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In this paper, a new approach to time series classification is proposed. It transforms the scalar time series into a two-dimensional space of amplitude (time series values) and a change of amplitude (increment). Subsequently, it uses this representation to plot the data. One figure is produced for each time series. In consequence, the time series classification problem is converted into the visual pattern recognition problem. This transformation allows applying a wide range of algorithms for standard pattern recognition – in this domain, there are more options to choose from than in the domain of time series classification. In this paper, we demonstrated the high effectiveness of the new method in a series of experiments on publicly available time series. We compare our results with several state-of-the-art approaches dedicated to time series classification. The new method is robust and stable. It works for time series of differing lengths and is easy to extend and alter. Even with a baseline variant presented in an empirical study in this paper, it achieves a satisfying classification accuracy. Furthermore, the proposed conversion of raw time series into images that are subjected to feature extraction opens the possibility to apply standard clustering algorithms.

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.

Facial Expression Recognition (FER) is an attractive and demanding problem in human computer interaction. Facial expressions are one of the most dominant and usual means for a human to communicate their feelings and intentions. This paper proposes the Enhanced Modified Decision Based Unsymmetric Trimmed Median Filter (EMDBUTMF) method which removes the noisy pixels from the face image. Also, this paper proposes the Local Directional Number (LDN) pattern, Dominant Gradient Local Ternary Pattern (DGLTP) descriptor for feature extraction and Support Vector Machine (SVM) classifier for classification. The histogram features are extracted from face images using the LDN and DGLTP descriptors. The experimental result shows that the proposed method gives an improved performance of 88% accuracy on two publicly available facial expression datasets JAFFE and CK.

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.

Today, VLSI technology has taken a fundamental role in developing most of the high-tech electronic circuits. Even though VLSI design is renowned for its smaller size, lower cost, lower power, high reliability and high functionality, the design process takes long time and produces high risk. So, to obtain the knowledge regarding the different contributions on VLSI design, about 52 papers on the design of VLSI using optimization are reviewed here. Accordingly, VLSI design optimization is analyzed through different bio-inspired algorithms and the performance measures of different VLSI experimentations are compared. Further, various improvements on Self-Adaptive Particle Swarm Optimization (SA-PSO) and VLSI design optimization, without the adoption of bio-inspired algorithms, are examined. Additionally, the floor planning problem of VLSI is considered and reviewed. Eventually, this paper provides the diverse research gaps and challenges in VLSI design, which may be helpful for the authors and the philosophers to contribute for further research.

Multiple-input multiple-output (MIMO) antennas integrated with orthogonal frequency division multiplexing (OFDM) gains high interest due to its potential reduction on interference as well as efficient data transmission. However, the rapid growth of voluminous data and their diverse characteristics necessitate the effectiveness of error correction schemes in the recent past. The conventional error correction schemes suffer from its compatibility over the diverse data. Despite the concatenated error correction schemes meet the requirements, the precision remains uncertain under data and image transmission. This paper intends to develop an error correction scheme with the combination of Polar Coding (PC), which is an advanced coding scheme, and the renowned Convolution coding (CC). The competiveness of the proposed error correction scheme is experimented under extensive simulation constraints. Accordingly, this paper simulates MIMO-OFDM system with three antenna configurations (2 × 2), (2 × 4) and (4 × 4), which are subjected to experimentation under three modulation schemes such as Binary Phase Shift Keying (BPSK), Quadrature Phase Shift Keying (QPSK), Quadrature Amplitude Modulation (QAM)-16 and QAM-64. Three practical channel models such as Additive White Gaussian Noise (AWGN), Rayleigh and Rician channel models are used in the proposed study. The performance of the proposed coding scheme is compared against the traditional error correction schemes such as CC, Reed Solomon Code (RSC)-CC, Low-Density Parity Check (LDPC)-CC and turbo-CC. The study is also extended by transmitting the data as well as image to ensure the fairness on diversity of the data characteristics. The study discloses the effectiveness of the proposed error correction scheme over the conventional error correction schemes in terms of BER (Bit Error Rate) and PSNR (Peak Signal to Noise Ratio), when data and images are transmitted, respectively. The proposed method is compared with the conventional techniques and the results have shown that the PSNR performance of the proposed PC-CC method is 37.95% better than CC, 31.73% better than RSC-CC, 24.52% better than LDPC-CC and 14.12% better than turbo-CC method.

