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Challenges in Cardiovascular Risk Prediction and Stratifi cation in Women 被引量:1
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作者 Sonia Henry,MD Rachel Bond,MD +2 位作者 Stacey Rosen,MD Cindy Grines,MD Jennifer Mieres,MD 《Cardiovascular Innovations and Applications》 2019年第B02期329-348,共20页
There has been an appropriate focus,since the turn of the 21 st century,on sex-and gender-specifi c cardiovascular disease(CVD)as increasing evidence suggests that there are substantial differences in the risk factor ... There has been an appropriate focus,since the turn of the 21 st century,on sex-and gender-specifi c cardiovascular disease(CVD)as increasing evidence suggests that there are substantial differences in the risk factor profi le,social and environmental factors,clinical presentation,diagnosis,and treatment of ischemic heart disease in women compared with men.As a result of increased awareness,detection,and treatment of ischemic heart disease in women,there has been signifi cant reduction(greater than 30%)in cardiovascular mortality,and in 2013,more US men than US women died of CVD.Nevertheless,continued efforts are required as CVD remains the leading cause of cardiovascular morbidity and death of women in the Western world,and in women younger than 55 years there has been a rise in cardiovascular mortality.In this article,we review several of the contributing factors that continue to cause challenges in accurate risk prediction and risk stratifi cation in women. 展开更多
关键词 SEX and gender DISPARITY RISK prediction RISK stratifi cation ISCHEMIC heart disease
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An Ordinal Multi-Dimensional Classification(OMDC)for Predictive Maintenance
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作者 Pelin Yildirim Taser 《Computer Systems Science & Engineering》 SCIE EI 2023年第2期1499-1516,共18页
Predictive Maintenance is a type of condition-based maintenance that assesses the equipment's states and estimates its failure probability and when maintenance should be performed.Although machine learning techniq... Predictive Maintenance is a type of condition-based maintenance that assesses the equipment's states and estimates its failure probability and when maintenance should be performed.Although machine learning techniques have been frequently implemented in this area,the existing studies disregard to the nat-ural order between the target attribute values of the historical sensor data.Thus,these methods cause losing the inherent order of the data that positively affects the prediction performances.To deal with this problem,a novel approach,named Ordinal Multi-dimensional Classification(OMDC),is proposed for estimating the conditions of a hydraulic system's four components by taking into the natural order of class values.To demonstrate the prediction ability of the proposed approach,eleven different multi-dimensional classification algorithms(traditional Binary Relevance(BR),Classifier Chain(CC),Bayesian Classifier Chain(BCC),Monte Carlo Classifier Chain(MCC),Probabilistic Classifier Chain(PCC),Clas-sifier Dependency Network(CDN),Classifier Trellis(CT),Classifier Dependency Trellis(CDT),Label Powerset(LP),Pruned Sets(PS),and Random k-Labelsets(RAKEL))were implemented using the Ordinal Class Classifier(OCC)algorithm.Besides,seven different classification algorithms(Multilayer Perceptron(MLP),Support Vector Machine(SVM),k-Nearest Neighbour(kNN),Decision Tree(C4.5),Bagging,Random Forest(RF),and Adaptive Boosting(AdaBoost))were chosen as base learners for the OCC algorithm.The experimental results present that the proposed OMDC approach using binary relevance multi-dimensional classification methods predicts the conditions of a hydraulic system's multiple components with high accuracy.Also,it is clearly seen from the results that the OMDC models that utilize ensemble-based classification algorithms give more reliable prediction performances with an average Hamming score of 0.853 than the others that use traditional algorithms as base learners. 展开更多
关键词 Machine learning multi-dimensional classification ordinal classification predictive maintenance
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Application of multi-component joint inversion in oil and gas exploration:A case study of reservoir and gas saturation prediction of the Xujiahe formation in the PLN area of the central Sichuan Basin
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作者 Wang Dong He Zhen-Hua +4 位作者 Wang Xu-Ben Li Le Yang Hai-Tao Fu Zhi-Guo Wang Hong-Yan 《Applied Geophysics》 SCIE CSCD 2020年第5期879-889,905,共12页
