Tianzhe Jiao, Chaopeng Guo, Xiaoyue Feng, Yuming Chen, Jie Song^*

CMC-Computers, Materials & Continua, Vol.80, No.1, pp. 1-35, 2024, DOI:10.32604/cmc.2024.053204

Abstract Multi-modal fusion technology gradually become a fundamental task in many fields, such as autonomous driving, smart healthcare, sentiment analysis, and human-computer interaction. It is rapidly becoming the dominant research due to its powerful perception and judgment capabilities. Under complex scenes, multi-modal fusion technology utilizes the complementary characteristics of multiple data streams to fuse different data types and achieve more accurate predictions. However, achieving outstanding performance is challenging because of equipment performance limitations, missing information, and data noise. This paper comprehensively reviews existing methods based on multi-modal fusion techniques and completes a detailed and in-depth analysis.… More >

Jiawen Li^1,2, Yuesheng Huang¹, Yayi Lu¹, Leijun Wang^1,*, Yongqi Ren¹, Rongjun Chen¹

CMC-Computers, Materials & Continua, Vol.80, No.1, pp. 1581-1599, 2024, DOI:10.32604/cmc.2024.052666

Abstract In the context of the accelerated pace of daily life and the development of e-commerce, online shopping is a mainstream way for consumers to access products and services. To understand their emotional expressions in facing different shopping experience scenarios, this paper presents a sentiment analysis method that combines the e-commerce review keyword-generated image with a hybrid machine learning-based model, in which the Word2Vec-TextRank is used to extract keywords that act as the inputs for generating the related images by generative Artificial Intelligence (AI). Subsequently, a hybrid Convolutional Neural Network and Support Vector Machine (CNN-SVM) model… More >

Anam Mughees^1,2,*, Muhammad Kamran^1,3

CMC-Computers, Materials & Continua, Vol.80, No.1, pp. 507-526, 2024, DOI:10.32604/cmc.2024.051820

Abstract Non-intrusive load monitoring is a method that disaggregates the overall energy consumption of a building to estimate the electric power usage and operating status of each appliance individually. Prior studies have mostly concentrated on the identification of high-power appliances like HVAC systems while overlooking the existence of low-power appliances. Low-power consumer appliances have comparable power consumption patterns, which can complicate the detection task and can be mistaken as noise. This research tackles the problem of classification of low-power appliances and uses turn-on current transients to extract novel features and develop unique appliance signatures. A hybrid… More >

Ahmad Ababneh¹, Yousef Sanjalawe², Salam Fraihat^3,*, Salam Al-E’mari⁴, Hamzah Alqudah⁵

CMC-Computers, Materials & Continua, Vol.80, No.1, pp. 1347-1379, 2024, DOI:10.32604/cmc.2024.050600

Abstract This study introduces the Orbit Weighting Scheme (OWS), a novel approach aimed at enhancing the precision and efficiency of Vector Space information retrieval (IR) models, which have traditionally relied on weighting schemes like tf-idf and BM25. These conventional methods often struggle with accurately capturing document relevance, leading to inefficiencies in both retrieval performance and index size management. OWS proposes a dynamic weighting mechanism that evaluates the significance of terms based on their orbital position within the vector space, emphasizing term relationships and distribution patterns overlooked by existing models. Our research focuses on evaluating OWS’s impact… More >

Nidhi Parashar¹, Prashant Johri¹, Arfat Ahmad Khan⁵, Nitin Gaur¹, Seifedine Kadry^2,3,4,*

CMC-Computers, Materials & Continua, Vol.80, No.1, pp. 389-425, 2024, DOI:10.32604/cmc.2024.050240

Abstract The growing global requirement for food and the need for sustainable farming in an era of a changing climate and scarce resources have inspired substantial crop yield prediction research. Deep learning (DL) and machine learning (ML) models effectively deal with such challenges. This research paper comprehensively analyses recent advancements in crop yield prediction from January 2016 to March 2024. In addition, it analyses the effectiveness of various input parameters considered in crop yield prediction models. We conducted an in-depth search and gathered studies that employed crop modeling and AI-based methods to predict crop yield. The… More >

