Systematic review and network meta-analysis of different nonsteroidal anti-inflammatory drugs for juvenile idiopathic arthritis

BACKGROUND Various non-steroidal anti-inflammatory drugs(NSAIDs)have been used for juvenile idiopathic arthritis(JIA).However,the optimal method for JIA has not yet been developed.AIM To perform a systematic review and network meta-analysis to determine the optimal instructions.METHODS We searched for randomized controlled trials(RCTs)from PubMed,EMBASE,Google Scholar,CNKI,and Wanfang without restriction for publication date or language at August,2023.Any RCTs that comparing the effectiveness of NSAIDs with each other or placebo for JIA were included in this network meta-analysis.The surface under the cumulative ranking curve(SUCRA)analysis was used to rank the treatments.P value less than 0.05 was identified as statistically significant.RESULTS We included 8 RCTs(1127 patients)comparing 8 different instructions including meloxicam(0.125 qd and 0.250 qd),Celecoxib(3 mg/kg bid and 6 mg/kg bid),piroxicam,Naproxen(5.0 mg/kg/d,7.5 mg/kg/d and 12.5 mg/kg/d),inuprofen(30-40 mg/kg/d),Aspirin(60-80 mg/kg/d,75 mg/kg/d,and 55 mg/kg/d),Tolmetin(15 mg/kg/d),Rofecoxib,and placebo.There were no significant differences between any two NSAIDs regarding ACR Pedi 30 response.The SUCRA shows that celecoxib(6 mg/kg bid)ranked first(SUCRA,88.9%),rofecoxib ranked second(SUCRA,68.1%),Celecoxib(3 mg/kg bid)ranked third(SUCRA,51.0%).There were no significant differences between any two NSAIDs regarding adverse events.The SUCRA shows that placebo ranked first(SUCRA,88.2%),piroxicam ranked second(SUCRA,60.5%),rofecoxib(0.6 mg/kg qd)ranked third(SUCRA,56.1%),meloxicam(0.125 mg/kg qd)ranked fourth(SUCRA,56.1%),and rofecoxib(0.3 mg/kg qd)ranked fifth(SUCRA,56.1%).CONCLUSION In summary,celecoxib(6 mg/kg bid)was found to be the most effective NSAID for treating JIA.Rofecoxib,piroxicam,and meloxicam may be safer options,but further research is needed to confirm these findings in larger trials with higher quality studies.

Threshold-Based Software-Defined Networking(SDN)Solution for Healthcare Systems against Intrusion Attacks

The healthcare sector holds valuable and sensitive data.The amount of this data and the need to handle,exchange,and protect it,has been increasing at a fast pace.Due to their nature,software-defined networks(SDNs)are widely used in healthcare systems,as they ensure effective resource utilization,safety,great network management,and monitoring.In this sector,due to the value of thedata,SDNs faceamajor challengeposed byawide range of attacks,such as distributed denial of service(DDoS)and probe attacks.These attacks reduce network performance,causing the degradation of different key performance indicators(KPIs)or,in the worst cases,a network failure which can threaten human lives.This can be significant,especially with the current expansion of portable healthcare that supports mobile and wireless devices for what is called mobile health,or m-health.In this study,we examine the effectiveness of using SDNs for defense against DDoS,as well as their effects on different network KPIs under various scenarios.We propose a threshold-based DDoS classifier(TBDC)technique to classify DDoS attacks in healthcare SDNs,aiming to block traffic considered a hazard in the form of a DDoS attack.We then evaluate the accuracy and performance of the proposed TBDC approach.Our technique shows outstanding performance,increasing the mean throughput by 190.3%,reducing the mean delay by 95%,and reducing packet loss by 99.7%relative to normal,with DDoS attack traffic.

How Marie Sklodowska-Curie Doctoral Networks Can Support European Refractory Ecosystem in Context of The European Green Deal

The movement of the Iron&Steelmaking(I&S)industry towards Net-Zero emissions and digitalized processes through disruptive,breakthrough technologies will be achieved through the use of Hydrogen.The biggest challenge for the refractory industry is to continue to meet the performance expectations while,at the same time,moving to a more sustainable production direction.The complexity and urgency of these technological changes,highlighted by the European Green Deal,requires ambitious,international,interdisciplinary and intersectoral projects,bringing together institutes from across the global value chain,to carry out cutting edge research.The European Union,through its flagship doctoral training program,MSCA,has,and continues to support research and development as well as the promotion of the refractory industry in Europe.An introduction to two MSCA projects and some of the results achieved are highlighted within this article.

