Distributed Denial-of-Service(DDoS)attacks pose severe threats to Industrial Control Networks(ICNs),where service disruption can cause significant economic losses and operational risks.Existing signature-based methods...Distributed Denial-of-Service(DDoS)attacks pose severe threats to Industrial Control Networks(ICNs),where service disruption can cause significant economic losses and operational risks.Existing signature-based methods are ineffective against novel attacks,and traditional machine learning models struggle to capture the complex temporal dependencies and dynamic traffic patterns inherent in ICN environments.To address these challenges,this study proposes a deep feature-driven hybrid framework that integrates Transformer,BiLSTM,and KNN to achieve accurate and robust DDoS detection.The Transformer component extracts global temporal dependencies from network traffic flows,while BiLSTM captures fine-grained sequential dynamics.The learned embeddings are then classified using an instance-based KNN layer,enhancing decision boundary precision.This cascaded architecture balances feature abstraction and locality preservation,improving both generalization and robustness.The proposed approach was evaluated on a newly collected real-time ICN traffic dataset and further validated using the public CIC-IDS2017 and Edge-IIoT datasets to demonstrate generalization.Comprehensive metrics including accuracy,precision,recall,F1-score,ROC-AUC,PR-AUC,false positive rate(FPR),and detection latency were employed.Results show that the hybrid framework achieves 98.42%accuracy with an ROC-AUC of 0.992 and FPR below 1%,outperforming baseline machine learning and deep learning models.Robustness experiments under Gaussian noise perturbations confirmed stable performance with less than 2%accuracy degradation.Moreover,detection latency remained below 2.1 ms per sample,indicating suitability for real-time ICS deployment.In summary,the proposed hybrid temporal learning and instance-based classification model offers a scalable and effective solution for DDoS detection in industrial control environments.By combining global contextual modeling,sequential learning,and instance-based refinement,the framework demonstrates strong adaptability across datasets and resilience against noise,providing practical utility for safeguarding critical infrastructure.展开更多
Container transportation is pivotal in global trade due to its efficiency,safety,and cost-effectiveness.However,structural defects—particularly in grapple slots—can result in cargo damage,financial loss,and elevated...Container transportation is pivotal in global trade due to its efficiency,safety,and cost-effectiveness.However,structural defects—particularly in grapple slots—can result in cargo damage,financial loss,and elevated safety risks,including container drops during lifting operations.Timely and accurate inspection before and after transit is therefore essential.Traditional inspection methods rely heavily on manual observation of internal and external surfaces,which are time-consuming,resource-intensive,and prone to subjective errors.Container roofs pose additional challenges due to limited visibility,while grapple slots are especially vulnerable to wear from frequent use.This study proposes a two-stage automated detection framework targeting defects in container roof grapple slots.In the first stage,YOLOv7 is employed to localize grapple slot regions with high precision.In the second stage,ResNet50 classifies the extracted slots as either intact or defective.The results from both stages are integrated into a human-machine interface for real-time visualization and user verification.Experimental evaluations demonstrate that YOLOv7 achieves a 99%detection rate at 100 frames per second(FPS),while ResNet50 attains 87%classification accuracy at 34 FPS.Compared to some state of the arts,the proposed system offers significant speed,reliability,and usability improvements,enabling efficient defect identification and visual reconfirmation via the interface.展开更多
Sudden wildfires cause significant global ecological damage.While satellite imagery has advanced early fire detection and mitigation,image-based systems face limitations including high false alarm rates,visual obstruc...Sudden wildfires cause significant global ecological damage.While satellite imagery has advanced early fire detection and mitigation,image-based systems face limitations including high false alarm rates,visual obstructions,and substantial computational demands,especially in complex forest terrains.To address these challenges,this study proposes a novel forest fire detection model utilizing audio classification and machine learning.We developed an audio-based pipeline using real-world environmental sound recordings.Sounds were converted into Mel-spectrograms and classified via a Convolutional Neural Network(CNN),enabling the capture of distinctive fire acoustic signatures(e.g.,crackling,roaring)that are minimally impacted by visual or weather conditions.Internet of Things(IoT)sound sensors were crucial for generating complex environmental parameters to optimize feature extraction.The CNN model achieved high performance in stratified 5-fold cross-validation(92.4%±1.6 accuracy,91.2%±1.8 F1-score)and on test data(94.93%accuracy,93.04%F1-score),with 98.44%precision and 88.32%recall,demonstrating reliability across environmental conditions.These results indicate that the audio-based approach not only improves detection reliability but also markedly reduces computational overhead compared to traditional image-based methods.The findings suggest that acoustic sensing integrated with machine learning offers a powerful,low-cost,and efficient solution for real-time forest fire monitoring in complex,dynamic environments.展开更多
The proliferation of Internet of Things(IoT)devices has established edge computing as a critical paradigm for real-time data analysis and low-latency processing.Nevertheless,the distributed nature of edge computing pr...The proliferation of Internet of Things(IoT)devices has established edge computing as a critical paradigm for real-time data analysis and low-latency processing.Nevertheless,the distributed nature of edge computing presents substantial security challenges,rendering it a prominent target for sophisticated malware attacks.Existing signature-based and behavior-based detection methods are ineffective against the swiftly evolving nature of malware threats and are constrained by the availability of resources.This paper suggests the Genetic Encoding for Novel Optimization of Malware Evaluation(GENOME)framework,a novel solution that is intended to improve the performance of malware detection and classification in peripheral computing environments.GENOME optimizes data storage and computa-tional efficiency by converting malware artifacts into compact,structured sequences through a Deoxyribonucleic Acid(DNA)encoding mechanism.The framework employs two DNA encoding algorithms,standard and compressed,which substantially reduce data size while preserving high detection accuracy.The Edge-IIoTset dataset was used to conduct experiments that showed that GENOME was able to achieve high classification performance using models such as Random Forest and Logistic Regression,resulting in a reduction of data size by up to 42%.Further evaluations with the CIC-IoT-23 dataset and Deep Learning models confirmed GENOME’s scalability and adaptability across diverse datasets and algorithms.The potential of GENOME to address critical challenges,such as the rapid mutation of malware,real-time processing demands,and resource limitations,is emphasized in this study.GENOME offers comprehensive protection for peripheral computing environments by offering a security solution that is both efficient and scalable.展开更多
Leaf disease identification is one of the most promising applications of convolutional neural networks(CNNs).This method represents a significant step towards revolutionizing agriculture by enabling the quick and accu...Leaf disease identification is one of the most promising applications of convolutional neural networks(CNNs).This method represents a significant step towards revolutionizing agriculture by enabling the quick and accurate assessment of plant health.In this study,a CNN model was specifically designed and tested to detect and categorize diseases on fig tree leaves.The researchers utilized a dataset of 3422 images,divided into four classes:healthy,fig rust,fig mosaic,and anthracnose.These diseases can significantly reduce the yield and quality of fig tree fruit.The objective of this research is to develop a CNN that can identify and categorize diseases in fig tree leaves.The data for this study was collected from gardens in the Amandi and Mamash Khail Bannu districts of the Khyber Pakhtunkhwa region in Pakistan.To minimize the risk of overfitting and enhance the model’s performance,early stopping techniques and data augmentation were employed.As a result,the model achieved a training accuracy of 91.53%and a validation accuracy of 90.12%,which are considered respectable.This comprehensive model assists farmers in the early identification and categorization of fig tree leaf diseases.Our experts believe that CNNs could serve as valuable tools for accurate disease classification and detection in precision agriculture.We recommend further research to explore additional data sources and more advanced neural networks to improve the model’s accuracy and applicability.Future research will focus on expanding the dataset by including new diseases and testing the model in real-world scenarios to enhance sustainable farming practices.展开更多
