Feature Selection for Document Classification : Case Study of Meta-heuristic Intelligence and Traditional Approaches

Doctor of Philosophy (Computer Engineering), 2020Nowadays, the culture for accessing news around the world is changed from paper to electronic format and the rate of publication for newspapers and magazines on website are increased dramatically. Meanwhile, text feature selection for the automatic document classification (ADC) is becoming a big challenge because of the unstructured nature of text feature, which is called “multi-dimension feature problem”. On the other hand, various powerful schemes dealing with text feature selection are being developed continuously nowadays, but there still exists a research gap for “optimization of feature selection problem (OFSP)”, which can be looked for the global optimal features. Meanwhile, the capacity of meta-heuristic intelligence for knowledge discovery process (KDP) is also become the critical role to overcome NP-hard problem of OFSP by providing effective performance and efficient computation time. Therefore, the idea of meta-heuristic based approach for optimization of feature selection is proposed in this research to search the global optimal features for ADC. In this thesis, case study of meta-heuristic intelligence and traditional approaches for feature selection optimization process in document classification is observed. It includes eleven meta-heuristic algorithms such as Ant Colony search, Artificial Bee Colony search, Bat search, Cuckoo search, Evolutionary search, Elephant search, Firefly search, Flower search, Genetic search, Rhinoceros search, and Wolf search, for searching the optimal feature subset for document classification. Then, the results of proposed model are compared with three traditional search algorithms like Best First search (BFS), Greedy Stepwise (GS), and Ranker search (RS). In addition, the framework of data mining is applied. It involves data preprocessing, feature engineering, building learning model and evaluating the performance of proposed meta-heuristic intelligence-based feature selection using various performance and computation complexity evaluation schemes. In data processing, tokenization, stop-words handling, stemming and lemmatizing, and normalization are applied. In feature engineering process, n-gram TF-IDF feature extraction is used for implementing feature vector and both filter and wrapper approach are applied for observing different cases. In addition, three different classifiers like J48, Naïve Bayes, and Support Vector Machine, are used for building the document classification model. According to the results, the proposed system can reduce the number of selected features dramatically that can deteriorate learning model performance. In addition, the selected global subset features can yield better performance than traditional search according to single objective function of proposed model

Determination of Oncogenic Human Papillomavirus (HPV) Genotypes in Anogenital Cancers in Myanmar

Molecular and epidemiologic investigations suggest a causal role for human papillomavirus (HPV) in anogenital cancers. This study identified oncogenic HPV genotypes in anogenital cancers among men and women in a 2013 cross-sectional descriptive study in Myanmar. In total, 100 biopsy tissues of histologically confirmed anogenital cancers collected in 2008-2012 were studied, including 30 penile and 9 anal cancers from Yangon General Hospital and 61 vulvar cancers from Central Women's Hospital, Yangon. HPV-DNA testing and genotyping were performed by polymerase chain reaction-restriction fragment length polymorphism. Overall, 34% of anogenital cancers were HPV-positive. HPV was found in 44.4% of anal (4/9), 36.1% of vulvar (22/61), and 26.7% of penile (8/30) cancers. The most frequent genotypes in anal cancers were HPV 16 (75%) and 18 (25%). In vulvar cancers, HPV 33 was most common (40.9%), followed by 16 (31.8%), 31 (22.7%), and 18 (4.6%). In penile cancers, HPV 16 (62.5%) was most common, followed by 33 (25%) and 18 (12.5%). This is the first report of evidencebased oncogenic HPV genotypes in anogenital cancers among men and women in Myanmar. This research provides valuable information for understanding the burden of HPV-associated cancers of the anus, penis, and vulva and considering the effectiveness of prophylactic HPV vaccination

Radon Concentration Measurement in Water from Lashio University, Using Solid State Nuclear Track Detectors

In this paper, the radon emanated from water at Lashio University, Myanmar has been measured. For the measurements, sensitive CR-39 plastic track detectors as Solid State Nuclear Track Detectors (SSNTDs) were used. Based upon the available data, radon concentration has been calculated

