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Virtual machine migration in MEC based artificial intelligence technique

10.11591/ijai.v10.i1.pp244-252
Ali OUACHA , Mohamed EL Ghmary
The whole world is inundated with smaller devices equipped with wireless communication interfaces. At the same time, the amount of data generated by these devices is becoming more important. The smaller size of these devices has the disadvantage of being short of processing and storage resources (memory, processes, energy,...), especially when it needs to process larger amounts of data. In order to overcome this weakness and process massive data, devices must help each other. A low-resource node can delegate the execution of a set of computionly heavy tasks to another machine in the network to process them for it. The machine with sufficient computational resources must also deposit the appropriate environment represented by the adapted virtual machine. Thus, in this paper, in order to migrate the virtual machine to an edge server in a mobile edge computing environment, we have proposed an approach based on artificial intelligence. More specifically, the main idea of this paper is to cut a virtual machine into several small pieces and then send them to an appropriate target node (Edge Server) using the ant colony algorithm. In order to test and prove the effectiveness of our approach, several simulations are made by NS3. The obtained results show that our approach is well adapted to mobile environments.
Volume: 10
Issue: 1
Page: 244-252
Publish at: 2021-03-01

Fusion for medical image based on discrete wavelet transform coefficient

10.11591/ijeecs.v21.i3.pp1407-1416
Zahraa Yaseen Hasan , Rusul Altaie , Hawraa Abd Al-kadum Hassan
More recent digital camera introduced enormous facilities for users from different specifications to take images easily, but the user still wants to improve these images, which it contains different problems like ambiguous and colors is not clear, because not enough light, cloudy weather, bright light, dark region and it's taken from remote distances. This paper aims to use a new approach for fusion images by using a wavelet coefficient based on PSNR and SNR measure (the technical result) instead of using the max, min, average values, and so on in the previous methods. The wavelet coefficient of each sub band is compared between them, the sub band had a value higher of measure is selected for fusion. Firstly, a discrete wavelet transform has been applied to the medical images with 2level. Then, the peak signal to noise ratio and signal to noise ratio measures have been computed for each sub-band. After that PSNR and SNR values have been compared for each sub-band to opposite sub-band and it selected the better value of measures. Secondly, PSNR and SNR values have been gathered for each image. Then select the image that contains value higher PSNR and lower value of SNR for purpose fusion. Finally, perform an inverse discrete wavelet on the fused image to transform it from the frequency to the spatial domain. The results of the work showed that the wavelet coefficient is used to preserve the image details and removed the noise. PSNR value of 1level of dwt is higher than 2level. This paper makes the image more clearer and informative than the original images. 
Volume: 21
Issue: 3
Page: 1407-1416
Publish at: 2021-03-01

An automatic system for detecting voltage leaks in houses to save people's lives

10.11591/ijeecs.v21.i3.pp1485-1492
Hussein M. Haglan , Hussam Jasim Ali
Many people are exposed to many dangers, such as electrical leakage into parts of the home that lead to death, injury, or loss of material resources. With the great developments that happen daily in the field of technology, one of the most important examples of these developments is the Arduino. The Arduino is a company that produces a software and electronic parts that are open source for companies and students in order to design and build digital devices, design and implement projects and systems that can be linked together or linked with the Internet to facilitate their use to serve society and humanity. It became easier to deploy this technology to solve problems that put people at risk. Many systems that detect leakage of gas, liquids and fires were built using these modern technologies to protect the lives of people and their resources, but no one has actually used these technologies to detect unexpected electrical voltage leaks into the home's water network or walls that resulting from damage in some parts of electrical devices. Therefore, in this paper, we have proposed and designed a system that can detect an unexpected voltage leak from some electric devices to the water network or walls in houses, alarm of house owners by sound, and cut off the electrical current to the house in order to save people's lives and resources.
Volume: 21
Issue: 3
Page: 1485-1492
Publish at: 2021-03-01