Breast cancer screening programs rely heavily on mammography for early detection;however,diagnostic performance is strongly affected by inter-reader variability,breast density,and the limitations of conven-tional comp...Breast cancer screening programs rely heavily on mammography for early detection;however,diagnostic performance is strongly affected by inter-reader variability,breast density,and the limitations of conven-tional computer-aided detection systems.Recent advances in deep learning have enabled more robust and scalable solutions for large-scale screening,yet a systematic comparison of modern object detection architectures on nationally representative datasets remains limited.This study presents a comprehensive quantitative comparison of prominent deep learning–based object detection architectures for Artificial Intelligence-assisted mammography analysis using the MammosighTR dataset,developed within the Turkish National Breast Cancer Screening Program.The dataset comprises 12,740 patient cases collected between 2016 and 2022,annotated with BI-RADS categories,breast density levels,and lesion localization labels.A total of 31 models were evaluated,including One-Stage,Two-Stage,and Transformer-based architectures,under a unified experimental framework at both patient and breast levels.The results demonstrate that Two-Stage architectures consistently outperform One-Stage models,achieving approximately 2%–4%higher Macro F1-Scores and more balanced precision–recall trade-offs,with Double-Head R-CNN and Dynamic R-CNN yielding the highest overall performance(Macro F1≈0.84–0.86).This advantage is primarily attributed to the region proposal mechanism and improved class balance inherent to Two-Stage designs.One-Stage detectors exhibited higher sensitivity and faster inference,reaching Recall values above 0.88,but experienced minor reductions in Precision and overall accuracy(≈1%–2%)compared with Two-Stage models.Among Transformer-based architectures,Deformable DEtection TRansformer demonstrated strong robustness and consistency across datasets,achieving Macro F1-Scores comparable to CNN-based detectors(≈0.83–0.85)while exhibiting minimal performance degradation under distributional shifts.Breast density–based analysis revealed increased misclassification rates in medium-density categories(types B and C),whereas Transformer-based architectures maintained more stable performance in high-density type D tissue.These findings quantitatively confirm that both architectural design and tissue characteristics play a decisive role in diagnostic accuracy.Overall,the study provides a reproducible benchmark and highlights the potential of hybrid approaches that combine the accuracy of Two-Stage detectors with the contextual modeling capability of Transformer architectures for clinically reliable breast cancer screening systems.展开更多
Spam emails remain one of the most persistent threats to digital communication,necessitating effective detection solutions that safeguard both individuals and organisations.We propose a spam email classification frame...Spam emails remain one of the most persistent threats to digital communication,necessitating effective detection solutions that safeguard both individuals and organisations.We propose a spam email classification frame-work that uses Bidirectional Encoder Representations from Transformers(BERT)for contextual feature extraction and a multiple-window Convolutional Neural Network(CNN)for classification.To identify semantic nuances in email content,BERT embeddings are used,and CNN filters extract discriminative n-gram patterns at various levels of detail,enabling accurate spam identification.The proposed model outperformed Word2Vec-based baselines on a sample of 5728 labelled emails,achieving an accuracy of 98.69%,AUC of 0.9981,F1 Score of 0.9724,and MCC of 0.9639.With a medium kernel size of(6,9)and compact multi-window CNN architectures,it improves performance.Cross-validation illustrates stability and generalization across folds.By balancing high recall with minimal false positives,our method provides a reliable and scalable solution for current spam detection in advanced deep learning.By combining contextual embedding and a neural architecture,this study develops a security analysis method.展开更多
The rapid advancement of large language models(LLMs)has driven the pervasive adoption of AI-generated content(AIGC),while also raising concerns about misinformation,academic misconduct,biased or harmful content,and ot...The rapid advancement of large language models(LLMs)has driven the pervasive adoption of AI-generated content(AIGC),while also raising concerns about misinformation,academic misconduct,biased or harmful content,and other risks.Detecting AI-generated text has thus become essential to safeguard the authenticity and reliability of digital information.This survey reviews recent progress in detection methods,categorizing approaches into passive and active categories based on their reliance on intrinsic textual features or embedded signals.Passive detection is further divided into surface linguistic feature-based and language model-based methods,whereas active detection encompasses watermarking-based and semantic retrieval-based approaches.This taxonomy enables systematic comparison of methodological differences in model dependency,applicability,and robustness.A key challenge for AI-generated text detection is that existing detectors are highly vulnerable to adversarial attacks,particularly paraphrasing,which substantially compromises their effectiveness.Addressing this gap highlights the need for future research on enhancing robustness and cross-domain generalization.By synthesizing current advances and limitations,this survey provides a structured reference for the field and outlines pathways toward more reliable and scalable detection solutions.展开更多
Manual inspection of onba earing casting defects is not realistic and unreliable,particularly in the case of some micro-level anomalies which lead to major defects on a large scale.To address these challenges,we propo...Manual inspection of onba earing casting defects is not realistic and unreliable,particularly in the case of some micro-level anomalies which lead to major defects on a large scale.To address these challenges,we propose BearFusionNet,an attention-based deep learning architecture with multi-stream,which merges both DenseNet201 and MobileNetV2 for feature extraction with a classification head inspired by VGG19.This hybrid design,figuratively beaming from one layer to another,extracts the enormity of representations on different scales,backed by a prepreprocessing pipeline that brings defect saliency to the fore through contrast adjustment,denoising,and edge detection.The use of multi-head self-attention enhances feature fusion,enabling the model to capture both large and small spatial features.BearFusionNet achieves an accuracy of 99.66%and Cohen’s kappa score of 0.9929 in Kaggle’s Real-life Industrial Casting Defects dataset.Both McNemar’s and Wilcoxon signed-rank statistical tests,as well as fivefold cross-validation,are employed to assess the robustness of our proposed model.To interpret the model,we adopt Grad-Cam visualizations,which are the state of the art standard.Furthermore,we deploy BearFusionNet as a webbased system for near real-time inference(5-6 s per prediction),which enables the quickest yet accurate detection with visual explanations.Overall,BearFusionNet is an interpretable,accurate,and deployable solution that can automatically detect casting defects,leading to significant advances in the innovative industrial environment.展开更多
