- I am an academician with eighteen years of post-PhD teaching experience in Pakistan and Saudi Arabia and a total of 3... moreI am an academician with eighteen years of post-PhD teaching experience in Pakistan and Saudi Arabia and a total of 30 years of post-graduate work experience. I did my PhD in computer science from France, in April 2000. I am a researcher with 2 monographs, 48 journal publications, and 62 conference papers. I am a PhD supervisor, approved by Higher Education Commission of Pakistan. I have successfully supervised 8 PhD theses, several MS theses, research surveys, research projects, and undergraduate projects.I have obtained and successfully completed 3 funded research projects.I have established a research group focused on computational intelligence and knowledge discovery. My research interests are in the areas of intelligent systems, soft computing, machine learning and data mining.edit
As social media and e-commerce on the Internet continue to grow, opinions have become one of the most important sources of information for users to base their future decisions on. Unfortunately, the large quantities of opinions make it... more
As social media and e-commerce on the Internet continue to grow, opinions have become one of the most important sources of information for users to base their future decisions on. Unfortunately, the large quantities of opinions make it difficult for an individual to comprehend and evaluate them all in a reasonable amount of time. The users have to read a large number of opinions of different entities before making any decision. Recently a new retrieval task in information retrieval known as Opinion-Based Entity Ranking (OpER) has emerged. OpER directly ranks relevant entities based on how well opinions on them are matched with a user's preferences that are given in the form of queries. With such a capability, users do not need to read a large number of opinions available for the entities. Previous research on OpER does not take into account the importance and subjectivity of query keywords in individual opinions of an entity. Entity relevance scores are computed primarily on the basis of occurrences of query keywords match, by assuming all opinions of an entity as a single field of text. Intuitively, entities that have positive judgments and strong relevance with query keywords should be ranked higher than those entities that have poor relevance and negative judgments. This paper outlines several ranking features and develops an intuitive framework for OpER in which entities are ranked according to how well individual opinions of entities are matched with the user's query keywords. As a useful ranking model may be constructed from many ranking features, we apply learning to rank approach based on genetic programming (GP) to combine features in order to develop an effective retrieval model for OpER task. The proposed approach is evaluated on two collections and is found to be significantly more effective than the standard OpER approach.
Association rules mining and classification rules discovery are two important data mining techniques used to expose the relations among large sets of data items. The technique aims to find out the rules that satisfy the predefined minimum... more
Association rules mining and classification rules discovery are two important data mining techniques used to expose the relations among large sets of data items. The technique aims to find out the rules that satisfy the predefined minimum support and the confidence. Association rules mining has successfully been implemented in biomedical research and has demonstrated encouraging results in analysing the gene expression data in order to discover the relevant biological association among different genes, gene expression, and various protein properties like protein functionality and sequence similarity. In this paper, we applied the association rule mining technique – the ACO-AC to the problem of classifying proteins into its correct fold of the SCOP dataset. The technique combines the association rules mining and supervised classification mechanism using ant colony optimisation. Experimental results reveal the classifier performance in protein classification problem as excellent by identifying most accurate and compact rules. Waseem Shahzad received his PhD at NUCES Islamabad, Pakistan and is currently working as an Assistant Professor in the same university. He has research interests in the fields of data mining, computational intelligence, machine learning, theory of computation and soft computing. He has several research publications in these areas. Abdul Rauf Baig is a Professor at Al Imam Mohammad Ibn Saud Islamic University (IMSIU), Saudi Arabia. He is also associated with NUCES, Islamabad, Pakistan. He received his PhD in Computer Science from University of Rennes-1, France. His research interests are in the areas of computational intelligence and machine learning. He has several research articles published in international journals and has completed many R&D projects. He is also a reviewer for several reputed international journals.
Security threat from senseless terrorist attacks on unarmed civilians is a major concern in today's society. The recent developments in data technology allow us to have scalable and flexible data capture, storage, processing and... more
Security threat from senseless terrorist attacks on unarmed civilians is a major concern in today's society. The recent developments in data technology allow us to have scalable and flexible data capture, storage, processing and analytics. We can utilize these capabilities to help us in dealing with our security related problems. This paper gives a new meaning to behavioral analytics and introduces a new opportunity for analytics in a typical university setting using data that is already present and being utilized in a university environment. We propose the basics of a system based on Big Data technologies that can be used to monitor students and predict whether some of them are becoming prone to deviant ideologies that may lead to terrorism.
