Showing papers in "Journal of Computer Science in 2016"
TL;DR: This study reviews some of the studies that have been conducted in this still-developing research area of text summarization and provides the theoretical explanation and the fundamental concepts related to it.
Abstract: It has been more than 50 years since the initial investigation on automatic text summarization was started. Various techniques have been successfully used to extract the important contents from text document to represent document summary. In this study, we review some of the studies that have been conducted in this still-developing research area. It covers the basics of text summarization, the types of summarization, the methods that have been used and some areas in which text summarization has been applied. Furthermore, this paper also reviews the significant efforts which have been put in studies concerning sentence extraction, domain specific summarization and multi document summarization and provides the theoretical explanation and the fundamental concepts related to it. In addition, the advantages and limitations concerning the approaches commonly used for text summarization are also highlighted in this study.
55 citations
TL;DR: This paper presents a constraint-handling technique for GA’s solely using the violation factor, called VCH (Violation Constraint-Handling) method, which was able to provide a consistent performance and match results from other GA-based techniques.
Abstract: Over the years, several meta-heuristic algorithms were proposed and are now emerging as common methods for constrained optimization problems. Among them, genetic algorithms (GA’s) shine as popular evolutionary algorithms (EA’s) in engineering optimization. Most engineering design problems are difficult to resolve with conventional optimization algorithms because they are highly nonlinear and contain constraints. In order to handle these constraints, the most common technique is to apply penalty functions. The major drawback is that they require tuning of parameters, which can be very challenging. In this paper, we present a constraint-handling technique for GA’s solely using the violation factor, called VCH (Violation Constraint-Handling) method. Several benchmark problems from the literature are examined. The VCH technique was able to provide a consistent performance and match results from other GA-based techniques.
51 citations
TL;DR: This research develops the efficiency of the information retrieval from the Holy Quran based on QAS and retrieving an accurate answer to the user's question through classifying the verses using the Neural Network (NN) technique depending on the purpose of the verses' contents, in order to match between questions and verses.
Abstract: In spite of great efforts that have been made to present systems that support the user's need of the answers from the Holy Quran, the current systems of English translation of Quran still need to do more investigation in order to develop the process of retrieving the accurate verse based on user's question. The Islamic terms are different from one document to another and might be undefined for the user. Thus, the need emerged for a Question Answering System (QAS) that retrieves the exact verse based on a semantic search of the Holy Quran. The main objective of this research is to develop the efficiency of the information retrieval from the Holy Quran based on QAS and retrieving an accurate answer to the user's question through classifying the verses using the Neural Network (NN) technique depending on the purpose of the verses' contents, in order to match between questions and verses. This research has used the most popular English translation of the Quran of Abdullah Yusuf Ali as the data set. In that respect, the QAS will tackle these problems by expanding the question, using WordNet and benefitting from the collection of Islamic terms in order to avoid differences in the terms of translations and question. In addition, this QAS classifies the Al-Baqarah surah into two classes, which are Fasting and Pilgrimage based on the NN classifier, to reduce the retrieval of irrelevant verses since the user's questions are asking for Fasting and Pilgrimage. Hence, this QAS retrieves the relevant verses to the question based on the N-gram technique, then ranking the retrieved verses based on the highest score of similarity to satisfy the desire of the user. According to F-measure, the evaluation of classification by using NN has shown an approximately 90% level and the evaluation of the proposed approach of this research based on the entire QAS has shown an approximately 87% level. This demonstrates that the QAS succeeded in providing a promising outcome in this critical field.
38 citations
TL;DR: The results show that in Arabic, a sentiments SVM with an n-gram feature achieved higher accuracy than NB both with using negative and positive classes only and with the neutral class.
Abstract: "This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited."
34 citations
TL;DR: A novel Fractional Lion Algorithm (FLA) as an optimization methodology for the clustering problems using the lion's unique characteristics such as pride, laggardness exploitation, territorial defence and territorial take over is presented.
