With the rapid advancement of visual generative models such as Generative Adversarial Networks(GANs)and stable Diffusion,the creation of highly realistic Deepfake through automated forgery has significantly progressed...With the rapid advancement of visual generative models such as Generative Adversarial Networks(GANs)and stable Diffusion,the creation of highly realistic Deepfake through automated forgery has significantly progressed.This paper examines the advancements inDeepfake detection and defense technologies,emphasizing the shift from passive detection methods to proactive digital watermarking techniques.Passive detection methods,which involve extracting features from images or videos to identify forgeries,encounter challenges such as poor performance against unknown manipulation techniques and susceptibility to counter-forensic tactics.In contrast,proactive digital watermarking techniques embed specificmarkers into images or videos,facilitating real-time detection and traceability,thereby providing a preemptive defense againstDeepfake content.We offer a comprehensive analysis of digitalwatermarking-based forensic techniques,discussing their advantages over passivemethods and highlighting four key benefits:real-time detection,embedded defense,resistance to tampering,and provision of legal evidence.Additionally,the paper identifies gaps in the literature concerning proactive forensic techniques and suggests future research directions,including cross-domain watermarking and adaptive watermarking strategies.By systematically classifying and comparing existing techniques,this review aims to contribute valuable insights for the development of more effective proactive defense strategies in Deepfake forensics.展开更多
Gas content serves as a critical indicator for assessing the resource potential of deep coal mines and forecasting coal mine gas outburst risks.However,existing sampling technologies face challenges in maintaining the...Gas content serves as a critical indicator for assessing the resource potential of deep coal mines and forecasting coal mine gas outburst risks.However,existing sampling technologies face challenges in maintaining the integrity of gas content within samples and are often constrained by estimation errors inherent in empirical formulas,which results in inaccurate gas content measurements.This study introduces a lightweight,in-situ pressure-and gas-preserved corer designed to collect coal samples under the pressure conditions at the sampling point,effectively preventing gas loss during transfer and significantly improving measurement accuracy.Additionally,a gas migration model for deep coal mines was developed to elucidate gas migration characteristics under pressure-preserved coring conditions.The model offers valuable insights for optimizing coring parameters,demonstrating that both minimizing the coring hole diameter and reducing the pressure difference between the coring-point pressure and the original pore pressure can effectively improve the precision of gas content measurements.Coring tests conducted at an experimental base validated the performance of the corer and its effectiveness in sample collection.Furthermore,successful horizontal coring tests conducted in an underground coal mine roadway demonstrated that the measured gas content using pressure-preserved coring was 34%higher than that obtained through open sampling methods.展开更多
The rapid evolution of malware presents a critical cybersecurity challenge,rendering traditional signature-based detection methods ineffective against novel variants.This growing threat affects individuals,organizatio...The rapid evolution of malware presents a critical cybersecurity challenge,rendering traditional signature-based detection methods ineffective against novel variants.This growing threat affects individuals,organizations,and governments,highlighting the urgent need for robust malware detection mechanisms.Conventional machine learning-based approaches rely on static and dynamicmalware analysis and often struggle to detect previously unseen threats due to their dependency on predefined signatures.Although machine learning algorithms(MLAs)offer promising detection capabilities,their reliance on extensive feature engineering limits real-time applicability.Deep learning techniques mitigate this issue by automating feature extraction but may introduce computational overhead,affecting deployment efficiency.This research evaluates classical MLAs and deep learningmodels to enhance malware detection performance across diverse datasets.The proposed approach integrates a novel text and imagebased detection framework,employing an optimized Support Vector Machine(SVM)for textual data analysis and EfficientNet-B0 for image-based malware classification.Experimental analysis,conducted across multiple train-test splits over varying timescales,demonstrates 99.97%accuracy on textual datasets using SVM and 96.7%accuracy on image-based datasets with EfficientNet-B0,significantly improving zero-day malware detection.Furthermore,a comparative analysis with existing competitive techniques,such as Random Forest,XGBoost,and CNN-based(Convolutional Neural Network)classifiers,highlights the superior performance of the proposed model in terms of accuracy,efficiency,and robustness.展开更多
In recent years,Speech Emotion Recognition(SER)has developed into an essential instrument for interpreting human emotions from auditory data.The proposed research focuses on the development of a SER system employing d...In recent years,Speech Emotion Recognition(SER)has developed into an essential instrument for interpreting human emotions from auditory data.The proposed research focuses on the development of a SER system employing deep learning and multiple datasets containing samples of emotive speech.The primary objective of this research endeavor is to investigate the utilization of Convolutional Neural Networks(CNNs)in the process of sound feature extraction.Stretching,pitch manipulation,and noise injection are a few of the techniques utilized in this study to improve the data quality.Feature extraction methods including Zero Crossing Rate,Chroma_stft,Mel⁃scale Frequency Cepstral Coefficients(MFCC),Root Mean Square(RMS),and Mel⁃Spectogram are used to train a model.By using these techniques,audio signals can be transformed into recognized features that can be utilized to train the model.Ultimately,the study produces a thorough evaluation of the models performance.When this method was applied,the model achieved an impressive accuracy of 94.57%on the test dataset.The proposed work was also validated on the EMO⁃BD and IEMOCAP datasets.These consist of further data augmentation,feature engineering,and hyperparameter optimization.By following these development paths,SER systems will be able to be implemented in real⁃world scenarios with greater accuracy and resilience.展开更多
