The detection of brain disease is an essential issue in medical and research areas.Deep learning techniques have shown promising results in detecting and diagnosing brain diseases using magnetic resonance imaging(MRI)...The detection of brain disease is an essential issue in medical and research areas.Deep learning techniques have shown promising results in detecting and diagnosing brain diseases using magnetic resonance imaging(MRI)images.These techniques involve training neural networks on large datasets of MRI images,allowing the networks to learn patterns and features indicative of different brain diseases.However,several challenges and limitations still need to be addressed further to improve the accuracy and effectiveness of these techniques.This paper implements a Feature Enhanced Stacked Auto Encoder(FESAE)model to detect brain diseases.The standard stack auto encoder’s results are trivial and not robust enough to boost the system’s accuracy.Therefore,the standard Stack Auto Encoder(SAE)is replaced with a Stacked Feature Enhanced Auto Encoder with a feature enhancement function to efficiently and effectively get non-trivial features with less activation energy froman image.The proposed model consists of four stages.First,pre-processing is performed to remove noise,and the greyscale image is converted to Red,Green,and Blue(RGB)to enhance feature details for discriminative feature extraction.Second,feature Extraction is performed to extract significant features for classification using DiscreteWavelet Transform(DWT)and Channelization.Third,classification is performed to classify MRI images into four major classes:Normal,Tumor,Brain Stroke,and Alzheimer’s.Finally,the FESAE model outperforms the state-of-theart,machine learning,and deep learning methods such as Artificial Neural Network(ANN),SAE,Random Forest(RF),and Logistic Regression(LR)by achieving a high accuracy of 98.61% on a dataset of 2000 MRI images.The proposed model has significant potential for assisting radiologists in diagnosing brain diseases more accurately and improving patient outcomes.展开更多
Damage detection in structures is performed via vibra-tion based structural identification. Modal information, such as fre-quencies and mode shapes, are widely used for structural dama-ge detection to indicate the hea...Damage detection in structures is performed via vibra-tion based structural identification. Modal information, such as fre-quencies and mode shapes, are widely used for structural dama-ge detection to indicate the health conditions of civil structures.The deep learning algorithm that works on a multiple layer neuralnetwork model termed as deep autoencoder is proposed to learnthe relationship between the modal information and structural stiff-ness parameters. This is achieved via dimension reduction of themodal information feature and a non-linear regression against thestructural stiffness parameters. Numerical tests on a symmetri-cal steel frame model are conducted to generate the data for thetraining and validation, and to demonstrate the efficiency of theproposed approach for vibration based structural damage detec-tion.展开更多
基于Python语言,结合“老师-学生”模型以及卷积自编码网络提出了Sound Auto Encoder算法,深入研究学生学习算法和实践研究,通过无监督特征学习的方式处理音频数据,根据自编码网络、卷积神经网络原理及相关工作,提出SoundAutoEncoder模...基于Python语言,结合“老师-学生”模型以及卷积自编码网络提出了Sound Auto Encoder算法,深入研究学生学习算法和实践研究,通过无监督特征学习的方式处理音频数据,根据自编码网络、卷积神经网络原理及相关工作,提出SoundAutoEncoder模型及SoundNet中的“老师-学生”模型.在音频数据的特征学习中,通过分析现有算法优势及局限,对SoundAutoEncoder算法模型网络结构及网络学习算法进行分析.通过实验,对算法Sound Auto Encoder和Sound Net进行对比,在五折上,Soimd Auto Encoder算法取得的结果要比Sound Net明显好;在低于600次迭代中,Soimd Auto Encoder算法的结果相较好,Sound Net算法基本处于训练初级阶段,且与可获得的最好结果尚有差距.展开更多
Blind image quality assessment(BIQA)is of fundamental importance in low-level computer vision community.Increasing interest has been drawn in exploiting deep neural networks for BIQA.Despite of the notable success ach...Blind image quality assessment(BIQA)is of fundamental importance in low-level computer vision community.Increasing interest has been drawn in exploiting deep neural networks for BIQA.Despite of the notable success achieved,there is a broad consensus that training deep convolutional neural networks(DCNN)heavily relies on massive annotated data.Unfortunately,BIQA is typically a small sample problem,resulting the generalization ability of BIQA severely restricted.In order to improve the accuracy and generalization ability of BIQA metrics,this work proposed a totally opinion-unaware BIQA in which no subjective annotations are involved in the training stage.Multiple full-reference image quality assessment(FR-IQA)metrics are employed to label the distorted image as a substitution of subjective quality annotation.A deep neural network(DNN)is trained to blindly predict the multiple FR-IQA score in absence of corresponding pristine image.In the end,a selfsupervised FR-IQA score aggregator implemented by adversarial auto-encoder pools the predictions of multiple FR-IQA scores into the final quality predicting score.Even though none of subjective scores are involved in the training stage,experimental results indicate that our proposed full reference induced BIQA framework is as competitive as state-of-the-art BIQA metrics.展开更多