A swarm-based metaheuristic algorithm, like artificial bee colony (ABC), embraces four key elements of collective intelligence: positive feedback, negative feedback, multiple interactions, and fluctuation. Fluctuation refers to population diversity which can be measured using dimension-wise diversity. This paper performed component-wise analysis of ABC algorithm using diversity measurement. The analysis revealed scout bees component as counterproductive and onlooker bees component with poor global search ability. Subsequently, an ABC algorithm without scout bees component and modified onlooker bees component is proposed in this paper. The effectiveness and efficiency of the proposed ScoutlessABC is validated on test suite of a dozen of benchmark functions. To further evaluate the performance, ScoutlessABC is employed on the parameter training problem of fuzzy neural network for solving eight classification problems. The experimental results show that ScoutlessABC maintains strong convergence ability than the original ABC algorithm. Overall, this study has two major contributions: (a) an effective component-wise analysis approach using diversity measurement and (b) a simplified and modified ABC variant with enhanced search efficiency.

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.

Underwater Wireless Sensor Network (UWSN) technology is widely used in various underwater monitoring and exploration applications and has proven its high stature. Since many years various UWSN protocols have been designed or existing protocols are improvised for effective and qualitative research analysis. The data aggregation is one of the schemes that is widely been used along with UWSN protocols to achieve better results. Thus it is foremost required to present a periodical review on data aggregation. Herein we present a paper based on the survey of UWSN with data aggregation to highlight its benefits and limitations. Ambition behind this paper is to build interest of research fraternity towards future challenges identified on the basis of survey of existing approaches. The existing techniques that aggregate data are divided into cluster based, non-cluster based and other approaches. The existing techniques are analysed along with their advantages and disadvantages. Moreover, the performances of K-means, Distributed underwater clustering scheme and Round-based clustering approaches are compared in terms of delay, packet drop and energy consumption, with and without aggregation. Also the performance of Receiver Oriented Sleep Scheduling, Intra and Inter Cluster Communication and Energy Efficient Distributed Time Synchronization techniques are compared. Keywords: Review, Survey, Future challenges, Data aggregation, UWSN

The latest research has shown that adaptive directional wavelet transform can constitute edges and textures in images efficiently due to the adaptive directional selectivity. This paper is primarily focused on the application of adaptive directional wavelet transform in conjunction with linear discriminant analysis (LDA) for capturing the discriminant directional multiresolution facial features. The intention of this paper is to explore the efficacy of adaptive directional wavelet transform in facial feature extraction and to offer a stepping stone for further research in this direction. The proposed approach is compared with existing subspace and local descriptor feature extraction methods. A performance comparison is also demonstrated with existing non-adaptive multiresolution analysis methods such as discrete wavelet transform (DWT), Gabor wavelet transform (GWT), curvelets, ridgelets, contourlets, and local Gabor binary pattern. Evaluation of the proposed approach on famous databases such as ORL, Essex Grimace, Yale, and Sterling face convinces the effectiveness of the adaptive directional wavelet transform based subspace features. Keywords: Face recognition, Adaptive directional wavelet transform, Linear discriminant analysis, Multiresolution analysis

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

Websites have evolved into an excellent medium of information dissemination and visibility. Hence governments and organizations around the world have websites as primary medium for information communication. The universal accessibility of this web medium remains a major challenge for both web developers and accessibility researchers. Conformance of WCAG 2.0 guidelines by web pages is a significant factor in measuring universal accessibility. This paper presents an exploratory study about the accessibility of Indian university website homepages. We have analyzed the homepages of 302 Indian universities under different conformance levels of WCAG 2.0 recommendation using automatic accessibility evaluation tools to find accessibility report of websites and then classified them comparatively into three groups namely low accessible websites called Tier-III, medium accessible websites called Tier-II and high accessible websites called Tier-I. Statistical classification and accessibility report of websites shows that an array of further improvements have to be made in order to make them more accessible and usable in terms of WCAG 2.0. Based on the results of the analysis, this paper proposes the necessary steps which shall be taken to further enhance the accessibility of the websites. Keywords: Web accessibility, Indian university websites, Classification, WCAG 2.0, Accessibility computing

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.