Many bioinformatics applications require determining the class of a newly sequenced Deoxyribonucleic acid(DNA)sequence,making DNA sequence classification an integral step in performing bioinformatics analysis,where la...Many bioinformatics applications require determining the class of a newly sequenced Deoxyribonucleic acid(DNA)sequence,making DNA sequence classification an integral step in performing bioinformatics analysis,where large biomedical datasets are transformed into valuable knowledge.Existing methods rely on a feature extraction step and suffer from high computational time requirements.In contrast,newer approaches leveraging deep learning have shown significant promise in enhancing accuracy and efficiency.In this paper,we investigate the performance of various deep learning architectures:Convolutional Neural Network(CNN),CNN-Long Short-Term Memory(CNNLSTM),CNN-Bidirectional Long Short-Term Memory(CNN-BiLSTM),Residual Network(ResNet),and InceptionV3 for DNA sequence classification.Various numerical and visual data representation techniques are utilized to represent the input datasets,including:label encoding,k-mer sentence encoding,k-mer one-hot vector,Frequency Chaos Game Representation(FCGR)and 5-Color Map(ColorSquare).Three datasets are used for the training of the models including H3,H4 and DNA Sequence Dataset(Yeast,Human,Arabidopsis Thaliana).Experiments are performed to determine which combination of DNA representation and deep learning architecture yields improved performance for the classification task.Our results indicate that using a hybrid CNN-LSTM neural network trained on DNA sequences represented as one-hot encoded k-mer sequences yields the best performance,achieving an accuracy of 92.1%.展开更多
In this investigation,we meticulously annotated a corpus of 21,174 auroral images captured by the THEMIS All-Sky Imager across diverse temporal instances.These images were categorized using an array of descriptors suc...In this investigation,we meticulously annotated a corpus of 21,174 auroral images captured by the THEMIS All-Sky Imager across diverse temporal instances.These images were categorized using an array of descriptors such as'arc','ab'(aurora but bright),'cloudy','diffuse','discrete',and'clear'.Subsequently,we utilized a state-of-the-art convolutional neural network,ConvNeXt(Convolutional Neural Network Next),deploying deep learning techniques to train the model on a dataset classified into six distinct categories.Remarkably,on the test set our methodology attained an accuracy of 99.4%,a performance metric closely mirroring human visual observation,thereby underscoring the classifier’s competence in paralleling human perceptual accuracy.Building upon this foundation,we embarked on the identification of large-scale auroral optical data,meticulously quantifying the monthly occurrence and Magnetic Local Time(MLT)variations of auroras from stations at different latitudes:RANK(high-latitude),FSMI(mid-latitude),and ATHA(low-latitude),under different solar wind conditions.This study paves the way for future explorations into the temporal variations of auroral phenomena in diverse geomagnetic contexts.展开更多
Deep learning(DL)has revolutionized time series forecasting(TSF),surpassing traditional statistical methods(e.g.,ARIMA)and machine learning techniques in modeling complex nonlinear dynamics and long-term dependencies ...Deep learning(DL)has revolutionized time series forecasting(TSF),surpassing traditional statistical methods(e.g.,ARIMA)and machine learning techniques in modeling complex nonlinear dynamics and long-term dependencies prevalent in real-world temporal data.This comprehensive survey reviews state-of-the-art DL architectures forTSF,focusing on four core paradigms:(1)ConvolutionalNeuralNetworks(CNNs),adept at extracting localized temporal features;(2)Recurrent Neural Networks(RNNs)and their advanced variants(LSTM,GRU),designed for sequential dependency modeling;(3)Graph Neural Networks(GNNs),specialized for forecasting structured relational data with spatial-temporal dependencies;and(4)Transformer-based models,leveraging self-attention mechanisms to capture global temporal patterns efficiently.We provide a rigorous analysis of the theoretical underpinnings,recent algorithmic advancements(e.g.,TCNs,attention mechanisms,hybrid architectures),and practical applications of each framework,supported by extensive benchmark datasets(e.g.,ETT,traffic flow,financial indicators)and standardized evaluation metrics(MAE,MSE,RMSE).Critical challenges,including handling irregular sampling intervals,integrating domain knowledge for robustness,and managing computational complexity,are thoroughly discussed.Emerging research directions highlighted include diffusion models for uncertainty quantification,hybrid pipelines combining classical statistical and DL techniques for enhanced interpretability,quantile regression with Transformers for riskaware forecasting,and optimizations for real-time deployment.This work serves as an essential reference,consolidating methodological innovations,empirical resources,and future trends to bridge the gap between theoretical research and practical implementation needs for researchers and practitioners in the field.展开更多