Early detection of Parkinson’s Disease(PD)using the PD patients’voice changes would avoid the intervention before the identification of physical symptoms.Various machine learning algorithms were developed to detect ...Early detection of Parkinson’s Disease(PD)using the PD patients’voice changes would avoid the intervention before the identification of physical symptoms.Various machine learning algorithms were developed to detect PD detection.Nevertheless,these ML methods are lack in generalization and reduced classification performance due to subject overlap.To overcome these issues,this proposed work apply graph long short term memory(GLSTM)model to classify the dynamic features of the PD patient speech signal.The proposed classification model has been further improved by implementing the recurrent neural network(RNN)in batch normalization layer of GLSTM and optimized with adaptive moment estimation(ADAM)on network hidden layer.To consider the importance of feature engineering,this proposed system use Linear Discriminant analysis(LDA)for dimensionality reduction and SparseAuto-Encoder(SAE)for extracting the dynamic speech features.Based on the computation of energy content transited from unvoiced to voice(onset)and voice to voiceless(offset),dynamic features are measured.The PD datasets is evaluated under 10 fold cross validation without sample overlap.The proposed smart PD detection method called RNN-GLSTM-ADAM is numerically experimented with persistent phonations in terms of accuracy,sensitivity,and specificity andMatthew correlation coefficient.The evaluated result of RNN-GLSTM-ADAM extremely improves the PD detection accuracy than static feature based conventional ML and DL approaches.展开更多
The medical community has more concern on lung cancer analysis.Medical experts’physical segmentation of lung cancers is time-consuming and needs to be automated.The research study’s objective is to diagnose lung tum...The medical community has more concern on lung cancer analysis.Medical experts’physical segmentation of lung cancers is time-consuming and needs to be automated.The research study’s objective is to diagnose lung tumors at an early stage to extend the life of humans using deep learning techniques.Computer-Aided Diagnostic(CAD)system aids in the diagnosis and shortens the time necessary to detect the tumor detected.The application of Deep Neural Networks(DNN)has also been exhibited as an excellent and effective method in classification and segmentation tasks.This research aims to separate lung cancers from images of Magnetic Resonance Imaging(MRI)with threshold segmentation.The Honey hook process categorizes lung cancer based on characteristics retrieved using several classifiers.Considering this principle,the work presents a solution for image compression utilizing a Deep Wave Auto-Encoder(DWAE).The combination of the two approaches significantly reduces the overall size of the feature set required for any future classification process performed using DNN.The proposed DWAE-DNN image classifier is applied to a lung imaging dataset with Radial Basis Function(RBF)classifier.The study reported promising results with an accuracy of 97.34%,whereas using the Decision Tree(DT)classifier has an accuracy of 94.24%.The proposed approach(DWAE-DNN)is found to classify the images with an accuracy of 98.67%,either as malignant or normal patients.In contrast to the accuracy requirements,the work also uses the benchmark standards like specificity,sensitivity,and precision to evaluate the efficiency of the network.It is found from an investigation that the DT classifier provides the maximum performance in the DWAE-DNN depending on the network’s performance on image testing,as shown by the data acquired by the categorizers themselves.展开更多
Generally,conventional methods for anomaly detection rely on clustering,proximity,or classification.With themassive growth in surveillance videos,outliers or anomalies find ingenious ways to obscure themselves in the ...Generally,conventional methods for anomaly detection rely on clustering,proximity,or classification.With themassive growth in surveillance videos,outliers or anomalies find ingenious ways to obscure themselves in the network and make conventional techniques inefficient.This research explores the structure of Graph neural networks(GNNs)that generalize deep learning frameworks to graph-structured data.Every node in the graph structure is labeled and anomalies,represented by unlabeled nodes,are predicted by performing random walks on the node-based graph structures.Due to their strong learning abilities,GNNs gained popularity in various domains such as natural language processing,social network analytics and healthcare.Anomaly detection is a challenging task in computer vision but the proposed algorithm using GNNs efficiently performs the identification of anomalies.The Graph-based deep learning networks are designed to predict unknown objects and outliers.In our case,they detect unusual objects in the form of malicious nodes.The edges between nodes represent a relationship of nodes