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384 results on '"Haron, Habibollah"'

2. The Hybrid of WOABAT-IFDO Optimization Algorithm and Its Application in Crowd Evacuation Simulation

Author

Sharbini, Hamizan, Sallehuddin, Roselina, Haron, Habibollah, Angrisani, Leopoldo, Series Editor, Arteaga, Marco, Series Editor, Panigrahi, Bijaya Ketan, Series Editor, Chakraborty, Samarjit, Series Editor, Chen, Jiming, Series Editor, Chen, Shanben, Series Editor, Chen, Tan Kay, Series Editor, Dillmann, Rüdiger, Series Editor, Duan, Haibin, Series Editor, Ferrari, Gianluigi, Series Editor, Ferre, Manuel, Series Editor, Hirche, Sandra, Series Editor, Jabbari, Faryar, Series Editor, Jia, Limin, Series Editor, Kacprzyk, Janusz, Series Editor, Khamis, Alaa, Series Editor, Kroeger, Torsten, Series Editor, Li, Yong, Series Editor, Liang, Qilian, Series Editor, Martín, Ferran, Series Editor, Ming, Tan Cher, Series Editor, Minker, Wolfgang, Series Editor, Misra, Pradeep, Series Editor, Möller, Sebastian, Series Editor, Mukhopadhyay, Subhas, Series Editor, Ning, Cun-Zheng, Series Editor, Nishida, Toyoaki, Series Editor, Oneto, Luca, Series Editor, Pascucci, Federica, Series Editor, Qin, Yong, Series Editor, Seng, Gan Woon, Series Editor, Speidel, Joachim, Series Editor, Veiga, Germano, Series Editor, Wu, Haitao, Series Editor, Zamboni, Walter, Series Editor, Zhang, Junjie James, Series Editor, Kang, Dae-Ki, editor, Alfred, Rayner, editor, Ismail, Zamhar Iswandono Bin Awang, editor, Baharum, Aslina, editor, and Thiruchelvam, Vinesh, editor

Published
2023
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5. Virtual Reality Prototype of a Linear Accelerator Simulator for Oncological Radiotherapy Training

Author

Chan, Vei S., Iglesias, Andrés, Haron, Habibollah, Prada, Pedro J., Ruiz, Samuel, Gálvez, Akemi, You, Lihua, Salleh, Faezah M., Mohamed, Farhan, Goos, Gerhard, Founding Editor, Hartmanis, Juris, Founding Editor, Bertino, Elisa, Editorial Board Member, Gao, Wen, Editorial Board Member, Steffen, Bernhard, Editorial Board Member, Yung, Moti, Editorial Board Member, Groen, Derek, editor, de Mulatier, Clélia, editor, Paszynski, Maciej, editor, Krzhizhanovskaya, Valeria V., editor, Dongarra, Jack J., editor, and Sloot, Peter M. A., editor

Published
2022
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7. Enhancing infectious disease prediction model selection with multi-objective optimization: an empirical study.

Author

Xu, Deren, Chan, Weng Howe, and Haron, Habibollah

Subjects
STANDARD deviations, DECISION support systems, DECISION trees, COMMUNICABLE diseases, PREDICTION models
Abstract

As the pandemic continues to pose challenges to global public health, developing effective predictive models has become an urgent research topic. This study aims to explore the application of multi-objective optimization methods in selecting infectious disease prediction models and evaluate their impact on improving prediction accuracy, generalizability, and computational efficiency. In this study, the NSGA-II algorithm was used to compare models selected by multi-objective optimization with those selected by traditional single-objective optimization. The results indicate that decision tree (DT) and extreme gradient boosting regressor (XGBoost) models selected through multi-objective optimization methods outperform those selected by other methods in terms of accuracy, generalizability, and computational efficiency. Compared to the ridge regression model selected through single-objective optimization methods, the decision tree (DT) and XGBoost models demonstrate significantly lower root mean square error (RMSE) on real datasets. This finding highlights the potential advantages of multi-objective optimization in balancing multiple evaluation metrics. However, this study's limitations suggest future research directions, including algorithm improvements, expanded evaluation metrics, and the use of more diverse datasets. The conclusions of this study emphasize the theoretical and practical significance of multi-objective optimization methods in public health decision support systems, indicating their wide-ranging potential applications in selecting predictive models. [ABSTRACT FROM AUTHOR]

Published
2024
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8. Thresholding-based approach for segmentation of melanocytic skin lesion in dermoscopic images.