Binding site identification of COVID-19 main protease 3D structure by homology modeling

10.11591/ijeecs.v21.i3.pp1713-1721
Marion Adebiyi , Oludayo O. Olugbara
The influx of coronavirus in 2019 (COVID-19) from Wuhan of China has led to a global pandemic, undesirable hiatus, and recorded millions of infection cases with several deaths worldwide. The strain of COVID-19 has neither known treatments nor vaccines, but recent studies have shown that a few of its enzymes may have been considered as potential drug target. Since its influx, the virus has been well-studied, but a lot is not known about its protease yet.  The purpose of this work was to identify the binding site in-silico and present 3D structure of COVID-19 main protease (Mpro) by homology modeling through multiple alignment followed by optimization and validation. The modeling was done by Swiss-Model template library and basic local alignment search tool (BLAST). The obtained homotrimer oligo-state model was verified for reliability using structural validation software such as PROCHECK, Verify3D, MolProbity and QMEAN. The HHBlits software was used to determine the structures that matched the target sequence by evolution. Best template, 6u7h.1.A was used to build a tertiary structure for Mpro with ProMod3 3.0.0 on the Swiss-Model workspace. Self-optimized prediction method with alignment (SOPMA) was applied to compute features of the secondary structure. The verification of quality of COVID-19 structure through Ramachandran plot showed an abundance of 99.3% of amino acid residues in allowed regions while 0.1% in disallowed region. The Verify3D rated the structure a 90.87% PASS of residues having an average 3D-1D score of at least 0.2, which validates a good environment profile for the COVID-19 Mpro model. The features of the secondary structure indicated that the modeled 3D structure of Mpro contains 32.05% α-helix and 37.17% random coil with 25.92 extended strand. DeepSite algorithm elucidates the binding site area that captured local patterns in the structure and exposed the surface cavity of the binding pocket of this protein. The main result of this study suggests that blocking expression of the protein may constitute an efficient approach for transmission blockage. Hence, our thought is that Mpro of COVID-19 may be considered a potential drug target. Nevertheless, more experimental analyses, verification and validation experiments will be required as a targeted drug or vaccine design against COVID-19 virus.
Volume: 21
Issue: 3
Page: 1713-1721
Publish at: 2021-03-01

High sensitivity sapphire FBG temperature sensors for the signal processing of data communications technology

10.11591/ijeecs.v21.i3.pp1567-1574
Mahmoud M. A. Eid , Ashraf S. Seliem , Ahmed Nabih Zaki Rashed , Abd El-Naser A. Mohammed , Mohamed Yassin Ali , Shaimaa S. Abaza
This study has outlined the fiber bragg grating (FBG) temperature sensors signal processing for data communications by using OptiGrating simulation software. The reflectivity of the silica and sapphire fiber grating spectrum is reported against the grating wavelength for internal and external temperature variations. As well as apodized Gaussian reflectivity of the silica and sapphire fiber grating spectrum is simulated and clarified against the grating wavelength for high temperature variations. The temperature sensitivity of sapphire FBG nearly 0.11 pm/0C, where its value is three times higher than silica FBG. It is observed that silica and Sapphire FBG sensors were tested up to 1000 0C by using Gaussian apodization type, side lobes in reflectivity spectrum are totally suppressed.
Volume: 21
Issue: 3
Page: 1567-1574
Publish at: 2021-03-01

Remote sensing data driven bathing water quality assessment using sentinel-3

10.11591/ijeecs.v21.i3.pp1634-1647
Antonia Senta , Ljiljana Šerić
In this paper we are investigating the possibility of usage of remote sensing satellite data, more precisely sentinel-3 OLCI and SLSTR data, for assessment of bathing water quality. In this research we used data driven approach and analysis of data in order to pinpoint aspects of remote sensing data that can be useful for bathing water quality assessment. For this purpose we collected satellite images for period from start of June till end of September of 2019 and results of in-situ measurement for the same period. Results of in-situ measurement were correlated with satellite images bands and analyzed. We propose a simple method for rapid assessment of possible deterioration of bathing water quality to be used by public health authorities for better planning of in situ measurements. Results of implementation of predictive models based on k-nearest neighbour (KNN) and decision tree (DT) are described.
Volume: 21
Issue: 3
Page: 1634-1647
Publish at: 2021-03-01

