Shaoxiong Wu¹, Ruoxin Li¹, Xiaofeng Tao¹, Hailong Wu^1,*, Ping Miao¹, Yang Lu¹, Yanyan Lu¹, Qi Liu², Li Pan²

CMC-Computers, Materials & Continua, Vol.81, No.2, pp. 3063-3077, 2024, DOI:10.32604/cmc.2024.055381 - 18 November 2024

Abstract Time series prediction has always been an important problem in the field of machine learning. Among them, power load forecasting plays a crucial role in identifying the behavior of photovoltaic power plants and regulating their control strategies. Traditional power load forecasting often has poor feature extraction performance for long time series. In this paper, a new deep learning framework Residual Stacked Temporal Long Short-Term Memory (RST-LSTM) is proposed, which combines wavelet decomposition and time convolutional memory network to solve the problem of feature extraction for long sequences. The network framework of RST-LSTM consists of two More >

Qingyue Zhao¹, Qiaoyu Gu², Zhijun Gao^3,*, 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 >

Tusongjiang Kari¹, Sun Guoliang², Lei Kesong¹, Ma Xiaojing^1,*, 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 >

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 >

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 >

Ayan Kumar¹, Abdul Quadir Md¹, J. Christy Jackson^1,*, 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 >

G. Arunachalam^1,*, 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 >

Saeed Masoud Alshahrani¹, Fatma S. Alrayes², Hamed Alqahtani³, Jaber S. Alzahrani⁴, Mohammed Maray⁵, Sana Alazwari⁶, Mohamed A. Shamseldin⁷, Mesfer Al Duhayyim^8,*

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 >

Siva Sankari Subbiah¹, Senthil Kumar Paramasivan^2,*, 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 >

R. Raja^1,*, 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 >