Tuesday, October 20, 2020

Journal of Information and Communication Technology (JICT) Vol.19, No.3, July 2020

  
Nur Nabila Mohamed, Yusnani Mohd Yussoff, Mohammed Ahmed Saleh & Habibah Hashim

 
Fatima-zahra El-Alami, Abdelkader El Mahdaouy, Said Ouatik El Alaoui & Noureddine En-Nahnahi 

Solomon Adelowo Adepoju, Ishaq Oyebisi Oyefolahan, Muhammed Bashir Abdullahi & Adamu Alhaji Mohammed
 
Abdullah Mohammed Rashid, Ali Adil Yassin, Ahmed Adel Abdel Wahed & Abdulla Jassim Yassin
  

 
HYBRID CRYPTOGRAPHIC APPROACH FOR INTERNET OF THINGS APPLICATIONS: A REVIEW
1Nur Nabila Mohamed, 2Yusnani Mohd Yussoff, 2Mohammed Ahmed Saleh & 2Habibah Hashim
1Faculty of Engineering & Built Environment, Mahsa University, Malaysia
2Faculty of Electrical Engineering, Universiti Teknologi MARA,Malaysia
nurnabila.m@mahsa.edu.my; mohamedswm@yahoo.com; yusna233, habib350@salam.uitm.edu.my 
 
 
ABSTRACT
 
Cryptography is described as the study of encrypting or secretCryptography is described as the study of encrypting or secretwriting of data using logical and mathematical principles toprotect information. This technique has grown in importancein computing technologies for banking services, medicalsystems, transportation and other Internet of Things (IoT)-based applications which have been subjected to increasingsecurity concerns. In cryptography, each scheme is built withits own respective strength, but the implementation of singlecryptographic scheme into the system has some disadvantages.For instance, symmetric encryption method provides a costeffectivetechnique of securing data without compromisingsecurity. However, sharing the secret key is a vital problem. Onthe other hand the asymmetric scheme solves the secret keydistribution issue; yet the standalone technique is slow andconsumes more computer resources compared to the symmetricencryption. In contrast, hashing function generates a unique andfixed-length signature for a message to provide data integritybut the method is only a one-way function which is infeasibleto invert. As an alternative to solve the security weakness of every single scheme, integration of several cryptographicof every single scheme, integration of several cryptographicschemes which are also called the hybridization techniqueis being proposed offering the efficiency of securing data andsolving the issue of key distribution. Herein, a review study ofarticles related to hybrid cryptographic approach from 2013to 2018 is presented. Current IoT domains that implementedhybrid approaches were identified and the review was conductedaccording to the category of the domain. The significant findingsfrom this literature review included the exploration of variousIoT domains that implemented hybrid cryptographic techniquesfor improving performance in related works. From the findings,it can be concluded that the hybrid cryptographic approach hasbeen implemented in many IoT cloud computing services. Inaddition, AES and ECC have been found to be the most popularmethods used in the hybrid approach due to its computing speedand security resistance among other schemes.
 
Keywords: Cryptography, Internet of things, encryption, public key cryptography, security.
 
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A PRACTICAL MODEL FROM MULTIDIMENSIONAL LAYERING: PERSONAL FINANCE INFORMATION FRAMEWORK USING MOBILE SOFTWARE INTERFACE OPERATIONS  
Meennapa Rukhiran & Paniti Netinant
College of Digital Innovation and Information Technology, Rangsit University,Thailand 
meennapa_ru@rmutto.ac.th; paniti.n@rsu.ac.th​ 
 
 
ABSTRACT
 
End user involvement is crucial in improving software development processes. Hence, nowadays user interface (UI) and user experience (UX) are particularly concerned with end user interactions in many software designs as most methodologies have inconsistencies between design and implementation. Besides, it is relatively difficult to make changes in complex software and personal finance application is one of the more complex software to design, develop, and adapt. This paper proposes the development of a mobile personal finance application using informative multidimensional layering. We have separated functional data cutting across the relationships of three categories and datasets showing operational semantics of dimensions, and combined layers of three-dimensional information including aspect elements through components. This study is concerned with the corresponsive composition of end user features using visual interfaces. It is illustrated in a Three-layer User Interface Composition Model to transfer and compose layers, functional data, aspect elements, and components to Graphical User Interfaces (GUIs). Therefore, an integrated view of the software system would make the design and implementation consistent to support our framework in a more straightforward manner. There have been a few studies which presented practical models of mobile informative multidimensional layering. This research applied aspect orientation and informative multidimensional layering to present a better features model for mobile personal finance application. We deliver a practical framework in the application in all four phases of analysis, design, implementation, and evaluation. In addressing the gap, this research proposes a clearer operation of three-dimensional models, functional data, and aspect elements that cut across through informative multidimensional layering.
 
Keywords: Functional data, multidimensional data, mobile, software, user interface.
 