In this paper, a fuzzy commitment scheme is applied with a machine vision gait-based biometric system to enhance system security. The proposed biometric cryptosystem has two phases: enrolment and verification. Each of them comprises three main stages: feature extraction, reliable components extraction, and fuzzy commitment scheme. Gait features are extracted from gait images using local ternary pattern (LTP), and then, the average of one complete gait cycle using the gait energy image (GEl) concept is calculated. The average images are joined using a 2D joint histogram, which is reduced using principal component analysis (PCA) to produce the final feature vector. To enhance the robustness of the system, only highly robust and reliable bits from the feature vector are extracted. Finally, the fuzzy commitment scheme is used to secure feature templates. Bose–Chaudhuri–Hocquenghem codes (BCH) are used for key encoding in the enrolment phase and for decoding in the verification phase. The proposed system is tested using the CMU MoBo and CASIA A databases. The experimental results show that the best error rate for the CMU MoBo database is obtained when using a fast walk for enrolment and verification, where we obtain 0% for the false acceptance rate (FAR) and 0% for the false rejection rate (FRR) for a key length equal to 50 bits. The best error rate for CASIA A dataset is obtained when using the 45-degree direction to the image plane view for enrolment and verification, where we obtain 0% for the false acceptance rate (FAR) and 0% for the false rejection rate (FRR) for a key length equal to 45 bits.

In the recent years, the advances in mobile technology have brought an exorbitant change in daily lifestyle of individuals. Smartphones/mobile devices are rampant in all aspects of human life. This has led to an extreme demand for developing software that runs on mobile devices. The developers have to keep up with this high demand and deliver high-quality app on time and within budget. For this, estimation of development and testing of apps play a pivotal role. In this paper, a Systematic Literature Review (SLR) is conducted to highlight development and testing estimation process for software/application. The goal of the present literature survey is to identify and compare existing test estimation techniques for traditional software (desktop/laptop) and for mobile software/application. The characteristics that make mobile software/application different from traditional software are identified in this literature survey. Further, the trend for developing the software is towards agile, thus this study also presents and compares estimation techniques used in agile software development for mobile applications. The analysis of literature review suggests filling a research gap to present formal models for estimating mobile application considering specific characteristics of mobile software.

The growth of social networks is ever-increasing. Many available scientific publications evidence the interest of researchers in this area. Within a time span of eight years from 2011 to 2018, approximately 2600, 230, 150, and 110 scientific articles were published from the USA, Iran, Saudi Arabia, and Turkey, respectively around this area of research. To comprehensively survey all the sub-fields and interests within this research area, the present paper proposes a novel density-based method for finding topic descriptors from academic articles. By employing a robust to noise fuzzy clustering algorithm, the terms are clustered, and by utilizing a modified Parzen window, k topic descriptors from each cluster are extracted. Besides, an optimization problem has been designed to detect the similarity between word pairs. By conducting the experiments, the research priorities for four countries within this time span have been found. Moreover, the closeness of the research in developing countries to the developed country have been measured. The experimental results show that for four years, the research topics in Turkey were close to the research topics in the USA on average, and the research topics in Saudi Arabia were close to the USA topics during the past two years. Additionally, the experimental comparison of the proposed method with two clustering baselines indicates the superiority of the proposed method in terms of precision, recall, and accuracy.

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.

The grasshopper optimization algorithm is one of the recently population-based optimization techniques inspired by the behaviours of grasshoppers in nature. It is an efficient optimization algorithm and since demonstrates excellent performance in solving continuous problems, but cannot resolve directly binary optimization problems. Many optimization problems have been modelled as binary problems since their decision variables varied in binary space such as feature selection in data classification. The main goal of feature selection is to find a small size subset of feature from a sizeable original set of features that optimize the classification accuracy. In this paper, a new binary variant of the grasshopper optimization algorithm is proposed and used for the feature subset selection problem. This proposed new binary grasshopper optimization algorithm is tested and compared to five well-known swarm-based algorithms used in feature selection problem. All these algorithms are implemented and experimented assessed on twenty data sets with various sizes. The results demonstrated that the proposed approach could outperform the other tested methods.

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.

