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  • Open Access

    ARTICLE

    Building Indoor Dangerous Behavior Recognition Based on LSTM-GCN with Attention Mechanism

    Qingyue Zhao1, Qiaoyu Gu2, Zhijun Gao3,*, Shipian Shao1, Xinyuan Zhang1

    CMES-Computer Modeling in Engineering & Sciences, Vol.137, No.2, pp. 1773-1788, 2023, DOI:10.32604/cmes.2023.027500 - 26 June 2023

    Abstract Building indoor dangerous behavior recognition is a specific application in the field of abnormal human recognition. A human dangerous behavior recognition method based on LSTM-GCN with attention mechanism (GLA) model was proposed aiming at the problem that the existing human skeleton-based action recognition methods cannot fully extract the temporal and spatial features. The network connects GCN and LSTM network in series, and inputs the skeleton sequence extracted by GCN that contains spatial information into the LSTM layer for time sequence feature extraction, which fully excavates the temporal and spatial features of the skeleton sequence. Finally, More >

  • Open Access

    ARTICLE

    Short-Term Wind Power Prediction Based on Combinatorial Neural Networks

    Tusongjiang Kari1, Sun Guoliang2, Lei Kesong1, Ma Xiaojing1,*, Wu Xian1

    Intelligent Automation & Soft Computing, Vol.37, No.2, pp. 1437-1452, 2023, DOI:10.32604/iasc.2023.037012 - 21 June 2023

    Abstract Wind power volatility not only limits the large-scale grid connection but also poses many challenges to safe grid operation. Accurate wind power prediction can mitigate the adverse effects of wind power volatility on wind power grid connections. For the characteristics of wind power antecedent data and precedent data jointly to determine the prediction accuracy of the prediction model, the short-term prediction of wind power based on a combined neural network is proposed. First, the Bi-directional Long Short Term Memory (BiLSTM) network prediction model is constructed, and the bi-directional nature of the BiLSTM network is used… More >

  • Open Access

    ARTICLE

    Enhanced Deep Learning for Detecting Suspicious Fall Event in Video Data

    Madhuri Agrawal*, Shikha Agrawal

    Intelligent Automation & Soft Computing, Vol.36, No.3, pp. 2653-2667, 2023, DOI:10.32604/iasc.2023.033493 - 15 March 2023

    Abstract

    Suspicious fall events are particularly significant hazards for the safety of patients and elders. Recently, suspicious fall event detection has become a robust research case in real-time monitoring. This paper aims to detect suspicious fall events during video monitoring of multiple people in different moving backgrounds in an indoor environment; it is further proposed to use a deep learning method known as Long Short Term Memory (LSTM) by introducing visual attention-guided mechanism along with a bi-directional LSTM model. This method contributes essential information on the temporal and spatial locations of ‘suspicious fall’ events in learning the

    More >

  • Open Access

    ARTICLE

    Short Term Traffic Flow Prediction Using Hybrid Deep Learning

    Mohandu Anjaneyulu, Mohan Kubendiran*

    CMC-Computers, Materials & Continua, Vol.75, No.1, pp. 1641-1656, 2023, DOI:10.32604/cmc.2023.035056 - 06 February 2023

    Abstract Traffic flow prediction in urban areas is essential in the Intelligent Transportation System (ITS). Short Term Traffic Flow (STTF) prediction impacts traffic flow series, where an estimation of the number of vehicles will appear during the next instance of time per hour. Precise STTF is critical in Intelligent Transportation System. Various extinct systems aim for short-term traffic forecasts, ensuring a good precision outcome which was a significant task over the past few years. The main objective of this paper is to propose a new model to predict STTF for every hour of a day. In… More >

  • Open Access

    ARTICLE

    Predicting and Curing Depression Using Long Short Term Memory and Global Vector

    Ayan Kumar1, Abdul Quadir Md1, J. Christy Jackson1,*, Celestine Iwendi2

    CMC-Computers, Materials & Continua, Vol.74, No.3, pp. 5837-5852, 2023, DOI:10.32604/cmc.2023.033431 - 28 December 2022

    Abstract In today’s world, there are many people suffering from mental health problems such as depression and anxiety. If these conditions are not identified and treated early, they can get worse quickly and have far-reaching negative effects. Unfortunately, many people suffering from these conditions, especially depression and hypertension, are unaware of their existence until the conditions become chronic. Thus, this paper proposes a novel approach using Bi-directional Long Short-Term Memory (Bi-LSTM) algorithm and Global Vector (GloVe) algorithm for the prediction and treatment of these conditions. Smartwatches and fitness bands can be equipped with these algorithms which… More >

  • Open Access

    ARTICLE

    Optimized Deep Learning Model for Effective Spectrum Sensing in Dynamic SNR Scenario

