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Emergent situations for smart cities: A survey

10.11591/ijece.v9i6.pp4777-4787
Ahmad Mohamad Al-Smadi , Mutasem K. Alsmadi , Abdel Karim Baareh , Ibrahim Almarashdeh , Hayam Abouelmagd , Osman Saad Shidwan Ahmed
A smart city is a community that uses communication and information technology to improve sustainability, livability, and feasibility. As any community, there are always unexpected emergencies, which must be treated to preserve the regular order. However, a smart system is needed to be able to respond effectively to these emergent situations. The contribution made in this survey is twofold. Firstly, it provides a comprehensive exhaustive and categorized overview of the existing surveys for smart cities.  The categorization is based on several criteria such as structures, benefits, advantages, applications, challenges, issues, and future directions. Secondly, it aims to analyze several studies with respect to emergent situations and management to smart cities. The analysis is based on several factors such as the challenges and issues discussed, the solutions proposed, and opportunities for future research. The challenges include security, privacy, reliability, performance, scalability, heterogeneity, scheduling, resource management, and latency. Few studies have investigated the emergent situations of smart cities and despite the importance of latency factor for smart city applications, it is rarely discussed.
Volume: 9
Issue: 6
Page: 4777-4787
Publish at: 2019-12-01

A review of cloud oriented mobile learning platform and frameworks

10.11591/ijece.v9i6.pp5529-5536
Ahmad Shukri Bin Moh Noor , Marwan Nasser Yousef Atoom , Rabiei Mamat
With the continued growth of mobile devices usage, wireless communications improvement, and cloud computing evolution, many educational institutions around the world, especially universities and colleges, began to provide their students with mobile learning systems based on cloud computing. The widespread, ubiquitous, and flexible natures of mobile devices make mobile learning an attractive alternative in education, particularly when integrating it with cloud computing which is the up-to-date technology that delivers computing hardware and software as services. However, the participatory between mobile learning and cloud computing as a cloud based mobile learning (CBML) becomes one of the important methods in the learning process. Many researches have attempted to combine the unique features of CBML in a form of frameworks. These frameworks have been designed to identify, categorize, or evaluate the major components of the CBML system. This paper is an attempt to identify the important role of cloud computing technology in mobile learning, investigate the main advantages and limitations of CBML systems, and explore the previously designed CBML frameworks.
Volume: 9
Issue: 6
Page: 5529-5536
Publish at: 2019-12-01

Performance comparison of artificial intelligence techniques in short term current forecasting for photovoltaic system

10.11591/ijpeds.v10.i4.pp2148-2156
Muhammad Murtadha Othman , Mohammad Fazrul Ashraf Mohd Fazil , Mohd Hafez Hilmi Harun , Ismail Musirin , Shahril Irwan Sulaiman
This paper presents artificial intelligence approach of artificial neural network (ANN) and random forest (RF) that used to perform short-term photovoltaic (PV) output current forecasting (STPCF) for the next 24-hours. The input data for ANN and RF is consists of multiple time lags of hourly solar irradiance, temperature, hour, power and current to determine the movement pattern of data that have been denoised by using wavelet decomposition. The Levenberg-Marquardt optimization technique is used as a back-propagation algorithm for ANN and the bagging based bootstrapping technique is used in the RF to improve the results of forecasting. The information of PV output current is obtained from Green Energy Research (GERC) University Technology Mara Shah Alam, Malaysia and is used as the case study in estimation of PV output current for the next 24-hours. The results have shown that both proposed techniques are able to perform forecasting of future hourly PV output current with less error.
Volume: 10
Issue: 4
Page: 2148-2156
Publish at: 2019-12-01

A reliable approach to customizing linux kernel using custom build tool-chain for ARM architecture and application to agriculture

