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A faulty situation occurred in Service Oriented Architecture (SOA) based systems can affect its functionality. It is challenging to guarantee the reliability of service utilization in a distributed, concurrent and dynamic composition of web services. This paper aims to present a fault detection approach of SOA using dependency analysis. Data and control dependencies are identified and a fault diagnosis approach based on these dependencies is proposed in this paper. The presented approach extends discrete state space techniques to simulate the concurrent and distributed behavior through Colored Petri Nets (CPNs) for the SOA-based systems in case of the faulty situation. Concrete translation from SOA based system to a CPN model is presented. Model-based technique is used and some heuristics are presented for dependency analysis and fault detection purpose. The experimental performance analysis shows the feasibility of our proposed approach.

In data mining, outlier detection is applied in different domains. It has very large applications such as energy consumption analysis, forecasting hurricanes in meteorological data, fraud and intrusion detection, event detection and system monitoring in sensor networks, etc. Most of existing outlier detection techniques depend on the properties of a particular type of data and can not deal with a large volume of data well, which mean that there is a necessity for improved methodologies and techniques to be applied to a large amount of data with different types in other application areas. In this paper, a parallel outlier detection technique is developed to detect the outliers in the sequential data. Although there are many types of outliers, this paper concentrates on the contextual anomalies. The proposed technique uses a graph approach to detect the outliers. It is very flexible, fast, and no labeled data is needed comparing to many previous approaches. The experimental results show the detected contextual outliers in the sequential data, as well as the efficient scaling up to handle the massive data by increasing the number of processors. The results prove that the parallelism of the proposed technique is very valuable.

Digital forensic aims to provide an assistance for making decisions about a crime by looking at a file content which usually involves image files such as GIF, BMP, JPEG and etc. JPEG is a very popular image file format. It has less structured contents than other images which makes its recovery possible in the absence of some file system metadata. However, an essential problem of which is fragmented JPEG file intertwined with non-JPEG files and/or Bifragmented in the scan area. This paper proposes RX_myKarve as a new file carving framework for solving a number of forensic recovery problems including fragmentation. The RX_myKarve basic design includes a structure-based and content-based carving approaches. It adopts machine learning and evolutionary algorithms in its main components of identification validation and reassembling. The identification and validation techniques encompass an Extreme Learning Machine (ELM) for identifying and filtering the image data in the scan area. The reassembling technique encompasses a genetic algorithm to reconstruct the data from fragmented pieces to a complete image. The main contribution of the paper lies on the reassembling of fragmented image file clusters in the scan area. The RX_myKarve is tested and evaluated by using the Digital Forensic Research Workshop (DFRWS) 2006 and 2007 forensic challenge datasets. The results show that the RX_myKarve is able to carve and fully recover all the giving cases of the DFRWS-2006 dataset, which are 19 images, and all the relevant cases of the DFRWS-2007 dataset, which are 18 images. This improvement in file carving is mostly attributed to the novel identification and reassembling techniques.

Internet has been source of knowledge for decades. The pool of information cannot be sustained in absence of the network of networks. Internet has many useful applications in commercial, social and educational areas. In today’s scenario, e-learning is also one of the useful applications in the world of Internet. The medium of e-learning has achieved advancement in various fields such as adaptive e-learning systems. The branch of computer science with psycholinguistics has done tremendous job in providing technical solutions to learners. However, learning disorders on the platform of e-learning still require lots of research. Therefore, this paper provides a personalized assessment model for alphabet learning with learning objects for children’s who face dyslexia. The cognitive inclination of dyslexic learner has been determined using assessment model. This paper studies the cognitive potential of dyslexic learner and has built a personalized e-learning platform to alleviate their alphabetical problems.

Due to the dynamic updating of real time spatial databases, the preservation of spatial association rules for dynamic database is a vital issue because the updates may not only invalidate some existing rules but also make other rules relevant. Consequently, the dynamic updating of spatial rules was handled by many researchers through the incremental association rule mining algorithm. Accordingly, in this paper we have developed an incremental topological association rule mining of geographical datasets using probabilistic approach. Initially, the spatial database is read out and it is passed through probability-based incremental association rule discovery algorithm to mine the topological spatial association rules. Once the rules are mined from the spatial database, the assumption here is that the database is dynamically updating for every time interval. In order to handle this dynamic nature, the proposed incremental topological association rule mining process is used in this paper. Here, the candidate topological rule generation is done from the spatial association rules using the topological relations such as, nearby, disjoint, intersects and inside/outside and the topological support is calculated using the proposed probabilistic topological support model. Finally, the spatial clustering is performed based on the mined spatial rules. From the experimentation, we proved that the maximum accuracy reached by the proposed method is 83.14% which is higher than the existing methods, which is defined as the ratio of the occurred rules and total number of topological data objects. Keywords: Spatial database, Association rule mining, Topological support, Probabilistic approach, Dynamic database

