Tuesday, April 07, 2020

 
These articles have been peer-reviewed and accepted for publication in JICT, but are pending final changes, are not yet published and may not appear here in their final order of publication until they are assigned to issues. Therefore, the content conforms to our standards but the presentation (e.g. typesetting and proof-reading) is not necessarily up to the JICT standard. Additionally, titles, authors, abstracts and keywords may change before publication.
 
A DEEP AUTOENCODER-BASED REPRESENTATION FOR ARABIC TEXT CATEGORIZATION
1Fatima-zahra El-Alami, 1Abdelkader El Mahdaouy, 2,1Said Ouatik El Alaoui & 1Noureddine En-Nahnahi 
1Laboratory of Informatics and Modeling, FSDM, Sidi Mohamed Ben Abdellah University, Fez, Morocco.
2Ibn Tofail University, National School of Applied Sciences, Kenitra, Morocco.
fatimazahraelalami1@gmail.com; abdelkader.elmahdaouy@usmba.ac.ma; s_ouatik@yahoo.com; nahnnourd@yahoo.fr 
 

ABSTRACT

Arabic text representation is a challenging assignment for several applications such as text categorization and clustering since the Arabic language is known for its variety, richness and complex morphology. Until recently, the Bag-Of-Words remains the most common method for Arabic text representation. However, it suffers from several shortcomings such as semantics deficiency and high dimensionality of the feature space. Moreover, most of existing methods ignore the explicit knowledge contained in semantic vocabularies as Arabic WordNet. To conquer these shortcomings, we propose a deep Autoencoder-based representation for Arabic text categorization. It consists of three stages: (1) Extracting from Arabic WordNet the most relevant concepts relying on feature selection process (2) Features learning via an unsupervised algorithm for text representation (3) Categorizing text using deep Autoencoder. Our method allows to consider document semantics by combining both implicit and explicit semantics and to reduce the feature space dimensionality. To evaluate our method, we conducted several experiments on the standard Arabic dataset OSAC. The obtained results show the effectiveness of the proposed method compared to the state-of-the-art ones.

Keywords: Arabic text representation, deep Autoencoder, feature selection, Restricted Boltzmann Machines, Text categorization.


 
A HYBRID LEAST SQUARES SUPPORT VECTOR MACHINE WITH BAT ALGORITHM AND CUCKOO SEARCH ALGORITHM FOR TIME SERIES FORECASTING
1Athraa Jasim Mohammed, 1Khalil Ibrahim Ghathwan & 2Yuhanis Yusof
1Computer Science Department, University of Technology - Iraq, Iraq
2School of Computing, Universiti Utara Malaysia, Malaysia
{10872, 110039}@uotechnology.edu.iq; k.i.ghathwan@gmail.com; yuhanis@uum.edu.my

 

ABSTRACT

Least Squares Support Vector Machine (LSSVM) has been known to be one of the effective forecasting models. However, its operation relies on two important parameters (regularization and kernel). Pre-determining the parameters values will affect the result of forecasting model; hence, to find the optimal value of these parameters, this study investigates the adaptation of Bat and Cuckoo Search Algorithms to optimize LSSVM parameters. Even though Cuckoo Search Algorithm has been proven to be able to solve global optimization in various areas, the algorithm leads to a slow convergence rate when the step size is large. Hence, to enhance the search ability of Cuckoo Search Algorithm, it is integrated with Bat algorithm that offers a balance search between global and local. To further analyze the strength of Bat and Cuckoo to optimize LSSVM parameters, evaluation was performed separately. Five evaluation metrics were utilized; Mean Average Percent Error (MAPE), Accuracy, Symmetric Mean Absolute Percent Error (SMAPE), Root Mean Square Percentage Error (RMSPE) and Fitness. Experimental results on diabetes forecasting demonstrate that the proposed BAT-LSSVM and CUCKOO-LSSVM generate lower MAPE and SMAPE, while producing higher Accuracy and Fitness compared to a PSO-LSSVM and a non-optimized LSSVM. Following to the success, this study integrates the two algorithms to optimize the LSSVM. The newly proposed forecasting algorithm, termed as CUCKOO-BAT-LSSVM, produces better forecasting in terms of MAPE, Accuracy and RMSPE. Such an outcome provides an alternative model to be used in facilitating decision making in forecasting.

Keywords: time series forecasting, Least Squares Support Vector Machine, Bat algorithm, Particle Swarm Optimization algorithm, Cuckoo Search algorithm.