among each other.In case of anomaly,such as the bike rider in Pedestrians data,the rider node has a negative value for the edge and it is identified as an anomaly.The encoding and decoding layers are crucial for determining how statistical measurements affect anomaly identification and for correcting the graph path to the best possible outcome.Results show that the proposed framework is a step ahead of the traditional approaches in detecting unusual activities,which shows a huge potential in automatically monitoring surveillance videos.Performing autonomous monitoring of CCTV,crime control and damage or destruction by a group of people or crowd can be identified and alarms may be triggered in unusual activities in streets or public places.The suggested GNN model improves accuracy by 4%for the Pedestrian 2 dataset and 12%for the Pedestrian 1 dataset compared to a few state-of the-art techniques.展开更多
Traffic flow prediction in urban areas is essential in the IntelligentTransportation System (ITS). Short Term Traffic Flow (STTF) predictionimpacts traffic flow series, where an estimation of the number of vehicleswil...Traffic flow prediction in urban areas is essential in the IntelligentTransportation System (ITS). Short Term Traffic Flow (STTF) predictionimpacts traffic flow series, where an estimation of the number of vehicleswill appear during the next instance of time per hour. Precise STTF iscritical in Intelligent Transportation System. Various extinct systems aim forshort-term traffic forecasts, ensuring a good precision outcome which was asignificant task over the past few years. The main objective of this paper is topropose a new model to predict STTF for every hour of a day. In this paper,we have proposed a novel hybrid algorithm utilizing Principal ComponentAnalysis (PCA), Stacked Auto-Encoder (SAE), Long Short Term Memory(LSTM), and K-Nearest Neighbors (KNN) named PALKNN. Firstly, PCAremoves unwanted information from the dataset and selects essential features.Secondly, SAE is used to reduce the dimension of input data using onehotencoding so the model can be trained with better speed. Thirdly, LSTMtakes the input from SAE, where the data is sorted in ascending orderbased on the important features and generates the derived value. Finally,KNN Regressor takes information from LSTM to predict traffic flow. Theforecasting performance of the PALKNN model is investigated with OpenRoad Traffic Statistics dataset, Great Britain, UK. This paper enhanced thetraffic flow prediction for every hour of a day with a minimal error value.An extensive experimental analysis was performed on the benchmark dataset.The evaluated results indicate the significant improvement of the proposedPALKNN model over the recent approaches such as KNN, SARIMA, LogisticRegression, RNN, and LSTM in terms of root mean square error (RMSE)of 2.07%, mean square error (MSE) of 4.1%, and mean absolute error (MAE)of 2.04%.展开更多
Service recommendation provides an effective solution to extract valuable information from the huge and ever-increasing volume of big data generated by the large cardinality of user devices.However,the distributed and...Service recommendation provides an effective solution to extract valuable information from the huge and ever-increasing volume of big data generated by the large cardinality of user devices.However,the distributed and rich multi-source big data resources raise challenges to the centralized cloud-based data storage and value mining approaches in terms of economic cost and effective service recommendation methods.In view of these challenges,we propose a deep neural collaborative filtering based service recommendation method with multi-source data(i.e.,NCF-MS)in this paper,which adopts the cloud-edge collaboration computing paradigm to build recommendation model.More specifically,the Stacked Denoising Auto Encoder(SDAE)module is adopted to extract user/service features from auxiliary user profiles and service attributes.The Multiple Layer Perceptron(MLP)module is adopted to integrate the auxiliary user/service features to train the recommendation model.Finally,we evaluate the effectiveness of the NCF-MS method on three public datasets.The experimental results show that our proposed method achieves better performance than existing methods.展开更多
In this study,we employ advanced data-driven techniques to investigate the complex relationships between the yields of five major crops and various geographical and spatiotemporal features in Senegal.We analyze how th...In this study,we employ advanced data-driven techniques to investigate the complex relationships between the yields of five major crops and various geographical and spatiotemporal features in Senegal.We analyze how these features influence crop yields by utilizing remotely sensed data.Our methodology incorporates clustering algorithms and correlation matrix analysis to identify significant patterns and dependencies,offering a comprehensive understanding of the factors affecting agricultural productivity in Senegal.To optimize the model's performance and identify the optimal hyperparameters,we implemented a comprehensive grid