Kabakus, Abdullah Talha/0000-0003-2181-4292; Kara, Resul/0000-0001-8902-6837 WOS: 000414049000010 The popularity of NoSQL databases has increased due to the need of (1) processing vast amount of data faster than the relational database management systems by taking the advantage of highly scalable architecture, (2) flexible (schema-free) data structure, and, (3) low latency and high performance. Despite that memory usage is not major criteria to evaluate performance of algorithms, since these databases serve the data from memory, their memory usages are also experimented alongside the time taken to complete each operation in the paper to reveal which one uses the memory most efficiently. Currently there exists over 225 NoSQL databases that provide different features and characteristics. So it is necessary to reveal which one provides better performance for different data operations. In this paper, we experiment the widely used in-memory databases to measure their performance in terms of (1) the time taken to complete operations, and (2) how efficiently they use memory during operations. As per the results reported in this paper, there is no database that provides the best performance for all data operations. It is also proved that even though a RDMS stores its data in memory, its overall performance is worse than NoSQL databases. (C) 2016 The Authors. Production and hosting by Elsevier B.V. on behalf of King Saud University.

Specifying consistent services at early project stages is a telecommunication service engineering challenge. Service logic inconsistencies, also known as feature interactions (FIs), can affect various types of services ranging from signaling protocol features to value-added end user services. This problem has been investigated for all those types of services. However, inconsistencies of Operation, Administration, Management and Provisioning (OAM&P) services have not been sufficiently addressed. The present paper studies the detection of OAM&P service inconsistencies at the software requirement specification stage. The aim is at the resolution of the problem before reaching the implementation step. The basic idea of the here reported approach is to consider service inconsistencies as software requirement conflicts. The contribution of the present paper consists of an OAM&P requirement modeling language and a requirement conflict detection method. A validation with a case study is reported.

Visualization of the knowledge extraction process is a front line to reveal the detail process and data structure, which is an advanced technique for the presentation of data modeling. However, the mechanisms for healthcare are challenging and dynamic processes to gain a clear insight or understanding of patient care. In this paper, we proposed a new approach of architecture and optimization of data mining modeling for visualization of knowledge extraction by analyzing clinical data sets to define the determinant attributes through modeling techniques. Therefore, architecture for the visualization of the knowledge extraction process is a systematic approach to support users to the best of their knowledge of the issues over the challenge of visualizing techniques. The proposed approach is capable and dynamic to handle and analyze large-scale data in its dimension and context. Such a variable is defined using various techniques to characterize them towards the detection of determinant variables as its influential circumstance. We focused on modeling based visualization as model representation, factor's interaction and integration. The detection process experimented in a different approach and justification as discussed in section five. The finding showed a deep understandability for an advanced and dynamic data mining modeling techniques to integrate applications with domain contexts for the optimal and understandable decision process. The strength of this approach is the depth for visualization towards the knowledge extraction process and its understandability for users as per their background and circumstances. It is also essential to inference for architecture based modeling and visualization for large scale data. Researchers, physicians, experts, and other users are the potentials to refer to these novel ideas and findings.

Change detection is the amount of changes that can guide to more concrete understandings into essential method concerning land cover, land usage and ecological variations. This paper deals with an intelligent methodology to optimize the solution out of the solution space that improves the efficiency of the change detection process. To support the theme, an integrated semi-supervised method is designed with a focus on image fusion, semi supervised clustering and binary swarm based optimization. A new approach of handling fusion using sparse coding is referred to expand the amount of information. Using the extracted information, change detection process is carried out by a constrained clustering technique to provide a solution reflecting the level of changes occurred in the region of investigation. To improve the accuracy by proposing a global optimum solution, still the result is refined through binary swarm based optimization process and hence the results are accelerated towards the increased level of accuracy followed by which the change map is reconstructed to show case changes prominently. To determine the accurateness of the proposed methodology quantitative and qualitative analysis has been done with different datasets. The proposed method has been evaluated with existing techniques such as k-means, AKM, FCM, ECKM and ASCC to show the efficiency and proved to be the preeminent change detection methodology compared to the state-of-art methods.

Over the last decade, virtualization technologies have seen unprecedented growth in the system-level domain and promptly have moved to the embedded system domain. While there are numerous applications of virtualization for off-the-shelf hardware such as security, sandboxing, testing tools, etc. However, there are few open-source virtualization solutions available for embedded systems. For real-time virtualization, good runtime performance is a deterministic factor for its practical use in the embedded domain. In this paper, the internal architecture of HTTM (an open-source type-2 hypervisor for MIPS64 architecture) was thoroughly analyzed. ISA virtualization unit and execution cycle control were two primary units that were profiled and explored for optimization. Dynamic Binary Translation (DBT) unit was modified to generate highly efficient code. While the reduction in switching between the translated code and management layer improved the overall execution cycle. The implementation of these strategies resulted in a 72–91% improvement in bandwidth benchmarks. Similarly, latency benchmarks show a 2–92% improvement from its vanilla version. Collectively producing an overall 1–5 times the improvement in execution time. The performance of optimized HTTM is also compared with Quick Emulator (QEMU). HTTM performs 44–80% better than QEMU in bandwidth benchmarks while QEMU performs better in latency operations.