Multi-component seismic exploration is an important technique in the utilization of P-waves and converted S-waves for oil and gas exploration.It has unique advantages in the structural imaging of gas zones,reservoir p... Multi-component seismic exploration is an important technique in the utilization of P-waves and converted S-waves for oil and gas exploration.It has unique advantages in the structural imaging of gas zones,reservoir prediction,lithology,and gas-water identifi cation,and the development direction and degree of fractures.Multi-component joint inversion is one of the most important steps in multi-component exploration.In this paper,starting from the basic principle of multi-component joint inversion,the diff erences between the method and single P-wave inversion are introduced.Next,the technique is applied to the PLN area of the Sichuan Basin,and the P-wave impedance,S-wave impedance,and density are obtained based on multi-component joint inversion.Through the velocity and lithology,porosity,and gas saturation fi tting formulas,prediction results are calculated,and the results are analyzed.Finally,multi-component joint inversion and single P-wave inversion are compared in eff ective reservoir prediction.The results show that multi-component joint inversion increases the constraints on the inversion conditions,reduces the multi-solution of a single P-wave inversion,and is more objective and reliable for the identification of reservoirs,effectively improving the accuracy of oil and gas reservoir prediction and development. 展开更多
关键词 multi-component joint inversion lithology identifi cation POROSITY gas saturation reservoir prediction
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Ab Initio Theoretical Prediction on Structures of Boron Cationic Cluster B_(17)^+
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作者 Xu-Guang HU Yu-Min CAI Qian-Shu LI(Institute of Theoretical Chemistry, National Key Laboratory of Theoretical and Computational Chemistry, Jilin University, Changchun 130023)(Department of Chemical Engineering, Xi an Petroleum Institute,Xi an, 710061)(Col 《Chinese Chemical Letters》 SCIE CAS CSCD 1997年第8期737-740,共4页
Four isomers of the three-dimensionally connected bare boron cationic cluster B were investigated by using ab initio molecular orbital theory at the HF/6-31G level. The results show that the D5h symmetric isomer of B ... Four isomers of the three-dimensionally connected bare boron cationic cluster B were investigated by using ab initio molecular orbital theory at the HF/6-31G level. The results show that the D5h symmetric isomer of B is a possible isomer candidate of its stable geometries with closed structure. 展开更多
关键词 Ab Initio Theoretical prediction on Structures of Boron cationic Cluster B
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SF-CNN: Deep Text Classification and Retrieval for Text Documents 被引量:2
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作者 R.Sarasu K.K.Thyagharajan N.R.Shanker 《Intelligent Automation & Soft Computing》 SCIE 2023年第2期1799-1813,共15页
Researchers and scientists need rapid access to text documents such as research papers,source code and dissertations.Many research documents are available on the Internet and need more time to retrieve exact documents... Researchers and scientists need rapid access to text documents such as research papers,source code and dissertations.Many research documents are available on the Internet and need more time to retrieve exact documents based on keywords.An efficient classification algorithm for retrieving documents based on keyword words is required.The traditional algorithm performs less because it never considers words’polysemy and the relationship between bag-of-words in keywords.To solve the above problem,Semantic Featured Convolution Neural Networks(SF-CNN)is proposed to obtain the key relationships among the searching keywords and build a structure for matching the words for retrieving correct text documents.The proposed SF-CNN is based on deep semantic-based bag-of-word representation for document retrieval.Traditional deep learning methods such as Convolutional Neural Network and Recurrent Neural Network never use semantic representation for bag-of-words.The experiment is performed with different document datasets for evaluating the performance of the proposed SF-CNN method.SF-CNN classifies the documents with an accuracy of 94%than the traditional algorithms. 展开更多
关键词 SEMANTIC classification convolution neural networks semantic enhancement
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A Quantum Spatial Graph Convolutional Network for Text Classification 被引量:2
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作者 Syed Mustajar Ahmad Shah Hongwei Ge +5 位作者 Sami Ahmed Haider Muhammad Irshad Sohail M.Noman Jehangir Arshad Asfandeyar Ahmad Talha Younas 《Computer Systems Science & Engineering》 SCIE EI 2021年第2期369-382,共14页
