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

Enhanced sentence extraction through neuro-fuzzy technique for text document summarization

A summary system comprises a subtraction of text documents to generate a new form that delivers the essentials contents of the documents. Due to the hassle of documents overload, getting the right information and effectively-developed summaries are essential in retrieving information. Reduction of information allows users to find the information needed quickly without the need to read the full document collection, in particular, multi documents. In the recent past, soft computing-based approaches have gained popularity in its ability to determine important information across documents. A number of studies have modelled summarization systems based on fuzzy logic reasoning in order to select important sentences to be included in the summary. Although past studies support the benefits of employing fuzzy based reasoning for extracting important sentences from the document, there is a limitation concerning this method. Human or linguistic experts are required to determine the rules for the fuzzy system. Furthermore, the membership functions need to be manually tuned. These can be a very tedious and time-consuming process. Moreover, the performance of the fuzzy system can be affected by the choice of rules and parameters of membership function. Therefore, this study proposes a text summarization model based on classification using neuro-fuzzy approach. A classifier is first trained to identify summary sentences. Then, we use the proposed model to score and filter high-quality summary sentences. We compare the performance of our proposed model with the existing approaches, which are based on fuzzy logic and neural network techniques. In this study, we also evaluate the performance of sentence scoring and clustering in the process of generating text summaries. The proposed neuro-fuzzy model was used to score the sentences and clustering were performed using K-Means and Hierarchical Clustering (HC) approaches. The proposed approach showed improved results compared to the previous techniques in terms of precision, recall and F-measure on the Document Understanding Conference (DUC) data corpus. However, it was found that no improvements in the quality of the generated summaries obtained by simply performing clustering

Surface Defect Detection And Polishing Parameter Optimization Using Image Processing For G3141 Cold Rolled Steel

Traditionally the surface quality inspection especially for metal polishing purpose is perform by human inspectors. Defect detection is a method of nondestructive testing of material and products to detect defects. This study consists of two parts where the first part is applying vision system to detect and measure surface defects that have been characterized to some level of surface roughness. Specimen of G3141 cold rolled steel is used in this research as it represent the actual material applied in local automotive manufacturer. Gray image of scratch defect on metal surface is detected and information about mean gray pixel value (Ga) is interpreted and converted to surface roughness (Ra) measurement. In this study a new technique is developed where the Ga only read on the specific scratch line without considering the whole image. To realize this, automatic cropping algorithm is developed to detect the region of interest and interpret the Ga value. This techniques will enables the polishing to be done at specific scratch defect area without necessary to develop polishing path throughout the whole surface which is time consuming. Second part is to obtain the optimum polishing parameter by using artificial intelligence technique which is able to predict the grit size, polishing time and polishing force parameter to remove the scratch by polishing process. For the purpose of this study, multiple ANFIS or MANFIS have been selected to predict optimum parameter for polishing parameters. Polishing parameter data can be generated by using MANFIS to predict optimum polishing parameters such as grit size, polishing time and polishing force in order to perform polishing process. However due to lack of study done in the field of flat and dry polishing, the polishing parameter data have to be developed. The polishing parameter data for flat and dry polishing is performed by using robotic polishing arm and the experiment runs design by using full factorial design. Results show that the defect detection algorithm able to detect defect only on the scratch area and able to read the Ga value at detected scratch line and transform it to surface roughness measurement at considerably good level of accuracy compared with manual method. Results from MANFIS have shown that the system is able to predict up to 95% accuracy which is considerably high. The overall results from both parts of this research would inspire further advancements to achieve robust machine vision based surface measurement systems for industrial robotic processes specifically in polishing process

A survey on utilization of data mining approaches for dermatological (skin) diseases prediction

Due to recent technology advances, large volumes of medical data is obtained. These data contain valuable information. Therefore data mining techniques can be used to extract useful patterns. This paper is intended to introduce data mining and its various techniques and a survey of the available literature on medical data mining. We emphasize mainly on the application of data mining on skin diseases. A categorization has been provided based on the different data mining techniques. The utility of the various data mining methodologies is highlighted. Generally association mining is suitable for extracting rules. It has been used especially in cancer diagnosis. Classification is a robust method in medical mining. In this paper, we have summarized the different uses of classification in dermatology. It is one of the most important methods for diagnosis of erythemato-squamous diseases. There are different methods like Neural Networks, Genetic Algorithms and fuzzy classifiaction in this topic. Clustering is a useful method in medical images mining. The purpose of clustering techniques is to find a structure for the given data by finding similarities between data according to data characteristics. Clustering has some applications in dermatology. Besides introducing different mining methods, we have investigated some challenges which exist in mining skin data

Sentiment Analysis of Movie Review using Machine Learning Approach

With development of Internet and Natural Language processing, use of regional languages is also grown for communication. Sentiment analysis is natural language processing task that extracts useful information from various data forms such as reviews and categorize them on basis of polarity. One of the sub-domain of opinion mining is sentiment analysis which is basically focused on the extraction of emotions and opinions of the people towards a particular topic from textual data. In this paper, sentiment analysis is performed on IMDB movie review database. We examine the sentiment expression to classify the polarity of the movie review on a scale of negative to positive and perform feature extraction and ranking and use these features to train our multilevel classifier to classify the movie review into its correct label. In this paper classification of movie reviews into positive and negative classes with the help of machine learning. Proposed approach using classification techniques has the best accuracy of about 99%

Recent advances in IoT, AI, and national technology resilience

Internet of Things (IoT) and Artificial Intelligence (AI) are the critical enablers of the Industrial Revolution 4.0. IoT can be used in many applications that require precision, such as agriculture, industrial automation, education, automotive, and smart cities, to name a few. In other words, IoT is a powerful technology that can solve various business problems. Nevertheless, its integration with AI can help to take automation to the next level. This talk aims to discuss the recent advances in IoT, edge computing, and its applications. First, the IoT and edge commercial adoption survey 2021 will be highlighted. Then, the IoT framework will be introduced to solve a complex problem, including Things, Connect, Collect, Learn, and Do. Especially, the Learn part is very much related to AI. Then, some applications using IoT and edge computing will be presented. Finally, national technology resilience is now a necessity rather than necessary due to the current world situation. Therefore, future directions to enhance national technology resilience will be elaborated