A novel multi-resolution network for the open-circuit faults diagnosis of automatic ramming drive system

The open-circuit fault is one of the most common faults of the automatic ramming drive system(ARDS),and it can be categorized into the open-phase faults of Permanent Magnet Synchronous Motor(PMSM)and the open-circuit faults of Voltage Source Inverter(VSI). The stator current serves as a common indicator for detecting open-circuit faults. Due to the identical changes of the stator current between the open-phase faults in the PMSM and failures of double switches within the same leg of the VSI, this paper utilizes the zero-sequence voltage component as an additional diagnostic criterion to differentiate them.Considering the variable conditions and substantial noise of the ARDS, a novel Multi-resolution Network(Mr Net) is proposed, which can extract multi-resolution perceptual information and enhance robustness to the noise. Meanwhile, a feature weighted layer is introduced to allocate higher weights to characteristics situated near the feature frequency. Both simulation and experiment results validate that the proposed fault diagnosis method can diagnose 25 types of open-circuit faults and achieve more than98.28% diagnostic accuracy. In addition, the experiment results also demonstrate that Mr Net has the capability of diagnosing the fault types accurately under the interference of noise signals(Laplace noise and Gaussian noise).

Joint Modeling of Citation Networks and User Preferences for Academic Tagging Recommender System

In the tag recommendation task on academic platforms,existing methods disregard users’customized preferences in favor of extracting tags based just on the content of the articles.Besides,it uses co-occurrence techniques and tries to combine nodes’textual content for modelling.They still do not,however,directly simulate many interactions in network learning.In order to address these issues,we present a novel system that more thoroughly integrates user preferences and citation networks into article labelling recommendations.Specifically,we first employ path similarity to quantify the degree of similarity between user labelling preferences and articles in the citation network.Then,the Commuting Matrix for massive node pair paths is used to improve computational performance.Finally,the two commonalities mentioned above are combined with the interaction paper labels based on the additivity of Poisson distribution.In addition,we also consider solving the model’s parameters by applying variational inference.Experimental results demonstrate that our suggested framework agrees and significantly outperforms the state-of-the-art baseline on two real datasets by efficiently merging the three relational data.Based on the Area Under Curve(AUC)and Mean Average Precision(MAP)analysis,the performance of the suggested task is evaluated,and it is demonstrated to have a greater solving efficiency than current techniques.

Systematic investigation of Radix Salviae for treating diabetic peripheral neuropathy disease based on network Pharmacology

BACKGROUND Diabetic peripheral neuropathy(DPN)is a debilitating complication of diabetes mellitus with limited available treatment options.Radix Salviae,a traditional Chinese herb,has shown promise in treating DPN,but its therapeutic mech-anisms have not been systematically investigated.AIM Radix Salviae(Danshen in pinin),a traditional Chinese medicine(TCM),is widely used to treat DPN in China.However,the mechanism through which Radix Salviae treats DPN remains unclear.Therefore,we aimed to explore the mechanism of action of Radix Salviae against DPN using network pharmacology.METHODS The active ingredients and target genes of Radix Salviae were screened using the TCM pharmacology database and analysis platform.The genes associated with DPN were obtained from the Gene Cards and OMIM databases,a drug-com-position-target-disease network was constructed,and a protein–protein inter-action network was subsequently constructed to screen the main targets.Gene Ontology(GO)functional annotation and pathway enrichment analysis were performed via the Kyoto Encyclopedia of Genes and Genomes(KEGG)using Bioconductor.RESULTS A total of 56 effective components,108 targets and 4581 DPN-related target genes of Radix Salviae were screened.Intervention with Radix Salviae for DPN mainly involved 81 target genes.The top 30 major targets were selected for enrichment analysis of GO and KEGG pathways.CONCLUSION These results suggested that Radix Salviae could treat DPN by regulating the AGE-RAGE signaling pathway and the PI3K-Akt signaling pathway.Therefore,Danshen may affect DPN by regulating inflammation and apoptosis.