Multimedia event classification has been one of the major endeavors in video event analysis. For event identification, feature extraction plays a critical role, merely distinguishing the right features becomes a challenging job. So, in this paper, feature extraction and selection for video event detection are proposed. For video event detection or classification, identification of object structure and its motion are a basic needs. So, for object detection, curvelet features are considered, due to its high directional selectivity and high anisotropic properties. Next, an algorithm for Shot Boundary Detection (SBD) called Motion based SBD (MSBD) is proposed to identify the shot boundaries. Also, to make the users to access the queried event in less time, an algorithm for representing few representative frames containing whole video content is proposed. To make an event search efficient, object-based features from dominant features are extracted and to enhance the features much more efficient and dominant, feature selection is performed using the ranking method. Lastly, an SVM classifier with RBF kernel is used for event classification. The proposed work is experimented using Columbia Consumer Video (CCV) dataset and evaluated using mean Average Precision (mAP) and find that, it outperforms various other existing methods.

The excessive growth of high dimensional big data has resulted in a greater challenge for data scientists to efficiently obtain valuable knowledge from these data. Traditional data mining techniques are not fit to process big data. Predictive analytics has grown in prominence alongside the emergence of big data. In this paper, an efficient predictive analytics system for high dimensional big data is proposed by enhancing scalable random forest (SRF) algorithm on the Apache Spark platform. SRF is enhanced by optimizing the hyperparameters and prediction performance is improved by reducing the dimensions. The effectiveness of the proposed system is examined on five real-world datasets. Experimental results demonstrated that the proposed system achieves the highly competitive performance compared with RF algorithm implemented by Spark MLlib.

Sentiment analysis SA is concerned with determining users’ opinions from text reviews. However, mining these opinions from massive amounts of unstructured and lengthy documents is a challenging task. In this paper, we propose methods that extract valuable opinions and values from online movie reviews to improve SA in Arabic. First, we propose a method that explores the role of n-gram and skip-n-gram models in opinion classification. Second, we study a method that exploits subjective words such as adjectives and nouns by applying Part-Of Speech tagging. Both of the methods are combined with a feature reduction technique to enhance SA results. Third, we present a method that seeks to extract relevant opinions such as review summaries and conclusion opinions. Then, a combined approach is proposed to augment opinion classification results. Forth, we introduce a method for analyzing customers’ opinions by determining factors impacting their attitudes based on the costumer value model. Experimental results conducted on two datasets prove that our proposed methods are effective and provide better scores than baseline sentiment classifiers. The best obtained classification results reached 96% in F-Measure. These results indicate also that the aesthetic factor is the most influent factor in Arabic movie reviews.

The efficient segmentation of customers of an enterprise is categorized into groups of similar behavior based on the RFM (Recency, Frequency and Monetary) values of the customers. The transactional data of a company over is analyzed over a specific period. Segmentation gives a good understanding of the need of the customers and helps in identifying the potential customers of the company. Dividing the customers into segments also increases the revenue of the company. It is believed that retaining the customers is more important than finding new customers. For instance, the company can deploy marketing strategies that are specific to an individual segment to retain the customers. This study initially performs an RFM analysis on the transactional data and then extends to cluster the same using traditional K-means and Fuzzy C- Means algorithms. In this paper, a novel idea for choosing the initial centroids in K- Means is proposed. The results obtained from the methodologies are compared with one another by their iterations, cluster compactness and execution time.

The aim of this work is to analyse the performance of the new proposed hybrid parameterisation model in handling problematic data. Three types of problematic data will be highlighted in this paper: i) big data set, ii) uncertain and inconsistent data set and iii) imbalanced data set. The proposed hybrid model is an integration of three main phases which consist of the data decomposition, parameter reduction and parameter selection phases. Three main methods, which are soft set and rough set theories, were implemented to reduce and to select the optimised parameter set, while a neural network was used to classify the optimised data set. This proposed model can process a data set that might contain uncertain, inconsistent and imbalanced data. Therefore, one additional phase, data decomposition, was introduced and executed after the pre-processing task was completed in order to manage the big data issue. Imbalanced data sets were used to evaluate the capability of the proposed hybrid model in handling problematic data. The experimental results demonstrate that the proposed hybrid model has the potential to be implemented with any type of data set in a classification task, especially with complex data sets.