search across four distinct machine learning regressors:Random Forest,Extreme Gradient Boosting(XGBoost),Categorical Boosting(CatBoost),and Light Gradient-Boosting Machine(LightGBM).Each regressor offers unique functionalities,enhancing our exploration of potential model configurations.The top-performing models were selected based on evaluating multiple performance metrics,ensuring robust and accurate predictive capabilities.The results demonstrated that XGBoost and CatBoost perform better than the other two.We introduce synthetic crop data generated using a Variational Auto Encoder to address the challenges posed by limited agricultural datasets.By achieving high similarity scores with real-world data,our synthetic samples enhance model robustness,mitigate overfitting,and provide a viable solution for small dataset issues in agriculture.Our approach distinguishes itself by creating a flexible model applicable to various crops together.By integrating five crop datasets and generating high-quality synthetic data,we improve model performance,reduce overfitting,and enhance realism.Our findings provide crucial insights for productivity drivers in key cropping systems,enabling robust recommendations and strengthening the decision-making capabilities of policymakers and farmers in datascarce regions.展开更多
Functional surfaces in relative contact and motion are prone to wear and tear,resulting in loss of efficiency and performance of the workpieces/machines.Wear occurs in the form of adhesion,abrasion,scuffing,galling,an...Functional surfaces in relative contact and motion are prone to wear and tear,resulting in loss of efficiency and performance of the workpieces/machines.Wear occurs in the form of adhesion,abrasion,scuffing,galling,and scoring between contacts.However,the rate of the wear phenomenon depends primarily on the physical properties and the surrounding environment.Monitoring the integrity of surfaces by offline inspections leads to significant wasted machine time.A potential alternate option to offline inspection currently practiced in industries is the analysis of sensors signatures capable of capturing the wear state and correlating it with the wear phenomenon,followed by in situ classification using a state-of-the-art machine learning(ML)algorithm.Though this technique is better than offline inspection,it possesses inherent disadvantages for training the ML models.Ideally,supervised training of ML models requires the datasets considered for the classification to be of equal weightage to avoid biasing.The collection of such a dataset is very cumbersome and expensive in practice,as in real industrial applications,the malfunction period is minimal compared to normal operation.Furthermore,classification models would not classify new wear phenomena from the normal regime if they are unfamiliar.As a promising alternative,in this work,we propose a methodology able to differentiate the abnormal regimes,i.e.,wear phenomenon regimes,from the normal regime.This is carried out by familiarizing the ML algorithms only with the distribution of the acoustic emission(AE)signals captured using a microphone related to the normal regime.As a result,the ML algorithms would be able to detect whether some overlaps exist with the learnt distributions when a new,unseen signal arrives.To achieve this goal,a generative convolutional neural network(CNN)architecture based on variational auto encoder(VAE)is built and trained.During the validation procedure of the proposed CNN architectures,we were capable of identifying acoustics signals corresponding to the normal and abnormal wear regime with an accuracy of 97%and 80%.Hence,our approach shows very promising results for in situ and real-time condition monitoring or even wear prediction in tribological applications.展开更多
Deep-Fake is an emerging technology used in synthetic media which manipulates individuals in existing images and videos with someone else’s likeness.This paper presents the comparative study of different deep neural ...Deep-Fake is an emerging technology used in synthetic media which manipulates individuals in existing images and videos with someone else’s likeness.This paper presents the comparative study of different deep neural networks employed for Deep-Fake video detection.In the model,the features from the training data are extracted with the intended Convolution Neural Network model to form feature vectors which are further analysed using a dense layer,a Long Short-Term Memoryand Gated Recurrent by adopting transfer learning with fine tuning for training the models.The model is evaluated to detect Artificial Intelligence based Deep fakes images and videos using benchmark datasets.Comparative analysis shows that the detections are majorly biased towards domain of the dataset but there is a noteworthy improvement in the model performance parameters by using Transfer Learning whereas Convolutional-Recurrent Neural Network has benefits in sequence detection.展开更多
基金supported by financial support from Universiti Sains Malaysia(USM)under FRGS Grant Number FRGS/1/2020/TK03/USM/02/1the School of Computer Sciences USM for their support.
