arXiv subjects

Ali Bou Nassif

Publications and source records attributed to Ali Bou Nassif.

At least 37 records · Page 2Linked to original sources

arXiv2021

CASA-Based Speaker Identification Using Cascaded GMM-CNN Classifier in Noisy and Emotional Talking Conditions

This work aims at intensifying text-independent speaker identification performance in real application situations such as noisy and emotional talking conditions. This is achieved by incorporating two different modules: a Computational Auditory Scene Analysis CASA based pre-processing module for noise reduction and cascaded Gaussian Mixture Model Convolutional Neural Network GMM-CNN classifier for speaker identification followed by emotion recognition. This research proposes and evaluates a novel algorithm to improve the accuracy of speaker identification in emotional and highly-noise susceptible conditions. Experiments demonstrate that the proposed model yields promising results in comparison with other classifiers when Speech Under Simulated and Actual Stress SUSAS database, Emirati Speech Database ESD, the Ryerson Audio-Visual Database of Emotional Speech and Song RAVDESS database and the Fluent Speech Commands database are used in a noisy environment.

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arXiv2021

Machine Learning Towards Intelligent Systems: Applications, Challenges, and Opportunities

The emergence and continued reliance on the Internet and related technologies has resulted in the generation of large amounts of data that can be made available for analyses. However, humans do not possess the cognitive capabilities to understand such large amounts of data. Machine learning (ML) provides a mechanism for humans to process large amounts of data, gain insights about the behavior of the data, and make more informed decision based on the resulting analysis. ML has applications in various fields. This review focuses on some of the fields and applications such as education, healthcare, network security, banking and finance, and social media. Within these fields, there are multiple unique challenges that exist. However, ML can provide solutions to these challenges, as well as create further research opportunities. Accordingly, this work surveys some of the challenges facing the aforementioned fields and presents some of the previous literature works that tackled them. Moreover, it suggests several research opportunities that benefit from the use of ML to address these challenges.

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arXiv2020

Predicting Software Effort from Use Case Points: A Systematic Review

Context: Predicting software project effort from Use Case Points (UCP) method is increasingly used among researchers and practitioners. However, unlike other effort estimation domains, this area of interest has not been systematically reviewed. Aims: There is a need for a systemic literature review to provide directions and supports for this research area of effort estimation. Specifically, the objective of this study is twofold: to classify UCP effort estimation papers based on four criteria: contribution type, research approach, dataset type and techniques used with UCP; and to analyze these papers from different views: estimation accuracy, favorable estimation context and impact of combined techniques on the accuracy of UCP. Method: We used the systematic literature review methodology proposed by Kitchenham and Charters. This includes searching for the most relevant papers, selecting quality papers, extracting data and drawing results. Result: The authors of UCP research paper, are generally not aware of previous published results and conclusions in the field of UCP effort estimation. There is a lack of UCP related publications in the top software engineering journals. This makes a conclusion that such papers are not useful for the community. Furthermore, most articles used small numbers of projects which cannot support generalizing the conclusion in most cases. Conclusions: There are multiple research directions for UCP method that have not been examined so far such as validating the algebraic construction of UCP based on industrial data. Also, there is a need for standard automated tools that govern the process of translating use case diagram into its corresponding UCP metrics. Although there is an increase interest among researchers to collect industrial data and build effort prediction models based on machine learning methods, the quality of data is still subject to debate

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arXiv2020

Multi-Stage Optimized Machine Learning Framework for Network Intrusion Detection

Cyber-security garnered significant attention due to the increased dependency of individuals and organizations on the Internet and their concern about the security and privacy of their online activities. Several previous machine learning (ML)-based network intrusion detection systems (NIDSs) have been developed to protect against malicious online behavior. This paper proposes a novel multi-stage optimized ML-based NIDS framework that reduces computational complexity while maintaining its detection performance. This work studies the impact of oversampling techniques on the models' training sample size and determines the minimal suitable training sample size. Furthermore, it compares between two feature selection techniques, information gain and correlation-based, and explores their effect on detection performance and time complexity. Moreover, different ML hyper-parameter optimization techniques are investigated to enhance the NIDS's performance. The performance of the proposed framework is evaluated using two recent intrusion detection datasets, the CICIDS 2017 and the UNSW-NB 2015 datasets. Experimental results show that the proposed model significantly reduces the required training sample size (up to 74%) and feature set size (up to 50%). Moreover, the model performance is enhanced with hyper-parameter optimization with detection accuracies over 99% for both datasets, outperforming recent literature works by 1-2% higher accuracy and 1-2% lower false alarm rate.