DeepSeek Chinese artificial intelligence(AI)open-source model,has gained a lot of attention due to its economical training and efficient inference.DeepSeek,a model trained on large-scale reinforcement learning without...DeepSeek Chinese artificial intelligence(AI)open-source model,has gained a lot of attention due to its economical training and efficient inference.DeepSeek,a model trained on large-scale reinforcement learning without supervised fine-tuning as a preliminary step,demonstrates remarkable reasoning capabilities of performing a wide range of tasks.DeepSeek is a prominent AI-driven chatbot that assists individuals in learning and enhances responses by generating insightful solutions to inquiries.Users possess divergent viewpoints regarding advanced models like DeepSeek,posting both their merits and shortcomings across several social media platforms.This research presents a new framework for predicting public sentiment to evaluate perceptions of DeepSeek.To transform the unstructured data into a suitable manner,we initially collect DeepSeek-related tweets from Twitter and subsequently implement various preprocessing methods.Subsequently,we annotated the tweets utilizing the Valence Aware Dictionary and sentiment Reasoning(VADER)methodology and the lexicon-driven TextBlob.Next,we classified the attitudes obtained from the purified data utilizing the proposed hybrid model.The proposed hybrid model consists of long-term,shortterm memory(LSTM)and bidirectional gated recurrent units(BiGRU).To strengthen it,we include multi-head attention,regularizer activation,and dropout units to enhance performance.Topic modeling employing KMeans clustering and Latent Dirichlet Allocation(LDA),was utilized to analyze public behavior concerning DeepSeek.The perceptions demonstrate that 82.5%of the people are positive,15.2%negative,and 2.3%neutral using TextBlob,and 82.8%positive,16.1%negative,and 1.2%neutral using the VADER analysis.The slight difference in results ensures that both analyses concur with their overall perceptions and may have distinct views of language peculiarities.The results indicate that the proposed model surpassed previous state-of-the-art approaches.展开更多
Addressing plant diseases and pests is not just crucial;it’s a matter of utmost importance for enhancing crop production and preventing economic losses. Recent advancements in artificial intelligence, machine learnin...Addressing plant diseases and pests is not just crucial;it’s a matter of utmost importance for enhancing crop production and preventing economic losses. Recent advancements in artificial intelligence, machine learning, and deep learning have revolutionised the precision and efficiency of this process, surpassing the limitations of manual identification. This study comprehensively reviews modern computer-based techniques, including recent advances in artificial intelligence, for detecting diseases and pests through images. This paper uniquely categorises methodologies into hyperspectral imaging, non-visualisation techniques, visualisation approaches, modified deep learning architectures, and transformer models, helping researchers gain detailed, insightful understandings. The exhaustive survey of recent works and comparative studies in this domain guides researchers in selecting appropriate and advanced state-of-the-art methods for plant disease and pest detection. Additionally, this paper highlights the consistent superiority of modern AI-based approaches, which often outperform older image analysis methods in terms of speed and accuracy. Further, this survey focuses on the efficiency of vision transformers against well-known deep learning architectures like MobileNetV3, which shows that Hierarchical Vision Transformer (HvT) can achieve accuracy upwards of 99.3% in plant disease detection. The study concludes by addressing the challenges of designing the systems, proposing potential solutions, and outlining directions for future research in this field.展开更多
The 3GPP standard defines the requirements for next-generation wireless networks,with particular attention to Ultra-Reliable Low-Latency Communications(URLLC),critical for applications such as Unmanned Aerial Vehicles...The 3GPP standard defines the requirements for next-generation wireless networks,with particular attention to Ultra-Reliable Low-Latency Communications(URLLC),critical for applications such as Unmanned Aerial Vehicles(UAVs).In this context,Non-Orthogonal Multiple Access(NOMA)has emerged as a promising technique to improve spectrum efficiency and user fairness by allowing multiple users to share the same frequency resources.However,optimizing key parameters–such as beamforming,rate allocation,and UAV trajectory–presents significant challenges due to the nonconvex nature of the problem,especially under stringent URLLC constraints.This paper proposes an advanced deep learning-driven approach to address the resulting complex optimization challenges.We formulate a downlink multiuser UAV,Rate-Splitting Multiple Access(RSMA),and Multiple Input Multiple Output(MIMO)system aimed at maximizing the achievable rate under stringent constraints,including URLLC quality-of-service(QoS),power budgets,rate allocations,and UAV trajectory limitations.Due to the highly nonconvex nature of the optimization problem,we introduce a novel distributed deep reinforcement learning(DRL)framework based on dual-agent deep deterministic policy gradient(DA-DDPG).The proposed framework leverages inception-inspired and deep unfolding architectures to improve feature extraction and convergence in beamforming and rate allocation.For UAV trajectory optimization,we design a dedicated actor-critic agent using a fully connected deep neural network(DNN),further enhanced through incremental learning.Simulation results validate the effectiveness of our approach,demonstrating significant performance gains over existing methods and confirming its potential for real-time URLLC in next-generation UAV communication networks.展开更多