10.11591/ijece.v9i6.pp4920-4928
Mahendra Swain , Rajesh Singh , Anita Gehlot , Md Farukh Hashmi , Shiv Kumar , Manish Parmar
ARM processors are receiving more attention as per IoT customized devices are concerned. A novel framework design tool for Linux kernel customization on ARM architecture has been illustrated. The tool is best suit from ARM based platformss like Raspberry pi, Beagle Bone, Intel Edison etc. The proposed techniques uses different tool chains for the kernel customization. The paper represents an integral framework that integrates all the cross compiling tools and simplifies the overall process. The framework has been used for the development of a customized kernel for Raspberry Pi on Ubuntu 14.04 host computer. The custom kernel has been ported in to Raspberry Pi and the performance evaluation has been done. Furthermore, the analysis aims to help users choose and configure their tracers based on their specific requirements to reduce their overhead and get the most of out of them. The testing of customized OS with raspberry Pi device in the field of agriculture. The smart node/mote is designed based on it to deploy in the agriculture field to test its feasibility. The group of nodes data is gathered using ThingSpeak cloud server. The gathered sensory data is analyzed and forecast on farmer’s mobile phone in the form of APP or handheld device for farmer.
Volume: 9
Issue: 6
Page: 4920-4928
Publish at: 2019-12-01

DC-DC converter based power management for go green applications

10.11591/ijpeds.v10.i4.pp2046-2054
Abitha Memala W , C. Bhuvaneswari , S.M. Shyni , G. Merlin Sheeba , Modi Surya Mahendra , V Jaishree
A non-isolated tri-port converter is a fully compact and functional system by the integration of basic converters. This can be used for renewable energy applications. This converter is capable of achieving different switching patterns of power flow between the source and load, interfaced sources of various voltage and current levels with the dc grid. This tri-port converter has to be used for continuous power distribution of rechargeable battery, photovoltaic panels and load. Due to the implementation of this dc-dc converter some operations like buck, boost and buck-boost operations became easy. Use of this converter helps in easy implementation of the system. The solar PV panel implementation boosts the system to a high level and bidirectional flow became easy from source to load and vice versa
Volume: 10
Issue: 4
Page: 2046-2054
Publish at: 2019-12-01

A significant energy management control strategy for a hybrid source EV

10.11591/ijece.v9i6.pp4580-4585
A Geetha , C Subramani
The conceptualization of Electric vehicles has been significant in solving the pollution due to the transportation problem. However, its design and working has not yet been perfected and can be optimized. An effective model of the electric vehicle is the integration of batteries and supercapacitors. In this paper, the optimal sharing of energy between the battery and the supercapacitor is provided by the Particle Swarm Optimization (PSO). The results reveal that the proposed algorithm can better share the energy between the hybrid sources. The simulations are carried out in MATLAB Simulink platform and the results obtained for ECE-15 driving cycle is presented.
Volume: 9
Issue: 6
Page: 4580-4585
Publish at: 2019-12-01

Predictive analytics of university student intake using supervised methods

10.11591/ijai.v8.i4.pp367-374
Muhammad Yunus Iqbal Basheer , Sofianita Mutalib , Nurzeatul Hamimah Abdul Hamid , Shuzlina Abdul-Rahman , Ariff Md Ab Malik
Predictive analytics extract important factors and patterns from historical data to predict future outcomes. This paper presents predictive analytics of university student intake using supervised methods. Every year, universities face a lot of academic offer rejection by the applicants. Hence, this research aims to predict student acceptance and rejection towards academic offer given by a university using supervised methods subject to past student intake data. To solve this problem, a lot of past studies had been reviewed starting from nineties era till now. From the analysis, two algorithms had been selected namely Decision Tree and k Nearest Neighbor. The dataset of past student intake was obtained with fifteen attributes, which are applicants’ gender, applicants studied stream during Sijil Peperiksaan Malaysia (SPM), university campuses, applicants’ hometown, disability, campus visit, course choice order in application form, applicant’s six SPM subjects result, orphan and status of acceptance. Several experiments were implemented to find the best model to predict the student’s offer acceptance by evaluating the model accuracy. Both models yield best accuracy at 66 percent with the selected attributes. This research gives a huge impact in selecting which applicants is suitable to be offered as well as adapting the university’s academic offering process in much intelligence way in the future.
Volume: 8
Issue: 4
Page: 367-374
Publish at: 2019-12-01

