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Ayad T . Imam

Isra University, Data Science and Artificial Intelligence, Faculty Member

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Prof. Dr. Ayad Tareq Imam (also Known as Ayad Al-Zobaydi) received his PhD degree in Computer Science / Artificial Intelligence from De Montfort University, Leicester, the UK in 2010, M.Sc. in Computer Science / Natural Language Processing from AlNahrain University in 1994, and B.Sc. in Computer Science from the University of Mosul in 1989. Currently, Dr. Ayad is a Proffesor at Al-Isra University / Amman / Jordan. Dr. Ayad has several published papers on various computer science and Software Engineering topics. Dr. Ayad is a reviewer in several journals and conferences on Computer and Information related areas.

In addition to using AI approaches and techniques to solve sorts of computing problems, my Interest in research includes the development of Intelligent CASE (I-CASE) tools, especially the use of Natural Language Processing (NLP) in the requirement engineering.

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University of Leeds

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Al Akhawayn University in Ifrane

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Papers by Ayad T . Imam

The Automatic Definition of the Intuitive Linguistic Heuristics Set to Recognize the Elements of UML Analysis and Design Models in English

IEEE Access

Elicitation of the elements of Unified Modelling Language (UML) analysis and design models from s... more Elicitation of the elements of Unified Modelling Language (UML) analysis and design models from sentences written in scripted English is essential in the production of analysis and design models. The correct elicitation of these elements depends on the intuitive, manually defined set of linguistic heuristics, which is used to map a word in the sentence to its correct semantics in the domain of UML analysis and design models. This paper proposes a Genetic Algorithm-based classification rule discovery approach and a developed Enhanced Intuitive Linguistic Heuristics (EILH) dataset to automate the definition of the intuitive linguistic heuristics set to elicit five elements of UML analysis and design models from English sentences. These elements are the use case, the actor, the sender, the receiver, and the message. The automatically defined intuitive linguistic heuristics set was evaluated by developing an Artificial Neural Network (ANN) to recognize the elements of the UML analysis and design models using both manually defined and automatically defined sets. This comparison shows the superiority of the automatically defined set over the manually defined one.

Uncertainty in Data Mining

Proceedings of the 1st International Conference on Computing and Emerging Sciences - ICCES, 2020

This paper aims to articulate the current approaches to handle the uncertainty problem in data mi... more This paper aims to articulate the current approaches to handle the uncertainty problem in data mining (DM). The difficulties in DM are given to show the impact of the uncertainty problem in DM's various applications. As it has been stated that is no common DM method to handle the uncertainty problem in DM and based upon the literature of cognitive science, this paper highlights a new classification approach to overcome the uncertainty problem in DM that is the Relative-Fuzzy (RF) approach and its ML/RFL-Based Net software tool.

GANCSA: A novel approach for finding the best path for migration of mobile-agents

Pervasive and Mobile Computing, 2022

A mobile agent is a mobile object, whose movement requires a wise determination of the best path,... more A mobile agent is a mobile object, whose movement requires a wise determination of the best path, as this path is applied to migrate a mobile agent from a source node to a destination node in an interconnected network of computers. Usually, the choice of the best path is made using optimization algorithms, which use the minimum time as a key measure for choosing the best path. This work proposes a new complex approach, which is called The Genetic Algorithm with Node Compression-based Search Algorithm (GANCSA). This approach utilizes a mathematical process and an optimization technique to achieve the finding of the best migration path in minimal time (sequential nodes in a time frame) for migrating a mobile agent in an interconnected network of computers. GANCSA encompasses the Genetic Algorithm (GA) as an optimization technique, and the Node Compression-based Search Algorithm (NCSA) to minimize the number of nodes. The testing of the proposed GANCSA shows that the combination of GA and NCSA reduces the time of selecting the best path from 336.448 ms to 286.29 ms after 10 iterations, believing that more time reduction can be achieved by increasing the iterations.

The Automatic Definition of the Intuitive Linguistic Heuristics Set to Recognize the Elements of UML Analysis and Design Models in English

by Ayad T . Imam and Eyad al-Zobaydi

IEEE ACCESS, 2023

Elicitation of the elements of Unified Modelling Language (UML) analysis and design models from s... more Elicitation of the elements of Unified Modelling Language (UML) analysis and design models from sentences written in scripted English is essential in the production of analysis and design models. The correct elicitation of these elements depends on the intuitive, manually defined set of linguistic heuristics, which is used to map a word in the sentence to its correct semantics in the domain of UML analysis and design models. This paper proposes a Genetic Algorithm-based classification rule discovery approach and a developed Enhanced Intuitive Linguistic Heuristics (EILH) dataset to automate the definition of the intuitive linguistic heuristics set to elicit five elements of UML analysis and design models from English sentences. These elements are the use case, the actor, the sender, the receiver, and the message. The automatically defined intuitive linguistic heuristics set was evaluated by developing an Artificial Neural Network (ANN) to recognize the elements of the UML analysis and design models using both manually defined and automatically defined sets. This comparison shows the superiority of the automatically defined set over the manually defined one.