This work addresses the problem of profiling drivers based on their driving features. A purpose-built hardware integrated with a software tool is used to record data from multiple drivers. The recorded data is then profiled using... more
This work addresses the problem of profiling
drivers based on their driving features. A purpose-built hardware
integrated with a software tool is used to record data
from multiple drivers. The recorded data is then profiled
using clustering techniques. k-means has been used for
clustering and the results are counterchecked with Fuzzy
c-means (FCM) and Model Based Clustering (MBC). Based
on the results of clustering, a classifier, i.e., an Artificial
Neural Network (ANN) is trained to classify a driver during
driving in one of the four discovered clusters (profiles). The
performance of ANN is compared with that of a Support
Vector Machine (SVM). Comparison of the clustering techniques
shows that different subsets of the recorded dataset
with a diverse combination of attributes provide approximately
the same number of profiles, i.e., four. Analysis of
features shows that average speed, maximum speed, number
of times brakes were applied, and number of times horn
was used provide the information regarding drivers’ driving
behavior, which is useful for clustering. Both one versus ne (SVM) and one versus rest (SVM) method for classification
have been applied. Average accuracy and average
mean square error achieved in the case of ANN was 84.2 %
and 0.05 respectively. Whereas the average performance for
SVM was 47 %, the maximum performance was 86 % using
RBF kernel. The proposed system can be used in modern
vehicles for early warning system, based on drivers’ driving
features, to avoid accidents.
drivers based on their driving features. A purpose-built hardware
integrated with a software tool is used to record data
from multiple drivers. The recorded data is then profiled
using clustering techniques. k-means has been used for
clustering and the results are counterchecked with Fuzzy
c-means (FCM) and Model Based Clustering (MBC). Based
on the results of clustering, a classifier, i.e., an Artificial
Neural Network (ANN) is trained to classify a driver during
driving in one of the four discovered clusters (profiles). The
performance of ANN is compared with that of a Support
Vector Machine (SVM). Comparison of the clustering techniques
shows that different subsets of the recorded dataset
with a diverse combination of attributes provide approximately
the same number of profiles, i.e., four. Analysis of
features shows that average speed, maximum speed, number
of times brakes were applied, and number of times horn
was used provide the information regarding drivers’ driving
behavior, which is useful for clustering. Both one versus ne (SVM) and one versus rest (SVM) method for classification
have been applied. Average accuracy and average
mean square error achieved in the case of ANN was 84.2 %
and 0.05 respectively. Whereas the average performance for
SVM was 47 %, the maximum performance was 86 % using
RBF kernel. The proposed system can be used in modern
vehicles for early warning system, based on drivers’ driving
features, to avoid accidents.
This paper presents an evolutionary algorithm based technique to solve multi-objective feature subset selection problem. The data used for classification contains large number of features called attributes. Some of these attributes are... more
This paper presents an evolutionary algorithm based technique to solve multi-objective feature subset selection problem. The data used for classification contains large number of features called attributes. Some of these attributes are not relevant and needs to be eliminated. In classification procedure, each feature has an effect on the accuracy, cost and learning time of the classifier. So, there is a strong requirement to select a subset of the features before building the classifier. This proposed technique treats feature subset selection as multi-objective optimization problem. This research uses one of the latest multi-objective genetic algorithms (NSGA-II). The fitness value of a particular feature subset is measured by using ID3. The testing accuracy acquired is then assigned to the fitness value. This technique is tested on several datasets taken from the UCI machine repository. The experiments demonstrate the feasibility of using NSGA-II for feature subset selection.
Imperfection of information is a part of our daily life; however, it is usually ignored in learning based on evolutionary approaches. In this paper we develop an Imperfect Evolutionary System that provides an uncertain and chaotic... more
Imperfection of information is a part of our daily life; however, it is usually ignored in learning based on evolutionary approaches. In this paper we develop an Imperfect Evolutionary System that provides an uncertain and chaotic imperfect environment that presents new challenges to its habitants. We then propose an intelligent methodology which is capable of learning in such environments. Detecting changes and adapting to the new environment is crucial to exploring the search space and exploiting any new opportunities that may arise. To deal with these uncertain and challenging environments, we propose a novel change detection strategy based on a Particle Swarm Optimization system which is hybridized with an Artificial Neural Network. This approach maintains a balance between exploitation and exploration during the search process. A comparison of approaches using different Particle Swarm Optimization algorithms show that the ability of our learning approach to detect changes and adapt as per the new demands of the environment is high.