Robust controller for an open irrigation canal prototype

10.11591/ijeecs.v17.i1.pp237-247
Nardjes Merabti , Arbaoui Faycel , Saidi Mohamed Larbi , Said Yahmedi
A Management and control of irrigation canals is a very important task whether for irrigation of agricultural land, to provide clean water, or to avoid floods. Irrigation canals are sometimes subject to intense variations due to climate change and inclement weather. For this reasons, a robust controller that allows dealing with large variation in operating conditions is proposed to control the water level of a multi-pool open irrigation canal prototype. The main objective of this study is to regulate the downstream level of each canal's pool at a constant value even with inflow disturbances.  The robust controller is designed and tested in simulation for different operating conditions. The results obtained show the good behavior and the effectiveness of the designed controller.
Volume: 17
Issue: 1
Page: 237-247
Publish at: 2019-11-14

Simulation hedge investment portfolios through options portfolio

10.11591/ijeecs.v16.i2.pp843-847
Miguel Jiménez-Gómez , Natalia Acevedo-Prins , Miguel David Rojas-López
This paper presents two hedging strategies with financial options to mitigate the market risk associated with the future purchase of investment portfolios that exhibit the same behavior as Colombia's COLCAP stock index. The first strategy consists in the purchase of a Call plain vanilla option and the second strategy in the purchase of a Call option and the sale of a Call option. The second strategy corresponds to a portfolio of options called Bull Call Spread. To determine the benefits of hedging and the best strategy, the Geometric Brownian Motion and Monte Carlo simulation is used. The results show that the two hedging strategies manage to mitigate market risk and the best strategy is the first one despite the fact that the Bull Call Spread strategy is lower cost.
Volume: 16
Issue: 2
Page: 843-847
Publish at: 2019-11-01

A descent extension of the Dai - Yuan conjugate gradient technique

10.11591/ijeecs.v16.i2.pp661-668
Basim Abbas Hassan , Zeyad M. Abdullah , Hawraz N. Jabbar
Based on Dai - Yuan technique, a new extension method was proposed. In contrary to the CG classical method, this proposed method employs a coefficient that has an important role in constructing the new formula with less computational efforts. This proposed method was found to be efficient on the basis of theory analysis and numerical results.
Volume: 16
Issue: 2
Page: 661-668
Publish at: 2019-11-01

Time performance analysis of advanced encryption standard and data encryption standard in data security transaction

10.11591/ijeecs.v16.i2.pp988-994
Zolidah Kasiran , Hikma Farah Ali , Noorhayati Mohamed Noor
The advancement of the data communication technologies has increased the traffic of data exchange over the internet and at the same time created the opportunity of data attack by various party.  This paper present  Time Performance Analysis Of Advanced Encryption Standard And Data Encryption Standard  in Data Security Transaction. In this study we proposed an AES algorithm with  different key size, and different file format. Our aim is to safely to transfer the file for using the AES algorithm. Proposed algorithm has done by analyzing the different time taken for both AES and DES, experiments were done by three different file  format which were text, image, and voice. Each file format type was tested with five different file sizes. The result of each experiments were analysed and it was confirmed that  the AES algorithm have better performance in term of time taken as compared to DES.
Volume: 16
Issue: 2
Page: 988-994
Publish at: 2019-11-01

Automating quranic verses labeling using machine learning approach

10.11591/ijeecs.v16.i2.pp925-931
A. Adeleke , N. Samsudin , A. Mustapha , S. Ahmad Khalid
Classification of Quranic verses into predefined categories is an essential task in Quranic studies. However, in recent times, with the advancement in information technology and machine learning, several classification algorithms have been developed for the purpose of text classification tasks. Automated text classification (ATC) is a well-known technique in machine learning. It is the task of developing models that could be trained to automatically assign to each text instances a known label from a predefined state. In this paper, four conventional ML classifiers: support vector machine (SVM), naïve bayes (NB), decision trees (J48), nearest neighbor (k-NN), are used in classifying selected Quranic verses into three predefined class labels: faith (iman), worship (ibadah), etiquettes (akhlak). The Quranic data comprises of verses in chapter two (al-Baqara) of the holy scripture. In the results, the classifiers achieved above 80% accuracy score with naïve bayes (NB) algorithm recording the overall highest scores of 93.9% accuracy and 0.964 AUC.
Volume: 16
Issue: 2
Page: 925-931
Publish at: 2019-11-01