Web Based Commercial Transaction System for Watch Stores

Web-based commercial transaction system is the process consumers go through to purchase products on the Internet. Nowadays, most transaction processing system (TPS) works in the online mode and online systems provide a tighter feedback loop by giving users more timely information. In fact, a web-based commercial transaction system is developed using Buy-Side Procurement System for watch stores. This system supports users with quick and accurate information. In addition, users can inquire the information about watch store. When specific information for watch or clock is needed, the appropriate data items are manipulated as necessary, and the user receive the resulting information. This information can then be applied to the specific purpose for which it was intended

Geographical analysis on the development of chain tea shops in Mandalay City

Since practicing of market oriented economy after 1988, private entrepreneurs have been participating in some formerly state controlled economic activities of Myanmar. Existing small scale economic activities have been also transforming into !be form that is more competitive in market economy. Among them, some kinds of spatial and functional transformation patterns of tea shop are witnessing in Mandalay City. Thus, this paper studies chain lea shops with followings research questions. (1) How do chain tea shops emerge in spatial context of Mandalay City? (2) How do locational and business strategies of chain tea shop differ in urban spatial context? (3) What are the major forces that cause the development of chain tea shops in Mandalay City? To answer above questions, chain tea shops were verified based on Mandalay City Directory 2009 and field surveys. Then, both spatial (location) and attribute (function) data of chain tea shops were collected by field surveys and structured interviews conducted to owners of chain tea shops in 2009. Then, their spatial transformation pattern was analysed by using ArcMap 9.2 software. The results revealed that chain tea shops _ emerged in Mandalay City (1) as a practice of spatial expansion based on !be market strategy of parent tea shop, (2) as a systematic development of family business system, and (3) vertical integration of tea shop related businesses under market oriented economy

Implementation of Neural Network Based Electricity Load Forecasting

This paper proposed a novel model for short term load forecast (STLF) in the electricity market. The prior electricity demand data are treated as time series. The model is composed of several neural networks whose data are processed using a wavelet technique. The model is created in the form of a simulation program written with MATLAB. The load data are treated as time series data. They are decomposed into several wavelet coefficient series using the wavelet transform technique known as Non-decimated Wavelet Transform (NWT). The reason for using this technique is the belief in the possibility of extracting hidden patterns from the time series data. The wavelet coefficient series are used to train the neural networks (NNs) and used as the inputs to the NNs for electricity load prediction. The Scale Conjugate Gradient (SCG) algorithm is used as the learning algorithm for the NNs. To get the final forecast data, the outputs from the NNs are recombined using the same wavelet technique. The model was evaluated with the electricity load data of Electronic Engineering Department in Mandalay Technological University in Myanmar. The simulation results showed that the model was capable of producing a reasonable forecasting accuracy in STLF

Consumption of fruits and vegetables and associations with risk factors for non-communicable diseases in the Yangon region of Myanmar: A cross-sectional study

OBJECTIVES: To explore the intake of fruits and vegetables in the Yangon region, Myanmar, and to describe associations between intake of fruits and vegetables (FV) and established risk factors for non-communicable diseases. DESIGN: 2 cross-sectional studies, using the STEPs methodology. SETTING: Urban and rural areas of the Yangon region of Myanmar. PARTICIPANTS: 1486, men and women, 25-74 years, were recruited through a multistage cluster sampling method. Institutionalised people, military personnel, Buddhist monks and nuns were not invited. Physically and mentally ill people were excluded. RESULTS: Mean intake of fruit was 0.8 (SE 0.1) and 0.6 (0.0) servings/day and of vegetables 2.2 (0.1) and 1.2 (0.1) servings/day, in urban and rural areas, respectively. Adjusted for included confounders (age, sex, location, income, education, smoking and low physical activity), men and women eating ≥2 servings of fruits and vegetables/day had lower odds than others of hypertriglyceridaemia (OR 0.72 (95% CI 0.56 to 0.94)). On average, women eating at least 2 servings of fruits and vegetables per day had cholesterol levels 0.28 mmol/L lower than the levels of other women. When only adjusted for sex and age, men eating at least 2 servings of fruits and vegetables per day had cholesterol levels 0.27 mmol/L higher than other men. CONCLUSIONS: A high intake of FV was associated with lower odds of hypertriglyceridaemia among men and women. It was also associated with cholesterol levels, negatively among women and positively among men