This study presents an enhanced convolutional neural network(CNN)model integrated with Explainable Artificial Intelligence(XAI)techniques for accurate prediction and interpretation of wheat crop diseases.The aim is to...This study presents an enhanced convolutional neural network(CNN)model integrated with Explainable Artificial Intelligence(XAI)techniques for accurate prediction and interpretation of wheat crop diseases.The aim is to streamline the detection process while offering transparent insights into the model’s decision-making to support effective disease management.To evaluate the model,a dataset was collected from wheat fields in Kotli,Azad Kashmir,Pakistan,and tested across multiple data splits.The proposed model demonstrates improved stability,faster conver-gence,and higher classification accuracy.The results show significant improvements in prediction accuracy and stability compared to prior works,achieving up to 100%accuracy in certain configurations.In addition,XAI methods such as Local Interpretable Model-agnostic Explanations(LIME)and Shapley Additive Explanations(SHAP)were employed to explain the model’s predictions,highlighting the most influential features contributing to classification decisions.The combined use of CNN and XAI offers a dual benefit:strong predictive performance and clear interpretability of outcomes,which is especially critical in real-world agricultural applications.These findings underscore the potential of integrating deep learning models with XAI to advance automated plant disease detection.The study offers a precise,reliable,and interpretable solution for improving wheat production and promoting agricultural sustainability.Future extensions of this work may include scaling the dataset across broader regions and incorporating additional modalities such as environmental data to enhance model robustness and generalization.展开更多
Early identification and treatment of stroke can greatly improve patient outcomes and quality of life.Although clinical tests such as the Cincinnati Pre-hospital Stroke Scale(CPSS)and the Face Arm Speech Test(FAST)are...Early identification and treatment of stroke can greatly improve patient outcomes and quality of life.Although clinical tests such as the Cincinnati Pre-hospital Stroke Scale(CPSS)and the Face Arm Speech Test(FAST)are commonly used for stroke screening,accurate administration is dependent on specialized training.In this study,we proposed a novel multimodal deep learning approach,based on the FAST,for assessing suspected stroke patients exhibiting symptoms such as limb weakness,facial paresis,and speech disorders in acute settings.We collected a dataset comprising videos and audio recordings of emergency room patients performing designated limb movements,facial expressions,and speech tests based on the FAST.We compared the constructed deep learning model,which was designed to process multi-modal datasets,with six prior models that achieved good action classification performance,including the I3D,SlowFast,X3D,TPN,TimeSformer,and MViT.We found that the findings of our deep learning model had a higher clinical value compared with the other approaches.Moreover,the multi-modal model outperformed its single-module variants,highlighting the benefit of utilizing multiple types of patient data,such as action videos and speech audio.These results indicate that a multi-modal deep learning model combined with the FAST could greatly improve the accuracy and sensitivity of early stroke identification of stroke,thus providing a practical and powerful tool for assessing stroke patients in an emergency clinical setting.展开更多
Organizations are adopting the Bring Your Own Device(BYOD)concept to enhance productivity and reduce expenses.However,this trend introduces security challenges,such as unauthorized access.Traditional access control sy...Organizations are adopting the Bring Your Own Device(BYOD)concept to enhance productivity and reduce expenses.However,this trend introduces security challenges,such as unauthorized access.Traditional access control systems,such as Attribute-Based Access Control(ABAC)and Role-Based Access Control(RBAC),are limited in their ability to enforce access decisions due to the variability and dynamism of attributes related to users and resources.This paper proposes a method for enforcing access decisions that is adaptable and dynamic,based on multilayer hybrid deep learning techniques,particularly the Tabular Deep Neural Network Tabular DNN method.This technique transforms all input attributes in an access request into a binary classification(allow or deny)using multiple layers,ensuring accurate and efficient access decision-making.The proposed solution was evaluated using the Kaggle Amazon access control policy dataset and demonstrated its effectiveness by achieving a 94%accuracy rate.Additionally,the proposed solution enhances the implementation of access decisions based on a variety of resource and user attributes while ensuring privacy through indirect communication with the Policy Administration Point(PAP).This solution significantly improves the flexibility of access control systems,making themmore dynamic and adaptable to the evolving needs ofmodern organizations.Furthermore,it offers a scalable approach to manage the complexities associated with the BYOD environment,providing a robust framework for secure and efficient access management.展开更多
Breast cancer(BC)is one of the leading causes of death among women worldwide,as it has emerged as the most commonly diagnosed malignancy in women.Early detection and effective treatment of BC can help save women’s li...Breast cancer(BC)is one of the leading causes of death among women worldwide,as it has emerged as the most commonly diagnosed malignancy in women.Early detection and effective treatment of BC can help save women’s lives.Developing an efficient technology-based detection system can lead to non-destructive and preliminary cancer detection techniques.This paper proposes a comprehensive framework that can effectively diagnose cancerous cells from benign cells using the Curated Breast Imaging Subset of the Digital Database for Screening Mammography(CBIS-DDSM)data set.The novelty of the proposed framework lies in the integration of various techniques,where the fusion of deep learning(DL),traditional machine learning(ML)techniques,and enhanced classification models have been deployed using the curated dataset.The analysis outcome proves that the proposed enhanced RF(ERF),enhanced DT(EDT)and enhanced LR(ELR)models for BC detection outperformed most of the existing models with impressive results.展开更多
基金supported by the National Fund Cultivation Project from China People’s Police University(Grant Number:JJPY202402)National Natural Science Foundation of China(Grant Number:62172165).
