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A Two-Tiered Bidirectional Atrous Spatial Pyramid Pooling-Based Semantic Segmentation Model for Landslide Classification Using Remote Sensing Images

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IEEE ACCESS 2024年 12卷 181316-181331页

作者： Nalinipriya, G. Lydia, E. Laxmi Alshenaifi, Reem Kavuri, Radhika Ishak, Mohamad Khairi Saveetha Engn Coll Dept Informat Technol Chennai 602105 India Siddhartha Acad Higher Educ Velagapudi Ramakrishna Siddhartha Engn Coll Dept Informat Technol Vijayawada 520007 India Majmaah Univ Coll Comp & Informat Sci Dept Informat Technol Al Majmaah 11952 Saudi Arabia Chaitanya Bharathi Inst Technol AI & DS Dept Hyderabad 500075 India Ajman Univ Coll Engn & Informat Technol Dept Elect & Comp Engn Ajman U Arab Emirates

Effective landslide representation from great spatial resolution images is significant in numerous applications. Many research works and techniques have been advertised. Still, these methods are very challenging to relate in real time since they depend on remotely sensing landslides from a solitary sensor with an exact spatial resolution. Precisely identifying landslides over a vast region with intricate background entities is difficult. Machine Learning (ML) and Deep Learning (DL) have attained extraordinary performance in classifying images utilizing remotely sensed images from numerous platforms. Moreover, techniques built within DL architectures tend to implement encoder-decoder network structures, where constant convolutions effortlessly strain out numerous landslide features. This study develops a Bidirectional Atrous Spatial Pyramid Pooling-Based Semantic Segmentation and Classification Model (BASPP-SSCM) technique for landslide Remote Sensing Images. The main goal of the BASPP-SSCM technique is to segment and classify the landslide areas. In the preprocessing stage, the BASPP-SSCM model employs an adaptive Wiener filtering (AWF) technique to eliminate the noise. Next, for the semantic segmentation method, the BASPP-SSCM technique utilizes the DeepLabV3 method with the backbone of the ConvNeXtLarge model for determining the landslide region. Furthermore, the CapsNet model is utilized for the feature extraction process. Besides, the Rigdelet neural network (RNN) technique is employed for the landslide classification process. At last, the pelican optimization algorithm (POA) methodology is implemented to fine-tune the parameters involved in the RNN model. A wide range of investigations is performed to highlight the superiority of the BASPP-SSCM method using a benchmark dataset. The performance validation of the BASPP-SSCM method underscored a superior accuracy value of 98.23% of other existing approaches.

关键词： Terrain factors Feature extraction Remote sensing Semantic segmentation Adaptation models Accuracy Image recognition Computational modeling Spatial resolution Reliability landslide remote sensing images pelican optimization algorithm feature extraction atrous spatial pyramid pooling

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IoT-Enabled Early Detection of Diabetes Diseases Using Deep Learning and Dimensionality Reduction Techniques

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IEEE ACCESS 2024年 12卷 143016-143028页

作者： Vaiyapuri, Thavavel Alharbi, Ghada Muttipoll Dharmarajlu, Santhi Bouteraa, Yassine Misra, Sanket Venkata Naga Ramesh, Janjhyam Nandan Mohanty, Sachi Prince Sattam Bin Abdulaziz Univ Coll Comp Engn & Sci Al Kharj 54112 Saudi Arabia Taibah Univ Coll Comp Sci & Engn Dept Artificial Intelligence & Data Sci Madinah 42353 Saudi Arabia Jazan Univ Coll Nursing & Hlth Sci Dept Nursing Jazan 45142 Saudi Arabia VIT AP Univ Sch Comp Sci & Engn SCOPE Amaravati 522241 Andhra Pradesh India Graph Era Hill Univ Dept CSE Dehra Dun 263136 India Graph Era Deemed Be Univ Dept CSE Dehra Dun 248002 Uttaranchal India