In linguistic steganography, languages’ features are employed to hide information. The Arabic language has a rich set of features which have not been utilized until now in this area. In particular, Arabic calligraphy contains multiple fonts and multiple shapes of Arabic alphabet letters. In this paper, a framework that uses Arabic calligraphy to hide information is proposed. The phases of the framework are preparation phase, embedding phase and extraction phase. The embedding phase uses string matching to generate stego text and accompanying letter shapes according to a secret message. The framework also includes corpus creation and a modification of the Aho-Corasick string-matching algorithm. The Arabic font Naskh was used as a case study. A set of Arabic poetry and proverbs were used as a dataset. The framework was evaluated on capacity and security. Because the visual difference between the cover and the stego-cover must be unnoticeable to the human in any stego-system, the security in this framework is satisfying due there is no cover used. The cover represents the secret message itself and it provides high capacity to hide data also. The evaluation showed the potential of using the multiple shapes of Arabic letters to satisfy steganography requirements.

The exponential growth of online textual data triggered the crucial need for an effective and powerful tool that automatically provides the desired content in a summarized form while preserving core information. In this paper, we propose an automatic, generic, and extractive Arabic single document summarizing method aiming at producing a sufficiently informative summary. The proposed extractive method evaluates each sentence based on a combination of statistical and semantic features in which a novel formulation is used taking into account sentence importance, coverage and diversity. Further, two summarizing techniques including score-based and supervised machine learning were employed to produce the summary and then assist leveraging the designed features. We demonstrate the effectiveness of the proposed method through a set of experiments under EASC corpus using ROUGE measure. Compared to some existing related work, the experimental evaluation shows the strength of the proposed method in terms of precision, recall, and F-score performance metrics.

A help desk system that acts as a single point of contact between users and IT staff is introduced in this paper. It utilizes an accurate ticket classification machine learning model to associate a help desk ticket with its correct service from the start and hence minimize ticket resolution time, save human resources, and enhance user satisfaction. The model is generated according to an empirically developed methodology that is comprised of the following steps: training tickets generation, ticket data preprocessing, words stemming, feature vectorization, and machine learning algorithm tuning. Nevertheless, the experimental results showed that including the ticket comments and description in the training data was one of the main factors that enhanced the model prediction accuracy from 53.8% to 81.4%. Furthermore, the system supports an administrator view that facilitates defining offered services, administering user roles, managing tickets and generating management reports. Also, it offers a user view that allows employees to report issues, request services, and exchange information with the IT staff via help desk tickets. Moreover, it supports automatic email notifications amongst collaborators for further action. Yet, it helps in defining business processes with well-defined activities and measuring KPIs to assess the performance of IT staff and processes.

The automatic processing of natural languages knew a remarkable growth in terms of techniques, methods and the variety of its potential applications. For instance, to automatize the processing of the Arabic language with its particular features, we selected the holy Quran as a study case. The latter has represented a challenge for the artificial intelligence. The main objective of the present paper is to design the semantic search engine for the text of the Quran using Quranic ontology. To determine the semantic fields of the words of the holy Quran, the Quranic ontology was developed, that presents the meaning of words and their relations. This method is used for each concept in order to enrich the query.

Explosive growth of data in digital world leads to the requirement of efficient technique to store and transmit data. Due to limited resources, data compression (DC) techniques are proposed to minimize the size of data being stored or communicated. As DC concepts results to effective utilization of available storage area and communication bandwidth, numerous approaches were developed in several aspects. In order to analyze how DC techniques and its applications have evolved, a detailed survey on many existing DC techniques is carried out to address the current requirements in terms of data quality, coding schemes, type of data and applications. A comparative analysis is also performed to identify the contribution of reviewed techniques in terms of their characteristics, underlying concepts, experimental factors and limitations. Finally, this paper insight to various open issues and research directions to explore the promising areas for future developments.

Bankruptcy prediction and credit risk analysis is one of the most significant problems in the field of accounting and financial decision making. Developing an effective classification rule induction (CRI) framework for bankruptcy prediction and credit risk analysis in appropriate time is essential to prevent the business communities from being bankrupt. Traditional statistical methods and artificial intelligence techniques play a major role to predict bankruptcy and credit risks. Most of the earlier research works were carried out on quantitative methods, while few studies have proposed on qualitative methods to improvise the performance of bankruptcy prediction models. The discovery of bankruptcy prediction in a qualitative way is an important task because it depends on the subjective knowledge of the experts. In this paper, a unified framework for qualitative and quantitative bankruptcy analysis using Ant Colony Optimization (ACO) based ant-miner algorithm is proposed. Three different natured datasets are used to present a trustworthy result. For this experiment, we have collected qualitative_bankruptcy dataset and benchmarked by UCI repository. The proposed method is successfully applied and the performance analysis prove that ant-miner method is better than existing classifiers namely Logistic Regression (LR), Multilayer Perceptron (MLP), Random Forest (RF) and Radial Basis Function (RBF) in terms of various performance analysis factors. Furthermore, the proposed ant-miner model is found to be a more suitable method for bankruptcy prediction when compared to other traditional statistical and artificial intelligence techniques.

