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Prototype development of smart parking system powered by solar photovoltaic

10.11591/ijeecs.v18.i3.pp1229-1235
M.Z.M. Nasir , S. Salimin , B. Chan , S.A. Jumaat
This paper proposed the prototype of smart car parking powered by photovoltaic. There is probability that cars will hit the wall or car out from the parking lot. The main objectives of this research are to determine the power rating and sizing of PV panel suitable for smart car parking system and design an ultrasonic parking sensor circuit to alert any obstacle while parking and development the prototype of smart car parking using PV. Smart car parking consists of distance sensors, LED, Arduino UNO potentiometer, resistors, and Node MCU. Where, Arduino UNO as a microcontroller was coding to receive the signal from the sensor and transmit signal to LED to display the car distance condition. To measure the distance between sensor positions to a car, HC-SR04 ultrasonic distance sensor is used. Whenever a car come near to the sensor within range specified, the red, yellow or green LED will light up and send the condition happening to the smartphone application. The application build-up can display the real situation and the PV system can supply enough electricity to the smart car parking. 
Volume: 18
Issue: 3
Page: 1229-1235
Publish at: 2020-06-01

Overview of soft intelligent computing technique for supercritical fluid extraction

10.11591/ijaas.v9.i2.pp117-124
Sitinoor Adeib Idris , Masturah Markom
Optimization of Supercritical Fluid Extraction process with mathematical modeling is essential for industrial applications. The response surface methodology (RSM) has been proven to be a useful and effective statistical method for studying the relationships between measured responses and independent factors. Recently there are growing interest in applying smart system or artificial technique to model and simulate a chemical process and also to predict, compute, classify and optimize as well as for process control. This system works by generalizing the experimental result and the process behavior and finally predict and estimate the problem. This smart system is a major assistance in the development of process from laboratory to pilot or industrial. The main advantage of intelligent systems is that the predictions can be performed easily, fast, and accurate way, which physical models unable to do. This paper shares several works that have been utilizing intelligent systems for modeling and simulating the supercritical fluid extraction process.
Volume: 9
Issue: 2
Page: 117-124
Publish at: 2020-06-01

Experiences and satisfaction of high-risk mothers who gave birth in select facilities in Legazpi City, Philippines

10.11591/ijphs.v9i2.20381
Maria Stephanie Fay S. Cagayan MD , Veincent Christian F. Pepito , Rita Mae Ang Bon MD MPM , Erlidia F. Llamas-Clark
Among the interventions implemented to curb maternal mortality in the Philippines, service delivery networks (SDNs) have been one of the most important. However, due to their recent implementation, frameworks to assess the quality of care they provide have not yet been established. To address this need, we had formulated the Integrated Patient-Centered Health Service Framework and used it to explore the satisfaction of mothers who gave birth in select facilities Legazpi City Philippines. We conducted key-informant interviews with 14 mothers. We found out that they were satisfied with the quality of care during pre-pregnancy, pregnancy, and post-partum; however, the quality of care during labor and delivery, adherence to referral protocols, and respect for patient‟s autonomy can still be improved. The framework should be used by program managers to have a qualitative measure of quality of care provided by SDNs, as well as to ensure that referral protocols were implemented.
Volume: 9
Issue: 2
Page: 137-144
Publish at: 2020-06-01

Predictors of stunting among pediatric children living with HIV/AIDS, Eastern Ethiopia