Chronic diseases, such as diabetes, cause serious challenges worldwide due to their long-lasting effect on health and quality of life. Diabetes, considered a high glucose level, requires continuous management to avoid complications like kidney failure, low vision, and heart disease. Leveraging state-of-the-art technology, especially IoT devices, shows great potential for optimizing the recognition and management of diabetes. These devices, including smart glucose monitors and wearable sensors, provide real-time data on crucial health metrics, enabling early intervention and proactive monitoring. Furthermore, incorporating deep learning (DL) techniques improves data analysis and recognizes risk factors and subtle patterns related to diabetes. By integrating IoT technology with DL techniques, the healthcare system can empower patients with tools for self-management and develop more conventional approaches for earlier diagnosis and personalized treatment plans, ultimately improving long-term health outcomes and reducing the burden of diabetes. This study develops a new IoT-enabled Driven Early Detection of Chronic Disease using the DL and Dimensionality Reduction (EDCD-DLDR) approach. The EDCD-DLDR method aims to enable IoT devices to collect patient medical data and employ the DL model for earlier diagnosis of diabetes. In the EDCD-DLDR technique, the IoT-based data acquisition process is initially involved, and the collected data gets normalized using Z-score normalization. The EDCD-DLDR technique uses an artificial rabbit optimizer-based feature selection (ARO-FS) approach for dimensionality reduction. In addition, the detection of diabetes is achieved by the attention bidirectional gated recurrent unit (ABiGRU) model. The pelican optimization algorithm (POA) based hyperparameter selection model is included to improve the performance of the ABiGRU network. A comprehensive set of simulations is made to highlight the performance of the EDCD-DLDR method on the Kaggle d

关键词： Diabetes Internet of Things Diseases Medical services Data models Feature extraction Convolutional neural networks Deep learning Machine learning Detection algorithms chronic disease deep learning pelican optimization algorithm machine learning

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An empirical wavelet transform based fault detection and hybrid convolutional recurrent neural network for fault classification in distribution network integrated power system

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MULTIMEDIA TOOLS AND APPLICATIONS 2024年第32期83卷 77445-77468页

作者： Mampilly, Binitha Joseph Sheeba, V. S. Univ Calicut Govt Engn Coll Dept Elect & Elect Trichur 680009 Kerala India Govt Engn Coll Dept Elect & Commun Engn Trichur 680009 Kerala India

The penetration of distributed renewable energy sources degrades the protection of microgrids, which leads to incorrect data flow in the energy systems. It is critical to detect faults, types of defects and location of faults in order to improve the protection and security of microgrids. To cater this issue in hybrid renewable energy system, a novel fault detection scheme is adopted using artificial intelligence. The renewable energy based microgrid system is implemented in the IEEE 13 bus power network to obtain the normal and faulty voltage and current data.. The system is simulated using MatLab/Simulink platform. From the time series data, the features are decomposed using empirical wavelet transform (EWT). First, EWT evaluates the frequency components in the signal, then calculates the bounds and gets the basis of the oscillating components. The obtained samples are classified using a Hybrid Convolutional Recurrent Neural Network (HCRNN) and optimized by the pelican optimization algorithm. The 11 types of faults are identified along with the location of fault in the system is obtained. The results are compared with the existing methods and found that the proposed method has improved the fault sample detection accuracy by 1.56%.

关键词： Empirical wavelet transform (EWT) Non-linear signals Solar photovoltaic Detection accuracy Signal decomposing Long short-term memory pelican optimization algorithm

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A Novel Classification Model Using Optimal Long Short-Term Memory for Classification of COVID-19 from CT Images

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JOURNAL OF DIGITAL IMAGING 2023年第6期36卷 2480-2493页

作者： Vinothini, R. Niranjana, G. Yakub, Fitri SRM Inst Sci & Technol Dept Comp Technol Kattankulathur India SRM Inst Sci & Technol Kattankulathur India Malaysia Japan Int Inst Technol Elect Syst Engn Dept Kuala Lumpur Malaysia

