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Traffic Vision:UAV-Based Vehicle Detection and Traffic Pattern Analysis via Deep Learning Classifier 认领 引用
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作者 Mohammed Alnusayri Ghulam Mujtaba +4 位作者 Nouf Abdullah Almujally Shuoa S.Aitarbi Asaad Algarni Ahmad Jalal Jeongmin Park 《Computers, Materials & Continua》 SCIE EI 2026年第3期266-284,共19页
This paper presents a unified Unmanned Aerial Vehicle-based(UAV-based)traffic monitoring framework that integrates vehicle detection,tracking,counting,motion prediction,and classification in a modular and co-optimized... This paper presents a unified Unmanned Aerial Vehicle-based(UAV-based)traffic monitoring framework that integrates vehicle detection,tracking,counting,motion prediction,and classification in a modular and co-optimized pipeline.Unlike prior works that address these tasks in isolation,our approach combines You Only Look Once(YOLO)v10 detection,ByteTrack tracking,optical-flow density estimation,Long Short-Term Memory-based(LSTM-based)trajectory forecasting,and hybrid Speeded-Up Robust Feature(SURF)+Gray-Level Co-occurrence Matrix(GLCM)feature engineering with VGG16 classification.Upon the validation across datasets(UAVDT and UAVID)our framework achieved a detection accuracy of 94.2%,and 92.3%detection accuracy when conducting a real-time UAV field validation.Our comprehensive evaluations,including multi-metric analyses,ablation studies,and cross-dataset validations,confirm the framework’s accuracy,efficiency,and generalizability.These results highlight the novelty of integrating complementary methods into a single framework,offering a practical solution for accurate and efficient UAV-based traffic monitoring. 展开更多
关键词 Smart traffic system drone devices machine learner dynamic complex scenes VGG-16 classifier
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A Hybrid Deep Learning Multi-Class Classification Model for Alzheimer’s Disease Using Enhanced MRI Images 认领 引用
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作者 Ghadah Naif Alwakid 《Computers, Materials & Continua》 SCIE EI 2026年第1期797-821,共25页
Alzheimer’s Disease(AD)is a progressive neurodegenerative disorder that significantly affects cognitive function,making early and accurate diagnosis essential.Traditional Deep Learning(DL)-based approaches often stru... Alzheimer’s Disease(AD)is a progressive neurodegenerative disorder that significantly affects cognitive function,making early and accurate diagnosis essential.Traditional Deep Learning(DL)-based approaches often struggle with low-contrast MRI images,class imbalance,and suboptimal feature extraction.This paper develops a Hybrid DL system that unites MobileNetV2 with adaptive classification methods to boost Alzheimer’s diagnosis by processing MRI scans.Image enhancement is done using Contrast-Limited Adaptive Histogram Equalization(CLAHE)and Enhanced Super-Resolution Generative Adversarial Networks(ESRGAN).A classification robustness enhancement system integrates class weighting techniques and a Matthews Correlation Coefficient(MCC)-based evaluation method into the design.The trained and validated model gives a 98.88%accuracy rate and 0.9614 MCC score.We also performed a 10-fold cross-validation experiment with an average accuracy of 96.52%(±1.51),a loss of 0.1671,and an MCC score of 0.9429 across folds.The proposed framework outperforms the state-of-the-art models with a 98%weighted F1-score while decreasing misdiagnosis results for every AD stage.The model demonstrates apparent separation abilities between AD progression stages according to the results of the confusion matrix analysis.These results validate the effectiveness of hybrid DL models with adaptive preprocessing for early and reliable Alzheimer’s diagnosis,contributing to improved computer-aided diagnosis(CAD)systems in clinical practice. 展开更多
关键词 Alzheimer’s disease deep learning MRI images MobileNetV2 contrast-limited adaptive histogram equalization(CLAHE) enhanced super-resolution generative adversarial networks(ESRGAN) multi-class classification
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Drone-Based Public Surveillance Using 3D Point Clouds and Neuro-Fuzzy Classifier 认领 引用 被引量：1
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作者 Yawar Abbas Aisha Ahmed Alarfaj +3 位作者 Ebtisam Abdullah Alabdulqader Asaad Algarni Ahmad Jalal Hui Liu 《Computers, Materials & Continua》 SCIE EI 2025年第3期4759-4776,共18页