The roles of decision making and empowerment in Jordanian web-based development organisations

Journal of Web Engineering, Nov 1, 2014

This study aims to explore how empowerment is enabled in Web-based project teams. It also aims to... more

The automation of the detection of large class bad smell by using genetic algorithm and deep learning

Journal of King Saud University - Computer and Information Sciences, 2022

In Software Engineering (SE), metrics are used for detecting software design problems (bad smells... more In Software Engineering (SE), metrics are used for detecting software design problems (bad smells) like the large-class bad smell, where a lot of different metrics were defined to find out the existence of this problem in the design of a class. Examples of these metrics are size metrics, cohesion metrics, and coupling metrics. Selecting the right metrics to detect the large-class bad smell is a common problem, and it is usually accomplished manually. The questions remain: Can a module with the best combination of two metrics, for detecting the problem of large-class bad smell, be formed automatically rather than manually? And how is this double valued threshold determined to be used to infer the existence of this problem? This paper proposes the Hybrid Approach to detect Large Class Bad Smell (HA-LCBS). This approach utilizes the Genetic Algorithm (GA) to automate the composing of a detecting module that consists of a cohesion metric type and a coupling metric type and passes its resulting paired value to a deep learning approach to automate the detection of the large class bad smell. The accuracy that has been gained from using this approach reached 94.21%.

DESIGN OF A TRILINGUAL ARABIC ENGLISH KURDISH

Iraqi Journal for Computers and Informatics, 1996

The research is an attempt to design a Triligual (Arabic, English, and Kurdish) computer system b... more The research is an attempt to design a Triligual (Arabic, English, and Kurdish) computer system by changing three I/O device drivers simultaneously; i.e. a user can change the computer language between these three languages by pressing the appropriate hotkeys. Such a system is very useful with application programs such as word processors.
The system has been implemented on an IBM-PC compatible using Turbo Pascal ver. 6.0, assembly macros and a tool program called MFont.

Automatic Diagnosis of Distortion Type of Arabic /r/ Phoneme Using Feed Forward Neural Network

Computer Engineering and Intelligent Systems, 2014

The paper is not for recognizing normal formed speech but for distorted speech via examining the ... more The paper is not for recognizing normal formed speech but for distorted speech via examining the ability of feed forward Artificial Neural Networks (ANN) to recognize speech flaws. In this paper, we consider the Arabic /r/ phoneme distortion that is somewhat common among native speakers as a case study. To do this, r-Distype program is developed as a script written using Praat speech processing software tool. r-Distype program automatically develops a feed forward ANN that tests the spoken word (which includes /r/ phoneme) to detect any possible type of distortion. Multiple feed forward ANNs of different architectures have been developed and their achievements reported. Training data and testing data of the developed ANNs are sets of spoken Arabic words that contain /r/ phoneme in different positions so they cover all distortion types of Arabic /r/ phoneme. These sets of words were produced by different genders and different ages. The results obtained from developed ANNs were used to draw a conclusion about automating the detection of pronunciation problems in general. Such computerised system would be a good tool for diagnosing speech flaws and gives a great help in speech therapy. Also, the idea itself may open a new research subarea of speech recognition that is automatic speech therapy.

Developing of Natural Language Interface to Robot - an Arabic Language Case Study

International Review on Computers and Software, Aug 20, 2014

In this paper, we report the development of an Arabic natural language interface to robot (ANLI2R... more In this paper, we report the development of an Arabic natural language interface to robot (ANLI2ROBOT). The imperative sentence in Arabic language is used to command a robot lifting arm to grasp small metal objects and move them from one place to another. Since this interface is a natural language processing (NLP) application, lexicon, syntactical, and morphological subsystems for the Arabic language interrogative sentence were created as components of this interface. A set of simple familiar Arabic command words, which are related to the operations of the robot lifting arm, have been defined in the lexicon of the interface, and different forms of these words have been created by using the morphological subsystem. ANLI2ROBOT is implemented using Prolog programming language since it gives a straightforward conversion from Definite Clause Grammar (DCG) to Prolog predicates. This work, which is classified as human–robot interaction (HRI), contributes the using of Arabic language to communicate with robot. ANLI2ROBOT is also considered as a written dialogue system (WDS) that uses Arabic written (not spoken) language. In addition to the success in handling the different forms of the imperative sentence, the work revealed other significant findings regarding the use of the Arabic natural language to instruct the robot (and similar machines).

Uncertainty in Data Mining

This paper aims to articulate the current approaches to handle the uncertainty problem in data mi... more This paper aims to articulate the current approaches to handle the uncertainty problem in data mining (DM). The difficulties in DM are given to show the impact of the uncertainty problem in DM's various applications. As it has been stated that is no common DM method to handle the uncertainty problem in DM and based upon the literature of cognitive science, this paper highlights a new classification approach to overcome the uncertainty problem in DM that is the Relative-Fuzzy (RF) approach and its ML/RFL-Based Net software tool.

MS/Ayad-Marwan Network: A New Architecture of an Artificial Neural Network

Social Science Research Network, 2006

Due to its parallelism property in processing the data, the Artificial Neural Networks (ANN) has ... more Due to its parallelism property in processing the data, the Artificial Neural Networks (ANN) has been gaining wide interest in recent years as a tool for processing data. Different ANN architectures have been defined for various applications. Yet, a number of difficulties existed when building an ANN like training time, over-training problem, retraining of the net for new types of data. This paper presents a new ANN architecture, which we called &quot;MS/Ayad-Marwan Network&quot; (MS stands for Multi-Stage), that compose of supernet and subnets (&quot;Stage&quot; architecture). The &quot;Computer-Assisted&quot; approach is used to develop this NN. Also, in this paper, a new learning algorithm has been proposed, which is required for adjusting weighting values for MS/Ayad-Marwan neural network. We called this algorithm a &quot;hybrid unsupervised/supervised learning paradigm&quot; This learning algorithm is used for training both supernet and subnets. Inheritance property of OOP plays an important role in the reasoning approach of MS/Ayad-Marwan Network, in that it deduces the final solution by gathering the property of the super network with the properties of its sub(s), to reach the ultimate goal. Thus, the results become more accurate. We called his reasoning approach &quot; Deductive auto-associative recalling&quot;. MS/Ayad-Marwan ANN has been applied to an Automatic Speech Recognition (ASR) application. This new network architecture is being able to recognize phonemes with &gt;91% accuracy. We note through the results a promising advancement has been reached and hence a Multi-Stage strategy is useful in applications of &quot;high changeable data&quot; property, like ASR. &quot;MS/Ayad-Marwan&quot; Neural Network has been implemented and tested using MATLAB, as a part of the ASR system.

