Fatma Harby¹, Mansor Alohali², Adel Thaljaoui^2,3,*, Amira Samy Talaat⁴

CMC-Computers, Materials & Continua, Vol., , DOI:10.32604/cmc.2024.046623

Abstract Machine Learning (ML) algorithms play a pivotal role in Speech Emotion Recognition (SER), although they encounter a formidable obstacle in accurately discerning a speaker’s emotional state. The examination of the emotional states of speakers holds significant importance in a range of real-time applications, including but not limited to virtual reality, human-robot interaction, emergency centers, and human behavior assessment. Accurately identifying emotions in the SER process relies on extracting relevant information from audio inputs. Previous studies on SER have predominantly utilized short-time characteristics such as Mel Frequency Cepstral Coefficients (MFCCs) due to their ability to capture the periodic nature of audio… More >

Vaishnawi Priyadarshni¹, Sanjay Kumar Sharma¹, Mohammad Khalid Imam Rahmani^2,*, Baijnath Kaushik³, Rania Almajalid^2,*

CMC-Computers, Materials & Continua, Vol., , DOI:10.32604/cmc.2024.044963

Abstract Breast cancer (BC) is one of the leading causes of death among women worldwide, as it has emerged as the most commonly diagnosed malignancy in women. Early detection and effective treatment of BC can help save women’s lives. Developing an efficient technology-based detection system can lead to non-destructive and preliminary cancer detection techniques. This paper proposes a comprehensive framework that can effectively diagnose cancerous cells from benign cells using the Curated Breast Imaging Subset of the Digital Database for Screening Mammography (CBIS-DDSM) data set. The novelty of the proposed framework lies in the integration of various techniques, where the fusion… More >

Saba Awan^1,*, Zahid Mehmood^2,*

CMC-Computers, Materials & Continua, Vol., , DOI:10.32604/cmc.2024.047337

Abstract Highway safety researchers focus on crash injury severity, utilizing deep learning—specifically, deep neural networks (DNN), deep convolutional neural networks (D-CNN), and deep recurrent neural networks (D-RNN)— as the preferred method for modeling accident severity. Deep learning’s strength lies in handling intricate relationships within extensive datasets, making it popular for accident severity level (ASL) prediction and classification. Despite prior success, there is a need for an efficient system recognizing ASL in diverse road conditions. To address this, we present an innovative Accident Severity Level Prediction Deep Learning (ASLP-DL) framework, incorporating DNN, D-CNN, and D-RNN models fine-tuned through iterative hyperparameter selection with… More >

Albert Osom¹, Krysta S. Barton², Katie Sexton^3,4, Lyndia Brumback¹, Joyce P. Yi-Frazier⁴, Abby R. Rosenberg^5,6, Ruth Engelberg⁷, Jill M. Steiner^8,*

Congenital Heart Disease, Vol., , DOI:10.32604/chd.2024.046656

Abstract Social determinants of health (SDOH) affect quality of life. We investigated SDOH impacts on self-perceived resilience among people with adult congenital heart disease (ACHD). Secondary analysis of data from two complementary studies: a survey study conducted May 2021–June 2022 and a qualitative study conducted June 2020–August 2021. Resilience was assessed through CD-RISC10 score (range 0–40, higher scores reflect greater self-perceived resilience) and interview responses. Sociodemographic and SDOH (education, employment, living situation, monetary stability, financial dependency, area deprivation index) data were collected by healthcare record review and self-report. We used linear regression with robust standard errors to analyze survey data and… More > Graphic Abstract

YANAN MA¹, SHANSHAN WANG^2,*, HUIGUO DING^1,*

BIOCELL, Vol., , DOI:10.32604/biocell.2024.048244

Abstract Background: Hepatocellular carcinoma (HCC) is a common malignant tumor with poor prognosis and high mortality worldwide. Although cystathionine-gamma-lyase (CSE) plays an important role in the development of multiple tumors, the clinical implication and potential mechanisms of CSE in HCC development remain elusive. Methods: In our study, the CSE expression in HCC was analyzed in Gene Expression Omnibus (GEO) and The Cancer Genome Atlas (TCGA) datasets and further confirmed by RT-qPCR and immunohistochemistry assays in HCC samples. Furthermore, the associations between CSE expression and HCC malignancy as well as survival were analyzed in GSE14520 and validated in HCC patients. Finally, the… More >