文摘The detection of brain disease is an essential issue in medical and research areas.Deep learning techniques have shown promising results in detecting and diagnosing brain diseases using magnetic resonance imaging(MRI)images.These techniques involve training neural networks on large datasets of MRI images,allowing the networks to learn patterns and features indicative of different brain diseases.However,several challenges and limitations still need to be addressed further to improve the accuracy and effectiveness of these techniques.This paper implements a Feature Enhanced Stacked Auto Encoder(FESAE)model to detect brain diseases.The standard stack auto encoder’s results are trivial and not robust enough to boost the system’s accuracy.Therefore,the standard Stack Auto Encoder(SAE)is replaced with a Stacked Feature Enhanced Auto Encoder with a feature enhancement function to efficiently and effectively get non-trivial features with less activation energy froman image.The proposed model consists of four stages.First,pre-processing is performed to remove noise,and the greyscale image is converted to Red,Green,and Blue(RGB)to enhance feature details for discriminative feature extraction.Second,feature Extraction is performed to extract significant features for classification using DiscreteWavelet Transform(DWT)and Channelization.Third,classification is performed to classify MRI images into four major classes:Normal,Tumor,Brain Stroke,and Alzheimer’s.Finally,the FESAE model outperforms the state-of-theart,machine learning,and deep learning methods such as Artificial Neural Network(ANN),SAE,Random Forest(RF),and Logistic Regression(LR)by achieving a high accuracy of 98.61% on a dataset of 2000 MRI images.The proposed model has significant potential for assisting radiologists in diagnosing brain diseases more accurately and improving patient outcomes.
文摘Damage detection in structures is performed via vibra-tion based structural identification. Modal information, such as fre-quencies and mode shapes, are widely used for structural dama-ge detection to indicate the health conditions of civil structures.The deep learning algorithm that works on a multiple layer neuralnetwork model termed as deep autoencoder is proposed to learnthe relationship between the modal information and structural stiff-ness parameters. This is achieved via dimension reduction of themodal information feature and a non-linear regression against thestructural stiffness parameters. Numerical tests on a symmetri-cal steel frame model are conducted to generate the data for thetraining and validation, and to demonstrate the efficiency of theproposed approach for vibration based structural damage detec-tion.
文摘基于Python语言,结合“老师-学生”模型以及卷积自编码网络提出了Sound Auto Encoder算法,深入研究学生学习算法和实践研究,通过无监督特征学习的方式处理音频数据,根据自编码网络、卷积神经网络原理及相关工作,提出SoundAutoEncoder模型及SoundNet中的“老师-学生”模型.在音频数据的特征学习中,通过分析现有算法优势及局限,对SoundAutoEncoder算法模型网络结构及网络学习算法进行分析.通过实验,对算法Sound Auto Encoder和Sound Net进行对比,在五折上,Soimd Auto Encoder算法取得的结果要比Sound Net明显好;在低于600次迭代中,Soimd Auto Encoder算法的结果相较好,Sound Net算法基本处于训练初级阶段,且与可获得的最好结果尚有差距.