Changes of China’s Status in the Global System and Its Influencing Factors:A Multiple Contact Networks Perspective

Clarifying China’s position in the global system is an important logical basis for developing national diplomacy.Although much research has been done on China’s development status,most studies have been based on country comparisons or institutional en-vironment.In today’s networked era in which the global economy,trade,personnel,and information are closely connected,studies on China’s global position and its status changes and influencing factors in multiple contact networks are still insufficient.In this study,from the perspective of diverse global contact networks,we constructed economic,cultural,and political influence indices to explore the changes and influencing factors on China’s status in the global system from 2005 to 2018.The results show that during the study period,China’s global influence in the fields of economic ties,cultural exchanges,and political contacts increased significantly,but its influ-ence in the fields of cultural exchanges and political contacts lagged far economic ties.The pattern of China’s economic influence on various economies around the world has shown a transformation from an‘upright pyramid’to an‘inverted pyramid’structure.The proportion of these economies in low-influence zones has decreased from more than 60%in 2005 to less than 20%in 2018.China’s cultural and political influence on various economies around the world has increased significantly;however,for the former,the percentage of high-influence areas is still less than 20%,whereas for the latter the percentage of these economies in medium-and high-influence areas is still less than 50%.Analyses such as a scatter plot matrix show that geographical proximity,economic globalization,close cooperation with developing countries,and a proactive and peaceful foreign policy are important factors in improving China’s status in the diverse global network system.

Systematic Risk Analysis of Listed Financial Institutions from the Perspective of Complex Network

Based on the complex network theory,this paper studies the systemic financial risks in China’s financial market.According to the industry classification of the China Securities Regulatory Commission in 2012,the daily closing prices of 45 listed financial institutions are collected and the daily return rates of each financial institution are measured according to the logarithmic return rate calculation formula.In this paper,the risk spillover value ΔCoVaR is used to measure the contribution degree of each financial institution to systemic risk.Finally,the relationship between the risk spillover valueΔCoVaR and the node topology index of the risk transmission network is investigated by using a regression model,and some policy suggestions are put forward based on the regression results.

The optimal atropine concentration for myopia control in Chinese children: a systematic review and network Metaanalysis

AIM:To figure out whether various atropine dosages may slow the progression of myopia in Chinese kids and teenagers and to determine the optimal atropine concentration for effectively slowing the progression of myopia.METHODS:A systematic search was conducted across the Cochrane Library,PubMed,Web of Science,EMBASE,CNKI,CBM,VIP,and Wanfang database,encompassing literature on slowing progression of myopia with varying atropine concentrations from database inception to January 17,2024.Data extraction and quality assessment were performed,and a network Meta-analysis was executed using Stata version 14.0 Software.Results were visually represented through graphs.RESULTS:Fourteen papers comprising 2475 cases were included;five different concentrations of atropine solution were used.The network Meta-analysis,along with the surface under the cumulative ranking curve(SUCRA),showed that 1%atropine(100%)>0.05%atropine(74.9%)>0.025%atropine(51.6%)>0.02%atropine(47.9%)>0.01%atropine(25.6%)>control in refraction change and 1%atropine(98.7%)>0.05%atropine(70.4%)>0.02%atropine(61.4%)>0.025%atropine(42%)>0.01%atropine(27.4%)>control in axial length(AL)change.CONCLUSION:In Chinese children and teenagers,the five various concentrations of atropine can reduce the progression of myopia.Although the network Meta-analysis showed that 1%atropine is the best one for controlling refraction and AL change,there is a high incidence of adverse effects with the use of 1%atropine.Therefore,we suggest that 0.05%atropine is optimal for Chinese children to slow myopia progression.

Assessment of challenges and strategies for driving energy transitions in emerging markets:A socio-technological systems perspective

The pursuit of improved quality of life standards has significantly influenced the contemporary mining model in the 21st century.This era is witnessing an unprecedented transformation driven by pressing concerns related to sustainability,climate change,the just energy transition,dynamic operating environments,and complex social challenges.Such transitions present both opportunities and obstacles.The aim of this study is to provide an extensive literature review on energy transition to identify the challenges and strategies associated with navigating transformations in energy systems.Understanding these transformations is particularly critical in the face of the severe consequences of global warming,where an accelerated energy transition is viewed as a universal remedy.Adopting a socio-technological systems perspective,specifically through the application of Actor Network Theory(ANT),this research provides a theoretical foundation while categorising challenges into five distinct domains and outlining strategies across these different dimensions.These insights are specifically tailored for emerging market countries to effectively navigate energy transition while fostering the development of resilient societies.Furthermore,our findings highlight that energy transition encompasses more than a mere technological shift;it entails fundamental changes in various systemic socio-economic imperatives.Through focusing on the role of social structures in transitions,this study makes a significant and innovative contribution to ANT,which has historically been criticised for its limited acknowledgement of social structures.Consequently,we propose an emerging market energy transition framework,which not only addresses technological aspects,but also integrates social considerations.This framework paves the way for future research and exploration of energy transition dynamics.The outcomes of this study offer valuable insights to policymakers,researchers,and practitioners engaged in the mining industry,enabling them to comprehend the multifaceted challenges involved and providing practical strategies for effective resolution.Through incorporating the social dimension into the analysis,we enhance the understanding of the complex nature of energy system transformations,facilitating a more holistic approach towards achieving sustainable and resilient energy transitions in emerging markets and beyond.