Abstract: Clustering divides the data available as bulk into meaningful, useful groups (Clusters) without any prior knowledge about the data. Cluster analysis provides an abstraction from individual data objects to the clusters in which those objects reside. It is a key technique in the data mining and has become an important issue in many fields. This paper presents a novel Fractional Lion Algorithm (FLA) as an optimization methodology for the clustering problems. The proposed algorithm utilizes the lion's unique characteristics such as pride, laggardness exploitation, territorial defence and territorial take over. The Lion algorithm is modified with the fractional theory to search the cluster centroids. The proposed fractional lion algorithm estimates the centroids with the systematic initialization itself. Proposed methodology is a robust one, since the parameters utilized are insensitive and not problem dependent. The performance of the proposed rapid centroid estimation is evaluated using the cluster accuracy, jaccard coefficient and rand coefficient. The quality of this approach is evaluated on the benchmarked iris and wine data sets. On comparing with the particle swarm clustering algorithm, experimental results shows that the clustering accuracy of about 75% is achieved by the proposed algorithm.
33 citations
TL;DR: This study reviews the suitability of SA approaches for application in the big data frameworks, as well as highlights the gaps and suggests future works that should be explored.
Abstract: The ability to exploit public sentiment in social media is increasingly considered as an important tool for market understanding, customer segmentation and stock price prediction for strategic marketing planning and manoeuvring. This evolution of technology adoption is energised by the healthy growth in big data framework, which caused applications based on Sentiment Analysis (SA) in big data to become common for businesses. However, scarce works have studied the gaps of SA application in big data. The contribution of this paper is two-fold: (i) this study reviews the state of the art of SA approaches. including sentiment polarity detection, SA features (explicit and implicit), sentiment classification techniques and applications of SA and (ii) this study reviews the suitability of SA approaches for application in the big data frameworks, as well as highlights the gaps and suggests future works that should be explored. SA studies are predicted to be expanded into approaches that utilise scalability, possess high adaptability for source variation, velocity and veracity to maximise value mining for the benefit of the users.
31 citations
TL;DR: A study of Arabic–English semantic similarity in short phrases and sentences using Dictionary and machine translation techniques to determine the relatedness between the cross-lingual texts from a monolingual perspective.
Abstract: Measuring cross-language semantic similarity between short texts is a task that is challenging in terms of human understanding. This paper addresses this problem by carrying out a study of Arabic–English semantic similarity in short phrases and sentences. Human-rated benchmark dataset was carefully constructed for this research. Dictionary and machine translation techniques were employed to determine the relatedness between the cross-lingual texts from a monolingual perspective. Three algorithms were developed to rate the semantic similarity and these were applied to the human-rated benchmark. An averaged maximum-translation similarity algorithm was proposed using the term sets produced by the dictionary-based technique. Noun-verb and term vectors obtained by the Machine Translation (MT) technique were also suggested to compute the semantic similarity. The results were compared with the human ratings in our benchmark using Pearson correlation coefficient and these were triangulated with the best, worst and mean for all human participants. MT-based term vector semantic similarity algorithm obtained the highest correlation (r = 0.8657) followed by averaged maximum-translation similarity algorithm (r = 0.7206). Further statistical analysis showed no significant difference between both algorithms and the humans’ judgement.
19 citations
TL;DR: A survey is done to highlight Arabic sentiment analysis challenging issues based on two main perspectives: Arabic-specific and general linguistic issues.
Abstract: Understanding what people think about an idea or how they evaluate a product, a service or a policy is important for individuals, companies and governments. Sentiment analysis is the process of automatically identifying opinions expressed in text on certain subjects. The accuracy of sentiment analysis has a direct effect on decision making in both business and government. Working with the Arabic language is very important because of the growing number of online contents in Arabic and the existing resources are limited and the accuracy of existing methods is low. In this study, we do a survey to highlight Arabic sentiment analysis challenging issues based on two main perspectives: Arabic-specific and general linguistic issues. The Arabic-specific challenges are mainly caused by Arabic morphological complexity, limited resources and dialects, while the general linguistic issues include polarity fuzziness, polarity strength, implicit sentiment, sarcasm, spam, review quality and domain dependence.
17 citations
TL;DR: A new CBIR technique that relies on extracting Speeded Up Robust Features (SURF) and Maximally Stable Extremal Regions (MSER) feature descriptors as well as the color features; color correlograms and Improved Color Coherence Vector (ICCV) to build a multidimensional feature vector is offered.