With computers becoming ubiquitous and high resolution graphics reaching the next level, computer games have become a major source of entertainment. It has been a tedious task for game developers to measure the entertainment value of the... more
With computers becoming ubiquitous and high resolution graphics reaching the next level, computer games have become a major source of entertainment. It has been a tedious task for game developers to measure the entertainment value of the computer games. The entertainment value of a game does depend upon the genre of the game in addition to the game contents. In this paper, we propose a set of entertainment metrics for the platform genre of games. The set of entertainment metrics is proposed based upon certain theories on entertainment in computer games. To test the metrics, we use an evolutionary algorithm for automated generation of game rules which are entertaining. The proposed approach starts with an initial set of randomly generated games and, based upon the proposed metrics as an objective function, guides the evolutionary process. The results produced are counterchecked against the entertainment criteria of humans by conducting a human user survey and a controller learning ability experiment. The proposed metrics and the evolutionary process of generating games can be employed by any platform game for the purpose of automatic generation of interesting games provided an initial search space is given.
In this study we report our research on learning an accurate and easily interpretable classifier model for authorship classification of typewritten digital texts. For this purpose we use Ant Colony Optimization; a meta-heuristic based on... more
In this study we report our research on learning an accurate and easily interpretable classifier model for authorship classification of typewritten digital texts. For this purpose we use Ant Colony Optimization; a meta-heuristic based on swarm intelligence. Unlike black box type classifiers, the decision making rules produced by the proposed method are understandable by people familiar to the domain and can be easily enhanced with the addition of domain knowledge. Our experimental results show that the method is feasible and more accurate than decision trees.
In this article, a novel unordered classification rule list discovery algorithm is presented based on Ant Colony Optimization (ACO). The proposed classifier is compared empirically with two other ACO-based classification techniques on 26... more
In this article, a novel unordered classification rule list discovery algorithm is presented based on
Ant Colony Optimization (ACO). The proposed classifier is compared empirically with two other ACO-based
classification techniques on 26 data sets, selected from miscellaneous domains, based on several performance
measures. As opposed to its ancestors, our technique has the flexibility of generating a list of IF-THEN rules with
unrestricted order. It makes the generated classification model more comprehensible and easily interpretable.
The results indicate that the performance of the proposed method is statistically significantly better as compared
with previous versions of AntMiner based on predictive accuracy and comprehensibility of the classification
model.
Ant Colony Optimization (ACO). The proposed classifier is compared empirically with two other ACO-based
classification techniques on 26 data sets, selected from miscellaneous domains, based on several performance
measures. As opposed to its ancestors, our technique has the flexibility of generating a list of IF-THEN rules with
unrestricted order. It makes the generated classification model more comprehensible and easily interpretable.
The results indicate that the performance of the proposed method is statistically significantly better as compared
with previous versions of AntMiner based on predictive accuracy and comprehensibility of the classification
model.
Games have always been a popular test bed for artificial intelligence techniques. Game developers are always in constant search for techniques that can automatically create computer games minimizing the developer's task. In this work we... more
Games have always been a popular test bed for artificial intelligence techniques. Game developers are always in constant search for techniques that can automatically create computer games minimizing the developer's task. In this work we present an evolutionary strategy based solution towards the automatic generation of two player board games. To guide the evolutionary process towards games, which are entertaining, we propose a set of metrics. These metrics are based upon different theories of entertainment in computer games. This work also compares the entertainment value of the evolved games with the existing popular board based games. Further to verify the entertainment value of the evolved games with the entertainment value of the human user a human user survey is conducted. In addition to the user survey we check the learnability of the evolved games using an artificial neural network based controller. The proposed metrics and the evolutionary process can be employed for generating new and entertaining board games, provided an initial search space is given to the evolutionary algorithm.