The data generated from non-Euclidean domains and its graphical representation(with complex-relationship object interdependence)applications has observed an exponential growth.The sophistication of graph data has pose... The data generated from non-Euclidean domains and its graphical representation(with complex-relationship object interdependence)applications has observed an exponential growth.The sophistication of graph data has posed consequential obstacles to the existing machine learning algorithms.In this study,we have considered a revamped version of a semi-supervised learning algorithm for graph-structured data to address the issue of expanding deep learning approaches to represent the graph data.Additionally,the quantum information theory has been applied through Graph Neural Networks(GNNs)to generate Riemannian metrics in closed-form of several graph layers.In further,to pre-process the adjacency matrix of graphs,a new formulation is established to incorporate high order proximities.The proposed scheme has shown outstanding improvements to overcome the deficiencies in Graph Convolutional Network(GCN),particularly,the information loss and imprecise information representation with acceptable computational overhead.Moreover,the proposed Quantum Graph Convolutional Network(QGCN)has significantly strengthened the GCN on semi-supervised node classification tasks.In parallel,it expands the generalization process with a significant difference by making small random perturbationsG of the graph during the training process.The evaluation results are provided on three benchmark datasets,including Citeseer,Cora,and PubMed,that distinctly delineate the superiority of the proposed model in terms of computational accuracy against state-of-the-art GCN and three other methods based on the same algorithms in the existing literature. 展开更多
关键词 Text classification deep learning graph convolutional networks semi-supervised learning GPUS performance improvements
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Disaster Monitoring of Satellite Image Processing Using Progressive Image Classification 被引量:1
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作者 Romany F.Mansour Eatedal Alabdulkreem 《Computer Systems Science & Engineering》 SCIE EI 2023年第2期1161-1169,共9页
The analysis of remote sensing image areas is needed for climate detec-tion and management,especially for monitoringflood disasters in critical environ-ments and applications.Satellites are mostly used to detect disast... The analysis of remote sensing image areas is needed for climate detec-tion and management,especially for monitoringflood disasters in critical environ-ments and applications.Satellites are mostly used to detect disasters on Earth,and they have advantages in capturing Earth images.Using the control technique,Earth images can be used to obtain detailed terrain information.Since the acquisi-tion of satellite and aerial imagery,this system has been able to detectfloods,and with increasing convenience,flood detection has become more desirable in the last few years.In this paper,a Big Data Set-based Progressive Image Classification Algorithm(PICA)system is introduced to implement an image processing tech-nique,detect disasters,and determine results with the help of the PICA,which allows disaster analysis to be extracted more effectively.The PICA is essential to overcoming strong shadows,for proper access to disaster characteristics to false positives by operators,and to false predictions that affect the impact of the disas-ter.The PICA creates tailoring and adjustments obtained from satellite images before training and post-disaster aerial image data patches.Two types of proposed PICA systems detect disasters faster and more accurately(95.6%). 展开更多
关键词 CLUSTERING SEGMENTATION progressive image classification algorithm satellite image disaster detection
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Stage-Wise Categorization and Prediction of Diabetic Retinopathy Using Ensemble Learning and 2D-CNN
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作者 N.M.Balamurugan K.Maithili +1 位作者 T.K.S.Rathish Babu M.Adimoolam 《Intelligent Automation & Soft Computing》 SCIE 2023年第4期499-514,共16页
Diabetic Eye Disease(DED)is a fundamental cause of blindness in human beings in the medical world.Different techniques are proposed to forecast and examine the stages in Prognostication of Diabetic Retinopathy(DR).The... Diabetic Eye Disease(DED)is a fundamental cause of blindness in human beings in the medical world.Different techniques are proposed to forecast and examine the stages in Prognostication of Diabetic Retinopathy(DR).The Machine Learning(ML)and the Deep Learning(DL)algorithms are the predomi-nant techniques to project and explore the images of DR.Even though some solu-tions were adapted to challenge the cause of DR disease,still there should be an efficient and accurate DR prediction to be adapted to refine its performance.In this work,a hybrid technique was proposed for classification and prediction of DR.The proposed hybrid technique consists of Ensemble Learning(EL),2 Dimensional-Conventional Neural Network(2D-CNN),Transfer Learning(TL)and Correlation method.Initially,the Stochastic Gradient Boosting(SGB)EL method was used to predict the DR.Secondly,the boosting based EL method was used to predict the DR of images.Thirdly 2D-CNN was applied to categorize the various stages of DR images.Finally,the TL was adopted to transfer the clas-sification prediction to training datasets.When this TL was applied,a new predic-tion feature was increased.From the experiment,the proposed technique has achieved 97.8%of accuracy in prophecies of DR images and 98%accuracy in grading of images.The experiment was also extended to measure the sensitivity(99.6%)and specificity(97.3%)metrics.The predicted accuracy rate was com-pared with existing methods. 展开更多