Considering the important applications in the military and the civilian domain, ship detection and classification based on optical remote sensing images raise considerable attention in the sea surface remote sensing f...Considering the important applications in the military and the civilian domain, ship detection and classification based on optical remote sensing images raise considerable attention in the sea surface remote sensing filed. This article collects the methods of ship detection and classification for practically testing in optical remote sensing images, and provides their corresponding feature extraction strategies and statistical data. Basic feature extraction strategies and algorithms are analyzed associated with their performance and application in ship detection and classification.Furthermore, publicly available datasets that can be applied as the benchmarks to verify the effectiveness and the objectiveness of ship detection and classification methods are summarized in this paper. Based on the analysis, the remaining problems and future development trends are provided for ship detection and classification methods based on optical remote sensing images.展开更多
Recently machine learning-based intrusion detection approaches have been subjected to extensive researches because they can detect both misuse and anomaly. In this paper, rough set classification (RSC), a modern learn...Recently machine learning-based intrusion detection approaches have been subjected to extensive researches because they can detect both misuse and anomaly. In this paper, rough set classification (RSC), a modern learning algorithm, is used to rank the features extracted for detecting intrusions and generate intrusion detection models. Feature ranking is a very critical step when building the model. RSC performs feature ranking before generating rules, and converts the feature ranking to minimal hitting set problem addressed by using genetic algorithm (GA). This is done in classical approaches using Support Vector Machine (SVM) by executing many iterations, each of which removes one useless feature. Compared with those methods, our method can avoid many iterations. In addition, a hybrid genetic algorithm is proposed to increase the convergence speed and decrease the training time of RSC. The models generated by RSC take the form of'IF-THEN' rules, which have the advantage of explication. Tests and comparison of RSC with SVM on DARPA benchmark data showed that for Probe and DoS attacks both RSC and SVM yielded highly accurate results (greater than 99% accuracy on testing set).展开更多
An ideal printed circuit board(PCB)defect inspection system can detect defects and classify PCB defect types.Existing defect inspection technologies can identify defects but fail to classify all PCB defect types.This ...An ideal printed circuit board(PCB)defect inspection system can detect defects and classify PCB defect types.Existing defect inspection technologies can identify defects but fail to classify all PCB defect types.This research thus proposes an algorithmic scheme that can detect and categorize all 14-known PCB defect types.In the proposed algorithmic scheme,fuzzy cmeans clustering is used for image segmentation via image subtraction prior to defect detection.Arithmetic and logic operations,the circle hough transform(CHT),morphological reconstruction(MR),and connected component labeling(CCL)are used in defect classification.The algorithmic scheme achieves 100%defect detection and 99.05%defect classification accuracies.The novelty of this research lies in the concurrent use of CHT,MR,and CCL algorithms to accurately detect and classify all 14-known PCB defect types and determine the defect characteristics such as the location,area,and nature of defects.This information is helpful in electronic parts manufacturing for finding the root causes of PCB defects and appropriately adjusting the manufacturing process.Moreover,the algorithmic scheme can be integrated into machine vision to streamline the manufacturing process,improve the PCB quality,and lower the production cost.展开更多
With recent breakthroughs in artificial intelligence,the use of deep learning models achieved remarkable advances in computer vision,ecommerce,cybersecurity,and healthcare.Particularly,numerous applications provided e...With recent breakthroughs in artificial intelligence,the use of deep learning models achieved remarkable advances in computer vision,ecommerce,cybersecurity,and healthcare.Particularly,numerous applications provided efficient solutions to assist radiologists for medical imaging analysis.For instance,automatic lesion detection and classification in mammograms is still considered a crucial task that requires more accurate diagnosis and precise analysis of abnormal lesions.In this paper,we propose an end-to-end system,which is based on You-Only-Look-Once(YOLO)model,to simultaneously localize and classify suspicious breast lesions from entire mammograms.The proposed system first preprocesses the raw images,then recognizes abnormal regions as breast lesions and determines their pathology classification as either mass or calcification.We evaluated the model on two publicly available datasets,with 2907 mammograms from the Curated Breast Imaging Subset of Digital Database for Screening Mammography(CBIS-DDSM)and 235 mammograms from INbreast database.We also used a privately collected dataset with 487 mammograms.Furthermore,we suggested a fusion models approach to report more precise detection and accurate classification.Our best results reached a detection accuracy rate of 95.7%,98.1%and 98%for mass lesions and 74.4%,71.8%and 73.2%for calcification lesions,respectively on CBIS-DDSM,INbreast and the private dataset.展开更多
Objective: Accurate detection and classification of breast lesions in early stage is crucial to timely formulate effective treatments for patients. We aim to develop a fully automatic system to detect and classify bre...Objective: Accurate detection and classification of breast lesions in early stage is crucial to timely formulate effective treatments for patients. We aim to develop a fully automatic system to detect and classify breast lesions using multiple contrast-enhanced mammography(CEM) images.Methods: In this study, a total of 1,903 females who underwent CEM examination from three hospitals were enrolled as the training set, internal testing set, pooled external testing set and prospective testing set. Here we developed a CEM-based multiprocess detection and classification system(MDCS) to perform the task of detection and classification of breast lesions. In this system, we introduced an innovative auxiliary feature fusion(AFF)algorithm that could intelligently incorporates multiple types of information from CEM images. The average freeresponse receiver operating characteristic score(AFROC-Score) was presented to validate system’s detection performance, and the performance of classification was evaluated by area under the receiver operating characteristic curve(AUC). Furthermore, we assessed the diagnostic value of MDCS through visual analysis of disputed cases,comparing its performance and efficiency with that of radiologists and exploring whether it could augment radiologists’ performance.Results: On the pooled external and prospective testing sets, MDCS always maintained a high standalone performance, with AFROC-Scores of 0.953 and 0.963 for detection task, and AUCs for classification were 0.909[95% confidence interval(95% CI): 0.822-0.996] and 0.912(95% CI: 0.840-0.985), respectively. It also achieved higher sensitivity than all senior radiologists and higher specificity than all junior radiologists on pooled external and prospective testing sets. Moreover, MDCS performed superior diagnostic efficiency with an average reading time of 5 seconds, compared to the radiologists’ average reading time of 3.2 min. The average performance of all radiologists was also improved to varying degrees with MDCS assistance.Conclusions: MDCS demonstrated excellent performance in the detection and classification of breast lesions,and greatly enhanced the overall performance of radiologists.展开更多
Automatic road damage detection using image processing is an important aspect of road maintenance.It is also a challenging problem due to the inhomogeneity of road damage and complicated background in the road images....Automatic road damage detection using image processing is an important aspect of road maintenance.It is also a challenging problem due to the inhomogeneity of road damage and complicated background in the road images.In recent years,deep convolutional neural network based methods have been used to address the challenges of road damage detection and classification.In this paper,we propose a new approach to address those challenges.This approach uses densely connected convolution networks as the backbone of the Mask R-CNN to effectively extract image feature,a feature pyramid network for combining multiple scales features,a region proposal network to generate the road damage region,and a fully convolutional neural network to classify the road damage region and refine the region bounding box.This method can not only detect and classify the road damage,but also create a mask of the road damage.Experimental results show that the proposed approach can achieve better results compared with other existing methods.展开更多