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arXiv2020

Bayesian Optimization with Machine Learning Algorithms Towards Anomaly Detection

Network attacks have been very prevalent as their rate is growing tremendously. Both organization and individuals are now concerned about their confidentiality, integrity and availability of their critical information which are often impacted by network attacks. To that end, several previous machine learning-based intrusion detection methods have been developed to secure network infrastructure from such attacks. In this paper, an effective anomaly detection framework is proposed utilizing Bayesian Optimization technique to tune the parameters of Support Vector Machine with Gaussian Kernel (SVM-RBF), Random Forest (RF), and k-Nearest Neighbor (k-NN) algorithms. The performance of the considered algorithms is evaluated using the ISCX 2012 dataset. Experimental results show the effectiveness of the proposed framework in term of accuracy rate, precision, low-false alarm rate, and recall.

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arXiv2020

Multi-split Optimized Bagging Ensemble Model Selection for Multi-class Educational Data Mining

Predicting students' academic performance has been a research area of interest in recent years with many institutions focusing on improving the students' performance and the education quality. The analysis and prediction of students' performance can be achieved using various data mining techniques. Moreover, such techniques allow instructors to determine possible factors that may affect the students' final marks. To that end, this work analyzes two different undergraduate datasets at two different universities. Furthermore, this work aims to predict the students' performance at two stages of course delivery (20% and 50% respectively). This analysis allows for properly choosing the appropriate machine learning algorithms to use as well as optimize the algorithms' parameters. Furthermore, this work adopts a systematic multi-split approach based on Gini index and p-value. This is done by optimizing a suitable bagging ensemble learner that is built from any combination of six potential base machine learning algorithms. It is shown through experimental results that the posited bagging ensemble models achieve high accuracy for the target group for both datasets.

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arXiv2020

Data Mining with Big Data in Intrusion Detection Systems: A Systematic Literature Review

Cloud computing has become a powerful and indispensable technology for complex, high performance and scalable computation. The exponential expansion in the deployment of cloud technology has produced a massive amount of data from a variety of applications, resources and platforms. In turn, the rapid rate and volume of data creation has begun to pose significant challenges for data management and security. The design and deployment of intrusion detection systems (IDS) in the big data setting has, therefore, become a topic of importance. In this paper, we conduct a systematic literature review (SLR) of data mining techniques (DMT) used in IDS-based solutions through the period 2013-2018. We employed criterion-based, purposive sampling identifying 32 articles, which constitute the primary source of the present survey. After a careful investigation of these articles, we identified 17 separate DMTs deployed in an IDS context. This paper also presents the merits and disadvantages of the various works of current research that implemented DMTs and distributed streaming frameworks (DSF) to detect and/or prevent malicious attacks in a big data environment.

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arXiv2020

Systematic Ensemble Model Selection Approach for Educational Data Mining

A plethora of research has been done in the past focusing on predicting student's performance in order to support their development. Many institutions are focused on improving the performance and the education quality; and this can be achieved by utilizing data mining techniques to analyze and predict students' performance and to determine possible factors that may affect their final marks. To address this issue, this work starts by thoroughly exploring and analyzing two different datasets at two separate stages of course delivery (20 percent and 50 percent respectively) using multiple graphical, statistical, and quantitative techniques. The feature analysis provides insights into the nature of the different features considered and helps in the choice of the machine learning algorithms and their parameters. Furthermore, this work proposes a systematic approach based on Gini index and p-value to select a suitable ensemble learner from a combination of six potential machine learning algorithms. Experimental results show that the proposed ensemble models achieve high accuracy and low false positive rate at all stages for both datasets.