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There exist numerous state of the art classification algorithms that are designed to handle the data with nominal or binary class labels. Unfortunately, less attention is given to the genre of classification problems where the classes are... more
There exist numerous state of the art classification algorithms that are designed to handle the data with nominal or binary class labels. Unfortunately, less attention is given to the genre of classification problems where the classes are organized as a structured hierarchy; such as protein function prediction (target area in this work), test scores, gene ontology, web page categorization, text categorization etc. The structured hierarchy is usually represented as a tree or a directed acyclic graph (DAG) where there exist IS-A relationship among the class labels. Class labels at upper level of the hierarchy are more abstract and easy to predict whereas class labels at deeper level are most specific and challenging for correct prediction. It is helpful to consider this class hierarchy for designing a hypothesis that can handle the tradeoff between prediction accuracy and prediction specificity. In this paper, a novel ant colony optimization (ACO) based single path hierarchical classification algorithm is proposed that incorporates the given class hierarchy during its learning phase. The algorithm produces IF–THEN ordered rule list and thus offer comprehensible classification model. Detailed discussion on the architecture and design of the proposed technique is provided which is followed by the empirical evaluation on six ion-channels data sets (related to protein function prediction) and two publicly available data sets. The performance of the algorithm is encouraging as compared to the existing methods based on the statistically significant Student's t-test (keeping in view, prediction accuracy and specificity) and thus confirm the promising ability of the proposed technique for hierarchical classification task.
This paper introduces a two-stage strategy for multi-class classification problems. The proposed technique is an advancement of tradition binary decomposition method. In the first stage, the classifiers are trained for each class versus... more
This paper introduces a two-stage strategy for multi-class classification problems. The proposed technique is an advancement of tradition binary decomposition method. In the first stage, the classifiers are trained for each class versus the remaining classes. A modified fitness value is used to select good discriminators for the imbalanced data. In the second stage, the classifiers are integrated and treated as a single chromosome that can classify any of the classes from the dataset. A population of such classifier-chromosomes is created from good classifiers (for individual classes) of the first phase. This population is evolved further, with a fitness that combines accuracy and conflicts. The proposed method encourages the classifier combination with good discrimination among all classes and less conflicts. The two-stage learning has been tested on several benchmark datasets and results are found encouraging.
The primary objective of this research is to propose and investigate a novel ant colony optimization-based classification rule discovery algorithm and its variants. The main feature of this algorithm is a new heuristic function based on... more
The primary objective of this research is to propose and investigate a novel ant colony optimization-based classification rule discovery algorithm and its variants. The main feature of this algorithm is a new heuristic function based on the correlation between attributes of a dataset. Several aspects and parameters of the proposed algorithm are investigated by experimentation on a number of benchmark datasets. We study the performance of our proposed approach and compare it with several state-of-the art commonly used classification algorithms. Experimental results indicate that the proposed approach builds more accurate models than the compared algorithms. The high accuracy supplemented by the comprehensibility of the discovered rule sets is the main advantage of this method.
This paper presents a comparison of evolutionary algorithm based technique and swarm based technique to solve multi-objective feature subset selection problem. The data used for classification contains large number of features called... more
This paper presents a comparison of evolutionary algorithm based technique and swarm based technique to solve multi-objective feature subset selection problem. The data used for classification contains large number of features called attributes. Some of these attributes are not significant and need to be removed. In the process of classification, a feature effects accuracy, cost and learning time of the classifier. So, before building a classifier there is a strong need to choose a subset of the attributes (features). This research treats feature subset selection as multi-objective optimization problem. The latest multi-objective techniques have been used for the comparison of evolutionary and swarm based algorithms. These techniques are Non-dominated Sorting Genetic Algorithms (NSGA – II) and Multi-objective Particle Swarm Optimization (MOPSO).MOPSO has also been converted into Binary MOPSO (BMOPSO) in order to deal with feature subset selection. The fitness value of a particular feature subset is measured by using ID3. The testing accuracy acquired is then assigned to the fitness value. The techniques are tested on several datasets taken from the UCI machine repository. The experiments demonstrate the feasibility of treating feature subset selection as multi-objective problem. NSGA-II has proved to be a better option for solving feature subset selection problem than BMOPSO.