文摘With the rapid advancement of visual generative models such as Generative Adversarial Networks(GANs)and stable Diffusion,the creation of highly realistic Deepfake through automated forgery has significantly progressed.This paper examines the advancements inDeepfake detection and defense technologies,emphasizing the shift from passive detection methods to proactive digital watermarking techniques.Passive detection methods,which involve extracting features from images or videos to identify forgeries,encounter challenges such as poor performance against unknown manipulation techniques and susceptibility to counter-forensic tactics.In contrast,proactive digital watermarking techniques embed specificmarkers into images or videos,facilitating real-time detection and traceability,thereby providing a preemptive defense againstDeepfake content.We offer a comprehensive analysis of digitalwatermarking-based forensic techniques,discussing their advantages over passivemethods and highlighting four key benefits:real-time detection,embedded defense,resistance to tampering,and provision of legal evidence.Additionally,the paper identifies gaps in the literature concerning proactive forensic techniques and suggests future research directions,including cross-domain watermarking and adaptive watermarking strategies.By systematically classifying and comparing existing techniques,this review aims to contribute valuable insights for the development of more effective proactive defense strategies in Deepfake forensics.
基金supported by the National Natural Science Foundation of China(Nos.51827901,42477191,and 52304033)the Fundamental Research Funds for the Central Universities(No.YJ202449)+1 种基金the Open Research Fund of State Key Laboratory of Geomechanics and Geotechnical Engineering,Institute of Rock and Soil Mechanics,Chinese Academy of Sciences(No.SKLGME022009)the China Postdoctoral Science Foundation(No.2023M742446).
文摘Gas content serves as a critical indicator for assessing the resource potential of deep coal mines and forecasting coal mine gas outburst risks.However,existing sampling technologies face challenges in maintaining the integrity of gas content within samples and are often constrained by estimation errors inherent in empirical formulas,which results in inaccurate gas content measurements.This study introduces a lightweight,in-situ pressure-and gas-preserved corer designed to collect coal samples under the pressure conditions at the sampling point,effectively preventing gas loss during transfer and significantly improving measurement accuracy.Additionally,a gas migration model for deep coal mines was developed to elucidate gas migration characteristics under pressure-preserved coring conditions.The model offers valuable insights for optimizing coring parameters,demonstrating that both minimizing the coring hole diameter and reducing the pressure difference between the coring-point pressure and the original pore pressure can effectively improve the precision of gas content measurements.Coring tests conducted at an experimental base validated the performance of the corer and its effectiveness in sample collection.Furthermore,successful horizontal coring tests conducted in an underground coal mine roadway demonstrated that the measured gas content using pressure-preserved coring was 34%higher than that obtained through open sampling methods.
基金supported and funded by the Deanship of Scientific Research at Imam Mohammad Ibn Saud Islamic University(IMSIU)(grant number IMSIU-DDRSP2504).
文摘The rapid evolution of malware presents a critical cybersecurity challenge,rendering traditional signature-based detection methods ineffective against novel variants.This growing threat affects individuals,organizations,and governments,highlighting the urgent need for robust malware detection mechanisms.Conventional machine learning-based approaches rely on static and dynamicmalware analysis and often struggle to detect previously unseen threats due to their dependency on predefined signatures.Although machine learning algorithms(MLAs)offer promising detection capabilities,their reliance on extensive feature engineering limits real-time applicability.Deep learning techniques mitigate this issue by automating feature extraction but may introduce computational overhead,affecting deployment efficiency.This research evaluates classical MLAs and deep learningmodels to enhance malware detection performance across diverse datasets.The proposed approach integrates a novel text and imagebased detection framework,employing an optimized Support Vector Machine(SVM)for textual data analysis and EfficientNet-B0 for image-based malware classification.Experimental analysis,conducted across multiple train-test splits over varying timescales,demonstrates 99.97%accuracy on textual datasets using SVM and 96.7%accuracy on image-based datasets with EfficientNet-B0,significantly improving zero-day malware detection.Furthermore,a comparative analysis with existing competitive techniques,such as Random Forest,XGBoost,and CNN-based(Convolutional Neural Network)classifiers,highlights the superior performance of the proposed model in terms of accuracy,efficiency,and robustness.
