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  • Open Access

    ARTICLE

    Smart Devices Based Multisensory Approach for Complex Human Activity Recognition

    Muhammad Atif Hanif1, Tallha Akram1, Aamir Shahzad2, Muhammad Attique Khan3, Usman Tariq4, Jung-In Choi5, Yunyoung Nam6,*, Zanib Zulfiqar7

    CMC-Computers, Materials & Continua, Vol.70, No.2, pp. 3221-3234, 2022, DOI:10.32604/cmc.2022.019815

    Abstract Sensors based Human Activity Recognition (HAR) have numerous applications in eHeath, sports, fitness assessments, ambient assisted living (AAL), human-computer interaction and many more. The human physical activity can be monitored by using wearable sensors or external devices. The usage of external devices has disadvantages in terms of cost, hardware installation, storage, computational time and lighting conditions dependencies. Therefore, most of the researchers used smart devices like smart phones, smart bands and watches which contain various sensors like accelerometer, gyroscope, GPS etc., and adequate processing capabilities. For the task of recognition, human activities can be broadly categorized as basic and complex… More >

  • Open Access

    ARTICLE

    IoT Information Status Using Data Fusion and Feature Extraction Method

    S. S. Saranya*, N. Sabiyath Fatima

    CMC-Computers, Materials & Continua, Vol.70, No.1, pp. 1857-1874, 2022, DOI:10.32604/cmc.2022.019621

    Abstract The Internet of Things (IoT) role is instrumental in the technological advancement of the healthcare industry. Both the hardware and the core level of software platforms are the progress resulted from the accompaniment of Medicine 4.0. Healthcare IoT systems are the emergence of this foresight. The communication systems between the sensing nodes and the processors; and the processing algorithms to produce output obtained from the data collected by the sensors are the major empowering technologies. At present, many new technologies supplement these empowering technologies. So, in this research work, a practical feature extraction and classification technique is suggested for handling… More >

  • Open Access

    ARTICLE

    Data and Machine Learning Fusion Architecture for Cardiovascular Disease Prediction

    Munir Ahmad1, Majed Alfayad2, Shabib Aftab1,3, Muhammad Adnan Khan4,*, Areej Fatima5, Bilal Shoaib6, Mohammad Sh. Daoud7, Nouh Sabri Elmitwally2,8

    CMC-Computers, Materials & Continua, Vol.69, No.2, pp. 2717-2731, 2021, DOI:10.32604/cmc.2021.019013

    Abstract Heart disease, which is also known as cardiovascular disease, includes various conditions that affect the heart and has been considered a major cause of death over the past decades. Accurate and timely detection of heart disease is the single key factor for appropriate investigation, treatment, and prescription of medication. Emerging technologies such as fog, cloud, and mobile computing provide substantial support for the diagnosis and prediction of fatal diseases such as diabetes, cancer, and cardiovascular disease. Cloud computing provides a cost-efficient infrastructure for data processing, storage, and retrieval, with much of the extant research recommending machine learning (ML) algorithms for… More >

  • Open Access

    ARTICLE

    An Optimized Data Fusion Paradigm for WSN Based on Neural Networks

    Moath Alsafasfeh1,*, Zaid A. Arida2, Omar A. Saraereh3, Qais Alsafasfeh4, Salem Alemaishat5

    CMC-Computers, Materials & Continua, Vol.69, No.1, pp. 1097-1108, 2021, DOI:10.32604/cmc.2021.018187

    Abstract Wireless sensor networks (WSNs) have gotten a lot of attention as useful tools for gathering data. The energy problem has been a fundamental constraint and challenge faced by many WSN applications due to the size and cost constraints of the sensor nodes. This paper proposed a data fusion model based on the back propagation neural network (BPNN) model to address the problem of a large number of invalid or redundant data. Using three layered-based BPNNs and a TEEN threshold, the proposed model describes the cluster structure and filters out unnecessary details. During the information transmission process, the neural network’s output… More >

  • Open Access

    ARTICLE

    Remote Sensing Monitoring Method Based on BDS-Based Maritime Joint Positioning Model

    Xiang Wang1,2, Jingxian Liu1, Osamah Ibrahim Khalaf3,*, Zhao Liu1,*

    CMES-Computer Modeling in Engineering & Sciences, Vol.127, No.2, pp. 801-818, 2021, DOI:10.32604/cmes.2021.013568

    Abstract Complicated sea conditions have a serious impact on ship navigation safety and even maritime accidents. Accordingly, this paper proposes a remote sensing monitoring method based on the Beidou Navigation Satellite System (BDS) maritime joint positioning model. This method is mainly based on the BDS and multiple Global Navigation Satellite Systems (GNSS) to build a data fusion model, which can capture more steady positioning, navigation, and timing (PNT) data. Compared with the current Global Positioning System (GPS) and Global Navigation Satellite System (GLONASS) mandatory used by the International Maritime Organization (IMO), this model has the characteristics of more accurate positioning data… More >