Automatic classification of paddy leaf disease

10.11591/ijeecs.v16.i2.pp767-774
Shafaf Ibrahim , Nurnazihah Wahab , Ahmad Firdaus Ahmad Fadzil , Nur Nabilah Abu Mangshor , Zaaba Ahmad
RiceisastaplefoodinmostoftheAsiancountries.Itisanimportantcrop, andoverhalfoftheworldpopulationreliesonitforfood.However,paddy leafdiseasecanaffectboththequalityandquantityofpaddyinagriculture production.Theclassificationofpaddyleafdiseaseisanimportantand urgenttaskasitdestroysabout10%to15%ofproductioninAsia.Thus,a studyonautomaticclassificationofpaddyleafdiseaseusingimage processingispresented.Featureextractiontechniquesofcolor,texture,and shapewereimplementedtoanalyzethecharacteristicsofthepaddyleaf disease.Inanother note,aSupportVector Machine(SVM)isused toclassify thefourtypesofpaddyleafdiseasewhicharethebrownspot,bacterialleaf blight,tungrovirus,andleaf scald.Theperformanceofthe proposedstudyis evaluatedto160testingimageswhichreturned86.25%ofclassification accuracy.Theoutcomeofthisstudyisexpectedtoassisttheagrotechnology industryinearlydetectionofpaddyleafdiseaseinwhichanappropriate actioncould be taken accordingly.
Volume: 16
Issue: 2
Page: 767-774
Publish at: 2019-11-01

Detecting candidates of depression, anxiety and stress through malay-written tweets: a preliminary study

10.11591/ijeecs.v16.i2.pp787-793
Muhammad Zahier Nasrudin , Ruhaila Maskat , Ramli Musa
Depression, anxiety and stress are not trivial conditions applicable for only the weak-hearted. They can be inflicted by anyone of all age groups, gender, race and social status. While some are courageous to acknowledge their condition, others shy away in shame or denial. In this paper, we proposed a “proactive” approach to detecting candidates of depression, anxiety and stress in an unobtrusive manner by tapping into what Malaysians tweet in Malay language. From this preliminary study, we constructed 165 Malay layman terms which describe depression, anxiety or stress as identified in M-DASS-42 scale. Since Twitter is an informal platform, construction of Malay layman terms is an essential step to the detection of candidates. Our study on 1,789 Malay tweets discovered 6 Twitter users as potential candidates, having high frequency of tweets with any of the layman terms. We can conclude that using tweets can be useful in unobtrusively detecting candidates of depression, anxiety or stress. This paper also identifies open research areas.
Volume: 16
Issue: 2
Page: 787-793
Publish at: 2019-11-01

Privacy preserving outsourcing algorithm for two-point linear boundary value problems

10.11591/ijeecs.v16.i2.pp1065-1069
Nedal Mohammed , Laman R. Sultan , Santosh Lomte
One of a powerful application in the age of cloud computing is the outsourcing of scientific computations to cloud computing which makes cloud computing a very powerful computing paradigm, where the customers with limited computing resource and storage devices can outsource the sophisticated computation workloads into powerful service providers. One of scientific computations problem is Two-Point Boundary Value Problems(BVP) is a basic engineering and scientific problem, which has application in various domains. In this paper, we propose a privacy-preserving, verifiable and efficient algorithm for Two-Point Boundary Value Problems in outsourcing paradigm. We implement the proposed schema on the customer side laptop and using AWS compute domain elastic compute cloud (EC2) for the cloud side.
Volume: 16
Issue: 2
Page: 1065-1069
Publish at: 2019-11-01
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