This paper presents a novel hybrid approach for detecting frauds in automobile insurance claims by applying Genetic Algorithm (GA) based Fuzzy C-Means (FCM) clustering and various supervised classifier models. Initially, a test set is extracted from the original insurance dataset. The remaining train set is subjected to the clustering technique for undersampling after generating some meaningful clusters. The test instances are then segregated into genuine, malicious or suspicious classes after subjecting to the clusters. The genuine and fraudulent records are discarded, while the suspicious cases are further analyzed by four classifiers – Decision Tree (DT), Support Vector Machine (SVM), Group Method of Data Handling (GMDH) and Multi-Layer Perceptron (MLP) individually. The 10-fold cross validation method is used throughout the work for training and validation of the models. The efficacy of the proposed system is illustrated by conducting several experiments on a real world automobile insurance dataset.

Search based test data generation has gained popularity in recent times. Mutation testing can assist in improving the effectiveness of the generated test data. We recently proposed PSO-MT (Particle Swarm Optimization along with Mutation Testing) for generation of test data. In this paper, we fortify our proposal by applying the proposed approach on larger programs from Software-artifact Infrastructure Repository (SIR). PSO exhibits similar working characteristics with those of Genetic Algorithm (GA) which has extensively been applied for evolution of test data with mutation testing. The results are evaluated against comparison with GA used with mutation testing (GA-MT) for generation of test data which is already implemented in the literature of Search based Mutation Testing. The results depict that PSO-MT exhibits better computational efficiency than GA-MT for most of the benchmark programs. Statistical test (MannWhitney U-test) has been conducted to statistically analyze the presented results.

Due to the large number of documents available in the internet, emails and digital libraries, document classification is becoming a crucial task extremely required. It is commonly achieved after performing feature selection that consists of selecting appropriate features to enhance the classification accuracy. Most of feature selection based text classification methods rely on building a term-frequency inverse-document frequency feature vector which is not usually efficient. In addition, numerous document classification studies are focused on English language. This paper deals with Arabic Text Classification which is not intensively studied due to the complexity of Arabic language. A new firefly algorithm based feature selection method is proposed. This algorithm has been successfully applied in different combinatorial problems. However, it has not been involved in feature selection concept to deal with Arabic Text Classification. To validate this technique, Support Vector Machine classifier is used as well as three evaluation measures including precision, recall and F-measure. Furthermore, experiments on OSAC real dataset along with a comparison with the state-of-the-art methods are performed. The proposed method achieves a precision value equals to 0.994. The results confirm the efficiency of the proposed feature selection method in improving Arabic Text Classification accuracy. Keywords: Arabic Natural Language Processing, Feature Selection, Firefly optimization method, Text Classification

In software development life cycle (SDLC), the testing phase is important to test the functionalities of any software. In this phase, test cases are generated to test software functionalities. This paper presents an approach on how to detect and refactor code smells from the source codes of an Android application in order to reduce the redundancy in test case generation. Refactoring is one of the vital activities in software development and maintenance. Refactoring is a process of code alteration that aims to make structural modifications to the source code without altering any external functionality. These changes often improve software quality such as readability, execution time and maintainability. The proposed approach is then implemented and evaluated in order to determine its effectiveness in reducing the redundancy of test case generation. Keywords: Lazy class, Small method, Duplicate code smell, Code smell detection, Refactoring

Scheduling or the allocation of user requests (tasks) in the cloud environment is an NP-hard optimization problem. According to the cloud infrastructure and the user requests, the cloud system is assigned with some load (that may be underloaded or overloaded or load is balanced). Situations like underloaded and overloaded cause different system failure concerning the power consumption, execution time, machine failure, etc. Therefore, load balancing is required to overcome all mentioned problems. This load balancing of tasks (those are may be dependent or independent) on virtual machines (VMs) is a significant aspect of task scheduling in clouds. There are various types of loads in the cloud network such as memory load, Computation (CPU) load, network load, etc. Load balancing is the mechanism of detecting overloaded and underloaded nodes and then balance the load among them. Researchers proposed various load balancing approaches in cloud computing to optimize different performance parameters. We have presented a taxonomy for the load balancing algorithms in the cloud. A brief explanation of considered performance parameters in the literature and their effects is presented in this paper. To analyze the performance of heuristic-based algorithms, the simulation is carried out in CloudSim simulator and the results are presented in detail. Keywords: Cloud computing, Energy consumption, Load balancing, Makespan, Virtualization, VM, Task allocation

