Bidirectional reservoir networks trained using SVM$$+$$privileged information for manufacturing process modeling - Rodan, Ali; Sheta, Alaa F.; Faris, Hossam (Soft Computing, 2017)
In the last decade, a wide range of machine learning approaches were proposed and experimented to model highly nonlinear manufacturing processes. However, improving the performance of such models is challenging due to the complexity and high dimensionality of the manufacturing processes in general. In this paper, we propose bidirectional echo state reservoir networks (Bi-ESNs) trained using support vector machine privileged information method (SVM$$+$$) to model a winding machine process. The proposed model will be applied, tested and compared to reported models in the literature such as classical ESN with linear regression, ESN with a linear SVM readout, genetic programming, feedfoward neural network with backpropagation, radial basis function network, adaptive neural fuzzy inference system and local linear wavelet neural network. The developed results show that Bi-ESNs trained with SVM$$+$$are promising. It was able to provide better generalization performance compared to other models.
19 citations (Crossref) [2023-10-31]
Keywords: Engineering process; Privileged information; Recurrent neural network; Reservoir computing; Support vector machines; Winding machines; dept.csc; peer-reviewed

Understanding the relationship between network traffic correlation and queueing behavior: A review based on the N-Burst ON/OFF model - Schwefel, Hans-Peter; Antonios, Imad; Lipsky, Lester (Performance Evaluation, 2017)
Understanding the impact of network traffic properties on performance behavior in bottleneck links or larger networks is of primary interest to traffic analysts and network designers. Among the contributing factors, variance and correlation properties have been thoroughly studied and a large set of individual results have been obtained. However, these individual contributing factors are not sufficient to predict performance behavior. In this paper we review a unifying and versatile class of ON/OFF models through which the relationship among these parameters can be characterized and their influence on network performance be understood. The analytic performance results from the model show that there is a radically different queueing behavior when the ON period duration follows truncated power-tail distributions (even if truncated), as opposed to model variants where these distribution types are used for the OFF periods. All these models create correlation functions that only decay slowly. This motivates the development of a simple data analysis scheme to distinguish performance relevant correlation. The scheme is described both for interarrival and count processes of traffic data and its effectiveness is shown using real data traces.
9 citations (Crossref) [2023-10-31] Citation Key Alias: lens.org/016-979-057-202-359, pop00159 tex.type: [object Object]
Keywords: Contributing factor; Correlation function; Correlation properties; Long range dependence; Long-range dependence; Network designer; Network traffic; ON/OFF models; Power-tail distributions; Queueing model; Queueing models; Queueing networks; Queueing theory; Tail distribution; dept.csc

Sentiment analysis of the correlation between regular tweets and retweets - Chen, Jundong; Li, He; Wu, Zeju; Hossain, Md Shafaeat (2017 IEEE 16th International Symposium on Network Computing and Applications (NCA), 2017)
In this paper, we study the influence from the sentiment of regular tweets on retweeting. We propose a method to calculate the sentiment score for each tweet and each Twitter user. This method enables us to place the tweets and retweets into the same time period to explore the sentiment factor. We adopt the correlation coefficient between the sentiment scores of regular tweets and those of retweets to measure the influence. We categorize the Twitter users in three different ways to investigate three factors, which are the number of followers, betweenness centrality and the types of accounts. Community detection and machine learning are integrated into our approach. We find that the difference for correlation coefficients exists between different levels of the number of followers, and different types of users. Our method sheds a light on better predicting the dynamics of tweets diffusion by including the sentiment factor into the prediction model. © 2017 IEEE.
6 citations (Crossref) [2023-10-31] Citation Key Alias: pop00210 tex.type: [object Object]
Keywords: Artificial intelligence; Betweenness centrality; Community detection; Correlation coefficient; Data mining; Learning systems; Prediction model; Sentiment analysis; Sentiment scores; Social networking (online); Time-periods; Twitter classification; dept.csc; machine learning; sentiment analysis; social network

An empirical study on verifier order selection in serial fusion based multi-biometric verification system - Hossain, Md Shafaeat; Rahman, Khandaker Abir (Advances in Artificial Intelligence: From Theory to Practice, 2017)
Selecting the order of verifier in a serial fusion based multi-biometric system is a crucial parameter to fix because of its high impact on verification errors. A wrong choice of verifier order might lead to tremendous user inconvenience by denying a large number of genuine users and might cause severe security breach by accepting impostors frequently. Unfortunately, this design issue has been poorly investigated in multi-biometric literature. In this paper, we address this design issue by performing experiments using three different serial fusion based multi-biometric verification schemes, in particular (1) symmetric scheme, (2) SPRT-based scheme, and (3) Marcialis et al.’s scheme. We experimented on publicly available NIST-BSSR1 multi-modal database. We tested 24 orders—all possible orders originated from four individual verifiers—on a four-stage biometric verification system. Our experimental results show that the verifier order “best-to-worst”, where the best performing individual verifier is placed in the first stage, the next best performing individual verifier is placed in the second stage, and so on, is the top performing order for all three serial fusion schemes mentioned above.
2 citations (Crossref) [2023-10-31] Citation Key Alias: hossainEmpiricalStudyVerifier2017, pop00277 tex.type: [object Object]
Keywords: Biometric verification; Biometrics; Crucial parameters; Intelligent systems; Multi biometrics; Multi-biometric verification; Multibiometric systems; Multimodal database; Order selection; Security breaches; Serial fusion; Verifier order selection; dept.csc