This paper presents an adaptive fuzzy-based cloud service brokering algorithm (AFBSB). The proposed algorithm employs an adaptive fuzzy-based engine to select the most appropriate data center for user cloud service requests considering user preferences in terms of cost and performance. The algorithm is implemented using an open-source cloud computing simulation tool. The algorithm results are tested against the results of other existing techniques within two types of cloud environments. First, a performance-constrained environment in which performance improvement is the main objective for cloud users. Second, a preference-aware environment, in which cloud users have different cost and performance preferences. Simulation results show that the proposed algorithm can achieve significant performance improvement in performance-constrained environments. As for the preference-aware environment, they show that AFBSB can operate in user-oriented manner that guarantees performance/cost improvement, as compared to other algorithms.

A computational grid needs to be cost-effective and highly reliable. It must have the ability to transfer maximum amount of data for end-to-end delivery. Many research works on computational grids conclude with a binary state of operation. Although some research reveals that, the grids operate with multiple states of connections and operations, they are silent about the mentioned constraints. Since there are multi-state operations, the reliability depends on the load, bandwidth and cost. This paper proposes a Genetic Algorithm (GA) based approach for optimization of reliability of computational grid with desired reliability under cost and bandwidth constraints. The method is well explained through illustration and its implementation on some Grid networks. The efficient encoding, cross-over, mutation and selection operation of the proposed scheme ensures the fast convergence to the optimal solution. On comparison, the proposed method is observed to be more efficient than the existing ones.

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.

Application of cellular automata (CA) to digital image processing has achieved considerable attention in last several years. CA are now employed in noise filtration of digital images; particularly many CA-based impulse noise filters have been proposed. Salt-and-pepper noise is a special kind of impulse noise and is introduced in an image during transmission over transmission media by external noise sources like atmospheric disturbances, or due to corrupted hardware memory locations or fault in camera sensors. In this paper, we present five salt-and-pepper noise filters based on modifications of outer totalistic cellular automata (OTCA) with the adaptive neighborhood. Use of OTCA model makes the proposed filters computationally simple on one hand and the use of adaptive neighborhood help the filters to provide efficient noise filtration at varying noise densities on the other. Comparative analysis of these filters followed by comparison with several standard and CA-based filters in terms of peak signal to noise ratio (PSNR) and structural similarity (SSIM) index is presented.

Characters segmentation is a necessity and the most critical stage in Arabic OCR system. It has attracted the interest of a wide range of researchers. However, the nature of the Arabic cursive script poses extra challenges that need further investigation. Therefore, having a reliable and efficient Arabic OCR system that is independent of font variations is highly required. In this paper, an indirect, font-in dependent word and character segmentation algorithm for printed Arabic text investigated. The proposed algorithm takes a binary line image as an input and produces a set of binary images consisting of one character or ligature as an output. The segmentation performed at two levels: a word segmentation performed in the first level, by employing a vertical projection at the input line image along with using Interquartile Range (IQR) method to differentiate between word gaps and within word gaps. A projection profile method used as a second level of segmentation along with a set of statistical and topological features, which are font-independent, to identify the correct segmentation points from all potential points. The APTI dataset used to test the proposed algorithm with a variety of font type, size, and style. The algorithm experimented on 1800 lines (approximately 24,816 words) with an average accuracy of 97.7% for words segmentation and 97.51% for characters segmentation.

In the cloud computing environment, scheduling algorithms show the vital role of finding a possible schedule of the tasks. Extant literatures have shown that the task scheduling problem is NP-Complete as the objective is to obtain the minimum overall execution time. In this paper, we address the problem of scheduling a set of l tasks with a set of |G| groups to a set of m clouds, such that the overall layover time is minimized. Note that overall layover time is the sum of the timing gaps between paired tasks. Here, we present a pair-based task scheduling algorithm for cloud computing environment, which is based on the well-known optimization algorithm, called Hungarian algorithm. The proposed algorithm considers an unequal number of tasks and clouds, and pairs the tasks to make the scheduling decision. We simulate the proposed algorithm and compare it with three existing algorithms, first-come-first-served, Hungarian algorithm with lease time and Hungarian algorithm with converse lease time in twenty-two different datasets. The performance evaluation shows that the proposed algorithm produces better layover time in comparison to existing algorithms. The proposed algorithm is analyzed theoretically and shown to require O (kpl2) time for k iterations, p repetitions and l tasks.

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.

