The increasing adoption of Industrial Internet of Things(IIoT)systems in smart manufacturing is leading to raise cyberattack numbers and pressing the requirement for intrusion detection systems(IDS)to be effective.How...The increasing adoption of Industrial Internet of Things(IIoT)systems in smart manufacturing is leading to raise cyberattack numbers and pressing the requirement for intrusion detection systems(IDS)to be effective.However,existing datasets for IDS training often lack relevance to modern IIoT environments,limiting their applicability for research and development.To address the latter gap,this paper introduces the HiTar-2024 dataset specifically designed for IIoT systems.As a consequence,that can be used by an IDS to detect imminent threats.Likewise,HiTar-2024 was generated using the AREZZO simulator,which replicates realistic smart manufacturing scenarios.The generated dataset includes five distinct classes:Normal,Probing,Remote to Local(R2L),User to Root(U2R),and Denial of Service(DoS).Furthermore,comprehensive experiments with popular Machine Learning(ML)models using various classifiers,including BayesNet,Logistic,IBK,Multiclass,PART,and J48 demonstrate high accuracy,precision,recall,and F1-scores,exceeding 0.99 across all ML metrics.The latter result is reached thanks to the rigorous applied process to achieve this quite good result,including data pre-processing,features extraction,fixing the class imbalance problem,and using a test option for model robustness.This comprehensive approach emphasizes meticulous dataset construction through a complete dataset generation process,a careful labelling algorithm,and a sophisticated evaluation method,providing valuable insights to reinforce IIoT system security.Finally,the HiTar-2024 dataset is compared with other similar datasets in the literature,considering several factors such as data format,feature extraction tools,number of features,attack categories,number of instances,and ML metrics.展开更多
Deep learning-based approaches are applied successfully in manyfields such as deepFake identification,big data analysis,voice recognition,and image recognition.Deepfake is the combination of deep learning in fake creati...Deep learning-based approaches are applied successfully in manyfields such as deepFake identification,big data analysis,voice recognition,and image recognition.Deepfake is the combination of deep learning in fake creation,which states creating a fake image or video with the help of artificial intelligence for political abuse,spreading false information,and pornography.The artificial intel-ligence technique has a wide demand,increasing the problems related to privacy,security,and ethics.This paper has analyzed the features related to the computer vision of digital content to determine its integrity.This method has checked the computer vision features of the image frames using the fuzzy clustering feature extraction method.By the proposed deep belief network with loss handling,the manipulation of video/image is found by means of a pairwise learning approach.This proposed approach has improved the accuracy of the detection rate by 98%on various datasets.展开更多
The Internet of Multimedia Things(IoMT)refers to a network of interconnected multimedia devices that communicate with each other over the Internet.Recently,smart healthcare has emerged as a significant application of ...The Internet of Multimedia Things(IoMT)refers to a network of interconnected multimedia devices that communicate with each other over the Internet.Recently,smart healthcare has emerged as a significant application of the IoMT,particularly in the context of knowledge‐based learning systems.Smart healthcare systems leverage knowledge‐based learning to become more context‐aware,adaptable,and auditable while maintain-ing the ability to learn from historical data.In smart healthcare systems,devices capture images,such as X‐rays,Magnetic Resonance Imaging.The security and integrity of these images are crucial for the databases used in knowledge‐based learning systems to foster structured decision‐making and enhance the learning abilities of AI.Moreover,in knowledge‐driven systems,the storage and transmission of HD medical images exert a burden on the limited bandwidth of the communication channel,leading to data trans-mission delays.To address the security and latency concerns,this paper presents a lightweight medical image encryption scheme utilising bit‐plane decomposition and chaos theory.The results of the experiment yield entropy,energy,and correlation values of 7.999,0.0156,and 0.0001,respectively.This validates the effectiveness of the encryption system proposed in this paper,which offers high‐quality encryption,a large key space,key sensitivity,and resistance to statistical attacks.展开更多
Snort rule-checking is one of the most popular forms of Network Intrusion Detection Systems (NIDS). In this article, we show that Snort priorities of true positive traffic (real attacks) can be approximated in real-ti...Snort rule-checking is one of the most popular forms of Network Intrusion Detection Systems (NIDS). In this article, we show that Snort priorities of true positive traffic (real attacks) can be approximated in real-time, in the context of high speed networks, by a decision tree classifier, using the information of only three easily extracted features (protocol, source port, and destination port), with an accuracy of 99%. Snort issues alert priorities based on its own default set of attack classes (34 classes) that are used by the default set of rules it provides. But the decision tree model is able to predict the priorities without using this default classification. The obtained tagger can provide a useful complement to an anomaly detection intrusion detection system.展开更多
Disasters may occur at any time and place without little to no presage in advance.With the development of surveillance and forecasting systems,it is now possible to forebode the most life-threatening and formidable di...Disasters may occur at any time and place without little to no presage in advance.With the development of surveillance and forecasting systems,it is now possible to forebode the most life-threatening and formidable disasters.However,forestfires are among the ones that are still hard to anticipate beforehand,and the technologies to detect and plot their possible courses are still in development.Unmanned Aerial Vehicle(UAV)image-basedfire detection systems can be a viable solution to this problem.However,these automatic systems use advanced deep learning and image processing algorithms at their core and can be tuned to provide accurate outcomes.Therefore,this article proposed a forestfire detection method based on a Convolutional Neural Network(CNN)architecture using a newfire detection dataset.Notably,our method also uses separable convolution layers(requiring less computational resources)for immediatefire detection and typical convolution layers.Thus,making it suitable for real-time applications.Consequently,after being trained on the dataset,experimental results show that the method can identify forestfires within images with a 97.63%accuracy,98.00%F1 Score,and 80%Kappa.Hence,if deployed in practical circumstances,this identification method can be used as an assistive tool to detectfire outbreaks,allowing the authorities to respond quickly and deploy preventive measures to minimize damage.展开更多