An Algorithmic Approach for Sketching Sequence Diagram (AASSD)

Sequence diagram is used to illustrate the exchanging of information between users and system&#39... more Sequence diagram is used to illustrate the exchanging of information between users and system's components. Sequence diagram helps in leveling up the requirement description. The analyst is the role who is manually performing the developing of sequence diagram. The skill of defining objects (or roles) and their exchanged messages are highly depending on the user's expert to recognize sequence diagram units from different sources of information. It is very hard to find UML's reference that describes enough direct rules (specially for many trainee developers) to develop sequence diagram. This paper contributes a definition of a systematic a semi-automated algorithmic approach for generating sequence diagram. Flow of events included in a use case model is used as an input. Semantic Role Labeling (SRL) is the process used to discover the thematic role, which is used to determine the elements of sequence diagram. The extracted elements of sequence diagram are to be fed to a ready-made software tool for sketching sequence diagram. This algorithmic approach provides a direct starting, clear linguistic process, and easy-to-follow practice for developing sequence diagram. The semi-automated approach is viewed as a step towards developing an Intelligent Computer Aided Software Engineering (I-CASE) tool for sketching sequence diagram from flow of events of a use case model.

The Definition of Intelligent Computer Aided Software Engineering (I-CASE) Tools

Social Science Research Network, Jan 15, 2015

The growing complexity of the software systems being developed and the use of different methodolo... more The growing complexity of the software systems being developed and the use of different methodologies indicate the need for more computer support for automating software development process and evolution activity. Currently, Computer-Aided Software Engineering (CASE), which is a set of software systems aimed to support set of software process activities, does this automation. While CASE tools prove its importance to develop high quality software, unfortunately CASE tools doesn't cover all software development activities. This is because some activities need intellectual human skills, which are not currently available as computer software. To solve this shortcoming, Artificial Intelligence (AI) approaches are the ones that can be used to develop software tools imitating these intellectual skills. This paper presents the definition of Intelligent Computer Aided Software Engineering (I-CASE). The definition encompasses two steps. The first step is a clear decomposition of each basic software development activity to sub activities, and classify each one of them whether it is an intellectual or procedural job. The second step is the addressing of each intellectual (un-automated) one to proper AI-based approach. These tools may be integrated into a package as an Integrated Development Environment (IDE) or could be used individually. The discussion and the next implementation step are reported.

The Use of Natural Language Processing Approach for Converting Pseudo Code to C# Code

Social Science Research Network, 2019

Although current computer-aided software engineering tools support developers in composing a prog... more Although current computer-aided software engineering tools support developers in composing a program, there is no doubt that more flexible supportive tools are needed to address the increases in the complexity of programs. This need can be met by automating the intellectual activities that are carried out by humans when composing a program. This paper aims to automate the composition of a programming language code from pseudocode, which is viewed here as a translation process for a natural language text, as pseudocode is a formatted text in natural English language. Based on this view, a new automatic code generator is developed that can convert pseudocode to C# programming language code. This new automatic code generator (ACG), which is called CodeComposer, uses natural language processing (NLP) techniques such as verb classification, thematic roles, and semantic role labeling (SRL) to analyze the pseudocode. The resulting analysis of linguistic information from these techniques is used by a semantic rule-based mapping machine to perform the composition process. CodeComposer can be viewed as an intelligent computer-aided software engineering (I_CASE) tool. An evaluation of the accuracy of CodeComposer using a binomial technique shows that it has a precision of 88%, a recall of 91%, and an F-measure of 89%.

The Genetic Algorithm and Binary Search Technique in the Program Path Coverage for Improving Software Testing Using Big Data

Intelligent Automation and Soft Computing, 2020

An Algorithmic Approach for Sketching Sequence Diagram (AASSD)

Proceedings of the International Conference on Advances in Image Processing, 2017

Sequence diagram is used to illustrate the exchanging of information between users and system&#39... more Sequence diagram is used to illustrate the exchanging of information between users and system's components. Sequence diagram helps in leveling up the requirement description. The analyst is the role who is manually performing the developing of sequence diagram. The skill of defining objects (or roles) and their exchanged messages are highly depending on the user's expert to recognize sequence diagram units from different sources of information. It is very hard to find UML's reference that describes enough direct rules (specially for many trainee developers) to develop sequence diagram. This paper contributes a definition of a systematic a semi-automated algorithmic approach for generating sequence diagram. Flow of events included in a use case model is used as an input. Semantic Role Labeling (SRL) is the process used to discover the thematic role, which is used to determine the elements of sequence diagram. The extracted elements of sequence diagram are to be fed to a ready-made software tool for sketching sequence diagram. This algorithmic approach provides a direct starting, clear linguistic process, and easy-to-follow practice for developing sequence diagram. The semi-automated approach is viewed as a step towards developing an Intelligent Computer Aided Software Engineering (I-CASE) tool for sketching sequence diagram from flow of events of a use case model.

