Polla Fattah

Salahaddin University - Erbil | SUH · Department of Software Engineering

Speech signal processing is a cornerstone of modern communication technologies, tasked with improving the clarity and comprehensibility of audio data in noisy environments. The primary challenge in this field is the effective separation and recognition of speech from background noise, crucial for applications ranging from voice-activated assistants...

Clustering assigns objects to clusters based on similarity, aiming to ensure that objects within the same cluster are similar and those in different clusters are dissimilar. Evaluating clustering quality is crucial and challenging. Thus, researchers have proposed clustering validation indices namely internal and external validation indices. Interna...

Handwriting recognition is one of the active and challenging areas of research in the field of image processing and pattern recognition. It has many applications that include: a reading aid for visual impairment, automated reading and processing for bank checks, making any handwritten document searchable, and converting them into structural text fo...

Economists have sought to predict stock market prices for decades with varying degrees of success. This study classifies stocks according to their stability in two consequent financial quarters (depending on whether the majority of stocks remain in the same stability group, which can indicate forecastability). However, classifying temporal informat...

Economists have sought to predict stock market prices for decades with varying degrees of success. This study classifies stocks according to their stability in two consequent financial quarters (depending on whether the majority of stocks remain in the same stability group, which can indicate forecastability). However, classifying temporal informat...

Handwriting recognition is regarded as a dynamic and inspiring topic in the exploration of pattern recognition and image processing. It has many applications including a blind reading aid, computerized reading, and processing for paper documents, making any handwritten document searchable and converting it into structural text form. High accuracy r...

p> The dragonfly algorithm developed in 2016. It is one of the algorithms used by the researchers to optimize an extensive series of uses and applications in various areas. At times, it offers superior performance compared to the most well-known optimization techniques. However, this algorithm faces several difficulties when it is utilized to enha...

To collect the handwritten format of separate Kurdish characters, each character has been printed on a grid of 14 × 9 of A4 paper. Each paper is filled with only one printed character so that the volunteers know what character should be written in each paper. Then each paper has been scanned, spliced, and cropped with a macro in photoshop to make s...

The dragonfly algorithm was developed in 2016. It is one of the algorithms used by researchers to optimize an extensive series of uses and applications in various areas. At times, it offers superior performance compared to the most well-known optimization techniques. However, this algorithm faces several difficulties when it is utilized to enhance...

This paper works on one of the most recent pedestrian crowd evacuation models-i.e., "a simulation model for pedestrian crowd evacuation based on various AI techniques"-which was developed in late 2019. This study adds a new feature to the developed model by proposing a new method and integrating it into the model. This method enables the developed...

p>This paper works on one of the most recent pedestrian crowd evacuation models, i.e., “a simulation model for pedestrian crowd evacuation based on various AI techniques”, developed in late 2019. This study adds a new feature to the developed model by proposing a new method and integrating it with the model. This method enables the developed model...

This paper works on one of the most recent pedestrian crowd evacuation models, i.e., "a simulation model for pedestrian crowd evacuation based on various AI techniques", developed in late 2019. This study adds a new feature to the developed model by proposing a new method and integrating it with the model. This method enables the developed model to...

p> Dragonfly algorithm developed in 2016. It is one of the algorithms used by the researchers to optimize an extensive series of uses and applications in various areas. At times, it offers superior performance compared to the most well-known optimization techniques. However, this algorithm faces several difficulties when it is utilized to enhance...

At present, deep learning is widely used in a broad range of arenas. A convolutional neural networks (CNN) is becoming the star of deep learning as it gives the best and most precise results when cracking real-world problems. In this work, a brief description of the applications of CNNs in two areas will be presented: First, in computer vision, gen...

Recurrent Neural Networks (RNNs) are possibly the most prevailing and advantageous type of neural network. On the other hand, these networks still have some weaknesses in terms of learning speed, error convergence, and accuracy due to long-term dependencies, which need to be solved. Long-term dependencies are mainly exploding and vanishing gradient...

The needs of communities and the new emerging technologies aspire researchers to come up with new and innovative ways to fulfil these needs. Sign languages are said to be a visual language that is used by the deaf community. Undoubtedly, there is a communication difficulty between the hearing-impaired people and the hearing community. To overcome t...

Stemming is one of the main important preprocessing techniques that can be used to enhance the accuracy of text classification. The key purpose of using the stemming is combining the number of words that have same stem to decrease high dimensionality of feature space. Reducing feature space cause to decline time to construct a model and minimize th...

An important issue in public goods game is whether player’s behaviour changes over time, and if so, how significant it is. In this game players can be classified into different groups according to the level of their participation in the public good. This problem can be considered as a concept drift problem by asking the amount of change that happen...

In the public goods game, players can be classified into different types according to their participation in the game. It is an important issue for economists to be able to measure players’ strategy changes over time which can be considered as concept drift. In this study, we present a method for measuring changes in items’ cluster membership in te...

Classifying items using temporal data, i.e. several readings of the same attribute in different time points, has many applications in the real world. The pivotal question which motivates this study is: ''Is it possible to quantify behavioural change in temporal data? And what is the best reference point to compare the behaviour change with?". The f...

An important issue in public goods game is whether player's behaviour changes over time, and if so, how significant it is. In this game players can be classified into different groups according to the level of their participation in the public good. This problem can be considered as a concept drift problem by asking the amount of change that happen...

This study optimises manually derived rule-based expert system classification of objects according to changes in their properties over time. One of the key challenges that this study tries to address is how to classify objects that exhibit changes in their behaviour over time, for example how to classify companies’ share price stability over a peri...

This study optimises manually derived rule-based expert system classification of objects according to changes in their properties over time. One of the key challenges that this study tries to address is how to classify objects that exhibit changes in their behaviour over time, for example how to classify companies’ share price stability over a peri...

This paper introduces an optimization approach for association rule mining in the time-memory domain. The approach splits the running mode of the traditional data mining algorithm into two phases. The first phase is designed to calculate all item sets in every transaction together with their frequencies (without pruning) and indexes their accumulat...

This paper introduces an optimization approach for association rule mining in the time-memory domain. The approach splits the running mode of the traditional data mining algorithm into two phases. The first phase is designed to calculate all item sets in every transaction together with their frequencies (without pruning) and indexes their accumulat...

The Web Based Application Visualization for Comprehensive Data Structures (WVCDS) project includes a Web based to sup- port the teaching of introductory computer-science statistics courses. The web application provides easy access to interface, watch sorting process dramatically when barter between each value regarding user need with ascending or d...

Well known categorizations for public goods game distribute players into four classes which they are conditional contributor, free rider, triangular contributor, and Uncategorized (Others). This classification entirely depends on the player’s answers for questions before starting the game about their willingness for contribution in a public good pr...

Many studies show that players’ contribution in public goods games are declining throughout a series of consequent game plays. Some studies take these results as an indication of decaying of conditional contributors while others argue that this is not the case and depends on other factors (e.g. boredom) which may affect players’ contribution. This...

Measuring Micro Changes over Time in Clustering

This study aims to find how people’s preferences change over time in public good games by clustering subjects into multiple groups of preference according to their behaviour inside an experimental game. After collecting data based on separate segments of time and clustering each segment individually the difference between any two segments is measur...

Modern humans found themselves living in an expanding universe of data in which there is too much data and too little information. The development of new techniques to find the required information from huge amount of data is one of the main challenges for software developers today. The process of knowledge discovering from data or databases is cal...