Tumor detection has been an active research topic in recent years due to the high mortality rate.Computer vision(CV)and image processing techniques have recently become popular for detecting tumors inMRI images.The au...Tumor detection has been an active research topic in recent years due to the high mortality rate.Computer vision(CV)and image processing techniques have recently become popular for detecting tumors inMRI images.The automated detection process is simpler and takes less time than manual processing.In addition,the difference in the expanding shape of brain tumor tissues complicates and complicates tumor detection for clinicians.We proposed a newframework for tumor detection aswell as tumor classification into relevant categories in this paper.For tumor segmentation,the proposed framework employs the Particle Swarm Optimization(PSO)algorithm,and for classification,the convolutional neural network(CNN)algorithm.Popular preprocessing techniques such as noise removal,image sharpening,and skull stripping are used at the start of the segmentation process.Then,PSO-based segmentation is applied.In the classification step,two pre-trained CNN models,alexnet and inception-V3,are used and trained using transfer learning.Using a serial approach,features are extracted from both trained models and fused features for final classification.For classification,a variety of machine learning classifiers are used.Average dice values on datasets BRATS-2018 and BRATS-2017 are 98.11 percent and 98.25 percent,respectively,whereas average jaccard values are 96.30 percent and 96.57%(Segmentation Results).The results were extended on the same datasets for classification and achieved 99.0%accuracy,sensitivity of 0.99,specificity of 0.99,and precision of 0.99.Finally,the proposed method is compared to state-of-the-art existingmethods and outperforms them.展开更多
The Internet of Things(IoT)has been transformed almost all fields of life,but its impact on the healthcare sector has been notable.Various IoTbased sensors are used in the healthcare sector and offer quality and safe ...The Internet of Things(IoT)has been transformed almost all fields of life,but its impact on the healthcare sector has been notable.Various IoTbased sensors are used in the healthcare sector and offer quality and safe care to patients.This work presents a deep learning-based automated patient discomfort detection system in which patients’discomfort is non-invasively detected.To do this,the overhead view patients’data set has been recorded.For testing and evaluation purposes,we investigate the power of deep learning by choosing a Convolution Neural Network(CNN)based model.The model uses confidence maps and detects 18 different key points at various locations of the body of the patient.Applying association rules and part affinity fields,the detected key points are later converted into six main body organs.Furthermore,the distance of subsequent key points is measured using coordinates information.Finally,distance and the time-based threshold are used for the classification of movements associated with discomfort or normal conditions.The accuracy of the proposed system is assessed on various test sequences.The experimental outcomes reveal the worth of the proposed system’by obtaining a True Positive Rate of 98%with a 2%False Positive Rate.展开更多
White blood cells(WBC)are immune system cells,which is why they are also known as immune cells.They protect the human body from a variety of dangerous diseases and outside invaders.The majority of WBCs come from red b...White blood cells(WBC)are immune system cells,which is why they are also known as immune cells.They protect the human body from a variety of dangerous diseases and outside invaders.The majority of WBCs come from red bone marrow,although some come from other important organs in the body.Because manual diagnosis of blood disorders is difficult,it is necessary to design a computerized technique.Researchers have introduced various automated strategies in recent years,but they still face several obstacles,such as imbalanced datasets,incorrect feature selection,and incorrect deep model selection.We proposed an automated deep learning approach for classifying white blood disorders in this paper.The data augmentation approach is initially used to increase the size of a dataset.Then,a Darknet-53 pre-trained deep learning model is used and finetuned according to the nature of the chosen dataset.On the fine-tuned model,transfer learning is used,and features engineering is done on the global average pooling layer.The retrieved characteristics are subsequently improved with a specified number of iterations using a hybrid reformed binary grey wolf optimization technique.Following that,machine learning classifiers are used to classify the selected best features for final classification.The experiment was carried out using a dataset of increased blood diseases imaging and resulted in an improved accuracy of over 99%.展开更多
The combination of spatiotemporal videos and essential features can improve the performance of human action recognition(HAR);however,the individual type of features usually degrades the performance due to similar acti...The combination of spatiotemporal videos and essential features can improve the performance of human action recognition(HAR);however,the individual type of features usually degrades the performance due to similar actions and complex backgrounds.The deep convolutional neural network has improved performance in recent years for several computer vision applications due to its spatial information.This article proposes a new framework called for video surveillance human action recognition dubbed HybridHR-Net.On a few selected datasets,deep transfer learning is used to pre-trained the EfficientNet-b0 deep learning model.Bayesian optimization is employed for the tuning of hyperparameters of the fine-tuned deep model.Instead of fully connected layer features,we considered the average pooling layer features and performed two feature selection techniques-an improved artificial bee colony and an entropy-based approach.Using a serial nature technique,the features that were selected are combined into a single vector,and then the results are categorized by machine learning classifiers.Five publically accessible datasets have been utilized for the experimental approach and obtained notable accuracy of 97%,98.7%,100%,99.7%,and 96.8%,respectively.Additionally,a comparison of the proposed framework with contemporarymethods is done to demonstrate the increase in accuracy.展开更多
This paper tests various scenarios of feature selection and feature reduction, with the objective of building a real-time anomaly-based intrusion detection system. These scenarios are evaluated on the realistic Kyoto ...This paper tests various scenarios of feature selection and feature reduction, with the objective of building a real-time anomaly-based intrusion detection system. These scenarios are evaluated on the realistic Kyoto 2006+ dataset. The influence of reducing the number of features on the classification performance and the execution time is measured for each scenario. The so-called HVS feature selection technique detailed in this paper reveals many advantages in terms of consistency, classification performance and execution time.展开更多
