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Journal receives papers in continuous flow and we will consider articles from a wide range of Information Technology disciplines encompassing the most basic research to the most innovative technologies. Please submit your papers electronically to our submission system at http://jatit.org/submit_paper.php in an MSWord, Pdf or compatible format so that they may be evaluated for publication in the upcoming issue. This journal uses a blinded review process; please remember to include all your personal identifiable information in the manuscript before submitting it for review, we will edit the necessary information at our side. Submissions to JATIT should be full research / review papers (properly indicated below main title).

Journal of Theoretical and Applied Information Technology
February 2020 | Vol. 98 No.04

Title:	SHIFTED LEGENDRE POLYNOMIAL BASED GALERKIN AND COLLOCATION METHODS FOR SOLVING FRACTIONAL ORDER DELAY DIFFERENTIAL EQUATIONS
Author:	R. M. JENA, S. CHAKRAVERTY, S. O. EDEKI, O. M. OFUYATAN
Abstract:	In this article, effective numerical methods for the solution of fractional order delay differential equations (FODDEs) are presented. The fractional derivative (FD) is defined in Caputo sense. Shifted Legendre polynomials are used in the Collocation and Galerkin methods to convert FDDEs to the linear and/or nonlinear system in algebraic form of equations. Example problems are addressed to show the powerfulness and efficacy of the methods.
Keywords:	Residual, Galerkin Method, Fractional Delay Differential Equation, Legendre Polynomial, Caputo Fractional Derivatives
Source:	Journal of Theoretical and Applied Information Technology 29^th February 2020 -- Vol. 98. No. 04 -- 2020
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Title:	RDF QUERY AND PROTOCOLS LANGUAGE USING FOR DESCRIPTION AND REPRESENTATION OF WEB ONTOLOGIES
Author:	PIÃƒâ€˜ERES-MELO, MARLON, ARIZA-COLPAS, PAOLA, 3MORALES-ORTEGA, ROBERTO, AYALA-MANTILLA, CRISTIAN, PELUFFO-MARTINEZ, GABRIEL, MENDOZA-PALECHOR, FABIO, COMBITA-NIÃƒâ€˜O, HAROLD, HERRERA-TAPIAS, BELIÃƒâ€˜A, DIAZ-MARTINEZ, JORGE
Abstract:	The purpose of this article is to expose the metadata structure based on RDF (Resource Description Framework) and the way in which queries can be made using SPARQL (Protocol and RDF Query Language), as a principle for searching the Semantic Web. It also describes what must be considered to build a Web Ontology and the tools that can help the Software developer to make querys using SPARQL.
Keywords:	SPARQL, RDF, OWL, Semantic Web, metadata.
Source:	Journal of Theoretical and Applied Information Technology 29^th February 2020 -- Vol. 98. No. 04 -- 2020
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Title:	HYBRID APPROACH TO AUTOMATIC SUMMARIZATION OF SCIENTIFIC AND TECHNICAL TEXTS
Author:	AIGERIM M. BAKIYEVA, TATIANA V. BATURA
Abstract:	The paper is devoted to the methods of automatic summarization, which use the representation of a text in the form of a graph. And contains an attempt at formal description of the text transformation in terms of the predicate calculus logic. The proposed method combines the use of a linguistic knowledge base, graph representation of texts and machine learning. The fragments of a text, such as words, sentences, paragraphs, are represented as graph nodes, and relations between nodes, for example, rhetorical relations, are denoted by edges. Automatic determination of rhetorical relations in the text allows you to set the location of the nucleus and satellite. To compile a brief annotation, it is necessary to transform the original text, based on the assumption that the nucleus contains the most important part of the statement. The relations between discursive markers in the text define a hierarchy that allows one to solve various problems of word processing in a natural language, including the task of automatically compiling a short abstract on a large volume of text. The summarization process created by the authors consists of six main steps: preprocessing, topic modeling, rhetorical analysis and transformation, weight evaluation, sentence selection, and smoothing. Topic modeling is used to discover key terms. First, unigram topiс models, that contain only one-word terms, are constructed. These models are further expanded by adding multiword terms. The most significant fragments of the source document are determined in the process of rhetorical analysis using discursive markers. Presentation of texts in the form of graphs helps to demonstrate the transformations with the text necessary to highlight important fragments. In assessing the importance of the text fragments are also included keywords, multiword and scientific terms, describing the scientific and technical texts. To store the marker information has created a linguistic knowledge base. The final step in the formation of the annotation is smoothing Ã¢â‚¬â€ a text conversion procedure that allows you to make the text of the abstract (annotation) received more coherent and consistent. The importance of sentences is determined using discursive markers and connectors. We used additive regularization for topic modeling (ARTM) to extract keywords and discover the topics. Our proposed BigARTM and Rake hybrid method for obtaining thematic models and the task of obtaining an abstract using RST markers, action and templates showed its effectiveness and efficiency in testing and in comparison with other methods as was shown in comparisons using the precision, recall and F- measure calculated in a way similar to [2, 10].