This research presents an optimization technique for multiple routes generation using simulated niche based particle swarm optimization for dynamic online route planning, optimization of the routes and proved to be an effective technique.... more
This research presents an optimization technique for multiple routes generation using simulated niche based particle swarm optimization for dynamic online route planning, optimization of the routes and proved to be an effective technique. It effectively deals with route planning in dynamic and unknown environments cluttered with obstacles and objects. A simulated niche based particle swarm optimization (SN-PSO) is proposed using modified particle swarm optimization algorithm for dealing with online route planning and is tested for randomly generated environments , obstacle ratio, grid sizes, and complex environments. The conventional techniques perform well in simple and less cluttered environments while their performance degrades with large and complex environments. The SN-PSO generates and optimizes multiple routes in complex and large environments with constraints. The traditional route optimization techniques focus on good solutions only and do not exploit the solution space completely. The SN-PSO is proved to be an efficient technique for providing safe, short, and feasible routes under dynamic constraints. The efficiency of the SN-PSO is tested in a mine field simulation with different environment configurations and successfully generates multiple feasible routes.
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Evolutionary algorithms (EA) have been used in data classification and data clustering task since the advent of these algorithms. Nonlinear complex optimization problems have been the area of interest since very long time. The EA have... more
Evolutionary algorithms (EA) have been used in data classification and data clustering task since the advent of these algorithms. Nonlinear complex optimization problems have been the area of interest since very long time. The EA have been applied successfully on these optimization problems. The evolutionary algorithms suffer a lot due to their slow convergence rate, mainly due to evolutionary nature of these algorithms. This paper presents a new mutation scheme for opposition based genetic algorithms (OGA-CM). This scheme tunes the population during evolutionary process effectively by using Cauchy Mutation (CM). The performance of the algorithm is tested over suit of 5 functions. Opposition based Genetic Algorithm (OGA) is used as competitor algorithm to compare the results of the proposed algorithm. The results show that the proposed method outperforms GA and OGA for most of the test functions.
Research Interests: Information Science, Evolutionary algorithms, Evolutionary Computation, Genetic Algorithms, Ant Colony Optimization, and 13 moreNeural Networks, Clustering Algorithms, Convergence, Genetic Algorithm, Learning, Optimization Problem, Convergence Rate, Data Clustering, Evolutionary Algorithm, Robustness, Data classification, Area of Interest, and Function optimization
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92 Int. J. Information Technology, Communications and Convergence, Vol. 1, No. 1, 2010 ... Measuring entertainment and automatic generation of entertaining games ... Zahid Halim*, A. Rauf Baig and Hasan Mujtaba ... FAST-National... more
92 Int. J. Information Technology, Communications and Convergence, Vol. 1, No. 1, 2010 ... Measuring entertainment and automatic generation of entertaining games ... Zahid Halim*, A. Rauf Baig and Hasan Mujtaba ... FAST-National University of Computer and Emerging ...
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... efficient Page 5. 508 S. Bashir and AR Baig bit-vector projection technique. In this paper we present a novel efficient bit-vector projection technique, which is better than the previous projected bitmap projection technique. The ...
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F AbstractHuman beings learn to do a task and then go on to learn some other task. However, they do not forget the previous learning. If need arises, they can call upon their previous learning and do not have to relearn from scratch... more
F AbstractHuman beings learn to do a task and then go on to learn some other task. However, they do not forget the previous learning. If need arises, they can call upon their previous learning and do not have to relearn from scratch again. In this paper, we build upon our ...
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Games have always been a popular test bed for artificial intelligence techniques. Game developers are always in constant search for techniques that can automatically create computer games minimizing the developer's task. In this work... more
Games have always been a popular test bed for artificial intelligence techniques. Game developers are always in constant search for techniques that can automatically create computer games minimizing the developer's task. In this work we present an evolutionary strategy based solution towards the automatic generation of two player board games. To guide the evolutionary process towards games, which are entertaining, we propose a set of metrics. These metrics are based upon different theories of entertainment in computer games. This work also compares the entertainment value of the evolved games with the existing popular board based games. Further to verify the entertainment value of the evolved games with the entertainment value of the human user a human user survey is conducted. In addition to the user survey we check the learnability of the evolved games using an artificial neural network based controller. The proposed metrics and the evolutionary process can be employed for ge...