Abstract: Content-Based Image Retrieval (CBIR) has received an extensive attention from researchers due to the rapid growing and widespread of image databases. Despite the massive research efforts consumed for CBIR, the completely satisfactory results have not yet been attained. In this article, we offer a new CBIR technique that relies on extracting Speeded Up Robust Features (SURF) and Maximally Stable Extremal Regions (MSER) feature descriptors as well as the color features; color correlograms and Improved Color Coherence Vector (ICCV). These features are joined and used to build a multidimensional feature vector. Bag-of-Visual-Words (BoVW) technique is utilized to quantize the extracted feature vector. Then, a multiclass Support Vector Machine (SVM) is implemented to classify the query images. The performance of the presented retrieval framework is analyzed and scrutinized by comparing it with three alternative approaches. The first one is based on extracting SURF descriptors while the second one is based on extracting SURF descriptors, color correlograms and ICCV. The third approach, on the other hand, is based on extracting MSER, color correlograms and ICCV. All implemented schemes are tested on two benchmark datasets; Corel-1000 and COIL-100 datasets. The empirical results show that our suggested approach has a superior discriminative classification and retrieval performance with respect to other approaches. The proposed method achieves average precisions of 88 and 93% for the Corel-1000 and COIL-100 datasets, respectively. Moreover, the proposed system has shown a substantial advance in the retrieval precision when compared with other existing systems.
15 citations
TL;DR: To overcome the limited data hiding capacity, suspiciousness, and data damaging effect due to modification, of traditional steganographic techniques, a new technique for information hiding in text file is proposed and concealed by using first, second, second last, and last letter of words of the cover text.
Abstract: In today’s electronic era, wealth of electronic information are accessing over the Internet. Several important information and private data transferring over the Internet are being hacked by attackers via latest communication technology. So, maintaining the security of secret data has been a great challenge. To tackle the security problem, cryptographic methods as well as steganographic techniques are essential. This paper focuses on hybrid security system using cryptographic algorithm and text steganographic technique to achieve a more robust security system. In this work, to overcome the limited data hiding capacity, suspiciousness, and data damaging effect due to modification, of traditional steganographic techniques, a new technique for information hiding in text file is proposed. The proposed approach conceals a message, without degrading cover, by using first, second, second last, and last letter of words of the cover text. Hence, from the embedding capacity point of view, its capacity depends on the similarity of characters of the words in cover text. In addition, as further improvement for security, secret message encryption is performed using Blowfish algorithm before hiding into the innocuous cover text.
14 citations
TL;DR: Experimental results justify the necessity of mitigating these observations and the policy of associations and then translations of bins of histogram may force the pixel values to exceed the extremes of gray scale which is indeed impossible.
Abstract: Purposely visual degradation of image quality is a newfangled idea in the area of Histogram Association Mapped (HAM) reversible data hiding. Such scheme first divides the color scale, i.e., 0-255, into some partitions by the range, difference of minimum and maximum value of a block. Next, the histogram of a block resided to a partition, original partition, is moved, e.g., shifted, to another partition named reflective partition. First, an association between the original and reflected partition are defined by a chunk of secret message and then the translations of the bins of the histogram of the original partition to the reflective partition are performed to complete the message concealment. In such schemes, the blocks are classified as Reflective Block (RB) and Non-Reflective Block (NRB) based on whether the range is less than 127 or not. Experimentally, it is observed that most of the RB blocks occupy the portion of two partitions which introduce a dilemma in defining original partition as well as associating and hence translating bins. Again, at NRB the range of occupied bins in the histogram of a block is enough longer than the available bins where to reflect. Therefore, the policy of associations and then translations of bins of histogram may force the pixel values to exceed the extremes of gray scale which is indeed impossible. Solutions to these issues are not presented by any literature so far we have studied. Besides, the side information database that is practiced to assist in decoding at receiver is made longer by storing bundle of information and sometimes it becomes near or more than both the secret data and size of image. Resolutions to these stated observations are presented in this article. Experimental results justify the necessity of mitigating these observations.
TL;DR: It is observed that available text watermarking algorithms are neither robust nor imperceptible and as such remain unsecured methods of protection, hence, research to improve the performance of text water marking algorithms is required.