The Internet of Vehicles(IoV)is a networking paradigm related to the intercommunication of vehicles using a network.In a dynamic network,one of the key challenges in IoV is traffic management under increasing vehicles...The Internet of Vehicles(IoV)is a networking paradigm related to the intercommunication of vehicles using a network.In a dynamic network,one of the key challenges in IoV is traffic management under increasing vehicles to avoid congestion.Therefore,optimal path selection to route traffic between the origin and destination is vital.This research proposed a realistic strategy to reduce traffic management service response time by enabling real-time content distribution in IoV systems using heterogeneous network access.Firstly,this work proposed a novel use of the Ant Colony Optimization(ACO)algorithm and formulated the path planning optimization problem as an Integer Linear Program(ILP).This integrates the future estimation metric to predict the future arrivals of the vehicles,searching the optimal routes.Considering the mobile nature of IOV,fuzzy logic is used for congestion level estimation along with the ACO to determine the optimal path.The model results indicate that the suggested scheme outperforms the existing state-of-the-art methods by identifying the shortest and most cost-effective path.Thus,this work strongly supports its use in applications having stringent Quality of Service(QoS)requirements for the vehicles.展开更多
The exponential growth of Internet and network usage has neces-sitated heightened security measures to protect against data and network breaches.Intrusions,executed through network packets,pose a significant challenge...The exponential growth of Internet and network usage has neces-sitated heightened security measures to protect against data and network breaches.Intrusions,executed through network packets,pose a significant challenge for firewalls to detect and prevent due to the similarity between legit-imate and intrusion traffic.The vast network traffic volume also complicates most network monitoring systems and algorithms.Several intrusion detection methods have been proposed,with machine learning techniques regarded as promising for dealing with these incidents.This study presents an Intrusion Detection System Based on Stacking Ensemble Learning base(Random For-est,Decision Tree,and k-Nearest-Neighbors).The proposed system employs pre-processing techniques to enhance classification efficiency and integrates seven machine learning algorithms.The stacking ensemble technique increases performance by incorporating three base models(Random Forest,Decision Tree,and k-Nearest-Neighbors)and a meta-model represented by the Logistic Regression algorithm.Evaluated using the UNSW-NB15 dataset,the pro-posed IDS gained an accuracy of 96.16%in the training phase and 97.95%in the testing phase,with precision of 97.78%,and 98.40%for taring and testing,respectively.The obtained results demonstrate improvements in other measurement criteria.展开更多
COVID’19 has caused the entire universe to be in existential healthcrisis by spreading globally in the year 2020. The lungs infection is detected inComputed Tomography (CT) images which provide the best way to increa...COVID’19 has caused the entire universe to be in existential healthcrisis by spreading globally in the year 2020. The lungs infection is detected inComputed Tomography (CT) images which provide the best way to increasethe existing healthcare schemes in preventing the deadly virus. Nevertheless,separating the infected areas in CT images faces various issues such as lowintensity difference among normal and infectious tissue and high changes inthe characteristics of the infection. To resolve these issues, a new inf-Net (LungInfection Segmentation Deep Network) is designed for detecting the affectedareas from the CT images automatically. For the worst segmentation results,the Edge-Attention Representation (EAR) is optimized using AdaptiveDonkey and Smuggler Optimization (ADSO). The edges which are identifiedby the ADSO approach is utilized for calculating dissimilarities. An IFCM(Intuitionistic Fuzzy C-Means) clustering approach is applied for computingthe similarity of the EA component among the generated edge maps andGround-Truth (GT) edge maps. Also, a Semi-Supervised Segmentation(SSS) structure is designed using the Randomly Selected Propagation (RP)technique and Inf-Net, which needs only less number of images and unlabelleddata. Semi-Supervised Multi-Class Segmentation (SSMCS) is designed usinga Bi-LSTM (Bi-Directional Long-Short-Term-memory), acquires all theadvantages of the disease segmentation done using Semi Inf-Net and enhancesthe execution of multi-class disease labelling. The newly designed SSMCSapproach is compared with existing U-Net++, MCS, and Semi-Inf-Net.factors such as MAE (Mean Absolute Error), Structure measure, Specificity(Spec), Dice Similarity coefficient, Sensitivity (Sen), and Enhance-AlignmentMeasure are considered for evaluation purpose.展开更多
Interactive learning tools can facilitate the learning process and increase student engagement,especially tools such as computer programs that are designed for human-computer interaction.Thus,this paper aims to help s...Interactive learning tools can facilitate the learning process and increase student engagement,especially tools such as computer programs that are designed for human-computer interaction.Thus,this paper aims to help students learn five different methods for solving nonlinear equations using an interactive learning tool designed with common principles such as feedback,visibility,affordance,consistency,and constraints.It also compares these methods by the number of iterations and time required to display the result.This study helps students learn these methods using interactive learning tools instead of relying on traditional teaching methods.The tool is implemented using the MATLAB app and is evaluated through usability testing with two groups of users that are categorized by their level of experience with root-finding.Users with no knowledge in root-finding confirmed that they understood the root-finding concept when interacting with the designed tool.The positive results of the user evaluation showed that the tool can be recommended to other users.展开更多
In order to research brain problems using MRI,PET,and CT neuroimaging,a correct understanding of brain function is required.This has been considered in earlier times with the support of traditional algorithms.Deep lea...In order to research brain problems using MRI,PET,and CT neuroimaging,a correct understanding of brain function is required.This has been considered in earlier times with the support of traditional algorithms.Deep learning process has also been widely considered in these genomics data processing system.In this research,brain disorder illness incliding Alzheimer’s disease,Schizophrenia and Parkinson’s diseaseis is analyzed owing to misdetection of disorders in neuroimaging data examined by means fo traditional methods.Moeover,deep learning approach is incorporated here for classification purpose of brain disorder with the aid of Deep Belief Networks(DBN).Images are stored in a secured manner by using DNA sequence based on JPEG Zig Zag Encryption algorithm(DBNJZZ)approach.The suggested approach is executed and tested by using the performance metric measure such as accuracy,root mean square error,Mean absolute error and mean absolute percentage error.Proposed DBNJZZ gives better performance than previously available methods.展开更多