Nowadays, individuals express experiences and opinions through online reviews. These has an influence on online marketing and obtaining real knowledge about products and services. However, some of the online reviews can be unreal. They may have been written to promote low-quality products/services or sabotage a product/service reputation to mislead potential customers. Such misleading reviews are known as spam reviews and require crucial attention. Prior spam detection research focused on English reviews, with less attention to other languages. The detection of spam reviews in Arabic online sources is a relatively new topic despite the relatively huge amount of data generated. Therefore, this paper contributes to such topic by presenting four different Arabic spam reviews detection methods, while putting more focus towards the construction and evaluation of an ensemble approach. The proposed ensemble method is based on integrating a rule-based classifier with machine learning techniques, while utilizing content-based features that depend on N-gram features and Negation handling. The four proposed methods are evaluated on two datasets of different sizes. The results indicate the efficiency of the ensemble approach where it achieves a classification accuracy of 95.25% and 99.98% for the two experimented datasets and outperforming existing related work by far of 25%.

Cloud computing facilitates the storage and management of huge volumes of data. It offers flexibility for retrieving the data anytime and anywhere. In recent years, storing data onto the cloud achieved fame among corporations as well as private users. Although, the cloud is drawing a lot of attention still there are data security, privacy, reliability and interoperability concerns that need to be taken care. To deal with these issues, cloud data encryption comes to the rescue. Encrypting the data before uploading it onto the cloud prevents unauthorized users from accessing the data. A lot of encryption algorithms have been developed to secure data stored on the cloud. In this paper, a novel cryptographic technique has been presented that uses client-side data encryption for encrypting the data before uploading it onto the cloud. It is a multifold symmetric-key cryptography technique which is based upon DNA cryptography. Besides presenting the detailed design of our approach, we have compared it with the existing symmetric-key algorithms (DNA, AES, DES and Blowfish). The experimental results illustrate that our proposed algorithm outperforms these traditional algorithms in terms of ciphertext size, encryption time and throughput. Hence, the newly proposed technique is more efficient and offers better performance.

Modern day computer vision tasks requires efficient solution to problems such as image recognition, natural language processing, object detection, object segmentation and language translation. Symbolic Artificial Intelligence with its hard coding rules is incapable of solving these complex problems resulting in the introduction of Deep Learning (DL) models such as Recurrent Neural Networks and Convolutional Neural Networks (CNN). However, CNNs require lots of training data and are incapable of recognizing pose and deformation of objects leading to the introduction of Capsule Networks. Capsule Networks are the new sensation in Deep Learning. They have lived to this expectation as their performance in relation to the above problems has been better than Convolutional Neural Networks. Even with this promise in performance, lack of architectural knowledge and inner workings of Capsules serves as a hindrance for researchers to take full advantage of this breakthrough. In this paper, we provide a comprehensive review of the state of the art architectures, tools and methodologies in existing implementations of capsule networks. We highlight the successes, failures and opportunities for further research to serve as a motivation to researchers and industry players to exploit the full potential of this new field. The main contribution of this survey article is that it explains and summarizes significant current state of the art Capsule Network architectures and implementations.

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.

Selection of an optimal path is a challenging issue in Mobile Ad-hoc network (MANET). This paper proposes a new mechanism for route selection combining Ad-hoc On-Demand Distance Vector (AODV) protocol with Ant Colony Optimization (ACO) to improve Quality of Service (QoS) in MANET. Based on the mechanism of ant colony with AODV, the finest route for data delivery is selected using pheromone value of the path. In the proposed work, pheromone value of a route is calculated based on end to end reliability of the path, congestion, number of hops and residual energy of the nodes along the path. The path which has highest pheromone value will be selected for transmission of the data packet. The simulation result shows that the proposed scheme outperforms AODV, Dynamic Source Routing (DSR) and Enhanced-Ant-DSR routing algorithms.

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.

An approach is presented that generalize OCR task including polish letters using deep learning technique. The paper extends EMNIST dataset so that two new classes of polish diacritics “Ą” and “C” are attached. Using this new dataset and deep learning technique one can analyze sensitives of standard as well as presented extended approach with different parameters of convolutional neural network. The analysis of the results shows that the shadows and noises that Polish characters, leave with their hooks leads, can be properly recognized and two similar letters “A” and “Ą” can be distinguished by convolutional neural network. On the other hand a neural network trained on dataset without Polish characters do not treat letters “Ą” and “C” properly.