文摘In recent years,Speech Emotion Recognition(SER)has developed into an essential instrument for interpreting human emotions from auditory data.The proposed research focuses on the development of a SER system employing deep learning and multiple datasets containing samples of emotive speech.The primary objective of this research endeavor is to investigate the utilization of Convolutional Neural Networks(CNNs)in the process of sound feature extraction.Stretching,pitch manipulation,and noise injection are a few of the techniques utilized in this study to improve the data quality.Feature extraction methods including Zero Crossing Rate,Chroma_stft,Mel⁃scale Frequency Cepstral Coefficients(MFCC),Root Mean Square(RMS),and Mel⁃Spectogram are used to train a model.By using these techniques,audio signals can be transformed into recognized features that can be utilized to train the model.Ultimately,the study produces a thorough evaluation of the models performance.When this method was applied,the model achieved an impressive accuracy of 94.57%on the test dataset.The proposed work was also validated on the EMO⁃BD and IEMOCAP datasets.These consist of further data augmentation,feature engineering,and hyperparameter optimization.By following these development paths,SER systems will be able to be implemented in real⁃world scenarios with greater accuracy and resilience.
基金funded by the Researchers Supporting Project number(RSPD2025R857),King Saud University,Riyadh,Saudi Arabia.
文摘Many bioinformatics applications require determining the class of a newly sequenced Deoxyribonucleic acid(DNA)sequence,making DNA sequence classification an integral step in performing bioinformatics analysis,where large biomedical datasets are transformed into valuable knowledge.Existing methods rely on a feature extraction step and suffer from high computational time requirements.In contrast,newer approaches leveraging deep learning have shown significant promise in enhancing accuracy and efficiency.In this paper,we investigate the performance of various deep learning architectures:Convolutional Neural Network(CNN),CNN-Long Short-Term Memory(CNNLSTM),CNN-Bidirectional Long Short-Term Memory(CNN-BiLSTM),Residual Network(ResNet),and InceptionV3 for DNA sequence classification.Various numerical and visual data representation techniques are utilized to represent the input datasets,including:label encoding,k-mer sentence encoding,k-mer one-hot vector,Frequency Chaos Game Representation(FCGR)and 5-Color Map(ColorSquare).Three datasets are used for the training of the models including H3,H4 and DNA Sequence Dataset(Yeast,Human,Arabidopsis Thaliana).Experiments are performed to determine which combination of DNA representation and deep learning architecture yields improved performance for the classification task.Our results indicate that using a hybrid CNN-LSTM neural network trained on DNA sequences represented as one-hot encoded k-mer sequences yields the best performance,achieving an accuracy of 92.1%.
基金supported by the General Program of the National Natural Science Foundation of China(Grant No.42374212)the National Magnetic Confinement Fusion Energy Research and Development Program of China(Grant No.2024YFE03020004).
文摘In this investigation,we meticulously annotated a corpus of 21,174 auroral images captured by the THEMIS All-Sky Imager across diverse temporal instances.These images were categorized using an array of descriptors such as'arc','ab'(aurora but bright),'cloudy','diffuse','discrete',and'clear'.Subsequently,we utilized a state-of-the-art convolutional neural network,ConvNeXt(Convolutional Neural Network Next),deploying deep learning techniques to train the model on a dataset classified into six distinct categories.Remarkably,on the test set our methodology attained an accuracy of 99.4%,a performance metric closely mirroring human visual observation,thereby underscoring the classifier’s competence in paralleling human perceptual accuracy.Building upon this foundation,we embarked on the identification of large-scale auroral optical data,meticulously quantifying the monthly occurrence and Magnetic Local Time(MLT)variations of auroras from stations at different latitudes:RANK(high-latitude),FSMI(mid-latitude),and ATHA(low-latitude),under different solar wind conditions.This study paves the way for future explorations into the temporal variations of auroral phenomena in diverse geomagnetic contexts.
基金funded by Natural Science Foundation of Heilongjiang Province,grant number LH2023F020.
文摘Deep learning(DL)has revolutionized time series forecasting(TSF),surpassing traditional statistical methods(e.g.,ARIMA)and machine learning techniques in modeling complex nonlinear dynamics and long-term dependencies prevalent in real-world temporal data.This comprehensive survey reviews state-of-the-art DL architectures forTSF,focusing on four core paradigms:(1)ConvolutionalNeuralNetworks(CNNs),adept at extracting localized temporal features;(2)Recurrent Neural Networks(RNNs)and their advanced variants(LSTM,GRU),designed for sequential dependency modeling;(3)Graph Neural Networks(GNNs),specialized for forecasting structured relational data with spatial-temporal dependencies;and(4)Transformer-based models,leveraging self-attention mechanisms to capture global temporal patterns efficiently.We provide a rigorous analysis of the theoretical underpinnings,recent algorithmic advancements(e.g.,TCNs,attention mechanisms,hybrid architectures),and practical applications of each framework,supported by extensive benchmark datasets(e.g.,ETT,traffic flow,financial indicators)and standardized evaluation metrics(MAE,MSE,RMSE).Critical challenges,including handling irregular sampling intervals,integrating domain knowledge for robustness,and managing computational complexity,are thoroughly discussed.Emerging research directions highlighted include diffusion models for uncertainty quantification,hybrid pipelines combining classical statistical and DL techniques for enhanced interpretability,quantile regression with Transformers for riskaware forecasting,and optimizations for real-time deployment.This work serves as an essential reference,consolidating methodological innovations,empirical resources,and future trends to bridge the gap between theoretical research and practical implementation needs for researchers and practitioners in the field.