X-Ray knee imaging is widely used to detect knee osteoarthritis due to ease of availability and lesser cost.However,the manual categorization of knee joint disorders is time-consuming,requires an expert person,and is ...X-Ray knee imaging is widely used to detect knee osteoarthritis due to ease of availability and lesser cost.However,the manual categorization of knee joint disorders is time-consuming,requires an expert person,and is costly.This article proposes a new approach to classifying knee osteoarthritis using deep learning and a whale optimization algorithm.Two pre-trained deep learning models(Efficientnet-b0 and Densenet201)have been employed for the training and feature extraction.Deep transfer learning with fixed hyperparameter values has been employed to train both selected models on the knee X-Ray images.In the next step,fusion is performed using a canonical correlation approach and obtained a feature vector that has more information than the original feature vector.After that,an improved whale optimization algorithm is developed for dimensionality reduction.The selected features are finally passed to the machine learning algorithms such as Fine-Tuned support vector machine(SVM)and neural networks for classification purposes.The experiments of the proposed framework have been conducted on the publicly available dataset and obtained the maximum accuracy of 90.1%.Also,the system is explained using Explainable Artificial Intelligence(XAI)technique called occlusion,and results are compared with recent research.Based on the results compared with recent techniques,it is shown that the proposed method’s accuracy significantly improved.展开更多
Intrusion detection systems that have emerged in recent decades can identify a variety of malicious attacks that target networks by employing several detection approaches.However,the current approaches have challenges...Intrusion detection systems that have emerged in recent decades can identify a variety of malicious attacks that target networks by employing several detection approaches.However,the current approaches have challenges in detecting intrusions,which may affect the performance of the overall detection system as well as network performance.For the time being,one of the most important creative technological advancements that plays a significant role in the professional world today is blockchain technology.Blockchain technology moves in the direction of persistent revolution and change.It is a chain of blocks that covers information and maintains trust between individuals no matter how far apart they are.Recently,blockchain was integrated into intrusion detection systems to enhance their overall performance.Blockchain has also been adopted in health-care,supply chain management,and the Internet of Things.Blockchain uses robust cryptography with private and public keys,and it has numerous properties that have leveraged security’s performance over peer-to-peer networks without the need for a third party.To explore and highlight the importance of integrating blockchain with intrusion detection systems,this paper provides a comprehensive background of intrusion detection systems and blockchain technology.Furthermore,a comprehensive review of emerging intrusion detection systems based on blockchain technology is presented.Finally,this paper suggests important future research directions and trending topics in intrusion detection systems based on blockchain technology.展开更多
A partial eigenstructure assignment method that keeps the open-loop stable eigenvalues and the corresponding eigenspace unchanged is presented. This method generalizes a large class of systems previous methods and can...A partial eigenstructure assignment method that keeps the open-loop stable eigenvalues and the corresponding eigenspace unchanged is presented. This method generalizes a large class of systems previous methods and can be applied to solve the constrained control problem for linear invariant continuous-time systems. Besides, it can be also applied to make a total eigenstructure assignment. Indeed, the problem of finding a stabilizing regulator matrix gain taking into account the asymmetrical control constraints is transformed to a Sylvester equation resolution. Examples are given to illustrate the obtained results.展开更多
The coronavirus(COVID19),also known as the novel coronavirus,first appeared in December 2019 in Wuhan,China.After that,it quickly spread throughout the world and became a disease.It has significantly impacted our ever...The coronavirus(COVID19),also known as the novel coronavirus,first appeared in December 2019 in Wuhan,China.After that,it quickly spread throughout the world and became a disease.It has significantly impacted our everyday lives,the national and international economies,and public health.However,early diagnosis is critical for prompt treatment and reducing trauma in the healthcare system.Clinical radiologists primarily use chest X-rays,and computerized tomography(CT)scans to test for pneumonia infection.We used Chest CT scans to predict COVID19 pneumonia and healthy scans in this study.We proposed a joint framework for prediction based on classical feature fusion and PSO-based optimization.We begin by extracting standard features such as discrete wavelet transforms(DWT),discrete cosine transforms(DCT),and dominant rotated local binary patterns(DRLBP).In addition,we extracted Shanon Entropy and Kurtosis features.In the following step,a Max-Covariance-based maximization approach for feature fusion is proposed.The fused features are optimized in the preliminary phase using Particle Swarm Optimization(PSO)and the ELM fitness function.For final prediction,PSO is used to obtain robust features,which are then implanted in a Support Vector Data Description(SVDD)classifier.The experiment is carried out using available COVID19 Chest CT Scans and scans from healthy patients.These images are from the Radiopaedia website.For the proposed scheme,the fusion and selection process accuracy is 88.6%and 93.1%,respectively.A detailed analysis is conducted,which supports the proposed system efficiency.展开更多