Arrhythmia is ubiquitous worldwide and cardiologists tend to provide solutions from the recent advancements in medicine.Detecting arrhythmia from ECG signals is considered a standard approach and hence,automating this...Arrhythmia is ubiquitous worldwide and cardiologists tend to provide solutions from the recent advancements in medicine.Detecting arrhythmia from ECG signals is considered a standard approach and hence,automating this process would aid the diagnosis by providing fast,costefficient,and accurate solutions at scale.This is executed by extracting the definite properties from the individual patterns collected from Electrocardiography(ECG)signals causing arrhythmia.In this era of applied intelligence,automated detection and diagnostic solutions are widely used for their spontaneous and robust solutions.In this research,our contributions are two-fold.Firstly,the Dual-Tree Complex Wavelet Transform(DT-CWT)method is implied to overhaul shift-invariance and aids signal reconstruction to extract significant features.Next,A neural attention mechanism is implied to capture temporal patterns from the extracted features of the ECG signal to discriminate distinct classes of arrhythmia and is trained end-to-end with the finest parameters.To ensure that the model’s generalizability,a set of five traintest variants are implied.The proposed model attains the highest accuracy of 98.5%for classifying 8 variants of arrhythmia on the MIT-BIH dataset.To test the resilience of the model,the unseen(test)samples are increased by 5x and the deviation in accuracy score and MSE was 0.12%and 0.1%respectively.Further,to assess the diagnostic model performance,AUC-ROC curves are plotted.At every test level,the proposed model is capable of generalizing new samples and leverages the advantage to develop a real-world application.As a note,this research is the first attempt to provide neural attention in arrhythmia classification using MIT-BIH ECG signals data with state-of-the-art performance.展开更多
Oral cancer is the most commonly occurring‘head and neck cancers’across the globe.Most of the oral cancer cases are diagnosed at later stages due to absence of awareness among public.Since earlier identification of ...Oral cancer is the most commonly occurring‘head and neck cancers’across the globe.Most of the oral cancer cases are diagnosed at later stages due to absence of awareness among public.Since earlier identification of disease is essential for improved outcomes,Artificial Intelligence(AI)and Machine Learning(ML)models are used in this regard.In this background,the current study introduces Artificial Intelligence with Deep Transfer Learning driven Oral Cancer detection and Classification Model(AIDTLOCCM).The primary goal of the proposed AIDTL-OCCM model is to diagnose oral cancer using AI and image processing techniques.The proposed AIDTL-OCCM model involves fuzzy-based contrast enhancement approach to perform data pre-processing.Followed by,the densely-connected networks(DenseNet-169)model is employed to produce a useful set of deep features.Moreover,Chimp Optimization Algorithm(COA)with Autoencoder(AE)model is applied for oral cancer detection and classification.Furthermore,COA is employed to determine optimal parameters involved in AE model.A wide range of experimental analyses was conducted on benchmark datasets and the results were investigated under several aspects.The extensive experimental analysis outcomes established the enhanced performance of AIDTLOCCM model compared to other approaches with a maximum accuracy of 90.08%.展开更多
Intrusion detection is a predominant task that monitors and protects the network infrastructure.Therefore,many datasets have been published and investigated by researchers to analyze and understand the problem of intr...Intrusion detection is a predominant task that monitors and protects the network infrastructure.Therefore,many datasets have been published and investigated by researchers to analyze and understand the problem of intrusion prediction and detection.In particular,the Network Security Laboratory-Knowledge Discovery in Databases(NSL-KDD)is an extensively used benchmark dataset for evaluating intrusion detection systems(IDSs)as it incorporates various network traffic attacks.It is worth mentioning that a large number of studies have tackled the problem of intrusion detection using machine learning models,but the performance of these models often decreases when evaluated on new attacks.This has led to the utilization of deep learning techniques,which have showcased significant potential for processing large datasets and therefore improving detection accuracy.For that reason,this paper focuses on the role of stacking deep learning models,including convolution neural network(CNN)and deep neural network(DNN)for improving the intrusion detection rate of the NSL-KDD dataset.Each base model is trained on the NSL-KDD dataset to extract significant features.Once the base models have been trained,the stacking process proceeds to the second stage,where a simple meta-model has been trained on the predictions generated from the proposed base models.The combination of the predictions allows the meta-model to distinguish different classes of attacks and increase the detection rate.Our experimental evaluations using the NSL-KDD dataset have shown the efficacy of stacking deep learning models for intrusion detection.The performance of the ensemble of base models,combined with the meta-model,exceeds the performance of individual models.Our stacking model has attained an accuracy of 99%and an average F1-score of 93%for the multi-classification scenario.Besides,the training time of the proposed ensemble model is lower than the training time of benchmark techniques,demonstrating its efficiency and robustness.展开更多
An efficient convolution neural network(CNN) plays a crucial role in various visual tasks like object classification or detection, etc. The most common way to construct a CNN is stacking the same convolution block or ...An efficient convolution neural network(CNN) plays a crucial role in various visual tasks like object classification or detection, etc. The most common way to construct a CNN is stacking the same convolution block or complex connection. These approaches may be efficient but the parameter size and computation(Comp) have explosive growth. So we present a novel architecture called"DLA+", which could obtain the feature from the different stages, and by the newly designed convolution block, could achieve better accuracy, while also dropping the computation six times compared to the baseline. We design some experiments about classification and object detection. On the CIFAR10 and VOC data-sets, we get better precision and faster speed than other architecture. The lightweight network even allows us to deploy to some low-performance device like drone, laptop, etc.展开更多
The detection of rice leaf disease is significant because,as an agricultural and rice exporter country,Pakistan needs to advance in production and lower the risk of diseases.In this rapid globalization era,information...The detection of rice leaf disease is significant because,as an agricultural and rice exporter country,Pakistan needs to advance in production and lower the risk of diseases.In this rapid globalization era,information technology has increased.A sensing system is mandatory to detect rice diseases using Artificial Intelligence(AI).It is being adopted in all medical and plant sciences fields to access and measure the accuracy of results and detection while lowering the risk of diseases.Deep Neural Network(DNN)is a novel technique that will help detect disease present on a rice leave because DNN is also considered a state-of-the-art solution in image detection using sensing nodes.Further in this paper,the adoption of the mixed-method approach Deep Convolutional Neural Network(Deep CNN)has assisted the research in increasing the effectiveness of the proposed method.Deep CNN is used for image recognition and is a class of deep-learning neural networks.CNN is popular and mostly used in the field of image recognition.A dataset of images with three main leaf diseases is selected for training and testing the proposed model.After the image acquisition and preprocessing process,the Deep CNN model was trained to detect and classify three rice diseases(Brown spot,bacterial blight,and blast disease).The proposed model achieved 98.3%accuracy in comparison with similar state-of-the-art techniques.展开更多
基金supported by the Extral High Voltage Power Transmission Company,China Southern Power Grid Co.,Ltd.