基金supported by the Public Welfare Technology Application Research Project of Zhejiang Province,China(No.LGF21F010001)the Key Research and Development Program of Zhejiang Province,China(Grant No.2019C01002)the Key Research and Development Program of Zhejiang Province,China(Grant No.2021C03138)。
文摘Blind image quality assessment(BIQA)is of fundamental importance in low-level computer vision community.Increasing interest has been drawn in exploiting deep neural networks for BIQA.Despite of the notable success achieved,there is a broad consensus that training deep convolutional neural networks(DCNN)heavily relies on massive annotated data.Unfortunately,BIQA is typically a small sample problem,resulting the generalization ability of BIQA severely restricted.In order to improve the accuracy and generalization ability of BIQA metrics,this work proposed a totally opinion-unaware BIQA in which no subjective annotations are involved in the training stage.Multiple full-reference image quality assessment(FR-IQA)metrics are employed to label the distorted image as a substitution of subjective quality annotation.A deep neural network(DNN)is trained to blindly predict the multiple FR-IQA score in absence of corresponding pristine image.In the end,a selfsupervised FR-IQA score aggregator implemented by adversarial auto-encoder pools the predictions of multiple FR-IQA scores into the final quality predicting score.Even though none of subjective scores are involved in the training stage,experimental results indicate that our proposed full reference induced BIQA framework is as competitive as state-of-the-art BIQA metrics.
文摘Early detection of Parkinson’s Disease(PD)using the PD patients’voice changes would avoid the intervention before the identification of physical symptoms.Various machine learning algorithms were developed to detect PD detection.Nevertheless,these ML methods are lack in generalization and reduced classification performance due to subject overlap.To overcome these issues,this proposed work apply graph long short term memory(GLSTM)model to classify the dynamic features of the PD patient speech signal.The proposed classification model has been further improved by implementing the recurrent neural network(RNN)in batch normalization layer of GLSTM and optimized with adaptive moment estimation(ADAM)on network hidden layer.To consider the importance of feature engineering,this proposed system use Linear Discriminant analysis(LDA)for dimensionality reduction and SparseAuto-Encoder(SAE)for extracting the dynamic speech features.Based on the computation of energy content transited from unvoiced to voice(onset)and voice to voiceless(offset),dynamic features are measured.The PD datasets is evaluated under 10 fold cross validation without sample overlap.The proposed smart PD detection method called RNN-GLSTM-ADAM is numerically experimented with persistent phonations in terms of accuracy,sensitivity,and specificity andMatthew correlation coefficient.The evaluated result of RNN-GLSTM-ADAM extremely improves the PD detection accuracy than static feature based conventional ML and DL approaches.
基金the Researchers Supporting Project Number(RSP2023R 509)King Saud University,Riyadh,Saudi ArabiaThis work was supported in part by the Higher Education Sprout Project from the Ministry of Education(MOE)and National Science and Technology Council,Taiwan,(109-2628-E-224-001-MY3)in part by Isuzu Optics Corporation.Dr.Shih-Yu Chen is the corresponding author.
文摘The medical community has more concern on lung cancer analysis.Medical experts’physical segmentation of lung cancers is time-consuming and needs to be automated.The research study’s objective is to diagnose lung tumors at an early stage to extend the life of humans using deep learning techniques.Computer-Aided Diagnostic(CAD)system aids in the diagnosis and shortens the time necessary to detect the tumor detected.The application of Deep Neural Networks(DNN)has also been exhibited as an excellent and effective method in classification and segmentation tasks.This research aims to separate lung cancers from images of Magnetic Resonance Imaging(MRI)with threshold segmentation.The Honey hook process categorizes lung cancer based on characteristics retrieved using several classifiers.Considering this principle,the work presents a solution for image compression utilizing a Deep Wave Auto-Encoder(DWAE).The combination of the two approaches significantly reduces the overall size of the feature set required for any future classification process performed using DNN.The proposed DWAE-DNN image classifier is applied to a lung imaging dataset with Radial Basis Function(RBF)classifier.The study reported promising results with an accuracy of 97.34%,whereas using the Decision Tree(DT)classifier has an accuracy of 94.24%.The proposed approach(DWAE-DNN)is found to classify the images with an accuracy of 98.67%,either as malignant or normal patients.In contrast to the accuracy requirements,the work also uses the benchmark standards like specificity,sensitivity,and precision to evaluate the efficiency of the network.It is found from an investigation that the DT classifier provides the maximum performance in the DWAE-DNN depending on the network’s performance on image testing,as shown by the data acquired by the categorizers themselves.