Copy-move image forgery detection has become a significant research subject in multimedia forensics and security due to its widespread use and its hard detection. In this type of image forging, a region of the image is copied and pasted elsewhere in the same image. Keypoint-based forgery detection approaches use local visual features to identify the duplicated regions. The performance of keypoint-based methods degrades in those cases when the duplicated regions are near to each other and when handling highly textured area. The clustering algorithm that mostly used in keypoint- based methods suffer from high complexity. In this paper, an improved approach for keypoint- based copy-move forgery detection is proposed. The proposed method is based on density-based clustering and Guaranteed Outlier Removal algorithm. Experimental results carried out on various benchmark datasets exhibit that the proposed method surpasses other similar state-of-the-art techniques under different challenging conditions, such as geometric attacks, post-processing attacks, and multiple cloning.

In recent years, research has been showing a great interest in using magnetic resonance imaging (MRI) as the only modality for radiation therapy (RT) for its superior soft-tissue visualisation and non-ionizing proprieties. Furthermore, MRI-only RT would be of great benefit to eliminate image registration errors, reduce cost and workload. In addition, machine-learning algorithms have been taking the lead in many fields. For instance, in MRI-only RT, machine learning is showing a notable performance compared to other methods owed to the flexibility of these methods towards data regardless of model complexity. In this paper, we present an ensemble learning approach with stacked generalisation to simulate a CT scan from multi-modal MR images from which patch-based shape, texture and spatial features were considered. Feature extraction, fusion and reduction were performed to get the most descriptive and informative features. The ensemble learning model was constructed with two levels of learning were the basic level consisted of three base learners namely: artificial neural networks (ANN), random forests (RF) and k-nearest neighbours (kNN) and the second level representing the stacking learner that takes predictions from the base learner and generates the final predictions. Multiple linear regression (MLR) was used for the stacked generalisation. The proposed ensemble learning with stacked generalisation (ES) approach produced an average mean absolute error (MAE) of 87.60 ± 19.70 and an average mean error (ME) of −4.68 ± 16.43 outperforming the RF method, which produced an average MAE of 106.88 ± 33.20 and an average ME of −5.38 ± 20.77. In addition, average Pearson correlation was 0.92 for the proposed approach compared to 0.89 for RF. Evaluation of the proposed approach shows that stacked generalisation can greatly improve prediction accuracy and reduce bias in electron density estimation.

Lung field segmentation is a significant step in the development of a Computer-Aided Diagnosis system and efficiently used for the quantitative analysis of lung CT images. However, this task is complicated especially in the presence of juxtapleural nodules, pulmonary vessels. In this paper, a more robust and accurate approach for lung segmentation is proposed. Our algorithm determines the dominant points indicating the concave and convex region along the lung boundary and these dominant points are connected by applying dominant point marching algorithm. The lung segmentation algorithm was tested on 36 subjects from the Lung Imaging Database Consortium. Results indicate that our proposed method can successfully re-include juxtapleural nodules and pulmonary vessels and achieves the average volumetric overlap fraction of 96.97%, average under segmentation rate of 2.324% and average over segmentation rate of 0.79%. The average processing time required for each subject is 182.448 s and for each slice is 0.953 s, which is comparatively faster than the manual segmentation done by radiologists. Experimental results suggest that our proposed method is faster and more robust than other state of the art methods. Hence proposed method can be used as an efficient tool for the lung segmentation in clinical practice.

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.

Informal availability of Internet and easy accessibility of network resources to a general public has created a new era of security design challenge for multimedia contents which is available in the form of image, text, video and audio etc. This paper presents a novel technique to employ a security mechanism for transmission of digital contents over public network. The proposed method uses a multilevel image encryption/decryption algorithm based on quantum chaos map and sparse sampling. In the initial phase an original image is divided into blocks of equal size and each block is further divided into sub-block using DWT technique. The pixels of the adjacent sub blocks are exchanged randomly by random matrix. Each frequency band of the blocks is encrypted by modulus function, and then combined each to get the new block. Next, pixels of the neighbor blocks are exchanged randomly by random matrix and subsequently each block is encrypted using modulus function. The result given in graphical and tabular form shows the correctness of the algorithm.