Advancements in the healthcare industry have given rise to the security threat to the ever growing e-medical data. The healthcare data management system records patient’s data in different formats such as text, numeric, pictures and videos leading to data which is big and unstructured. Also, hospitals may have several branches in different geographical locations. Sometimes, for research purposes, there is a need to integrate patients’ health data stored at different locations. In view of this, a cloud-based healthcare management system can be an effective solution for efficient health care data management. But the major concern of cloud-based healthcare system is the security aspect. It includes theft of identity, tax fraudulence, bank fraud, insurance frauds, medical frauds and defamation of high profile patients. Hence, a secure data access and retrieval is needed in order to provide security of critical medical records in healthcare management system. Biometric based authentication mechanism is suitable in this scenario since it overcomes the limitations of token theft and forgetting passwords in the conventional token id-password mechanism used for providing security. It also has high accuracy rate for secure data access and retrieval. In the present paper, a cloud-based system for management of healthcare data BAMHealthCloud is proposed, which ensures the security of e-medical data access through a behavioral biometric signature-based authentication. Training of the signature samples for authentication purpose has been performed in parallel on Hadoop MapReduce framework using Resilient Backpropagation neural network. From rigorous experiments, it can be concluded that it achieves a speedup of 9 times, Equal error rate (EER) of 0.12, the sensitivity of 0.98 and specificity of 0.95. Performance comparison of the system with other state-of-art-algorithms shows that the proposed system preforms better than the existing systems in literature. Keywords: Biometric, Authentication, Healthcare, Cloud, Healthcare cloud, Hadoop

Software quality in use (QinU) relates to human-software interactions when a software product is used in a particular context. Currently, QinU measurement models are bound to ineffective measurement formulation and many models are subjectively incoherent. This paper proposes a novel QinU framework (QinUF) to measure QinU competently consuming software reviews. The framework has three components: QinU prediction, polarity classification, and QinU scoring. The QinU prediction component computationally maps software review-sentences to its respective QinU characteristics (topics) of the ISO 25010 model based on a text similarity measure. The topic prediction problem is run as a text to text similarity; where the first text (test) is the actual unlabeled review-sentence and the second text is the set of selected features (keywords) from a benchmark dataset. The polarity classification component classifies each test sentence to its polarity orientation; the respective sentimental values are recorded. To score QinU, the sentimental values are grouped and summarized into their respective QinU topics. The QinUF evaluation over real-life scenarios showed that the QinUF automates software QinU measurement; therefore, users could compare and acquire software on the fly. The framework is consistent and superior to related compared works. Keywords: ISO25010, Quality in use, Sentiment analysis, Software quality, Text similarity

A hybrid watermarking scheme for multimedia data such as digital image, digital video is proposed in this paper. The scheme utilizes various image processing transforms and Compressive Sensing (CS) to achieved fragility and security for multimedia data. The compressive sensing is applied on the singular value of wavelet coefficients of watermark image to get the CS measurements. These CS measurements are embedded with embedding factor into the hybrid coefficients (high frequency curvelet coefficients of DT coefficients) of the host medium. In the proposed scheme, host medium may be digital image or video. The generated watermarked data have different security layers such as four levels from CS such as knowledge of transform basis matrix, measurement matrix, orthogonal matrices, information of wavelet basis matrix and one level from watermarking such as embedding factor. The scheme offers fragility and security to multimedia data against various standard watermarking attacks such as signal processing attacks, geometric attacks, and compression attacks. This scheme can be provided high payload capacity and used for host multimedia authentication. A comparison of proposed scheme with existed schemes shows that the proposed scheme performed faster and better than existed schemes available in the literature. Keywords: Compressive sensing, Curvelet, Image, Multimedia, Video, Watermarking

Cancer refers to a disease in which a group of cells show uncontrolled growth, invasion and metastasis. Data mining and machine learning are common approaches for clinical diagnosis. An important issue in this filed is that these data often include missing value which reduces the diagnostic accuracy. In this paper, a modified tensor factorization method is used for estimating the missing data. When tensor approach intends to estimate the missing value, there should be a class balance in the dataset and tensor does not properly estimate data in the case of data insufficiency. In the proposed schema, particle swarm optimization algorithm with adaptive adjustment (RAPSO) which is improved by chaotic search is used to solve this problem. In this approach, in order to inhibit the random initialization of the particle swarm algorithm, a distinctive approach is adopted and Bayesian network is used. Finally, the accuracy of disease diagnosis is estimated through different classifiers and RMSE measure. The results suggested that the proposed method is superior to other methods in terms of RMSE, accuracy, sensitivity and specificity. Keywords: Breast cancer diagnosis, Tensor factorization, Particle swarm optimization, Bayesian networks, Support vector machine, Chaos

Disguising the presence of communication has become a severe concern in this highly digitalized world due to the unauthorized data access and network policy violations that are emerging rapidly. These issues have led to the application of cryptography technique as a mean for securing data by encrypting them. However, since the encrypted data can be seen by sophisticated intruders during the transmission, this may lead to its suspicion which can results in unauthorized access. Thereby, steganography is another technique for securing communication. Steganography is the practice of concealing confidential information in the codes that make up digital files. Different from encryption, however, steganography provides security by disguising the presence of communication. In this context, this paper presents an improved information hiding implemented based on difference expansion and modulus function. The previous method has only considered the image smooth areas where the difference value is 0 or 1 while ignoring other values for hiding data. These limitations may result in decreasing the embedding capacity for all images having few smooth areas. Hence, a new scheme that considers both positive and negative difference values to conceal secret data is developed. The experimental results prove that the proposed scheme achieves better results than the existing methods. Keywords: Confidential data, Data hiding, Data protection, Difference expansion, Information security