Abstract: Data protection from malicious attacks and misuse has become a crucial issue. Various types of data, including images, videos, audio and text documents, have given cause for the development of different methods for their protection. Cryptography, digital signatures and steganography are the most well known technologies used to protect data. During the last decade, digital watermarking technology has also been utilized as an alternative to prevent media forgery and tampering or falsification to ensure both copyright and authentication. Much work has been done to protect images, videos and audio but only a few algorithms have been considered for text document protection with digital watermarking. However, our survey observed that available text watermarking algorithms are neither robust nor imperceptible and as such remain unsecured methods of protection. Hence, research to improve the performance of text watermarking algorithms is required. This paper reviews current watermarking algorithms for text documents and categorizes text watermarking methods based on the way the text was treated during the watermarking process. It also discusses merits and demerits of available methods as well as recent proposed methods for evaluating text watermarking systems and the need for further research on digital text watermarking.
TL;DR: This study compares the accuracy and Kappa Coefficient of six machine learning techniques namely Maximum Likelihood, Minimum Distance, Mahalanobis Distance, Parallelepiped, Neural Network and Support Vector Machines on three type of images; single optical multispectral, single SAR and fused image and shows that classification using SVM on single multispects image has the highest accuracy among all.
Abstract: Shoreline is a very important element to identify exact boundary at the coastal areas of a country However, in order to identify land-water boundary for a large region using traditional ground survey technique is very time consuming Alternatively, shoreline can be extracted by using satellite images that minimizes the mapping errors The trend of extracting shoreline has been shifted from image processing to machine learning and data mining techniques By using machine learning technique, the satellite images could be classified into land and water classes in order to extract shoreline However, the result is meaningless if it has cloud and shadow on the water-land boundary In this study, we compare the accuracy and Kappa Coefficient of six machine learning techniques namely Maximum Likelihood, Minimum Distance, Mahalanobis Distance, Parallelepiped, Neural Network and Support Vector Machines on three type of images; single optical multispectral, single SAR and fused image A case study for this research is done alongside Tumpat beach, located at the Northeast Coast of Peninsular Malaysia All the machine learning techniques have been tested on the three types of images The experimental results show that classification using SVM on single multispectral image has the highest accuracy among all However, the classified of fused image using SVM is considered much more accurate because it can cater the cloud and shadow problem Additionally, the classification on 5 and 10 m fused images also tested and the result shows that with the increase of spatial resolution of fused image, the classification accuracy also increases
TL;DR: The goal of this work is to highlight the most important challenges in the field of object tracking and provide a survey of the WSN architectural design and implementation approaches for tackling this problem, and analyze how each approach responds to each challenge and where it falls short.
Abstract: Wireless Sensor Networks (WSNs) are small, inexpensive and battery-operated sensor nodes that are deployed over a geographical area. WSNs are used in many applications such border patrolling, military intrusion detection, wildlife animal monitoring, surveillance of natural disasters and healthcare systems. Mobile object tracking is a vital task in all these applications. The goal of this work is to highlight the most important challenges in the field of object tracking and provide a survey of the WSN architectural design and implementation approaches for tackling this problem. To that end, we analyze how each approach responds to each challenge and where it falls short. This analysis should provide researchers with a state-of-the-art review and inspire them to propose novel solutions.
TL;DR: This paper introduces and discusses a control strategy for nonholonomic wheeled mobile robots based on the robust sliding mode control technique and shows the superiority of the proposed controller compared with the computed torque method.
Abstract: This paper introduces and discusses a control strategy for nonholonomic wheeled mobile robots. The models of the robots include the kinematic and dynamic equations of motion. Trajectory tracking control problem of parallel wheeled differential drive mobile robot is considered, where the robot should reach the final position by following a referenced trajectory for different initial conditions. A motion control strategy for a mobile robot by only assuming the kinematic model was developed by many researchers. In the case of high-speed robot motion, the dynamical model is important. In this study, two stages of the proposed control strategy are presented. The first one is dealing with the kinematics of the system and denoted as ‘steering’ controller. The second one, a velocity controller is developed based on the robust sliding mode control technique. A new design of the sliding surface is proposed. The switching feedback gain is determined based on a novel mathematical simple rule, considering the initial state of the system. Robustness to parameters uncertainties and stability of the controlled system are achieved. A simulation model of the controlled system is developed in MATLAB-SIMULINK software. Simulation results show the performances of the developed controller. In the case of presence of uncertainties, the results show the superiority of the proposed controller compared with the computed torque method.