Jian Luo^1,*, Bo Xu¹, Tardi Tjahjadi², Jian Yi¹

CMC-Computers, Materials & Continua, Vol.80, No.1, pp. 235-261, 2024, DOI:10.32604/cmc.2024.050018

Abstract Subject identification via the subject’s gait is challenging due to variations in the subject’s carrying and dressing conditions in real-life scenes. This paper proposes a novel targeted 3-dimensional (3D) gait model (3DGait) represented by a set of interpretable 3DGait descriptors based on a 3D parametric body model. The 3DGait descriptors are utilised as invariant gait features in the 3DGait recognition method to address object carrying and dressing. The 3DGait recognition method involves 2-dimensional (2D) to 3DGait data learning based on 3D virtual samples, a semantic gait parameter estimation Long Short Time Memory (LSTM) network (3D-SGPE-LSTM), a feature fusion… More >

Abdul Sattar Laghari¹, Mohammad Waqas Chandio¹, Laveet Kumar^2,*, Mamdouh El Haj Assad³

Energy Engineering, Vol.121, No.8, pp. 2023-2038, 2024, DOI:10.32604/ee.2024.051082

Abstract The selection of working fluid significantly impacts the geothermal ORC’s Efficiency. Using a mixture as a working fluid is a strategy to improve the output of geothermal ORC. In the current study, modelling and thermodynamic analysis of ORC, using geothermal as a heat source, is carried out at fixed operating conditions. The model is simulated in the Engineering Equation Solver (EES). An environment-friendly mixture of fluids, i.e., R245fa/R600a, with a suitable mole fraction, is used as the operating fluid. The mixture provided the most convenient results compared to the pure working fluid under fixed operating More >

Haoping Yao¹, Xinyu Zhong², Chunxia He^1,*

Journal of Renewable Materials, Vol.12, No.5, pp. 993-1006, 2024, DOI:10.32604/jrm.2024.047758

Abstract To make full use of plant shell fibers (rice husk, walnut shell, chestnut shell), three kinds of wood-plastic composites of plant shell fibers and polyvinyl chloride (PVC) were prepared. X-ray diffraction analysis was carried out on three kinds of plant shell fibers to test their crystallinity. The aging process of the composites was conducted under 2 different conditions. One was artificial seawater immersion and xenon lamp irradiation, and the other one was deionized water spray and xenon lamp irradiation. The mechanical properties (tensile strength, flexural strength, impact strength), changes in color, water absorption, Fourier transform… More >

WEI JIANG¹, MEI ZHOU^2,*

Oncology Research, Vol.32, No.8, pp. 1359-1368, 2024, DOI:10.32604/or.2024.043423

Abstract Multiple myeloma (MM) is a plasma cell malignancy and remains incurable as it lacks effective curative approaches; thus, novel therapeutic strategies are desperately needed. The study aimed to explore the therapeutic role of dihydromyricetin (DHM) in MM and explore its mechanisms. Human MM and normal plasma samples, human MM cell lines, and normal plasma cells were used for in vitro experiments. Cell counting kit-8 (CCK-8), flow cytometry, and trans-well assays were performed for the assessment of cell viability, apoptosis, migration, and invasion, respectively. Quantitative real-time polymerase chain reaction (qRT-PCR) was employed to assess the mRNA expression… More > Graphic Abstract

Saravanan Arumugam^*

Computer Systems Science and Engineering, Vol.48, No.4, pp. 989-1008, 2024, DOI:10.32604/csse.2024.051770

Abstract Text mining presents unique challenges in extracting meaningful information from the vast volumes of digital documents. Traditional filter feature selection methods often fall short in handling the complexities of short text data. To address this issue, this paper presents a novel approach to feature selection in text classification, aiming to overcome challenges posed by high dimensionality and reduced accuracy in the face of increasing digital document volumes. Unlike traditional filter feature selection techniques, the proposed method, Multivariate Relevance Frequency Analysis, offers a tailored solution for diverse text data types. By integrating positive, negative, and dependency… More >