12 research outputs found
An Intelligent Classification System For Aggregate Based On Image Processing And Neural Network
Bentuk dan tekstur permukaan aggregat mempengaruhi kekuatan dan struktur konkrit. Secara tradisi, mesin pengayakan mekanikal dan pengukuran manual digunakan bagi menentukan kedua-dua saiz dan bentuk aggregat.
Aggregate’s shape and surface texture immensely influence the strength and structure of the resulting concrete. Traditionally, mechanical sieving and manual gauging are used
to determine both the size and shape of the aggregates
Development Of An Intelligent System For River Water Quality Classification Based On Algae Composition
Throughout the years, many researches have been conducted on the potential
applications of Artificial Intelligence (AI) in the biological monitoring of river quality.
This project will provide an overview regarding the feasibility of the application of
neural networks for direct classification of river water quality based on algae
composition. A brief introduction to neural networks and the suitability of neural
network for use in river water quality determination will be investigated. In this project,
several neural networks will be developed and their performance are compared to yield
the most suitable network that will be used to model the classification system for
determination of river water quality based on algae composition. Among the types of
neural network that will be developed are Multilayer Perceptron network (MLP),
Radial Basis Function (RBF) network and Hybrid Multilayer Perceptron (HMLP)
network. This study proves that the HMLP network trained using the MRPE algorithm
achieves the best performance as compared to the MLP and RBF network. The HMLP
network produces 90% accuracy. In this study, an intelligent system is developed for
the classification of river water quality using the HMLP network. The proposed system
provides several advantages in terms of its applicability, high accuracy, user-friendliness and as well as yields faster results compared to conventional system
Pembinaan Sistem Pintar Untuk Penentuan Kualiti Air Berdasarkan Rangkaian Neural.
Alga merupakan organisma mikro yang digunakan dalam pemerhatian secara biologi bagi penentuan kualiti air sungai.
Algae are microorganisms which are being used in biological monitoring to determine the quality of river's water
Multiple Adaptive Neuro-Fuzzy Inference System with Automatic Features Extraction Algorithm for Cervical Cancer Recognition
To date, cancer of uterine cervix is still a leading cause of cancer-related deaths in women worldwide. The current methods (i.e., Pap smear and liquid-based cytology (LBC)) to screen for cervical cancer are time-consuming and dependent on the skill of the cytopathologist and thus are rather subjective. Therefore, this paper presents an intelligent computer vision system to assist pathologists in overcoming these problems and, consequently, produce more accurate results. The developed system consists of two stages. In the first stage, the automatic features extraction (AFE) algorithm is performed. In the second stage, a neuro-fuzzy model called multiple adaptive neuro-fuzzy inference system (MANFIS) is proposed for recognition process. The MANFIS contains a set of ANFIS models which are arranged in parallel combination to produce a model with multi-input-multioutput structure. The system is capable of classifying cervical cell image into three groups, namely, normal, low-grade squamous intraepithelial lesion (LSIL) and high-grade squamous intraepithelial lesion (HSIL). The experimental results prove the capability of the AFE algorithm to be as effective as the manual extraction by human experts, while the proposed MANFIS produces a good classification performance with 94.2% accuracy
Selección de perceptrones multicapa usando aprendizaje bayesiano
La Regularización Bayesiana de perceptrones multicapa pretende resolver el problema de optimización de los pesos de la red neuronal simultáneamente con el problema de generalización. En este trabajo se realiza un análisis de la regularización Bayesiana, que parece ser una de las más poderosas técnicas de entrenamiento de perceptrones multicapa, para luego hacer un comparativo con los resultados obtenidos usando Regla Delta Generalizada. Finalmente se discute alguna implicación de los resultados obtenidos respecto a la técnica basada en algoritmos constructivos para la selección final de neuronas en la capa oculta
Selección de perceptrones multicapa usando aprendizaje bayesiano
La Regularización Bayesiana de perceptrones multicapa pretende resolver el problema de optimización de los pesos de la red neuronal simultáneamente con el problema de generalización. En este trabajo se realiza un análisis de la regularización Bayesiana, que parece ser una de las más poderosas técnicas de entrenamiento de perceptrones multicapa, para luego hacer un comparativo con los resultados obtenidos usando Regla Delta Generalizada. Finalmente se discute alguna implicación de los resultados obtenidos respecto a la técnica basada en algoritmos constructivos para la selección final de neuronas en la capa oculta
Selección de perceptrones multicapa usando aprendizaje bayesiano
La Regularización Bayesiana de perceptrones multicapa pretende resolver el problema de optimización de los pesos de la red neuronal simultáneamente con el problema de generalización. En este trabajo se realiza un análisis de la regularización Bayesiana, que parece ser una de las más poderosas técnicas de entrenamiento de perceptrones multicapa, para luego hacer un comparativo con los resultados obtenidos usando Regla Delta Generalizada. Finalmente se discute alguna implicación de los resultados obtenidos respecto a la técnica basada en algoritmos constructivos para la selección final de neuronas en la capa oculta
Crab and cockle shells as heterogeneous catalysts in the production of biodiesel
In the present study, the waste crab and cockle shells were utilized as source of calcium oxide to transesterify palm olein into methyl esters (biodiesel). Characterization results revealed that the main component of the shells are calcium carbonate which transformed into calcium oxide
upon activated above 700 °C for 2 h. Parametric studies have been investigated and optimal conditions were found to be catalyst amount, 5 wt.% and methanol/oil mass ratio, 0.5:1. The waste catalysts perform equally well as laboratory CaO, thus creating another low-cost catalyst source for producing biodiesel. Reusability results confirmed that the prepared catalyst is able to be reemployed up to five times. Statistical analysis has been
performed using a Central Composite Design to evaluate the contribution and performance of the
parameters on biodiesel purity