文摘Generally,conventional methods for anomaly detection rely on clustering,proximity,or classification.With themassive growth in surveillance videos,outliers or anomalies find ingenious ways to obscure themselves in the network and make conventional techniques inefficient.This research explores the structure of Graph neural networks(GNNs)that generalize deep learning frameworks to graph-structured data.Every node in the graph structure is labeled and anomalies,represented by unlabeled nodes,are predicted by performing random walks on the node-based graph structures.Due to their strong learning abilities,GNNs gained popularity in various domains such as natural language processing,social network analytics and healthcare.Anomaly detection is a challenging task in computer vision but the proposed algorithm using GNNs efficiently performs the identification of anomalies.The Graph-based deep learning networks are designed to predict unknown objects and outliers.In our case,they detect unusual objects in the form of malicious nodes.The edges between nodes represent a relationship of nodes among each other.In case of anomaly,such as the bike rider in Pedestrians data,the rider node has a negative value for the edge and it is identified as an anomaly.The encoding and decoding layers are crucial for determining how statistical measurements affect anomaly identification and for correcting the graph path to the best possible outcome.Results show that the proposed framework is a step ahead of the traditional approaches in detecting unusual activities,which shows a huge potential in automatically monitoring surveillance videos.Performing autonomous monitoring of CCTV,crime control and damage or destruction by a group of people or crowd can be identified and alarms may be triggered in unusual activities in streets or public places.The suggested GNN model improves accuracy by 4%for the Pedestrian 2 dataset and 12%for the Pedestrian 1 dataset compared to a few state-of the-art techniques.
文摘Traffic flow prediction in urban areas is essential in the IntelligentTransportation System (ITS). Short Term Traffic Flow (STTF) predictionimpacts traffic flow series, where an estimation of the number of vehicleswill appear during the next instance of time per hour. Precise STTF iscritical in Intelligent Transportation System. Various extinct systems aim forshort-term traffic forecasts, ensuring a good precision outcome which was asignificant task over the past few years. The main objective of this paper is topropose a new model to predict STTF for every hour of a day. In this paper,we have proposed a novel hybrid algorithm utilizing Principal ComponentAnalysis (PCA), Stacked Auto-Encoder (SAE), Long Short Term Memory(LSTM), and K-Nearest Neighbors (KNN) named PALKNN. Firstly, PCAremoves unwanted information from the dataset and selects essential features.Secondly, SAE is used to reduce the dimension of input data using onehotencoding so the model can be trained with better speed. Thirdly, LSTMtakes the input from SAE, where the data is sorted in ascending orderbased on the important features and generates the derived value. Finally,KNN Regressor takes information from LSTM to predict traffic flow. Theforecasting performance of the PALKNN model is investigated with OpenRoad Traffic Statistics dataset, Great Britain, UK. This paper enhanced thetraffic flow prediction for every hour of a day with a minimal error value.An extensive experimental analysis was performed on the benchmark dataset.The evaluated results indicate the significant improvement of the proposedPALKNN model over the recent approaches such as KNN, SARIMA, LogisticRegression, RNN, and LSTM in terms of root mean square error (RMSE)of 2.07%, mean square error (MSE) of 4.1%, and mean absolute error (MAE)of 2.04%.
基金supported by the Natural Science Foundation of Zhejiang Province(Nos.LQ21F020021 and LZ21F020008)Zhejiang Provincial Natural Science Foundation of China(No.LZ22F020002)the Research Start-up Project funded by Hangzhou Normal University(No.2020QD2035).
文摘Service recommendation provides an effective solution to extract valuable information from the huge and ever-increasing volume of big data generated by the large cardinality of user devices.However,the distributed and rich multi-source big data resources raise challenges to the centralized cloud-based data storage and value mining approaches in terms of economic cost and effective service recommendation methods.In view of these challenges,we propose a deep neural collaborative filtering based service recommendation method with multi-source data(i.e.,NCF-MS)in this paper,which adopts the cloud-edge collaboration computing paradigm to build recommendation model.More specifically,the Stacked Denoising Auto Encoder(SDAE)module is adopted to extract user/service features from auxiliary user profiles and service attributes.The Multiple Layer Perceptron(MLP)module is adopted to integrate the auxiliary user/service features to train the recommendation model.Finally,we evaluate the effectiveness of the NCF-MS method on three public datasets.The experimental results show that our proposed method achieves better performance than existing methods.