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In this study we describe a method for extending particle swarm optimization. We have presented a novel approach for avoiding premature convergence to local minima by the introduction of diversity in the swarm. The swarm is made more... more
In this study we describe a method for extending particle swarm optimization. We have presented a novel approach for avoiding premature convergence to local minima by the introduction of diversity in the swarm. The swarm is made more diverse and is encouraged to explore by employing a mechanism which allows each particle to use a different equation to update its velocity. This equation is also continuously evolved through the use of genetic programming to ensure adaptability. We compare two variations of our algorithm, one utilizing random initialization while in the second one we utilize partial non-random initalization which forces some particles to use the standard PSO velocity update equation. Results from experimentation suggest that the modified PSO with complete random initialization shows promise and has potential for improvement. It is particularly very good at finding the exact optimum.
Data classification has received increasing interest lately. It is a challenging task due to uncertainty, unpredictability and inconsistency of data. This challenge increases in the case of multi-class classification. Genetic Programming... more
Data classification has received increasing interest lately. It is a challenging task due to uncertainty, unpredictability and inconsistency of data. This challenge increases in the case of multi-class classification. Genetic Programming (GP) has shown promising results as an efficient and robust classification strategy. For multiclass classification, multi-tree chromosome classifiers can be used, where each tree is an arithmetic expression that discriminates between one and rest of the classes. In this paper, we have emphasized fitness of an individual tree in multi-tree classifiers which adds to the fitness of whole chromosome and results in better classifier efficiency. A series of experiments have been conducted to support the efficiency of proposed algorithm and the results have been found encouraging.
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This experimental study investigated the effect of the use of video games based on the curriculum on students' performance levels in Fourth, Fifth and Sixth grade Mathematics compared to traditional learning methods. The participants... more
This experimental study investigated the effect of the use of video games based on the curriculum on students' performance levels in Fourth, Fifth and Sixth grade Mathematics compared to traditional learning methods. The participants were seven hundred and eighty-nine female students from Fourth, Fifth and Sixth grades, and nineteen teachers, from different six schools in Riyadh city in Saudi Arabia. Three research null hypotheses were tested to explore students’ performance when they received two different instructional treatments: traditional learning methods (textbooks or worksheets) and a video game based on the Mathematics curriculum. The results indicate that video games based on Mathematics curriculum had a positively effect on students’ performance based on their score average in standard tests, when compared to traditional learning methods.
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Research Interests: Computer Science, Relational Database, Data Mining, Graph Theory, Complexity Theory, and 15 moreDatabase Management Systems, Data Warehouse, Communication model, Pattern Mining, Social Networking Services, Graph Mining, Algorithm Design, Internet technology, Routing Protocol, Protein Complex Detection, Approximate Algorithm, Social Network, Area of Interest, Database System, and Probability Distribution
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Games have always been a popular test bed for artificial intelligence techniques. Game developers are always in constant search for techniques that can automatically create computer games minimizing the developer's task. In this work... more
Games have always been a popular test bed for artificial intelligence techniques. Game developers are always in constant search for techniques that can automatically create computer games minimizing the developer's task. In this work we present an evolutionary strategy based solution towards the automatic generation of two player board games. To guide the evolutionary process towards games, which are entertaining, we propose a set of metrics. These metrics are based upon different theories of entertainment in computer games. This work also compares the entertainment value of the evolved games with the existing popular board based games. Further to verify the entertainment value of the evolved games with the entertainment value of the human user a human user survey is conducted. In addition to the user survey we check the learnability of the evolved games using an artificial neural network based controller. The proposed metrics and the evolutionary process can be employed for gen...
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Abstract. In this study we present an extension to the PSOGP algorithm for multimodal optimization problems. PSOGP avoids premature convergence by utilizing a method wherein the swarm is made more diverse by employing a mechanism which... more
Abstract. In this study we present an extension to the PSOGP algorithm for multimodal optimization problems. PSOGP avoids premature convergence by utilizing a method wherein the swarm is made more diverse by employing a mechanism which allows each particle to use a ...