Semantic duplicates in databases represent today an important data quality challenge which leads to bad decisions. In large databases, we sometimes find ourselves with tens of thousands of duplicates, which necessitat...Semantic duplicates in databases represent today an important data quality challenge which leads to bad decisions. In large databases, we sometimes find ourselves with tens of thousands of duplicates, which necessitates an automatic deduplication. For this, it is necessary to detect duplicates, with a fairly reliable method to find as many duplicates as possible and powerful enough to run in a reasonable time. This paper proposes and compares on real data effective duplicates detection methods for automatic deduplication of files based on names, working with French texts or English texts, and the names of people or places, in Africa or in the West. After conducting a more complete classification of semantic duplicates than the usual classifications, we introduce several methods for detecting duplicates whose average complexity observed is less than O(2n). Through a simple model, we highlight a global efficacy rate, combining precision and recall. We propose a new metric distance between records, as well as rules for automatic duplicate detection. Analyses made on a database containing real data for an administration in Central Africa, and on a known standard database containing names of restaurants in the USA, have shown better results than those of known methods, with a lesser complexity.展开更多
文摘The increasing adoption of Industrial Internet of Things(IIoT)systems in smart manufacturing is leading to raise cyberattack numbers and pressing the requirement for intrusion detection systems(IDS)to be effective.However,existing datasets for IDS training often lack relevance to modern IIoT environments,limiting their applicability for research and development.To address the latter gap,this paper introduces the HiTar-2024 dataset specifically designed for IIoT systems.As a consequence,that can be used by an IDS to detect imminent threats.Likewise,HiTar-2024 was generated using the AREZZO simulator,which replicates realistic smart manufacturing scenarios.The generated dataset includes five distinct classes:Normal,Probing,Remote to Local(R2L),User to Root(U2R),and Denial of Service(DoS).Furthermore,comprehensive experiments with popular Machine Learning(ML)models using various classifiers,including BayesNet,Logistic,IBK,Multiclass,PART,and J48 demonstrate high accuracy,precision,recall,and F1-scores,exceeding 0.99 across all ML metrics.The latter result is reached thanks to the rigorous applied process to achieve this quite good result,including data pre-processing,features extraction,fixing the class imbalance problem,and using a test option for model robustness.This comprehensive approach emphasizes meticulous dataset construction through a complete dataset generation process,a careful labelling algorithm,and a sophisticated evaluation method,providing valuable insights to reinforce IIoT system security.Finally,the HiTar-2024 dataset is compared with other similar datasets in the literature,considering several factors such as data format,feature extraction tools,number of features,attack categories,number of instances,and ML metrics.
文摘Deep learning-based approaches are applied successfully in manyfields such as deepFake identification,big data analysis,voice recognition,and image recognition.Deepfake is the combination of deep learning in fake creation,which states creating a fake image or video with the help of artificial intelligence for political abuse,spreading false information,and pornography.The artificial intel-ligence technique has a wide demand,increasing the problems related to privacy,security,and ethics.This paper has analyzed the features related to the computer vision of digital content to determine its integrity.This method has checked the computer vision features of the image frames using the fuzzy clustering feature extraction method.By the proposed deep belief network with loss handling,the manipulation of video/image is found by means of a pairwise learning approach.This proposed approach has improved the accuracy of the detection rate by 98%on various datasets.
文摘The Internet of Multimedia Things(IoMT)refers to a network of interconnected multimedia devices that communicate with each other over the Internet.Recently,smart healthcare has emerged as a significant application of the IoMT,particularly in the context of knowledge‐based learning systems.Smart healthcare systems leverage knowledge‐based learning to become more context‐aware,adaptable,and auditable while maintain-ing the ability to learn from historical data.In smart healthcare systems,devices capture images,such as X‐rays,Magnetic Resonance Imaging.The security and integrity of these images are crucial for the databases used in knowledge‐based learning systems to foster structured decision‐making and enhance the learning abilities of AI.Moreover,in knowledge‐driven systems,the storage and transmission of HD medical images exert a burden on the limited bandwidth of the communication channel,leading to data trans-mission delays.To address the security and latency concerns,this paper presents a lightweight medical image encryption scheme utilising bit‐plane decomposition and chaos theory.The results of the experiment yield entropy,energy,and correlation values of 7.999,0.0156,and 0.0001,respectively.This validates the effectiveness of the encryption system proposed in this paper,which offers high‐quality encryption,a large key space,key sensitivity,and resistance to statistical attacks.
文摘Snort rule-checking is one of the most popular forms of Network Intrusion Detection Systems (NIDS). In this article, we show that Snort priorities of true positive traffic (real attacks) can be approximated in real-time, in the context of high speed networks, by a decision tree classifier, using the information of only three easily extracted features (protocol, source port, and destination port), with an accuracy of 99%. Snort issues alert priorities based on its own default set of attack classes (34 classes) that are used by the default set of rules it provides. But the decision tree model is able to predict the priorities without using this default classification. The obtained tagger can provide a useful complement to an anomaly detection intrusion detection system.
文摘Disasters may occur at any time and place without little to no presage in advance.With the development of surveillance and forecasting systems,it is now possible to forebode the most life-threatening and formidable disasters.However,forestfires are among the ones that are still hard to anticipate beforehand,and the technologies to detect and plot their possible courses are still in development.Unmanned Aerial Vehicle(UAV)image-basedfire detection systems can be a viable solution to this problem.However,these automatic systems use advanced deep learning and image processing algorithms at their core and can be tuned to provide accurate outcomes.Therefore,this article proposed a forestfire detection method based on a Convolutional Neural Network(CNN)architecture using a newfire detection dataset.Notably,our method also uses separable convolution layers(requiring less computational resources)for immediatefire detection and typical convolution layers.Thus,making it suitable for real-time applications.Consequently,after being trained on the dataset,experimental results show that the method can identify forestfires within images with a 97.63%accuracy,98.00%F1 Score,and 80%Kappa.Hence,if deployed in practical circumstances,this identification method can be used as an assistive tool to detectfire outbreaks,allowing the authorities to respond quickly and deploy preventive measures to minimize damage.