Human Activity Recognition(HAR)in drone-captured videos has become popular because of the interest in various fields such as video surveillance,sports analysis,and human-robot interaction.However,recognizing actions f... Human Activity Recognition(HAR)in drone-captured videos has become popular because of the interest in various fields such as video surveillance,sports analysis,and human-robot interaction.However,recognizing actions from such videos poses the following challenges:variations of human motion,the complexity of backdrops,motion blurs,occlusions,and restricted camera angles.This research presents a human activity recognition system to address these challenges by working with drones’red-green-blue(RGB)videos.The first step in the proposed system involves partitioning videos into frames and then using bilateral filtering to improve the quality of object foregrounds while reducing background interference before converting from RGB to grayscale images.The YOLO(You Only Look Once)algorithm detects and extracts humans from each frame,obtaining their skeletons for further processing.The joint angles,displacement and velocity,histogram of oriented gradients(HOG),3D points,and geodesic Distance are included.These features are optimized using Quadratic Discriminant Analysis(QDA)and utilized in a Neuro-Fuzzy Classifier(NFC)for activity classification.Real-world evaluations on the Drone-Action,Unmanned Aerial Vehicle(UAV)-Gesture,and Okutama-Action datasets substantiate the proposed system’s superiority in accuracy rates over existing methods.In particular,the system obtains recognition rates of 93%for drone action,97%for UAV gestures,and 81%for Okutama-action,demonstrating the system’s reliability and ability to learn human activity from drone videos. 展开更多
关键词 Activity recognition geodesic distance pattern recognition neuro fuzzy classifier
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船海学术语篇摘要中名词词组形式表征的认知分析——以“Classifier +Thing”为例 认领 引用
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作者 田苗 张宇新 《山东外语教学》 CSSCI 北大核心 2025年第3期19-29,共11页
“Classifier+Thing”结构在船海学术语篇摘要中俯拾皆是,其认知路径和理据亟待深入探究。本研究聚焦“Classifier+Thing”名词词组,分析船海学术语篇摘要中该词组的认知路径及理据。研究发现:(1)“Classifier+Thing”在概念结构-语义... “Classifier+Thing”结构在船海学术语篇摘要中俯拾皆是,其认知路径和理据亟待深入探究。本研究聚焦“Classifier+Thing”名词词组,分析船海学术语篇摘要中该词组的认知路径及理据。研究发现:(1)“Classifier+Thing”在概念结构-语义层的认知过程体现了语法转喻机制,船海摘要语料库中主要通过“过程-动作”“过程-结果”“用途-结构”实现概念结构-语义间的动、静态转换;(2)“Classifier+Thing”的形式表征过程为先确定“核心词(Thing)”,后在大脑词库中匹配“类别语(Classifier)”,遵循认知经济性原则;(3)该词组形式表征过程受学术语篇类型影响,遵循受限语言说。研究结果一定程度上深化了对学术语篇中名词词组的认识,提升学界对于船海学科学术话语的关注。 展开更多
关键词 “Classifier+Thing” 认知路径及理据 学术摘要 名词词组
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Enhancing Multi-Class Cyberbullying Classification with Hybrid Feature Extraction and Transformer-Based Models 认领 引用 被引量：1
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作者 Suliman Mohamed Fati Mohammed A.Mahdi +4 位作者 Mohamed A.G.Hazber Shahanawaj Ahamad Sawsan A.Saad Mohammed Gamal Ragab Mohammed Al-Shalabi 《Computer Modeling in Engineering & Sciences》 SCIE EI 2025年第5期2109-2131,共23页
Cyberbullying on social media poses significant psychological risks,yet most detection systems over-simplify the task by focusing on binary classification,ignoring nuanced categories like passive-aggressive remarks or... Cyberbullying on social media poses significant psychological risks,yet most detection systems over-simplify the task by focusing on binary classification,ignoring nuanced categories like passive-aggressive remarks or indirect slurs.To address this gap,we propose a hybrid framework combining Term Frequency-Inverse Document Frequency(TF-IDF),word-to-vector(Word2Vec),and Bidirectional Encoder Representations from Transformers(BERT)based models for multi-class cyberbullying detection.Our approach integrates TF-IDF for lexical specificity and Word2Vec for semantic relationships,fused with BERT’s contextual embeddings to capture syntactic and semantic complexities.We evaluate the framework on a publicly available dataset of 47,000 annotated social media posts across five cyberbullying categories:age,ethnicity,gender,religion,and indirect aggression.Among BERT variants tested,BERT Base Un-Cased achieved the highest performance with 93%accuracy(standard deviation across±1%5-fold cross-validation)and an average AUC of 0.96,outperforming standalone TF-IDF(78%)and Word2Vec(82%)models.Notably,it achieved near-perfect AUC scores(0.99)for age and ethnicity-based bullying.A comparative analysis with state-of-the-art benchmarks,including Generative Pre-trained Transformer 2(GPT-2)and Text-to-Text Transfer Transformer(T5)models highlights BERT’s superiority in handling ambiguous language.This work advances cyberbullying detection by demonstrating how hybrid feature extraction and transformer models improve multi-class classification,offering a scalable solution for moderating nuanced harmful content. 展开更多
关键词 Cyberbullying classification multi-class classification BERT models machine learning TF-IDF Word2Vec social media analysis transformer models
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Selective Multiple Classifiers for Weakly Supervised Semantic Segmentation 认领 引用
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作者 Zilin Guo Dongyue Wu +1 位作者 Changxin Gao Nong Sang 《CAAI Transactions on Intelligence Technology》 SCIE EI CSCD 2025年第6期1688-1702,共15页