Effect of service quality and motivation on the consumption behavior of students in the academic services

10.11591/ijere.v10i1.20794
Kuswanto Kuswanto , Irzal Anderson
This study aimed to determine the effect of service quality and motivation to the consumption behavior of students in utilizing academic services. This research involved 87 students of Economic Education Study Program in University of Jambi, Indonesia. The data were analyzed using structural equation modeling with partial least squares technique (SEM-PLS). Student consumption behavior is an illustration of the level of satisfaction in utilizing academic services provided by universities. Satisfaction with academic services is a measure of the success of higher education performance in providing education. A good indicator of consumer behavior variables, namely making the University of Jambi academic services the best choice and choosing them as institutions of higher education to meet learning needs on an ongoing basis. The quality of academic services is shown by indicators of the reliability of officers in providing academic services, accuracy in the delivery of information, responsiveness in responding to complaints and student requests and harmonization of service relationships between officers and students. While consumption motivation is indicated by an indicator of trust in the services consumed it will facilitate the completion of academic affairs and the existence of attractive offers from higher education institutions. The quality of academic services has a significant effect on consumer behavior of students. Moreover, the consumption motivation variable is proven to be a significant mediator on the effect of service quality on student’s consumption behavior.
Volume: 10
Issue: 1
Page: 86-96
Publish at: 2021-03-01

Plant disease prediction using classification algorithms

10.11591/ijai.v10.i1.pp257-264
Maria Morgan , Carla Blank , Raed Seetan
This paper investigates the capability of six existing classification algorithms (Artificial Neural Network, Naïve Bayes, k-Nearest Neighbor, Support Vector Machine, Decision Tree and Random Forest) in classifying and predicting diseases in soybean and mushroom datasets using datasets with numerical or categorical attributes. While many similar studies have been conducted on datasets of images to predict plant diseases, the main objective of this study is to suggest classification methods that can be used for disease classification and prediction in datasets that contain raw measurements instead of images. A fungus and a plant dataset, which had many differences, were chosen so that the findings in this paper could be applied to future research for disease prediction and classification in a variety of datasets which contain raw measurements. A key difference between the two datasets, other than one being a fungus and one being a plant, is that the mushroom dataset is balanced and only contained two classes while the soybean dataset is imbalanced and contained eighteen classes. All six algorithms performed well on the mushroom dataset, while the Artificial Neural Network and k-Nearest Neighbor algorithms performed best on the soybean dataset. The findings of this paper can be applied to future research on disease classification and prediction in a variety of dataset types such as fungi, plants, humans, and animals.
Volume: 10
Issue: 1
Page: 257-264
Publish at: 2021-03-01

Ubiquitous learning in occupational health and safety for vocational education

10.11591/ijere.v10i1.20823
Ketut Ima Ismara , Amin Suharjono , Didi Supriadi
This research aimed to develop instructional media for occupational safety and health android-based for vocational schools. The second aim is to determine the feasibility of instructional media and student responses. The type of research is research and development (R&D) with the Analysis, Design, Development, Implementation, Evaluation (ADDIE) method, namely, analysis, design, development, implementation, and evaluation. The instrument used for data collection was a questionnaire with fourth scales. Data collection used an assessment questionnaire for two experts and 103 users. Research data analysis techniques used quantitative descriptive analysis. This study's results have produced products in the form of android-based instructional media for Occupational Safety and Health. In this study, we designed and developed an application called Zerosicks for mobile devices to help students learn occupational safety and health principles. The results of testing for all features and navigation work well and under their functions. The assessment results by content experts, media experts, and students as users indicate that this media has a very decent level of eligibility. Suggestions for product development are to add videos to the media, provide selected music, and add discussion forums for users.
Volume: 10
Issue: 1
Page: 285-292
Publish at: 2021-03-01