关键词 Diabetic retinopathy prediction and classification ensemble learning conventional neural network diabetic eye disease
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Cephalopods Classification Using Fine Tuned Lightweight Transfer Learning Models
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作者 P.Anantha Prabha G.Suchitra R.Saravanan 《Intelligent Automation & Soft Computing》 SCIE 2023年第3期3065-3079,共15页
Cephalopods identification is a formidable task that involves hand inspection and close observation by a malacologist.Manual observation and iden-tification take time and are always contingent on the involvement of expe... Cephalopods identification is a formidable task that involves hand inspection and close observation by a malacologist.Manual observation and iden-tification take time and are always contingent on the involvement of experts.A system is proposed to alleviate this challenge that uses transfer learning techni-ques to classify the cephalopods automatically.In the proposed method,only the Lightweight pre-trained networks are chosen to enable IoT in the task of cephalopod recognition.First,the efficiency of the chosen models is determined by evaluating their performance and comparing thefindings.Second,the models arefine-tuned by adding dense layers and tweaking hyperparameters to improve the classification of accuracy.The models also employ a well-tuned Rectified Adam optimizer to increase the accuracy rates.Third,Adam with Gradient Cen-tralisation(RAdamGC)is proposed and used infine-tuned models to reduce the training time.The framework enables an Internet of Things(IoT)or embedded device to perform the classification tasks by embedding a suitable lightweight pre-trained network.Thefine-tuned models,MobileNetV2,InceptionV3,and NASNet Mobile have achieved a classification accuracy of 89.74%,87.12%,and 89.74%,respectively.Thefindings have indicated that thefine-tuned models can classify different kinds of cephalopods.The results have also demonstrated that there is a significant reduction in the training time with RAdamGC. 展开更多
关键词 CEPHALOPODS transfer learning lightweight models classification deep learning fish IOT
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Optimal Sparse Autoencoder Based Sleep Stage Classification Using Biomedical Signals
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作者 Ashit Kumar Dutta Yasser Albagory +2 位作者 Manal Al Faraj Yasir A.M.Eltahir Abdul Rahaman Wahab Sait 《Computer Systems Science & Engineering》 SCIE EI 2023年第2期1517-1529,共13页
The recently developed machine learning(ML)models have the ability to obtain high detection rate using biomedical signals.Therefore,this article develops an Optimal Sparse Autoencoder based Sleep Stage Classification M... The recently developed machine learning(ML)models have the ability to obtain high detection rate using biomedical signals.Therefore,this article develops an Optimal Sparse Autoencoder based Sleep Stage Classification Model on Electroencephalography(EEG)Biomedical Signals,named OSAE-SSCEEG technique.The major intention of the OSAE-SSCEEG technique is tofind the sleep stage disorders using the EEG biomedical signals.The OSAE-SSCEEG technique primarily undergoes preprocessing using min-max data normalization approach.Moreover,the classification of sleep stages takes place using the Sparse Autoencoder with Smoothed Regularization(SAE-SR)with softmax(SM)approach.Finally,the parameter optimization of the SAE-SR technique is carried out by the use of Coyote Optimization Algorithm(COA)and it leads to boosted classification efficiency.In order to ensure the enhanced performance of the OSAE-SSCEEG technique,a wide ranging simulation analysis is performed and the obtained results demonstrate the betterment of the OSAE-SSCEEG tech-nique over the recent methods. 展开更多
关键词 Biomedical signals EEG sleep stage classification machine learning autoencoder softmax parameter tuning
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Optimal Deep Belief Network Enabled Malware Detection and Classification Model
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作者 P.Pandi Chandran N.Hema Rajini M.Jeyakarthic 《Intelligent Automation & Soft Computing》 SCIE 2023年第3期3349-3364,共16页
