Mian Muhammad Awais

On Vowels Segmentation and Identification Using Formant Transitions in Continuous Recitation of Quranic Arabic

This paper provides an analysis of cues to identify Arabic vowels. A new algorithm for vowel identification has been developed that uses formant frequencies. The algorithm extracts the formants of already segmented recitation audio files and recognizes the vowels on the basis of these extracted formants. The investigation has been done in context of recitation principles of Holy Quran which are commonly known as Tajweed rules. Primary objective of this work is to be able to identify zabar /a/, zair /e/ and pesh /u/ mistakes of the recitor during the recitation. Acoustic Analysis was performed on 150 samples of different recitors and a corpus comprising recitation of five experts was used to validate the results. The vowel identification system developed here has shown up to 90% average accuracy on continuous speech files comprising around 1000 vowels.

Eliminating process of normalization in relational database design

The relational database design approach requires the process of normalization in order to minimize data redundancy and update anomalies in the relational schema. Algorithms defined in normalization theory depend upon various dependencies namely functional, multivalued, join and inclusion dependencies that should be carefully defined for a database application. Identification of these dependencies and a minimal cover is a complex and time consuming task for almost all practical problems. This work discusses how the normalization process can be eliminated from the required steps of database design. It explores various constructs of entity relationship diagram (ERD) and their transformation to relational schema. This work elaborates how un-normalized relations are created during the entity relationship (ER) model to relational schema transformation. A set of rules is presented which if followed at the stage of conceptual modeling would always generate a relational schema that satisfies normal forms up to Boyce-Codd normal form (BCNF), thus eliminating the need for normalization. The motivation behind this paper is to save the database designers valuable time and effort otherwise required in defining dependencies, in finding a minimal cover and in normalizing a given relational schema.

Comparative Study of Various Artificial Intelligence Techniques to Predict Software Quality

Software quality prediction models are used to identify software modules that may cause potential quality problems. These models are based on various metrics available during the early stages of software development life cycle like product size, software complexity, coupling and cohesion. In this survey paper, we have compared and discussed some software quality prediction approaches based on Bayesian belief network, neural networks, fuzzy logic, support vector machine, expectation maximum likelihood algorithm and case-based reasoning. This study gives better comparative insight about these approaches, and helps to select an approach based on available resources and desired level of quality.

Gastro-Intestinal Tract Inspired Computational Paradigm

The dynamic nature of handling undesirable, irritant and toxic items during digestion process by the defense mechanism associated with the Human Gastrointestinal Tract helps avoid intake of hazardous material in the body. The defense mechanism acts in coordination with the sensory organs and nervous system to keep a human healthy. In this paper, we have mapped the defense mechanism associated with the Human Gastrointestinal Tract from the biological/nature domain to the computer science/information technology domain for proposing a Gastrointestinal Tract Inspired Computing Model. The proposed model has its roots purely in the biological domain with the softbots used as the main building block for processing. The processing is facilitated by a centralized learning center that mimics the human nervous system functionality.The dynamic nature of handling undesirable, irritant and toxic items during digestion process by the defense mechanism associated with the human gastrointestinal tract helps avoid intake of hazardous material in the body. The defense mechanism acts in coordination with the sensory organs and nervous system to keep a human healthy. In this paper, we have mapped the defense mechanism associated with the human gastrointestinal tract from the biological/nature domain to the computer science/information technology domain for proposing a gastrointestinal tract inspired computing model. The proposed model has its roots purely in the biological domain with the softbots used as the main building block for processing. The processing is facilitated by a centralized learning center that mimics the human nervous system functionality.