Existing weakly supervised semantic segmentation(WSSS)methods based on image-level labels always rely on class activation maps(CAMs),which measure the relationships between features and classifiers.However,CAMs only f... Existing weakly supervised semantic segmentation(WSSS)methods based on image-level labels always rely on class activation maps(CAMs),which measure the relationships between features and classifiers.However,CAMs only focus on the most discriminative regions of images,resulting in their poor coverage performance.We attribute this to the deficiency in the recognition ability of a single classifier and the negative impacts caused by magnitudes during the CAMs normalisation process.To address the aforementioned issues,we propose to construct selective multiple classifiers(SMC).During the training process,we extract multiple prototypes for each class and store them in the corresponding memory bank.These prototypes are divided into foreground and background prototypes,with the former used to identify foreground objects and the latter aimed at preventing the false activation of background pixels.As for the inference stage,multiple prototypes are adaptively selected from the memory bank for each image as SMC.Subsequently,CAMs are generated by measuring the angle between SMC and features.We enhance the recognition ability of classifiers by adaptively constructing multiple classifiers for each image,while only relying on angle measurement to generate CAMs can alleviate the suppression phenomenon caused by magnitudes.Furthermore,SMC can be integrated into other WSSS approaches to help generate better CAMs.Extensive experiments conducted on standard WSSS benchmarks such as PASCAL VOC 2012 and MS COCO 2014 demonstrate the superiority of our proposed method. 展开更多
关键词 image segmentation multiple classifiers weakly supervised learning
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A dual-approach to genomic predictions:leveraging convolutional networks and voting classifiers 认领 引用
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作者 Raghad K.Mohammed Azmi Tawfeq Hussein Alrawi Ali Jbaeer Dawood 《Biomedical Engineering Communications》 CAS 2025年第1期3-11,共9页
Background:In the field of genetic diagnostics,DNA sequencing is an important tool because the depth and complexity of this field have major implications in light of the genetic architectures of diseases and the ident... Background:In the field of genetic diagnostics,DNA sequencing is an important tool because the depth and complexity of this field have major implications in light of the genetic architectures of diseases and the identification of risk factors associated with genetic disorders.Methods:Our study introduces a novel two-tiered analytical framework to raise the precision and reliability of genetic data interpretation.It is initiated by extracting and analyzing salient features from DNA sequences through a CNN-based feature analysis,taking advantage of the power inherent in Convolutional neural networks(CNNs)to attain complex patterns and minute mutations in genetic data.This study embraces an elite collection of machine learning classifiers interweaved through a stern voting mechanism,which synergistically joins the predictions made from multiple classifiers to generate comprehensive and well-balanced interpretations of the genetic data.Results:This state-of-the-art method was further tested by carrying out an empirical analysis on a variants'dataset of DNA sequences taken from patients affected by breast cancer,juxtaposed with a control group composed of healthy people.Thus,the integration of CNNs with a voting-based ensemble of classifiers returned outstanding outcomes,with performance metrics accuracy,precision,recall,and F1-scorereaching the outstanding rate of 0.88,outperforming previous models.Conclusions:This dual accomplishment underlines the transformative potential that integrating deep learning techniques with ensemble machine learning might provide in real added value for further genetic diagnostics and prognostics.These results from this study set a new benchmark in the accuracy of disease diagnosis through DNA sequencing and promise future studies on improved personalized medicine and healthcare approaches with precise genetic information. 展开更多
关键词 CNN DNA sequencing ensemble machine learning genetic disease voting classifier
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Multi-Class Support Vector Machine Classifier Based on Jeffries-Matusita Distance and Directed Acyclic Graph 认领 引用 被引量：1
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作者 Miao Zhang Zhen-Zhou Lai +1 位作者 Dan Li Yi Shen 《Journal of Harbin Institute of Technology(New Series)》 EI CAS 2013年第5期113-118,共6页
Based on the framework of support vector machines （SVM） using one-against-one （OAO） strategy, a new multi-class kernel method based on directed aeyclie graph （DAG） and probabilistic distance is proposed to raise... Based on the framework of support vector machines （SVM） using one-against-one （OAO） strategy, a new multi-class kernel method based on directed aeyclie graph （DAG） and probabilistic distance is proposed to raise the multi-class classification accuracies. The topology structure of DAG is constructed by rearranging the nodes＇ sequence in the graph. DAG is equivalent to guided operating SVM on a list, and the classification performance depends on the nodes＇ sequence in the graph. Jeffries-Matusita distance （JMD） is introduced to estimate the separability of each class, and the implementation list is initialized with all classes organized according to certain sequence in the list. To testify the effectiveness of the proposed method, numerical analysis is conducted on UCI data and hyperspectral data. Meanwhile, comparative studies using standard OAO and DAG classification methods are also conducted and the results illustrate better performance and higher accuracy of the orooosed JMD-DAG method. 展开更多