基金This work was supported by“Human Resources Program in Energy Technology”of the Korea Institute of Energy Technology Evaluation and Planning(KETEP)granted financial resources from the Ministry of Trade,Industry&Energy,Republic of Korea.(No.20204010600090).
文摘Tumor detection has been an active research topic in recent years due to the high mortality rate.Computer vision(CV)and image processing techniques have recently become popular for detecting tumors inMRI images.The automated detection process is simpler and takes less time than manual processing.In addition,the difference in the expanding shape of brain tumor tissues complicates and complicates tumor detection for clinicians.We proposed a newframework for tumor detection aswell as tumor classification into relevant categories in this paper.For tumor segmentation,the proposed framework employs the Particle Swarm Optimization(PSO)algorithm,and for classification,the convolutional neural network(CNN)algorithm.Popular preprocessing techniques such as noise removal,image sharpening,and skull stripping are used at the start of the segmentation process.Then,PSO-based segmentation is applied.In the classification step,two pre-trained CNN models,alexnet and inception-V3,are used and trained using transfer learning.Using a serial approach,features are extracted from both trained models and fused features for final classification.For classification,a variety of machine learning classifiers are used.Average dice values on datasets BRATS-2018 and BRATS-2017 are 98.11 percent and 98.25 percent,respectively,whereas average jaccard values are 96.30 percent and 96.57%(Segmentation Results).The results were extended on the same datasets for classification and achieved 99.0%accuracy,sensitivity of 0.99,specificity of 0.99,and precision of 0.99.Finally,the proposed method is compared to state-of-the-art existingmethods and outperforms them.
文摘The Internet of Things(IoT)has been transformed almost all fields of life,but its impact on the healthcare sector has been notable.Various IoTbased sensors are used in the healthcare sector and offer quality and safe care to patients.This work presents a deep learning-based automated patient discomfort detection system in which patients’discomfort is non-invasively detected.To do this,the overhead view patients’data set has been recorded.For testing and evaluation purposes,we investigate the power of deep learning by choosing a Convolution Neural Network(CNN)based model.The model uses confidence maps and detects 18 different key points at various locations of the body of the patient.Applying association rules and part affinity fields,the detected key points are later converted into six main body organs.Furthermore,the distance of subsequent key points is measured using coordinates information.Finally,distance and the time-based threshold are used for the classification of movements associated with discomfort or normal conditions.The accuracy of the proposed system is assessed on various test sequences.The experimental outcomes reveal the worth of the proposed system’by obtaining a True Positive Rate of 98%with a 2%False Positive Rate.
基金This research project was supported by the Deanship of Scientific Research,Prince Sattam Bin Abdulaziz University,KSA,Project Grant No.2021/01/18613.
文摘White blood cells(WBC)are immune system cells,which is why they are also known as immune cells.They protect the human body from a variety of dangerous diseases and outside invaders.The majority of WBCs come from red bone marrow,although some come from other important organs in the body.Because manual diagnosis of blood disorders is difficult,it is necessary to design a computerized technique.Researchers have introduced various automated strategies in recent years,but they still face several obstacles,such as imbalanced datasets,incorrect feature selection,and incorrect deep model selection.We proposed an automated deep learning approach for classifying white blood disorders in this paper.The data augmentation approach is initially used to increase the size of a dataset.Then,a Darknet-53 pre-trained deep learning model is used and finetuned according to the nature of the chosen dataset.On the fine-tuned model,transfer learning is used,and features engineering is done on the global average pooling layer.The retrieved characteristics are subsequently improved with a specified number of iterations using a hybrid reformed binary grey wolf optimization technique.Following that,machine learning classifiers are used to classify the selected best features for final classification.The experiment was carried out using a dataset of increased blood diseases imaging and resulted in an improved accuracy of over 99%.
文摘The combination of spatiotemporal videos and essential features can improve the performance of human action recognition(HAR);however,the individual type of features usually degrades the performance due to similar actions and complex backgrounds.The deep convolutional neural network has improved performance in recent years for several computer vision applications due to its spatial information.This article proposes a new framework called for video surveillance human action recognition dubbed HybridHR-Net.On a few selected datasets,deep transfer learning is used to pre-trained the EfficientNet-b0 deep learning model.Bayesian optimization is employed for the tuning of hyperparameters of the fine-tuned deep model.Instead of fully connected layer features,we considered the average pooling layer features and performed two feature selection techniques-an improved artificial bee colony and an entropy-based approach.Using a serial nature technique,the features that were selected are combined into a single vector,and then the results are categorized by machine learning classifiers.Five publically accessible datasets have been utilized for the experimental approach and obtained notable accuracy of 97%,98.7%,100%,99.7%,and 96.8%,respectively.Additionally,a comparison of the proposed framework with contemporarymethods is done to demonstrate the increase in accuracy.
文摘This paper tests various scenarios of feature selection and feature reduction, with the objective of building a real-time anomaly-based intrusion detection system. These scenarios are evaluated on the realistic Kyoto 2006+ dataset. The influence of reducing the number of features on the classification performance and the execution time is measured for each scenario. The so-called HVS feature selection technique detailed in this paper reveals many advantages in terms of consistency, classification performance and execution time.