Resilience Augmentation in Unmanned Weapon Systems via Multi-Layer Attention Graph Convolutional Neural Networks

The collective Unmanned Weapon System-of-Systems(UWSOS)network represents a fundamental element in modern warfare,characterized by a diverse array of unmanned combat platforms interconnected through hetero-geneous network architectures.Despite its strategic importance,the UWSOS network is highly susceptible to hostile infiltrations,which significantly impede its battlefield recovery capabilities.Existing methods to enhance network resilience predominantly focus on basic graph relationships,neglecting the crucial higher-order dependencies among nodes necessary for capturing multi-hop meta-paths within the UWSOS.To address these limitations,we propose the Enhanced-Resilience Multi-Layer Attention Graph Convolutional Network(E-MAGCN),designed to augment the adaptability of UWSOS.Our approach employs BERT for extracting semantic insights from nodes and edges,thereby refining feature representations by leveraging various node and edge categories.Additionally,E-MAGCN integrates a regularization-based multi-layer attention mechanism and a semantic node fusion algo-rithm within the Graph Convolutional Network(GCN)framework.Through extensive simulation experiments,our model demonstrates an enhancement in resilience performance ranging from 1.2% to 7% over existing algorithms.

Weighted Forwarding in Graph Convolution Networks for Recommendation Information Systems

Recommendation Information Systems(RIS)are pivotal in helping users in swiftly locating desired content from the vast amount of information available on the Internet.Graph Convolution Network(GCN)algorithms have been employed to implement the RIS efficiently.However,the GCN algorithm faces limitations in terms of performance enhancement owing to the due to the embedding value-vanishing problem that occurs during the learning process.To address this issue,we propose a Weighted Forwarding method using the GCN(WF-GCN)algorithm.The proposed method involves multiplying the embedding results with different weights for each hop layer during graph learning.By applying the WF-GCN algorithm,which adjusts weights for each hop layer before forwarding to the next,nodes with many neighbors achieve higher embedding values.This approach facilitates the learning of more hop layers within the GCN framework.The efficacy of the WF-GCN was demonstrated through its application to various datasets.In the MovieLens dataset,the implementation of WF-GCN in LightGCN resulted in significant performance improvements,with recall and NDCG increasing by up to+163.64%and+132.04%,respectively.Similarly,in the Last.FM dataset,LightGCN using WF-GCN enhanced with WF-GCN showed substantial improvements,with the recall and NDCG metrics rising by up to+174.40%and+169.95%,respectively.Furthermore,the application of WF-GCN to Self-supervised Graph Learning(SGL)and Simple Graph Contrastive Learning(SimGCL)also demonstrated notable enhancements in both recall and NDCG across these datasets.

Temporal variations of food web in a marine bay ecosystem based on LIM-MCMC model

Climate change has led to significant fluctuations in marine ecosystems,including alterations in the structure and function of food webs and ecosystem status.Coastal ecosystems are critical to the functioning of the earth’s lifesupporting systems.However,temporal variations in most of these ecosystems have remained unclear so far.In this study,we employed a linear inverse model with Markov Chain Monte Carlo(LIM-MCMC)combined with ecological network analysis to reveal the temporal variations of the food web in Haizhou Bay of China.Food webs were constructed based on diet composition data in this ecosystem during the year of 2011 and 2018.Results indicated that there were obvious temporal variations in the composition of food webs in autumn of 2011 and 2018.The number of prey and predators for most species in food web decreased in 2018 compared with 2011,especially for Trichiurus lepturus,zooplankton,Amblychaeturichthys hexanema,and Loligo sp.Ecological network analysis showed that the complexity of food web structure could be reflected by comprehensive analysis of compartmentalized indicators.Haizhou Bay ecosystem was more mature and stable in 2011,while the ecosystem’s self-sustainability and recovery from disturbances were accelerated from 2011 to 2018.These findings contribute to our understanding of the dynamics of marine ecosystems and highlight the importance of comprehensive analysis of marine food webs.This work provides a framework for assessing and comparing temporal variations in marine ecosystems,which provides essential information and scientific guidance for the Ecosystem-based Fisheries Management.