Author

Zebari, Dilovan Asaad, Yusoff, Yusliza, Al-Zebari, Adel, Haron, Habibollah, and Zebari, Nechirvan Asaad

Subjects
DERMOSCOPY, IMAGE segmentation, NEVUS, SKIN
Abstract

The early and effective identification of skin lesion relies heavily on skin lesion segmentation. In contrast, automatic lesion segmentation in dermoscopic images is a difficult task due to several issues such as artifacts (hairs, gel bubbles, and ruler markers), indistinct boundaries, low contrast, and differences in sizes and lesion shapes. Nevertheless, this task can be accomplished. This work attempts to segment skin lesions from normal lesions based on the thresholding technique. The proposed work improved the segmented lesion by removing unwanted objects from an image using morphological operation. This work can effectively segment the lesion region from the normal skin. The segmentation accuracy provided by the system is 91.21%, and comparative analysis has been carried out with thresholding techniques. Based on the findings of the investigation, it has been determined that this work can segment lesions effectively. [ABSTRACT FROM AUTHOR]

Published
2024
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11. A Proposed Gradient Tree Boosting with Different Loss Function in Crime Forecasting and Analysis

Author

Khairuddin, Alif Ridzuan, Alwee, Razana, Haron, Habibollah, Kacprzyk, Janusz, Series Editor, Pal, Nikhil R., Advisory Editor, Bello Perez, Rafael, Advisory Editor, Corchado, Emilio S., Advisory Editor, Hagras, Hani, Advisory Editor, Kóczy, László T., Advisory Editor, Kreinovich, Vladik, Advisory Editor, Lin, Chin-Teng, Advisory Editor, Lu, Jie, Advisory Editor, Melin, Patricia, Advisory Editor, Nedjah, Nadia, Advisory Editor, Nguyen, Ngoc Thanh, Advisory Editor, Wang, Jun, Advisory Editor, Saeed, Faisal, editor, Mohammed, Fathey, editor, and Gazem, Nadhmi, editor

Published
2020
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12. Minimizing Material Consumption of 3D Printing with Stress-Guided Optimization

Author

Zheng, Anzong, Bian, Shaojun, Chaudhry, Ehtzaz, Chang, Jian, Haron, Habibollah, You, Lihua, Zhang, Jianjun, Goos, Gerhard, Founding Editor, Hartmanis, Juris, Founding Editor, Bertino, Elisa, Editorial Board Member, Gao, Wen, Editorial Board Member, Steffen, Bernhard, Editorial Board Member, Woeginger, Gerhard, Editorial Board Member, Yung, Moti, Editorial Board Member, Krzhizhanovskaya, Valeria V., editor, Závodszky, Gábor, editor, Lees, Michael H., editor, Dongarra, Jack J., editor, Sloot, Peter M. A., editor, Brissos, Sérgio, editor, and Teixeira, João, editor

Published
2020
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13. Appropriate Combination of Crossover Operator andMutation Operator in Genetic Algorithms for the Travelling Salesman Problem.

Author

Ahmed, Zakir Hussain, Haron, Habibollah, and Al-Tameem, Abdullah

Abstract

Genetic algorithms (GAs) are very goodmetaheuristic algorithms that are suitable for solving NP-hard combinatorial optimization problems. A simpleGA begins with a set of solutions represented by a population of chromosomes and then uses the idea of survival of the fittest in the selection process to select some fitter chromosomes. It uses a crossover operator to create better offspring chromosomes and thus, converges the population. Also, it uses a mutation operator to explore the unexplored areas by the crossover operator, and thus, diversifies the GA search space. A combination of crossover and mutation operators makes the GA search strong enough to reach the optimal solution. However, appropriate selection and combination of crossover operator and mutation operator can lead to a very good GA for solving an optimization problem. In this present paper, we aim to study the benchmark traveling salesman problem (TSP). We developed several genetic algorithms using seven crossover operators and six mutation operators for the TSP and then compared them to some benchmark TSPLIB instances. The experimental studies show the effectiveness of the combination of a comprehensive sequential constructive crossover operator and insertion mutation operator for the problem. The GA using the comprehensive sequential constructive crossover with insertion mutation could find average solutions whose average percentage of excesses from the best-known solutions are between 0.22 and 14.94 for our experimented problem instances. [ABSTRACT FROM AUTHOR]

Published
2024
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15. A comparative study on applied null hypothesis feature selection technique in crime forecasting.