Keywords:	Automatic Text Processing, Theory of Rhetorical Structures, Discursive Marker, Text Analysis, Rhetorical Relationships, Semantics.
Source:	Journal of Theoretical and Applied Information Technology 29^th February 2020 -- Vol. 98. No. 04 -- 2020
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Title:	ARABIC DISORDERED SPEECH PHONETIC DICTIONARY GENERATOR FOR AUTOMATIC SPEECH RECOGNITION
Author:	ASSAL A. M. ALQUDAH, MOHAMMAD A. M. ALSHRAIDEH, AHMAD A. S. SHARIEH
Abstract:	The phonetic dictionary, which is also known as pronunciation dictionary and lexicon, contains a mapping between the words of the intended language and their corresponding phoneme sequences. It is an essential component for developing an Automatic Speech Recognition (ASR) system. The phonetic dictionary acts as an intermediary component with other ASR components such as the acoustic model and the language model. This research aims to design and develop a phonetic dictionary generator (PDG) for Modern Standard Arabic (MSA) and Arabic disordered speech that can be used for ASR research and development. This rule-based PDG receives Arabic texts and transforms them into their corresponding phoneme sequences using pre-defined rules. Since ASR systems must accommodate different needs of speakers and users including the speech of normal speakers and speakers with speech and articulation disorders, this PDG will be able to produce phoneme sequences for both normal speech and disordered speech using pre-defined rules too. The input speech from speakers with speech and articulation disorders such as substitution and distortion articulation speech disorders can be different from the speech of normal speakers. In this release of the PDG, the substitution and distortion articulation speech disorders are considered for their importance and frequent recurrence. The PDG is evaluated using an Arabic text that contains 1,623 unique words. The output phonetic dictionary for normal speakers contains 2,473 phoneme sequences, whereas the output phonetic dictionary for speakers with disordered speech contains 62,997 phoneme sequences. This indicates that the output phonetic dictionary for speakers with disordered speech is more comprehensive and contains more possibilities and variations of the same unique word, which would ease the recognition task in ASR systems in a manner that suits different speakers with different pronunciation variations.
Keywords:	Arabic Language, Articulation Disorders, Automatic Speech Recognition, Disordered Speech, Phonetic Dictionary
Source:	Journal of Theoretical and Applied Information Technology 29^th February 2020 -- Vol. 98. No. 04 -- 2020
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Title:	MODERATING ROLE OF CONTENT MARKETING ON THE RELATIONSHIP BETWEEN PERCEIVED RISK AND THE INTENTION TO ONLINE SHOPPING
Author:	JASSIM AHMAD AL-GASAWNEH, KHALID MOHAMMAD OMAR
Abstract:	This paper examined the moderation role of content marketing on the relationship between Perceived risk and Online shopping intention in Jordan based on perceived risk theory (PRT), Stimulus-Organism-Response theory (SOR), and e-technology acceptance model (TAM). The questionnaire was used to obtain data from 215 customers. Partial least squares structural equation modelling was used to analyses the results, and this study concluded a negative relationship between the Perceived risk and online shopping intention. Also, content marketing was found to moderate the perceived risk in order to increase the intent of customers toward online shopping. In practice, this study proves the importance of adequate content marketing particularly on the product and the online shopping procedure, as this can motivate the intention of online shopping among customers. This study is of value to online sellers, both individuals and companies.