Recommendation System Based on Perceptron and Graph Convolution Network

The relationship between users and items,which cannot be recovered by traditional techniques,can be extracted by the recommendation algorithm based on the graph convolution network.The current simple linear combination of these algorithms may not be sufficient to extract the complex structure of user interaction data.This paper presents a new approach to address such issues,utilizing the graph convolution network to extract association relations.The proposed approach mainly includes three modules:Embedding layer,forward propagation layer,and score prediction layer.The embedding layer models users and items according to their interaction information and generates initial feature vectors as input for the forward propagation layer.The forward propagation layer designs two parallel graph convolution networks with self-connections,which extract higher-order association relevance from users and items separately by multi-layer graph convolution.Furthermore,the forward propagation layer integrates the attention factor to assign different weights among the hop neighbors of the graph convolution network fusion,capturing more comprehensive association relevance between users and items as input for the score prediction layer.The score prediction layer introduces MLP(multi-layer perceptron)to conduct non-linear feature interaction between users and items,respectively.Finally,the prediction score of users to items is obtained.The recall rate and normalized discounted cumulative gain were used as evaluation indexes.The proposed approach effectively integrates higher-order information in user entries,and experimental analysis demonstrates its superiority over the existing algorithms.

Review on uncertainty analysis and information fusion diagnosis of aircraft control system

In the aircraft control system,sensor networks are used to sample the attitude and environmental data.As a result of the external and internal factors(e.g.,environmental and task complexity,inaccurate sensing and complex structure),the aircraft control system contains several uncertainties,such as imprecision,incompleteness,redundancy and randomness.The information fusion technology is usually used to solve the uncertainty issue,thus improving the sampled data reliability,which can further effectively increase the performance of the fault diagnosis decision-making in the aircraft control system.In this work,we first analyze the uncertainties in the aircraft control system,and also compare different uncertainty quantitative methods.Since the information fusion can eliminate the effects of the uncertainties,it is widely used in the fault diagnosis.Thus,this paper summarizes the recent work in this aera.Furthermore,we analyze the application of information fusion methods in the fault diagnosis of the aircraft control system.Finally,this work identifies existing problems in the use of information fusion for diagnosis and outlines future trends.

A Security Trade-Off Scheme of Anomaly Detection System in IoT to Defend against Data-Tampering Attacks

Internet of Things(IoT)is vulnerable to data-tampering(DT)attacks.Due to resource limitations,many anomaly detection systems(ADSs)for IoT have high false positive rates when detecting DT attacks.This leads to the misreporting of normal data,which will impact the normal operation of IoT.To mitigate the impact caused by the high false positive rate of ADS,this paper proposes an ADS management scheme for clustered IoT.First,we model the data transmission and anomaly detection in clustered IoT.Then,the operation strategy of the clustered IoT is formulated as the running probabilities of all ADSs deployed on every IoT device.In the presence of a high false positive rate in ADSs,to deal with the trade-off between the security and availability of data,we develop a linear programming model referred to as a security trade-off(ST)model.Next,we develop an analysis framework for the ST model,and solve the ST model on an IoT simulation platform.Last,we reveal the effect of some factors on the maximum combined detection rate through theoretical analysis.Simulations show that the ADS management scheme can mitigate the data unavailability loss caused by the high false positive rates in ADS.

