Terrorism Event Classification Using Fuzzy Inference Systems

Terrorism has led to many problems in Thai societies, not only property damage but also civilian casualties. Predicting terrorism activities in advance can help prepare and manage risk from sabotage by these activities. This paper proposes a framework focusing on event classification in terrorism domain using fuzzy inference systems (FISs). Each FIS is a decision-making model combining fuzzy logic and approximate reasoning. It is generated in five main parts: the input interface, the fuzzification interface, knowledge base unit, decision making unit and output defuzzification interface. Adaptive neuro-fuzzy inference system (ANFIS) is a FIS model adapted by combining the fuzzy logic and neural network. The ANFIS utilizes automatic identification of fuzzy logic rules and adjustment of membership function (MF). Moreover, neural network can directly learn from data set to construct fuzzy logic rules and MF implemented in various applications. FIS settings are evaluated based on two comparisons. The first evaluation is the comparison between unstructured and structured events using the same FIS setting. The second comparison is the model settings between FIS and ANFIS for classifying structured events. The data set consists of news articles related to terrorism events in three southern provinces of Thailand. The experimental results show that the classification performance of the FIS resulting from structured events achieves satisfactory accuracy and is better than the unstructured events. In addition, the classification of structured events using ANFIS gives higher performance than the events using only FIS in the prediction of terrorism events.Comment: IEEE Publication format, ISSN 1947 5500, http://sites.google.com/site/ijcsis

Machine Learning Aided Static Malware Analysis: A Survey and Tutorial

Malware analysis and detection techniques have been evolving during the last decade as a reflection to development of different malware techniques to evade network-based and host-based security protections. The fast growth in variety and number of malware species made it very difficult for forensics investigators to provide an on time response. Therefore, Machine Learning (ML) aided malware analysis became a necessity to automate different aspects of static and dynamic malware investigation. We believe that machine learning aided static analysis can be used as a methodological approach in technical Cyber Threats Intelligence (CTI) rather than resource-consuming dynamic malware analysis that has been thoroughly studied before. In this paper, we address this research gap by conducting an in-depth survey of different machine learning methods for classification of static characteristics of 32-bit malicious Portable Executable (PE32) Windows files and develop taxonomy for better understanding of these techniques. Afterwards, we offer a tutorial on how different machine learning techniques can be utilized in extraction and analysis of a variety of static characteristic of PE binaries and evaluate accuracy and practical generalization of these techniques. Finally, the results of experimental study of all the method using common data was given to demonstrate the accuracy and complexity. This paper may serve as a stepping stone for future researchers in cross-disciplinary field of machine learning aided malware forensics.Comment: 37 Page

Data-driven Soft Sensors in the Process Industry

In the last two decades Soft Sensors established themselves as a valuable alternative to the traditional means for the acquisition of critical process variables, process monitoring and other tasks which are related to process control. This paper discusses characteristics of the process industry data which are critical for the development of data-driven Soft Sensors. These characteristics are common to a large number of process industry fields, like the chemical industry, bioprocess industry, steel industry, etc. The focus of this work is put on the data-driven Soft Sensors because of their growing popularity, already demonstrated usefulness and huge, though yet not completely realised, potential. A comprehensive selection of case studies covering the three most important Soft Sensor application fields, a general introduction to the most popular Soft Sensor modelling techniques as well as a discussion of some open issues in the Soft Sensor development and maintenance and their possible solutions are the main contributions of this work

Neuro-fuzzy software for intelligent control and education

Tese de mestrado integrado. Engenharia Electrotécnica e de Computadores (Major Automação). Faculdade de Engenharia. Universidade do Porto. 200

Measurements, Models, Systems and Design

531 s.

An Intelligent Method for Industrial Location Selection: Application to Markazi Province, Iran

Decision-making and selection are important and sensitive aspects of planning. An important part of land-use planning is the location of human activities. Locating activities in the right places determines the future space of a region. Selection and definition of natural and human indices and criteria for location always face uncertainty. Thus, this study aimed to develop an intelligent method for industrial location. In this study a developmental-applied approach was used along with a descriptive-analytical method for data analysis. Through the review of related literature and a Delphi survey, 18 criteria were extracted and 6 main components were categorized. The data were analyzed and modeled by GIS, MATLAB software, and the Fuzzy Inference System (FIS) and Adaptive Neuro-Fuzzy Inference System (ANFIS) methods. For each modeling three industrial domains were extracted, i.e. weak, medium, and premium. A total of 42,968 hectares of premium industrial location with a score higher than 0.7 resulted from combining the produced maps. Other important findings were related to the architecture and methodology applied in the research based on computational intelligence and knowledge-based systems to analyze and understand the processes that influence the score of locations. The novelty of this method lies in the use of high computing power and information evaluation based on artificial intelligence (AI), making it possible to analyze and understand the processes influencing industrial location.   Abstrak. Pengambilan keputusan dan seleksi adalah aspek-aspek penting dan sensitive dalam perencanaan. Bagian yang penting dalam sebuah perencaan penggunaan lahan adalah terkait lokasi kegiatan manusia. Alokasi kegiatan manusia pada tempat yang benar adalah penentu ruang masa depan dari suatu wilayah. Dalam hal seleksi dan definisi index, juga kriteria lokasi selalu menghadapi ketidakpastian. Sehingga, studi ini dilakukan untuk mengembangkan metode yang berguna dalam alokasi industri. Pada artikel ini, digunakan pendekatan terapan-terkembangkan dengan metode analisis deskriptif dalam hal analisis data. Berdasarkan tinjauan pada literatur terkait dan survey Delphi, 18 kritersia diekstraksi yang dikategorikan pada 6 komponen utama. Data dianalisis dan dimodelkan menggunakan GIS, MATLAB, Fuzzy Inference System (FIS), dan metode Adaptive Neuro-Fuzzy Inference System (ANFIS). Untuk setiap model, tiga domain industry ditentukan, yakni: lemah, moderat, dan premium. Terdapat lokasi industry premium dengan total 42,968 ha dengan nilai lebih dari 0.7. Hasil penting lainnya berkaitan dengan arsitektur dan metode terapan dalam penelitian yang berdasar kepada ilmu komputasi untuk memahami proses yang memengaruhi nilai untuk suatu lokasi. Kebaruan dari metode ini ada pada penggunaan model komputasi tinggi dan evaluasi informasi berdasarkan kecerdasan buatan (AI) yang memungkinkan untuk melakukan analisis dan memahami proses yang memengaruhi lokasi industri.   Kata kunci. Fuzzy Inference System (FIS), Adaptive Neuro-Fuzzy Inference System (ANFIS), Artificial Neural Network (ANN), lokasi industri, Provinsi Markazi