基金supported by“Human Resources Program in Energy Technology”of the Korea Institute of Energy Technology Evaluation and Planning(KETEP),granted financial resources from the Ministry of Trade,Industry&Energy,Republic of Korea.(No.20204010600090).
文摘The Internet of Vehicles(IoV)is a networking paradigm related to the intercommunication of vehicles using a network.In a dynamic network,one of the key challenges in IoV is traffic management under increasing vehicles to avoid congestion.Therefore,optimal path selection to route traffic between the origin and destination is vital.This research proposed a realistic strategy to reduce traffic management service response time by enabling real-time content distribution in IoV systems using heterogeneous network access.Firstly,this work proposed a novel use of the Ant Colony Optimization(ACO)algorithm and formulated the path planning optimization problem as an Integer Linear Program(ILP).This integrates the future estimation metric to predict the future arrivals of the vehicles,searching the optimal routes.Considering the mobile nature of IOV,fuzzy logic is used for congestion level estimation along with the ACO to determine the optimal path.The model results indicate that the suggested scheme outperforms the existing state-of-the-art methods by identifying the shortest and most cost-effective path.Thus,this work strongly supports its use in applications having stringent Quality of Service(QoS)requirements for the vehicles.
文摘The exponential growth of Internet and network usage has neces-sitated heightened security measures to protect against data and network breaches.Intrusions,executed through network packets,pose a significant challenge for firewalls to detect and prevent due to the similarity between legit-imate and intrusion traffic.The vast network traffic volume also complicates most network monitoring systems and algorithms.Several intrusion detection methods have been proposed,with machine learning techniques regarded as promising for dealing with these incidents.This study presents an Intrusion Detection System Based on Stacking Ensemble Learning base(Random For-est,Decision Tree,and k-Nearest-Neighbors).The proposed system employs pre-processing techniques to enhance classification efficiency and integrates seven machine learning algorithms.The stacking ensemble technique increases performance by incorporating three base models(Random Forest,Decision Tree,and k-Nearest-Neighbors)and a meta-model represented by the Logistic Regression algorithm.Evaluated using the UNSW-NB15 dataset,the pro-posed IDS gained an accuracy of 96.16%in the training phase and 97.95%in the testing phase,with precision of 97.78%,and 98.40%for taring and testing,respectively.The obtained results demonstrate improvements in other measurement criteria.
文摘COVID’19 has caused the entire universe to be in existential healthcrisis by spreading globally in the year 2020. The lungs infection is detected inComputed Tomography (CT) images which provide the best way to increasethe existing healthcare schemes in preventing the deadly virus. Nevertheless,separating the infected areas in CT images faces various issues such as lowintensity difference among normal and infectious tissue and high changes inthe characteristics of the infection. To resolve these issues, a new inf-Net (LungInfection Segmentation Deep Network) is designed for detecting the affectedareas from the CT images automatically. For the worst segmentation results,the Edge-Attention Representation (EAR) is optimized using AdaptiveDonkey and Smuggler Optimization (ADSO). The edges which are identifiedby the ADSO approach is utilized for calculating dissimilarities. An IFCM(Intuitionistic Fuzzy C-Means) clustering approach is applied for computingthe similarity of the EA component among the generated edge maps andGround-Truth (GT) edge maps. Also, a Semi-Supervised Segmentation(SSS) structure is designed using the Randomly Selected Propagation (RP)technique and Inf-Net, which needs only less number of images and unlabelleddata. Semi-Supervised Multi-Class Segmentation (SSMCS) is designed usinga Bi-LSTM (Bi-Directional Long-Short-Term-memory), acquires all theadvantages of the disease segmentation done using Semi Inf-Net and enhancesthe execution of multi-class disease labelling. The newly designed SSMCSapproach is compared with existing U-Net++, MCS, and Semi-Inf-Net.factors such as MAE (Mean Absolute Error), Structure measure, Specificity(Spec), Dice Similarity coefficient, Sensitivity (Sen), and Enhance-AlignmentMeasure are considered for evaluation purpose.
文摘Interactive learning tools can facilitate the learning process and increase student engagement,especially tools such as computer programs that are designed for human-computer interaction.Thus,this paper aims to help students learn five different methods for solving nonlinear equations using an interactive learning tool designed with common principles such as feedback,visibility,affordance,consistency,and constraints.It also compares these methods by the number of iterations and time required to display the result.This study helps students learn these methods using interactive learning tools instead of relying on traditional teaching methods.The tool is implemented using the MATLAB app and is evaluated through usability testing with two groups of users that are categorized by their level of experience with root-finding.Users with no knowledge in root-finding confirmed that they understood the root-finding concept when interacting with the designed tool.The positive results of the user evaluation showed that the tool can be recommended to other users.
文摘In order to research brain problems using MRI,PET,and CT neuroimaging,a correct understanding of brain function is required.This has been considered in earlier times with the support of traditional algorithms.Deep learning process has also been widely considered in these genomics data processing system.In this research,brain disorder illness incliding Alzheimer’s disease,Schizophrenia and Parkinson’s diseaseis is analyzed owing to misdetection of disorders in neuroimaging data examined by means fo traditional methods.Moeover,deep learning approach is incorporated here for classification purpose of brain disorder with the aid of Deep Belief Networks(DBN).Images are stored in a secured manner by using DNA sequence based on JPEG Zig Zag Encryption algorithm(DBNJZZ)approach.The suggested approach is executed and tested by using the performance metric measure such as accuracy,root mean square error,Mean absolute error and mean absolute percentage error.Proposed DBNJZZ gives better performance than previously available methods.
