Suchergebnisse

With the pressure of population growth and environmental pollution, it is particularly important to develop and utilize water resources more rationally, safely, and efficiently. Due to safety concerns, the government today adopts a pessimistic method, single factor assessment, for the evaluation of domestic water quality. At the same time, however, it is impossible to grasp the timely comprehensive pollution status of each area, so effective measures cannot be taken in time to reverse or at least alleviate its deterioration. Thus, the main propose of this paper is to establish a comprehensive evaluation model of water quality, which can provide the managers with timely information of water pollution in various regions. After considering various evaluation methods, this paper finally decided to use the fuzzy support vector machine method (FSVM) to establish the model that is mentioned above. The FSVM method is formed by applying the membership function to the support vector machine. However, the existing membership functions have some shortcomings, so after some improvements in these functions, a new membership function is finally formed. The model is then tested on the artificial data, UCI dataset, and water quality evaluation historical data. The results show that the improvement is meaningful, the improved fuzzy support vector machine has good performance, and it can deal with noise and outliers well. Thus, the model can completely solve the problem of comprehensive evaluation of water quality.

We present a novel approach for measuring democracy, which enables a very detailed and sensitive index. This method is based on Support Vector Machines, a mathematical algorithm for pattern recognition. Our implementation evaluates 188 countries in the period between 1981 and 2011. The Support Vector Machines Democracy Index (SVMDI) is continuously on the 0-1-Interval and robust to variations in the numerical process parameters. The algorithm introduced here can be used for every concept of democracy without additional adjustments, and due to its exibility it is also a valuable tool for comparison studies.

Abstrak - Berita adalah sebuah informasi mengenai peristiwa yang terjadi di suatu lokasi yang bisa disajikan dalam bentuk teks maupun visual. Berita bisa ditemukan di berbagai portal berita dan media cetak. Umumnya setiap berita dikelompokan berdasarkan kategori umum seperti ekonomi, politik, olahraga, dll. Permasalahan yang muncul adalah bagaimana cara untuk melakukan pengelompokan pada data berita yang biasanya berjumlah hingga ribuan karakter kedalam kategori yang lebih spesifik. Permasalah ini dapat diselesaikan dengan cara menerapkan text mining dengan memanfatakan algoritma klasifikasi untuk mendapatkan sebuah model fungsi yang merepresentasikan tiap kategori berita. Salah satu algoritma klasifikasi yang cukup tangguh untuk melakukan proses klasifikasi teks adalah Support Vector Machine. Penelitian ini menggunakan 510 data berita dengan batasan klasifikasi 3 kategori berita. Algoritma SVM mendapatkan hasil akurasi tertinggi di 88% untuk nilai parameter C =1, kernel Linear dengan pembagian data uji dan data latih sebesar 90% dan 10 %.Kata kunci : Berita, Klasifikasi, Support Vector Machine, Text Mining Abstract - News is information about events that occur in a location that can be presented in text or visual form. News can be found on various news portals and print media.Generally each news is grouped by general categories such as economics, politics, sports, etc. The problem is how to group news data into more specific categories.This problem can be solved by applying text mining using the classification algorithm to obtain a function model that represents each news category. One of the classification algorithms that is strong enough to do the text classification process is the Support Vector Machine. This study uses 510 news sample with a classification limit of 3 news categories. The SVM algorithm gets the highest accuracy at 88% for the parameter value C = 1, and Linear kernel with the distribution of test data and training data is 90% and 10%.Keywords : Classification, News, Support Vector Machine, ...

Recently, Content-Based Image Retrieval is a widely popular and efficient searching and indexing approach used by knowledge seekers. Use of images by e-commerce sites, by product and by service industries is not new nowadays. Travel and tourism are the largest service industries in India. Every year people visit tourist places and upload pictures of their visit on social networking sites or share via the mobile device with friends and relatives. Classification of the monuments is helpful to hoteliers for the development of a new hotel with state of the art amenities, to travel service providers, to restaurant owners, to government agencies for security, etc. The proposed system had extracted features and classified the Indian monuments visited by the tourists based on the linear Support Vector Machine (SVM). The proposed system was divided into 3 main phases: preprocessing, feature vector creation and classification. The extracted features are based on Local Binary Pattern, Histogram, Co-occurrence Matrix and Canny Edge Detection methods. Once the feature vector had been constructed, classification was performed using Linear SVM. The Database of 10 popular Indian monuments was generated with 50 images for each class. The proposed system is implemented in MATLAB and achieves very high accuracy. The proposed system was also tested on other popular benchmark databases.