Cybercrime has increased considerably in recent times by creating new methods of stealing,changing,and destroying data in daily lives.Portable Docu-ment Format(PDF)has been traditionally utilized as a popular way of s... Cybercrime has increased considerably in recent times by creating new methods of stealing,changing,and destroying data in daily lives.Portable Docu-ment Format(PDF)has been traditionally utilized as a popular way of spreading malware.The recent advances of machine learning(ML)and deep learning(DL)models are utilized to detect and classify malware.With this motivation,this study focuses on the design of mayfly optimization with a deep belief network for PDF malware detection and classification(MFODBN-MDC)technique.The major intention of the MFODBN-MDC technique is for identifying and classify-ing the presence of malware exist in the PDFs.The proposed MFODBN-MDC method derives a new MFO algorithm for the optimal selection of feature subsets.In addition,Adamax optimizer with the DBN model is used for PDF malware detection and classification.The design of the MFO algorithm to select features and Adamax based hyperparameter tuning for PDF malware detection and classi-fication demonstrates the novelty of the work.For demonstrating the improved outcomes of the MFODBN-MDC model,a wide range of simulations are exe-cuted,and the results are assessed in various aspects.The comparison study high-lighted the enhanced outcomes of the MFODBN-MDC model over the existing techniques with maximum precision,recall,and F1 score of 97.42%,97.33%,and 97.33%,respectively. 展开更多
关键词 PDF malware data classification SECURITY deep learning feature selection metaheuristics
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A Novel Outlier Detection with Feature Selection Enabled Streaming Data Classification
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作者 R.Rajakumar S.Sathiya Devi 《Intelligent Automation & Soft Computing》 SCIE 2023年第2期2101-2116,共16页
Due to the advancements in information technologies,massive quantity of data is being produced by social media,smartphones,and sensor devices.The investigation of data stream by the use of machine learning(ML)approach... Due to the advancements in information technologies,massive quantity of data is being produced by social media,smartphones,and sensor devices.The investigation of data stream by the use of machine learning(ML)approaches to address regression,prediction,and classification problems have received consid-erable interest.At the same time,the detection of anomalies or outliers and feature selection(FS)processes becomes important.This study develops an outlier detec-tion with feature selection technique for streaming data classification,named ODFST-SDC technique.Initially,streaming data is pre-processed in two ways namely categorical encoding and null value removal.In addition,Local Correla-tion Integral(LOCI)is used which is significant in the detection and removal of outliers.Besides,red deer algorithm(RDA)based FS approach is employed to derive an optimal subset of features.Finally,kernel extreme learning machine(KELM)classifier is used for streaming data classification.The design of LOCI based outlier detection and RDA based FS shows the novelty of the work.In order to assess the classification outcomes of the ODFST-SDC technique,a series of simulations were performed using three benchmark datasets.The experimental results reported the promising outcomes of the ODFST-SDC technique over the recent approaches. 展开更多
关键词 Streaming data classification outlier removal feature selection machine learning metaheuristics
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Big Data Analytics with Optimal Deep Learning Model for Medical Image Classification
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作者 Tariq Mohammed Alqahtani 《Computer Systems Science & Engineering》 SCIE EI 2023年第2期1433-1449,共17页
In recent years,huge volumes of healthcare data are getting generated in various forms.The advancements made in medical imaging are tremendous owing to which biomedical image acquisition has become easier and quicker.... In recent years,huge volumes of healthcare data are getting generated in various forms.The advancements made in medical imaging are tremendous owing to which biomedical image acquisition has become easier and quicker.Due to such massive generation of big data,the utilization of new methods based on Big Data Analytics(BDA),Machine Learning(ML),and Artificial Intelligence(AI)have become essential.In this aspect,the current research work develops a new Big Data Analytics with Cat Swarm Optimization based deep Learning(BDA-CSODL)technique for medical image classification on Apache Spark environment.The aim of the proposed BDA-CSODL technique is to classify the medical images and diagnose the disease accurately.BDA-CSODL technique involves different stages of operations such as preprocessing,segmentation,fea-ture extraction,and classification.In addition,BDA-CSODL technique also fol-lows multi-level thresholding-based image segmentation approach for the detection of infected regions in medical image.Moreover,a deep convolutional neural network-based Inception v3 method is utilized in this study as feature extractor.Stochastic Gradient Descent(SGD)model is used for parameter tuning process.Furthermore,CSO with Long Short-Term Memory(CSO-LSTM)model is employed as a classification model to determine the appropriate class labels to it.Both SGD and CSO design approaches help in improving the overall image classification performance of the proposed BDA-CSODL technique.A wide range of simulations was conducted on benchmark medical image datasets and the com-prehensive comparative results demonstrate the supremacy of the proposed BDA-CSODL technique under different measures. 展开更多