关键词 multi-class classification support vector machine directed acyclic graph Jeffries-Matusitadistance hyperspcctral data
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PIDINet-MC:Real-Time Multi-Class Edge Detection with PiDiNet 认领 引用
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作者 Mingming Huang Yunfan Ye Zhiping Cai 《Computers, Materials & Continua》 SCIE EI 2026年第2期1983-1999,共17页
As a fundamental component in computer vision,edges can be categorized into four types based on discontinuities in reflectance,illumination,surface normal,or depth.While deep CNNs have significantly advanced generic e... As a fundamental component in computer vision,edges can be categorized into four types based on discontinuities in reflectance,illumination,surface normal,or depth.While deep CNNs have significantly advanced generic edge detection,real-time multi-class semantic edge detection under resource constraints remains challenging.To address this,we propose a lightweight framework based on PiDiNet that enables fine-grained semantic edge detection.Our model simultaneously predicts background and four edge categories from full-resolution inputs,balancing accuracy and efficiency.Key contributions include:a multi-channel output structure expanding binary edge prediction to five classes,supported by a deep supervision mechanism;a dynamic class-balancing strategy combining adaptive weighting with physical priors to handle extreme class imbalance;and maintained architectural efficiency enabling real-time inference.Extensive evaluations on BSDS-RIND show our approach achieves accuracy competitive with state-of-the-art methods while operating in real time. 展开更多
关键词 Multi-class edge detection real-time lightweight deep supervision
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RankXLAN:An explainable ensemble-based machine learning framework for biomarker detection,therapeutic target identification,and classification using transcriptomic and epigenomic stomach cancer data 认领 引用
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作者 Kasmika Borah Himanish Shekhar Das +1 位作者 Mudassir Khan Saurav Mallik 《Medical Data Mining》 CAS 2026年第1期13-31,共19页
Background:Stomach cancer(SC)is one of the most lethal malignancies worldwide due to late-stage diagnosis and limited treatment.The transcriptomic,epigenomic,and proteomic,etc.,omics datasets generated by high-through... Background:Stomach cancer(SC)is one of the most lethal malignancies worldwide due to late-stage diagnosis and limited treatment.The transcriptomic,epigenomic,and proteomic,etc.,omics datasets generated by high-throughput sequencing technology have become prominent in biomedical research,and they reveal molecular aspects of cancer diagnosis and therapy.Despite the development of advanced sequencing technology,the presence of high-dimensionality in multi-omics data makes it challenging to interpret the data.Methods:In this study,we introduce RankXLAN,an explainable ensemble-based multi-omics framework that integrates feature selection(FS),ensemble learning,bioinformatics,and in-silico validation for robust biomarker detection,potential therapeutic drug-repurposing candidates’identification,and classification of SC.To enhance the interpretability of the model,we incorporated explainable artificial intelligence(SHapley Additive exPlanations analysis),as well as accuracy,precision,F1-score,recall,cross-validation,specificity,likelihood ratio(LR)+,LR−,and Youden index results.Results:The experimental results showed that the top four FS algorithms achieved improved results when applied to the ensemble learning classification model.The proposed ensemble model produced an area under the curve(AUC)score of 0.994 for gene expression,0.97 for methylation,and 0.96 for miRNA expression data.Through the integration of bioinformatics and ML approach of the transcriptomic and epigenomic multi-omics dataset,we identified potential marker genes,namely,UBE2D2,HPCAL4,IGHA1,DPT,and FN3K.In-silico molecular docking revealed a strong binding affinity between ANKRD13C and the FDA-approved drug Everolimus(binding affinity−10.1 kcal/mol),identifying ANKRD13C as a potential therapeutic drug-repurposing target for SC.Conclusion:The proposed framework RankXLAN outperforms other existing frameworks for serum biomarker identification,therapeutic target identification,and SC classification with multi-omics datasets. 展开更多
关键词 stomach cancer bioinformatics ensemble learning classifier biomarker targets
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Towards Decentralized IoT Security: Optimized Detection of Zero-Day Multi-Class Cyber-Attacks Using Deep Federated Learning 认领 引用
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作者 Misbah Anwer Ghufran Ahmed +3 位作者 Maha Abdelhaq Raed Alsaqour Shahid Hussain Adnan Akhunzada 《Computers, Materials & Continua》 SCIE EI 2026年第1期744-758,共15页