文摘Distributed Denial-of-Service(DDoS)attacks pose severe threats to Industrial Control Networks(ICNs),where service disruption can cause significant economic losses and operational risks.Existing signature-based methods are ineffective against novel attacks,and traditional machine learning models struggle to capture the complex temporal dependencies and dynamic traffic patterns inherent in ICN environments.To address these challenges,this study proposes a deep feature-driven hybrid framework that integrates Transformer,BiLSTM,and KNN to achieve accurate and robust DDoS detection.The Transformer component extracts global temporal dependencies from network traffic flows,while BiLSTM captures fine-grained sequential dynamics.The learned embeddings are then classified using an instance-based KNN layer,enhancing decision boundary precision.This cascaded architecture balances feature abstraction and locality preservation,improving both generalization and robustness.The proposed approach was evaluated on a newly collected real-time ICN traffic dataset and further validated using the public CIC-IDS2017 and Edge-IIoT datasets to demonstrate generalization.Comprehensive metrics including accuracy,precision,recall,F1-score,ROC-AUC,PR-AUC,false positive rate(FPR),and detection latency were employed.Results show that the hybrid framework achieves 98.42%accuracy with an ROC-AUC of 0.992 and FPR below 1%,outperforming baseline machine learning and deep learning models.Robustness experiments under Gaussian noise perturbations confirmed stable performance with less than 2%accuracy degradation.Moreover,detection latency remained below 2.1 ms per sample,indicating suitability for real-time ICS deployment.In summary,the proposed hybrid temporal learning and instance-based classification model offers a scalable and effective solution for DDoS detection in industrial control environments.By combining global contextual modeling,sequential learning,and instance-based refinement,the framework demonstrates strong adaptability across datasets and resilience against noise,providing practical utility for safeguarding critical infrastructure.
文摘Container transportation is pivotal in global trade due to its efficiency,safety,and cost-effectiveness.However,structural defects—particularly in grapple slots—can result in cargo damage,financial loss,and elevated safety risks,including container drops during lifting operations.Timely and accurate inspection before and after transit is therefore essential.Traditional inspection methods rely heavily on manual observation of internal and external surfaces,which are time-consuming,resource-intensive,and prone to subjective errors.Container roofs pose additional challenges due to limited visibility,while grapple slots are especially vulnerable to wear from frequent use.This study proposes a two-stage automated detection framework targeting defects in container roof grapple slots.In the first stage,YOLOv7 is employed to localize grapple slot regions with high precision.In the second stage,ResNet50 classifies the extracted slots as either intact or defective.The results from both stages are integrated into a human-machine interface for real-time visualization and user verification.Experimental evaluations demonstrate that YOLOv7 achieves a 99%detection rate at 100 frames per second(FPS),while ResNet50 attains 87%classification accuracy at 34 FPS.Compared to some state of the arts,the proposed system offers significant speed,reliability,and usability improvements,enabling efficient defect identification and visual reconfirmation via the interface.
基金funded by the Directorate of Research and Community Service,Directorate General of Research and Development,Ministry of Higher Education,Science and Technologyin accordance with the Implementation Contract for the Operational Assistance Program for State Universities,Research Program Number:109/C3/DT.05.00/PL/2025.
文摘Sudden wildfires cause significant global ecological damage.While satellite imagery has advanced early fire detection and mitigation,image-based systems face limitations including high false alarm rates,visual obstructions,and substantial computational demands,especially in complex forest terrains.To address these challenges,this study proposes a novel forest fire detection model utilizing audio classification and machine learning.We developed an audio-based pipeline using real-world environmental sound recordings.Sounds were converted into Mel-spectrograms and classified via a Convolutional Neural Network(CNN),enabling the capture of distinctive fire acoustic signatures(e.g.,crackling,roaring)that are minimally impacted by visual or weather conditions.Internet of Things(IoT)sound sensors were crucial for generating complex environmental parameters to optimize feature extraction.The CNN model achieved high performance in stratified 5-fold cross-validation(92.4%±1.6 accuracy,91.2%±1.8 F1-score)and on test data(94.93%accuracy,93.04%F1-score),with 98.44%precision and 88.32%recall,demonstrating reliability across environmental conditions.These results indicate that the audio-based approach not only improves detection reliability but also markedly reduces computational overhead compared to traditional image-based methods.The findings suggest that acoustic sensing integrated with machine learning offers a powerful,low-cost,and efficient solution for real-time forest fire monitoring in complex,dynamic environments.
基金supported by the Institute of Information&Communications Technology Planning&Evaluation(IITP)(Project Nos.RS-2024-00438551,30%,2022-11220701,30%,2021-0-01816,30%)the National Research Foundation of Korea(NRF)grant funded by the Korean Government(Project No.RS2023-00208460,10%).
文摘The proliferation of Internet of Things(IoT)devices has established edge computing as a critical paradigm for real-time data analysis and low-latency processing.Nevertheless,the distributed nature of edge computing presents substantial security challenges,rendering it a prominent target for sophisticated malware attacks.Existing signature-based and behavior-based detection methods are ineffective against the swiftly evolving nature of malware threats and are constrained by the availability of resources.This paper suggests the Genetic Encoding for Novel Optimization of Malware Evaluation(GENOME)framework,a novel solution that is intended to improve the performance of malware detection and classification in peripheral computing environments.GENOME optimizes data storage and computa-tional efficiency by converting malware artifacts into compact,structured sequences through a Deoxyribonucleic Acid(DNA)encoding mechanism.The framework employs two DNA encoding algorithms,standard and compressed,which substantially reduce data size while preserving high detection accuracy.The Edge-IIoTset dataset was used to conduct experiments that showed that GENOME was able to achieve high classification performance using models such as Random Forest and Logistic Regression,resulting in a reduction of data size by up to 42%.Further evaluations with the CIC-IoT-23 dataset and Deep Learning models confirmed GENOME’s scalability and adaptability across diverse datasets and algorithms.The potential of GENOME to address critical challenges,such as the rapid mutation of malware,real-time processing demands,and resource limitations,is emphasized in this study.GENOME offers comprehensive protection for peripheral computing environments by offering a security solution that is both efficient and scalable.
基金the Deanship of Graduate Studies and Scientific Research at Qassim University for financial support(QU-APC-2025).