An algorithmic approach to extract actions and actors (AAEAA)

Proceedings of the International Conference on Geoinformatics and Data Analysis, 2018

Use case is a model delivered by requirements engineering phase, which is considered as an input ... more Use case is a model delivered by requirements engineering phase, which is considered as an input to the forthcoming design phase and test phase. A use case model is a simplest representation of an actor's interactions with the system in which the user is involved. The development of a use case model requires the finding out the use case itself and the actor that uses this use case to interact with the system. These two tasks are achieved manually via analyst's experience, who starts with different sources of data to develop use case model. User requirements document is a common source of data that may be started with to develop use case model. The extracting of actors and their actions (use cases) is subjected to the linguistic properties of each on. The aim of this paper is to define a new algorithmic approach for extracting actors and their use cases by using thematic role technique. This algorithmic approach had been manually tested using known examples, and shown its validity. The success of this technique will lead to develop an Intelligent Computer Aided Software Engineering (I-CASE) tool that automatically extracts actions and actors of use case model from functional requirements by using Semantic Role Labelling (SRL) of Natural Language Processing (NLP) approach.

Automatic Diagnosis of Distortion Type of Arabic /r/ Phoneme Using Feed Forward Neural Network

Computer Engineering and Intelligent Systems, 2014

The paper is not for recognizing normal formed speech but for distorted speech via examining the ... more The paper is not for recognizing normal formed speech but for distorted speech via examining the ability of feed forward Artificial Neural Networks (ANN) to recognize speech flaws. In this paper we take the Arabic /r/ phoneme distortion that is somewhat common among native speakers as a case study.To do this, r-Distype program is developed as a script written using Praat speech processing software tool. r-Distype program automatically develops a feed forward ANN that tests the spoken word (which includes /r/ phoneme) to detect any possible type of distortion. Multiple feed forward ANNs of different architectures have been developed and their achievements reported. Training data and testing data of the developed ANNs are sets of spoken Arabic words that contain /r/ phoneme in different positions so they cover all distortion types of Arabic /r/ phoneme. These sets of words were produced by different genders and different ages.The results obtained from developed ANNs were used to draw a conclusion about automating the detection of pronunciation problems in general.Such computerised system would be a good tool for diagnosing speech flaws and gives a great help in speech therapy. Also, the idea itself may open a new research subarea of speech recognition that is automatic speech therapy.

An expert code generator using rule-based and frames knowledge representation techniques

2014 5th International Conference on Information and Communication Systems (ICICS), 2014

ABSTRACT This paper aims to demonstrate the development of an expert code generator using rule-ba... more ABSTRACT This paper aims to demonstrate the development of an expert code generator using rule-based and frames knowledge representation techniques (ECG-RF). The ECG-RF system presented in this paper is a passive code generator that carries out the task of automatic code generation in fixed-structure software. To develop an ECG-RF system, the artificial intelligence (AI) of rule-based system and frames knowledge representation techniques was applied to a code generation task. ECG-RF fills a predefined frame of a certain fixed-structure program with code chunks retrieved from ECG-RF&#39;s knowledge base. The filling operation is achieved by ECG-RF&#39;s inference engine and is guided by the information collected from the user via a graphic user interface (GUI). In this paper, an ECG-RF system for generating a device driver program is presented and implemented with VBasic software. The results show that the ECG-RF design concept is reasonably reliable.

MS/Ayad-Marwan Network: A New Architecture of an Artificial Neural Network

SSRN Electronic Journal, 2006

Due to its parallelism property in processing the data, the Artificial Neural Networks (ANN) has ... more Due to its parallelism property in processing the data, the Artificial Neural Networks (ANN) has been gaining wide interest in recent years as a tool for processing data. Different ANN architectures have been defined for various applications. Yet, a number of difficulties existed when building an ANN like training time, over-training problem, retraining of the net for new types of data. This paper presents a new ANN architecture, which we called &quot;MS/Ayad-Marwan Network&quot; (MS stands for Multi-Stage), that compose of supernet and subnets (&quot;Stage&quot; architecture). The &quot;Computer-Assisted&quot; approach is used to develop this NN. Also, in this paper, a new learning algorithm has been proposed, which is required for adjusting weighting values for MS/Ayad-Marwan neural network. We called this algorithm a &quot;hybrid unsupervised/supervised learning paradigm&quot; This learning algorithm is used for training both supernet and subnets. Inheritance property of OOP plays an important role in the reasoning approach of MS/Ayad-Marwan Network, in that it deduces the final solution by gathering the property of the super network with the properties of its sub(s), to reach the ultimate goal. Thus, the results become more accurate. We called his reasoning approach &quot; Deductive auto-associative recalling&quot;. MS/Ayad-Marwan ANN has been applied to an Automatic Speech Recognition (ASR) application. This new network architecture is being able to recognize phonemes with &gt;91% accuracy. We note through the results a promising advancement has been reached and hence a Multi-Stage strategy is useful in applications of &quot;high changeable data&quot; property, like ASR. &quot;MS/Ayad-Marwan&quot; Neural Network has been implemented and tested using MATLAB, as a part of the ASR system.

