Papers by Raneem Qaddoura
EvoCC: An Open-Source Classification-Based Nature-Inspired Optimization Clustering Framework in Python
Applications of Evolutionary Computation
Temporal prediction of traffic characteristics on real road scenarios in Amman
Journal of Ambient Intelligence and Humanized Computing
IEEE Access
Online media has an increasing presence on the restaurants' activities through social media websi... more Online media has an increasing presence on the restaurants' activities through social media websites, coinciding with an increase in customers' reviews of these restaurants. These reviews become the main source of information for both customers and decision-makers in this field. Any customer who is seeking such places will check their reviews first, which usually affect their final choice. In addition, customers' experiences can be enhanced by utilizing other customers' suggestions. Consequently, customers' reviews can influence the success of restaurant business since it is considered the final judgment of the overall quality of any restaurant. Thus, decision-makers need to analyze their customers' underlying sentiments in order to meet their expectations and improve the restaurants' services, in terms of food quality, ambiance, price range, and customer service. The number of reviews available for various products and services has dramatically increased these days and so has the need for automated methods to collect and analyze these reviews. Sentiment Analysis (SA) is a field of machine learning that helps analyze and predict the sentiments underlying these reviews. Usually, SA for customers' reviews face imbalanced datasets challenge, as the majority of these sentiments fall into supporters or resistors of the product or service. This work proposes a hybrid approach by combining the Support Vector Machine (SVM) algorithm with Particle Swarm Optimization (PSO) and different oversampling techniques to handle the imbalanced data problem. SVM is applied as a machine learning classification technique to predict the sentiments of reviews by optimizing the dataset, which contains different reviews of several restaurants in Jordan. Data were collected from Jeeran, a well-known social network for Arabic reviews. A PSO technique is used to optimize the weights of the features, as well as four different oversampling techniques, namely, the Synthetic Minority Oversampling Technique (SMOTE), SVM-SMOTE, Adaptive Synthetic Sampling (ADASYN) and borderline-SMOTE were examined to produce an optimized dataset and solve the imbalanced problem of the dataset. This study shows that the proposed PSO-SVM approach produces the best results compared to different classification techniques in terms of accuracy, F-measure, G-mean and AUC, for different versions of the datasets.
A Grey Wolf-Based Clustering Algorithm for Medical Diagnosis Problems
Comparative Study for the Effect of CPU Speed in Fog Networks
2018 Fifth International Symposium on Innovation in Information and Communication Technology (ISIICT), 2018
Fog computing is recently introduced to work along with cloud computing in the promise of providi... more Fog computing is recently introduced to work along with cloud computing in the promise of providing better performance in contrast with using the cloud computing by itself. This work measures how CPU speed of the fog devices which is measured in million instruction per second (MIPS) effects the overall performance of the fog network in terms of energy consumption and end-to-end latency. The experiments conducted in this study proves that the CPU speed of the fog devices can effect the consumption of energy and the latency of the network.
Requirements Prioritization Techniques Review and Analysis
2017 International Conference on New Trends in Computing Sciences (ICTCS), 2017
Requirements prioritization is considered as one of the most important activities in the requirem... more Requirements prioritization is considered as one of the most important activities in the requirement engineering process. This paper gives an overview of the requirements prioritization activities and techniques. It also presents how data mining and machine learning techniques have been used to prioritize the software project requirements. A comparison between these techniques is also presented.
Symmetry
Due to the accelerated growth of symmetrical sentiment data across different platforms, experimen... more Due to the accelerated growth of symmetrical sentiment data across different platforms, experimenting with different sentiment analysis (SA) techniques allows for better decision-making and strategic planning for different sectors. Specifically, the emergence of COVID-19 has enriched the data of people’s opinions and feelings about medical products. In this paper, we analyze people’s sentiments about the products of a well-known e-commerce website named Alibaba.com. People’s sentiments are experimented with using a novel evolutionary approach by applying advanced pre-trained word embedding for word presentations and combining them with an evolutionary feature selection mechanism to classify these opinions into different levels of ratings. The proposed approach is based on harmony search algorithm and different classification techniques including random forest, k-nearest neighbor, AdaBoost, bagging, SVM, and REPtree to achieve competitive results with the least possible features. The...