QI XIA¹, XING CHEN², QINGHUA MA³, XIANXIU WEN^2,*

BIOCELL, Vol., , DOI:10.32604/biocell.2023.027414

Abstract Background: Breast cancer (BC) is the most common cancer and the leading cause of cancer death in women. Immune features play an important role in improving the prognosis prediction of BC. However, while previous immune signatures consisted mainly of immune genes, immune cell-based signatures have been rarely reported. Methods: In this study, we report that a novel immune cell signature is effective in improving prognostic prediction by combining M2 macrophages. We identified 17 differentially infiltrating immune cells between cancer and normal groups. Prognostic features of the four immune cells identified by LASSO COX analysis showed good performance for survival risk… More >

Yanqiong Zhang, Xianwei Jiang^*

CMES-Computer Modeling in Engineering & Sciences, Vol., , DOI:10.32604/cmes.2023.045731

Abstract Sign language, a visual-gestural language used by the deaf and hard-of-hearing community, plays a crucial role in facilitating communication and promoting inclusivity. Sign language recognition (SLR), the process of automatically recognizing and interpreting sign language gestures, has gained significant attention in recent years due to its potential to bridge the communication gap between the hearing impaired and the hearing world. The emergence and continuous development of deep learning techniques have provided inspiration and momentum for advancing SLR. This paper presents a comprehensive and up-to-date analysis of the advancements, challenges, and opportunities in deep learning-based sign language recognition, focusing on the… More >

Longle Cheng^1,2, Xiru Li¹, Shiyu Fang², Wansu Pan¹, He Zhao^1,*, Haibo Tan¹, Xiaofeng Li^1,2

CMC-Computers, Materials & Continua, Vol., , DOI:10.32604/cmc.2024.047166

Abstract Peer-to-peer (P2P) overlay networks provide message transmission capabilities for blockchain systems. Improving data transmission efficiency in P2P networks can greatly enhance the performance of blockchain systems. However, traditional blockchain P2P networks face a common challenge where there is often a mismatch between the upperlayer traffic requirements and the underlying physical network topology. This mismatch results in redundant data transmission and inefficient routing, severely constraining the scalability of blockchain systems. To address these pressing issues, we propose FPSblo, an efficient transmission method for blockchain networks. Our inspiration for FPSblo stems from the Farthest Point Sampling (FPS) algorithm, a well-established technique widely… More >

Xue Li, Hongying Zhang^*, Zixun Ye, Xiaoru Huang

CMC-Computers, Materials & Continua, Vol., , DOI:10.32604/cmc.2024.047093

Abstract Transformer-based stereo image super-resolution reconstruction (Stereo SR) methods have significantly improved image quality. However, existing methods have deficiencies in paying attention to detailed features and do not consider the offset of pixels along the epipolar lines in complementary views when integrating stereo information. To address these challenges, this paper introduces a novel epipolar line window attention stereo image superresolution network (EWASSR). For detail feature restoration, we design a feature extractor based on Transformer and convolutional neural network (CNN), which consists of (shifted) window-based self-attention ((S)W-MSA) and feature distillation and enhancement blocks (FDEB). This combination effectively solves the problem of global… More >

Chirag Chandrashekar¹, Pradeep Krishnadoss^1,*, Vijayakumar Kedalu Poornachary¹, Balasundaram Ananthakrishnan^1,2

CMC-Computers, Materials & Continua, Vol., , DOI:10.32604/cmc.2024.046304

Abstract Cloud computing provides a diverse and adaptable resource pool over the internet, allowing users to tap into various resources as needed. It has been seen as a robust solution to relevant challenges. A significant delay can hamper the performance of IoT-enabled cloud platforms. However, efficient task scheduling can lower the cloud infrastructure’s energy consumption, thus maximizing the service provider’s revenue by decreasing user job processing times. The proposed Modified Chimp-Whale Optimization Algorithm called Modified ChimpWhale Optimization Algorithm (MCWOA), combines elements of the Chimp Optimization Algorithm (COA) and the Whale Optimization Algorithm (WOA). To enhance MCWOA’s identification precision, the Sobol sequence… More >