The exponential growth of the Internet of Things(IoT)has introduced significant security challenges,with zero-day attacks emerging as one of the most critical and challenging threats.Traditional Machine Learning(ML)an... The exponential growth of the Internet of Things(IoT)has introduced significant security challenges,with zero-day attacks emerging as one of the most critical and challenging threats.Traditional Machine Learning(ML)and Deep Learning(DL)techniques have demonstrated promising early detection capabilities.However,their effectiveness is limited when handling the vast volumes of IoT-generated data due to scalability constraints,high computational costs,and the costly time-intensive process of data labeling.To address these challenges,this study proposes a Federated Learning(FL)framework that leverages collaborative and hybrid supervised learning to enhance cyber threat detection in IoT networks.By employing Deep Neural Networks(DNNs)and decentralized model training,the approach reduces computational complexity while improving detection accuracy.The proposed model demonstrates robust performance,achieving accuracies of 94.34%,99.95%,and 87.94%on the publicly available kitsune,Bot-IoT,and UNSW-NB15 datasets,respectively.Furthermore,its ability to detect zero-day attacks is validated through evaluations on two additional benchmark datasets,TON-IoT and IoT-23,using a Deep Federated Learning(DFL)framework,underscoring the generalization and effectiveness of the model in heterogeneous and decentralized IoT environments.Experimental results demonstrate superior performance over existing methods,establishing the proposed framework as an efficient and scalable solution for IoT security. 展开更多
关键词 Cyber-attack intrusion detection system(IDS) deep federated learning(DFL) zero-day attack distributed denial of services(DDoS) multi-class Internet of Things(IoT)
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Fault Diagnosis Based on BA-SVM Trapezoidal Region Pole Classification 认领 引用
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作者 DU Zi-wei YAO Bo WANG Fu-zhong 《Chinese Quarterly Journal of Mathematics》 2026年第2期197-206,共10页
For a class of linear constant systems, the problem of continuous gain type fault diagnosis and reliable control of a single component of an actuator is investigated based on the trapezoidal region. Firstly, in order ... For a class of linear constant systems, the problem of continuous gain type fault diagnosis and reliable control of a single component of an actuator is investigated based on the trapezoidal region. Firstly, in order to solve the problem that the pole information of closed-loop system is difficult to observe, a design scheme of full-dimensional state observer is given to realize the real-time observation of pole information and form a pole classification database for system failure. Secondly, according to the characteristics that the poles are located in different areas when different channels have faults, support vector machine is applied to design a pole classifier to diagnose faults in the system and achieve accurate and reliable control of the system based on the fault diagnosis results.Then, in order to solve the problem of difficulty in selecting parameters for support vector machines, the bat algorithm(BA) is proposed to achieve automatic parameter optimization, which has the advantages of strong robustness and easy to combine with other methods. 展开更多
关键词 Pole classifier Support vector machine Bat algorithm Regional pole assignment Fault diagnosis
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Bridging innovation and clinical reality:Interpreting the comparative study of deep learning models for multi-class upper gastrointestinal disease segmentation 认领 引用
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作者 Yu-Han Yang 《World Journal of Gastroenterology》 SCIE CAS 2026年第8期1-9,共9页
In this editorial we comment on the article by Chan et al.The study presents the most comprehensive comparative evaluation to date of deep learning models for multi-class segmentation of upper gastrointestinal disease... In this editorial we comment on the article by Chan et al.The study presents the most comprehensive comparative evaluation to date of deep learning models for multi-class segmentation of upper gastrointestinal diseases,leveraging a novel 3313-image,nine-class clinical dataset alongside the public EDD2020 benchmark.Their results demonstrate that hierarchical,pre-trained encoders(notably Swin-UMamba-D)deliver the highest segmentation accuracy,while SegFormer balances accuracy with computational efficiency,an important consideration for clinical deployment.Beyond raw performance metrics,the work confronts core translational barriers:Limited and biased datasets,lighting and imaging variability,boundary ambiguity,and multi-label complexity.This Editorial argues that the manuscript marks a pivotal shift from isolated technical advances toward clinically-minded validation of segmentation systems,and proposes a concrete agenda for the field to accelerate safe,generalizable,and ethically responsible adoption of automated endoscopic assistance. 展开更多
关键词 Gastrointestinal disease Endoscopy Automated segmentation Deep learning Multi-class model Comparative study Artificial intelligence