关键词 Big data analytics healthcare deep learning image classification biomedical imaging machine learning
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An Intelligent Deep Neural Sentiment Classification Network
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作者 Umamaheswari Ramalingam Senthil Kumar Murugesan +1 位作者 Karthikeyan Lakshmanan Chidhambararajan Balasubramaniyan 《Intelligent Automation & Soft Computing》 SCIE 2023年第5期1733-1744,共12页
A Deep Neural Sentiment Classification Network(DNSCN)is devel-oped in this work to classify the Twitter data unambiguously.It attempts to extract the negative and positive sentiments in the Twitter database.The main go... A Deep Neural Sentiment Classification Network(DNSCN)is devel-oped in this work to classify the Twitter data unambiguously.It attempts to extract the negative and positive sentiments in the Twitter database.The main goal of the system is tofind the sentiment behavior of tweets with minimum ambiguity.A well-defined neural network extracts deep features from the tweets automatically.Before extracting features deeper and deeper,the text in each tweet is represented by Bag-of-Words(BoW)and Word Embeddings(WE)models.The effectiveness of DNSCN architecture is analyzed using Twitter-Sanders-Apple2(TSA2),Twit-ter-Sanders-Apple3(TSA3),and Twitter-DataSet(TDS).TSA2 and TDS consist of positive and negative tweets,whereas TSA3 has neutral tweets also.Thus,the proposed DNSCN acts as a binary classifier for TSA2 and TDS databases and a multiclass classifier for TSA3.The performances of DNSCN architecture are evaluated by F1 score,precision,and recall rates using 5-fold and 10-fold cross-validation.Results show that the DNSCN-WE model provides more accuracy than the DNSCN-BoW model for representing the tweets in the feature encoding.The F1 score of the DNSCN-BW based system on the TSA2 database is 0.98(binary classification)and 0.97(three-class classification)for the TSA3 database.This system provides better a F1 score of 0.99 for the TDS database. 展开更多
关键词 Deep neural network word embeddings BAG-OF-WORDS sentiment analysis text classification
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An Automatic Deep Neural Network Model for Fingerprint Classification
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作者 Amira Tarek Mahmoud Wael AAwad +4 位作者 Gamal Behery Mohamed Abouhawwash Mehedi Masud Hanan Aljuaid Ahmed Ismail Ebada 《Intelligent Automation & Soft Computing》 SCIE 2023年第5期2007-2023,共17页
The accuracy offingerprint recognition model is extremely important due to its usage in forensic and securityfields.Anyfingerprint recognition system has particular network architecture whereas many other networks achiev... The accuracy offingerprint recognition model is extremely important due to its usage in forensic and securityfields.Anyfingerprint recognition system has particular network architecture whereas many other networks achieve higher accuracy.To solve this problem in a unified model,this paper proposes a model that can automatically specify itself.So,it is called an automatic deep neural net-work(ADNN).Our algorithm can specify the appropriate architecture of the neur-al network used and some significant parameters of this network.These parameters are the number offilters,epochs,and iterations.It guarantees the high-est accuracy by updating itself until achieving 99%accuracy then it stops and out-puts the result.Moreover,this paper proposes an end-to-end methodology for recognizing a person’s identity from the inputfingerprint image based on a resi-dual convolutional neural network.It is a complete system and is fully automated whether in the features extraction stage or the classification stage.Our goal is to automate thisfingerprint recognition system because the more automatic the sys-tem is,the more time and effort it saves.Our model also allows users to react by inputting the initial values of these parameters.Then,the model updates itself until itfinds the optimal values for the parameters and achieves the best accuracy.Another advantage of our algorithm is that it can recognize people from their thumb and otherfingers and its ability to recognize distorted samples.Our algo-rithm achieved 99.75%accuracy on the publicfingerprint dataset(SOCOFing).This is the best accuracy compared with other models. 展开更多
关键词 Automatic system fingerprint classification residual networks deep learning
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Multi-Level Deep Generative Adversarial Networks for Brain Tumor Classification on Magnetic Resonance Images