Network Security Enhanced with Deep Neural Network-Based Intrusion Detection System

This study describes improving network security by implementing and assessing an intrusion detection system(IDS)based on deep neural networks(DNNs).The paper investigates contemporary technical ways for enhancing intrusion detection performance,given the vital relevance of safeguarding computer networks against harmful activity.The DNN-based IDS is trained and validated by the model using the NSL-KDD dataset,a popular benchmark for IDS research.The model performs well in both the training and validation stages,with 91.30%training accuracy and 94.38%validation accuracy.Thus,the model shows good learning and generalization capabilities with minor losses of 0.22 in training and 0.1553 in validation.Furthermore,for both macro and micro averages across class 0(normal)and class 1(anomalous)data,the study evaluates the model using a variety of assessment measures,such as accuracy scores,precision,recall,and F1 scores.The macro-average recall is 0.9422,the macro-average precision is 0.9482,and the accuracy scores are 0.942.Furthermore,macro-averaged F1 scores of 0.9245 for class 1 and 0.9434 for class 0 demonstrate the model’s ability to precisely identify anomalies precisely.The research also highlights how real-time threat monitoring and enhanced resistance against new online attacks may be achieved byDNN-based intrusion detection systems,which can significantly improve network security.The study underscores the critical function ofDNN-based IDS in contemporary cybersecurity procedures by setting the foundation for further developments in this field.Upcoming research aims to enhance intrusion detection systems by examining cooperative learning techniques and integrating up-to-date threat knowledge.

Ultimately Bounded Output Feedback Control for Networked Nonlinear Systems With Unreliable Communication Channel: A Buffer-Aided Strategy

This paper concerns ultimately bounded output-feedback control problems for networked systems with unknown nonlinear dynamics. Sensor-to-observer signal transmission is facilitated over networks that has communication constraints.These transmissions are carried out over an unreliable communication channel. In order to enhance the utilization rate of measurement data, a buffer-aided strategy is novelly employed to store historical measurements when communication networks are inaccessible. Using the neural network technique, a novel observer-based controller is introduced to address effects of signal transmission behaviors and unknown nonlinear dynamics.Through the application of stochastic analysis and Lyapunov stability, a joint framework is constructed for analyzing resultant system performance under the introduced controller. Subsequently, existence conditions for the desired output-feedback controller are delineated. The required parameters for the observerbased controller are then determined by resolving some specific matrix inequalities. Finally, a simulation example is showcased to confirm method efficacy.

Optimal decision-making method for equipment maintenance to enhance the resilience of power digital twin system under extreme disaster

Digital twins and the physical assets of electric power systems face the potential risk of data loss and monitoring failures owing to catastrophic events,causing surveillance and energy loss.This study aims to refine maintenance strategies for the monitoring of an electric power digital twin system post disasters.Initially,the research delineates the physical electric power system along with its digital counterpart and post-disaster restoration processes.Subsequently,it delves into communication and data processing mechanisms,specifically focusing on central data processing(CDP),communication routers(CRs),and phasor measurement units(PMUs),to re-establish an equipment recovery model based on these data transmission methodologies.Furthermore,it introduces a mathematical optimization model designed to enhance the digital twin system’s post-disaster monitoring efficacy by employing the branch-and-bound method for its resolution.The efficacy of the proposed model was corroborated by analyzing an IEEE-14 system.The findings suggest that the proposed branch-and-bound algorithm significantly augments the observational capabilities of a power system with limited resources,thereby bolstering its stability and emergency response mechanisms.

IDS-INT:Intrusion detection system using transformer-based transfer learning for imbalanced network traffic

A network intrusion detection system is critical for cyber security against llegitimate attacks.In terms of feature perspectives,network traffic may include a variety of elements such as attack reference,attack type,a subcategory of attack,host information,malicious scripts,etc.In terms of network perspectives,network traffic may contain an imbalanced number of harmful attacks when compared to normal traffic.It is challenging to identify a specific attack due to complex features and data imbalance issues.To address these issues,this paper proposes an Intrusion Detection System using transformer-based transfer learning for Imbalanced Network Traffic(IDS-INT).IDS-INT uses transformer-based transfer learning to learn feature interactions in both network feature representation and imbalanced data.First,detailed information about each type of attack is gathered from network interaction descriptions,which include network nodes,attack type,reference,host information,etc.Second,the transformer-based transfer learning approach is developed to learn detailed feature representation using their semantic anchors.Third,the Synthetic Minority Oversampling Technique(SMOTE)is implemented to balance abnormal traffic and detect minority attacks.Fourth,the Convolution Neural Network(CNN)model is designed to extract deep features from the balanced network traffic.Finally,the hybrid approach of the CNN-Long Short-Term Memory(CNN-LSTM)model is developed to detect different types of attacks from the deep features.Detailed experiments are conducted to test the proposed approach using three standard datasets,i.e.,UNsWNB15,CIC-IDS2017,and NSL-KDD.An explainable AI approach is implemented to interpret the proposed method and develop a trustable model.