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From Binary to Multi-Class:LLM-Judged Synthetic Annotation Applied to Hate Speech Detection 认领 引用
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作者 Antonio Moreno-Cediel Antonio Garcia-Cabot Eva Garcia-Lopez 《Computers, Materials & Continua》 SCIE EI 2026年第10期1332-1377,共46页
The increasing prevalence of hate speech on social media platforms has spurred research aimed at mitigating this societal harm.However,the development of effective machine learning solutions is hindered by a lack of l... The increasing prevalence of hate speech on social media platforms has spurred research aimed at mitigating this societal harm.However,the development of effective machine learning solutions is hindered by a lack of labelled hate speech data in languages beyond English,particularly when attempting granular,multi-class classification.This research aims to address this data scarcity by introducing a novel methodology leveraging the‘Large Language Model as a judge’paradigm to transform existing binary-labelled hate speech data into multi-class datasets.Our approach aims to generate balanced datasets and enables classification across seven identity groups:race,religion,origin,gender,sexuality,age,and disability.The methodology has been applied to the Spanish Hate Speech Superset,and it has been validated using the Measuring Hate Speech dataset,demonstrating significant efficacy and broad applicability.Specifically,our approach obtains a higher match rate with human labels and a lower number of mismatches when compared with prompt-only strategies.In addition,Cohen’s Kappa scores demonstrate that our approach exhibits a moderate strength of agreement with human annotators,outperforming prompt-only strategies’scores by 5%.As a result of the application of the proposed strategy to the Spanish Hate Speech Superset dataset,a multi-class version is obtained,comprising 6325 hateful samples classified among the seven identity groups.The proposed strategy offers a versatile solution for nuanced classification tasks beyond hate speech,providing a valuable technique for detailed categorisation in various domains. 展开更多
关键词 Hate speech synthetic data annotation dataset LLM-as-a-judge multi-class classification
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A Multi-Classifier Based Prediction Model for Phishing Emails Detection Using Topic Modelling, Named Entity Recognition and Image Processing 认领 引用
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作者 C. Emilin Shyni S. Sarju S. Swamynathan 《Circuits and Systems》 2016年第9期2507-2520,共14页
Phishing is the act of attempting to steal a user’s financial and personal information, such as credit card numbers and passwords by pretending to be a trustworthy participant, during online communication. Attackers ... Phishing is the act of attempting to steal a user’s financial and personal information, such as credit card numbers and passwords by pretending to be a trustworthy participant, during online communication. Attackers may direct the users to a fake website that could seem legitimate, and then gather useful and confidential information using that site. In order to protect users from Social Engineering techniques such as phishing, various measures have been developed, including improvement of Technical Security. In this paper, we propose a new technique, namely, “A Prediction Model for the Detection of Phishing e-mails using Topic Modelling, Named Entity Recognition and Image Processing”. The features extracted are Topic Modelling features, Named Entity features and Structural features. A multi-classifier prediction model is used to detect the phishing mails. Experimental results show that the multi-classification technique outperforms the single-classifier-based prediction techniques. The resultant accuracy of the detection of phishing e-mail is 99% with the highest False Positive Rate being 2.1%. 展开更多
关键词 Phishing Conditional Random Field Classifier Latent Dirichlet Allocation Natural Language Processing Machine Learning Image Segmentation Image Processing
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Multi-Class Classification Methods of Cost-Conscious LS-SVM for Fault Diagnosis of Blast Furnace 认领 引用 被引量：17
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作者 LIU Li-mei WANG An-na +1 位作者 SHA Mo ZHAO Feng-yun 《Journal of Iron and Steel Research International》 SCIE EI CAS CSCD 2011年第10期17-23,33,共7页
Aiming at the limitations of rapid fault diagnosis of blast furnace,a novel strategy based on cost-conscious least squares support vector machine(LS-SVM)is proposed to solve this problem.Firstly,modified discrete part... Aiming at the limitations of rapid fault diagnosis of blast furnace,a novel strategy based on cost-conscious least squares support vector machine(LS-SVM)is proposed to solve this problem.Firstly,modified discrete particle swarm optimization is applied to optimize the feature selection and the LS-SVM parameters.Secondly,cost-con-scious formula is presented for fitness function and it contains in detail training time,recognition accuracy and the feature selection.The CLS-SVM algorithm is presented to increase the performance of the LS-SVM classifier.The new method can select the best fault features in much shorter time and have fewer support vectbrs and better general-ization performance in the application of fault diagnosis of the blast furnace.Thirdly,a gradual change binary tree is established for blast furnace faults diagnosis.It is a multi-class classification method based on center-of-gravity formula distance of cluster.A gradual change classification percentage ia used to select sample randomly.The proposed new metbod raises the sped of diagnosis,optimizes the classifieation scraraey and has good generalization ability for fault diagnosis of the application of blast furnace. 展开更多