Author

Khairuddin, Alif Ridzuan, Alwee, Razana, and Haron, Habibollah

Abstract

In criminology study, crime trend and occurrence are mostly influenced by various factors such as social mistreatment and economic disadvantages. From the literature, there are various approaches used by researchers in identifying the significant factors that influence crime. Among these, an application of the feature selection technique in identifying the significant relationship between factors and crime became a popular option. Feature selection is helpful in providing statistical evidence in determining the relevance of the factors towards crime data. Motivated by this, this study aims to conduct a study on the application of three null hypothesis feature selection techniques namely F-test, T-test, and sign test in identifying the significant factors. The identified significant factors are then used to develop the crime forecasting model based on artificial intelligence (AI) technique called gradient tree boosting (GTB). The developed crime model is then used in predicting the United States (US) crime rate data. The experimental results show that F-test and T-test yield a significant improvement in terms of quantitative error towards the GTB crime model while a sign test shows the opposites. The contribution of this study is to highlight the importance of identifying significant factors that affect the accuracy of the crime model in forecasting crime rate. [ABSTRACT FROM AUTHOR]

Published
2024
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21. Adaptive Neuro Integral Sliding Mode Control on Synchronization of Two Robot Manipulators

Author

Esmaili, Parvaneh, Haron, Habibollah, Hutchison, David, Series Editor, Kanade, Takeo, Series Editor, Kittler, Josef, Series Editor, Kleinberg, Jon M., Series Editor, Mattern, Friedemann, Series Editor, Mitchell, John C., Series Editor, Naor, Moni, Series Editor, Pandu Rangan, C., Series Editor, Steffen, Bernhard, Series Editor, Terzopoulos, Demetri, Series Editor, Tygar, Doug, Series Editor, Weikum, Gerhard, Series Editor, Nguyen, Ngoc Thanh, editor, Papadopoulos, George A., editor, Jędrzejowicz, Piotr, editor, Trawiński, Bogdan, editor, and Vossen, Gottfried, editor

Published
2017
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22. Metaheuristic Optimization on Conventional Freeman Chain Code Extraction Algorithm for Handwritten Character Recognition

Author

Mohamad, Muhammad A., Haron, Habibollah, Hasan, Haswadi, Hutchison, David, Series editor, Kanade, Takeo, Series editor, Kittler, Josef, Series editor, Kleinberg, Jon M., Series editor, Mattern, Friedemann, Series editor, Mitchell, John C., Series editor, Naor, Moni, Series editor, Pandu Rangan, C., Series editor, Steffen, Bernhard, Series editor, Terzopoulos, Demetri, Series editor, Tygar, Doug, Series editor, Weikum, Gerhard, Series editor, Nguyen, Ngoc Thanh, editor, Tojo, Satoshi, editor, Nguyen, Le Minh, editor, and Trawiński, Bogdan, editor

Published
2017
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24. An Overview of Retinal Blood Vessels Segmentation

Author

Jusoh, Fatimatufaridah, Haron, Habibollah, Ibrahim, Roliana, Che Azemin, Mohd Zulfaezal, Sulaiman, Hamzah Asyrani, editor, Othman, Mohd Azlishah, editor, Othman, Mohd Fairuz Iskandar, editor, Rahim, Yahaya Abd, editor, and Pee, Naim Che, editor

Published
2016
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25. An Enhanced Ant Colony System Algorithm Based on Subpaths for Solving the Capacitated Vehicle Routing Problem.

Author

Ahmed, Zakir Hussain, Hameed, Asaad Shakir, Mutar, Modhi Lafta, and Haron, Habibollah

Subjects
VEHICLE routing problem, ROUTING algorithms, ANT algorithms, SIMULATED annealing, METAHEURISTIC algorithms, POLYNOMIAL time algorithms, NP-hard problems, ALGORITHMS
Abstract

The capacitated vehicle routing problem (CVRP) is regarded as an NP-hard problem. Moreover, the CVRP is described as a model that can be used in many applications such as transport, logistics, and distribution. The exact algorithms can find exact optimal solutions on the small-sized problem instances; however, for large-sized instances it is difficult to find the exact optimal solutions in polynomial time. This reason motivated the researchers to present heuristic/metaheuristic algorithms to solve large-sized problem instances within a reasonable computational time. One of the good algorithms that deal with the CVRP is the ant colony optimization (ACO) algorithm. Several ACO algorithms have been suggested in the literature, such as the ant system (AS) algorithm, ant colony system (ACS) algorithm, and so on. On the other hand, ACO is designed to solve the path problem that finds the best way. However, this algorithm still lacks exploratory mechanisms, which results in premature convergence and stagnation issues. Therefore, we propose to develop an enhanced ACS (EACS) algorithm for solving the CVRP based on subpaths. In our proposed algorithm, we propose to utilize the K-nearest neighbour (KNN) algorithm for finding the best initial solution and then enhance the diversity mechanism of the proposed algorithm by avoiding the generation of the same solution using subpaths. This uses the diversity of the generated solution to find a better solution with a shorter route in a reasonable amount of computational time. Furthermore, we propose to apply the three-opt algorithm to the completed subtour and the k-opt algorithm to the subpath gained from the experience of the subpath. Finally, to verify the effectiveness of the proposed EACS algorithm, the algorithm is tested on some CVRP instances and is compared with one of the state-of-the-art methods, namely, the enhanced simulated annealing algorithm. The comparative study showed a better performance of our EACS compared to the enhanced simulated annealing algorithm. [ABSTRACT FROM AUTHOR]

