Many Next-Generation consumer electronic devices would be distributed hybrid electronic systems, such as UAVs (Unmanned Aerial Vehicles) and smart electronic cars. The safety and risk control are the key issues for th...
详细信息
Industrial Internet of Things(IIoT)offers efficient communication among business partners and *** an enlargement of IoT tools connected through the internet,the ability of web traffic gets *** to the raise in the size...
详细信息
Industrial Internet of Things(IIoT)offers efficient communication among business partners and *** an enlargement of IoT tools connected through the internet,the ability of web traffic gets *** to the raise in the size of network traffic,discovery of attacks in IIoT and malicious traffic in the early stages is a very demanding issues.A novel technique called Maximum Posterior Dichotomous Quadratic Discriminant Jaccardized Rocchio Emphasis Boost Classification(MPDQDJREBC)is introduced for accurate attack detection wi th minimum time consumption in *** proposed MPDQDJREBC technique includes feature selection and ***,the network traffic features are collected from the *** applying the Maximum Posterior Dichotomous Quadratic Discriminant analysis to find the significant features for accurate classification and minimize the time *** the significant features selection,classification is performed using the Jaccardized Rocchio Emphasis Boost *** Rocchio Emphasis Boost Classification technique combines the weak learner result into strong *** Rocchio classification technique is considered as the weak learners to identify the normal and ***,proposed MPDQDJREBC technique gives strong classification results through lessening the quadratic *** assists for proposed MPDQDJREBC technique to get better the accuracy for attack detection with reduced time *** assessment is carried out with UNSW_NB15 Dataset using different factors such as accuracy,precision,recall,F-measure and attack detection *** observed results exhibit the MPDQDJREBC technique provides higher accuracy and lesser time consumption than the conventional techniques.
Effective data communication is a crucial aspect of the Social Internet of Things(SIoT)and continues to be a significant research *** paper proposes a data forwarding algorithm based on Multidimensional Social Relatio...
详细信息
Effective data communication is a crucial aspect of the Social Internet of Things(SIoT)and continues to be a significant research *** paper proposes a data forwarding algorithm based on Multidimensional Social Relations(MSRR)in SIoT to solve this *** proposed algorithm separates message forwarding into intra-and cross-community forwarding by analyzing interest traits and social connections among *** new metrics are defined:the intensity of node social relationships,node activity,and community *** the community,messages are sent by determining which node is most similar to the sender by weighing the strength of social connections and node *** a node performs cross-community forwarding,the message is forwarded to the most reasonable relay community by measuring the node activity and the connection between *** proposed algorithm was compared to three existing routing algorithms in simulation *** indicate that the proposed algorithmsubstantially improves message delivery efficiency while lessening network overhead and enhancing connectivity and coordination in the SIoT context.
With the prosperity of the mobile Internet, the abundance of data makes it difficult for users to choose their favorite app. Thus, mobile app recommendation as an emerging topic attracts lots of attention. However, ex...
详细信息
The development and use of Internet of Things(IoT)devices have grown significantly in recent *** IoT device characteristics are mainly to blame for the wide range of applications that may now be achieved with IoT *** ...
详细信息
The development and use of Internet of Things(IoT)devices have grown significantly in recent *** IoT device characteristics are mainly to blame for the wide range of applications that may now be achieved with IoT *** have begun to embrace the IoT *** true and suitable devices,security faults that might be used for bad reasons,and administration of such devices are only a few of the issues that IoT,a new concept in technological progress,*** some ways,IoT device traffic differs from regular device *** with particular features can be classified into categories,irrespective of their function or ***-changing and complex environments,like a smart home,demand this classification scheme.A total of 41 IoT devices were employed in this *** build a multiclass classification model,IoT devices contributed 13 network traffic *** further preprocess the raw data received,preprocessing techniques like Normalization and Dataset Scaling were *** engineering techniques can extract features from the text data.A total of 117,423 feature vectors are contained in the dataset after stratification,which are used to further improve the classification *** this study,a variety of performance indicators were employed to show the performance of the logiboosted ***-XGB scored 80.2%accuracy following application of the logit-boosted algorithms to the dataset for classification,whereas Logi-GBC achieved 77.8%***,Logi-ABC attained 80.7%***-CBC,on the other hand,received the highest Accuracy score of 85.6%.The accuracy of Logi-LGBM and Logi-HGBC was the same at 81.37%*** suggested Logi-CBC showed the highest accuracy on the dataset when compared to existing Logit-Boosted Algorithms used in earlier studies.
Significant research efforts are currently devoted to wireless sensor networks due to its broad range of applications. WSNs face various constraints, encompassing challenges related to communication, clustering manage...
