The novel SoftwareDefined Networking(SDN)architecture potentially resolves specific challenges arising from rapid internet growth of and the static nature of conventional networks to manage organizational business req...
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The novel SoftwareDefined Networking(SDN)architecture potentially resolves specific challenges arising from rapid internet growth of and the static nature of conventional networks to manage organizational business requirements with distinctive ***,such benefits lead to a more adverse environment entailing network breakdown,systems paralysis,and online banking fraudulence and *** one of the most common and dangerous threats in SDN,probe attack occurs when the attacker scans SDN devices to collect the necessary knowledge on system susceptibilities,which is thenmanipulated to undermine the entire ***,high performance,and real-time systems prove pivotal in successful goal attainment through feature selection to minimize computation time,optimize prediction performance,and provide a holistic understanding of machine learning *** the extension of astute machine learning algorithms into an Intrusion Detection System(IDS)through SDN has garnered much scholarly attention within the past decade,this study recommended an effective IDS under the Grey-wolf optimizer(GWO)and Light Gradient Boosting Machine(Light-GBM)classifier for probe attack *** InSDN dataset was employed to train and test the proposed IDS,which is deemed to be a novel benchmarking dataset in *** proposed IDS assessment demonstrated an optimized performance against that of peer IDSs in probe attack detection within *** results revealed that the proposed IDS outperforms the state-of-the-art IDSs,as it achieved 99.8%accuracy,99.7%recall,99.99%precision,and 99.8%F-measure.
In this paper, we model and characterize an unconventionally wide GaN HEMT device. Each finger of the device has a width of 150 μ m which allows the modeling methodology to examine the wave propagation effects at hig...
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Accurate prediction of drivers' gaze is an important component of vision-based driver monitoring and assistive systems. Of particular interest are safety-critical episodes, such as performing maneuvers or crossing...
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In this study, we introduce SAVAGE, a novel framework for sparse vicious adversarial link prediction attacks in graph neural networks (GNNs). While GNNs have been successful in link prediction tasks, they are suscepti...
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This study explores the influence of social media marketing on consumers' decisions to purchase green software and identifies key factors affecting those decisions. The findings contribute to effective marketing s...
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Supervisory control and data acquisition (SCADA) systems are vital in monitoring and controlling industrial processes through the web. However, while such systems result in lower costs, greater utilisation efficiency,...
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Agriculture is the major source of food and significantly contributes to Indian employment, and the economy is intricately tied to the outcomes of crop management, where the final yield and market prices play crucial ...
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Large-quantity and high-quality data is critical to the success of machine learning in diverse *** with the dilemma of data silos where data is difficult to circulate,emerging data markets attempt to break the dilemma...
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Large-quantity and high-quality data is critical to the success of machine learning in diverse *** with the dilemma of data silos where data is difficult to circulate,emerging data markets attempt to break the dilemma by facilitating data exchange on the ***,on the other hand,is one of the important methods to efficiently collect large amounts of data with high-value in data *** this paper,we investigate the joint problem of efficient data acquisition and fair budget distribution across the crowdsourcing and data *** propose a new metric of data value as the uncertainty reduction of a Bayesian machine learning model by integrating the data into model *** by this data value metric,we design a mechanism called Shapley Value Mechanism with Individual Rationality(SV-IR),in which we design a greedy algorithm with a constant approximation ratio to greedily select the most cost-efficient data brokers,and a fair compensation determination rule based on the Shapley value,respecting the individual rationality *** further propose a fair reward distribution method for the data holders with various effort levels under the charge of a data *** demonstrate the fairness of the compensation determination rule and reward distribution rule by evaluating our mechanisms on two real-world *** evaluation results also show that the selection algorithm in SV-IR could approach the optimal solution,and outperforms state-of-the-art methods.
An imbalanced dataset often challenges machine learning, particularly classification methods. Underrepresented minority classes can result in biased and inaccurate models. The Synthetic Minority Over-Sampling Techniqu...
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An imbalanced dataset often challenges machine learning, particularly classification methods. Underrepresented minority classes can result in biased and inaccurate models. The Synthetic Minority Over-Sampling Technique (SMOTE) was developed to address the problem of imbalanced data. Over time, several weaknesses of the SMOTE method have been identified in generating synthetic minority class data, such as overlapping, noise, and small disjuncts. However, these studies generally focus on only one of SMOTE’s weaknesses: noise or overlapping. Therefore, this study addresses both issues simultaneously by tackling noise and overlapping in SMOTE-generated data. This study proposes a combined approach of filtering, clustering, and distance modification to reduce noise and overlapping produced by SMOTE. Filtering removes minority class data (noise) located in majority class regions, with the k-nn method applied for filtering. The use of Noise Reduction (NR), which removes data that is considered noise before applying SMOTE, has a positive impact in overcoming data imbalance. Clustering establishes decision boundaries by partitioning data into clusters, allowing SMOTE with modified distance metrics to generate minority class data within each cluster. This SMOTE clustering and distance modification approach aims to minimize overlap in synthetic minority data that could introduce noise. The proposed method is called “NR-Clustering SMOTE,” which has several stages in balancing data: (1) filtering by removing minority classes close to majority classes (data noise) using the k-nn method;(2) clustering data using K-means aims to establish decision boundaries by partitioning data into several clusters;(3) applying SMOTE oversampling with Manhattan distance within each cluster. Test results indicate that the proposed NR-Clustering SMOTE method achieves the best performance across all evaluation metrics for classification methods such as Random Forest, SVM, and Naїve Bayes, compared t
Hyperspectral sensing is a valuable tool for detecting anomalies and distinguishing between materials in a scene. Hyperspectral anomaly detection (HS-AD) helps characterize the captured scenes and separates them into ...
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