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  • Open Access

    REVIEW

    Wireless Positioning: Technologies, Applications, Challenges, and Future Development Trends

    Xingwang Li1,2, Hua Pang1, Geng Li1,*, Junjie Jiang1, Hui Zhang3, Changfei Gu4, Dong Yuan4

    CMES-Computer Modeling in Engineering & Sciences, Vol.139, No.2, pp. 1135-1166, 2024, DOI:10.32604/cmes.2023.031534

    Abstract The development of the fifth-generation (5G) mobile communication systems has entered the commercialization stage. 5G has a high data rate, low latency, and high reliability that can meet the basic demands of most industries and daily life, such as the Internet of Things (IoT), intelligent transportation systems, positioning, and navigation. The continuous progress and development of society have aroused wide concern. Positioning accuracy is the core demand for the applications, especially in complex environments such as airports, warehouses, supermarkets, and basements. However, many factors also affect the accuracy of positioning in those environments, for example, multipath effects, non-line-of-sight, and clock… More >

  • Open Access

    REVIEW

    Emerging Trends in Damage Tolerance Assessment: A Review of Smart Materials and Self-Repairable Structures

    Ali Akbar Firoozi1,*, Ali Asghar Firoozi2

    Structural Durability & Health Monitoring, Vol.18, No.1, pp. 1-18, 2024, DOI:10.32604/sdhm.2023.044573

    Abstract The discipline of damage tolerance assessment has experienced significant advancements due to the emergence of smart materials and self-repairable structures. This review offers a comprehensive look into both traditional and innovative methodologies employed in damage tolerance assessment. After a detailed exploration of damage tolerance concepts and their historical progression, the review juxtaposes the proven techniques of damage assessment with the cutting-edge innovations brought about by smart materials and self-repairable structures. The subsequent sections delve into the synergistic integration of smart materials with self-repairable structures, marking a pivotal stride in damage tolerance by establishing an autonomous system for immediate damage identification… More >

  • Open Access

    REVIEW

    Exploring Deep Learning Methods for Computer Vision Applications across Multiple Sectors: Challenges and Future Trends

    Narayanan Ganesh1, Rajendran Shankar2, Miroslav Mahdal3, Janakiraman Senthil Murugan4, Jasgurpreet Singh Chohan5, Kanak Kalita6,*

    CMES-Computer Modeling in Engineering & Sciences, Vol.139, No.1, pp. 103-141, 2024, DOI:10.32604/cmes.2023.028018

    Abstract Computer vision (CV) was developed for computers and other systems to act or make recommendations based on visual inputs, such as digital photos, movies, and other media. Deep learning (DL) methods are more successful than other traditional machine learning (ML) methods in CV. DL techniques can produce state-of-the-art results for difficult CV problems like picture categorization, object detection, and face recognition. In this review, a structured discussion on the history, methods, and applications of DL methods to CV problems is presented. The sector-wise presentation of applications in this paper may be particularly useful for researchers in niche fields who have… More >

  • Open Access

    ARTICLE

    Time Highlighted Multi-Interest Network for Sequential Recommendation

    Jiayi Ma, Tianhao Sun*, Xiaodong Zhang

    CMC-Computers, Materials & Continua, Vol.76, No.3, pp. 3569-3584, 2023, DOI:10.32604/cmc.2023.040005

    Abstract Sequential recommendation based on a multi-interest framework aims to analyze different aspects of interest based on historical interactions and generate predictions of a user’s potential interest in a list of items. Most existing methods only focus on what are the multiple interests behind interactions but neglect the evolution of user interests over time. To explore the impact of temporal dynamics on interest extraction, this paper explicitly models the timestamp with a multi-interest network and proposes a time-highlighted network to learn user preferences, which considers not only the interests at different moments but also the possible trends of interest over time.… More >

  • Open Access

    REVIEW

    An Overview of Modern Cartographic Trends Aligned with the ICA’s Perspective

    Maan Habib1,*, Maan Okayli2

    Revue Internationale de Géomatique, Vol.32, pp. 1-16, 2023, DOI:10.32604/rig.2023.043399

    Abstract This study provides a comprehensive overview of modern cartography innovations and emerging trends, highlighting the importance of geospatial representation in various fields. It discusses recent advancements in geospatial data collection techniques, including satellite and aerial imagery, Light Detection and Ranging (LiDAR) technology, and crowdsourcing. The research also investigates the integration of big data, machine learning, and real-time processing in Geographic Information Systems (GIS), as well as advances in geospatial visualization. In addition, it examines the role of cartography in addressing global challenges such as climate change, disaster management, and urban planning in line with the International Cartographic Association’s (ICA) perspectives.… More >