TL;DR: This study covers the introduction of menu planning, the state of the arts for nutrition care expert systems and the approaches that have been applied, and the advantages and limitations of the methods commonly used for menu planning.
Abstract: Corresponding Author: Hea Choon Ngo Faculty of Information and Communication Technology, Universiti Teknikal Malaysia Melaka, 76100, Melaka, Malaysia Email: heachoon@utem.edu.my Abstract: Planning is widely been used in many areas such as in medicine, administration, business, logistics, education, environment and family matters. In automated planning research, the word “plans” refers specifically to plans of action. It is about the representation of future behavior, generally a series of action, with temporal and other constraints on them for execution by some agents. Theoretically, planning is an important part of rational behavior. In this study, we review some of the studies that have been conducted in menu planning. It covers the introduction of menu planning, the state of the arts for nutrition care expert systems and the approaches that have been applied. Furthermore, the advantages and limitations of the methods commonly used for menu planning are also highlighted in this study.
TL;DR: This study utilized a quantitative approach based a Technology Acceptance Model to evaluate the designing Eight Queens Chess Puzzle Game from 1×1 to 25×25 levels and outlined the details of each construct and its relevance toward the research issue.
Abstract: In the game of chess, the queen can attack any piece that lies on the same row, on the same column, or along a diagonal. The eight-queens is a classic logic puzzle. The task is to place eight queens on a chessboard in such a fashion that no queen can attack any other queen. The eight-queen puzzle is often used to illustrate problem-solving or backtracking techniques. Consequently, in this study Technology Acceptance Model has designed to investigate the user acceptance of designing Eight Queens Chess Puzzle Game. The purpose of this study is to design a quantitative approach based on the technology acceptance model questionnaire as its primary research methodology. It utilized a quantitative approach based a Technology Acceptance Model (TAM) to evaluate the designing Eight Queens Chess Puzzle Game from 1×1 to 25×25 levels. The related constructs for evaluation are: Perceived of Usefulness, Perceived Ease of Use, User Satisfaction and Attribute of Usability. All these constructs are modified to suit the context of the study. Moreover, this study outlines the details of each construct and its relevance toward the research issue. The outcome of the study represents series of approaches that will apply for checking the advantages of collecting all levels in a single game with series of episodes and how well it achieves the aims and objectives of the design.
TL;DR: A novel approach which consists of a combination of global twister with circular technique and initial congruential generation with complete stochastic sequences is proposed, which has been experimentally confirmed that for complete sequences this type of generation provides uniformity in distribution of random numbers.
Abstract: Generators of uniformly distributed random numbers are broadly applied in simulations of stochastic processes that rely on normal and other distributions. In a point of fact, the uniform random numbers are actively used for applications that range from, modeling different phenomena such as theoretical mathematics and technical designing, to evidence-based medicine. This paper proposes a novel approach which consists of a combination of global twister with circular technique and initial congruential generation with complete stochastic sequences. It has been experimentally confirmed that for complete sequences this type of generation provides uniformity in distribution of random numbers. The offered program codes include the tuning methods for the generation technique where random numbers may take any bit length. Moreover, the automatic switching of generator parameters such as initial congruential constants depending on intervals for generated numbers is considered as well. Demonstrated results of testing confirm the uniformity of distribution without any repeated or skipped generated elements.
TL;DR: A robot equipped with vision and tactile sensors capable of receiving natural instructions is examined, which accomplishes a retrieving and passing task using the natural instructions of finger pointing and tapping with the palm.
Abstract: Stress-free interaction between humans and robots is necessary to support humans in daily life. In order to achieve this, we anticipate the development of new robots equipped with tactile and vision sensors for receiving human instructions. In this article, we focus on spontaneous movements that do not require training, such as pointing and force adjustment and that are suitable for daily care. These movements, which we call natural instructions, involve the transmission of human instructions to robots. In this experiment, we examine a robot equipped with vision and tactile sensors capable of receiving natural instructions. Our new robot accomplishes a retrieving and passing task using the natural instructions of finger pointing and tapping with the palm.