10.11591/ijphs.v9i2.20422
Dawit Gezahegn , Gudina Egata , Tesfaye Gobena , Berhanu Abebaw
Globally, there were about 3.4 million pediatric children (<15 years of age) who were living with HIV/AIDS. Ethiopia has one of the highest rates of malnutrition in Sub-Saharan Africa. As of 2013, there were about 160,000 pediatric children living with HIV/AIDS in Ethiopia. Even though undernutrition makes it difficult to combat HIV/AIDS, there is paucity of information on the magnitude of stunting and its predictors among seropositive pediatric children in low-income countries like Ethiopia. Institution based quantitative cross sectional study design was employed on 414 randomly selected pediatric (5-15 years) children living with HIV/AIDS in Harari Region and Dire Dawa City Administration Public Hospitals, Eastern Ethiopia. Pretested interviewer administered questionnaire and patient card review was held to collect data. Data were entered through Epi-data and exported to SPSS for analysis. The WHO Anthros plus software was used to calculate the anthropometric indices. Bivariate and Multivariable analysis along with 95%CI were done to identify predictors of stunting. Level of statistical significance was declared at P-value <0.05. The prevalence of stunting was found to be 30.9% (95%CI: 26.0-36.0%). Rural residence [AOR=4.0, (95%CI: 2.22, 7.17)], family monthly income of ≤500 ETB [AOR=5.79, (95%CI: 2.82, 11.60)], being anemic [AOR=3.17, (95% CI: 2.13, 4.93)] and the presence of diarrhea [AOR=6.21, 95% (CI: 3.39, 9.24)] were predictors of stunting. Thus, collaborative measures should be undertaken (to decrease frequent infections and to improve the economic status) to combat chronic malnutrition during HIV/AIDS treatment.
Volume: 9
Issue: 2
Page: 82-89
Publish at: 2020-06-01

Transceiver error reduction by design prototype system based on neural network analysis method

10.11591/ijeecs.v18.i3.pp1244-1251
Muthna Jasim Fadhil , Maitham Ali Naji , Ghalib Ahmed Salman
Code words traditional can be decoding when applied in artificial neural network. Nevertheless, explored rarely for encoding of artificial neural network so that it proposed encoder for artificial neural network forward with major structure built by Self Organizing Feature Map (SOFM). According to number of bits codeword and bits source mentioned the dimension of forward neural network at first then sets weight of distribution proposal choosing after that algorithm appropriate using for sets weight initializing and finally sets code word uniqueness check so that matching with existing. The spiking neural network (SNN) using as decoder of neural network for processing of decoding where depending on numbers of bits codeword and bits source dimension the spiking neural network structure built at first then generated sets codeword by network neural forward using for train spiking neural network after that when whole error reached minimum the process training stop and at last sets code word decode accepted. In tests simulation appear that feasible decoding and encoding neural network while performance better for structure network neural forward a proper condition is achieved with γ node output degree. The methods of mathematical traditional can not using for decoding generated Sets codeword by encoder network of neural so it is prospect good for communication security. 
Volume: 18
Issue: 3
Page: 1244-1251
Publish at: 2020-06-01

Patient satisfaction and associated factors among in-patients in Primary Hospitals of North Shoa Zone, Amhara Regional State, Ethiopia

10.11591/ijphs.v9i2.20412
Zenebe Wondimhunegn Weldearegay , Sisay Shewasinad Yehualahet , Shimelse Ololo Sinkie , Tilahun Fufa Debela
Satisfaction of patient is the preferred item for the consumption of care. It is related to health services consumption. The objective of the study was to determine the level of patient satisfaction and associated factors among patients Primary Hospitals of North Shoa Zone. Institutional-based cross-sectional study design were implemented from March 15-April 25, 2019. Sample sizes of 422 inpatients were included. Participants were selected by lottery method. Factor analysis was used in order to generate factor scores for further analysis. Variables P<0.25 on the bivariate analysis were included in the multiple linear regression and variables with p<0.05 with 95% confidence interval were considered as statistically significant. There was 59.1% inpatient was satisfied. Sex, religion, place of residence, previous admission and occupational status of a housewife, ggovernment employee and educational status of primary school were associated with patient satisfaction.
Volume: 9
Issue: 2
Page: 76-81
Publish at: 2020-06-01

The development of simulation logic model that dealing with uncertainty for piping construction industry