Satellite image inpainting with deep generative adversarial neural networks

10.11591/ijai.v10.i1.pp121-130
Mohamed Akram Zaytar , Chaker El Amrani
This work addresses the problem of recovering lost or damaged satellite image pixels (gaps) caused by sensor processing errors or by natural phenomena like cloud presence. Such errors decrease our ability to monitor regions of interest and significantly increase the average revisit time for all satellites. This paper presents a novel neural system based on conditional deep generative adversarial networks (cGAN) optimized to fill satellite imagery gaps using surrounding pixel values and static high-resolution visual priors. Experimental results show that the proposed system outperforms traditional and neural network baselines. It achieves a normalized least absolute deviations error of (  &  decrease in error compared with the two baselines) and a mean squared error loss of  (  &  decrease in error) over the test set. The model can be deployed within a remote sensing data pipeline to reconstruct missing pixel measurements for near-real-time monitoring and inference purposes, thus empowering policymakers and users to make environmentally informed decisions.
Volume: 10
Issue: 1
Page: 121-130
Publish at: 2021-03-01

Pedagogical pattern of running a course on secondary school students’ achievement in waves

10.11591/ijere.v10i1.20729
Nkwo Inyang Nkwo , Margaret Ndidiamaka Anugwo , J. O. Ugama
This study investigated the comparative effectiveness of pedagogical pattern of running a course and talk-chalk methods on senior secondary school students’ achievement in waves. It is triggered by reports of persistent students’ low achievement in physics contributed largely by students’ poor performance in waves-related items. It adopted the quasi-experimental pretest-posttest control group design. Three research questions and hypotheses guided the study. There were 216 students who participated in the study. Physics Achievement Test (PAT) containing 50 multiple-choice researcher-developed items were used as instrument for data collection. Mean and standard deviation were used to answer the research questions while ANCOVA was used to test the hypotheses at 0.05 level of significance. Results showed that: the pedagogical pattern of running a course method was superior method in fostering students’ achievement in waves; female students achieved higher than male students using pedagogical pattern of running a course strategy to teach waves; and there was no significant interaction effect of teaching methods and gender on students’ achievement in waves. From the findings, it was recommended that the pedagogical pattern of running a course strategy should be used in teaching physics in secondary school education system and in training of teachers.
Volume: 10
Issue: 1
Page: 336-344
Publish at: 2021-03-01

A systematic review of the relationship between motivational constructs and self-regulated learning

10.11591/ijere.v10i1.21006
Sook Ling Lim , Kee Jiar Yeo
The aim of this review was to identify the motivational constructs which were mostly associated with self-regulated learning and how these motivational constructs were related to self-regulated learning. There were 20 studies (N=8,759) met inclusion criteria for this review. In overall, the evidence of the included studies showed that motivational constructs such as self-efficacy, intrinsic goal orientation, task value, and control of learning beliefs were positively and significantly related to and in predicting self-regulated learning; test anxiety was negatively and insignificantly related to and in predicting self-regulated learning; inconsistent results were observed for extrinsic goal orientation as it could be positively or negatively related to and in predicting self-regulated learning.
Volume: 10
Issue: 1
Page: 330-335
Publish at: 2021-03-01

Second order noise shaping for data-weighted averaging technique to improve sigma-delta DAC performance