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作者 Abdullah A.Asiri Ahmad Shaf +7 位作者 Tariq Ali Muhammad Aamir Ali Usman Muhammad Irfan Hassan A.Alshamrani Khlood M.Mehdar Osama M.Alshehri Samar M.Alqhtani 《Intelligent Automation & Soft Computing》 SCIE 2023年第4期127-143,共17页
The brain tumor is an abnormal and hysterical growth of brain tissues,and the leading cause of death affected patients worldwide.Even in this technol-ogy-based arena,brain tumor images with proper labeling and acquisi... The brain tumor is an abnormal and hysterical growth of brain tissues,and the leading cause of death affected patients worldwide.Even in this technol-ogy-based arena,brain tumor images with proper labeling and acquisition still have a problem with the accurate and reliable generation of realistic images of brain tumors that are completely different from the original ones.The artificially created medical image data would help improve the learning ability of physicians and other computer-aided systems for the generation of augmented data.To over-come the highlighted issue,a Generative Adversarial Network(GAN)deep learn-ing technique in which two neural networks compete to become more accurate in creating artificially realistic data for MRI images.The GAN network contains mainly two parts known as generator and discriminator.Commonly,a generator is the convolutional neural network,and a discriminator is the deconvolutional neural network.In this research,the publicly accessible Contrast-Enhanced Mag-netic Resonance Imaging(CE-MRI)dataset collected from 2005-to 2020 from different hospitals in China consists of four classes has been used.Our proposed method is simple and achieved an accuracy of 96%.We compare our technique results with the existing results,indicating that our proposed technique outper-forms the best results associated with the existing methods. 展开更多
关键词 GAN network CE-MRI images convolutional neural network brain tumor classification
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Efficient Object Detection and Classification Approach Using HTYOLOV4 and M^(2)RFO-CNN
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作者 V.Arulalan Dhananjay Kumar 《Computer Systems Science & Engineering》 SCIE EI 2023年第2期1703-1717,共15页
Object detection and classification are the trending research topics in thefield of computer vision because of their applications like visual surveillance.However,the vision-based objects detection and classification met... Object detection and classification are the trending research topics in thefield of computer vision because of their applications like visual surveillance.However,the vision-based objects detection and classification methods still suffer from detecting smaller objects and dense objects in the complex dynamic envir-onment with high accuracy and precision.The present paper proposes a novel enhanced method to detect and classify objects using Hyperbolic Tangent based You Only Look Once V4 with a Modified Manta-Ray Foraging Optimization-based Convolution Neural Network.Initially,in the pre-processing,the video data was converted into image sequences and Polynomial Adaptive Edge was applied to preserve the Algorithm method for image resizing and noise removal.The noiseless resized image sequences contrast was enhanced using Contrast Limited Adaptive Edge Preserving Algorithm.And,with the contrast-enhanced image sequences,the Hyperbolic Tangent based You Only Look Once V4 was trained for object detection.Additionally,to detect smaller objects with high accuracy,Grasp configuration was observed for every detected object.Finally,the Modified Manta-Ray Foraging Optimization-based Convolution Neural Network method was carried out for the detection and the classification of objects.Comparative experiments were conducted on various benchmark datasets and methods that showed improved accurate detection and classification results. 展开更多
关键词 Object detection hyperbolic tangent YOLO manta-ray foraging object classification
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A Deep Trash Classification Model on Raspberry Pi 4
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作者 Thien Khai Tran Kha Tu Huynh +2 位作者 Dac-Nhuong Le Muhammad Arif Hoa Minh Dinh 《Intelligent Automation & Soft Computing》 SCIE 2023年第2期2479-2491,共13页