Keywords:	Perceived risk; Content marketing; Intention to Online Shopping
Source:	Journal of Theoretical and Applied Information Technology 29^th February 2020 -- Vol. 98. No. 04 -- 2020
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Title:	ENTROPY-BASED FUZZY RANDOM FOREST FOR IMBALANCED DATASETS
Author:	SHROUK EL-AMIR, SAMIRELMOUGY
Abstract:	With the recent wave of data analytics accessing every domain, there is a growing interest in handling an imbalanced classification problem. In many datasets, the positive class size is extra smaller compared with the major class (negative class), as in the case of disease detection, cyber-attacks, and many data mining applications. Among the different algorithms that addressed this problem, Random Forest (RF) attracted many researchers because of its general robustness. But, RSs and other cost-sensitive algorithms are suffering from low sensitivity and low precision according to positive class when dealing with imbalanced dataset problem. In this paper, we propose and develop an Entropy-based Fuzzy Random Forest (EFRF) algorithm to deal with imbalanced classification problem. Fuzzy membership is applied to the training instances such that different instances offer different contributions to the classifiers. Samples that have a higher class certainty are assigned to larger fuzzy memberships. EFRF uses the entropy to pay more attention to the samples with higher class certainty to result in more robust decision making to avoid losing information like other undersampling algorithms. The proposed algorithm showed promising results compared to other imbalanced classification techniques including Entropy-based Fuzzy Support Vector Machine (EFSVM) technique. It featured both high precision and high recall which makes it an excellent choice for security-wise application.
Keywords:	Imbalanced Dataset, Random Forest, Cost-Sensitive Learning, Sampling, Information Entropy
Source:	Journal of Theoretical and Applied Information Technology 29^th February 2020 -- Vol. 98. No. 04 -- 2020
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Title:	SENTIMENT ANALYSIS FOR ARABIC TWEETS DATASETS: LEXICON-BASED AND MACHINE LEARNING APPROACHES
Author:	AHMAD ALOQAILY, MALAK AL-HASSAN, KAMAL SALAH, BASIMA ELSHQEIRAT, MONTAHA ALMASHAGBAH
Abstract:	Recently, Sentiment Analysis applied to social media data has gradually become one of the significant research interest in the data mining domain due to the large volume of data available on social media networks. Sentiment Analysis is concerned with analyzing text to identify opinions or emotions and categorizing them as positive, negative or neutral. Applying sentiment analysis to short texts such as Twitter messages is a challenging task because tweets might contain a combination of formal and informal language, special characters, emojis and symbols. Therefore, it is often difficult to understand the semantics of the text and it is complex to extract the proper emotions expressed by users. In this paper, sentiment analysis approaches, namely: lexicon-based and machine learning approaches, are applied and evaluated on an Arabic tweets dataset (short texts) regarding the Syrian civil war and crises. The experimental results revealed that machine learning approaches outperformed the lexicon-based in the context of predicting the subjectivity of tweets. In terms of machine learning, five popular machine learning algorithms were applied and evaluated. According to the experimental results, the Logistic Model Trees (LMT) algorithm achieved the highest performance results, followed by the simple logistic and the SVM algorithms, respectively. The results also showed that there are enhancements in performance when utilizing feature selection approaches. Based on all performance evaluation measures, the LMT algorithms reported the best performance results (Acc= 85.55, F1= 0.92 and AUC= 0.86).
Keywords:	Machine Learning; Lexicon-Based Approach; Sentiment Analysis; Opinion Mining; Social Media; Twitter Datasets.