    G. Arunachalam1,*, P. SureshKumar2

    Computer Systems Science and Engineering, Vol.45, No.2, pp. 1279-1294, 2023, DOI:10.32604/csse.2023.031001 - 03 November 2022

    Abstract The main components of Cognitive Radio networks are Primary Users (PU) and Secondary Users (SU). The most essential method used in Cognitive networks is Spectrum Sensing, which detects the spectrum band and opportunistically accesses the free white areas for different users. Exploiting the free spaces helps to increase the spectrum efficiency. But the existing spectrum sensing techniques such as energy detectors, cyclo-stationary detectors suffer from various problems such as complexity, non-responsive behaviors under low Signal to Noise Ratio (SNR) and computational overhead, which affects the performance of the sensing accuracy. Many algorithms such as Long-Short… More >

  • Open Access

    ARTICLE

    IoT-Cloud Assisted Botnet Detection Using Rat Swarm Optimizer with Deep Learning

    Saeed Masoud Alshahrani1, Fatma S. Alrayes2, Hamed Alqahtani3, Jaber S. Alzahrani4, Mohammed Maray5, Sana Alazwari6, Mohamed A. Shamseldin7, Mesfer Al Duhayyim8,*

    CMC-Computers, Materials & Continua, Vol.74, No.2, pp. 3085-3100, 2023, DOI:10.32604/cmc.2023.032972 - 31 October 2022

    Abstract Nowadays, Internet of Things (IoT) has penetrated all facets of human life while on the other hand, IoT devices are heavily prone to cyberattacks. It has become important to develop an accurate system that can detect malicious attacks on IoT environments in order to mitigate security risks. Botnet is one of the dreadful malicious entities that has affected many users for the past few decades. It is challenging to recognize Botnet since it has excellent carrying and hidden capacities. Various approaches have been employed to identify the source of Botnet at earlier stages. Machine Learning… More >

  • Open Access

    ARTICLE

    Deep Learning for Wind Speed Forecasting Using Bi-LSTM with Selected Features

    Siva Sankari Subbiah1, Senthil Kumar Paramasivan2,*, Karmel Arockiasamy3, Saminathan Senthivel4, Muthamilselvan Thangavel2

    Intelligent Automation & Soft Computing, Vol.35, No.3, pp. 3829-3844, 2023, DOI:10.32604/iasc.2023.030480 - 17 August 2022

    Abstract Wind speed forecasting is important for wind energy forecasting. In the modern era, the increase in energy demand can be managed effectively by forecasting the wind speed accurately. The main objective of this research is to improve the performance of wind speed forecasting by handling uncertainty, the curse of dimensionality, overfitting and non-linearity issues. The curse of dimensionality and overfitting issues are handled by using Boruta feature selection. The uncertainty and the non-linearity issues are addressed by using the deep learning based Bi-directional Long Short Term Memory (Bi-LSTM). In this paper, Bi-LSTM with Boruta feature… More >

  • Open Access

    ARTICLE

    Routing with Cooperative Nodes Using Improved Learning Approaches

    R. Raja1,*, N. Satheesh2, J. Britto Dennis3, C. Raghavendra4

    Intelligent Automation & Soft Computing, Vol.35, No.3, pp. 2857-2874, 2023, DOI:10.32604/iasc.2023.026153 - 17 August 2022

    Abstract In IoT, routing among the cooperative nodes plays an incredible role in fulfilling the network requirements and enhancing system performance. The evaluation of optimal routing and related routing parameters over the deployed network environment is challenging. This research concentrates on modelling a memory-based routing model with Stacked Long Short Term Memory (s − LSTM) and Bi-directional Long Short Term Memory (b − LSTM). It is used to hold the routing information and random routing to attain superior performance. The proposed model is trained based on the searching and detection mechanisms to compute the packet delivery ratio (PDR), end-to-end (E2E) delay, throughput,… More >

  • Open Access

    ARTICLE

    Harnessing LSTM Classifier to Suggest Nutrition Diet for Cancer Patients

    S. Raguvaran1,*, S. Anandamurugan2, A. M. J. Md. Zubair Rahman3

    Intelligent Automation & Soft Computing, Vol.35, No.2, pp. 2171-2187, 2023, DOI:10.32604/iasc.2023.028605 - 19 July 2022

    Abstract A customized nutrition-rich diet plan is of utmost importance for cancer patients to intake healthy and nutritious foods that help them to be strong enough to maintain their body weight and body tissues. Consuming nutrition-rich diet foods will prevent them from the side effects caused before and after treatment thereby minimizing it. This work is proposed here to provide them with an effective diet assessment plan using deep learning-based automated medical diet system. Hence, an Enhanced Long-Short Term Memory (E-LSTM) has been proposed in this paper, especially for cancer patients. This proposed method will be… More >

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