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  • Open Access

    ARTICLE

    Cross-Modal Consistency with Aesthetic Similarity for Multimodal False Information Detection

    Weijian Fan1,*, Ziwei Shi2

    CMC-Computers, Materials & Continua, Vol.79, No.2, pp. 2723-2741, 2024, DOI:10.32604/cmc.2024.050344

    Abstract With the explosive growth of false information on social media platforms, the automatic detection of multimodal false information has received increasing attention. Recent research has significantly contributed to multimodal information exchange and fusion, with many methods attempting to integrate unimodal features to generate multimodal news representations. However, they still need to fully explore the hierarchical and complex semantic correlations between different modal contents, severely limiting their performance detecting multimodal false information. This work proposes a two-stage detection framework for multimodal false information detection, called ASMFD, which is based on image aesthetic similarity to segment and… More >

  • Open Access

    ARTICLE

    DNBP-CCA: A Novel Approach to Enhancing Heterogeneous Data Traffic and Reliable Data Transmission for Body Area Network

    Abdulwadood Alawadhi1,*, Mohd. Hasbullah Omar1, Abdullah Almogahed2, Noradila Nordin3, Salman A. Alqahtani4, Atif M. Alamri5

    CMC-Computers, Materials & Continua, Vol.79, No.2, pp. 2851-2878, 2024, DOI:10.32604/cmc.2024.050154

    Abstract The increased adoption of Internet of Medical Things (IoMT) technologies has resulted in the widespread use of Body Area Networks (BANs) in medical and non-medical domains. However, the performance of IEEE 802.15.4-based BANs is impacted by challenges related to heterogeneous data traffic requirements among nodes, including contention during finite backoff periods, association delays, and traffic channel access through clear channel assessment (CCA) algorithms. These challenges lead to increased packet collisions, queuing delays, retransmissions, and the neglect of critical traffic, thereby hindering performance indicators such as throughput, packet delivery ratio, packet drop rate, and packet delay.… More >

  • Open Access

    ARTICLE

    Developing Lexicons for Enhanced Sentiment Analysis in Software Engineering: An Innovative Multilingual Approach for Social Media Reviews

    Zohaib Ahmad Khan1, Yuanqing Xia1,*, 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.… More >

  • Open Access

    ARTICLE

    Harnessing ML and GIS for Seismic Vulnerability Assessment and Risk Prioritization

    Shalu1, Twinkle Acharya1, Dhwanilnath Gharekhan1,*, Dipak Samal2

    Revue Internationale de Géomatique, Vol.33, pp. 111-134, 2024, DOI:10.32604/rig.2024.051788

    Abstract Seismic vulnerability modeling plays a crucial role in seismic risk assessment, aiding decision-makers in pinpointing areas and structures most prone to earthquake damage. While machine learning (ML) algorithms and Geographic Information Systems (GIS) have emerged as promising tools for seismic vulnerability modeling, there remains a notable gap in comprehensive geospatial studies focused on India. Previous studies in seismic vulnerability modeling have primarily focused on specific regions or countries, often overlooking the unique challenges and characteristics of India. In this study, we introduce a novel approach to seismic vulnerability modeling, leveraging ML and GIS to address… More >

  • Open Access

    ARTICLE

    Deep-Ensemble Learning Method for Solar Resource Assessment of Complex Terrain Landscapes

    Lifeng Li1, Zaimin Yang1, Xiongping Yang1, Jiaming Li2, Qianyufan Zhou3,*, Ping Yang3

    Energy Engineering, Vol.121, No.5, pp. 1329-1346, 2024, DOI:10.32604/ee.2023.046447

    Abstract As the global demand for renewable energy grows, solar energy is gaining attention as a clean, sustainable energy source. Accurate assessment of solar energy resources is crucial for the siting and design of photovoltaic power plants. This study proposes an integrated deep learning-based photovoltaic resource assessment method. Ensemble learning and deep learning methods are fused for photovoltaic resource assessment for the first time. The proposed method combines the random forest, gated recurrent unit, and long short-term memory to effectively improve the accuracy and reliability of photovoltaic resource assessment. The proposed method has strong adaptability and More >

  • Open Access

    ARTICLE

    E2E-MFERC: A Multi-Face Expression Recognition Model for Group Emotion Assessment