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ABSTRACT Swarm intelligence and evolutionary techniques are heavily used by the researchers to solve combinatorial and NP hard problems. The n-Queen problem is a combinatorial problem which become intractable for large values of ‘n’ and... more
ABSTRACT Swarm intelligence and evolutionary techniques are heavily used by the researchers to solve combinatorial and NP hard problems. The n-Queen problem is a combinatorial problem which become intractable for large values of ‘n’ and thus placed in NP (Non-Deterministic Polynomial) class problem. In this paper, a solution is proposed for n-Queen problem based on ACO (Ant Colony Optimization). The n-Queen problem is basically a generalized form of 8-Queen problem. In 8-Queen problem, the goal is to place eight queens such that no queen can kill the other using standard chess queen moves. The environment for the ants is a directed graph which we call search space is constructed for efficiently searching the valid placement of n-queens such that they do not harm each other. We also develop an intelligent heuristic function that helps in finding the solution very quickly and effectively. The paper contains the detail discussion of problem background, problem complexity, Ant Colony Optimization (Swarm Intelligence), proposed technique design and architecture and a fair amount of experimental results.
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ABSTRACT There exist numerous state of the art classification algorithms that are designed to handle the data with nominal or binary class labels. Unfortunately, less attention is given to the genre of classification problems where the... more
ABSTRACT There exist numerous state of the art classification algorithms that are designed to handle the data with nominal or binary class labels. Unfortunately, less attention is given to the genre of classification problems where the classes are organized as a structured hierarchy; such as protein function prediction (target area in this work), test scores, gene ontology, web page categorization, text categorization etc. The structured hierarchy is usually represented as a tree or a directed acyclic graph (DAG) where there exist IS-A relationship among the class labels. Class labels at upper level of the hierarchy are more abstract and easy to predict whereas class labels at deeper level are most specific and challenging for correct prediction. It is helpful to consider this class hierarchy for designing a hypothesis that can handle the tradeoff between prediction accuracy and prediction specificity. In this paper, a novel ant colony optimization (ACO) based single path hierarchical classification algorithm is proposed that incorporates the given class hierarchy during its learning phase. The algorithm produces IF–THEN ordered rule list and thus offer comprehensible classification model. Detailed discussion on the architecture and design of the proposed technique is provided which is followed by the empirical evaluation on six ion-channels data sets (related to protein function prediction) and two publicly available data sets. The performance of the algorithm is encouraging as compared to the existing methods based on the statistically significant Student's t-test (keeping in view, prediction accuracy and specificity) and thus confirm the promising ability of the proposed technique for hierarchical classification task.
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... Ils ouvrent des perspectives à la technique mise en œuvre pour qu'elle soit étendue à des bases de données multi-locuteurs et à un plus grand vocabulaire, ceci dans un contexte temps réel. Source / Source. ... Reconnaissance... more
... Ils ouvrent des perspectives à la technique mise en œuvre pour qu'elle soit étendue à des bases de données multi-locuteurs et à un plus grand vocabulaire, ceci dans un contexte temps réel. Source / Source. ... Reconnaissance parole. ; Reconnaissance image. ; ...
ABSTRACT Advent of Evolutionary algorithms (EA) is a major milestone in the field of data mining. Many research has been made to solve complicated mathematical and optimization problems since long. The Evolutionary Algorithm has been used... more
ABSTRACT Advent of Evolutionary algorithms (EA) is a major milestone in the field of data mining. Many research has been made to solve complicated mathematical and optimization problems since long. The Evolutionary Algorithm has been used effectively to resolve these optimization problems. Due to the evolutionary and stochastic nature of these algorithms, slow convergence rate is the major problem of these algorithms. We propose a new scheme to mutate the opposition Genetic Algorithm (GA). This technique is used to improve the population effectively by using the Gaussian Mutation (GM) and Cauchy Mutation (CM). Both the mutation schemes are used probabilistically. A suit of 5 optimization functions has been used to test the performance of the algorithm. The results are compared with Opposition based Genetic Algorithm (OGA) to evaluate the effectiveness of the presented algorithm. Proposed method shows results superior to GA and OGA for the majority of the test functions and shows comparable results over some functions.
Abstract. In this paper we present the details of a processing tech-nique utilised to extract parameters from the images of a talking person's mouth region. These parameters are converted into... more
Abstract. In this paper we present the details of a processing tech-nique utilised to extract parameters from the images of a talking person's mouth region. These parameters are converted into impulse sequences for a given series of images. Spatio-temporal ST coding of the impulses ...