Source:	Journal of Theoretical and Applied Information Technology 29^th February 2020 -- Vol. 98. No. 04 -- 2020
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Title:	HYBRID MODEL FOR TWITTER DATA SENTIMENT ANALYSIS BASED ON ENSEMBLE OF DICTIONARY BASED CLASSIFIER AND STACKED MACHINE LEARNING CLASSIFIERS-SVM, KNN AND C5.0
Author:	SANGEETA RANI, NASIB SINGH GILL
Abstract:	Social Networking sites like Twitter and Facebook has offered the possibility to users to express their opinion on various topics and events. Opinion mining is a technique to find the sentiment of people about these topics, which can be useful in decision support. Various government policies can also be monitored by doing the sentiment analysis of related tweets. The objective of this research is to enhance the accuracy of twitter sentiment classification. The paper proposes a framework for a hybrid approach with an ensemble of stacked machine learning algorithms and dictionary based classifier. Sentiment Score extracted from dictionary based classifier is added as additional feature in the feature set. Three machine learning algorithms SVM, KNN and C5.0 are stacked to build an ensemble by using two Meta learners RF and GLM. Real time manually labeled tweets based on Ã¢â‚¬Å“Clean India MissionÃ¢â‚¬Â an Indian government policy is used for implementation of the model. Proposed model is compared with different machine learning and ensemble classifiers. Proposed hybrid model recorded higher accuracy of 0.9066377 for 5 fold cross validation and 0.9124793 for 10 fold cross validation as compared to 0.8667328 in case of stacked ensemble of SVMRadial, KNN and C5.0 by using RF as Meta classifier. RF Meta classifier performed better as compared to GLM in all stacked based ensemble. Proposed model also recorded higher accuracy as compared to machine learning classifiers-SVM, NaÃƒÂ¯ve Bayes, Decision Tree, Random forest and Maximum Entropy. The contribution of the research is to enhance the accuracy of stacked based ensemble classifiers for twitter sentiment classification by using additional sentiment score provided by dictionary based classifier.
Keywords:	Clean India Mission, C5.0, KNN, Sentiment Analysis, Stack Ensemble, SVM, Swatch Bharat.
Source:	Journal of Theoretical and Applied Information Technology 29^th February 2020 -- Vol. 98. No. 04 -- 2020
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Title:	CONFLICT DISTRIBUTION PREDICTION AND OPTIMIZATION OF AIRCRAFT IN GROUND MOVEMENTS
Author:	YASSINE DABACHINE, MOHAMED BINIZ, BELAID BOUIKHALENE, ABDESSAMAD BALOUKI
Abstract:	During the participation in the 2018 International Conference on Air Transport Research, "ICRAT" the scientific community of air transport stressed the importance of traffic control within airport taxiways. Scheduling aircraft (ASCP) is addressed in this document for taxiways (single-track road). For different aircraft categories, a detailed examination of the variable response to aircraft delay is provided. A heuristic method based on the prediction of the global conflict distribution (CDPG) is the first approach presented. In the CDPG, two problems that restricted the implementation of the system are fixed: the impasse situation and an optimal travel strategy. The second part details a "first come, first served"(FCFS) planner to develop an integrated departure and arrival management system at Mohamed V Airport. Improved traffic flow management has been implemented to take into account directional constraints on traffic lane links as well as crossing constraints at traffic lane intersections. Rather than using preset itineraries, a route assignment mechanism is added. Scheduling is applied to each route. Numerical experiments demonstrate an optimal solution for the CDPG in a very short calculation time can be achieved.
Keywords:	Air Traffic Management (ATM), First-Come First-Served (FCFS),, Aircraft Scheduling (ASCP) ,Information System, Conflicts Distribution
Source:	Journal of Theoretical and Applied Information Technology 29^th February 2020 -- Vol. 98. No. 04 -- 2020
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Title:	HYBRID ANT COLONY OPTIMIZATION AND ITERATED LOCAL SEARCH FOR RULES-BASED CLASSIFICATION
Author:	HAYDER NASER KHRAIBET AL-BEHADILI, KU RUHANA KU-MAHAMUD, RAFID SAGBAN
Abstract:	This research presents the ILS-AntMiner rules-based algorithm, a hybrid Iterated Local Search and Ant Colony Optimization, to improve classification accuracy and the size of the classification model. This hybridisation aims to enhance the classification performance in both accuracy and simplicity by increasing the profit of neighbourhood structures in the exploitation mechanism. The experimental results in this research are compared with the most related ant-mining classifiers, including ACO/PSO2 and ACO/SA across various datasets. The results indicate that the proposed classification algorithm can effectively search the training space based on multiple structures to escape from local optima and achieve high classification accuracy and model size.