 
A PRACTICAL MODEL FROM MULTIDIMENSIONAL LAYERING PERSONAL FINANCIAL INFORMATION FRAMEWORK TO MOBILE SOFTWARE INTERFACE OPERATIONS
Meennapa Rukhiran & Paniti Netinant*
College of Digital Innovation and Information Technology (DIIT), Rangsit University, Bangkok 12000, Thailand
meennapa_ru@rmutto.ac.th; paniti.n@rsu.ac.th​
 
 
ABSTRACT
 
End users are involved in improving software development processes. Nowadays, User Interface (UI) and User Experience (UX) are particularly concerned for end user interactions in many software designs. Most methodologies have inconsistencies between design and implementation. While complex software is more difficult to make changes, personal finance application is one of the more complex software to design, development, and adapt. This paper proposes a development of the mobile personal financial application towards informative multidimensional layering. We have separated the functional data cutting across the relationships of the three categories and datasets. We have represented operational semantics of dimensions, and combined layers of three-dimensional information and the aspect elements through components. The corresponsive composition of end user features using visual interfaces is concerned. Three-layer User Interface Composition Model is illustrated to transfer and compose layers, functional data, aspect elements, and components to Graphical User Interfaces. Therefore, an integrated view of the software system seems to make the design and implementation consistent to support our framework more straightforward. Few works have been represented by a practical model of mobile informative multidimensional layering. This research applied aspect orientation and informative multidimensional layering to represent better feature models for mobile personal finance application development. We deliver a practical framework of the mobile personal finance application for all four phases of analysis, design, implementation, and evaluation. In addition, to dispense the gap, this research presents more clearly the operations of three-dimensional models, functional data, and aspect elements that are cut across through informative multidimensional layering.
 
Keywords: Functional data, multidimensional data, three-dimensional layering, personal finance, user interface.
 

 
ESTIMATION OF MISSING VALUES USING OPTIMIZED HYBRID FUZZY C-MEANS AND MAJORITY VOTE FOR MICROARRAY DATA
Shamini Raja Kumaran, Mohd Shahizan Othman & Lizawati Mi Yusuf
Faculty Engineering, School of Computing, Universiti Teknologi Malaysia   
shamini.rajakumaran@hotmail.com; shahizan@utm.my; lizawati@utm.my
 
 
ABSTRACT
 
Missing values were a huge constraint in microarray technologies toward improving and identifying the disease-causing genes. Estimating missing values is an undeniable scenario faced by the experts and the imputation methods are an effective way to impute the proper values to proceed with the next processes in microarray technology. Missing value imputation approaches may increase the classification accuracy. Although these approaches might predict the values, the accuracy rates prove its abilities of these approaches to identify the missing values in the gene expression data. In this article, a novel approach, optimized hybrid fuzzy c-Means and majority vote (opt-FCMMV), is proposed to identify the missing values in the data. Using the majority vote (MV) and optimization through particle swarm optimization (PSO), this approach predicts the missing values in the data to form more informative and solid data. In order to verify the effectiveness of opt-FCMMV, several experiments were carried out on two publicly available microarray datasets (ovary and lung cancer samples) under three missing value mechanisms with five different percentage values in the biomedical domain using Support Vector Machine (SVM) classifier. The experimental results showed that the proposed approach functioned efficiently by showcasing highest accuracy rates compared to no imputations, FCM and FCMMV. For exemplary, the accuracy rates for Ovary data with 5% missing values were no imputation (64.0%), FCM (81.8%), FCMMV (90.0%) and opt-FCMMV (93.7%). While for future work, other metaheuristic algorithms can be used to solve missing values via optimization to improve the performance of data.
 
Keywords: fuzzy c-Means, majority vote, missing values, microarray data, data optimization.
 

MODIFIED PASSIVE AVAILABLE BANDWIDTH ESTIMATION IN IEEE 802.11 WLAN
1Yoanes Bandung & 1Joshua Tanuraharja
1School of Electrical Engineering and Informatics, Institut Teknologi Bandung, Indonesia 
bandung@stei.itb.ac.id; joshua.tanuraharja@hotmail.com
 
