Fuzzy Clustering Image Segmentation Based on Particle Swarm Optimization

Image segmentation refers to the technology to segment the image into different regions with different characteristics and to extract useful objectives, and it is a key step from image processing to image analysis. Based on the comprehensive study of image segmentation technology, this paper analyzes the advantages and disadvantages of the existing fuzzy clustering algorithms; integrates the particle swarm optimization (PSO) with the characteristics of global optimization and rapid convergence and fuzzy clustering (FC) algorithm with fuzzy clustering effects starting from the perspective of particle swarm and fuzzy membership restrictions and gets a PSO-FC image segmentation algorithm so as to effectively avoid being trapped into the local optimum and improve the stability and reliability of clustering algorithm. The experimental results show that this new PSO-FC algorithm has excellent image segmentation effects

Prediction of Biochemical Oxygen Demand Using Radial Basis Function Network

Biochemical oxygen demand shows the amount of oxygen needed by microorganisms to decompose dissolved organic substances suspended in water. This variable determines water quality. The higher value indicates lower water quality. Obtaining this value requires a lengthy procedure of five days in typical laboratories. This paper proposes to predict biochemical oxygen demand using a radial basis function network with improvement relational fuzzy c-means clustering to set centroid by using 11 parameters that come from water quality records. The dataset used in testing consisting of weekly parameters between 2014-2019. Testing results show performance measurement of mean absolute error, mean square error, root mean square error, mean absolute percentage error, and accuracy using centroid with improvement relational fuzzy c-means 0.15016, 0.3677, 0.19082, 21.64490 and 78.35510 comparing with centroid from fuzzy c-means 0.16002, 0.04021, 0.19963, 22.83184, and 77.16816

Perkiraan Nilai Biochemical Oxygen Demand Berdasarkan Nilai Parameter Air Baku Menggunakan Radial Basis Function Network Dan Improvement Relational Fuzzy C-Means Clustering

Air merupakan salah satu sumber daya alam yang menunjang kelangsungan hidup dan kehidupan manusia. Peraturan Pemerintah nomor 82 tahun 2001 tentang pengelolaan kualitas dan pengendalian pencemaran air telah menetapkan salah satu parameter dalam penentuan kualitas air, yaitu Biochemical Oxygen Demand (BOD). Nilai BOD menunjukkan jumlah oksigen yang dibutuhkan oleh mikro organisme untuk menguraikan zat organik terlarut dan sebagian zat-zat organik yang tersuspensi di dalam air. Semakin tinggi nilainya menandakan bahwa semakin rendah kualitas air. Nilai BOD didapatkan melalui prosedur panjang di laboratorium dan waktu selama lima hari. Oleh karena itu, diperlukan sebuah sistem untuk memperkirakannya secara langsung pada saat pengambilan sampel. Penelitian ini melakukan perkiraan nilai BOD menggunakan metode Radial Basis Function Network dengan Improvement Relational Fuzzy C-Means Clustering (IRFCM). IRFCM digunakan untuk menentukan centroid dalam perhitungan fungsi aktifasi Gaussian. Data yang digunakan berasal dari rekaman data kualitas air baku mingguan antara tahun 2014-2019. Hasil pengujian menunjukkan Mean Absolute Error sebesar 2.15465, Mean Square Error sebesar 7.72187, Root Mean Square Error sebesar 2.75870, Mean Absolute Percentage Error sebesar 20.11702, dan akurasi sebesar 79.88298 %. ================================================================================================================================ Water is one of the natural resources that support living things. Government Regulation number 82 of 2001 concerning quality management and control of water pollution has set one of the parameters in determining water quality, namely Biochemical Oxygen Demand (BOD). Biochemical Oxygen Demand indicates the amount of oxygen needed by microorganisms to decompose dissolved organic substances that are suspended in water. The high value of BOD indicates low water quality. Obtaining this value requires a lengthy procedure of five days in typical laboratories. This research proposes to estimate the BOD value using the Radial Basis Function Network with Improvement Relational Fuzzy C-Means Clustering (IRFCM). IRFCM is used to set centroid in the calculation of the Gaussian activation function. The dataset comes from weekly raw water quality data records between 2014-2019. The result shows Mean Absolute Error 2.15465, Mean Square Error 7.72187, Root Mean Square Error 2.75870, Mean Absolute Percentage Error 20.11702, and accuracy 79.88298 %