Papers by SAMERKAE SOMHOM
Orchids Classification Using Spatial Transformer Network with Adaptive Scaling
The orchids families are large, diverse flowering plants in the tropical areas. It is a challengi... more The orchids families are large, diverse flowering plants in the tropical areas. It is a challenging task to classify orchid species from images. In this paper, we proposed an adaptive classification model of the orchid images by using a Deep Convolutional Neural Network (D-CNN). The first part of the model improved the quality of input feature maps using an adaptive Spatial Transformer Network (STN) module by performing a spatial transformation to warp an input image which was split into different locations and scales. We applied D-CNN to extract the image features from the previous step and warp into four branches. Then, we concatenated the feature channels and reduced the dimension by an estimation block. Finally, the feature maps would be forwarded to the prediction network layers to predict the orchid species. We verified the efficiency of the proposed method by conducting experiments on our data set of 52 classes of orchid flowers, containing 3,559 samples. Our results achieved...
Self-organizing neural network algorithm in routing problems(配送問題における自己組織化ニューラルネットワークアルゴリズムの開発)
Ontology for Blood Group Phenotyping and ABO Discrepancy Screening*
2021 18th International Joint Conference on Computer Science and Software Engineering (JCSSE)
Enhancing the efficiency of heuristic placement algorithm for two-dimensional orthogonal knapsack packing problem
2015 6th IEEE International Conference on Software Engineering and Service Science (ICSESS), 2015
A Guided Genetic Algorithm for Bilateral Negotiation with Incomplete Information
Advanced Materials Research, Jul 2, 2014
Reaching an agreement between negotiators is a complex process. The complexity of the problem is ... more Reaching an agreement between negotiators is a complex process. The complexity of the problem is depicted by the difference preference of negotiators, the size of the solution space and the negotiation procedure. The aim of this study is to develop an automated negotiation method using a genetic algorithm as a mechanism. The proposed method uses theestimation of the zone of agreementtoguide negotiation. Time and joint utility are used as performance indicators. The result shows that the proposed method hasa better time usage than others.However, our method could have poor value of joint utility in some cases. A likely explanation is that the progress rate of the negotiators affects the joint payoff.
DNA Sequencing Analysis Framework for ABO Genotyping and ABO Discrepancy Resolution *
2021 18th International Conference on Electrical Engineering/Electronics, Computer, Telecommunications and Information Technology (ECTI-CON)
An iterative bidirectional heuristic placement algorithm for solving the two-dimensional knapsack packing problem
Engineering Optimization
A self-organizing neural network for multiple traveling salesman and vehicle routing problems
Or, 1999
Short Term Stock Prediction Using SOM
Lecture Notes in Business Information Processing, 2009
Feature Selection for Neural Network Based Stock Prediction
Communications in Computer and Information Science, 2010
ABSTRACT We propose a new methodology of feature selection for stock movement prediction. The met... more ABSTRACT We propose a new methodology of feature selection for stock movement prediction. The methodology is based upon finding those features which minimize the correlation relation function. We first produce all the combination of feature and evaluate each of them by using our evaluate function. We search through the generated set with hill climbing approach. The self-organizing map based stock prediction model is utilized as the prediction method. We conduct the experiment on data sets of the Microsoft Corporation, General Electric Co. and Ford Motor Co. The results show that our feature selection method can improve the efficiency of the neural network based stock prediction.
A Study of Structure and Evolution of Marketing Research Collaboration Network
International Conference on Software Technology and Engineering (ICSTE 2012), 2012
A Guided Genetic Algorithm for Bilateral Negotiation with Incomplete Information
Advanced Materials Research, 2014
Reaching an agreement between negotiators is a complex process. The complexity of the problem is ... more Reaching an agreement between negotiators is a complex process. The complexity of the problem is depicted by the difference preference of negotiators, the size of the solution space and the negotiation procedure. The aim of this study is to develop an automated negotiation method using a genetic algorithm as a mechanism. The proposed method uses theestimation of the zone of agreementtoguide negotiation. Time and joint utility are used as performance indicators. The result shows that the proposed method hasa better time usage than others.However, our method could have poor value of joint utility in some cases. A likely explanation is that the progress rate of the negotiators affects the joint payoff.
ScienceAsia, 2014
In this paper, a framework based on algebraic structures to formalize various types of neural net... more In this paper, a framework based on algebraic structures to formalize various types of neural networks is presented. The working strategy is to break down neural networks into building blocks, relationships between each building block, and their operations. Building blocks are collections of primary components or neurons. In turn, neurons are collections of properties functioning as single entities, transforming an input into an output. We perceive a neuron as a function. Thus the flow of information in a neural network is a composition between functions. Moreover, we also define an abstract data structure called a layer which is a collection of entities which exist in the same time step. This layer concept allows the parallel computation of our model. There are two types of operation in our model; recalling operators and training operators. The recalling operators are operators that challenge the neural network with data. The training operators are operators that change parameters of neurons to fit with the data. This point of view means that all neural networks can be constructed or modelled using the same structures with different parameters.
International Transactions in Operational Research, 1999
This paper addresses several algorithms based on self-organizing neural network approach for rout... more This paper addresses several algorithms based on self-organizing neural network approach for routing problems. The algorithm for Traveling Salesman Problem is elaborated and the extension of the proposed algorithm to more complex problems namely, Multiple Traveling Salesmen and Vehicle Routing is discussed. In order to investigate the performance of the algorithms, a comprehensive empirical study has been provided. The simulations, which are conducted on standard data, evaluate the overall performance of this approach by comparing the results with the best known or the optimal solutions of the problems. The proposed algorithm shows signi®cant advances in both qualities of the solution and computational eorts for most of the experimented data.
Competition-based neural network for the multiple travelling salesmen problem with minmax objective
Computers & Operations Research, 1999
... 2 Abdolhamid Modares is a Research Associate in the Department of Industrial Engineering and ... more ... 2 Abdolhamid Modares is a Research Associate in the Department of Industrial Engineering and Management at Tokyo Institute of Technology (TIT). He received his MS from Isfahan University of Technology, Iran, and his Ph.D. from TIT. ...
A self-organising model for the travelling salesman problem
The Journal of the Operational Research Society, Sep 1, 1997
This work describes a new algorithm, based on a self-organising neural network approach, to solve... more This work describes a new algorithm, based on a self-organising neural network approach, to solve the Travelling Salesman Problem (TSP). Firstly, various features of the available adaptive neural network algorithms for TSP are reviewed and a new algorithm is ...
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Papers by SAMERKAE SOMHOM