Empirical Evaluation of Distance Measures for Nearest Point with Indexing Ratio Clustering Algorithm
Proceedings of the 17th ACM Symposium on QoS and Security for Wireless and Mobile Networks
The traffic characteristics over the road network vary from a country to another. The geometric p... more The traffic characteristics over the road network vary from a country to another. The geometric parameters of the designed roads and the driving habits and behavior represent the main factors in this aspect. The traffic volume, traffic speed, traffic density, and traffic context are the main interesting characteristics for investigation. Predicting these characteristics is also affected by several factors including the position and speed of each vehicle. In this work, we aim to use some machine learning techniques including Linear Regression, K-Nearest Neighbor, and Decision Tree to predict the traffic characteristics of six main selected roads in Jordan, the Kingdom of Saudi Arabia (KSA), and the United Arab Emirates (UAE). From the experimental results, we can infer that the KNN algorithm gives better results in terms of and 2 for the six roads when predicting the vehicles' information for future periods of time. CCS CONCEPTS • Networks → Application layer protocols.
Privacy Preservation Tools and Techniques in Artificial Intelligence
Cybersecurity
A Classification Approach Based on Evolutionary Clustering and Its Application for Ransomware Detection
Evolutionary Data Clustering: Algorithms and Applications
Predicting Different Types of Imbalanced Intrusion Activities Based on a Multi-Stage Deep Learning Approach
2021 International Conference on Information Technology (ICIT)
Matrix multiplication of big data using MapReduce: A review
2017 2nd International Conference on the Applications of Information Technology in Developing Renewable Energy Processes & Systems (IT-DREPS)
SN Computer Science
EvoCluster is an open source and cross-platform framework implemented in Python language, which i... more EvoCluster is an open source and cross-platform framework implemented in Python language, which includes the most wellknown and recent nature-inspired metaheuristic optimizers that are customized to perform partitional clustering tasks. This paper is an extension to the existing EvoCluster framework in which it includes different distance measures for the objective function, different techniques of detecting the k value, and a user option to consider either supervised or unsupervised datasets. The current implementation of the framework includes ten metaheuristic optimizers, thirty datasets, five objective functions, twelve evaluation measures, more than twenty distance measures, and ten different ways for detecting the k value.
Sensors
The security of IoT networks is an important concern to researchers and business owners, which is... more The security of IoT networks is an important concern to researchers and business owners, which is taken into careful consideration due to its direct impact on the availability of the services offered by IoT devices and the privacy of the users connected with the network. An intrusion detection system ensures the security of the network and detects malicious activities attacking the network. In this study, a deep multi-layer classification approach for intrusion detection is proposed combining two stages of detection of the existence of an intrusion and the type of intrusion, along with an oversampling technique to ensure better quality of the classification results. Extensive experiments are made for different settings of the first stage and the second stage in addition to two different strategies for the oversampling technique. The experiments show that the best settings of the proposed approach include oversampling by the intrusion type identification label (ITI), 150 neurons for ...