Keywords:	Data Mining, Rule Discovery, Ant-Miner, Metaheuristics, Swarm Intelligence.
Source:	Journal of Theoretical and Applied Information Technology 29^th February 2020 -- Vol. 98. No. 04 -- 2020
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Title:	SUPERVISED MACHINE LEARNING FOR SMART DATA ANALYSIS IN INTERNET OF THINGS ENVIRONMENT: AN OVERVIEW
Author:	MOHAMMED H. ALSHARIF, WILLIAM A. MOSIER, OSAMA AHMAD ALOMARI, KHALID YAHYA
Abstract:	Machine learning techniques will contribution to making Internet of Things (IoT) applications that are considered the most significant sources of new data in the coming future more intelligent, where the systems will be able to access raw data from different resources over the network and analyze this information in order to extract knowledge. This study focuses on supervised machine learning techniques that is considered the main pillar of the IoT smart data analysis. This study includes reviews and discussions of substantial issues related to supervised machine learning techniques, highlighting the advantages and limitations of each algorithm, and discusses the research trends and recommendations for further study.
Keywords:	Machine learning; Artificial intelligence; Supervised learning; Big data; Internet of Things.
Source:	Journal of Theoretical and Applied Information Technology 29^th February 2020 -- Vol. 98. No. 04 -- 2020
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Title:	RECOMMENDATION ALGORITHM BASED ON TIME CONTEXT AND TAG OPTIMIZATION
Author:	ZHANG KAI, LI FEI DA, ZHANG XU QIAN
Abstract:	At present, some progress has been made in the research of context-based recommendation algorithm and label-based recommendation algorithm. However, there are some problems such as sparse scoring data for items by users and low precision of recommendation results. In response to the above problems, this paper proposes a recommendation algorithm that integrates time context and tag optimization. The recommendation algorithm is improved by integrating user behavior time interval and user attribute label information. Firstly, Long Short-Term Memory (LSTM) is introduced to study the effect of time interval on tags. Then, each output layer is combined with Latent Dirichlet Allocation (LDA) to weigh the tags with high importance. Finally, the prediction value is obtained by fusing the scoring information. Experiments show that the new algorithm has effectively alleviated the problem of sparse scoring data and improves the precision of recommendation results.
Keywords:	Recommender system; time context; tags; Long Short-Term Memory; topic model；Scoring Matrix
Source:	Journal of Theoretical and Applied Information Technology 29^th February 2020 -- Vol. 98. No. 04 -- 2020
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Title:	THE IMPLICATION OF THE EUROPEAN UNIONÃ¢â‚¬â„¢S GENERAL DATA PROTECTION REGULATION (GDPR) ON THE GLOBAL DATA PRIVACY
Author:	DR. ABDULAH M. ASERI
Abstract:	The study examined the implications of the European Union's General Data Protection Regulations on Global Data Privacy. Based on a qualitative technique, primary data were collected using semi-structured interviews. The thematic method was used in the analysis of the interview transcripts collected from 10 interviewees. The results indicate that the new data protection is effective and could negatively impact companies that fail to comply especially those located outside the EU. The policy in promoting data privacy necessitates a practical methodology in seeking data consent from participants while enforcing the viability of information acquisition and usage through affirmative action. As part of the user-centric initiative, GDPR has developed a sense of tailored accountability while reducing security breaches that impact on business potentials. The requirement for enhancing content-sensitive information by global firms and organizations increases responsibility on organizational compliance with GDPR data policies thereby with the realization of a considerable improvement in data security and privacy. The finding also shows that the GDPR is likely to address most of the privacy issues associated with the development of digital technology in the world. The policy controls the key privacy issues associated with the data collection and management such as the privacy, security, integrity and access among many others. Through the GDPR, the companies will embrace strategies to enhance the rights to be forgotten and the access aspects, hence, addressing the privacy issues.