TL;DR: In this study a hardware architecture for moving object tracking using Kalman filter on a FPGA board, is proposed.
Abstract: Intelligent video is a new area of research fairly wide allowing to do a study, analysis, or interpretation of digital video such as motion analysis. However, for a video surveillance system, a motion analysis task of digital video includes the detection of moving objects and their tracking. The object detection allows the location of the regions of interest, which represents a change of movement. The purpose of tracking is to maintain the identity of objects detected over time by the estimation or the location of their position in each frame of the sequence. The most popular tracking algorithm is the Kalman filtering. In this study a hardware architecture for moving object tracking using Kalman filter on a FPGA board, is proposed.
TL;DR: This paper provides a literature review, discussion and analysis of the existing solutions for implementing ontologies in SE, and establishes that ontologies are suitable for providing a common vocabulary to avoid misunderstanding between different parties in SE.
Abstract: One of the main goals of the Software Engineering (SE) discipline is to find higher abstraction levels and ways to reuse software in order to increase its productivity and quality. Ontologies, which are typically considered as a technique or an artifact used in one or more software lifecycle phases, may be used to help achieve that goal. This paper provides a literature review, discussion and analysis of the existing solutions for implementing ontologies in SE. We selected several software development paradigms (including Software Product Lines, Component-Based Development, Generative Programming and Model-Driven Engineering) for our classification and discussion of different approaches proposed in the literature. It was established that ontologies are suitable for providing a common vocabulary to avoid misunderstanding between different parties in SE, requirements specification, features specification, variability management, components specification, components matching, model transformations and code generation. Based on the conducted review, guidelines for further research are given.
TL;DR: A rigorous survey of various routing protocols as well as a comparison of diverse routing strategies regarding significant issues in DTN are put forward.
Abstract: Delay Tolerant Network (DTN) are promising techniques to enable data transmission in challenging scenarios where sophisticated infrastructure is not available and the end-to-end path does not exist at the moment of data transmission. These networks are characterized by a long delay, intermittent connectivity and high error rates. Furthermore, the dynamic topology of the network may change randomly. Therefore, routing is one of the most crucial issues that affect the performance of DTN in terms of data delivery, latency and using resources if node mobility is considered. The routing design in DTN raises many challenges to the networks. Therefore, the problem of how to route a packet from one node to another in DTN is of the essence. This paper puts forward a rigorous survey of various routing protocols as well as performs a comparison of diverse routing strategies regarding significant issues in DTN.
TL;DR: A feature extraction method that incorporates the properties of the peripheral auditory system to improve robustness in noisy speech recognition and outperformed the classical feature extraction methods in terms of speech recognition rate is presented.
Abstract: The paper presents a feature extraction method, named as Normalized Gammachirp Cepstral Coefficients (NGCC) that incorporates the properties of the peripheral auditory system to improve robustness in noisy speech recognition. The proposed method is based on a second order low-pass filter and normalized gammachirp filterbank to emulate the mechanisms performed in the outer/middle ear and cochlea. The speech recognition performance of this method is conducted on the speech signals in real-world noisy environments. Experimental results demonstrate that method outperformed the classical feature extraction methods in terms of speech recognition rate. The used Hidden Markov Models based speech recognition system is employed on the HTK 3.4.1 platform (Hidden Markov Model Toolkit).
TL;DR: The results using the EDC approach are more accurate than collaborative filtering and existing methods of matrix factorization namely SVD, baseline, Matrix factorization and neighbours-base, which indicates the significance of the latent feedback of both users and items against the different factorization features in improving the prediction accuracy of the collaborative filtering technique.