Continuous Arabic Speech Segmentation using FFT Spectrogram

This paper describes a phoneme segmentation algorithm that uses fast Fourier transform (FFT) spectrogram. The algorithm has been implemented and tested for utterances of continuous Arabic speech of 10 male speakers that contain almost 2346 phonemes in total. The recognition system determines the phoneme boundaries and identifies them as pauses, vowels and consonants. The system uses intensity and phoneme duration for separating pauses from consonants. Intensity in particular is used to detect two specific consonants (/r/, /hf) when they are not detected through the spectrographic information. Segmentation accuracy of 95.39% for the overall system has been achieved

Ineffectiveness of Use of Software Science Metrics as Predictors of Defects in Object Oriented Software

Software science metrics (SSM) have been widely used as predictors of software defects. The usage of SSM is an effect of correlation of size and complexity metrics with number of defects. The SSM have been proposed keeping in view the procedural paradigm and structural nature of the programs. There has been a shift in software development paradigm from procedural to object oriented (OO) and SSM have been used as defect predictors of OO software as well. However, the effectiveness of SSM in OO software needs to be established. This paper investigates the effectiveness of use of SSM for: a)classification of defect prone modules in OO software b) prediction of number of defects. Various binary and numeric classification models have been applied on dataset kc1 with class level data to study the role of SSM. The results show that the removal of SSM from the set of independent variables does not significantly affect the classification of modules as defect prone and the prediction of number of defects. In most of the cases the accuracy and mean absolute error has improved when SSM were removed from the set of independent variables. The results thus highlight the ineffectiveness of use of SSM in defect prediction in OO software.

A novel approach to increase the robustness of speaker independent Arabic speech recognition

This work presents a two-tier approach through sequential application of intensity contours and formant tracks for accurate Arabic phoneme identification. The recognition system developed is based on data sets of 40 speakers for each Arabic phonetic sound. As a first step towards recognition of phonemes, the sound is sampled and then preprocessed to get formant frequencies and intensity contours. In order to automate the intensity and formant based feature extraction, a generalized regression neural network has been implemented, trained and validated on 21 input features.

Self-Configuration in Autonomic Systems Using Clustered CBR Approach

Self-configuration is one of the key properties of autonomic systems. We apply an experience-based artificial intelligence approach known as case-based reasoning (CBR) in order to help autonomic manager to devise new configuration solution. Searching the entire case-base on occurrences of every new problem is a time consuming task. We propose to cluster the case-base and classify each new problem among one of the clusters. Our approach to reduce the search space promises to achieve efficiency as well as accuracy. We performed experiments on a simulation of autonomic forest fire application and achieved inspiring results.

Towards a generic model for software quality prediction

Various models and techniques have been proposed and applied in literature for software quality prediction. Specificity of each suggested model is one of the impediments in development of a generic model. A few models have been quality factor specific whereas others are software development paradigm specific. The models can even be company specific or domain specific. The amount of work done for software quality prediction compels the researchers to get benefit from the existing models and develop a relatively generic model. Development of a generic model will facilitate the quality managers by letting them focus on how to improve the quality instead of employing time on deciding which technique best suites their scenario. This paper suggests a generic model which takes software as input and predicts a quality factor value using existing models. This approach captures the specificity of existing models in various dimensions (like quality factor, software development paradigm, and software development life cycle phase etc.), and calculates quality factor value based on the model with higher accuracy. Application of the model has been discussed with the help of an example.

An FIS for early detection of defect prone modules

Early prediction of defect prone modules helps in better resource planning, test planning and reducing the cost of defect correction in later stages of software lifecycle. Early prediction models based on design and code metrics are difficult to develop because precise values of the model inputs are not available. Conventional prediction techniques require exact inputs, therefore such models cannot always be used for early predictions. Innovative prediction methods that use imprecise inputs, however, can be applied to overcome the requirement of exact inputs. This paper presents a fuzzy inference system (FIS) that predicts defect proneness in software using vague inputs defined as fuzzy linguistic variables. The paper outlines the methodology for developing the FIS and applies the model to a real dataset. Performance analysis in terms of recall, accuracy, misclassification rate and a few other measures has been conducted resulting in useful insight to the FIS application. The FIS model predictions at an early stage have been compared with conventional prediction methods (i.e. classification trees, linear regression and neural networks) based on exact values. In case of the FIS model, the maximum and the minimum performance shortfalls were noticed for true negative rate (TNRate) and F measure respectively. Whereas for Recall, the FIS model performed better than the other models even with the imprecise inputs.