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  • Open Access

    ARTICLE

    Improved Lightweight Deep Learning Algorithm in 3D Reconstruction

    Tao Zhang1,*, Yi Cao2

    CMC-Computers, Materials & Continua, Vol.72, No.3, pp. 5315-5325, 2022, DOI:10.32604/cmc.2022.027083

    Abstract The three-dimensional (3D) reconstruction technology based on structured light has been widely used in the field of industrial measurement due to its many advantages. Aiming at the problems of high mismatch rate and poor real-time performance caused by factors such as system jitter and noise, a lightweight stripe image feature extraction algorithm based on You Only Look Once v4 (YOLOv4) network is proposed. First, Mobilenetv3 is used as the backbone network to effectively extract features, and then the Mish activation function and Complete Intersection over Union (CIoU) loss function are used to calculate the improved target frame regression loss, which… More >

  • Open Access

    ARTICLE

    Object Detection for Cargo Unloading System Based on Fuzzy C Means

    Sunwoo Hwang1, Jaemin Park1, Jongun Won2, Yongjang Kwon3, Youngmin Kim1,*

    CMC-Computers, Materials & Continua, Vol.71, No.2, pp. 4167-4181, 2022, DOI:10.32604/cmc.2022.023295

    Abstract With the recent increase in the utilization of logistics and courier services, it is time for research on logistics systems fused with the fourth industry sector. Algorithm studies related to object recognition have been actively conducted in convergence with the emerging artificial intelligence field, but so far, algorithms suitable for automatic unloading devices that need to identify a number of unstructured cargoes require further development. In this study, the object recognition algorithm of the automatic loading device for cargo was selected as the subject of the study, and a cargo object recognition algorithm applicable to the automatic loading device is… More >

  • Open Access

    ARTICLE

    Design of Intelligent Mosquito Nets Based on Deep Learning Algorithms

    Yuzhen Liu1,3, Xiaoliang Wang1,*, Xinghui She1, Ming Yi1, Yuelong Li1, Frank Jiang2

    CMC-Computers, Materials & Continua, Vol.69, No.2, pp. 2261-2276, 2021, DOI:10.32604/cmc.2021.015501

    Abstract An intelligent mosquito net employing deep learning has been one of the hotspots in the field of Internet of Things as it can reduce significantly the spread of pathogens carried by mosquitoes, and help people live well in mosquito-infested areas. In this study, we propose an intelligent mosquito net that can produce and transmit data through the Internet of Medical Things. In our method, decision-making is controlled by a deep learning model, and the proposed method uses infrared sensors and an array of pressure sensors to collect data. Moreover the ZigBee protocol is used to transmit the pressure map which… More >

  • Open Access

    ARTICLE

    Cryptographic Based Secure Model on Dataset for Deep Learning Algorithms

    Muhammad Tayyab1,*, Mohsen Marjani1, N. Z. Jhanjhi1, Ibrahim Abaker Targio Hashim2, Abdulwahab Ali Almazroi3, Abdulaleem Ali Almazroi4

    CMC-Computers, Materials & Continua, Vol.69, No.1, pp. 1183-1200, 2021, DOI:10.32604/cmc.2021.017199

    Abstract Deep learning (DL) algorithms have been widely used in various security applications to enhance the performances of decision-based models. Malicious data added by an attacker can cause several security and privacy problems in the operation of DL models. The two most common active attacks are poisoning and evasion attacks, which can cause various problems, including wrong prediction and misclassification of decision-based models. Therefore, to design an efficient DL model, it is crucial to mitigate these attacks. In this regard, this study proposes a secure neural network (NN) model that provides data security during model training and testing phases. The main… More >

  • Open Access

    ARTICLE

    Power System Resiliency and Wide Area Control Employing Deep Learning Algorithm

    Pandia Rajan Jeyaraj1, Aravind Chellachi Kathiresan1, Siva Prakash Asokan1, Edward Rajan Samuel Nadar1, Hegazy Rezk2,3,*, Thanikanti Sudhakar Babu4

    CMC-Computers, Materials & Continua, Vol.68, No.1, pp. 553-567, 2021, DOI:10.32604/cmc.2021.015128

    Abstract The power transfer capability of the smart transmission grid-connected networks needs to be reduced by inter-area oscillations. Due to the fact that inter-area modes of oscillations detain and make instability of power transmission networks. This fact is more noticeable in smart grid-connected systems. The smart grid infrastructure has more renewable energy resources installed for its operation. To overcome this problem, a deep learning wide-area controller is proposed for real-time parameter control and smart power grid resilience on oscillations inter-area modes. The proposed Deep Wide Area Controller (DWAC) uses the Deep Belief Network (DBN). The network weights are updated based on… More >

  • Open Access

    ARTICLE

    Deep Learning and Holt-Trend Algorithms for Predicting Covid-19 Pandemic

    Theyazn H. H. Aldhyani1,*, Melfi Alrasheed2, Mosleh Hmoud Al-Adaileh3, Ahmed Abdullah Alqarni4, Mohammed Y. Alzahrani4, Ahmed H. Alahmadi5

    CMC-Computers, Materials & Continua, Vol.67, No.2, pp. 2141-2160, 2021, DOI:10.32604/cmc.2021.014498

    Abstract The Covid-19 epidemic poses a serious public health threat to the world, where people with little or no pre-existing human immunity can be more vulnerable to its effects. Thus, developing surveillance systems for predicting the Covid-19 pandemic at an early stage could save millions of lives. In this study, a deep learning algorithm and a Holt-trend model are proposed to predict the coronavirus. The Long-Short Term Memory (LSTM) and Holt-trend algorithms were applied to predict confirmed numbers and death cases. The real time data used has been collected from the World Health Organization (WHO). In the proposed research, we have… More >

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