文摘Leaf disease identification is one of the most promising applications of convolutional neural networks(CNNs).This method represents a significant step towards revolutionizing agriculture by enabling the quick and accurate assessment of plant health.In this study,a CNN model was specifically designed and tested to detect and categorize diseases on fig tree leaves.The researchers utilized a dataset of 3422 images,divided into four classes:healthy,fig rust,fig mosaic,and anthracnose.These diseases can significantly reduce the yield and quality of fig tree fruit.The objective of this research is to develop a CNN that can identify and categorize diseases in fig tree leaves.The data for this study was collected from gardens in the Amandi and Mamash Khail Bannu districts of the Khyber Pakhtunkhwa region in Pakistan.To minimize the risk of overfitting and enhance the model’s performance,early stopping techniques and data augmentation were employed.As a result,the model achieved a training accuracy of 91.53%and a validation accuracy of 90.12%,which are considered respectable.This comprehensive model assists farmers in the early identification and categorization of fig tree leaf diseases.Our experts believe that CNNs could serve as valuable tools for accurate disease classification and detection in precision agriculture.We recommend further research to explore additional data sources and more advanced neural networks to improve the model’s accuracy and applicability.Future research will focus on expanding the dataset by including new diseases and testing the model in real-world scenarios to enhance sustainable farming practices.
文摘Breast cancer screening programs rely heavily on mammography for early detection;however,diagnostic performance is strongly affected by inter-reader variability,breast density,and the limitations of conven-tional computer-aided detection systems.Recent advances in deep learning have enabled more robust and scalable solutions for large-scale screening,yet a systematic comparison of modern object detection architectures on nationally representative datasets remains limited.This study presents a comprehensive quantitative comparison of prominent deep learning–based object detection architectures for Artificial Intelligence-assisted mammography analysis using the MammosighTR dataset,developed within the Turkish National Breast Cancer Screening Program.The dataset comprises 12,740 patient cases collected between 2016 and 2022,annotated with BI-RADS categories,breast density levels,and lesion localization labels.A total of 31 models were evaluated,including One-Stage,Two-Stage,and Transformer-based architectures,under a unified experimental framework at both patient and breast levels.The results demonstrate that Two-Stage architectures consistently outperform One-Stage models,achieving approximately 2%–4%higher Macro F1-Scores and more balanced precision–recall trade-offs,with Double-Head R-CNN and Dynamic R-CNN yielding the highest overall performance(Macro F1≈0.84–0.86).This advantage is primarily attributed to the region proposal mechanism and improved class balance inherent to Two-Stage designs.One-Stage detectors exhibited higher sensitivity and faster inference,reaching Recall values above 0.88,but experienced minor reductions in Precision and overall accuracy(≈1%–2%)compared with Two-Stage models.Among Transformer-based architectures,Deformable DEtection TRansformer demonstrated strong robustness and consistency across datasets,achieving Macro F1-Scores comparable to CNN-based detectors(≈0.83–0.85)while exhibiting minimal performance degradation under distributional shifts.Breast density–based analysis revealed increased misclassification rates in medium-density categories(types B and C),whereas Transformer-based architectures maintained more stable performance in high-density type D tissue.These findings quantitatively confirm that both architectural design and tissue characteristics play a decisive role in diagnostic accuracy.Overall,the study provides a reproducible benchmark and highlights the potential of hybrid approaches that combine the accuracy of Two-Stage detectors with the contextual modeling capability of Transformer architectures for clinically reliable breast cancer screening systems.
基金funded by Princess Nourah bint Abdulrahman University Researchers Supporting Project number(PNURSP2026R234)Princess Nourah bint Abdulrahman University,Riyadh,Saudi Arabia.
文摘Spam emails remain one of the most persistent threats to digital communication,necessitating effective detection solutions that safeguard both individuals and organisations.We propose a spam email classification frame-work that uses Bidirectional Encoder Representations from Transformers(BERT)for contextual feature extraction and a multiple-window Convolutional Neural Network(CNN)for classification.To identify semantic nuances in email content,BERT embeddings are used,and CNN filters extract discriminative n-gram patterns at various levels of detail,enabling accurate spam identification.The proposed model outperformed Word2Vec-based baselines on a sample of 5728 labelled emails,achieving an accuracy of 98.69%,AUC of 0.9981,F1 Score of 0.9724,and MCC of 0.9639.With a medium kernel size of(6,9)and compact multi-window CNN architectures,it improves performance.Cross-validation illustrates stability and generalization across folds.By balancing high recall with minimal false positives,our method provides a reliable and scalable solution for current spam detection in advanced deep learning.By combining contextual embedding and a neural architecture,this study develops a security analysis method.
基金supported in part by the Science and Technology Innovation Program of Hunan Province under Grant 2025RC3166the National Natural Science Foundation of China under Grant 62572176the National Key R&D Program of China under Grant 2024YFF0618800.
文摘The rapid advancement of large language models(LLMs)has driven the pervasive adoption of AI-generated content(AIGC),while also raising concerns about misinformation,academic misconduct,biased or harmful content,and other risks.Detecting AI-generated text has thus become essential to safeguard the authenticity and reliability of digital information.This survey reviews recent progress in detection methods,categorizing approaches into passive and active categories based on their reliance on intrinsic textual features or embedded signals.Passive detection is further divided into surface linguistic feature-based and language model-based methods,whereas active detection encompasses watermarking-based and semantic retrieval-based approaches.This taxonomy enables systematic comparison of methodological differences in model dependency,applicability,and robustness.A key challenge for AI-generated text detection is that existing detectors are highly vulnerable to adversarial attacks,particularly paraphrasing,which substantially compromises their effectiveness.Addressing this gap highlights the need for future research on enhancing robustness and cross-domain generalization.By synthesizing current advances and limitations,this survey provides a structured reference for the field and outlines pathways toward more reliable and scalable detection solutions.
基金funded by Multimedia University,Cyberjaya,Selangor,Malaysia(Grant Number:PostDoc(MMUI/240029)).
文摘Manual inspection of onba earing casting defects is not realistic and unreliable,particularly in the case of some micro-level anomalies which lead to major defects on a large scale.To address these challenges,we propose BearFusionNet,an attention-based deep learning architecture with multi-stream,which merges both DenseNet201 and MobileNetV2 for feature extraction with a classification head inspired by VGG19.This hybrid design,figuratively beaming from one layer to another,extracts the enormity of representations on different scales,backed by a prepreprocessing pipeline that brings defect saliency to the fore through contrast adjustment,denoising,and edge detection.The use of multi-head self-attention enhances feature fusion,enabling the model to capture both large and small spatial features.BearFusionNet achieves an accuracy of 99.66%and Cohen’s kappa score of 0.9929 in Kaggle’s Real-life Industrial Casting Defects dataset.Both McNemar’s and Wilcoxon signed-rank statistical tests,as well as fivefold cross-validation,are employed to assess the robustness of our proposed model.To interpret the model,we adopt Grad-Cam visualizations,which are the state of the art standard.Furthermore,we deploy BearFusionNet as a webbased system for near real-time inference(5-6 s per prediction),which enables the quickest yet accurate detection with visual explanations.Overall,BearFusionNet is an interpretable,accurate,and deployable solution that can automatically detect casting defects,leading to significant advances in the innovative industrial environment.