文摘DeepSeek Chinese artificial intelligence(AI)open-source model,has gained a lot of attention due to its economical training and efficient inference.DeepSeek,a model trained on large-scale reinforcement learning without supervised fine-tuning as a preliminary step,demonstrates remarkable reasoning capabilities of performing a wide range of tasks.DeepSeek is a prominent AI-driven chatbot that assists individuals in learning and enhances responses by generating insightful solutions to inquiries.Users possess divergent viewpoints regarding advanced models like DeepSeek,posting both their merits and shortcomings across several social media platforms.This research presents a new framework for predicting public sentiment to evaluate perceptions of DeepSeek.To transform the unstructured data into a suitable manner,we initially collect DeepSeek-related tweets from Twitter and subsequently implement various preprocessing methods.Subsequently,we annotated the tweets utilizing the Valence Aware Dictionary and sentiment Reasoning(VADER)methodology and the lexicon-driven TextBlob.Next,we classified the attitudes obtained from the purified data utilizing the proposed hybrid model.The proposed hybrid model consists of long-term,shortterm memory(LSTM)and bidirectional gated recurrent units(BiGRU).To strengthen it,we include multi-head attention,regularizer activation,and dropout units to enhance performance.Topic modeling employing KMeans clustering and Latent Dirichlet Allocation(LDA),was utilized to analyze public behavior concerning DeepSeek.The perceptions demonstrate that 82.5%of the people are positive,15.2%negative,and 2.3%neutral using TextBlob,and 82.8%positive,16.1%negative,and 1.2%neutral using the VADER analysis.The slight difference in results ensures that both analyses concur with their overall perceptions and may have distinct views of language peculiarities.The results indicate that the proposed model surpassed previous state-of-the-art approaches.
文摘Addressing plant diseases and pests is not just crucial;it’s a matter of utmost importance for enhancing crop production and preventing economic losses. Recent advancements in artificial intelligence, machine learning, and deep learning have revolutionised the precision and efficiency of this process, surpassing the limitations of manual identification. This study comprehensively reviews modern computer-based techniques, including recent advances in artificial intelligence, for detecting diseases and pests through images. This paper uniquely categorises methodologies into hyperspectral imaging, non-visualisation techniques, visualisation approaches, modified deep learning architectures, and transformer models, helping researchers gain detailed, insightful understandings. The exhaustive survey of recent works and comparative studies in this domain guides researchers in selecting appropriate and advanced state-of-the-art methods for plant disease and pest detection. Additionally, this paper highlights the consistent superiority of modern AI-based approaches, which often outperform older image analysis methods in terms of speed and accuracy. Further, this survey focuses on the efficiency of vision transformers against well-known deep learning architectures like MobileNetV3, which shows that Hierarchical Vision Transformer (HvT) can achieve accuracy upwards of 99.3% in plant disease detection. The study concludes by addressing the challenges of designing the systems, proposing potential solutions, and outlining directions for future research in this field.
基金supported by the Deputyship of Research&Innovation,Ministry of Education in Saudi Arabia for funding this research work through the project number RI-44-0291.
文摘The 3GPP standard defines the requirements for next-generation wireless networks,with particular attention to Ultra-Reliable Low-Latency Communications(URLLC),critical for applications such as Unmanned Aerial Vehicles(UAVs).In this context,Non-Orthogonal Multiple Access(NOMA)has emerged as a promising technique to improve spectrum efficiency and user fairness by allowing multiple users to share the same frequency resources.However,optimizing key parameters–such as beamforming,rate allocation,and UAV trajectory–presents significant challenges due to the nonconvex nature of the problem,especially under stringent URLLC constraints.This paper proposes an advanced deep learning-driven approach to address the resulting complex optimization challenges.We formulate a downlink multiuser UAV,Rate-Splitting Multiple Access(RSMA),and Multiple Input Multiple Output(MIMO)system aimed at maximizing the achievable rate under stringent constraints,including URLLC quality-of-service(QoS),power budgets,rate allocations,and UAV trajectory limitations.Due to the highly nonconvex nature of the optimization problem,we introduce a novel distributed deep reinforcement learning(DRL)framework based on dual-agent deep deterministic policy gradient(DA-DDPG).The proposed framework leverages inception-inspired and deep unfolding architectures to improve feature extraction and convergence in beamforming and rate allocation.For UAV trajectory optimization,we design a dedicated actor-critic agent using a fully connected deep neural network(DNN),further enhanced through incremental learning.Simulation results validate the effectiveness of our approach,demonstrating significant performance gains over existing methods and confirming its potential for real-time URLLC in next-generation UAV communication networks.
