Bahari Idrus

A linear model based on Kalman filter for improving neural network classification performance

This paper proposes a method to improve neural network classification performance.A linear model was used as post processing of neural network.The parameters of linear model was estimated using Kalman filter iteration.The method can be applied to classify an object regardless of the type of feature.The method has been validated with five different datasets. Neural network has been applied in several classification problems such as in medical diagnosis, handwriting recognition, and product inspection, with a good classification performance. The performance of a neural network is characterized by the neural networks structure, transfer function, and learning algorithm. However, a neural network classifier tends to be weak if it uses an inappropriate structure. The neural networks structure depends on the complexity of the relationship between the input and the output. There are no exact rules that can be used to determine the neural networks structure. Therefore, studies in improving neural network classification performance without changing the neural networks structure is a challenging issue. This paper proposes a method to improve neural network classification performance by constructing a linear model based on the Kalman filter as a post processing. The linear model transforms the predicted output of the neural network to a value close to the desired output by using the linear combination of the object features and the predicted output. This simple transformation will reduce the error of neural network and improve classification performance. The Kalman filter iteration is used to estimate the parameters of the linear model. Five datasets from various domains with various characteristics, such as attribute types, the number of attributes, the number of samples, and the number of classes, were used for empirical validation. The validation results show that the linear model based on the Kalman filter can improve the performance of the original neural network.

Monte Carlo Method with Heuristic Adjustment for Irregularly Shaped Food Product Volume Measurement

Volume measurement plays an important role in the production and processing of food products. Various methods have been proposed to measure the volume of food products with irregular shapes based on 3D reconstruction. However, 3D reconstruction comes with a high-priced computational cost. Furthermore, some of the volume measurement methods based on 3D reconstruction have a low accuracy. Another method for measuring volume of objects uses Monte Carlo method. Monte Carlo method performs volume measurements using random points. Monte Carlo method only requires information regarding whether random points fall inside or outside an object and does not require a 3D reconstruction. This paper proposes volume measurement using a computer vision system for irregularly shaped food products without 3D reconstruction based on Monte Carlo method with heuristic adjustment. Five images of food product were captured using five cameras and processed to produce binary images. Monte Carlo integration with heuristic adjustment was performed to measure the volume based on the information extracted from binary images. The experimental results show that the proposed method provided high accuracy and precision compared to the water displacement method. In addition, the proposed method is more accurate and faster than the space carving method.

Critical Review of Measurement for Multipartite Entanglement: Detection and Quantification

Quantum entanglement is one of the fields in quantum mechanics. Recently, quantum entanglement becomes the heart of many tasks in quantum information such as quantum cryptography, quantum teleportation and quantum computing due to the capability it to compute data more efficient compare classical computer. Therefore, the measurement of entanglement become important to determine either the state is entangled or separable. The aim of this paper is to view all possible methods of measurement in term of detection and quantification for multipartite entanglement cases. The outcome of this paper is to classify the method of detection and quantification including summarize the criteria and advantage or disadvantage of each method.

Separability criteria and method of measurement for entanglement

Quantum computers have the potentials to solve certain problems faster than classical computers. In quantum computer, entanglement is one of the elements beside superposition. Recently, with the advent of quantum information theory, entanglement has become an important resource for Quantum Information and Computation. The purpose of this paper is to discuss the separability criteria and method of measurement for entanglement. This paper is aimed at viewing the method that has been proposed in previous works in bipartite and multipartite entanglement. The outcome of this paper is to classify the different method that used to measure entanglement for bipartite and multipartite cases including the advantage and disadvantage of each method.