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ABSTRACT The abilities to accept new information from the environment and use it to update our existing knowledge thus adapting to the changes of our environment have played a crucial role in the success of human beings as a species.... more
ABSTRACT The abilities to accept new information from the environment and use it to update our existing knowledge thus adapting to the changes of our environment have played a crucial role in the success of human beings as a species. Incorporating these abilities in machines has been an age long desire of artificial intelligence. In this paper, we present a learning technique based on evolutionary approaches that enables artificial agents to detect changes in their environment and adapt accordingly. Our focus is on enabling the agents to learn new tasks without any human intervention, relying only on stimulus from their environment. We argue that learning in such a dynamic environment should be a continuous process and past experiences must be retained for future scenar-ios. The learning method itself provides a mechanism where the decrease in performance, forced by the change in goals, triggers new learning. We conduct experimentation to show how this approach works and results from these experiments are very encouraging.
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AbstractWith the rapid growth in business size, today's businesses orient towards electronic technologies. Amazon.com and e-bay.com are some of the major stakeholders in this regard. Unfortunately the enormous size and hugely... more
AbstractWith the rapid growth in business size, today's businesses orient towards electronic technologies. Amazon.com and e-bay.com are some of the major stakeholders in this regard. Unfortunately the enormous size and hugely unstructured data on the web, even ...
... Fahad Maqbool, Shariq Bashir, and A. Rauf Baig ... In this paper, we propose a novel algorithm E-MAP (Efficient Mining of Asynchronous Periodic Patterns) for efficient mining of asynchronous periodic patterns in large temporal... more
... Fahad Maqbool, Shariq Bashir, and A. Rauf Baig ... In this paper, we propose a novel algorithm E-MAP (Efficient Mining of Asynchronous Periodic Patterns) for efficient mining of asynchronous periodic patterns in large temporal datasets. ...
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AUTOMATIC GENERATION AND OPTIMIZATION OF FUZZY RULES Syed Atif Mehdiatif.mehdi@umt.edu.pk School of Science and Technology University of Management and Technology Lahore, Pakistan Dr. A. Rauf Baig rauf.baig@nu.edu.pk National University... more
AUTOMATIC GENERATION AND OPTIMIZATION OF FUZZY RULES Syed Atif Mehdiatif.mehdi@umt.edu.pk School of Science and Technology University of Management and Technology Lahore, Pakistan Dr. A. Rauf Baig rauf.baig@nu.edu.pk National University of ...
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Abstract. Classification rule discovery and association rules mining are two important data mining tasks. Association rules mining discovers all those rules from the training set that satisfies minimum support and confidence threshold... more
Abstract. Classification rule discovery and association rules mining are two important data mining tasks. Association rules mining discovers all those rules from the training set that satisfies minimum support and confidence threshold while classification rule mining ...
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... is not only more natural but also it is more feasible for many non-Latin languages (eg Chinese, Arabic, Persian) where the number ... In this paper, we extend the earlier work [3,4,7], on onlineisolated ... [5] R. Plamondon, and S.... more
... is not only more natural but also it is more feasible for many non-Latin languages (eg Chinese, Arabic, Persian) where the number ... In this paper, we extend the earlier work [3,4,7], on onlineisolated ... [5] R. Plamondon, and S. Srihari, On-Line and Off-Line Handwriting Recognition ...
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The goal of this work is to provide a purely neuronal solution with no preprocessing for on-line handwritten character recognition problem. The idea consists of util-ising the neurons enriched by a spatio-temporal (ST} coding developed in... more
The goal of this work is to provide a purely neuronal solution with no preprocessing for on-line handwritten character recognition problem. The idea consists of util-ising the neurons enriched by a spatio-temporal (ST} coding developed in our laboratory. The coding, defined in ...
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Abstract. In this paper we present the details of a processing tech-nique utilised to extract parameters from the images of a talking person's mouth region. These parameters are converted into... more
Abstract. In this paper we present the details of a processing tech-nique utilised to extract parameters from the images of a talking person's mouth region. These parameters are converted into impulse sequences for a given series of images. Spatio-temporal ST coding of the impulses ...
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AbstractComputer games have always been a source of entertainment for all age groups. From the point of view of game developer it has always been difficult to quantify the entertainment value of the human player, as the entertainment... more
AbstractComputer games have always been a source of entertainment for all age groups. From the point of view of game developer it has always been difficult to quantify the entertainment value of the human player, as the entertainment value is very subjective. The two factors ...