Data-intensive science is a critical science paradigm that interferes with all other sciences. Data mining (DM) is a powerful and useful technology with wide potential users focusing on important meaningful patterns and discovers a new knowledge from a collected dataset. Any predictive task in DM uses some attribute to classify an unknown class. Classification algorithms are a class of prominent mathematical techniques in DM. Constructing a model is the core aspect of such algorithms. However, their performance highly depends on the algorithm behavior upon manipulating data. Focusing on binarazaition as an approach for preprocessing, this paper analysis and evaluates different classification algorithms when construct a model based on accuracy in the classification task. The Mixed National Institute of Standards and Technology (MNIST) handwritten digits dataset provided by Yann LeCun has been used in evaluation. The paper focuses on machine learning approaches for handwritten digits detection. Machine learning establishes classification methods, such as K-Nearest Neighbor(KNN), Decision Tree (DT), and Neural Networks (NN). Results showed that the knowledge-based method, i.e. NN algorithm, is more accurate in determining the digits as it reduces the error rate. The implication of this evaluation is providing essential insights for computer scientists and practitioners for choosing the suitable DM technique that fit with their data.
Network Simulator-2(NS-2) is one of the most popular simulation systems that is widely used in the network community. C++ and the object-oriented Tool Command Language (TCL) are both used to write this simulator. C++ works as a background for this simulator, whereas TCL is responsible for scheduling discrete events and network configuration objects. The TCL language is used to write the code of the simulation scenario. NS-2 does not present enough graphical interfaces that could help a researcher reduce the time spent on writing long TCL scripts. Therefore, network researchers spend a great deal of time focusing on how to write the TCL simulation script, which consequently makes the simulation process more difficult. This study presents a novel tool that enhances simulation by using graphical interfaces. The graphical interface is used to create the network topology and convert it into a TCL script. Thus, the process is visualized easily, efficiently, and quickly. This work describes the Network Topology Tool(NTT),which is intended to help researchers who work under the network simulation environment of NS-2. In such a scenario, researchers can create the network topology through an interactive graphical user interface and also they can retrieve and edit it which considered a very important and unique service from the other previous works. This tool will allow professional users to focus on the development of new algorithms or architectures rather than spend time writing scripts for data processing. .
In today’s world, the data generated by many applications are increasing drastically, and finding an optimal subset of features from the data has become a crucial task. The main objective of this review is to analyze and comprehend different stochastic local search algorithms to find an optimal feature subset. Simulated annealing, tabu search, genetic programming, genetic algorithm, particle swarm optimization, artificial bee colony, grey wolf optimization, and bat algorithm, which have been used in feature selection, are discussed. This review also highlights the filter and wrapper approaches for feature selection. Furthermore, this review highlights the main components of stochastic local search algorithms, categorizes these algorithms in accordance with the type, and discusses the promising research directions for such algorithms in future research of feature selection.