Published
2023
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26. Performance of Different Techniques Applied in Genetic Algorithm towards Benchmark Functions

Author

Lim, Seng Poh, Haron, Habibollah, Hutchison, David, editor, Kanade, Takeo, editor, Kittler, Josef, editor, Kleinberg, Jon M., editor, Mattern, Friedemann, editor, Mitchell, John C., editor, Naor, Moni, editor, Nierstrasz, Oscar, editor, Pandu Rangan, C., editor, Steffen, Bernhard, editor, Sudan, Madhu, editor, Terzopoulos, Demetri, editor, Tygar, Doug, editor, Vardi, Moshe Y., editor, Weikum, Gerhard, editor, Goebel, Randy, editor, Siekmann, Jörg, editor, Wahlster, Wolfgang, editor, Selamat, Ali, editor, Nguyen, Ngoc Thanh, editor, and Haron, Habibollah, editor

Published
2013
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39. False Positives Reduction on Segmented Multiple Sclerosis Lesions Using Fuzzy Inference System by Incorporating Atlas Prior Anatomical Knowledge: A Conceptual Model

Author

Khastavaneh, Hassan, Haron, Habibollah, Hutchison, David, Series editor, Kanade, Takeo, Series editor, Kittler, Josef, Series editor, Kleinberg, Jon M., Series editor, Kobsa, Alfred, Series editor, Mattern, Friedemann, Series editor, Mitchell, John C., Series editor, Naor, Moni, Series editor, Nierstrasz, Oscar, Series editor, Pandu Rangan, C., Series editor, Steffen, Bernhard, Series editor, Terzopoulos, Demetri, Series editor, Tygar, Doug, Series editor, Weikum, Gerhard, Series editor, Goebel, Randy, Series editor, Tanaka, Yuzuru, Series editor, Wahlster, Wolfgang, Series editor, Siekmann, Jörg, Series editor, Hwang, Dosam, editor, Jung, Jason J., editor, and Nguyen, Ngoc-Thanh, editor

Published
2014
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40. Applying NURBS Surfaces Approximation with Different Parameterization Methods on CKSOM Model Closed Surfaces Data

Author

Lim, Seng Poh, Haron, Habibollah, Hutchison, David, Series editor, Kanade, Takeo, Series editor, Kittler, Josef, Series editor, Kleinberg, Jon M., Series editor, Kobsa, Alfred, Series editor, Mattern, Friedemann, Series editor, Mitchell, John C., Series editor, Naor, Moni, Series editor, Nierstrasz, Oscar, Series editor, Pandu Rangan, C., Series editor, Steffen, Bernhard, Series editor, Terzopoulos, Demetri, Series editor, Tygar, Doug, Series editor, Weikum, Gerhard, Series editor, Elmoataz, Abderrahim, editor, Lezoray, Olivier, editor, Nouboud, Fathallah, editor, and Mammass, Driss, editor

Published
2014
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41. Artificial Neural Network Based Prediction Model of the Sliding Mode Control in Coordinating Two Robot Manipulators

Author

Esmaili, Parvaneh, Haron, Habibollah, Hutchison, David, editor, Kanade, Takeo, editor, Kittler, Josef, editor, Kleinberg, Jon M., editor, Mattern, Friedemann, editor, Mitchell, John C., editor, Naor, Moni, editor, Nierstrasz, Oscar, editor, Pandu Rangan, C., editor, Steffen, Bernhard, editor, Sudan, Madhu, editor, Terzopoulos, Demetri, editor, Tygar, Doug, editor, Vardi, Moshe Y., editor, Weikum, Gerhard, editor, Goebel, Randy, editor, Siekmann, Jörg, editor, Wahlster, Wolfgang, editor, Nguyen, Ngoc Thanh, editor, Attachoo, Boonwat, editor, Trawiński, Bogdan, editor, and Somboonviwat, Kulwadee, editor