The human respiratory system is affected when an individual is infected with COVID-19, which became a global pandemic in 2020 and affected millions of people worldwide. However, accurate diagnosis of COVID-19 can be challenging due to small variations in typical and COVID-19 pneumonia, as well as the complexities involved in classifying infection regions. Currently, various deep learning (DL)-based methods are being introduced for the automatic detection of COVID-19 using computerized tomography (CT) scan images. In this paper, we propose the pelican optimization algorithm-based long short-term memory (POA-LSTM) method for classifying coronavirus using CT scan images. The data preprocessing technique is used to convert raw image data into a suitable format for subsequent steps. Here, we develop a general framework called no new U-Net (nnU-Net) for region of interest (ROI) segmentation in medical images. We apply a set of heuristic guidelines derived from the domain to systematically optimize the ROI segmentation task, which represents the dataset's key properties. Furthermore, high-resolution net (HRNet) is a standard neural network design developed for feature extraction. HRNet chooses the top-down strategy over the bottom-up method after considering the two options. It first detects the subject, generates a bounding box around the object and then estimates the relevant feature. The POA is used to minimize the subjective influence of manually selected parameters and enhance the LSTM's parameters. Thus, the POA-LSTM is used for the classification process, achieving higher performance for each performance metric such as accuracy, sensitivity, F1-score, precision, and specificity of 99%, 98.67%, 98.88%, 98.72%, and 98.43%, respectively.

关键词： Computerized tomography scan images Coronavirus High-resolution net pelican optimization algorithm No new U-Net Disease prediction

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Predicting Rock Burst in Underground Engineering Leveraging a Novel Metaheuristic-Based LightGBM Model

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Computer Modeling in Engineering & Sciences 2024年第7期140卷 229-253页

作者： Kai Wang Biao He Pijush Samui Jian Zhou No.Three Engineering Co. Ltd.CCCC First Highway Engineering CompanyBeijing101102China Department of Civil Engineering Faculty of EngineeringUniversiti MalayaKuala Lumpur50603Malaysia Department of Civil Engineering National Institute of Technology PatnaBihar800005India School of Resources and Safety Engineering Central South UniversityChangsha410083China

Rock bursts represent a formidable challenge in underground engineering,posing substantial risks to both infrastructure and human *** sudden and violent failures of rock masses are characterized by the rapid release of accumulated stress within the rock,leading to severe seismic events and structural ***,the development of reliable prediction models for rock bursts is paramount to mitigating these *** study aims to propose a tree-based model—a Light Gradient Boosting Machine(LightGBM)—to predict the intensity of rock bursts in underground engineering.322 actual rock burst cases are collected to constitute an exhaustive rock burst dataset,which serves to train the *** population-basedmetaheuristic algorithms are used to optimize the hyperparameters of the LightGBM ***,the sensitivity analysis is used to identify the predominant factors that may incur the occurrence of rock *** results show that the population-based metaheuristic algorithms have a good ability to search out the optimal hyperparameters of the LightGBM *** developed LightGBM model yields promising performance in predicting the intensity of rock bursts,with which accuracy on training and testing sets are 0.972 and 0.944,*** sensitivity analysis discloses that the risk of occurring rock burst is significantly sensitive to three factors:uniaxial compressive strength(σc),stress concentration factor(SCF),and elastic strain energy index(Wet).Moreover,this study clarifies the particular impact of these three factors on the intensity of rock bursts through the partial dependence plot.

关键词： Rock burst prediction LightGBM coati optimization algorithm pelican optimization algorithm partial dependence plot

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A New Method for Fault Diagnosis of Hydraulic System Based on Improved Empirical Wavelet Transform and Kernel Extreme Learning Machine

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IEEE ACCESS 2023年 11卷 92135-92149页

作者： Chen, Chao Kelimu, Muhetaer Yang, Bo Xinjiang Univ Coll Mech Engn Urumqi 830017 Xinjiang Peoples R China Xinjiang Univ Sch Mech Engn Urumqi Xinjiang Peoples R China