关键词 blast furnace fault diagnosis eosc-conscious LS-SVM multi-class classification
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Mapping of cropland,cropping patterns and crop types by combining optical remote sensing images with decision tree classifier and random forest 认领 引用 被引量：23
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作者 Aqil Tariq Jianguo Yan +2 位作者 Alexandre S.Gagnon Mobushir Riaz Khan Faisal Mumtaz 《Geo-Spatial Information Science》 SCIE EI CSCD 2023年第3期302-320,共19页
Mapping and monitoring the distribution of croplands and crop types support policymakers and international organizations by reducing the risks to food security,notably from climate change and,for that purpose,remote s... Mapping and monitoring the distribution of croplands and crop types support policymakers and international organizations by reducing the risks to food security,notably from climate change and,for that purpose,remote sensing is routinely used.However,identifying specific crop types,cropland,and cropping patterns using space-based observations is challenging because different crop types and cropping patterns have similarity spectral signatures.This study applied a methodology to identify cropland and specific crop types,including tobacco,wheat,barley,and gram,as well as the following cropping patterns:wheat-tobacco,wheat-gram,wheat-barley,and wheat-maize,which are common in Gujranwala District,Pakistan,the study region.The methodology consists of combining optical remote sensing images from Sentinel-2 and Landsat-8 with Machine Learning(ML)methods,namely a Decision Tree Classifier(DTC)and a Random Forest(RF)algorithm.The best time-periods for differentiating cropland from other land cover types were identified,and then Sentinel-2 and Landsat 8 NDVI-based time-series were linked to phenological parameters to determine the different crop types and cropping patterns over the study region using their temporal indices and ML algorithms.The methodology was subsequently evaluated using Landsat images,crop statistical data for 2020 and 2021,and field data on cropping patterns.The results highlight the high level of accuracy of the methodological approach presented using Sentinel-2 and Landsat-8 images,together with ML techniques,for mapping not only the distribution of cropland,but also crop types and cropping patterns when validated at the county level.These results reveal that this methodology has benefits for monitoring and evaluating food security in Pakistan,adding to the evidence base of other studies on the use of remote sensing to identify crop types and cropping patterns in other countries. 展开更多
关键词 Sentinel-2 Random Forest cropland crop types cropping patterns Decision Tree Classifier
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Effect of rotor cage rotary speed on classification accuracy in turbo air classifier 认领 引用 被引量：13
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作者 高利苹 于源 刘家祥 《化工学报》 EI CSCD 北大核心 2012年第4期1056-1062,共7页
The numerical simulation of flow field was performed with Fluent, in order to indicate the effect of rotor cage rotary speed on classification accuracy in turbo air classifier. With the same wind speed and different r... The numerical simulation of flow field was performed with Fluent, in order to indicate the effect of rotor cage rotary speed on classification accuracy in turbo air classifier. With the same wind speed and different rotary speeds of rotor cage, airflow velocity distribution in the rotor classifier was analyzed and compared, and a reasonable rotor cage rotary speed was obtained, which made a small velocity gradient both in the annular zone and between the neighboring blades, and a stable flow field. Moreover, the pressure distributions between the neighboring blades were analyzed. In this reasonable range of rotor cage rotary speed, the pressure between onward surface and regression surface of rotor blades was evenly distributed, and the formed vortex was weak, which helped to improve the accuracy of classification. Pulvis talci and quartz sand material experiments showed that when wind speed was 8 m· s^-1 , rotating speed was 600-800 r· min^-1, the accuracy of turbo air classifier was high. These actual experimental results confirmed the simulation results using Fluent. Through this simulation, the corresponding reasonable range of the rotor cage speeds with different wind speeds could be obtained. For example, when wind speed was 20 m - s^-1 , reasonable rotating speed was 1000-1400 r· rain^-1 and when the wind speed was 30 m · s^-1, reasonable rotating speed was 1500-2000 r ·min^- 1. This provides a method to obtain suitable operating parameters of turbo air classifier. 展开更多