基金supported by“Human Resources Program in Energy Technology”of the Korea Institute of Energy Technology Evaluation and Planning (KETEP),granted financial resources from the Ministry of Trade,Industry&Energy,Republic of Korea.(No.20204010600090).
文摘X-Ray knee imaging is widely used to detect knee osteoarthritis due to ease of availability and lesser cost.However,the manual categorization of knee joint disorders is time-consuming,requires an expert person,and is costly.This article proposes a new approach to classifying knee osteoarthritis using deep learning and a whale optimization algorithm.Two pre-trained deep learning models(Efficientnet-b0 and Densenet201)have been employed for the training and feature extraction.Deep transfer learning with fixed hyperparameter values has been employed to train both selected models on the knee X-Ray images.In the next step,fusion is performed using a canonical correlation approach and obtained a feature vector that has more information than the original feature vector.After that,an improved whale optimization algorithm is developed for dimensionality reduction.The selected features are finally passed to the machine learning algorithms such as Fine-Tuned support vector machine(SVM)and neural networks for classification purposes.The experiments of the proposed framework have been conducted on the publicly available dataset and obtained the maximum accuracy of 90.1%.Also,the system is explained using Explainable Artificial Intelligence(XAI)technique called occlusion,and results are compared with recent research.Based on the results compared with recent techniques,it is shown that the proposed method’s accuracy significantly improved.
基金This work was supported by Universiti Sains Malaysia under external grant(Grant number 304/PNAV/650958/U154).
文摘Intrusion detection systems that have emerged in recent decades can identify a variety of malicious attacks that target networks by employing several detection approaches.However,the current approaches have challenges in detecting intrusions,which may affect the performance of the overall detection system as well as network performance.For the time being,one of the most important creative technological advancements that plays a significant role in the professional world today is blockchain technology.Blockchain technology moves in the direction of persistent revolution and change.It is a chain of blocks that covers information and maintains trust between individuals no matter how far apart they are.Recently,blockchain was integrated into intrusion detection systems to enhance their overall performance.Blockchain has also been adopted in health-care,supply chain management,and the Internet of Things.Blockchain uses robust cryptography with private and public keys,and it has numerous properties that have leveraged security’s performance over peer-to-peer networks without the need for a third party.To explore and highlight the importance of integrating blockchain with intrusion detection systems,this paper provides a comprehensive background of intrusion detection systems and blockchain technology.Furthermore,a comprehensive review of emerging intrusion detection systems based on blockchain technology is presented.Finally,this paper suggests important future research directions and trending topics in intrusion detection systems based on blockchain technology.
文摘A partial eigenstructure assignment method that keeps the open-loop stable eigenvalues and the corresponding eigenspace unchanged is presented. This method generalizes a large class of systems previous methods and can be applied to solve the constrained control problem for linear invariant continuous-time systems. Besides, it can be also applied to make a total eigenstructure assignment. Indeed, the problem of finding a stabilizing regulator matrix gain taking into account the asymmetrical control constraints is transformed to a Sylvester equation resolution. Examples are given to illustrate the obtained results.
基金supported by Korea Institute for Advancement of Technology(KIAT)grant funded by the Korea Government(MOTIE)(P0012724,The Competency Development Program for Industry Specialist)the Soonchunhyang University Research Fund.
文摘The coronavirus(COVID19),also known as the novel coronavirus,first appeared in December 2019 in Wuhan,China.After that,it quickly spread throughout the world and became a disease.It has significantly impacted our everyday lives,the national and international economies,and public health.However,early diagnosis is critical for prompt treatment and reducing trauma in the healthcare system.Clinical radiologists primarily use chest X-rays,and computerized tomography(CT)scans to test for pneumonia infection.We used Chest CT scans to predict COVID19 pneumonia and healthy scans in this study.We proposed a joint framework for prediction based on classical feature fusion and PSO-based optimization.We begin by extracting standard features such as discrete wavelet transforms(DWT),discrete cosine transforms(DCT),and dominant rotated local binary patterns(DRLBP).In addition,we extracted Shanon Entropy and Kurtosis features.In the following step,a Max-Covariance-based maximization approach for feature fusion is proposed.The fused features are optimized in the preliminary phase using Particle Swarm Optimization(PSO)and the ELM fitness function.For final prediction,PSO is used to obtain robust features,which are then implanted in a Support Vector Data Description(SVDD)classifier.The experiment is carried out using available COVID19 Chest CT Scans and scans from healthy patients.These images are from the Radiopaedia website.For the proposed scheme,the fusion and selection process accuracy is 88.6%and 93.1%,respectively.A detailed analysis is conducted,which supports the proposed system efficiency.
文摘Semantic duplicates in databases represent today an important data quality challenge which leads to bad decisions. In large databases, we sometimes find ourselves with tens of thousands of duplicates, which necessitates an automatic deduplication. For this, it is necessary to detect duplicates, with a fairly reliable method to find as many duplicates as possible and powerful enough to run in a reasonable time. This paper proposes and compares on real data effective duplicates detection methods for automatic deduplication of files based on names, working with French texts or English texts, and the names of people or places, in Africa or in the West. After conducting a more complete classification of semantic duplicates than the usual classifications, we introduce several methods for detecting duplicates whose average complexity observed is less than O(2n). Through a simple model, we highlight a global efficacy rate, combining precision and recall. We propose a new metric distance between records, as well as rules for automatic duplicate detection. Analyses made on a database containing real data for an administration in Central Africa, and on a known standard database containing names of restaurants in the USA, have shown better results than those of known methods, with a lesser complexity.