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arXiv2020

Software Effort Estimation from Use Case Diagrams Using Nonlinear Regression Analysis

Software effort estimation in the early stages of the software life cycle is one of the most essential and daunting tasks for project managers. In this research, a new model based on non-linear regression analysis is proposed to predict software effort from use case diagrams. It is concluded that, where software size is classified from small to very large, one linear or non-linear equation for effort estimation cannot be applied. Our model with three different non-linear regression equations can incorporate the different ranges in software size.

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arXiv2020

EEG Wheelchair for People of Determination

The aim of this paper is to design and construct an electroencephalograph (EEG) based brain-controlled wheelchair to provide a communication bridge from the nervous system to the external technical device for people of determination or individuals suffering from partial or complete paralysis. EEG is a technique that reads the activity of the brain by capturing brain signals non-invasively using a special EEG headset. The signals acquired go through pre-processing, feature extraction and classification. This technique allows human thoughts alone to be converted to control the wheelchair. The commands used are moving to the right, left, forward, and backward and stop. The brain signals are acquired using the Emotiv Epoc headset. Discrete Wavelet Transform is used for feature extraction and Support Vector Machine (SVM) is used for classification.

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arXiv2019

Emirati-Accented Speaker Identification in Stressful Talking Conditions

This research is dedicated to improving text-independent Emirati-accented speaker identification performance in stressful talking conditions using three distinct classifiers: First-Order Hidden Markov Models (HMM1s), Second-Order Hidden Markov Models (HMM2s), and Third-Order Hidden Markov Models (HMM3s). The database that has been used in this work was collected from 25 per gender Emirati native speakers uttering eight widespread Emirati sentences in each of neutral, shouted, slow, loud, soft, and fast talking conditions. The extracted features of the captured database are called Mel-Frequency Cepstral Coefficients (MFCCs). Based on HMM1s, HMM2s, and HMM3s, average Emirati-accented speaker identification accuracy in stressful conditions is 58.6%, 61.1%, and 65.0%, respectively. The achieved average speaker identification accuracy in stressful conditions based on HMM3s is so similar to that attained in subjective assessment by human listeners.

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arXiv2019

Speaker Verification in Emotional Talking Environments based on Third-Order Circular Suprasegmental Hidden Markov Model

Speaker verification accuracy in emotional talking environments is not high as it is in neutral ones. This work aims at accepting or rejecting the claimed speaker using his/her voice in emotional environments based on the Third-Order Circular Suprasegmental Hidden Markov Model (CSPHMM3) as a classifier. An Emirati-accented (Arabic) speech database with Mel-Frequency Cepstral Coefficients as the extracted features has been used to evaluate our work. Our results demonstrate that speaker verification accuracy based on CSPHMM3 is greater than that based on the state-of-the-art classifiers and models such as Gaussian Mixture Model (GMM), Support Vector Machine (SVM), and Vector Quantization (VQ).

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arXiv2019

Can we rely on smartphone applications?

Smartphones are becoming necessary tools in the daily lives of mil-lions of users who rely on these devices and their applications. There are thou-sands of applications for smartphone devices such as the iPhone, Blackberry, and Android, thus their reliability has become paramount for their users. This work aims to answer two related questions: (1) Can we assess the reliability of mobile applications by using the traditional reliability models? (2) Can we model adequately the failure data collected from many users? Firstly, it has been proved that the three most used software reliability models have fallen short of the mark when applied to smartphone applications; their failures were traced back to specific features of mobile applications. Secondly, it has been demonstrated that the Weibull and Gamma distribution models can adequately fit the observed failure data, thus providing better means to predict the reliability of smartphone applications.