文摘Considering the important applications in the military and the civilian domain, ship detection and classification based on optical remote sensing images raise considerable attention in the sea surface remote sensing filed. This article collects the methods of ship detection and classification for practically testing in optical remote sensing images, and provides their corresponding feature extraction strategies and statistical data. Basic feature extraction strategies and algorithms are analyzed associated with their performance and application in ship detection and classification.Furthermore, publicly available datasets that can be applied as the benchmarks to verify the effectiveness and the objectiveness of ship detection and classification methods are summarized in this paper. Based on the analysis, the remaining problems and future development trends are provided for ship detection and classification methods based on optical remote sensing images.
文摘Recently machine learning-based intrusion detection approaches have been subjected to extensive researches because they can detect both misuse and anomaly. In this paper, rough set classification (RSC), a modern learning algorithm, is used to rank the features extracted for detecting intrusions and generate intrusion detection models. Feature ranking is a very critical step when building the model. RSC performs feature ranking before generating rules, and converts the feature ranking to minimal hitting set problem addressed by using genetic algorithm (GA). This is done in classical approaches using Support Vector Machine (SVM) by executing many iterations, each of which removes one useless feature. Compared with those methods, our method can avoid many iterations. In addition, a hybrid genetic algorithm is proposed to increase the convergence speed and decrease the training time of RSC. The models generated by RSC take the form of'IF-THEN' rules, which have the advantage of explication. Tests and comparison of RSC with SVM on DARPA benchmark data showed that for Probe and DoS attacks both RSC and SVM yielded highly accurate results (greater than 99% accuracy on testing set).
基金This research is supported by the National Research Council of Thailand(NRCT).Project ID:618211.
文摘An ideal printed circuit board(PCB)defect inspection system can detect defects and classify PCB defect types.Existing defect inspection technologies can identify defects but fail to classify all PCB defect types.This research thus proposes an algorithmic scheme that can detect and categorize all 14-known PCB defect types.In the proposed algorithmic scheme,fuzzy cmeans clustering is used for image segmentation via image subtraction prior to defect detection.Arithmetic and logic operations,the circle hough transform(CHT),morphological reconstruction(MR),and connected component labeling(CCL)are used in defect classification.The algorithmic scheme achieves 100%defect detection and 99.05%defect classification accuracies.The novelty of this research lies in the concurrent use of CHT,MR,and CCL algorithms to accurately detect and classify all 14-known PCB defect types and determine the defect characteristics such as the location,area,and nature of defects.This information is helpful in electronic parts manufacturing for finding the root causes of PCB defects and appropriately adjusting the manufacturing process.Moreover,the algorithmic scheme can be integrated into machine vision to streamline the manufacturing process,improve the PCB quality,and lower the production cost.
文摘With recent breakthroughs in artificial intelligence,the use of deep learning models achieved remarkable advances in computer vision,ecommerce,cybersecurity,and healthcare.Particularly,numerous applications provided efficient solutions to assist radiologists for medical imaging analysis.For instance,automatic lesion detection and classification in mammograms is still considered a crucial task that requires more accurate diagnosis and precise analysis of abnormal lesions.In this paper,we propose an end-to-end system,which is based on You-Only-Look-Once(YOLO)model,to simultaneously localize and classify suspicious breast lesions from entire mammograms.The proposed system first preprocesses the raw images,then recognizes abnormal regions as breast lesions and determines their pathology classification as either mass or calcification.We evaluated the model on two publicly available datasets,with 2907 mammograms from the Curated Breast Imaging Subset of Digital Database for Screening Mammography(CBIS-DDSM)and 235 mammograms from INbreast database.We also used a privately collected dataset with 487 mammograms.Furthermore,we suggested a fusion models approach to report more precise detection and accurate classification.Our best results reached a detection accuracy rate of 95.7%,98.1%and 98%for mass lesions and 74.4%,71.8%and 73.2%for calcification lesions,respectively on CBIS-DDSM,INbreast and the private dataset.
基金supported by the National Natural Science Foundation of China (No.82001775, 82371933)the Natural Science Foundation of Shandong Province of China (No.ZR2021MH120)+1 种基金the Special Fund for Breast Disease Research of Shandong Medical Association (No.YXH2021ZX055)the Taishan Scholar Foundation of Shandong Province of China (No.tsgn202211378)。
文摘Objective: Accurate detection and classification of breast lesions in early stage is crucial to timely formulate effective treatments for patients. We aim to develop a fully automatic system to detect and classify breast lesions using multiple contrast-enhanced mammography(CEM) images.Methods: In this study, a total of 1,903 females who underwent CEM examination from three hospitals were enrolled as the training set, internal testing set, pooled external testing set and prospective testing set. Here we developed a CEM-based multiprocess detection and classification system(MDCS) to perform the task of detection and classification of breast lesions. In this system, we introduced an innovative auxiliary feature fusion(AFF)algorithm that could intelligently incorporates multiple types of information from CEM images. The average freeresponse receiver operating characteristic score(AFROC-Score) was presented to validate system’s detection performance, and the performance of classification was evaluated by area under the receiver operating characteristic curve(AUC). Furthermore, we assessed the diagnostic value of MDCS through visual analysis of disputed cases,comparing its performance and efficiency with that of radiologists and exploring whether it could augment radiologists’ performance.Results: On the pooled external and prospective testing sets, MDCS always maintained a high standalone performance, with AFROC-Scores of 0.953 and 0.963 for detection task, and AUCs for classification were 0.909[95% confidence interval(95% CI): 0.822-0.996] and 0.912(95% CI: 0.840-0.985), respectively. It also achieved higher sensitivity than all senior radiologists and higher specificity than all junior radiologists on pooled external and prospective testing sets. Moreover, MDCS performed superior diagnostic efficiency with an average reading time of 5 seconds, compared to the radiologists’ average reading time of 3.2 min. The average performance of all radiologists was also improved to varying degrees with MDCS assistance.Conclusions: MDCS demonstrated excellent performance in the detection and classification of breast lesions,and greatly enhanced the overall performance of radiologists.
基金supported by the School Doctoral Fund of Zhengzhou University of Light Industry No.2015BSJJ051.
文摘Automatic road damage detection using image processing is an important aspect of road maintenance.It is also a challenging problem due to the inhomogeneity of road damage and complicated background in the road images.In recent years,deep convolutional neural network based methods have been used to address the challenges of road damage detection and classification.In this paper,we propose a new approach to address those challenges.This approach uses densely connected convolution networks as the backbone of the Mask R-CNN to effectively extract image feature,a feature pyramid network for combining multiple scales features,a region proposal network to generate the road damage region,and a fully convolutional neural network to classify the road damage region and refine the region bounding box.This method can not only detect and classify the road damage,but also create a mask of the road damage.Experimental results show that the proposed approach can achieve better results compared with other existing methods.
基金This research was partially supported by JNTU Hyderabad,India under Grant proceeding number:JNTUH/TEQIP-III/CRS/2019/CSE/08.The authors are grateful for the support provided by the TEQIP-III team.