Abstract: The rating matrix of a personalized recommendation system contains a high percentage of unknown rating scores which lowers the quality of the prediction. Besides, during data streaming into memory, some rating scores are misplaced from its appropriate cell in the rating matrix which also decrease the quality of the prediction. The singular value decomposition algorithm predicts the unknown rating scores based on the relation between the implicit feedback of both users and items, but exploiting neither the user similarity nor item similarity which leads to low accuracy predictions. There are several factorization methods used in improving the prediction performance of the collaborative filtering technique such as baseline, matrix factorization, neighbour-base. However, the prediction performance of the collaborative filtering using factorization methods is still low while baseline and neighbours-base have limitations in terms of over fitting. Therefore, this paper proposes Ensemble Divide and Conquer (EDC) approach for solving 2 main problems which are the data sparsity and the rating scores’ deviation (misplace). The EDC approach is founded by the Singular Value Decomposition (SVD) algorithm which extracts the relationship between the latent feedback of users and the latent feedback of the items. Furthermore, this paper addresses the scale of rating scores as a sub problem which effect on the rank approximation among the users’ features. The latent feedback of the users and items are also SVD factors. The results using the EDC approach are more accurate than collaborative filtering and existing methods of matrix factorization namely SVD, baseline, matrix factorization and neighbours-base. This indicates the significance of the latent feedback of both users and items against the different factorization features in improving the prediction accuracy of the collaborative filtering technique.
TL;DR: Component-based software engineering and generative programming are common approaches in software engineering, each approach has some benefits and drawbacks.
Abstract: Component-based software engineering and generative programming are common approaches in software engineering. Each approach has some benefits and domain of usage. Component-based development is used to build autonomous components that can be further combined in different ways, while generative programming is more suitable when building systems that have different variants. Before a variable component based system can be build, it needs to be modeled. In this article, a new common metamodel that aims to enable modeling a system which combines both component-based development and generative programming is introduced. The introduced metamodel proposed in this paper combines the component diagram that is used to model systems in component-based development and the feature diagram that is employed in modeling systems in generative programming. The combined metamodel enables modeling of variable systems using components.
TL;DR: A self-organizing time synchronization algorithm that was adapted from the traditional PCO model of fireflies flashing synchronization was proposed, and a significant improvement in energy efficiency was observed, as reflected by an improved transmission scheduling and a coordinated duty cycling and data gathering ratio.
Abstract: Various types of natural phenomena are regarded as primary sources of information for artificial occurrences that involve spontaneous synchronization. Among the artificial occurrences that mimic natural phenomena are Wireless Sensor Networks (WSNs) and the Pulse Coupled Oscillator (PCO), which utilizes firefly synchronization for attracting mating partners. However, the PCO model was not appropriate for wireless sensor networks because sensor nodes are typically not capable to collect sensor data packets during transmission (because of packet collision and deafness). To avert these limitations, this study proposed a self-organizing time synchronization algorithm that was adapted from the traditional PCO model of fireflies flashing synchronization. Energy consumption and transmission delay will be reduced by using this method. Using the proposed model, a simulation exercise was performed and a significant improvement in energy efficiency was observed, as reflected by an improved transmission scheduling and a coordinated duty cycling and data gathering ratio. Therefore, the energy-efficient data gathering is enhanced in the proposed model than in the original PCO-based wave-traveling model. The battery lifetime of the Sensor Nodes (SNs) was also extended by using the proposed model.
TL;DR: This paper suggests a smart and an activesystem in which both unique and multiple soft computing classifier techniques are used to examine performance analysis of lecturers of college of engineering at Salahaddin University-Erbil (SUE).
Abstract: Lecturer performance analysis has enormous influence on the educational life of lecturers in universities. The existing system in universities in Kurdistan-Iraq is conducted conventionally, what is more, the evaluation process of performance analysis of lecturers is assessed by the managers at various branches at the university andin view of that, in some cases the outcomes of this process cause a low level of endorsement among staffs who believe that most of these cases are opinionated. This paper suggests a smart and an activesystem in which both unique and multiple soft computing classifier techniques are used to examine performance analysis of lecturers of college of engineering at Salahaddin University-Erbil (SUE). The dataset collected from the quality assurancedepartment at SUE. The dataset composes of three sub-datasets namely: Student Feedback (FB), Continuous Academic Development (CAD) and lecturer’s portfolio (PRF). Each of the mentioned sub-datasets is classified with a different classifier technique. FB uses Back-Propagation Neural Network (BPNN), CAD uses Naive Bayes Classifier (NBC) and the third sub-dataset uses Support Vector Machine (SVM) as a classifier technique. After implementing the system, the results of the above sub-datasets are collected and then fed as input data to BPNN technique to obtain the final result and accordingly, the lectures are awarded, warned or punished.