Keywords:	Data Protection, Privacy, Global, Regulations, Compliance
Source:	Journal of Theoretical and Applied Information Technology 29^th February 2020 -- Vol. 98. No. 04 -- 2020
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Title:	AUTOMATIC SPEECH RECOGNITION SYSTEM FOR KAZAKH LANGUAGE USING CONNECTIONIST TEMPORAL CLASSIFIER
Author:	YEDILKHAN AMIRGALIYEV, DARKHAN KUANYSHBAY, DIDAR YEDILKHAN, SHOIYNBEK A
Abstract:	This scientific report illustrates the performance evaluation of the well-known, recently popular neural network Connectionist Temporal Classifier (CTC) for speech recognition. The CTC contains LSTM layers with 256 cells and Momentum Optimizer with learning rate 0.005 and momentum 0.9. Dataset that we have used has 35 native speakers with 360 utterances. For expanding the size of our dataset with overall performance augmentation techniques has been applied using Adobe Audition software, which output 20 more speakers to our original dataset. The result of our experiment has been evaluated with LER (Label error rate). LER measures the inaccuracy between predicted an actual texts. The output of the experiment reported training LER 0.000 and validation LER 0.5.
Keywords:	Recurrent Neural Network, Language Model, Acoustic Model, CTC, Data Augmentation, Time Warping.
Source:	Journal of Theoretical and Applied Information Technology 29^th February 2020 -- Vol. 98. No. 04 -- 2020
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Title:	ROBOT NAVIGATION SYSTEM USING LASER AND A MONOCULAR CAMERA
Author:	TAMER ABUKHALIL, MALEK ALKSASBEH, BASSAM ALQARALLEH, ANAS ABUKARAKI
Abstract:	The key contribution in this research work is introducing a technique that can be used to avoid unknown objects to a mobile robot. Most vision-based obstacle avoidance systems tend to have high computational complexity in order to compensate for accuracy. The proposed method tries to overcome that using pattern recognition of two laser pointers in the view of a single camera. This vision combination along with the program that runs on a base station is designed as a module that detects objects around the robot. Such obstacles are detected by calculating the intensity of the red laser points found on each frame being captured by the camera in real-time. Distance and angle to the objects is measured using Lagrange interpolation formula applied separately to each laser projection in the framed image. A map is created to show the robotÃ¢â‚¬â„¢s actual distances versus estimated ones as the robot keeps track of objects which are in the cameraÃ¢â‚¬â„¢s view. The algorithm successfully manages to avoid obstacles as shown in the experiments. The effectiveness of the proposed system is evaluated by deploying the robot and performing a simple navigation task. The results show that the concept algorithm along with the hardware module is able to utilize the monocular vision with classification error of 3.63%.
Keywords:	Monocular Camera, Obstacle Avoidance, Distance Measure, Lagrange Formula, Mobile Robots
Source:	Journal of Theoretical and Applied Information Technology 29^th February 2020 -- Vol. 98. No. 04 -- 2020
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Title:	ANALYSIS OF PATH LOSS PROPAGATION MODELS IN MOBILE COMMUNICATION
Author:	SIRIAKSORN JAKBORVORNPHAN
Abstract:	The trend of exchanging information data will continuously increase due to the rapid development of mobile communication networks. The new fifth-generation (5G) technology is designed to support the ever increasing demand for internet traffic volume over wide coverage ranges. This paper focuses on the studies of empirical path loss prediction models for network planning of 5G mobile communication systems. The relationship between path loss and other wireless propagation parameters such as transmitter-receiver antennas separation distance, antenna heights, operating frequency are presented to improve the performance optimisation of wireless networks. The data provided in this paper was analysed in MATLAB computer program to predict signal path loss; estimate radio coverage; avoid interferences; and determine received power level. The results based on the studied model showed that the propagation path loss decreases in accordance with the increase in base station tower antenna height. Okumura model is the most suitable model for short-haul applications in 5G radio network communication.
Keywords:	Path Loss Propagation, Free Space Propagation, Outdoor Propagation Model, Line-Of-Sight Signal, Wireless Communication
Source:	Journal of Theoretical and Applied Information Technology 29^th February 2020 -- Vol. 98. No. 04 -- 2020
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