The Automatic Definition of the Intuitive Linguistic Heuristics Set to Recognize the Elements of UML Analysis and Design Models in English

IEEE Access

Elicitation of the elements of Unified Modelling Language (UML) analysis and design models from s... more Elicitation of the elements of Unified Modelling Language (UML) analysis and design models from sentences written in scripted English is essential in the production of analysis and design models. The correct elicitation of these elements depends on the intuitive, manually defined set of linguistic heuristics, which is used to map a word in the sentence to its correct semantics in the domain of UML analysis and design models. This paper proposes a Genetic Algorithm-based classification rule discovery approach and a developed Enhanced Intuitive Linguistic Heuristics (EILH) dataset to automate the definition of the intuitive linguistic heuristics set to elicit five elements of UML analysis and design models from English sentences. These elements are the use case, the actor, the sender, the receiver, and the message. The automatically defined intuitive linguistic heuristics set was evaluated by developing an Artificial Neural Network (ANN) to recognize the elements of the UML analysis and design models using both manually defined and automatically defined sets. This comparison shows the superiority of the automatically defined set over the manually defined one.

Uncertainty in Data Mining

Proceedings of the 1st International Conference on Computing and Emerging Sciences - ICCES, 2020

This paper aims to articulate the current approaches to handle the uncertainty problem in data mi... more This paper aims to articulate the current approaches to handle the uncertainty problem in data mining (DM). The difficulties in DM are given to show the impact of the uncertainty problem in DM's various applications. As it has been stated that is no common DM method to handle the uncertainty problem in DM and based upon the literature of cognitive science, this paper highlights a new classification approach to overcome the uncertainty problem in DM that is the Relative-Fuzzy (RF) approach and its ML/RFL-Based Net software tool.

GANCSA: A novel approach for finding the best path for migration of mobile-agents

Pervasive and Mobile Computing, 2022

A mobile agent is a mobile object, whose movement requires a wise determination of the best path,... more A mobile agent is a mobile object, whose movement requires a wise determination of the best path, as this path is applied to migrate a mobile agent from a source node to a destination node in an interconnected network of computers. Usually, the choice of the best path is made using optimization algorithms, which use the minimum time as a key measure for choosing the best path. This work proposes a new complex approach, which is called The Genetic Algorithm with Node Compression-based Search Algorithm (GANCSA). This approach utilizes a mathematical process and an optimization technique to achieve the finding of the best migration path in minimal time (sequential nodes in a time frame) for migrating a mobile agent in an interconnected network of computers. GANCSA encompasses the Genetic Algorithm (GA) as an optimization technique, and the Node Compression-based Search Algorithm (NCSA) to minimize the number of nodes. The testing of the proposed GANCSA shows that the combination of GA and NCSA reduces the time of selecting the best path from 336.448 ms to 286.29 ms after 10 iterations, believing that more time reduction can be achieved by increasing the iterations.

The Automatic Definition of the Intuitive Linguistic Heuristics Set to Recognize the Elements of UML Analysis and Design Models in English

by Ayad T . Imam and Eyad al-Zobaydi

IEEE ACCESS, 2023

Elicitation of the elements of Unified Modelling Language (UML) analysis and design models from s... more Elicitation of the elements of Unified Modelling Language (UML) analysis and design models from sentences written in scripted English is essential in the production of analysis and design models. The correct elicitation of these elements depends on the intuitive, manually defined set of linguistic heuristics, which is used to map a word in the sentence to its correct semantics in the domain of UML analysis and design models. This paper proposes a Genetic Algorithm-based classification rule discovery approach and a developed Enhanced Intuitive Linguistic Heuristics (EILH) dataset to automate the definition of the intuitive linguistic heuristics set to elicit five elements of UML analysis and design models from English sentences. These elements are the use case, the actor, the sender, the receiver, and the message. The automatically defined intuitive linguistic heuristics set was evaluated by developing an Artificial Neural Network (ANN) to recognize the elements of the UML analysis and design models using both manually defined and automatically defined sets. This comparison shows the superiority of the automatically defined set over the manually defined one.

The roles of decision making and empowerment in Jordanian web-based development organisations

Journal of Web Engineering, Nov 1, 2014

This study aims to explore how empowerment is enabled in Web-based project teams. It also aims to... more

The automation of the detection of large class bad smell by using genetic algorithm and deep learning

Journal of King Saud University - Computer and Information Sciences, 2022

In Software Engineering (SE), metrics are used for detecting software design problems (bad smells... more In Software Engineering (SE), metrics are used for detecting software design problems (bad smells) like the large-class bad smell, where a lot of different metrics were defined to find out the existence of this problem in the design of a class. Examples of these metrics are size metrics, cohesion metrics, and coupling metrics. Selecting the right metrics to detect the large-class bad smell is a common problem, and it is usually accomplished manually. The questions remain: Can a module with the best combination of two metrics, for detecting the problem of large-class bad smell, be formed automatically rather than manually? And how is this double valued threshold determined to be used to infer the existence of this problem? This paper proposes the Hybrid Approach to detect Large Class Bad Smell (HA-LCBS). This approach utilizes the Genetic Algorithm (GA) to automate the composing of a detecting module that consists of a cohesion metric type and a coupling metric type and passes its resulting paired value to a deep learning approach to automate the detection of the large class bad smell. The accuracy that has been gained from using this approach reached 94.21%.

DESIGN OF A TRILINGUAL ARABIC ENGLISH KURDISH

Iraqi Journal for Computers and Informatics, 1996

The research is an attempt to design a Triligual (Arabic, English, and Kurdish) computer system b... more The research is an attempt to design a Triligual (Arabic, English, and Kurdish) computer system by changing three I/O device drivers simultaneously; i.e. a user can change the computer language between these three languages by pressing the appropriate hotkeys. Such a system is very useful with application programs such as word processors.
The system has been implemented on an IBM-PC compatible using Turbo Pascal ver. 6.0, assembly macros and a tool program called MFont.