 
ABSTRACT
 
QoS provisioning for real-time multimedia applications is largely determined by network’s available bandwidth. Until now there is no standard method for estimating bandwidth on wireless networks. Therefore, in this study a mathematical model called Modified Passive Available Bandwidth Estimation (MPABE) was developed to estimate the available bandwidth passively on a Distributed Coordination Function (DCF) wireless network on the IEEE 802.11 protocol. The mathematical model developed was a modification of the three existing mathematical models, namely Available Bandwidth Estimation (ABE), Cognitive Passive Estimation of Available Bandwidth V2 (cPEAB-V2), and Passive Available Bandwidth Estimation (PABE). The proposed mathematical model gives emphasis on what will be faced to estimate available bandwidth and will help in the coming up of the strategies to estimate available bandwidth on the IEEE 802.11. The developed mathematical model consisted of idle-period synchronization between sender and receiver, the overhead probability occurring in the Medium Access Control (MAC) layer, as well as the successful packet transmission probability. A successful packet transmission is influenced by three variables, the packet collision probability caused by a number of neighboring nodes, the packet collision probability caused by traffic from the hidden nodes, and the packet error probability. The proposed mathematical model was tested by comparing it with other relevant mathematical models. The performance of the four mathematical models was compared with the actual bandwidth. Using a series of experiments that have been done, it is found that the proposed mathematical model is approximately 26% more accurate than the ABE, 36% more accurate than the cPEAB-V2, and 32% more accurate than the PABE.
 
Keywords: Available bandwidth estimation, distributed coordination function, IEEE 802.11, hidden nodes.
 

 
SMART CITY SECURITY: FACE-BASED IMAGE RETRIEVAL MODEL USING GRAY LEVEL CO-OCCURANCE MATRIX
1Abdullah Mohammed Rashid,2Ali A.Yassin,1Ahmed A. Alkadhmawee & 2Abdulla J. Yassin
1Education College for Human Science, University of Basrah.
2Computer Dept., Education College for Pure Sciences, University of Basrah.
 Abdalla_rshd@yahoo.com; Abdullah.rashid@uobasrah.edu.iq
 
 
ABSTRACT
 
Nowadays, a lot of images and documents are saved on data sets and cloud servers such as certificates, personal images, and passport. These images and documents are utilized in several applications to serve the resident living in smart cities. Image similarity is considered as one of the smart city's applications. The major challenges faced in the field of image management are searching and retrieving the images. This is because searching based on image content requires a long time. In this paper, the researchers present a secure scheme to retrieve images in the smart city to identify the wanted criminals by using the Gray Level Co-occurrence Matrix (GLCM). The proposed scheme extracts only five features of the query image which are (Contrast, Homogeneity, Entropy, Energy, and Dissimilarity). This work consists of six phases which are registration, authentication, face detection, features extraction, image similarity, and image retrieval. The current study runs on a database of 810 images which was borrowed from face94 to measure the performance of image retrieving. The results of the experiment show the average of precision (AP) is 97.6 and average of recall (AR) is 6.3. Compared with the results of two previous studies, the current study comes out with inspiring results.
 
Keywords: Image Retrieves, Image Similarity, Extracted Features, Smart City Security.
 

 
HYBRID CRYPTOGRAPHIC APPROACH FOR INTERNET OF THINGS APPLICATIONS: A LITERATURE REVIEW
Nur Nabila Mohamed, Yusnani Mohd Yussoff, Mohammed Ahmed Mohammed Saleh & Habibah Hashim
Faculty of Electrical Engineering, Universiti Teknologi MARA, Selangor, Malaysia
nurnabilamohamed@gmail.com; yusna233@salam.uitm.edu.my; mohamedswm@yahoo.com; habib350@salam.uitm.edu.my
 
 
ABSTRACT
 
Cryptography is described as the study of encrypting or secret writing of data using logical and mathematical principles to protect information. This technique has grown in importance in computing technologies for banking service, medical system, transportation and other Internet of Things (IoT)-based applications which have been subjected to increasing security concerns. In cryptography, each of the schemes is built with its own strength respectively, but implementation of single cryptographic scheme into the system has some disadvantages. For instance, symmetric encryption method provides a cost-effective technique of securing data without compromising security, however, sharing the secret key is a vital problem. On the other hand, asymmetric scheme solves the secret key distribution issue yet the standalone technique is slow and consumes more computer resources compared to the symmetric encryption. In contrast, hashing function generates a unique and fixed-length signature for a message to provide data integrity but the method is only a one-way function which is infeasible to invert. As an alternative to solve the security weakness of every single scheme, integration of several cryptographic schemes which also called the hybridization technique is being proposed offering the efficiency of securing data and solving the issue of key distribution. Herein, a review study of articles related to hybrid cryptographic approach from the year 2013 until 2018 is presented. Current IoT domains that implemented hybrid approaches have been identified and the review has been done according to the category of the domain. The significant finding from this literature review is the exploration of various IoT domains that implemented hybrid cryptographic techniques for improving the performance in related works. From the findings, it can be concluded that the hybrid cryptographic approach has been implemented in many IoT cloud computing services. Additionally, AES and ECC are found to be the most popular methods used in the hybrid approach due to its computing speed and security resistance among other schemes.
 
Keywords: hybrid cryptographic approach; internet of things; symmetric encryption; asymmetric encryption; cryptographic hash function.
 
 

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