10.11591/ijaas.v10.i1.pp79-87
Ali Kerem Nahar , Ansam Subhi Jaddar , Hussain K. Khleaf , Mohmmed Jawad Mortada Mobarek
In general, the noise shaping responses, a cyclic second-order response is delivered by the method of data weighted averaging (DWA) in which the output of the digital-to-analog converter (DAC) is restricted to one of two states. DWA works efficiently for rather low levels of quantizing; it begins presenting considerable difficulties when internal levels of quantizing are extended further. Though, each added bit of internal quantizing causes an exponentially increasing in power dissipation, complexity, and size of the DWA logic and the DAC. This gives a controlled second-order response accounting for the mismatch of the elements of DAC. The multi-bit DAC is made up of numerous single-bit DACs having values thereof chosen via a digital encoder. This research presents a discussion of the influence of mismatching between unit elements of the delta-sigma DAC. This results in a constrained second-order response accounting for a mismatch of DAC elements. The results of the simulation showed how the effectiveness of the DWA method in reducing band tones. Furthermore, the DWA method has proved its efficiency in solving the mismatching of DAC unit elements. The noise of the mismatching elements is enhanced by 11 dB at 0.01 with the proposed DWA, thereby enhancing the efficiency of the DAC in comparison to the efficiency of the DAC with no use of the circuit of DWA.
Volume: 10
Issue: 1
Page: 79-87
Publish at: 2021-03-01

Prediction of the effects of environmental factors towards COVID-19 outbreak using AI-based models

10.11591/ijai.v10.i1.pp35-42
Khalid Mahmoud , Hatice Bebiş , A. G. Usman , A. N. Salihu , M. S. Gaya , Umar Farouk Dalhat , R. A. Abdulkadir , M. B. Jibril , S. I. Abba
The need for elucidating the effects of environmental factors in the determination of the novel corona virus (COVID-19) is very vital. This study is a methodological study to compare three different test models (1. Artificial neural networks (ANN), 2. Adaptive neuro fuzzy inference system (ANFIS), 3. A linear classical model (MLR)) used to determine the relationship between COVID-19 spread and environmental factors (temperature, humidity and wind). These data were obtained from the studies (Pirouz, Haghshenas, Haghshenas, & Piro, 2020) with confirmed COVID-19 patients in Wuhan, China, using temperature, humidity and wind as the independent variables. The measured and the predicted results were checked based on three different performance indices; Root mean square error (RMSE), determination coefficient (R2) and correlation coefficient (R). The results showed that ANFIS and ANN are more promising over the classical MLR models having an average R-values of 0.90 in both calibration and verification stages. The findings indicated that ANFIS outperformed MLR and ANN. In addition, their performance skills boosted up to 25% and 9% respectively based on the determination coefficient for the prediction of confirmed COVID-19 cases in Wuhan city of China. Overall, the results depict the reliability and ability of AI-based models (ANFIS and ANN) for the simulation of COVID-19 using the effects of various environmental variables. 
Volume: 10
Issue: 1
Page: 35-42
Publish at: 2021-03-01

Enhancement in data security and integrity using minhash technique

10.11591/ijeecs.v21.i3.pp1739-1750
Sa'ed Abed , Lamis Waleed , Ghadeer Aldamkhi , Khaled Hadi
Data encryption process and key generation techniques protect sensitive data against any various attacks. This paper focuses on generating secured cipher keys to raise the level of security and the speed of the data integrity checking by using the MinHash function. The methodology is based on applying the cryptographic algorithms rivest-shamir-adleman (RSA) and advanced encryption standard (AES) to generate the cipher keys. These keys are used in the encryption/decryption process by utilizing the Pearson Hash and the MinHash techniques. The data is divided into shingles that are used in the Hash function to generate integers and in the MinHash function to generate the public and the private keys. MinHash technique is used to check the data integrity by comparing the sender’s and the receiver’s encrypted digest. The experimental results show that the RSA and AES algorithms based on the MinHash function have less encryption time compared to the normal hash functions by 17.35% and 43.93%, respectively. The data integrity between two large sets is improved by 100% against the original algorithm in terms of completion time, and 77% for small/medium data and 100% for large set data in terms of memory utilization.
Volume: 21
Issue: 3
Page: 1739-1750
Publish at: 2021-03-01
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