Zhigao Zeng^1,2, Aoting Tang^1,2, Shengqiu Yi^1,2, Xinpan Yuan^1,2, Yanhui Zhu^1,2,*

CMC-Computers, Materials & Continua, Vol.79, No.2, pp. 2277-2293, 2024, DOI:10.32604/cmc.2024.047989

Abstract Radiomics is a non-invasive method for extracting quantitative and higher-dimensional features from medical images for diagnosis. It has received great attention due to its huge application prospects in recent years. We can know that the number of features selected by the existing radiomics feature selection methods is basically about ten. In this paper, a heuristic feature selection method based on frequency iteration and multiple supervised training mode is proposed. Based on the combination between features, it decomposes all features layer by layer to select the optimal features for each layer, then fuses the optimal features to form a local optimal… More >

Norah Abdullah Al-Johany^1,*, Sanaa Abdullah Sharaf^1,2, Fathy Elbouraey Eassa^1,2, Reem Abdulaziz Alnanih^1,2,*

CMC-Computers, Materials & Continua, Vol.79, No.2, pp. 3139-3173, 2024, DOI:10.32604/cmc.2024.047392

Abstract The Message Passing Interface (MPI) is a widely accepted standard for parallel computing on distributed memory systems. However, MPI implementations can contain defects that impact the reliability and performance of parallel applications. Detecting and correcting these defects is crucial, yet there is a lack of published models specifically designed for correcting MPI defects. To address this, we propose a model for detecting and correcting MPI defects (DC_MPI), which aims to detect and correct defects in various types of MPI communication, including blocking point-to-point (BPTP), nonblocking point-to-point (NBPTP), and collective communication (CC). The defects addressed by the DC_MPI model include illegal… More >

J. Sheril Angel¹, A. Diana Andrushia^1,*, T. Mary Neebha¹, Oussama Accouche², Louai Saker², N. Anand³

CMC-Computers, Materials & Continua, Vol.79, No.2, pp. 2427-2448, 2024, DOI:10.32604/cmc.2024.047326

Abstract Facial emotion recognition (FER) has become a focal point of research due to its widespread applications, ranging from human-computer interaction to affective computing. While traditional FER techniques have relied on handcrafted features and classification models trained on image or video datasets, recent strides in artificial intelligence and deep learning (DL) have ushered in more sophisticated approaches. The research aims to develop a FER system using a Faster Region Convolutional Neural Network (FRCNN) and design a specialized FRCNN architecture tailored for facial emotion recognition, leveraging its ability to capture spatial hierarchies within localized regions of facial features. The proposed work enhances… More >

Zohaib Ahmad Khan¹, Yuanqing Xia^1,*, Ahmed Khan², Muhammad Sadiq², Mahmood Alam³, Fuad A. Awwad⁴, Emad A. A. Ismail⁴

CMC-Computers, Materials & Continua, Vol.79, No.2, pp. 2771-2793, 2024, DOI:10.32604/cmc.2024.046897

Abstract Sentiment analysis is becoming increasingly important in today’s digital age, with social media being a significant source of user-generated content. The development of sentiment lexicons that can support languages other than English is a challenging task, especially for analyzing sentiment analysis in social media reviews. Most existing sentiment analysis systems focus on English, leaving a significant research gap in other languages due to limited resources and tools. This research aims to address this gap by building a sentiment lexicon for local languages, which is then used with a machine learning algorithm for efficient sentiment analysis. In the first step, a… More >

Hui Du, Zhiyuan Hu^*, Depeng Lu, Jingrui Liu

CMC-Computers, Materials & Continua, Vol.79, No.2, pp. 3239-3259, 2024, DOI:10.32604/cmc.2024.046314

Abstract Traditional clustering algorithms often struggle to produce satisfactory results when dealing with datasets with uneven density. Additionally, they incur substantial computational costs when applied to high-dimensional data due to calculating similarity matrices. To alleviate these issues, we employ the KD-Tree to partition the dataset and compute the K-nearest neighbors (KNN) density for each point, thereby avoiding the computation of similarity matrices. Moreover, we apply the rules of voting elections, treating each data point as a voter and casting a vote for the point with the highest density among its KNN. By utilizing the vote counts of each point, we develop… More >

Khaldon Azzam Kordi¹, Mardeni Roslee^1,*, Mohamad Yusoff Alias¹, Abdulraqeb Alhammadi², Athar Waseem³, Anwar Faizd Osman⁴

CMC-Computers, Materials & Continua, Vol.79, No.2, pp. 3261-3298, 2024, DOI:10.32604/cmc.2024.044890

