With the acceleration of urbanization leading to a general decrease in air quality, accurate PM2.5 concentration prediction is of the utmost practical meaning for the control and prevention of air pollution in the reg...
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With the acceleration of urbanization leading to a general decrease in air quality, accurate PM2.5 concentration prediction is of the utmost practical meaning for the control and prevention of air pollution in the region. Therefore, a new hybrid prediction model for PM2.5 concentration based on complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN), approximate entropy (ApEn), variational mode decomposition optimized by capuchin search algorithm (CVMD), long short-term memory optimized by pelican optimization algorithm (POA-LSTM) and error correction (EC), named CEEMDAN-ApEn-CVMD-POA-LSTM-EC, is proposed. First, CEEMDAN is used to acquire a limited amount of intrinsic mode functions (IMFs). Second, calculate ApEn value for each IMF component, and divide each IMF component into high-complexity and low-complexity components by the size of ApEn values. Third, variational mode decomposition optimized by capuchin search algorithm (CVMD), named CVMD, is proposed. CVMD is used as a secondary decomposition method to further decompose high-complexity components adaptively into a finite number of IMFs. Fourth, long short-term memory optimized by pelican optimization algorithm, named POA-LSTM, is proposed. POA-LSTM predicts all IMF components, and the results of their predictions are combined to generate the original prediction results. Final, error sequence is decomposed and predicted again by the EC module CVMD-POA-LSTM to obtain prediction results of error sequence, and final prediction results are acquired by combining original prediction results and prediction results of error sequence. The datasets in Beijing, Shanghai, and Xi'an were selected for simulation experiments to demonstrate the superiority of the proposed model. Taking Beijing as an example, RMSE, MAE, MAPE and R2 values are 1.9947, 1.5577, 0.1157 and 0.9947, which are superior to other comparison models and have the best performance.
The Internet of Things (IoT) network is a fast-growing technology, which is efficiently used in various applications. In an IoT network, the massive amount of connecting nodes is the existence of day-to-day communicat...
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The Internet of Things (IoT) network is a fast-growing technology, which is efficiently used in various applications. In an IoT network, the massive amount of connecting nodes is the existence of day-to-day communication challenges. The platform of IoT uses a cloud service as a backend for processing information and maintaining remote control. To manage the developing intricacy of cyberattacks, it is critical to have an effectual intrusion detection system (IDS), which can monitor computer sources and create data on suspicious or abnormal actions. The IoT network's security can progressively become a critical concern as IoT technology obtains extensive use. Protecting IoT systems with traditional IDS is challenging due to the vast variety and volume of IoT devices. Currently, Machine Learning (ML) and Deep Learning (DL) techniques are utilized to address the security threats in IoT networks. This manuscript proposes a Cybersecurity through an Attention-based Stacked Autoencoder with a pelican optimization algorithm for the Detection and Mitigation of Attacks (CASAE-POADMA) methodology on an IoT-assisted network. The main purpose of the CASAE-POADMA methodology is to identify and mitigate the presence of cybersecurity attack behavior in the IoT-assisted network. At first, the presented CASAE-POADMA approach utilizes min-max normalization to scale input data into a uniform design. Besides, the greylag goose optimization (GGO) method is employed for the feature selection process. For the detection and mitigation of attack, the presented CASAE-POADMA approach employs the attention-based stacked autoencoder (ASAE) method. Eventually, the hyperparameter tuning of the ASAE method is executed by using pelican optimization algorithm (POA) method. The simulation validation of the CASAE-POADMA approach is verified under a benchmark database. The experimental validation of the CASAE-POADMA approach exhibited a superior accuracy value of 99.50% over existing techniques.
Assisted living facilities cater to the demands of the elderly population, providing assistance and support with day-to-day activities. Fall detection is fundamental to ensuring their well-being and safety. Falls are ...
