Classification Of Abnormal Cervical Cells Using Hierarchical Multilayered Perceptron Network.

The paper discusses the use of neural network to classify the types of cervical cells based on Bethesda system; which are normal, low-grade squamous intraepithelial lesion (LSIL) and high-grade squamous intraepithelial lesion (HSIL). The current study proposed new neural network architecture, namely hierarchical multilayered perceptron (HiMLP) network

Behavior of schmutzdecke with varied filtration rates of slow sand filter to remove total coliforms

The previous research showed that slow sand filtration (SSF) can remove the total coli by approximately 99% because of the schmutzecke layer in the filter. The presented study aimed to complete the previous research on SSF, especially on the schmuztdecke layer mechanism, to remove total coli. Total coli is a parameter of water quality standard in Indonesia, and the behavior of schmutzdecke affects the total coli removal. In the present study, the raw water from Amprong River was treated using horizontal roughing filter (HRF) and SSF. The variations in SSF rate used were 0.2 and 0.4 m/h. Total coliforms were analyzed using the most probable number test, and schmutzdecke visualization was conducted through scanning electron microscopy–energy-dispersive X-ray spectroscopy (SEM–EDX). The best coliform concentration in water treated by the combination of HRF and SSF was 4,386 colonies per 100 mL of sample using the filtration rate of 0.2 m/h, and its removal efficiency was 99.60%. However, the quality of water treated by the combination of HRF and SSF did not meet the drinking water quality standard because the removal of total coli must be 100%. The SEM–EDX visualization results in schmutzdecke showed that the average bacteria in the schmutzdecke layer were small, white, opaque, and circular, with entire edge and flat elevation. The Gram test results showed that the schmutzdecke bacteria consisted of Gram-positive and Gram-negative bacteria with basil as the common cell form

Classifying The Shape Of Aggregate Using Hybrid Multilayered Perceptron Network.

In concrete production, shape of aggregate reflects the quality of concrete produced. The well-shaped aggregates are said to produce high quality concrete by reducing water to cement ratio. On the contrary, poor-shaped aggregates often require higher water to cement ratio in concrete production

3D Object Recognition Using Multiple Views And Neural Networks.

This paper proposes a method for recognition and classification of 3D objects. The method is based on 2D moments and neural networks. The 2D moments are calculated based on 2D intensity images taken from multiple cameras that have been arranged using multiple views technique. 2D moments are commonly used for 2D pattern recognition

On-Line Modelling And Forecasting Of Carbon Monoxide Concentrations Using Hybird Multilayered Perceptron Network.

This paper discusses on-line modelling and forecasting of carbon monoxide (CO) concentrations using Hybrid Multilayered Perceptron (HMLP) Network. Th¢ HMLP network is trained using Modified Recursive Prediction Error (MRPE) algorithm

Performance Comparison of Segmentation Techniques for Nucleus in Chronics Leukemia

Morphological criteria have been used by haematologists to identify malignant cells in the blood smear sample under a light microscope. Experienced hematologist must perform this screening operation. However, manual screening using microscope is time-consuming and tedious. Thus, an automated or semi-automated image screening and diagnosis system are very helpful. An ideal automated screening system will acts as a human expert during the procedure. To formulate this idea, there are few steps involves in this process which is the acquisition of image, image segmentation, features extraction and recognition of image data for further analysis in computer-based. However, segmentation of a region of interest is the most crucial task to extract features for further learning and diagnose. This paper represents two segmentation techniques and their performance comparison based on clustering approach which are k-means and moving k-means clustering algorithms. The segmentation process is performed on ten chronics leukaemia images. The performance of segmentation based on the proposed techniques was evaluated. The proposed segmentation techniques offer high accuracies of segmentation which is more than 97% for both techniques

Automated Intelligent real-time system for aggregate classification

Traditionally, mechanical sieving and manual gauging are used to determine the quality of the aggregates. In order to obtain aggregates with better characteristics, it must pass a series of mechanical, chemical and physical tests which are often performed manually, and are slow, highly subjective and laborious. This research focuses on developing an intelligent real-time classification system called NeuralAgg which consists of 3 major subsystems namely the real-time machine vision, the intelligent classification and the database system. The image capturing system can send high quality images of moving aggregates to the image processing subsystem, and then to the intelligent system for shape classification using artificial neural network. Finally, the classification information is stored in the database system for data archive, which can be used for post analysis purposes. These 3 subsystems are integrated to work in real-time mode which takes an average of 1.23 s for a complete classification process. The system developed in this study has an accuracy of approximately 87% and has the potential to significantly reduce the processing and/or classification time and workload

Exploiting Heterogeneity in Operational Neural Networks by Synaptic Plasticity

The recently proposed network model, Operational Neural Networks (ONNs), can generalize the conventional Convolutional Neural Networks (CNNs) that are homogenous only with a linear neuron model. As a heterogenous network model, ONNs are based on a generalized neuron model that can encapsulate any set of non-linear operators to boost diversity and to learn highly complex and multi-modal functions or spaces with minimal network complexity and training data. However, the default search method to find optimal operators in ONNs, the so-called Greedy Iterative Search (GIS) method, usually takes several training sessions to find a single operator set per layer. This is not only computationally demanding, also the network heterogeneity is limited since the same set of operators will then be used for all neurons in each layer. To address this deficiency and exploit a superior level of heterogeneity, in this study the focus is drawn on searching the best-possible operator set(s) for the hidden neurons of the network based on the Synaptic Plasticity paradigm that poses the essential learning theory in biological neurons. During training, each operator set in the library can be evaluated by their synaptic plasticity level, ranked from the worst to the best, and an elite ONN can then be configured using the top ranked operator sets found at each hidden layer. Experimental results over highly challenging problems demonstrate that the elite ONNs even with few neurons and layers can achieve a superior learning performance than GIS-based ONNs and as a result the performance gap over the CNNs further widens.Comment: 15 pages, 19 figures, journal manuscrip

Self-Organized Operational Neural Networks with Generative Neurons

Operational Neural Networks (ONNs) have recently been proposed to address the well-known limitations and drawbacks of conventional Convolutional Neural Networks (CNNs) such as network homogeneity with the sole linear neuron model. ONNs are heterogenous networks with a generalized neuron model that can encapsulate any set of non-linear operators to boost diversity and to learn highly complex and multi-modal functions or spaces with minimal network complexity and training data. However, Greedy Iterative Search (GIS) method, which is the search method used to find optimal operators in ONNs takes many training sessions to find a single operator set per layer. This is not only computationally demanding, but the network heterogeneity is also limited since the same set of operators will then be used for all neurons in each layer. Moreover, the performance of ONNs directly depends on the operator set library used, which introduces a certain risk of performance degradation especially when the optimal operator set required for a particular task is missing from the library. In order to address these issues and achieve an ultimate heterogeneity level to boost the network diversity along with computational efficiency, in this study we propose Self-organized ONNs (Self-ONNs) with generative neurons that have the ability to adapt (optimize) the nodal operator of each connection during the training process. Therefore, Self-ONNs can have an utmost heterogeneity level required by the learning problem at hand. Moreover, this ability voids the need of having a fixed operator set library and the prior operator search within the library in order to find the best possible set of operators. We further formulate the training method to back-propagate the error through the operational layers of Self-ONNs.Comment: 14 pages, 14 figures, journal articl