Abstract This study comprehensively examines the current state of deep learning (DL) usage in indoor positioning. It emphasizes the significance and efficiency of convolutional neural networks (CNNs) and recurrent neural networks (RNNs). Unlike prior studies focused on single sensor modalities like Wi-Fi or Bluetooth, this research explores the integration of multiple sensor modalities (e.g., Wi-Fi, Bluetooth, Ultra-Wideband, ZigBee) to expand indoor localization methods, particularly in obstructed environments. It addresses the challenge of precise object localization, introducing a novel hybrid DL approach using received signal information (RSI), Received Signal Strength (RSS), and Channel State Information (CSI) data to enhance accuracy and stability.… More >

Kamolthip Ruckwongpatr^1,#, Chirawat Paratthakonkun^2,#, Usanut Sangtongdee^3,4,*, Iqbal Pramukti⁵, Ira Nurmala⁶, Kanokwan Angkasith⁷, Weena Thanachaisakul⁷, Jatuphum Ketchatturat⁸, Mark D. Griffiths⁹, Yi-Kai Kao^10,*, Chung-Ying Lin^1,5,11,12

International Journal of Mental Health Promotion, Vol.26, No.4, pp. 293-302, 2024, DOI:10.32604/ijmhp.2024.047023

Abstract Background: In recent years, there has been increased research interest in both smartphone addiction and social media addiction as well as the development of psychometric instruments to assess these constructs. However, there is a lack of psychometric evaluation for instruments assessing smartphone addiction and social media addiction in Thailand. The present study evaluated the psychometric properties and gender measurement invariance of the Thai version of the Smartphone Application-Based Addiction Scale (SABAS) and Bergen Social Media Addiction Scale (BSMAS). Method: A total of 801 Thai university students participated in an online survey from January 2022 to July 2022 which included demographic… More >

Yanjing Cao¹, Chenchen Xu^1,2, Qi Li¹, Shan Lu^1,2,*, Jing Xiao^1,*

International Journal of Mental Health Promotion, Vol.26, No.4, pp. 261-269, 2024, DOI:10.32604/ijmhp.2024.043729

Abstract Background: Valid and reliable measures of depressive symptoms are crucial for understanding risk factors, outcomes, and interventions across rural and urban settings. Despite this need, the longitudinal invariance of these measures over time remains understudied. This research explores the structural components of the Center for Epidemiological Studies Depression Scale (CES-D) and examines its consistency across various living environments and temporal stability in a cohort of Chinese teenagers. Method: In the initial phase, 1,042 adolescents furnished demographic details and undertook the CES-D assessment. After a three-month interval, 967 of these participants repeated the CES-D evaluation. The study employed Confirmatory factor analysis… More >

Jingcheng Zhang¹, Xingjian Zhou¹, Dong Shen¹, Qimeng Yu¹, Lin Yuan^2,*, Yingying Dong³

Phyton-International Journal of Experimental Botany, Vol.93, No.4, pp. 745-762, 2024, DOI:10.32604/phyton.2024.049734

Abstract As an important rice disease, rice bacterial leaf blight (RBLB, caused by the bacterium Xanthomonas oryzae pv. oryzae), has become widespread in east China in recent years. Significant losses in rice yield occurred as a result of the disease’s epidemic, making it imperative to monitor RBLB at a large scale. With the development of remote sensing technology, the broad-band sensors equipped with red-edge channels over multiple spatial resolutions offer numerous available data for large-scale monitoring of rice diseases. However, RBLB is characterized by rapid dispersal under suitable conditions, making it difficult to track the disease at a regional scale with… More >

Guangwei Sun^1,2,#, Xuanhao Zhang^3,#, Yi Liu³, Liguang Chai², Daisong Liu², Zhenguo Chen^1,*, Shiyou Lü^3,*

Phyton-International Journal of Experimental Botany, Vol.93, No.4, pp. 641-659, 2024, DOI:10.32604/phyton.2024.049285

Abstract The Nicotiana genus, commonly known as tobacco, holds significant importance as a crucial economic crop. Confronted with an abundance of herbivorous insects that pose a substantial threat to yield, tobacco has developed a diverse and sophisticated array of mechanisms, establishing itself as a model of plant ecological defense. This review provides a concise overview of the current understanding of tobacco’s defense strategies against herbivores. Direct defenses, exemplified by its well-known tactic of secreting the alkaloid nicotine, serve as a potent toxin against a broad spectrum of herbivorous pests. Moreover, in response to herbivore attacks, tobacco enhances the discharge of volatile… More >