10.11591/ijeecs.v18.i3.pp1311-1318
Zirawani Baharum , HairulNizam Mahdin , Fauziah Abdul Rahman
Basically, multi processes and activities involve in the piping construction (PC) projects needs to be followed before the project is handed over to the customer or client, called multi-project construction environment (MPCE). In PC, the MPCEs exist where more than one project is managed simultaneously within an organization become as a common phenomenon faced by managers that highly possible to face with uncertainty issues, lead to project completion late delivery (PCLD). Dealing with uncertainty in MPCE is a common, though managing uncertainty in PC become more complicated rather that others industry. Therefore, the objective of this paper is to develop the simulation logic model to deal with the uncertainty for PC projects that focused on environmental issues (EI). This result will be proceed for uncertainty model development in the next phase of this research. The simulation logic model development began with qualitative data collection towards the case study, to get all the activities throughout the water supply company. Then, the business model is developed with 14 activities before integrated with the uncertainty factors in EI. The integration of business model and uncertainty factors generated the simulation logic model as main contribution. Once the uncertainty model completely developed, it will provide a medium for PC that confront with MPCE to monitor the uncertainties and prepared to encounter any matters in future. Therefore, it is important to pursue in providing the PC company to all inclusive-model, and helping them in managing and tackling the uncertainty, especially for EI.
Volume: 18
Issue: 3
Page: 1311-1318
Publish at: 2020-06-01

Lung cancer transition rate by stages using discrete time markov model

10.11591/ijeecs.v18.i3.pp1295-1302
Muhammad Hakeem Omar , Syazreen Niza Shair , Nurin Haniah Asmuni
Morbidity risk is linked to the health status or a disease within a population. Morbidity risk is the risk of illness associated with health status or disease within a population. Cancer is one of the prime causes of both morbidity and mortality in majority of countries worldwide. In year 2016, the probability of Malaysian diagnosed with cancer before they reach age 75 is one over four. It has been reported that lung cancer has the highest deaths and it increased by 16.03% from 2012 to 2016. Malaysian National Cancer Registry reported that in year 2007 until 2011, 69.9% of lung cancer are men and the remaining 30.1% are women. Chinese become the dominant lung cancer cases representing 51.04% of total lung cancer patients in Malaysia followed by Malay, 44.81% and Indian, 4.16%. Treatments for lung cancer patients may vary by cancer stages. If cancer were just spread in one place, doctor may recommend a local treatment to get rid of cancer completely. Whereas, if a cancer has spread throughout the whole body, more comprehensive treatments may be needed. Therefore, knowing the probability of transition rate between cancer stages is important for healthcare cost effectiveness evaluation and expected cost calculation. This paper aims to estimate lung cancer transition rate by stages using the Functional Markov model. The Lung cancer transition rate will be calculated based on discrete time on a yearly basis. As a result, the probability of a Lung cancer patient recovering or deteriorating can be estimated.
Volume: 18
Issue: 3
Page: 1295-1302
Publish at: 2020-06-01

Determinants of iodine deficiency among school age children in Guraghe Zone, Southwest Ethiopia

10.11591/ijphs.v9i2.20387
Berhanu Abebaw , Abdu Oumer
Globally, insufficient iodine intake is found to be the most common preventable cause of mental retardation that results in diminished immunity, decreased school performance and infant and young child death. Ethiopia is the first of the top iodine deficient countries in the world. About 12 million children (school age) get inadequate iodine. Community based cross sectional study was conducted on 792 school age children in Guraghe Zone, Ethiopia to assess the determinants of iodine deficiency. Multistage sampling technique was used to select the study subjects. Data were collected by using pretested questionnaire. Clinical examinations were taken following standard procedures. The collected data were entered into Epi-data and exported to SPSS for analysis. Descriptive statistics was calculated and presented accordingly. Bivariate and multivariable Logistic regression with odds ratios along with the 95% confidence interval was computed and interpreted accordingly. A P-value <0.05 was declared as statistically significant association. Total goiter rate was 8.7%. Factors that had significant association with goiter were: School age children who utilized non iodized salt (AOR=3.12, 95% CI=1.73-5.63), those who consumed cabbage >2 times per week (AOR=1.94, 95% CI=1.10-3.52), and children who got elder (AOR=1.22, 95% CI=1.10-1.41). The study area had mild iodine deficiency disorder. Non iodized salt utilization; frequent cabbage consumption and increased age of children were found to be predictors of goiter. Thus, Interventions should focus on universal salt iodization, besides familiarizing goiterogens to the community at large.
Volume: 9
Issue: 2
Page: 90-96
Publish at: 2020-06-01