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Assisted living facilities cater to the demands of the elderly population, providing assistance and support with day-to-day activities. Fall detection is fundamental to ensuring their well-being and safety. Falls are frequent among older persons and might cause severe injuries and complications. Incorporating computer vision techniques into assisted living environments is revolutionary for these issues. By leveraging cameras and complicated approaches, a computer vision (CV) system can monitor residents' movements continuously and identify any potential fall events in real time. CV, driven by deep learning (DL) techniques, allows continuous surveillance of people through cameras, investigating complicated visual information to detect potential fall risks or any instances of falls quickly. This system can learn from many visual data by leveraging DL, improving its capability to identify falls while minimalizing false alarms precisely. Incorporating CV and DL enhances the efficiency and reliability of fall detection and allows proactive intervention, considerably decreasing response times in emergencies. This study introduces a new Deep Feature Fusion with Computer Vision for Fall Detection and Classification (DFFCV-FDC) technique. The primary purpose of the DFFCV-FDC approach is to employ the CV concept for detecting fall events. Accordingly, the DFFCV-FDC approach uses the Gaussian filtering (GF) approach for noise eradication. Besides, a deep feature fusion process comprising MobileNet, DenseNet, and ResNet models is involved. To improve the performance of the DFFCV-FDC technique, improved pelican optimization algorithm (IPOA) based hyperparameter selection is performed. Finally, the detection of falls is identified using the denoising autoencoder (DAE) model. The performance analysis of the DFFCV-FDC methodology was examined on the benchmark fall database. A widespread comparative study reported the supremacy of the DFFCV-FDC approach with existing techniques.
Since the rolling bearing fault signal captured by a vibration sensor contains a large amount of background noise, fault features cannot be accurately extracted. To address this problem, a rolling bearing fault featur...
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Since the rolling bearing fault signal captured by a vibration sensor contains a large amount of background noise, fault features cannot be accurately extracted. To address this problem, a rolling bearing fault feature extraction algorithm based on improved pelican optimization algorithm (IPOA)-variable modal decomposition (VMD) and multipoint optimal minimum entropy deconvolution adjustment (MOMEDA) methods is proposed. Firstly, the pelican optimization algorithm (POA) was improved using a reverse learning strategy for dimensional-by-dimensional lens imaging and circle mapping, and the optimization performance of IPOA was verified. Secondly, the kurtosis-square envelope Gini coefficient criterion was used to select the optimal modal components from the decomposed components of the signal, and MOMEDA was used to process the optimal modal components in order to obtain the optimal deconvolution signal. Finally, the Teager energy operator (TEO) was employed to demodulate and analyze the optimally deconvoluted signal in order to enhance the transient shock component of the original fault signal. The effectiveness of the proposed method was verified using simulated and actual signals. The results showed that the proposed method can accurately extract failure characteristics in the presence of strong background noise interference.
Recently, sentiment analysis (SA) has become more popular as it is crucial to moderate and examine the data from the internet. It contains several applications, such as social media monitoring, market research, and op...
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Recently, sentiment analysis (SA) has become more popular as it is crucial to moderate and examine the data from the internet. It contains several applications, such as social media monitoring, market research, and opinion mining. Aspect Based Sentiment Analysis (ABSA) is a domain of SA that manages sentiment at a better level. ABSA classifies sentiment in terms of all the aspects for obtaining superior insights as sentiment expressed. A major contribution has been developed in ABSA, and then this progress can be restricted only to some languages with suitable resources. One common method is to utilize machine learning (ML) approaches, namely Neural Networks (NN), Support Vector Machines (SVM), and Naive Bayes (NB), together with Asian and low language-specific resources. These resources offer data on the sentiment polarity (neutral, positive, or negative) of phrases and words that are generally utilized in low-resource languages. In this aspect, this study develops a new pelican optimization algorithm with Deep Learning for ABSA (POADL-ABSA) on Asian and Low Resource Languages. The proposed POADL-ABSA technique focuses on the detection and classification of sentiments. To accomplish this, the POADL-ABSA technique encompasses various levels of operations such as pre-processed, feature vector conversion, and classification. In addition, the POADL-ABSA technique employs the BERT model for feature vector extraction. Besides, attention-based bi-directional long short-term memory (ABiLSTM) system was used for the recognition and classification of sentiments. Finally, the POA was utilized for optimum hyperparameter selection of the ABiLSTM model, and it helps in attaining enhanced sentiment classification results. To ensure the improvised performance of the IAOADL-ABSA technique, an extensive experimental outcome the IAOADL-ABSA technique surpassed other models with \(acc{u}_y\), \(pre{c}_n\), \(rec{a}_l\), and \({F}_{score}\) of 98.72%, 98.71%, 98.72%, and 98.71%, respec
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