文摘This study presents an enhanced convolutional neural network(CNN)model integrated with Explainable Artificial Intelligence(XAI)techniques for accurate prediction and interpretation of wheat crop diseases.The aim is to streamline the detection process while offering transparent insights into the model’s decision-making to support effective disease management.To evaluate the model,a dataset was collected from wheat fields in Kotli,Azad Kashmir,Pakistan,and tested across multiple data splits.The proposed model demonstrates improved stability,faster conver-gence,and higher classification accuracy.The results show significant improvements in prediction accuracy and stability compared to prior works,achieving up to 100%accuracy in certain configurations.In addition,XAI methods such as Local Interpretable Model-agnostic Explanations(LIME)and Shapley Additive Explanations(SHAP)were employed to explain the model’s predictions,highlighting the most influential features contributing to classification decisions.The combined use of CNN and XAI offers a dual benefit:strong predictive performance and clear interpretability of outcomes,which is especially critical in real-world agricultural applications.These findings underscore the potential of integrating deep learning models with XAI to advance automated plant disease detection.The study offers a precise,reliable,and interpretable solution for improving wheat production and promoting agricultural sustainability.Future extensions of this work may include scaling the dataset across broader regions and incorporating additional modalities such as environmental data to enhance model robustness and generalization.
基金supported by the Ministry of Science and Technology of China,No.2020AAA0109605(to XL)Meizhou Major Scientific and Technological Innovation PlatformsProjects of Guangdong Provincial Science & Technology Plan Projects,No.2019A0102005(to HW).
文摘Early identification and treatment of stroke can greatly improve patient outcomes and quality of life.Although clinical tests such as the Cincinnati Pre-hospital Stroke Scale(CPSS)and the Face Arm Speech Test(FAST)are commonly used for stroke screening,accurate administration is dependent on specialized training.In this study,we proposed a novel multimodal deep learning approach,based on the FAST,for assessing suspected stroke patients exhibiting symptoms such as limb weakness,facial paresis,and speech disorders in acute settings.We collected a dataset comprising videos and audio recordings of emergency room patients performing designated limb movements,facial expressions,and speech tests based on the FAST.We compared the constructed deep learning model,which was designed to process multi-modal datasets,with six prior models that achieved good action classification performance,including the I3D,SlowFast,X3D,TPN,TimeSformer,and MViT.We found that the findings of our deep learning model had a higher clinical value compared with the other approaches.Moreover,the multi-modal model outperformed its single-module variants,highlighting the benefit of utilizing multiple types of patient data,such as action videos and speech audio.These results indicate that a multi-modal deep learning model combined with the FAST could greatly improve the accuracy and sensitivity of early stroke identification of stroke,thus providing a practical and powerful tool for assessing stroke patients in an emergency clinical setting.
基金partly supported by the University of Malaya Impact Oriented Interdisci-plinary Research Grant under Grant IIRG008(A,B,C)-19IISS.
文摘Organizations are adopting the Bring Your Own Device(BYOD)concept to enhance productivity and reduce expenses.However,this trend introduces security challenges,such as unauthorized access.Traditional access control systems,such as Attribute-Based Access Control(ABAC)and Role-Based Access Control(RBAC),are limited in their ability to enforce access decisions due to the variability and dynamism of attributes related to users and resources.This paper proposes a method for enforcing access decisions that is adaptable and dynamic,based on multilayer hybrid deep learning techniques,particularly the Tabular Deep Neural Network Tabular DNN method.This technique transforms all input attributes in an access request into a binary classification(allow or deny)using multiple layers,ensuring accurate and efficient access decision-making.The proposed solution was evaluated using the Kaggle Amazon access control policy dataset and demonstrated its effectiveness by achieving a 94%accuracy rate.Additionally,the proposed solution enhances the implementation of access decisions based on a variety of resource and user attributes while ensuring privacy through indirect communication with the Policy Administration Point(PAP).This solution significantly improves the flexibility of access control systems,making themmore dynamic and adaptable to the evolving needs ofmodern organizations.Furthermore,it offers a scalable approach to manage the complexities associated with the BYOD environment,providing a robust framework for secure and efficient access management.
文摘Breast cancer(BC)is one of the leading causes of death among women worldwide,as it has emerged as the most commonly diagnosed malignancy in women.Early detection and effective treatment of BC can help save women’s lives.Developing an efficient technology-based detection system can lead to non-destructive and preliminary cancer detection techniques.This paper proposes a comprehensive framework that can effectively diagnose cancerous cells from benign cells using the Curated Breast Imaging Subset of the Digital Database for Screening Mammography(CBIS-DDSM)data set.The novelty of the proposed framework lies in the integration of various techniques,where the fusion of deep learning(DL),traditional machine learning(ML)techniques,and enhanced classification models have been deployed using the curated dataset.The analysis outcome proves that the proposed enhanced RF(ERF),enhanced DT(EDT)and enhanced LR(ELR)models for BC detection outperformed most of the existing models with impressive results.