详细信息
Significant research efforts are currently devoted to wireless sensor networks due to its broad range of applications. WSNs face various constraints, encompassing challenges related to communication, clustering management and the finite battery life of nodes. Thus, Energy conservation in such networks is indispensable. Given a constant energy consumption rate during information sensing and reception, the highest energy consumption among sensor nodes occurs during data transmission. One of promising solution to reduce energy consumption is organizing WSN in clusters. Clustering in Wireless Sensor Networks (WSN) involves grouping sensor nodes into clusters to facilitate efficient data aggregation, communication, and management within the network. This organizational structure helps optimize energy consumption, enhance scalability, and prolong the overall lifespan of the WSN. However determining the optimal criteria for selecting cluster heads is challenging, as it involves balancing energy efficiency, network connectivity, and load distribution. In this paper, a dual-phase approach is proposed, firstly Reinforcement learning (RL) approach has been applied to clustering in WSNs which enables nodes to autonomously adapt their clustering strategies, leading to more efficient and adaptive network configurations. Further Particle Swarm Optimization (PSO) can be utilized for cluster head selection in Wireless Sensor Networks (WSNs) to optimize the formation of clusters. The consideration of both local and global perspectives in the proposed approach results in a more balanced and efficient clustering solution. The outcomes of our experiments demonstrate the enhanced performance of the integrated approach as compared to traditional clustering algorithms. Results show considerable improvement in terms of reduced energy consumption, accuracy and efficiency in fault detection specifically tailored for Wireless Sensor Networks (WSNs). In addition the proposed algorithm show enha
A scheme to improve the quality in ghost imaging(GI)by controlling the bandwidth of light source(BCGI)is *** theoretical and numerical results show that the reconstruction result with high quality can be obtained by a...
详细信息
A scheme to improve the quality in ghost imaging(GI)by controlling the bandwidth of light source(BCGI)is *** theoretical and numerical results show that the reconstruction result with high quality can be obtained by adjusting the bandwidth range of the light source appropriately,and the selection criterion of the bandwidth is analyzed by the power distribution of the imaging target.A proof-of-principle experiment is implemented to verify the theoretical and numerical *** addition,the BCGI also presents better anti-noise performance when compared with some popular GI methods.
One of the hot research topics in propagation dynamics is identifying a set of critical nodes that can influence maximization in a complex *** importance and dispersion of critical nodes among them are both vital fact...
详细信息
One of the hot research topics in propagation dynamics is identifying a set of critical nodes that can influence maximization in a complex *** importance and dispersion of critical nodes among them are both vital factors that can influence *** therefore propose a multiple influential spreaders identification algorithm based on spectral graph *** algorithm first quantifies the role played by the local structure of nodes in the propagation process,then classifies the nodes based on the eigenvectors of the Laplace matrix,and finally selects a set of critical nodes by the constraint that nodes in the same class are not adjacent to each other while different classes of nodes can be adjacent to each *** results on real and synthetic networks show that our algorithm outperforms the state-of-the-art and classical algorithms in the SIR model.
Melanoma is the most lethal malignant tumour,and its prevalence is *** detection and diagnosis of skin cancer can alert patients to manage precautions and dramatically improve the lives of ***,deep learning has grown ...
详细信息
Melanoma is the most lethal malignant tumour,and its prevalence is *** detection and diagnosis of skin cancer can alert patients to manage precautions and dramatically improve the lives of ***,deep learning has grown increasingly popular in the extraction and categorization of skin cancer features for effective prediction.A deep learning model learns and co-adapts representations and features from training data to the point where it fails to perform well on test *** a result,overfitting and poor performance *** deal with this issue,we proposed a novel Consecutive Layerwise weight Con-straint MaxNorm model(CLCM-net)for constraining the norm of the weight vector that is scaled each time and bounding to a *** method uses deep convolutional neural networks and also custom layer-wise weight constraints that are set to the whole weight matrix directly to learn features *** this research,a detailed analysis of these weight norms is performed on two distinct datasets,International Skin Imaging Collaboration(ISIC)of 2018 and 2019,which are challenging for convolutional networks to *** to thefindings of this work,CLCM-net did a better job of raising the model’s performance by learning the features efficiently within the size limit of weights with appropriate weight constraint *** results proved that the proposed techniques achieved 94.42%accuracy on ISIC 2018,91.73%accuracy on ISIC 2019 datasets and 93%of accuracy on combined dataset.
As the trend to use the latestmachine learning models to automate requirements engineering processes continues,security requirements classification is tuning into the most researched field in the software engineering ...
详细信息
As the trend to use the latestmachine learning models to automate requirements engineering processes continues,security requirements classification is tuning into the most researched field in the software engineering *** literature studies have proposed numerousmodels for the classification of security ***,adopting those models is constrained due to the lack of essential datasets permitting the repetition and generalization of studies employing more advanced machine learning ***,most of the researchers focus only on the classification of requirements with security *** did not consider other nonfunctional requirements(NFR)directly or indirectly related to *** has been identified as a significant research gap in security requirements *** major objective of this study is to propose a security requirements classification model that categorizes security and other relevant security *** use PROMISE_exp and DOSSPRE,the two most commonly used datasets in the software engineering *** proposed methodology consists of two *** the first step,we analyze all the nonfunctional requirements and their relation with security *** found 10 NFRs that have a strong relationship with security *** the second step,we categorize those NFRs in the security requirements *** proposedmethodology is a hybridmodel based on the ConvolutionalNeural Network(CNN)and Extreme Gradient Boosting(XGBoost)***,we evaluate the model by updating the requirement type column with a binary classification column in the dataset to classify the requirements into security and non-security *** performance is evaluated using four metrics:recall,precision,accuracy,and F1 Score with 20 and 28 epochs number and batch size of 32 for PROMISE_exp and DOSSPRE datasets and achieved 87.3%and 85.3%accuracy,*** proposed study shows an enhancement in metrics
暂无评论