Among meta-heuristic algorithms, differential evolution (DE) is one of the most powerful nature-inspired algorithm used to solve the complex problems in various application areas. In DE algorithm at higher generations, there is an increase in the computational cost because existing mutation operator may not provide more diversity. In this paper, a new variant of DE has been proposed by incorporating the homeostasis adaption based mutation operator (HABMO), which maintains the diversity when it stuck to the local optimum problem. This operator with DE is applied for the cost estimation in software development, where proposed optimization technique is used with constructive cost model (COCOMO) for optimizing the tuning parameters. The main objective of this work is accurate prediction and minimization of the error like MMRE, MMER, MSE and RMSE in less number of iteration, for COCOMO model. Further, the proposed variant of DE has been compared with different versions of DE and it has been concluded that the proposed HABDE is able to improve the performance of DE algorithm.

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.

Highlights: • A novel chaotic asymmetric-key color image encryption algorithm is proposed. • The multiplicative coupled Chebyshev-based encryption scheme allows arbitrary sizes of keyspace. • One pixel may appear in any position throughout the image. • The proposed technique uses tries to achieve the benefits of the one-time pad. Abstract: In this paper, a novel randomized chaotic asymmetric-key algorithm is proposed for color image encryption. Using the multiplicative coupled Chebyshev-based encryption algorithm, a novel chaotic key establishment algorithm using Chebyshev polynomials is proposed. This key establishment algorithm is used to generate three chaotic pseudo-random number sequences to permute the rows and the columns of the plain image randomly. Then, each pixel is XORed with respect to a random value obtained based on its new position to mask its value. The whole encryption algorithm is randomized, so it can resist chosen plaintext attacks. The experimental and security analysis of the proposed algorithm shows that the proposed algorithm has a large keyspace, a good level of security, and an acceptable level of robustness.

In this paper a novel multilevel thresholding algorithm using a meta-heuristic Krill Herd Optimization (KHO) algorithm has been proposed for solving the image segmentation problem. The optimum threshold values are determined by the maximization of Kapur’s or Otsu’s objective function using Krill Herd Optimization technique. The proposed method reduces the computational time for computing the optimum thresholds for multilevel thresholding. The applicability and computational efficiency of the Krill Herd Optimization based multilevel thresholding is demonstrated using various benchmark images. A detailed comparative analysis with other existing bio-inspired techniques based multilevel thresholding techniques such as Bacterial Foraging (BF), Particle Swarm Optimization (PSO), Genetic Algorithm (GA) and Moth-Flame Optimization (MFO) has been performed to prove the superior performance of the proposed method.

Vehicle plate number recognition plays an important role in traffic control and surveillance systems. A key stage in any vehicle plate number recognition system is to first locate the vehicle plate number. In this paper, we present a modified GrabCut algorithm for localizing vehicle plate numbers. In contrast with the traditional interactive GrabCut technique, a modified GrabCut algorithm was designed to identify and extract vehicle plate numbers in a completely automatic manner. Our approach extends the use of the traditional GrabCut algorithm with addition of a feature extraction method which uses geometric information to give accurate foreground extraction. Finally, to evaluate the performance of the proposed technique, the localization accuracy is tested with a dataset of 500 vehicle images with vehicle plates from different countries. An accuracy of 99.8% was achieved for the localization of vehicle plates. Comparative analysis is also reported.

Most of the key management schemes do not consider the attacking behavior of the adversary making such schemes less practical in real world. By knowing the adversarial behavior, several countermeasures against them can be effectively and efficiently designed by the defender/network designer. In this paper, we investigate the problem of node capture attack and propose a secure hybrid key predistribution scheme (HKP-HD) for wireless sensor networks (WSN). This scheme combines the robustness of the q-composite scheme with threshold resistant polynomial scheme. The proposed scheme aims to make the network more resistant against the node capture attacks. Adversary is assumed to be intelligent that tends to exploit different vulnerabilities present in network to build an attack matrix. It aims to destroy complete network with least number of nodes based on the attack matrix. As a countermeasure, the network designer constructs similar attack matrix based on the vulnerabilities in the network by considering sink as a major influencing factor. This matrix is used to calculate the attack coefficient of every node of the network that determines its probability of attack by the adversary. A hash chain based on maximum value of attack coefficient along with the multiple key pools is used to reduce the probability of the key compromise and communication overhead of the proposed scheme. The simulation results demonstrate that the proposed scheme has reduced probability of key compromise, communication overhead and storage overhead as compared to other schemes.