Automatic Diagnosis of Distortion Type of Arabic /r/ Phoneme Using Feed Forward Neural Network

Computer Engineering and Intelligent Systems, 2014

The paper is not for recognizing normal formed speech but for distorted speech via examining the ... more The paper is not for recognizing normal formed speech but for distorted speech via examining the ability of feed forward Artificial Neural Networks (ANN) to recognize speech flaws. In this paper, we consider the Arabic /r/ phoneme distortion that is somewhat common among native speakers as a case study. To do this, r-Distype program is developed as a script written using Praat speech processing software tool. r-Distype program automatically develops a feed forward ANN that tests the spoken word (which includes /r/ phoneme) to detect any possible type of distortion. Multiple feed forward ANNs of different architectures have been developed and their achievements reported. Training data and testing data of the developed ANNs are sets of spoken Arabic words that contain /r/ phoneme in different positions so they cover all distortion types of Arabic /r/ phoneme. These sets of words were produced by different genders and different ages. The results obtained from developed ANNs were used to draw a conclusion about automating the detection of pronunciation problems in general. Such computerised system would be a good tool for diagnosing speech flaws and gives a great help in speech therapy. Also, the idea itself may open a new research subarea of speech recognition that is automatic speech therapy.

Developing of Natural Language Interface to Robot - an Arabic Language Case Study

International Review on Computers and Software, Aug 20, 2014

In this paper, we report the development of an Arabic natural language interface to robot (ANLI2R... more In this paper, we report the development of an Arabic natural language interface to robot (ANLI2ROBOT). The imperative sentence in Arabic language is used to command a robot lifting arm to grasp small metal objects and move them from one place to another. Since this interface is a natural language processing (NLP) application, lexicon, syntactical, and morphological subsystems for the Arabic language interrogative sentence were created as components of this interface. A set of simple familiar Arabic command words, which are related to the operations of the robot lifting arm, have been defined in the lexicon of the interface, and different forms of these words have been created by using the morphological subsystem. ANLI2ROBOT is implemented using Prolog programming language since it gives a straightforward conversion from Definite Clause Grammar (DCG) to Prolog predicates. This work, which is classified as human–robot interaction (HRI), contributes the using of Arabic language to communicate with robot. ANLI2ROBOT is also considered as a written dialogue system (WDS) that uses Arabic written (not spoken) language. In addition to the success in handling the different forms of the imperative sentence, the work revealed other significant findings regarding the use of the Arabic natural language to instruct the robot (and similar machines).

Uncertainty in Data Mining

This paper aims to articulate the current approaches to handle the uncertainty problem in data mi... more This paper aims to articulate the current approaches to handle the uncertainty problem in data mining (DM). The difficulties in DM are given to show the impact of the uncertainty problem in DM's various applications. As it has been stated that is no common DM method to handle the uncertainty problem in DM and based upon the literature of cognitive science, this paper highlights a new classification approach to overcome the uncertainty problem in DM that is the Relative-Fuzzy (RF) approach and its ML/RFL-Based Net software tool.

MS/Ayad-Marwan Network: A New Architecture of an Artificial Neural Network

Social Science Research Network, 2006

Due to its parallelism property in processing the data, the Artificial Neural Networks (ANN) has ... more Due to its parallelism property in processing the data, the Artificial Neural Networks (ANN) has been gaining wide interest in recent years as a tool for processing data. Different ANN architectures have been defined for various applications. Yet, a number of difficulties existed when building an ANN like training time, over-training problem, retraining of the net for new types of data. This paper presents a new ANN architecture, which we called &quot;MS/Ayad-Marwan Network&quot; (MS stands for Multi-Stage), that compose of supernet and subnets (&quot;Stage&quot; architecture). The &quot;Computer-Assisted&quot; approach is used to develop this NN. Also, in this paper, a new learning algorithm has been proposed, which is required for adjusting weighting values for MS/Ayad-Marwan neural network. We called this algorithm a &quot;hybrid unsupervised/supervised learning paradigm&quot; This learning algorithm is used for training both supernet and subnets. Inheritance property of OOP plays an important role in the reasoning approach of MS/Ayad-Marwan Network, in that it deduces the final solution by gathering the property of the super network with the properties of its sub(s), to reach the ultimate goal. Thus, the results become more accurate. We called his reasoning approach &quot; Deductive auto-associative recalling&quot;. MS/Ayad-Marwan ANN has been applied to an Automatic Speech Recognition (ASR) application. This new network architecture is being able to recognize phonemes with &gt;91% accuracy. We note through the results a promising advancement has been reached and hence a Multi-Stage strategy is useful in applications of &quot;high changeable data&quot; property, like ASR. &quot;MS/Ayad-Marwan&quot; Neural Network has been implemented and tested using MATLAB, as a part of the ASR system.

An Algorithmic Approach for Sketching Sequence Diagram (AASSD)

Sequence diagram is used to illustrate the exchanging of information between users and system&#39... more Sequence diagram is used to illustrate the exchanging of information between users and system's components. Sequence diagram helps in leveling up the requirement description. The analyst is the role who is manually performing the developing of sequence diagram. The skill of defining objects (or roles) and their exchanged messages are highly depending on the user's expert to recognize sequence diagram units from different sources of information. It is very hard to find UML's reference that describes enough direct rules (specially for many trainee developers) to develop sequence diagram. This paper contributes a definition of a systematic a semi-automated algorithmic approach for generating sequence diagram. Flow of events included in a use case model is used as an input. Semantic Role Labeling (SRL) is the process used to discover the thematic role, which is used to determine the elements of sequence diagram. The extracted elements of sequence diagram are to be fed to a ready-made software tool for sketching sequence diagram. This algorithmic approach provides a direct starting, clear linguistic process, and easy-to-follow practice for developing sequence diagram. The semi-automated approach is viewed as a step towards developing an Intelligent Computer Aided Software Engineering (I-CASE) tool for sketching sequence diagram from flow of events of a use case model.

