Challenging Issues of Spatio-Temporal Data Mining

The spatio-temporal database (STDB) has received considerable attention during the past few years, due to the emergence of numerous applications (e.g., flight control systems, weather forecast, mobile computing, etc.) that demand efficient management of moving objects. These applications record objects' geographical locations (sometimes also shapes) at various timestamps and support queries that explore their historical and future (predictive) behaviors. The STDB significantly extends the traditional spatial database, which deals with only stationary data and hence is inapplicable to moving objects, whose dynamic behavior requires re-investigation of numerous topics including data modeling, indexes, and the related query algorithms. In many application areas, huge amounts of data are generated, explicitly or implicitly containing spatial or spatiotemporal information. However, the ability to analyze these data remains inadequate, and the need for adapted data mining tools becomes a major challenge. In this paper, we have presented the challenging issues of spatio-temporal data mining. Keywords: database, data mining, spatial, temporal, spatio-tempora

Analysis of Dual Core Hexagonal PCF Based Polarization Beam Splitter

In this research work an analysis has been carried out on symmetric dual core hexagonal PCF-based polarization beam splitter by using finite element method (FEM). The splitter designs are carried out with hexagonal PCFs with simple symmetric design by varying only air holes diameter. The results of numerical calculation show that coupling lengths are higher for polarization splitters with larger air hole diameters and with the increase of operating wavelength coupling length decreases. Furthermore it is possible to obtain an 8.4 mm-long polarization beam splitter with high extinction ratio (250dB). This study will be very helpful to design and manufacture simple PCF based splitters with better performance. Keywords: photonic crystal fiber (PCF), finite element method (FEM), polarization beam splitter, birefringence, extinction ratio, coupling lengt

Performance Evaluation of MPEG-4 Video Transmission over IP-Networks: Best-Effort and Quality-of-Service

The demand for video communication over internet has been growing rapidly in recent years and the quality of video has become a challenging issue for video transmission. Different types of video coding standards like MPEG-2 and MPEG-4 have been developed to support application like video transmission. MPEG-2 which requires high bit rate transmission has been successful video standard for DVD and satellite digital broadcasting. On the other hand, MPEG-4 supports low bit rate and is suitable for transmitting video over IP networks. In this paper, MPEG-4 Video standard has been used for evaluating the performance of video transmission over two IP networks:- Best-effort and Quality of Service (QoS). For both of the best-effort and QoS IP networks, peak signal noise ratio (PSNR), throughput, frame and packet statistics have been considered as performance metrics. The calculated values of these performance metrics reflect that video transmission over QoS IP network is better than that of the best-effort network. Keywords: video transmission, mpeg, ip networks, best-effort, quality of service, ns-

A novel penalty-based wrapper objective function for feature selection in big data using cooperative co-evolution

The rapid progress of modern technologies generates a massive amount of high-throughput data, called Big Data, which provides opportunities to find new insights using machine learning (ML) algorithms. Big Data consist of many features (also called attributes); however, not all these are necessary or relevant, and they may degrade the performance of ML algorithms. Feature selection (FS) is an essential preprocessing step to reduce the dimensionality of a dataset. Evolutionary algorithms (EAs) are widely used search algorithms for FS. Using classification accuracy as the objective function for FS, EAs, such as the cooperative co-evolutionary algorithm (CCEA), achieve higher accuracy, even with a higher number of features. Feature selection has two purposes: reducing the number of features to decrease computations and improving classification accuracy, which are contradictory but can be achieved using a single objective function. For this very purpose, this paper proposes a penalty-based wrapper objective function. This function can be used to evaluate the FS process using CCEA, hence called Cooperative Co-Evolutionary Algorithm-Based Feature Selection (CCEAFS). An experiment was performed using six widely used classifiers on six different datasets from the UCI ML repository with FS and without FS. The experimental results indicate that the proposed objective function is efficient at reducing the number of features in the final feature subset without significantly reducing classification accuracy. Based on different performance measures, in most cases, naïve Bayes outperforms other classifiers when using CCEAFS

Cooperative co-evolution for feature selection in big data with random feature grouping

© 2020, The Author(s). A massive amount of data is generated with the evolution of modern technologies. This high-throughput data generation results in Big Data, which consist of many features (attributes). However, irrelevant features may degrade the classification performance of machine learning (ML) algorithms. Feature selection (FS) is a technique used to select a subset of relevant features that represent the dataset. Evolutionary algorithms (EAs) are widely used search strategies in this domain. A variant of EAs, called cooperative co-evolution (CC), which uses a divide-and-conquer approach, is a good choice for optimization problems. The existing solutions have poor performance because of some limitations, such as not considering feature interactions, dealing with only an even number of features, and decomposing the dataset statically. In this paper, a novel random feature grouping (RFG) has been introduced with its three variants to dynamically decompose Big Data datasets and to ensure the probability of grouping interacting features into the same subcomponent. RFG can be used in CC-based FS processes, hence called Cooperative Co-Evolutionary-Based Feature Selection with Random Feature Grouping (CCFSRFG). Experiment analysis was performed using six widely used ML classifiers on seven different datasets from the UCI ML repository and Princeton University Genomics repository with and without FS. The experimental results indicate that in most cases [i.e., with naïve Bayes (NB), support vector machine (SVM), k-Nearest Neighbor (k-NN), J48, and random forest (RF)] the proposed CCFSRFG-1 outperforms an existing solution (a CC-based FS, called CCEAFS) and CCFSRFG-2, and also when using all features in terms of accuracy, sensitivity, and specificity