文摘In this study,we employ advanced data-driven techniques to investigate the complex relationships between the yields of five major crops and various geographical and spatiotemporal features in Senegal.We analyze how these features influence crop yields by utilizing remotely sensed data.Our methodology incorporates clustering algorithms and correlation matrix analysis to identify significant patterns and dependencies,offering a comprehensive understanding of the factors affecting agricultural productivity in Senegal.To optimize the model's performance and identify the optimal hyperparameters,we implemented a comprehensive grid search across four distinct machine learning regressors:Random Forest,Extreme Gradient Boosting(XGBoost),Categorical Boosting(CatBoost),and Light Gradient-Boosting Machine(LightGBM).Each regressor offers unique functionalities,enhancing our exploration of potential model configurations.The top-performing models were selected based on evaluating multiple performance metrics,ensuring robust and accurate predictive capabilities.The results demonstrated that XGBoost and CatBoost perform better than the other two.We introduce synthetic crop data generated using a Variational Auto Encoder to address the challenges posed by limited agricultural datasets.By achieving high similarity scores with real-world data,our synthetic samples enhance model robustness,mitigate overfitting,and provide a viable solution for small dataset issues in agriculture.Our approach distinguishes itself by creating a flexible model applicable to various crops together.By integrating five crop datasets and generating high-quality synthetic data,we improve model performance,reduce overfitting,and enhance realism.Our findings provide crucial insights for productivity drivers in key cropping systems,enabling robust recommendations and strengthening the decision-making capabilities of policymakers and farmers in datascarce regions.
基金This work was funded by the Austrian COMET Program(project InTribology,No.872176)via the Austrian Research Promotion Agency(FFG)the Provinces of Niederösterreich and Vorarlberg and has been carried out within the Austrian Excellence Centre of Tribology(AC2T Research GmbH)Experiments were carried out within the framework of a project funded by the government of Lower Austria(No.K3-F-760/001-2017).
文摘Functional surfaces in relative contact and motion are prone to wear and tear,resulting in loss of efficiency and performance of the workpieces/machines.Wear occurs in the form of adhesion,abrasion,scuffing,galling,and scoring between contacts.However,the rate of the wear phenomenon depends primarily on the physical properties and the surrounding environment.Monitoring the integrity of surfaces by offline inspections leads to significant wasted machine time.A potential alternate option to offline inspection currently practiced in industries is the analysis of sensors signatures capable of capturing the wear state and correlating it with the wear phenomenon,followed by in situ classification using a state-of-the-art machine learning(ML)algorithm.Though this technique is better than offline inspection,it possesses inherent disadvantages for training the ML models.Ideally,supervised training of ML models requires the datasets considered for the classification to be of equal weightage to avoid biasing.The collection of such a dataset is very cumbersome and expensive in practice,as in real industrial applications,the malfunction period is minimal compared to normal operation.Furthermore,classification models would not classify new wear phenomena from the normal regime if they are unfamiliar.As a promising alternative,in this work,we propose a methodology able to differentiate the abnormal regimes,i.e.,wear phenomenon regimes,from the normal regime.This is carried out by familiarizing the ML algorithms only with the distribution of the acoustic emission(AE)signals captured using a microphone related to the normal regime.As a result,the ML algorithms would be able to detect whether some overlaps exist with the learnt distributions when a new,unseen signal arrives.To achieve this goal,a generative convolutional neural network(CNN)architecture based on variational auto encoder(VAE)is built and trained.During the validation procedure of the proposed CNN architectures,we were capable of identifying acoustics signals corresponding to the normal and abnormal wear regime with an accuracy of 97%and 80%.Hence,our approach shows very promising results for in situ and real-time condition monitoring or even wear prediction in tribological applications.
文摘Deep-Fake is an emerging technology used in synthetic media which manipulates individuals in existing images and videos with someone else’s likeness.This paper presents the comparative study of different deep neural networks employed for Deep-Fake video detection.In the model,the features from the training data are extracted with the intended Convolution Neural Network model to form feature vectors which are further analysed using a dense layer,a Long Short-Term Memoryand Gated Recurrent by adopting transfer learning with fine tuning for training the models.The model is evaluated to detect Artificial Intelligence based Deep fakes images and videos using benchmark datasets.Comparative analysis shows that the detections are majorly biased towards domain of the dataset but there is a noteworthy improvement in the model performance parameters by using Transfer Learning whereas Convolutional-Recurrent Neural Network has benefits in sequence detection.