A chi-square-SVM based pedagogical rule extraction method for microarray data analysis

10.11591/ijaas.v9.i2.pp93-100
Mukhtar Damola Salawu , Micheal Olaolu Arowolo , Sulaiman Olaniyi Abdulsalam , Rafiu Mope Isiaka , Bilkisu Jimada-Ojuolape , Mudashiru Lateef Olumide , Kazeem A Gbolagade
Support Vector Machine (SVM) is currently an efficient classification technique due to its ability to capture nonlinearities in diagnostic systems, but it does not reveal the knowledge learnt during training. It is important to understand of how a decision is reached in the machine learning technology, such as bioinformatics. On the other hand, a decision tree has good comprehensibility; the process of converting such incomprehensible models into an understandable model is often regarded as rule extraction. In this paper we proposed an approach for extracting rules from SVM for microarray dataset by combining the merits of both the SVM and decision tree. The proposed approach consists of three steps; the SVM-CHI-SQUARE is employed to reduce the feature set. Dataset with reduced features is used to obtain SVM model and synthetic data is generated. Classification and Regression Tree (CART) is used to generate Rules as the Last phase. We use breast masses dataset from UCI repository where comprehensibility is a key requirement. From the result of the experiment as the reduced feature dataset is used, the proposed approach extracts smaller length rules, thereby improving the comprehensibility of the system. We obtained accuracy of 93.53%, sensitivity of 89.58%, specificity of 96.70%, and training time of 3.195 seconds. A comparative analysis is carried out done with other algorithms.
Volume: 9
Issue: 2
Page: 93-100
Publish at: 2020-06-01

Development of stratospheric communication platforms (SCP) as backbone to terrestrial networks

10.11591/ijeecs.v18.i3.pp1679-1688
Dimov Stojce Ilcev
In this paper are introduced the new airship techniques and technologies as cost effective solutions of Stratospheric Communication Platforms (SCP) as future backbone of terrestrial networks for rural communications. The launch or putting in position the airship is not critical point such as launch of satellite and controlling support services in the creation of space-based communication technology and the most expensive phase of the total system cost. Therefore, with few cost effective remote controlled and solar powered airships can be covered some region or country including remote and rural areas with low density of population. The airship SCP network offers better solutions than cellular radio systems, with greater speed of transmission than even optical modes, roaming will be enhanced without severe shadowing or obstacle problems and disturbances inside of buildings and service will cost less. The SPS mission system is more autonomous and discrete, can be integrated with current satellite and cellular systems, and will be the best solution for rural, mobile transportation and military applications. The SCP airship can be seen well from all positions inside coverage area, because they are overlapping the total coverage and because of elevation angle. In any circumstances mountains, buildings and even trees cannot cause obstructions like to cellular network. For these reasons, there is currently a revival of interest for SCP constellations and application types of various system concepts are being studied. 
Volume: 18
Issue: 3
Page: 1679-1688
Publish at: 2020-06-01

Population based optimization algorithms improvement using the predictive particles