To address the problem that multivariable faults in hydraulic systems are difficult to diagnose accurately in a short time, a new signal processing method combining an improved empirical wavelet transform with a kernel extreme learning machine optimized by the pelican optimization algorithm is proposed. First, the problem of unreasonable spectral partitioning of the empirical wavelet transform is solved using an improved k-means clustering method, which upgrades the empirical wavelet transform in the maximum scale space and then adaptively decomposes the acquired pressure signal to obtain a series of sub-signal components, thus reducing the dimensionality of multivariate faults and saving computation time. Second, 17 features of each sub-signal component are calculated and input to the kernel extreme learning machine for training, and the sequence-forward selection strategy is used to select the optimal features from the kernel extreme learning machine to ensure the basic accuracy and efficiency of prediction. Finally, a pelican optimization algorithm improved kernel extreme learning machine algorithm is proposed for fast classification of faults. Experiments show that the method can diagnose a variety of faults in hydraulic systems quickly and accurately, with a diagnostic accuracy of 97.24 percent, which is better than other methods.

关键词： Hydraulic systems Wavelet transforms Transforms Fault diagnosis Feature extraction Vibrations Kernel Extreme learning machines Improved empirical wavelet transform hydraulic systems multivariable fault diagnosis pelican optimization algorithm kernel extreme learning machine

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Cardiac Arrhythmia multiclass classification using optimized FLS-based 3D-CNN

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JOURNAL OF INTELLIGENT & FUZZY SYSTEMS 2024年第1期46卷 1543-1566页

作者： Pashikanti, Rajesh Patil, C. Y. Shinde, Amita Coll Engn Dept Instrumentat & Control Engn Pune 411005 Maharashtra India AISSMS IOIT Dept Instrumentat Engn Pune Maharashtra India

Arrhythmia is the medical term for any irregularities in the normal functioning of the heart. Due to their ease of use and non-invasive nature, electrocardiograms (ECGs) are frequently used to identify heart problems. Analyzing a huge number of ECG data manually by medical professionals uses excessive medical resources. Consequently, identifying ECG characteristics based on machine learning has become increasingly popular. However, these conventional methods have some limitations, including the need for manual feature recognition, complex models, and lengthy training periods. This research offers a unique hybrid POA-F3DCNN method for arrhythmia classification that combines the pelican Optimisation algorithm with fuzzy-based 3D-CNN (F3DCNN) to alleviate the shortcomings of the existing methods. The POA is applied to hyper-tune the parameters of 3DCNN and determine the ideal parameters of the Gaussian Membership Functions used for FLSs. The experimental results were obtained by testing the performance of five and thirteen categories of arrhythmia classification, respectively, on UCI-arrhythmia and the MIT-BIH Arrhythmia datasets. Standard measures such as F1-score, Precision, Accuracy, Specificity, and Recall enabled the classification results to be expressed appropriately. The outcomes of the novel framework achieved testing average accuracies after ten-fold crossvalidation are 98.96 % on the MIT-BIH dataset and 99.4% on the UCI arrhythmia datasets compared to state-of-the-art approaches.

关键词： Deep learning optimization algorithm ECG classification cardiac arrhythmia feature extraction 3D-CNN pelican optimization algorithm

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Efficiency of Hybrid Decision Tree algorithms in Evaluating the Academic Performance of Students

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INTERNATIONAL JOURNAL OF ADVANCED COMPUTER SCIENCE AND APPLICATIONS 2024年第2期15卷 480-491页

作者： Xie, Yanxin Jilin Agr Sci & Technol Coll Elect & Informat Engn Coll Jilin 132101 Jilin Peoples R China

Educational institutions are anticipated to take substantial and proactive roles in guaranteeing students' successful program completion. Academic performance is conventionally employed to categorize and forecast students' future ability to confront post-graduation challenges. A student's academic accomplishments are instrumental in shaping exceptional individuals who may become future leaders. Using algorithms to assess and predict academic performance is a well-established practice in machine learning, encompassing techniques such as neural networks(NN), logistic regression(LR), decision trees(DT), and others. The goal of this project is to improve decision trees' ability to predict students' academic achievement via the use of data mining methods and meta-heuristic algorithms. Educational data mining involves the utilization of data analysis methodologies and tools to examine the extensive data generated within educational establishments as a result of students' interactions and activities throughout their academic journey. pelican optimization algorithm (POA) and Runge Kutta optimization (RKO) are utilized algorithms in developing hybrid models, both of which can efficiently search for optimal or near-optimal splits by fine-tuning the hyperparameters of decision tree models. Students' final grades were predicted through training and testing models and categorized into four classes: Excellent, Good, Acceptable, and Poor. The classification capability of a single model and optimized counterparts was evaluated using Accuracy, Recall, Precision, and F1-score in separate phases for each category. Obtained results for all models revealed that POA and RKO developed Accuracy of DTC by 1.86% and 0.87%. Also, Precision and Recall metric analysis further manifest the superiority of DTPO. Prediction based on classifiers, especially workable optimized versions such as DTPO, paves the way for institutions to raise student success rates.