文摘Arrhythmia is ubiquitous worldwide and cardiologists tend to provide solutions from the recent advancements in medicine.Detecting arrhythmia from ECG signals is considered a standard approach and hence,automating this process would aid the diagnosis by providing fast,costefficient,and accurate solutions at scale.This is executed by extracting the definite properties from the individual patterns collected from Electrocardiography(ECG)signals causing arrhythmia.In this era of applied intelligence,automated detection and diagnostic solutions are widely used for their spontaneous and robust solutions.In this research,our contributions are two-fold.Firstly,the Dual-Tree Complex Wavelet Transform(DT-CWT)method is implied to overhaul shift-invariance and aids signal reconstruction to extract significant features.Next,A neural attention mechanism is implied to capture temporal patterns from the extracted features of the ECG signal to discriminate distinct classes of arrhythmia and is trained end-to-end with the finest parameters.To ensure that the model’s generalizability,a set of five traintest variants are implied.The proposed model attains the highest accuracy of 98.5%for classifying 8 variants of arrhythmia on the MIT-BIH dataset.To test the resilience of the model,the unseen(test)samples are increased by 5x and the deviation in accuracy score and MSE was 0.12%and 0.1%respectively.Further,to assess the diagnostic model performance,AUC-ROC curves are plotted.At every test level,the proposed model is capable of generalizing new samples and leverages the advantage to develop a real-world application.As a note,this research is the first attempt to provide neural attention in arrhythmia classification using MIT-BIH ECG signals data with state-of-the-art performance.
基金The authors extend their appreciation to the Deanship of Scientific Research at King Khalid University for funding this work under grant number(RGP 1/322/42)Princess Nourah bint Abdulrahman University Researchers Supporting Project number(PNURSP2022R161)Princess Nourah bint Abdulrahman University,Riyadh,Saudi Arabia.The authors would like to thank the Deanship of Scientific Research at Umm Al-Qura University for supporting this work by Grant Code:(22UQU4310373DSR06).
文摘Oral cancer is the most commonly occurring‘head and neck cancers’across the globe.Most of the oral cancer cases are diagnosed at later stages due to absence of awareness among public.Since earlier identification of disease is essential for improved outcomes,Artificial Intelligence(AI)and Machine Learning(ML)models are used in this regard.In this background,the current study introduces Artificial Intelligence with Deep Transfer Learning driven Oral Cancer detection and Classification Model(AIDTLOCCM).The primary goal of the proposed AIDTL-OCCM model is to diagnose oral cancer using AI and image processing techniques.The proposed AIDTL-OCCM model involves fuzzy-based contrast enhancement approach to perform data pre-processing.Followed by,the densely-connected networks(DenseNet-169)model is employed to produce a useful set of deep features.Moreover,Chimp Optimization Algorithm(COA)with Autoencoder(AE)model is applied for oral cancer detection and classification.Furthermore,COA is employed to determine optimal parameters involved in AE model.A wide range of experimental analyses was conducted on benchmark datasets and the results were investigated under several aspects.The extensive experimental analysis outcomes established the enhanced performance of AIDTLOCCM model compared to other approaches with a maximum accuracy of 90.08%.
文摘Intrusion detection is a predominant task that monitors and protects the network infrastructure.Therefore,many datasets have been published and investigated by researchers to analyze and understand the problem of intrusion prediction and detection.In particular,the Network Security Laboratory-Knowledge Discovery in Databases(NSL-KDD)is an extensively used benchmark dataset for evaluating intrusion detection systems(IDSs)as it incorporates various network traffic attacks.It is worth mentioning that a large number of studies have tackled the problem of intrusion detection using machine learning models,but the performance of these models often decreases when evaluated on new attacks.This has led to the utilization of deep learning techniques,which have showcased significant potential for processing large datasets and therefore improving detection accuracy.For that reason,this paper focuses on the role of stacking deep learning models,including convolution neural network(CNN)and deep neural network(DNN)for improving the intrusion detection rate of the NSL-KDD dataset.Each base model is trained on the NSL-KDD dataset to extract significant features.Once the base models have been trained,the stacking process proceeds to the second stage,where a simple meta-model has been trained on the predictions generated from the proposed base models.The combination of the predictions allows the meta-model to distinguish different classes of attacks and increase the detection rate.Our experimental evaluations using the NSL-KDD dataset have shown the efficacy of stacking deep learning models for intrusion detection.The performance of the ensemble of base models,combined with the meta-model,exceeds the performance of individual models.Our stacking model has attained an accuracy of 99%and an average F1-score of 93%for the multi-classification scenario.Besides,the training time of the proposed ensemble model is lower than the training time of benchmark techniques,demonstrating its efficiency and robustness.
基金supported by University Synergy Innovation Program of Anhui Province (No. GXXT-2019-007)Corporative Information Processing and Deep Mining for Intelligent Robot (No. JCYJ20170817155854115)+1 种基金Major Project for New Generation of AI (No.2018AAA0100400)Anhui Provincial Natural Science Foundation (No. 1908085MF206)。
文摘An efficient convolution neural network(CNN) plays a crucial role in various visual tasks like object classification or detection, etc. The most common way to construct a CNN is stacking the same convolution block or complex connection. These approaches may be efficient but the parameter size and computation(Comp) have explosive growth. So we present a novel architecture called"DLA+", which could obtain the feature from the different stages, and by the newly designed convolution block, could achieve better accuracy, while also dropping the computation six times compared to the baseline. We design some experiments about classification and object detection. On the CIFAR10 and VOC data-sets, we get better precision and faster speed than other architecture. The lightweight network even allows us to deploy to some low-performance device like drone, laptop, etc.
基金funded by the University of Haripur,KP Pakistan Researchers Supporting Project number (PKURFL2324L33)。
文摘The detection of rice leaf disease is significant because,as an agricultural and rice exporter country,Pakistan needs to advance in production and lower the risk of diseases.In this rapid globalization era,information technology has increased.A sensing system is mandatory to detect rice diseases using Artificial Intelligence(AI).It is being adopted in all medical and plant sciences fields to access and measure the accuracy of results and detection while lowering the risk of diseases.Deep Neural Network(DNN)is a novel technique that will help detect disease present on a rice leave because DNN is also considered a state-of-the-art solution in image detection using sensing nodes.Further in this paper,the adoption of the mixed-method approach Deep Convolutional Neural Network(Deep CNN)has assisted the research in increasing the effectiveness of the proposed method.Deep CNN is used for image recognition and is a class of deep-learning neural networks.CNN is popular and mostly used in the field of image recognition.A dataset of images with three main leaf diseases is selected for training and testing the proposed model.After the image acquisition and preprocessing process,the Deep CNN model was trained to detect and classify three rice diseases(Brown spot,bacterial blight,and blast disease).The proposed model achieved 98.3%accuracy in comparison with similar state-of-the-art techniques.