    Lin Wang1, Juan Zhao2, Hu Song3, Xiaolong Xu4,*

    CMC-Computers, Materials & Continua, Vol.79, No.1, pp. 1105-1135, 2024, DOI:10.32604/cmc.2024.048688

    Abstract In smart classrooms, conducting multi-face expression recognition based on existing hardware devices to assess students’ group emotions can provide educators with more comprehensive and intuitive classroom effect analysis, thereby continuously promoting the improvement of teaching quality. However, most existing multi-face expression recognition methods adopt a multi-stage approach, with an overall complex process, poor real-time performance, and insufficient generalization ability. In addition, the existing facial expression datasets are mostly single face images, which are of low quality and lack specificity, also restricting the development of this research. This paper aims to propose an end-to-end high-performance multi-face… More >

  • Open Access

    ARTICLE

    Spinal Vertebral Fracture Detection and Fracture Level Assessment Based on Deep Learning

    Yuhang Wang1,*, Zhiqin He1, Qinmu Wu1, Tingsheng Lu2, Yu Tang1, Maoyun Zhu1

    CMC-Computers, Materials & Continua, Vol.79, No.1, pp. 1377-1398, 2024, DOI:10.32604/cmc.2024.047379

    Abstract This paper addresses the common orthopedic trauma of spinal vertebral fractures and aims to enhance doctors’ diagnostic efficiency. Therefore, a deep-learning-based automated diagnostic system with multi-label segmentation is proposed to recognize the condition of vertebral fractures. The whole spine Computed Tomography (CT) image is segmented into the fracture, normal, and background using U-Net, and the fracture degree of each vertebra is evaluated (Genant semi-qualitative evaluation). The main work of this paper includes: First, based on the spatial configuration network (SCN) structure, U-Net is used instead of the SCN feature extraction network. The attention mechanism and… More >

  • Open Access

    ARTICLE

    Durable Aroma Finishing of Wool Fabric with Microencapsulated Vetiver Essential Oil and Assessment of its Properties

    SHALINI RUKHAYA*, NEELAM M. ROSE, SAROJ YADAV

    Journal of Polymer Materials, Vol.38, No.3-4, pp. 179-190, 2021, DOI:10.32381/JPM.2021.38.3-4.1

    Abstract The present study was conducted to develop aroma wool fabric using microencapsulated vetiver essential oil without deteriorating the fabric properties. SEM analysis of the treated wool fabric depicted adhesion of numerous microcapsules of spherical shape and FTIR analysis indicated presence of different functional groups on the fabric. The aroma treatment improved most of the tested physical properties of wool fabric. Aroma treated fabric displayed good antimoth efficacy in terms of weight reduction (7.57%) and moth mortality (40%). Treated wool fabric also exhibited antibacterial activity with 63.45 and 61.37 percent reduction in bacterial growth against S. More >

  • Open Access

    ARTICLE

    Assessment of Particle Matter Pollution during Post-Earthquake Debris Removal in Adiyaman City

    Ercan Vural*

    Revue Internationale de Géomatique, Vol.33, pp. 37-50, 2024, DOI:10.32604/rig.2024.047908

    Abstract Severe earthquakes in the world and in Turkey can cause great loss of life and property, environmental problems and health problems. In addition to the loss of life and property, earthquakes are closely related to ecosystems, air, water, and soil pollution. Particularly in post-earthquake debris removal, very large amounts of particulate matter are released and may have negative effects on the health of the local population. This study aimed to detect two types of particle matter pollution during debris removal in 25 different locations in Adiyaman City using a CEM DT 9880 particle matter measuring… More >

  • Open Access

    ARTICLE

    Movement Function Assessment Based on Human Pose Estimation from Multi-View

    Lingling Chen1,2,*, Tong Liu1, Zhuo Gong1, Ding Wang1

    Computer Systems Science and Engineering, Vol.48, No.2, pp. 321-339, 2024, DOI:10.32604/csse.2023.037865

    Abstract Human pose estimation is a basic and critical task in the field of computer vision that involves determining the position (or spatial coordinates) of the joints of the human body in a given image or video. It is widely used in motion analysis, medical evaluation, and behavior monitoring. In this paper, the authors propose a method for multi-view human pose estimation. Two image sensors were placed orthogonally with respect to each other to capture the pose of the subject as they moved, and this yielded accurate and comprehensive results of three-dimensional (3D) motion reconstruction that… More >

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