Social Network Analysis

International Journal of Computer and Applications

Hybrid structures in time series modeling and forecasting: A review

The impulse features in a condition monitoring (CM) signal usually imply the occurrence of a defect in a rotating machine. To accurately capture the impulse components in a CM signal, a concentrated time-frequency analysis (TFA) method based on time-reassigned synchrosqueezing transform (TSST) is proposed. First, the limitation of the TSST method in dealing with strong frequency-varying signals is explored. Second, an iteration procedure is introduced to address the blurry time frequency representation problem of TSST. The convergence of the iteration algorithm is also analyzed. Finally, an algorithm is proposed to extract the impulse features for signal reconstructions, which are also useful for an accurate diagnosis of the fault type. A simulated noise-contaminated signal and three sets of experimental data are employed in this article to evaluate the performance of the proposed method. Results obtained from this article confirm that the proposed method has a better performance in dealing with impulsive-like signals than other TFA methods.

Time-Reassigned Multisynchrosqueezing Transform for Bearing Fault Diagnosis of Rotating Machinery

The fault detection and diagnosis (FDD) along with condition monitoring (CM) and of rotating machinery (RM) have critical importance for early diagnosis to prevent severe damage of infrastructure in industrial environments. Importantly, valuable industrial equipment needs continuous monitoring to enhance the safety, reliability, and availability and to decrease the cost of maintenance of modern industrial systems and applications. However, induction motor (IM) has been extensively used in several industrial processes because it is cheap, reliable, and robust. Rolling bearings are considered to be the main component of IM. Undoubtedly, any failure of this basic component can lead to a serious breakdown of IM and for whole industrial system. Thus, many current methods based on different techniques are employed as a fault prognosis and diagnosis of rolling elements bearing of IM. Moreover, these techniques include signal/image processing, intelligent diagnostics, data fusion, data mining, and expert systems for time and frequency as well as time-frequency domains. Artificial intelligence (AI) techniques have proven their significance in every field of digital technology. Industrial machines, automation, and processes are the net frontiers of AI adaptation. There are quite developed literatures that have been approaching the issues using signals and data processing techniques. However, the key contribution of this work is to present an extensive review of CM and FDD of the IM, especially for rolling elements bearings, based on artificial intelligent (AI) methods. This study highlights the advantages and performance limitations of each method. Finally, challenges and future trends are also highlighted.

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A Review of Artificial Intelligence Methods for Condition Monitoring and Fault Diagnosis of Rolling Element Bearings for Induction Motor

Empirical Analysis of Data Clustering Algorithms

A bearing data analysis based on kurtogram and deep learning sequence models

A blockchain is a growing list of records, called blocks, that are linked using cryptography. Blockchain private and public keys are stored in a cryptocurrency wallet, but not the actual currency values. Wallets provide customers with the ability to send and receive virtual currency / tokens and tune their balance through interaction with blockchains. Multi-currency wallets may be broken down into 3 categories: software, hardware, and paper. Software wallets are web, mobile and desktop. Growing penetration of blockchain in many industries makes one to understand wallets in detail. There are a variety of wallet kinds to pick out from. This paper focuses on multi-currency wallets review exploring on features like supported currencies, anonymity, cost, platform support, key management, wallet recovery methods and fiat currencies supported.

Cryptocurrency Wallet: A Review

Forecasting is an integral part of any organization for their decision-making process so that they can predict their targets and modify their strategy in order to improve their sales or productivity in the coming future. This paper evaluates and compares various machine learning models, namely, ARIMA, Auto Regressive Neural Network(ARNN), XGBoost, SVM, Hy-brid Models like Hybrid ARIMA-ARNN, Hybrid ARIMA-XGBoost, Hybrid ARIMA-SVM and STL Decomposition (using ARIMA, Snaive, XGBoost) to forecast sales of a drug store company called Rossmann. Training data set contains past sales and supplemental information about drug stores. Accuracy of these models is measured by metrics such as MAE and RMSE. Initially, linear model such as ARIMA has been applied to forecast sales. ARIMA was not able to capture nonlinear patterns precisely, hence nonlinear models such as Neural Network, XGBoost and SVM were used. Nonlinear models performed better than ARIMA and gave low RMSE. Then, to further optimize the performance, composite models were designed using hybrid technique and decomposition technique. Hybrid ARIMA-ARNN, Hybrid ARIMA-XGBoost, Hybrid ARIMA-SVM were used and all of them performed better than their respective individual models. Then, the composite model was designed using STL Decomposition where the decomposed components namely seasonal, trend and remainder components were forecasted by Snaive, ARIMA and XGBoost. STL gave better results than individual and hybrid models. This paper evaluates and analyzes why composite models give better results than an individual model and state that decomposition technique is better than the hybrid technique for this application.

Forecasting of sales by using fusion of machine learning techniques

Social networks display community structures of interest, which have to be uncovered to understand the latent relationships present in them. These networks are composed of loosely connected small clusters, whose structure is more convenient for analysis. Graph clustering algorithms have been developed to identify communities in real or artificial networks using structural characteristics of the network. Determining the efficiency of such techniques with respect to accuracy and computational time is an open question in the absence of ground truth labels for the underlying communities as is seen in most real-world situations. In this study, performance of eight state-of-the-art graph clustering algorithms are demonstrated on small egocentric graphs, obtained from Facebook. The results are used for objective evaluation and a critical discussion is presented.

Sunil Bhirud

Papers

Empirical Analysis of Data Clustering Algorithms

A bearing data analysis based on kurtogram and deep learning sequence models

Cryptocurrency Wallet: A Review

Forecasting of sales by using fusion of machine learning techniques

A Comparative Analysis of Community Detection Algorithms on Social Networks