|
AIML ISSN:
1687-4846 Print,
1687-4854 Online,
1687-4862 CD-ROM
|
@ARTICLE |
{P1121004977, |
| AUTHOR = |
{Alaa M. Elsayad}, |
| TITLE = |
{Diagnosis of Breast Tumor using Boosted Decision Trees}, |
| JOURNAL = |
{ICGST International Journal on Artificial Intelligence and Machine Learning, AIML}, |
| YEAR = |
{2010}, |
| MONTH= |
{October}, |
| VOLUME = |
{10}, |
| ISSUE = |
{I}, |
| PAGES= |
{1--11}, |
| ABSTRACT= |
{
Decision tree (DT) is one of the popular and effective data mining methods. DT provides a pathway to find “rules” that could be evaluated for separating the input samples into one of several groups without having to express the functional relationship directly. They avoid the limitations of the parametric models and are well suited for the analysis of nonlinear events. The purpose of this study is to examine the performance of the recent invented DT model algorithm (C5.0) on the diagnosis of breast cancer using cytologically proven tumor dataset . The objective is to classify a tumor as either benign or malignant based on cell descriptions gathered by microscopic examination. The classification performance of C5.0 DT is evaluated and compared to the one that achieved by radial basis function kernel support vector machine (RBF-SVM). The dataset has been partitioned by the ratio 70:30% into training and test subsets respectively . Experimental results show that the generalization of the C5.0 DT has been increased radically using boosting, winnowing and tree pruning methods. The C5.0 DT model has achieved a remarkable performance with 98.95% classification accuracy on training subset and 100% of test one while RBF-SVM has achieved 100% success on both training and test subsets}, |
| NOTE= |
{Breast cancer, cytology patterns, decision tree, support vector machine, performance measures }}
|
(Status:
Accepted)
| @ARTICLE |
{P1121002967, |
| AUTHOR = |
{Y. Mechqrane and R. Ezzahir and C. Bessiere and E.H. Bouyakhf}, |
| TITLE = |
{A Constraint Based Approach To Air Traffic Control}, |
| JOURNAL = |
{ICGST International Journal on Artificial Intelligence and Machine Learning, AIML}, |
| YEAR = |
{2010}, |
| MONTH= |
{October}, |
| VOLUME = |
{10}, |
| ISSUE = |
{I}, |
| PAGES= |
{13--22}, |
| ABSTRACT= |
{During the fly, a conflict between two aircraft occurs if the two aircraft are closer than a given safety distance. In this paper we propose a constraint based approach to solve conflicts between aircraft during the fly: Each pair of aircraft in the controlled airspace is related by a separation constraint which specifies that at any moment, the two aircraft must be separated by a minimum distance guaranteeing safety; the issue is to determine the maneuvers to be implemented by the pilots such that the separation constraints are satisfied. To this end, we discretized the time and used discrete variables and constraints. We formulated the separation constraints so that the number of tests required to check a constraint is independent of the time step. We also identified some useful properties of these constraints and used them to infer infeasible values during the search. Moreover, the filtering algorithm we used suspends forward checks until they are required by the search and avoids searching large domains for consistent values until it has to. These techniques save search efforts and reduce the computational times. In addition, to minimize the delays and additional consumption, "least- aggressive" maneuvers are tested first. Our approach make possible to solve difficult Air Traffic Control situations in a few seconds}, |
| NOTE= |
{Air traffic control, conflict resolution, constraint satisfaction problem, constraint propagation}}
|
(Status: Accepted)
| @ARTICLE |
{P1121022077, |
| AUTHOR = |
{Tarek Aboueldahab and Mahumod Fakhreldin}, |
| TITLE = |
{Stock Market Indices Prediction via Hybrid Sigmoid Diagonal Recurrent Neural Networks and Enhanced Particle Swarm Optimization}, |
| JOURNAL = |
{ICGST International Journal on Artificial Intelligence and Machine Learning, AIML}, |
| YEAR = |
{2010}, |
| MONTH= |
{October}, |
| VOLUME = |
{10}, |
| ISSUE = |
{I}, |
| PAGES= |
{23--30}, |
| ABSTRACT= |
{Recently, the usage of hybrid intelligent model comprising both Neural Networks (NN) and Particle Swarm Optimization (PSO) for stock market prediction has been widely established. Although, t his model has shown its fast search speed in the complicated optimization and search problem for stock market prediction, however, PSO could often easily fall into local optima, causing the decrease of prediction accuracy. This paper presents an Enhanced PSO (EPSO) to enhance the prediction accuracy and avoid premature convergence. The proposed method employs comparison between cognitive term and randomly perturbation term in particle relocation and the used network architecture is Sigmoid Diagonal Recurrent Neural Network (SDRNN). Experimental results on the most well known stock market indices have shown that EPSO could successfully deal with those difficulties while maintaining fast search speed}, |
| NOTE= |
{Sigmoid Diagonal Recurrent Neural Networks, Enhanced Particle Swarm Optimization, Time Series Prediction, Stock Market}}
|
(Status: Accepted)
| @ARTICLE |
{P1121026153, |
| AUTHOR = |
{S. Abu Naser and R. Al-Dahdooh and A. Mushtaha and M. El-Naffar}, |
| TITLE = |
{Knowledge Management in ESMDA: Expert System for Medical Diagnostic Assistance}, |
| JOURNAL = |
{ICGST International Journal on Artificial Intelligence and Machine Learning, AIML}, |
| YEAR = |
{2010}, |
| MONTH= |
{October}, |
| VOLUME = |
{10}, |
| ISSUE = |
{I}, |
| PAGES= |
{31--40}, |
| ABSTRACT= |