IEEE Access
We are currently witnessing an immense proliferation of natural language processing (NLP) applica... more We are currently witnessing an immense proliferation of natural language processing (NLP) applications. Natural language generation (NLG) has emerged from NLP and is now commonly utilized in various applications, including chatting applications. The objective of this paper is to propose a deep learning-based language generation model that simplifies the process of writing medical recommendations for doctors in an Arabic context, to improve service satisfaction and patient-doctor interactions. The developed language generation model is a predictive text system intended for next word prediction in a telemedicine service. Altibbi-a digital platform for telemedicine and teleconsultations services in the Middle East and the North Africa (MENA) region-was utilized as a case study for the textual prediction process. The proposed model was trained using data obtained from Altibbi databases related to medical recommendations, particularly gynecology, dermatology, psychiatric diseases, urology, and internist diseases. Variants of deep learning models were implemented and optimized for next word prediction, based on the unidirectional and bidirectional long short-term memory (LSTM and BiLSTM), the one-dimensional convolutional neural network (CONV1D), and a combination of LSTM and CONV1D (LSTM-CONV1D). The algorithms were trained using two versions of the datasets (i.e., 3-gram and 4-gram representations) and evaluated in terms of their training accuracy and loss, validation accuracy and loss, and testing accuracy per their matching scores. The proposed models' performances were comparable. CONV1D produced the most promising matching score. INDEX TERMS Altibbi, natural language processing, deep learning, telemedicine, Arabic, predictive text.
Sensors
Maintaining a high quality of conversation between doctors and patients is essential in telehealt... more Maintaining a high quality of conversation between doctors and patients is essential in telehealth services, where efficient and competent communication is important to promote patient health. Assessing the quality of medical conversations is often handled based on a human auditory-perceptual evaluation. Typically, trained experts are needed for such tasks, as they follow systematic evaluation criteria. However, the daily rapid increase of consultations makes the evaluation process inefficient and impractical. This paper investigates the automation of the quality assessment process of patient–doctor voice-based conversations in a telehealth service using a deep-learning-based classification model. For this, the data consist of audio recordings obtained from Altibbi. Altibbi is a digital health platform that provides telemedicine and telehealth services in the Middle East and North Africa (MENA). The objective is to assist Altibbi’s operations team in the evaluation of the provided c...
IEEE Access
In recent years, Ransomware has been a critical threat that attacks smartphones. Ransomware is a ... more In recent years, Ransomware has been a critical threat that attacks smartphones. Ransomware is a kind of malware that blocks the mobile's system and prevents the user of the infected device from accessing their data until a ransom is paid. Worldwide, Ransomware attacks have led to serious losses for individuals and stakeholders. However, the dramatic increase of Ransomware families makes to the process of identifying them more challenging due to their continuously evolved characteristics. Traditional malware detection methods (e.g., statistical-based prevention methods) fail to combat the evolving Ransomware since they result in a high percentage of false positives. Indeed, developing a non-classical, intelligent technique to safeguarding against Ransomware is of significant importance. This paper introduces a new methodology for the detection of Ransomware that is depending on an evolutionary-based machine learning approach. The binary particle swarm optimization algorithm is utilized for tuning the hyperparameters of the classification algorithm, as well as performing feature selection. The support vector machines (SVM) algorithm is used alongside the synthetic minority oversampling technique (SMOTE) for classification. The utilized dataset is collected from various sources, which consists of 10,153 Android applications, where 500 of them are Ransomware. The performance of the proposed approach SMOTE-tBPSO-SVM achieved merits over traditional machine learning algorithms by having the highest scores in terms of sensitivity, specificity, and g-mean.
Applied Sciences
Intrusion detection of IoT-based data is a hot topic and has received a lot of interests from res... more Intrusion detection of IoT-based data is a hot topic and has received a lot of interests from researchers and practitioners since the security of IoT networks is crucial. Both supervised and unsupervised learning methods are used for intrusion detection of IoT networks. This paper proposes an approach of three stages considering a clustering with reduction stage, an oversampling stage, and a classification by a Single Hidden Layer Feed-Forward Neural Network (SLFN) stage. The novelty of the paper resides in the technique of data reduction and data oversampling for generating useful and balanced training data and the hybrid consideration of the unsupervised and supervised methods for detecting the intrusion activities. The experiments were evaluated in terms of accuracy, precision, recall, and G-mean and divided into four steps: measuring the effect of the data reduction with clustering, the evaluation of the framework with basic classifiers, the effect of the oversampling technique,...
EvoCluster: An Open-Source Nature-Inspired Optimization Clustering Framework in Python
Applications of Evolutionary Computation
Uploads
Papers by Raneem Qaddoura