关键词： Academic performance decision tree pelican optimization algorithm runge kutta optimization

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Augmenting cybersecurity through attention based stacked autoencoder with optimization algorithm for detection and mitigation of attacks on IoT assisted networks

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SCIENTIFIC REPORTS 2024年第1期14卷 1-23页

作者： Prasad, Kashi Sai Lydia, E. Laxmi Rajesh, M. V. Radhika, K. Ramesh, Janjhyam Venkata Naga Neelima, N. Pokuri, Rao MLR Inst Technol Dept CSE AI&ML Hyderabad India Siddhartha Acad Higher Educ Univ VR Siddhartha Engn Coll A Dept Informat Technol Vijayawada India Aditya Univ Assoc Dean Sch Engn IT Dept Surampalem India Chaitanya Bharathi Inst Technol AI&DS Dept Hyderabad 500075 India Graphic Era Hill Univ Dept Comp Sci & Engn Dehra Dun India Graphic Era Univ Dept CSE Dehra Dun Uttarakhand India Koneru Lakshmaiah Educ Fdn Dept Comp Sci & Engn Guntur 522302 Andhra Pradesh India VIT AP Univ Sch Comp Sci & Engn Scope Amaravati Andhra Pradesh India

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.

关键词： Cybersecurity attack Feature selection pelican optimization algorithm Internet of Things Deep learning

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The implementation of a least square support vector regression model for predicting the ultimate bearing capacity of rock-socketed piles

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MULTISCALE AND MULTIDISCIPLINARY MODELING EXPERIMENTS AND DESIGN 2024年第4期7卷 4605-4618页

作者： Yang, Xiaochuan Chongqing Coll Finance & Econ Coll Design Chongqing 402160 Peoples R China

The ultimate load-bearing capacity (Qu) of rock-socketed piles, drilled shafts, or piers is contingent on various factors. Rock-socketed piles find widespread application in foundation engineering, particularly when it comes to supporting structures erected on rock formations. Determining the Qu of these piles presents a challenging engineering endeavor, demanding a comprehensive grasp of geotechnical and structural principles, consideration of site-specific circumstances, and meticulous planning and execution during design and construction phases. Accurately predicting the Qu of rock-socketed piles is of paramount importance for various reasons, including ensuring safety, optimizing costs, complying with regulations, and mitigating environmental impacts. Precise forecasts enable the optimization of foundation designs and significantly minimize potential risks inherent in construction projects. The primary aim of this research is to employ two machine learning-based models for the Qu prediction process as innovative research. These models comprise Radial Basis Function and Least Squares Support Vector Regression. Additionally, two distinct meta-heuristic optimization techniques, namely the African Vultures optimization algorithm and the pelican optimization algorithm, were chosen to fine-tune the base models, aiming to optimize their predictive performance. Various performance criteria, such as R2, RMSE, MSE, MAPE, and the SI index, were utilized to assess the predictive performance of the models. These assessments illustrate that, when it comes to practical utility, the LSPO (LSSVR + POA) model emerges as the most superior predictor, attaining an impressive maximum R2train of 0.994 and a minimal RMSEtrain value of 762.93. Properly designed foundations based on accurate Qu predictions can minimize the environmental impact of construction projects. This includes reducing the need for excessive excavation or altering the natural landscape.

关键词： Ultimate bearing capacity Rock-socketed piles Radial basis function Least square support vector regression pelican optimization algorithm African Vultures optimization algorithm

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