{This research involved designing a prototype expert system that helps patients in diagnosing their diseases and offering them the proper advice; furthermore, the knowledge management used in the expert system is discussed. One of the main objectives of this research was to find a proper language for representing patient's medical history and current situation into a knowledge base for the expert systems to be able to carry out the consultation effectively. Production rules were used to capture the knowledge. The expert system was developed using CLIPS(C Language Integrated Production System) with Java Interface. The expert system yielded good results in the analysis of the medical cases tested and the system was able to determine the correct diagnosis in all cases}, |
| NOTE= |
{Knowledge Management, Expert System, CLIPS, Production System, Medical System}} |
(Status: Accepted)
| @ARTICLE |
{P1121022077, |
| AUTHOR = |
{Tarek Aboueldahab and Mahumod Fakhreldin}, |
| TITLE = |
{Stock Market Indices Prediction via Hybrid Sigmoid Diagonal Recurrent Neural Networks and Enhanced Particle Swarm Optimization}, |
| JOURNAL = |
{ICGST International Journal on Artificial Intelligence and Machine Learning, AIML}, |
| YEAR = |
{2010}, |
| MONTH= |
{October}, |
| VOLUME = |
{10}, |
| ISSUE = |
{I}, |
| PAGES= |
{23--30}, |
| ABSTRACT= |
{Recently, the usage of hybrid intelligent model comprising both Neural Networks (NN) and Particle Swarm Optimization (PSO) for stock market prediction has been widely established. Although, t his model has shown its fast search speed in the complicated optimization and search problem for stock market prediction, however, PSO could often easily fall into local optima, causing the decrease of prediction accuracy. This paper presents an Enhanced PSO (EPSO) to enhance the prediction accuracy and avoid premature convergence. The proposed method employs comparison between cognitive term and randomly perturbation term in particle relocation and the used network architecture is Sigmoid Diagonal Recurrent Neural Network (SDRNN). Experimental results on the most well known stock market indices have shown that EPSO could successfully deal with those difficulties while maintaining fast search speed}, |
| NOTE= |
{Sigmoid Diagonal Recurrent Neural Networks, Enhanced Particle Swarm Optimization, Time Series Prediction, Stock Market}}
|
(Status: Accepted)
| @ARTICLE |
{P1121052912, |
| AUTHOR = |
{A. Thobbi and R. Kadam and W. Sheng}, |
| TITLE = |
{Achieving Remote Presence using a Humanoid Robot Controlled by a Non-Invasive BCI Device}, |
| JOURNAL = |
{ICGST International Journal on Artificial Intelligence and Machine Learning, AIML}, |
| YEAR = |
{2010}, |
| MONTH= |
{October}, |
| VOLUME = |
{10}, |
| ISSUE = |
{I}, |
| PAGES= |
{41--45}, |
| ABSTRACT= |
{This paper presents a platform for ‘Remote Presence' which enables a person to be present at a remote location through the embodiment of a humanoid robot. We specifically propose the use of a humanoid robot since it will endow human like capabilities for manipulating the remote environment. The numerous sensors available on the humanoid robot such as vision, microphones are essential to give feedback to the human controller about the remote environment. In addition to this, the humanoid has capabilities such as speech synthesis, obstacle avoidance, and ability to grasp objects which can be used to perform a wide array of tasks. To control the actions of the robot we propose the use of non-invasive Brain Computer Interface. The BCI enables the user to conveniently control the robot in the remote environment. The human user receives audio and video feedback from the robot on a personal media viewer such as video goggles. This would help the user to feel total immersion in the remote environment. This system could immensely benefit a variety of sectors such as military, medicine, disaster management etc. for carrying out dangerous or physically intensive tasks}, |
| NOTE= |
{Humanoid Robot, Tele-presence, Brain Computer Interface, Human Robot Interaction, Tele-operation}} |
(Status: Accepted)
| @ARTICLE |
{P1121052911, |
| AUTHOR = |
{S.Kokila and P.Gomathi and T.Manigandan}, |
| TITLE = |
{Design of Energy Efficient Humidification Plant for Textile Processing}, |
| JOURNAL = |
{ICGST International Journal on Artificial Intelligence and Machine Learning, AIML}, |
| YEAR = |
{2010}, |
| MONTH= |
{October}, |
| VOLUME = |
{10}, |
| ISSUE = |
{I}, |
| PAGES= |
{47--54}, |
| ABSTRACT= |
{Humidification is an important ancillary process in a textile industry that is supportive to the production of yarns and fabrics. It improves not only the production but also the quality. Besides, humidification is the second-largest power consuming component next to textile mills and accounts for nearly 15% of the power bill of a textile mills. The increasing power cost at the rate of 12% per year, any effort to save power will be received in the industry. Energy saving in the tune of 25% to 65% in the existing condition is possible by incorporating control in the textile mill depending on the outside climate. Energy efficient control system for humidification plant in textile mills for making the existing humidification plants are more energy efficient in operation. Control consists of variable speed drives for air supply fans, exhaust fans and pumps. In this paper energy efficient humidification plant for textile processing is designed and the power of humidification plant is predicted using Fuzzy Logic. The circuit is simulated in MATLAB- SIMULINK and the results are shown. This Energy Efficient Humidification Plant reduces the power consumption and maintains the quality of the product}, |
| NOTE= |
{Mathematical model, Humidification Plant, Fuzzy Logic, Energy Efficient Controller}} |
(Status: Accepted)
|