10.11591/ijece.v10i3.pp3261-3274
M. M. H. Elroby , S. F. Mekhamer , H. E. A. Talaat , M. A. Moustafa Hassan
A new efficient improvement, called Predictive Particle Modification (PPM), is proposed in this paper. This modification makes the particle look to the near area before moving toward the best solution of the group. This modification can be applied to any population algorithm. The basic philosophy of PPM is explained in detail. To evaluate the performance of PPM, it is applied to Particle Swarm Optimization (PSO) algorithm and Teaching Learning Based Optimization (TLBO) algorithm then tested using 23 standard benchmark functions. The effectiveness of these modifications are compared with the other unmodified population optimization algorithms based on the best solution, average solution, and convergence rate.
Volume: 10
Issue: 3
Page: 3261-3274
Publish at: 2020-06-01

Pipelined vedic multiplier with manifold adder complexity levels

10.11591/ijece.v10i3.pp2951-2958
Ansiya Eshack , S. Krishnakumar
Recently, the increased use of portable devices, has driven the research world to design systems with low power-consumption and high throughput. Vedic multiplier provides least delay even in complex multiplications when compared to other conventional multipliers. In this paper, a 64-bit multiplier is created using the Urdhava Tiryakbhyam sutra in Vedic mathematics. The design of this 64-bit multiplier is implemented in five different ways with the pipelining concept applied at different stages of adder complexities. The different architectures show different delay and power consumption. It is noticed that as complexity of adders in the multipliers reduce, the systems show improved speed and least hardware utilization. The architecture designed using 2 x 2 – bit pipelined Vedic multiplier is, then, compared with existing Vedic multipliers and conventional multipliers and shows least delay.
Volume: 10
Issue: 3
Page: 2951-2958
Publish at: 2020-06-01

Predictive geospatial analytics using principal component regression

10.11591/ijece.v10i3.pp2651-2658
Kyilai Lai Khine , ThiThi Soe Nyunt
Nowadays, exponential growth in geospatial or spatial data all over the globe, geospatial data analytics is absolutely deserved to pay attention in manipulating voluminous amount of geodata in various forms increasing with high velocity. In addition, dimensionality reduction has been playing a key role in high-dimensional big data sets including spatial data sets which are continuously growing not only in observations but also in features or dimensions. In this paper, predictive analytics on geospatial big data using Principal Component Regression (PCR), traditional Multiple Linear Regression (MLR) model improved with Principal Component Analysis (PCA), is implemented on distributed, parallel big data processing platform. The main objective of the system is to improve the predictive power of MLR model combined with PCA which reduces insignificant and irrelevant variables or dimensions of that model. Moreover, it is contributed to present how data mining and machine learning approaches can be efficiently utilized in predictive geospatial data analytics. For experimentation, OpenStreetMap (OSM) data is applied to develop a one-way road prediction for city Yangon, Myanmar. Experimental results show that hybrid approach of PCA and MLR can be efficiently utilized not only in road prediction using OSM data but also in improvement of traditional MLR model.
Volume: 10
Issue: 3
Page: 2651-2658
Publish at: 2020-06-01

High functionality reversible arithmetic logic unit

10.11591/ijece.v10i3.pp2329-2335
Shaveta Thakral , Dipali Bansal
Energy loss is a big challenge in digital logic design primarily due to impending end of Moore’s Law. Increase in power dissipation not only affects portability but also overall life span of a device. Many applications cannot afford this loss. Therefore, future computing will rely on reversible logic for implementation of power efficient and compact circuits. Arithmetic and logic unit (ALU) is a fundamental component of all processors and designing it with reversible logic is tedious. The various ALU designs using reversible logic gates exist in literature but operations performed by them are limited. The main aim of this paper is to propose a new design of reversible ALU and enhance number of operations in it. This paper critically analyzes proposed ALU with existing designs and demonstrates increase in functionality with 56% reduction in gates, 17 % reduction in garbage lines, 92 % reduction in ancillary lines and 53 % reduction in quantum cost. The proposed ALU design is coded in Verilog HDL, synthesized and simulated using EDA (Electronic Design Automation) tool-Xilinx ISE design suit 14.2. RCViewer+ tool has been used to validate quantum cost of proposed design.
Volume: 10
Issue: 3
Page: 2329-2335
Publish at: 2020-06-01
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