TL;DR: The architecture of the neural networks models based on the different combination of inputs and number of hidden neurons to obtain the optimum classification were verified in this study and showed, in terms of classification accuracy, BPN model performed better than the RFN model, however, in term of consistency, theRFN model outperformed BPNmodel.
Abstract: Rainfall is one of the important weather variables that vary in space and time. High mean daily rainfall (> 30 mm) has a high possibility of resulting in flood. Accurate prediction of this variable would save human lives and properties. Soft computing methods have been widely applied in this field. Among the various soft computing methods, Artificial Neural Network (ANN) is the most commonly used methodology. While numerous ANN algorithms were applied, the most commonly applied are the Backpropagation (BPN) and Radial Basis Function (RFN) models. However, there was no research conducted to verify which model among these two produces a superior result. Therefore, this study will fill this gap. In this study, using the meteorology data, the two ANN models were trained to classify the rainfall intensity based on four different classes: Light ( 51 mm). The architecture of the neural networks models based on the different combination of inputs and number of hidden neurons to obtain the optimum classification were verified in this study. The influence of the number of training data on the classification results was also analyzed. Results obtained showed, in term of classification accuracy, BPN model performed better than the RFN model. However, in term of consistency, the RFN model outperformed BPN model.
TL;DR: A model that identifies common patterns for the jobs submitted to the cloud is proposed that outperform both Genetic Algorithm and Round Robin from energy efficiency perspective.
Abstract: Cloud Computing is a paradigm that delivers services by providing an access to wide range of shared resources which are hosted in cloud data centers. One of the recent challenges in this paradigm is to enhance the energy efficiency in these data centers. In this study, a model that identifies common patterns for the jobs submitted to the cloud is proposed. This model is able to predict the type of the job submitted and accordingly, the set of users’ jobs is classified into four subsets. Each subset contains jobs that have similar requirements. In addition to the jobs’ common pattern and requirements, the users’ history is considered in the jobs’ type prediction model. The goal of job classification is to find a way to propose useful strategy that helps to improve power efficiency. Based on the process of jobs’ classification, the best fit virtual machine is allocated to each job. Then, the virtual machines are placed on the physical machines according to a novel strategy, called Mixed Type Placement strategy. The core idea of the proposed strategy is to place virtual machines of the jobs of different types in the same physical machine whenever possible. The placement process is based on Multi Choice Knapsack Problem which is a generalization of the classical Knapsack Problem. This is because different types of jobs do not intensively use the same compute or storage resources in the physical machine. This strategy minimizes the number of active physical machines which, in turn, leads to major reduction in the total energy consumption in the data center. The total execution time and the cost of executing the jobs submitted are considered in the placement process. To evaluate the performance of the proposed strategy, the CloudSim simulator is used with a real workload trace to simulate the cloud computing environment. The results show that the proposed strategy outperform both Genetic Algorithm and Round Robin from energy efficiency perspective.
TL;DR: The aim of this paper is to conduct experiments for determining the best network structure that has effective variable and fitting parameters in predicting the spread of the dengue outbreak and it is proven that the hybrid Genetic Algorithm and Neural network (GANN) model significantly outperforms standalone models namely regression and Neural Network.
Abstract: Early prediction of diseases especially dengue fever in the case of Malaysia, is very crucial to enable health authorities to develop response strategies and context preventive intervention programs such as awareness campaigns for the high risk population before an outbreak occurs. Some of the deficiencies in dengue epidemiology are insufficient awareness on the parameter as well as the combination among them. Most of the studies on dengue prediction use standalone models which face problem of finding the appropriate parameter since they need to apply try and error approach. The aim of this paper is to conduct experiments for determining the best network structure that has effective variable and fitting parameters in predicting the spread of the dengue outbreak. Four model structures were designed in order to attain optimum prediction performance. The best model structure was selected as predicting model to solve the time series prediction of dengue. The result showed that neighboring location of dengue cases was very effective in predicting the dengue outbreak and it is proven that the hybrid Genetic Algorithm and Neural Network (GANN) model significantly outperforms standalone models namely regression and Neural Network (NN).