The Definition of Intelligent Computer Aided Software Engineering (I-CASE) Tools

Social Science Research Network, Jan 15, 2015

The growing complexity of the software systems being developed and the use of different methodolo... more The growing complexity of the software systems being developed and the use of different methodologies indicate the need for more computer support for automating software development process and evolution activity. Currently, Computer-Aided Software Engineering (CASE), which is a set of software systems aimed to support set of software process activities, does this automation. While CASE tools prove its importance to develop high quality software, unfortunately CASE tools doesn't cover all software development activities. This is because some activities need intellectual human skills, which are not currently available as computer software. To solve this shortcoming, Artificial Intelligence (AI) approaches are the ones that can be used to develop software tools imitating these intellectual skills. This paper presents the definition of Intelligent Computer Aided Software Engineering (I-CASE). The definition encompasses two steps. The first step is a clear decomposition of each basic software development activity to sub activities, and classify each one of them whether it is an intellectual or procedural job. The second step is the addressing of each intellectual (un-automated) one to proper AI-based approach. These tools may be integrated into a package as an Integrated Development Environment (IDE) or could be used individually. The discussion and the next implementation step are reported.

The Use of Natural Language Processing Approach for Converting Pseudo Code to C# Code

Social Science Research Network, 2019

Although current computer-aided software engineering tools support developers in composing a prog... more Although current computer-aided software engineering tools support developers in composing a program, there is no doubt that more flexible supportive tools are needed to address the increases in the complexity of programs. This need can be met by automating the intellectual activities that are carried out by humans when composing a program. This paper aims to automate the composition of a programming language code from pseudocode, which is viewed here as a translation process for a natural language text, as pseudocode is a formatted text in natural English language. Based on this view, a new automatic code generator is developed that can convert pseudocode to C# programming language code. This new automatic code generator (ACG), which is called CodeComposer, uses natural language processing (NLP) techniques such as verb classification, thematic roles, and semantic role labeling (SRL) to analyze the pseudocode. The resulting analysis of linguistic information from these techniques is used by a semantic rule-based mapping machine to perform the composition process. CodeComposer can be viewed as an intelligent computer-aided software engineering (I_CASE) tool. An evaluation of the accuracy of CodeComposer using a binomial technique shows that it has a precision of 88%, a recall of 91%, and an F-measure of 89%.

The Genetic Algorithm and Binary Search Technique in the Program Path Coverage for Improving Software Testing Using Big Data

Intelligent Automation and Soft Computing, 2020

An Algorithmic Approach for Sketching Sequence Diagram (AASSD)

Proceedings of the International Conference on Advances in Image Processing, 2017

Sequence diagram is used to illustrate the exchanging of information between users and system&#39... more Sequence diagram is used to illustrate the exchanging of information between users and system's components. Sequence diagram helps in leveling up the requirement description. The analyst is the role who is manually performing the developing of sequence diagram. The skill of defining objects (or roles) and their exchanged messages are highly depending on the user's expert to recognize sequence diagram units from different sources of information. It is very hard to find UML's reference that describes enough direct rules (specially for many trainee developers) to develop sequence diagram. This paper contributes a definition of a systematic a semi-automated algorithmic approach for generating sequence diagram. Flow of events included in a use case model is used as an input. Semantic Role Labeling (SRL) is the process used to discover the thematic role, which is used to determine the elements of sequence diagram. The extracted elements of sequence diagram are to be fed to a ready-made software tool for sketching sequence diagram. This algorithmic approach provides a direct starting, clear linguistic process, and easy-to-follow practice for developing sequence diagram. The semi-automated approach is viewed as a step towards developing an Intelligent Computer Aided Software Engineering (I-CASE) tool for sketching sequence diagram from flow of events of a use case model.

An algorithmic approach to extract actions and actors (AAEAA)

Proceedings of the International Conference on Geoinformatics and Data Analysis, 2018

Use case is a model delivered by requirements engineering phase, which is considered as an input ... more Use case is a model delivered by requirements engineering phase, which is considered as an input to the forthcoming design phase and test phase. A use case model is a simplest representation of an actor's interactions with the system in which the user is involved. The development of a use case model requires the finding out the use case itself and the actor that uses this use case to interact with the system. These two tasks are achieved manually via analyst's experience, who starts with different sources of data to develop use case model. User requirements document is a common source of data that may be started with to develop use case model. The extracting of actors and their actions (use cases) is subjected to the linguistic properties of each on. The aim of this paper is to define a new algorithmic approach for extracting actors and their use cases by using thematic role technique. This algorithmic approach had been manually tested using known examples, and shown its validity. The success of this technique will lead to develop an Intelligent Computer Aided Software Engineering (I-CASE) tool that automatically extracts actions and actors of use case model from functional requirements by using Semantic Role Labelling (SRL) of Natural Language Processing (NLP) approach.

