continua, Vol.76, No.2, pp. 2483-2502, 2023, DOI:10.32604/cmc.2023.040485
Abstract Computing-intensive and latency-sensitive user requests pose significant challenges to traditional cloud computing. In response to these challenges, mobile edge computing (MEC) has emerged as a new paradigm that extends the computational, caching, and communication capabilities of cloud computing. By caching certain services on edge nodes, computational support can be provided for requests that are offloaded to the edges. However, previous studies on task offloading have generally not considered the impact of caching mechanisms and the cache space occupied by services. This oversight can lead to problems, such as high delays in task executions and invalidation of offloading decisions. To optimize… More >

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Qinhui Liu, Zhijie Gao, Jiang Li^*, Shuo Li, Laizheng Zhu
CMC-Computers, Materials & Continua, Vol.76, No.2, pp. 2503-2530, 2023, DOI:10.32604/cmc.2023.040505
Abstract With the rapid development of intelligent manufacturing and the changes in market demand, the current manufacturing industry presents the characteristics of multi-varieties, small batches, customization, and a short production cycle, with the whole production process having certain flexibility. In this paper, a mathematical model is established with the minimum production cycle as the optimization objective for the dual-resource batch scheduling of the flexible job shop, and an improved nested optimization algorithm is designed to solve the problem. The outer layer batch optimization problem is solved by the improved simulated annealing algorithm. The inner double resource scheduling problem is solved by… More >

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215

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ARTICLE

Shoucai Zhao, Lifeng Cao^*, Jinhui Li, Jiling Wan, Jinlong Bai
CMC-Computers, Materials & Continua, Vol.76, No.2, pp. 2531-2549, 2023, DOI:10.32604/cmc.2023.040776
Abstract With the application and development of blockchain technology, many problems faced by blockchain traceability are gradually exposed. Such as cross-chain information collaboration, data separation and storage, multi-system, multi-security domain collaboration, etc. To solve these problems, it is proposed to construct trust domains based on federated chains. The public chain is used as the authorization chain to build a cross-domain data traceability mechanism applicable to multi-domain collaboration. First, the architecture of the blockchain cross-domain model is designed. Combined with the data access strategy and the decision mechanism, the open and transparent judgment of cross-domain permission and cross-domain identity authentication is realized.… More >

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208

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ARTICLE

Umair Muneer Butt^1,2,*, Rimsha Arif², Sukumar Letchmunan^1,*, Babur Hayat Malik², Muhammad Adil Butt²
CMC-Computers, Materials & Continua, Vol.76, No.2, pp. 2551-2570, 2023, DOI:10.32604/cmc.2023.039164
Abstract The detection of brain disease is an essential issue in medical and research areas. Deep learning techniques have shown promising results in detecting and diagnosing brain diseases using magnetic resonance imaging (MRI) images. These techniques involve training neural networks on large datasets of MRI images, allowing the networks to learn patterns and features indicative of different brain diseases. However, several challenges and limitations still need to be addressed further to improve the accuracy and effectiveness of these techniques. This paper implements a Feature Enhanced Stacked Auto Encoder (FESAE) model to detect brain diseases. The standard stack auto encoder’s results are… More >

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RETRACTION

Siti Julia Rosli^1,2, Hasliza A Rahim^1,2,*, Khairul Najmy Abdul Rani^1,2, Ruzelita Ngadiran^2,3, Wan Azani Mustafa^3,4, Muzammil Jusoh^1,2, Mohd Najib Mohd Yasin^1,2, Thennarasan Sabapathy^1,2, Mohamedfareq Abdulmalek⁵, Wan Suryani Firuz Wan Ariffin², Ahmed Alkhayyat⁶
CMC-Computers, Materials & Continua, Vol.76, No.2, pp. 2571-2571, 2023, DOI:10.32604/cmc.2023.045533
Abstract This article has no abstract. More >

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