  • Open Access

    ARTICLE

    Data Fusion about Serviceability Reliability Prediction for the Long-Span Bridge Girder Based on MBDLM and Gaussian Copula Technique

    Xueping Fan*, Guanghong Yang, Zhipeng Shang, Xiaoxiong Zhao, Yuefei Liu*

    Structural Durability & Health Monitoring, Vol.15, No.1, pp. 69-83, 2021, DOI:10.32604/sdhm.2021.011922

    Abstract This article presented a new data fusion approach for reasonably predicting dynamic serviceability reliability of the long-span bridge girder. Firstly, multivariate Bayesian dynamic linear model (MBDLM) considering dynamic correlation among the multiple variables is provided to predict dynamic extreme deflections; secondly, with the proposed MBDLM, the dynamic correlation coefficients between any two performance functions can be predicted; finally, based on MBDLM and Gaussian copula technique, a new data fusion method is given to predict the serviceability reliability of the long-span bridge girder, and the monitoring extreme deflection data from an actual bridge is provided to illustrated the feasibility and application… More >

  • Open Access

    ARTICLE

    Deep Learning Based Optimal Multimodal Fusion Framework for Intrusion Detection Systems for Healthcare Data

    Phong Thanh Nguyen1, Vy Dang Bich Huynh2, Khoa Dang Vo1, Phuong Thanh Phan1, Mohamed Elhoseny3, Dac-Nhuong Le4,5,*

    CMC-Computers, Materials & Continua, Vol.66, No.3, pp. 2555-2571, 2021, DOI:10.32604/cmc.2021.012941

    Abstract Data fusion is a multidisciplinary research area that involves different domains. It is used to attain minimum detection error probability and maximum reliability with the help of data retrieved from multiple healthcare sources. The generation of huge quantity of data from medical devices resulted in the formation of big data during which data fusion techniques become essential. Securing medical data is a crucial issue of exponentially-pacing computing world and can be achieved by Intrusion Detection Systems (IDS). In this regard, since singular-modality is not adequate to attain high detection rate, there is a need exists to merge diverse techniques using… More >

  • Open Access

    ARTICLE

    Autonomous Parking-Lots Detection with Multi-Sensor Data Fusion Using Machine Deep Learning Techniques

    Kashif Iqbal1,2, Sagheer Abbas1, Muhammad Adnan Khan3,*, Atifa Athar4, Muhammad Saleem Khan1, Areej Fatima3, Gulzar Ahmad1

    CMC-Computers, Materials & Continua, Vol.66, No.2, pp. 1595-1612, 2021, DOI:10.32604/cmc.2020.013231

    Abstract The rapid development and progress in deep machine-learning techniques have become a key factor in solving the future challenges of humanity. Vision-based target detection and object classification have been improved due to the development of deep learning algorithms. Data fusion in autonomous driving is a fact and a prerequisite task of data preprocessing from multi-sensors that provide a precise, well-engineered, and complete detection of objects, scene or events. The target of the current study is to develop an in-vehicle information system to prevent or at least mitigate traffic issues related to parking detection and traffic congestion detection. In this study… More >

  • Open Access

    ARTICLE

    An Adjust Duty Cycle Method for Optimized Congestion Avoidance and Reducing Delay for WSNs

    Ting Xu1, Ming Zhao1, Xin Yao1, *, Kun He2

    CMC-Computers, Materials & Continua, Vol.65, No.2, pp. 1605-1624, 2020, DOI:10.32604/cmc.2020.011458

    Abstract With the expansion of the application range and network scale of wireless sensor networks in recent years, WSNs often generate data surges and delay queues during the transmission process, causing network paralysis, even resulting in local or global congestion. In this paper, a dynamically Adjusted Duty Cycle for Optimized Congestion based on a real-time Queue Length (ADCOC) scheme is proposed. In order to improve the resource utilization rate of network nodes, we carried out optimization analysis based on the theory and applied it to the adjustment of the node’s duty cycle strategy. Using this strategy to ensure that the network… More >

  • Open Access

    ARTICLE

    Study for Multi-Resources Spatial Data Fusion Methods in Big Data Environment

    Zhiquan Huanga,b, Yu Fua, Fuchu Daia

    Intelligent Automation & Soft Computing, Vol.24, No.1, pp. 29-34, 2018, DOI:10.1080/10798587.2016.1267237

    Abstract The rapid development and extensive application of geographic information system (GIS) and the advent of the age of big data bring about the generation of multi-resources spatial data, which makes data integration and fusion share more difficult due to the differences on data source, data accuracy and data modal. Meanwhile, study for multi-resources spatial data fusion methods has an important practical significance for reducing the production cost of geographic data, accelerating the updating speed of existing geographical information and improving the quality of GIS big data. To expound the formation and developing trends of multi-resources spatial data fusion methods systematically,… More >

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