Published
2014
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43. A New Framework in Solving Tailing and Necking Problems of Thinned Binary Image

Author

Abu Bakar, Sabarina, Yuhaniz, SitiSophiayati, Majid, HairudinAbd, Haron, Habibollah, Akan, Ozgur, Series editor, Bellavista, Paolo, Series editor, Cao, Jiannong, Series editor, Dressler, Falko, Series editor, Ferrari, Domenico, Series editor, Gerla, Mario, Series editor, Kobayashi, Hisashi, Series editor, Palazzo, Sergio, Series editor, Sahni, Sartaj, Series editor, Shen, Xuemin (Sherman), Series editor, Stan, Mircea, Series editor, Xiaohua, Jia, Series editor, Zomaya, Albert, Series editor, Coulson, Geoffrey, Series editor, Das, Vinu V., editor, Ariwa, Ezendu, editor, and Rahayu, Syarifah Bahiyah, editor

Published
2012
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44. The Mapping Algorithm of Triangular Vertex Chain Code from Thinned Binary Image

Author

Wulandhari, Lili A., Haron, Habibollah, Sallehuddin, Roselina, Akan, Ozgur, Series editor, Bellavista, Paolo, Series editor, Cao, Jiannong, Series editor, Dressler, Falko, Series editor, Ferrari, Domenico, Series editor, Gerla, Mario, Series editor, Kobayashi, Hisashi, Series editor, Palazzo, Sergio, Series editor, Sahni, Sartaj, Series editor, Shen, Xuemin (Sherman), Series editor, Stan, Mircea, Series editor, Xiaohua, Jia, Series editor, Zomaya, Albert, Series editor, Coulson, Geoffrey, Series editor, Das, Vinu V., editor, Ariwa, Ezendu, editor, and Rahayu, Syarifah Bahiyah, editor

Published
2012
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45. Logical Heuristic Algorithm in Extracting 2D Structure Thinned Binary Image into Freeman Chain Code (FCC)

Author

Hasan, Haswadi, Haron, Habibollah, Hashim, Siti Zaiton, Syakirin Omar, Fakhrul, Hutchison, David, editor, Kanade, Takeo, editor, Kittler, Josef, editor, Kleinberg, Jon M., editor, Mattern, Friedemann, editor, Mitchell, John C., editor, Naor, Moni, editor, Nierstrasz, Oscar, editor, Pandu Rangan, C., editor, Steffen, Bernhard, editor, Sudan, Madhu, editor, Terzopoulos, Demetri, editor, Tygar, Doug, editor, Vardi, Moshe Y., editor, Weikum, Gerhard, editor, Badioze Zaman, Halimah, editor, Robinson, Peter, editor, Petrou, Maria, editor, Olivier, Patrick, editor, Schröder, Heiko, editor, and Shih, Timothy K., editor

Published
2009
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48. Hybrid Neighbourhood Component Analysis with Gradient Tree Boosting for Feature Selection in Forecasting Crime Rate.

Author

Khairuddin, Alif Ridzuan, Alwee, Razana, and Haron, Habibollah

Subjects
CRIME forecasting, CRIME prevention, CRIME statistics, NEIGHBORHOODS, LITERATURE reviews, MEASUREMENT errors
Abstract

Crime forecasting is beneficial as it provides valuable information to the government and authorities in planning an efficient crime prevention measure. Most criminology studies found that influence from several factors, such as social, demographic, and economic factors, significantly affects crime occurrence. Therefore, most criminology experts and researchers study and observe the effect of factors on criminal activities as it provides relevant insight into possible future crime trends. Based on the literature review, the applications of proper analysis in identifying significant factors that influence crime are scarce and limited. Therefore, this study proposed a hybrid model that integrates Neighbourhood Component Analysis (NCA) with Gradient Tree Boosting (GTB) in modelling the United States (US) crime rate data. NCA is a feature selection technique used in this study to identify the significant factors influencing crime rate. Once the significant factors were identified, an artificial intelligence technique, i.e., GTB, was implemented in modelling the crime data, where the crime rate value was predicted. The performance of the proposed model was compared with other existing models using quantitative measurement error analysis. Based on the result, the proposed NCA-GTB model outperformed other crime models in predicting the crime rate. As proven by the experimental result, the proposed model produced the smallest quantitative measurement error in the case study. [ABSTRACT FROM AUTHOR]

Published
2023
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