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arXiv2019

Software Development Effort Estimation Using Regression Fuzzy Models

Software effort estimation plays a critical role in project management. Erroneous results may lead to overestimating or underestimating effort, which can have catastrophic consequences on project resources. Machine-learning techniques are increasingly popular in the field. Fuzzy logic models, in particular, are widely used to deal with imprecise and inaccurate data. The main goal of this research was to design and compare three different fuzzy logic models for predicting software estimation effort: Mamdani, Sugeno with constant output and Sugeno with linear output. To assist in the design of the fuzzy logic models, we conducted regression analysis, an approach we call regression fuzzy logic. State-of-the-art and unbiased performance evaluation criteria such as standardized accuracy, effect size and mean balanced relative error were used to evaluate the models, as well as statistical tests. Models were trained and tested using industrial projects from the International Software Benchmarking Standards Group (ISBSG) dataset. Results showed that data heteroscedasticity affected model performance. Fuzzy logic models were found to be very sensitive to outliers. We concluded that when regression analysis was used to design the model, the Sugeno fuzzy inference system with linear output outperformed the other models.

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arXiv2018

Ensemble of Learning Project Productivity in Software Effort Based on Use Case Points

It is well recognized that the project productivity is a key driver in estimating software project effort from Use Case Point size metric at early software development stages. Although, there are few proposed models for predicting productivity, there is no consistent conclusion regarding which model is the superior. Therefore, instead of building a new productivity prediction model, this paper presents a new ensemble construction mechanism applied for software project productivity prediction. Ensemble is an effective technique when performance of base models is poor. We proposed a weighted mean method to aggregate predicted productivities based on average of errors produced by training model. The obtained results show that the using ensemble is a good alternative approach when accuracies of base models are not consistently accurate over different datasets, and when models behave diversely.

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arXiv2018

v-SVR Polynomial Kernel for Predicting the Defect Density in New Software Projects

An important product measure to determine the effectiveness of software processes is the defect density (DD). In this study, we propose the application of support vector regression (SVR) to predict the DD of new software projects obtained from the International Software Benchmarking Standards Group (ISBSG) Release 2018 data set. Two types of SVR (e-SVR and v-SVR) were applied to train and test these projects. Each SVR used four types of kernels. The prediction accuracy of each SVR was compared to that of a statistical regression (i.e., a simple linear regression, SLR). Statistical significance test showed that v-SVR with polynomial kernel was better than that of SLR when new software projects were developed on mainframes and coded in programming languages of third generation

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arXiv2018

Machine Learning Classifications of Coronary Artery Disease

Coronary Artery Disease (CAD) is one of the leading causes of death worldwide, and so it is very important to correctly diagnose patients with the disease. For medical diagnosis, machine learning is a useful tool, however features and algorithms must be carefully selected to get accurate classification. To this effect, three feature selection methods have been used on 13 input features from the Cleveland dataset with 297 entries, and 7 were selected. The selected features were used to train three different classifiers, which are SVM, Naïve Bayes and KNN using 10-fold cross-validation. The resulting models evaluated using Accuracy, Recall, Specificity and Precision. It is found that the Naïve Bayes classifier performs the best on this dataset and features, outperforming or matching SVM and KNN in all the four evaluation parameters used and achieving an accuracy of 84%.

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arXiv2018

Novel Cascaded Gaussian Mixture Model-Deep Neural Network Classifier for Speaker Identification in Emotional Talking Environments

This research is an effort to present an effective approach to enhance text-independent speaker identification performance in emotional talking environments based on novel classifier called cascaded Gaussian Mixture Model-Deep Neural Network (GMM-DNN). Our current work focuses on proposing, implementing and evaluating a new approach for speaker identification in emotional talking environments based on cascaded Gaussian Mixture Model-Deep Neural Network as a classifier. The results point out that the cascaded GMM-DNN classifier improves speaker identification performance at various emotions using two distinct speech databases: Emirati speech database (Arabic United Arab Emirates dataset) and Speech Under Simulated and Actual Stress (SUSAS) English dataset. The proposed classifier outperforms classical classifiers such as Multilayer Perceptron (MLP) and Support Vector Machine (SVM) in each dataset. Speaker identification performance that has been attained based on the cascaded GMM-DNN is similar to that acquired from subjective assessment by human listeners.

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