In this paper, we present a simple and novel approach for steganography through transliteration. A phonetic keyboard layout is very popular for writing languages having non-roman alphabets. Bengali, a language spoken by 230 million people, is a fair example and in this work we utilize Bengali digital text for data hiding by the proposed technique. For several characters in the Bengali alphabet, there are multiple options to represent a character in it’s equivalent roman form using a phonetic keyboard layout. The main idea of the proposed method is to exploit this special feature of Bengali phonetic keyboard layouts to hide secret information in form of bits. One of these options can be used to represent the bit ‘0’ and the other option can represent the bit ‘1’ in a document without any risk of understanding by any intermediate user. The results show that the proposed method is very prominent to be a successful steganography technique. Steganalysis results show that the capacity of the method is 1.2%, which is adequate for a text steganography system with very low risk of machine detection. This method can be easily adapted and applied for any other language having non-roman alphabet. Keywords: Steganography, Transliteration, Phonetic keyboard, Capacity, Steganalysis, SVM

Data mining and machine learning approaches can be used to predict breast cancer recurrence. However, real datasets often include missing values for various reasons. In this paper, a hybrid imputation method is proposed with respect to the dependency between the attributes and the type of incomplete attributes in order to especially improve the prediction of breast cancer recurrence. After splitting the dataset into two discrete and numerical subsets, first missing values of the discrete fields are imputed using Bayesian network. Then, using Tensor factorization, the integrated dataset, which comprises of the filled-subset of the previous stage and numerical missing values subset, is constructed so that both continuous missing values are imputed and the accuracy of imputation is enhanced. We evaluated the proposed method versus six imputation methods i.e. mean, Hot-deck, K-NN, Weighted K-NN, Tensor factorization and Bayesian network on three datasets and used three classifiers, namely decision tree, K-Nearest Neighbor and Support Vector Machine for recurrence prediction. Experimental results show that the proposed method has as average 0.26 prediction improvement. Also, the prediction performance of the proposed approach outperforms all other imputation-classifier pairs in terms of specificity, sensitivity and accuracy. Keywords: Breast cancer recurrence, Missing value imputation, Classification, Tensor factorization, Bayesian network

WOS: 000462516600009, Neural Network (NN), hybrid NN methods and Rotation Forest (RF) ensemble classifier are preferred in pattern analysis owing to their ability for finding efficient solutions on different problems. NN architecture usually includes backpropagation type algorithms in which error is exposed to fluctuations. Hybrid NN methods are generally designed to improve the classification performance of NN. Scout Particle Swarm Optimization (ScPSO) is one of these optimization algorithms including the effective parts of Particle Swarm Optimization (PSO) and Artificial Bee Colony Optimization (ABC). Moreover, RF algorithm usually indicates the same performance as in hybrid NN methods, although it is comprised of Decision Tree (DT) classifiers. At this point, our paper investigates whether RF using the hybrid NNs can outperform other ensemble classifiers in binary-medical pattern classification, or not. With this intention, PSO, ABC and ScPSO are placed in NN algorithms instead of back propagation, and hybrid methods (PSO-NN, ABC-NN and ScPSO-NN) are realized. As a result, RF (PSO-NN), RF (ABC-NN) and RF (ScPSO-NN) architectures are obtained. Classification Accuracy (CA), Area Under Curve (AUC), Sensitivity, Specificity, F-measure, Gmean and Precision metrics are used for a statistical performance comparison, and a test based on 2-fold cross validation method was realized on five medical datasets. (C) 2017 The Authors. Production and hosting by Elsevier B.V. on behalf of King Saud University.

Data stream classification plays a vital role in data mining techniques which extracts the most important patterns from the real world database. Nowadays, many applications like sensor network, video surveillance and network traffic generate a huge amount of data streams. Due to the ambiguity in input data, imprecise input information and concept drift, some problems arise in classifying the data stream. To resolve these problems, we propose a HRNeuro fuzzy system in this paper based on rough set theory and holoentropy function. At first, the input database is given to the PCA algorithm to reduce the dimension of the data. An adaptive neuro fuzzy classifier is utilized where the designing of membership function and rule base are the two important aspects. Then, neuro-fuzzy system undergoes updating when the change of detection occurs between the data streams. Here, the updating behaviour of membership function and rules are performed using rough set theory and holoentropy function. The experimental results are evaluated for the datasets and the performance is analysed by some metrics and compared with the existing systems such as JIT adaptive K-NN and HRFuzzy system. From the result, it is concluded that our proposed fuzzy classifier attains the higher accuracy of 96% which proves the efficient performance of data stream classification algorithm. Keywords: Data stream, Neuro fuzzy, Change of detection, Rough set theory, Holoentropy function