  • Open Access

    REVIEW

    Review of Recent Trends in the Hybridisation of Preprocessing-Based and Parameter Optimisation-Based Hybrid Models to Forecast Univariate Streamflow

    Baydaa Abdul Kareem1,2, Salah L. Zubaidi2,3, Nadhir Al-Ansari4,*, Yousif Raad Muhsen2,5

    CMES-Computer Modeling in Engineering & Sciences, Vol.138, No.1, pp. 1-41, 2024, DOI:10.32604/cmes.2023.027954

    Abstract Forecasting river flow is crucial for optimal planning, management, and sustainability using freshwater resources. Many machine learning (ML) approaches have been enhanced to improve streamflow prediction. Hybrid techniques have been viewed as a viable method for enhancing the accuracy of univariate streamflow estimation when compared to standalone approaches. Current researchers have also emphasised using hybrid models to improve forecast accuracy. Accordingly, this paper conducts an updated literature review of applications of hybrid models in estimating streamflow over the last five years, summarising data preprocessing, univariate machine learning modelling strategy, advantages and disadvantages of standalone ML techniques, hybrid models, and performance… More > Graphic Abstract

    Review of Recent Trends in the Hybridisation of Preprocessing-Based and Parameter Optimisation-Based Hybrid Models to Forecast Univariate Streamflow

  • Open Access

    ARTICLE

    Increasing Crop Quality and Yield with a Machine Learning-Based Crop Monitoring System

    Anas Bilal1,*, Xiaowen Liu1, Haixia Long1,*, 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 >

  • Open Access

    REVIEW

    A Review on Intelligent Detection and Classification of Power Quality Disturbances: Trends, Methodologies, and Prospects

    Yanjun Yan, Kai Chen*, Hang Geng, Wenqian Fan, Xinrui Zhou

    CMES-Computer Modeling in Engineering & Sciences, Vol.137, No.2, pp. 1345-1379, 2023, DOI:10.32604/cmes.2023.027252

    Abstract With increasing global concerns about clean energy in smart grids, the detection of power quality disturbances (PQDs) caused by energy instability is becoming more and more prominent. It is well acknowledged that the PQD effects on power grid equipment are destructive and hazardous, which causes irreversible damage to underlying electrical/electronic equipment of the concerned intelligent grids. In order to ensure safe and reliable equipment implementation, appropriate PQD detection technologies must be adopted to avoid such adverse effects. This paper summarizes the newly proposed and traditional PQD detection techniques in order to give a quick start to new researchers in the… More > Graphic Abstract

    A Review on Intelligent Detection and Classification of Power Quality Disturbances: Trends, Methodologies, and Prospects

  • Open Access

    ARTICLE

    IoT-Based Women Safety Gadgets (WSG): Vision, Architecture, and Design Trends

    Sharad Saxena1, Shailendra Mishra2,*, Mohammed Baljon2,*, Shamiksha Mishra3, Sunil Kumar Sharma2, Prakhar Goel1, Shubham Gupta1, Vinay Kishore1

    CMC-Computers, Materials & Continua, Vol.76, No.1, pp. 1027-1045, 2023, DOI:10.32604/cmc.2023.039677

    Abstract In recent years, the growth of female employees in the commercial market and industries has increased. As a result, some people think travelling to distant and isolated locations during odd hours generates new threats to women’s safety. The exponential increase in assaults and attacks on women, on the other hand, is posing a threat to women’s growth, development, and security. At the time of the attack, it appears the women were immobilized and needed immediate support. Only self-defense isn’t sufficient against abuse; a new technological solution is desired and can be used as quickly as hitting a switch or button.… More >

  • Open Access

    ARTICLE

    Predicting Bitcoin Trends Through Machine Learning Using Sentiment Analysis with Technical Indicators

    Hae Sun Jung1, Seon Hong Lee1, Haein Lee1, Jang Hyun Kim2,*

    Computer Systems Science and Engineering, Vol.46, No.2, pp. 2231-2246, 2023, DOI:10.32604/csse.2023.034466

    Abstract Predicting Bitcoin price trends is necessary because they represent the overall trend of the cryptocurrency market. As the history of the Bitcoin market is short and price volatility is high, studies have been conducted on the factors affecting changes in Bitcoin prices. Experiments have been conducted to predict Bitcoin prices using Twitter content. However, the amount of data was limited, and prices were predicted for only a short period (less than two years). In this study, data from Reddit and LexisNexis, covering a period of more than four years, were collected. These data were utilized to estimate and compare the… More >

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