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A HYBRID LEAST SQUARES SUPPORT VECTOR MACHINE WITH BAT AND CUCKOO SEARCH ALGORITHMS FOR TIME SERIES FORECASTING 
1Athraa Jasim Mohammed, 1Khalil Ibrahim Ghathwan & 2Yuhanis Yusof
1Computer Science Department, University of Technology, Iraq
2School of Computing, Universiti Utara Malaysia, Malaysia 
10872, 110039@uotechnology.edu.iq; 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 values of parameters will affect the results of the 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 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, it is integrated with Bat algorithm that offers a balanced search between global and local. Evaluation was performed separately to further analyze the strength of Bat and Cuckoo Search to optimize LSSVM parameters. Five evaluation metrics were utilized; mean average percent error (MAPE), accuracy, symmetric mean absolute percent error (SMAPE), root mean square percent error (RMSPE) and fitness value. Experimental results on diabetes forecasting demonstrated that the proposed BAT-LSSVM and CUCKOO-LSSVM generated lower MAPE and SMAPE, at the same time produced higher accuracy and fitness value compared to particle swarm optimization (PSO)-LSSVM and a non-optimized LSSVM. Following the success, this study has integrated the two algorithms to better 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: Machine learning, data mining, time series forecasting, least squares support vector machine, particle swarm optimization.
 
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A DEEP AUTOENCODER-BASED REPRESENTATION FOR ARABIC TEXT CATEGORIZATION
1Fatima-zahra El-Alami, 1Abdelkader El Mahdaouy, 1,2Said Ouatik El Alaoui & 1Noureddine En-Nahnahi 
1Laboratory of Informatics and Modeling, FSDM, Sidi Mohamed Ben Abdellah University, Morocco
2National School of Applied Sciences, Ibn Tofail University, 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 feature space. Moreover, most existing methods ignore the explicit knowledge contained in semantic vocabularies such as Arabic WordNet. To overcome these shortcomings, we proposed a deep Autoencoder based representation for Arabic text categorization. It consisted of three stages: (1) Extracting from Arabic WordNet the most relevant concepts based on feature selection processes (2) Features learning via an unsupervised algorithm for text representation (3) Categorizing text using deep Autoencoder. Our method allowed for the consideration of document semantics by combining both implicit and explicit semantics and reducing feature space dimensionality. To evaluate our method, we conducted several experiments on the standard Arabic dataset, OSAC. The obtained results showed the effectiveness of the proposed method compared to state-of-the-art ones.
 
Keywords: Arabic text representation, deep autoencoder, feature selection, machine learning, text categorization.
 
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MULTI-CRITERIA DECISION-MAKING BASED APPROACHES IN WEBSITE QUALITY AND USABILITY EVALUATION: A SYSTEMATIC REVIEW 
1Solomon Adelowo Adepoju, 2Ishaq Oyebisi Oyefolahan, 1Muhammed Bashir Abdullahi & 3Adamu Alhaji Mohammed
1Department of Computer Science, Federal University of Technology Minna, Nigeria
2Department of Information and Media Technology, Federal University of Technology Minna, Nigeria
3Department of Mathematics, Federal University of Technology Minna, Nigeria 
solo.adepoju, o.ishaq, el.bashir02, adamu.alhaj@futminna.edu.ng   
 
 
ABSTRACT 
 
Websites are important in every organisation and tremendous effort is made to design websites that not only look and feel good, but are usable and of high quality. Nevertheless, one critical task is how to evaluate these websites to ensure that users are satisfied with its quality and usability. Although a variety of methods and approaches have been proposed, there is currently an increase in research efforts to model website quality and usability evaluation from the point of view of decision-makers which existing methods do not handle. Thus, this has led to the application of multi-criteria decision-making (MCDM) approaches in the evaluation of websites to handle complexity in decision-making. This paper, therefore, provides a review of the various MCDM methods that have been used in the usability and quality evaluation of websites. The search strategy which was adopted identified a total of 63 published articles in peer-reviewed journals and international conferences between 2005 and 2017. From the research questions formulated for the study, the papers were classified into various MCDM approaches, website genre, number and list of criteria used over the years and the localization of websites based on country. Some of the findings showed that the Analytical Hierarchy Process approach integrated with fuzzy logic has been the most common method over the years. In addition, e-commerce websites make up the most common website genre. Besides, currently most active websites are from Turkey and five is the average number of criteria for the evaluation of website quality and usability.
 
Keywords: Decision making, website, usability, website evaluation, quality.
 
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SMART CITY SECURITY: FACE-BASED IMAGE RETRIEVAL MODEL USING GRAY LEVEL CO-OCCURRENCE MATRIX 
1Abdullah Mohammed Rashid, 2Ali Adil Yassin, 1Ahmed Adel Abdel Wahed & 2Abdulla Jassim Yassin
1Education College for Human Science, University of Basrah. Iraq
2Computer Department, University of Basrah. Iraq  
Abdalla_rshd@yahoo.com, aliadel79yassin, ahmedadel1949, abdullajas@gmail.com  
 
 
ABSTRACT 
 
Nowadays, a lot of images and documents are saved on data sets and cloud servers such as certificates, personal images, and passports. These images and documents are utilized in several applications to serve residents living in smart cities. Image similarity is considered as one of the applications of smart cities. The major challenges faced in the field of image management are searching and retrieving 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 smart cities to identify wanted criminals by using the Gray Level Co-occurrence Matrix. 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 retrieval. The results of the experiment showed that the average precision is 97.6 and average recall is 6.3., Results of the current study have been relatively inspiring compared with the results of two previous studies.
 
Keywords: Image retrieval, image similarity, extracted features, smart city, security.
 
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