Interoperability in healthcare environment deals with sharing of patient’s Electronic Health Records (EHR) with fellow professionals in inter as well as intra departments or organizations. Healthcare environment experiences frequent shifting of doctors, paramedical staff in inter as well as intra departments or hospitals. The system exhibits dynamic attributes of users and resources managed through access control policies defined for that environment. Rules obtained on merging of such policies often generate policy-conflicts thereby resulting in undue data leakages to unintended users. This paper proposes an access control framework that applies a Hierarchy Similarity Analyzer (HSA) on the policies need to be merged. It calculates a Security_Level (SL) and assigns it to the users sharing data. The SL determines the authorized amount of data that can be shared on successful collaboration of two policies. The proposed framework allows integration of independent policies and identifies the possible policy-conflicts arising due to attribute disparities in defined rules. The framework is implemented on XACML policies and compared with other access models designed using centralized and decentralized approaches. Conditional constraints and properties are defined that generate policy-conflicts as prevalent in the policies.

Cosine similarity is one of the most popular distance measures in text classification problems. In this paper, we used this important measure to investigate the performance of Arabic language text classification. For textual features, vector space model (VSM) is generally used as a model to represent textual information as numerical vectors. However, Latent Semantic Indexing (LSI) is a better textual representation technique as it maintains semantic information between the words. Hence, we used the singular value decomposition (SVD) method to extract textual features based on LSI. In our experiments, we conducted comparison between some of the well-known classification methods such as Naive Bayes, k-Nearest Neighbors, Neural Network, Random Forest, Support Vector Machine, and classification tree. We used a corpus that contains 4,000 documents of ten topics (400 document for each topic). The corpus contains 2,127,197 words with about 139,168 unique words. The testing set contains 400 documents, 40 documents for each topics. As a weighing scheme, we used Term Frequency.Inverse Document Frequency (TF.IDF). This study reveals that the classification methods that use LSI features significantly outperform the TF.IDF-based methods. It also reveals that k-Nearest Neighbors (based on cosine measure) and support vector machine are the best performing classifiers.

VoIP usage is rapidly growing due to its cost effectiveness, dramatic functionality over the traditional telephone network and its compatibility with public switched telephone network (PSTN). In some countries, like Pakistan, the commercial usage of VoIP is prohibited. Internet service providers (ISPs) and telecommunication authorities are interested in detecting VoIP calls to either block or prioritize them. So detection of VoIP calls is important for both types of authorities. Signature-based, port-based, and pattern-based VoIP detection techniques are inefficient due to complex and confidential security and tunneling mechanisms used by VoIP. In this paper, we propose a generic, robust, efficient, and practically implementable statistical analysis-based solution to identify encrypted, non-encrypted, or tunneled VoIP media (voice) flows using threshold values of flow statistical parameters. We have made a comparison with existing techniques and evaluated our system with respect to accuracy and efficiency. Our system has 97.54% direct rate and .00015% false positive rate.

Research has increased notably in vision-based automatic sign language recognition (ASLR). However, there has been little attention given to building a uniform platform for these purposes. Sign language (SL) includes not only static hand gestures, finger spelling, hand motions (which are called manual signs “MS”) but also facial expressions, lip reading, and body language (which are called non-manual signs “NMS”). Building up a database (DB) that includes both MS and NMS is the main first step for any SL recognition task. In addition to this, the Arabic Sign Language (ArSL) has no standard database. For this purpose, this paper presents a DB developed for the ArSL MS and NM signs which we call SignsWorld Atlas. The postures, gestures, and motions included in this DB are collected in lighting and background laboratory conditions. Individual facial expression recognition and static hand gestures recognition tasks were tested by the authors using the SignsWorld Atlas, achieving a recognition rate of 97% and 95.28%, respectively.

Sentiment analysis is the process of determining a predefined sentiment from text written in a natural language with respect to the entity to which it is referring. A number of lexical resources are available to facilitate this task in English. One such resource is the SentiWordNet, which assigns sentiment scores to words found in the English WordNet. In this paper, we present an Arabic sentiment lexicon that assigns sentiment scores to the words found in the Arabic WordNet. Starting from a small seed list of positive and negative words, we used semi-supervised learning to propagate the scores in the Arabic WordNet by exploiting the synset relations. Our algorithm assigned a positive sentiment score to more than 800, a negative score to more than 600 and a neutral score to more than 6000 words in the Arabic WordNet. The lexicon was evaluated by incorporating it into a machine learning-based classifier. The experiments were conducted on several Arabic sentiment corpora, and we were able to achieve a 96% classification accuracy.