Automatic Diagnosis of Distortion Type of Arabic /r/ Phoneme Using Feed Forward Neural Network

Computer Engineering and Intelligent Systems, 2014

The paper is not for recognizing normal formed speech but for distorted speech via examining the ... more The paper is not for recognizing normal formed speech but for distorted speech via examining the ability of feed forward Artificial Neural Networks (ANN) to recognize speech flaws. In this paper we take the Arabic /r/ phoneme distortion that is somewhat common among native speakers as a case study.To do this, r-Distype program is developed as a script written using Praat speech processing software tool. r-Distype program automatically develops a feed forward ANN that tests the spoken word (which includes /r/ phoneme) to detect any possible type of distortion. Multiple feed forward ANNs of different architectures have been developed and their achievements reported. Training data and testing data of the developed ANNs are sets of spoken Arabic words that contain /r/ phoneme in different positions so they cover all distortion types of Arabic /r/ phoneme. These sets of words were produced by different genders and different ages.The results obtained from developed ANNs were used to draw a conclusion about automating the detection of pronunciation problems in general.Such computerised system would be a good tool for diagnosing speech flaws and gives a great help in speech therapy. Also, the idea itself may open a new research subarea of speech recognition that is automatic speech therapy.

An expert code generator using rule-based and frames knowledge representation techniques

2014 5th International Conference on Information and Communication Systems (ICICS), 2014

ABSTRACT This paper aims to demonstrate the development of an expert code generator using rule-ba... more ABSTRACT This paper aims to demonstrate the development of an expert code generator using rule-based and frames knowledge representation techniques (ECG-RF). The ECG-RF system presented in this paper is a passive code generator that carries out the task of automatic code generation in fixed-structure software. To develop an ECG-RF system, the artificial intelligence (AI) of rule-based system and frames knowledge representation techniques was applied to a code generation task. ECG-RF fills a predefined frame of a certain fixed-structure program with code chunks retrieved from ECG-RF&#39;s knowledge base. The filling operation is achieved by ECG-RF&#39;s inference engine and is guided by the information collected from the user via a graphic user interface (GUI). In this paper, an ECG-RF system for generating a device driver program is presented and implemented with VBasic software. The results show that the ECG-RF design concept is reasonably reliable.

MS/Ayad-Marwan Network: A New Architecture of an Artificial Neural Network

SSRN Electronic Journal, 2006

Due to its parallelism property in processing the data, the Artificial Neural Networks (ANN) has ... more Due to its parallelism property in processing the data, the Artificial Neural Networks (ANN) has been gaining wide interest in recent years as a tool for processing data. Different ANN architectures have been defined for various applications. Yet, a number of difficulties existed when building an ANN like training time, over-training problem, retraining of the net for new types of data. This paper presents a new ANN architecture, which we called &quot;MS/Ayad-Marwan Network&quot; (MS stands for Multi-Stage), that compose of supernet and subnets (&quot;Stage&quot; architecture). The &quot;Computer-Assisted&quot; approach is used to develop this NN. Also, in this paper, a new learning algorithm has been proposed, which is required for adjusting weighting values for MS/Ayad-Marwan neural network. We called this algorithm a &quot;hybrid unsupervised/supervised learning paradigm&quot; This learning algorithm is used for training both supernet and subnets. Inheritance property of OOP plays an important role in the reasoning approach of MS/Ayad-Marwan Network, in that it deduces the final solution by gathering the property of the super network with the properties of its sub(s), to reach the ultimate goal. Thus, the results become more accurate. We called his reasoning approach &quot; Deductive auto-associative recalling&quot;. MS/Ayad-Marwan ANN has been applied to an Automatic Speech Recognition (ASR) application. This new network architecture is being able to recognize phonemes with &gt;91% accuracy. We note through the results a promising advancement has been reached and hence a Multi-Stage strategy is useful in applications of &quot;high changeable data&quot; property, like ASR. &quot;MS/Ayad-Marwan&quot; Neural Network has been implemented and tested using MATLAB, as a part of the ASR system.

Automatic Processing of Interrogative Words in The Arabic Language

MSc Thesis, 1994

This MSc thesis explores interrogatives (question words) in Arabic linguistically and computation... more This MSc thesis explores interrogatives (question words) in Arabic linguistically and computationally. It aims to create a framework for automating their processing within Natural Language Processing (NLP) systems, focusing on their grammatical, syntactic, and semantic roles. The research aims to study interrogative sentences in Arabic and develop automated tools for their analysis. This involves understanding the grammatical and semantic structure of these sentences.
Interrogatives are of great importance in formulating the interrogative sentence that is used to retrieve information or inquire about something, each according to its importance. It is certain that the interrogative sentence is indispensable when using natural language to retrieve information from computers or other computer applications of natural language.
Natural language processing by computers involves developing a lexicon and calculating subsystems for morphology, syntax, semantics, phonetics, and pragmatics, the latter two of which require additional means for their automated processing, which were not well dealt with until that time (Note that this MSc thesis was performed in 1993 at Al-Nahrain University, Baghdad/Iraq)
In the proposed system in this research, which is called Arabic Interrogative Sentence (ALIS), the lexicon, morphology, grammar, and semantics of all interrogative tools in Arabic were computerized, and the distortions of those tools were treated using Turbo Prolog programming language. Also, an automation of the syntactic analysis of Arabic interrogative sentences was developed, which extracts the corresponding syntactic tree as an intermediate form representing the system’s understanding of the question asked. Finally, an experimental Arabic text was defined using the semantic network representation method to test the ability of ALIS to retrieve information from free structured Arabic text. The ALIS system produced complete answers in the form of natural sentences that were grammatically and semantically correct to the questions asked.