Environmental pollution has had substantial impacts on human life,and trash is one of the main sources of such pollution in most countries.Trash classi-fication from a collection of trash images can limit the overloadi... Environmental pollution has had substantial impacts on human life,and trash is one of the main sources of such pollution in most countries.Trash classi-fication from a collection of trash images can limit the overloading of garbage dis-posal systems and efficiently promote recycling activities;thus,development of such a classification system is topical and urgent.This paper proposed an effective trash classification system that relies on a classification module embedded in a hard-ware setup to classify trash in real time.An image dataset isfirst augmented to enhance the images before classifying them as either inorganic or organic trash.The deep learning–based ResNet-50 model,an improved version of the ResNet model,is used to classify trash from the dataset of trash images.The experimental results,which are tested both on the dataset and in real time,show that ResNet-50 had an average accuracy of 96%,higher than that of related models.Moreover,integrating the classification module into a Raspberry Pi computer,which con-trolled the trash bin slide so that garbage fell into the appropriate bin for inorganic or organic waste,created a complete trash classification system.This proves the efficiency and high applicability of the proposed system. 展开更多
关键词 Trash classification ResNet raspberry pi internet of things(IoT) deep learning
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Deep LearningModel for Big Data Classification in Apache Spark Environment
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作者 T.M.Nithya R.Umanesan +2 位作者 T.Kalavathidevi C.Selvarathi A.Kavitha 《Intelligent Automation & Soft Computing》 SCIE 2023年第9期2537-2547,共11页
Big data analytics is a popular research topic due to its applicability in various real time applications.The recent advent of machine learning and deep learning models can be applied to analyze big data with better p... Big data analytics is a popular research topic due to its applicability in various real time applications.The recent advent of machine learning and deep learning models can be applied to analyze big data with better performance.Since big data involves numerous features and necessitates high computational time,feature selection methodologies using metaheuristic optimization algorithms can be adopted to choose optimum set of features and thereby improves the overall classification performance.This study proposes a new sigmoid butterfly optimization method with an optimum gated recurrent unit(SBOA-OGRU)model for big data classification in Apache Spark.The SBOA-OGRU technique involves the design of SBOA based feature selection technique to choose an optimum subset of features.In addition,OGRU based classification model is employed to classify the big data into appropriate classes.Besides,the hyperparameter tuning of the GRU model takes place using Adam optimizer.Furthermore,the Apache Spark platform is applied for processing big data in an effective way.In order to ensure the betterment of the SBOA-OGRU technique,a wide range of experiments were performed and the experimental results highlighted the supremacy of the SBOA-OGRU technique. 展开更多
关键词 Big data apache spark classification feature selection gated recurrent unit adam optimizer
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Machine Learning-Based Models for Magnetic Resonance Imaging(MRI)-Based Brain Tumor Classification
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作者 Abdullah A.Asiri Bilal Khan +5 位作者 Fazal Muhammad Shams ur Rahman Hassan A.Alshamrani Khalaf A.Alshamrani Muhammad Irfan Fawaz F.Alqhtani 《Intelligent Automation & Soft Computing》 SCIE 2023年第4期299-312,共14页
In the medical profession,recent technological advancements play an essential role in the early detection and categorization of many diseases that cause mortality.The technique rising on daily basis for detecting illn... In the medical profession,recent technological advancements play an essential role in the early detection and categorization of many diseases that cause mortality.The technique rising on daily basis for detecting illness in magnetic resonance through pictures is the inspection of humans.Automatic(computerized)illness detection in medical imaging has found you the emergent region in several medical diagnostic applications.Various diseases that cause death need to be identified through such techniques and technologies to overcome the mortality ratio.The brain tumor is one of the most common causes of death.Researchers have already proposed various models for the classification and detection of tumors,each with its strengths and weaknesses,but there is still a need to improve the classification process with improved efficiency.However,in this study,we give an in-depth analysis of six distinct machine learning(ML)algorithms,including Random Forest(RF),Naïve Bayes(NB),Neural Networks(NN),CN2 Rule Induction(CN2),Support Vector Machine(SVM),and Decision Tree(Tree),to address this gap in improving accuracy.On the Kaggle dataset,these strategies are tested using classification accuracy,the area under the Receiver Operating Characteristic(ROC)curve,precision,recall,and F1 Score(F1).The training and testing process is strengthened by using a 10-fold cross-validation technique.The results show that SVM outperforms other algorithms,with 95.3%accuracy. 展开更多
关键词 MRI images brain tumor machine learning-based classification
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