关键词 turbo air classifier airflow velocity distribution flow pressure classification accuracy
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Classification performance of model coal mill classifiers with swirling and non-swirling inlets 认领 引用 被引量：6
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作者 Lele Feng Hai Zhang +4 位作者 Lilin Hu Yang Zhang Yuxin Wu Yuzhao Wang Hairui Yang 《Chinese Journal of Chemical Engineering》 SCIE EI CAS CSCD 2020年第3期777-784,共8页
The classification performance of model coal mill classifiers with different bottom incoming flow inlets was experimentally and numerically studied.The flow field adjacent to two neighboring impeller blades was measur... The classification performance of model coal mill classifiers with different bottom incoming flow inlets was experimentally and numerically studied.The flow field adjacent to two neighboring impeller blades was measured using the particle image velocimetry technique.The results showed that the flow field adjacent to two neighboring blades with the swirling inlet was significantly different from that with the non-swirling inlet.With the swirling inlet,there was a vortex located between two neighboring blades,while with the nonswirling inlet,the vortex was attached to the blade tip.The vorticity of the vortex with the non-swirling inlet was much lower than that with the swirling inlet.The classifier with the non-swirling inlet demonstrated a larger cut size than that with the swirling inlet when the impeller was stationary(~0 r·min-1).As the impeller rotational speed increased,the cut size of the cases with non-swirling and swirling inlets both decreased,and the one with the non-swirling inlet decreased more dramatically.The values of the cut size of the two classifiers were close to each other at a high impeller rotational speed(≥120 r·min-1).The overall separation efficiency of the classifier with the non-swirling inlet was lower than that with the swirling inlet,and monotonically increased as the impeller rotational speed increased.With the swirling inlet,the overall separation efficiency first increased with the impeller rotational speed and then decreased when the rotational speed was above 120 r·min-1,and the variation trend of the separation efficiency was more moderate.As the initial particle concentration increased,the cut sizes of both swirling and non-swirling inlet cases decreased first and then barely changed.At a low initial particle concentration(b 0.04 kg·m-3),the classifier with the swirling inlet had a larger cut size than that with the non-swirling inlet. 展开更多
关键词 Coal mill classifier Cut size Non-swirling inlet Particle image velocimetry Impeller rotational speed
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An Improved LSTM-PCA Ensemble Classifier for SQL Injection and XSS Attack Detection 认领 引用 被引量：2
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作者 Deris Stiawan Ali Bardadi +7 位作者 Nurul Afifah Lisa Melinda Ahmad Heryanto Tri Wanda Septian Mohd Yazid Idris Imam Much Ibnu Subroto Lukman Rahmat Budiarto 《Computer Systems Science & Engineering》 SCIE EI 2023年第8期1759-1774,共16页
The Repository Mahasiswa(RAMA)is a national repository of research reports in the form of final assignments,student projects,theses,dissertations,and research reports of lecturers or researchers that have not yet been... The Repository Mahasiswa(RAMA)is a national repository of research reports in the form of final assignments,student projects,theses,dissertations,and research reports of lecturers or researchers that have not yet been published in journals,conferences,or integrated books from the scientific repository of universities and research institutes in Indonesia.The increasing popularity of the RAMA Repository leads to security issues,including the two most widespread,vulnerable attacks i.e.,Structured Query Language(SQL)injection and cross-site scripting(XSS)attacks.An attacker gaining access to data and performing unauthorized data modifications is extremely dangerous.This paper aims to provide an attack detection system for securing the repository portal from the abovementioned attacks.The proposed system combines a Long Short–Term Memory and Principal Component Analysis(LSTM-PCA)model as a classifier.This model can effectively solve the vanishing gradient problem caused by excessive positive samples.The experiment results show that the proposed system achieves an accuracy of 96.85%using an 80%:20%ratio of training data and testing data.The rationale for this best achievement is that the LSTM’s Forget Gate works very well as the PCA supplies only selected features that are significantly relevant to the attacks’patterns.The Forget Gate in LSTM is responsible for deciding which information should be kept for computing the cell state and which one is not relevant and can be discarded.In addition,the LSTM’s Input Gate assists in finding out crucial information and stores specific relevant data in the memory. 展开更多
关键词 LSTM PCA ensemble classifier SQL injection XSS
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