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29,922 Article Results

ANFIS controller for vector control of three phase induction motor

10.11591/ijeecs.v19.i3.pp1177-1185
Girisha Joshi , Pinto Pius A J
For variable speed drive applications such as electric vehicles, 3 phase induction motor is used and is controlled by fuzzy logic controllers. For the steady functioning of the vehicle drive, it is essential to generate required torque and speed during starting, coasting, free running, braking and reverse operating regions. The drive performance under these transient conditions are studied and presented. In the present paper, vector control technique is implemented using three fuzzy logic controllers. Separate Fuzzy logic controllers are used to control the direct axis current, quadrature axis current and speed of the motor. In this paper performance of the indirect vector controller containing artificial neural network based fuzzy logic (ANFIS) based control system is studied and compared with regular fuzzy logic system, which is developed without using artificial neural network. Data required to model the artificial neural network based fuzzy inference system is obtained from the PI controlled induction motor system. Results obtained in MATLAB-SIMULINK simulation shows that the ANFIS controller is superior compared to controller which is implemented only using fuzzy logic, under all dynamic conditions.
Volume: 19
Issue: 3
Page: 1177-1185
Publish at: 2020-09-01

Automatic control system based on iot data identification

10.11591/ijeecs.v19.i3.pp1525-1532
Hankil Kim , Jaehyun Park , Hoekyung Jung
Recently, as the internet of things (IoT) technology has developed, researches are rigorously conducted to construct smart environments such as smart home, smart grid, and industrial IoT. However, currently existing systems consists of a series of events, and even if an existing task is running, unnecessary work still occurs as both works happen simultaneously. In this paper, we propose an automatic work control system to solve this problem. The proposed system transmits the data measured by the sensor to the server and identifies non-real-time tasks such as real-time work which is related to the dangerous situations, ventilation and temperature control. In addition, priority among the tasks is set in a way that existing tasks are stopped when high priority tasks occur. Accordingly, this can reduce the unnecessary waste of power, and the user is able to receive a proactive service.
Volume: 19
Issue: 3
Page: 1525-1532
Publish at: 2020-09-01

Intelligent reputation system for safety messages in VANET

10.11591/ijai.v9.i3.pp439-447
Ghassan Samara
Nowadays Vehicle Ad - hoc Nets (VANET) applications have become very important in our lives because VANET provides drivers with safety messages, warnings, and instructions to ensure drivers have a safe and enjoyable journey. VANET Security is one of the hottest topics in computer networks research, Falsifying VANET system information violates VANET safety objectives and may lead to hazardous situations and loss of life. In this paper, an Intelligent Reputation System (IRS) aims to identify attacking vehicles will be proposed; the proposed system will rely on opinion generation, trust value collection, traffic analysis, position based, data collection, and intelligent decision making by utilizing the multi-parameter Greedy Best First algorithm. The results of this research will enhance VANET's safety level and will facilitate the identification of misbehaving vehicles and their messages. The results of the proposed system have also proven to be superior to other reputational systems.
Volume: 9
Issue: 3
Page: 439-447
Publish at: 2020-09-01

Application of artificial neural network to predict amount of carried weight of cargo train in rail transportation system

10.11591/ijai.v9.i3.pp480-487
Siti Nasuha Zubir , S. Sarifah Radiah Shariff , Siti Meriam Zahari
Derailments of cargo have frequently occurred in Malaysian train services during the last decade. Many factors contribute to this incident, especially its total amount of carried weight. It is found that severe derailments cause damage to both lives and properties every year. If the amount of carried weight of cargo train could be accurately forecasted in advance, then its detrimental effect could be greatly minimized. This paper presents the application of Artificial Neural Network (ANN) to predict the amount of carried weight of cargo train, with KTMB used as the study case. As there are many types of cargo being carried by KTMB, this study focuses only on cement that being carried in twelve (12) different routes. In this study, Artificial Neural Network (ANN) has been incorporated for developing a predictive model with three (3) different training algorithms, Levenberg-Marquardt (LM), Quick Propagation (QP) and Conjugate Gradient Descent (CGD). The best training algorithm is selected to predict the amount of carried weight by comparing the error measures of all the training algorithm which are Root Mean Squared Error (RMSE) and Mean Absolute Percentage Error (MAPE). The obtained results indicated that the ANN technique is suitable for predicting the amount of carried weight.
Volume: 9
Issue: 3
Page: 480-487
Publish at: 2020-09-01

Machine learning-based technique for big data sentiments extraction

10.11591/ijai.v9.i3.pp473-479
Noraini Seman , Nurul Atiqah Razmi
A huge amount of data is generated every minute for social networking and content sharing via Social media sites that can be in a form of structured, unstructured or semi-structured data.  One of the largest used social media sites is Twitter, where each and every day millions of data generated in the form of unstructured tweets. Tweets or opinions of the people can be used to extract sentiments of the people. Sentiment analysis is beneficial for organizations to improve their products and make required changes on demand to increase their profit. In this paper, three machine learning algorithms Support Vector Machine (SVM), Decision Trees (DT), and Naive Bayes (NB) for classifying sentiments of twitters data. The purpose of this research is to compare the outcomes of these algorithms to identify best machine learning method which gives most accurate and efficient results for classifying twitter data. Our experimental result shows that same preprocessing methods on a different dataset affect similarly the classifiers performance. After analyzing the results it is observed that SVM provides 64.96%, 71.26% and 91.25% precision which is better than other two algorithms. Also, overall Recall and F-measure rate of SVM is greater than NB and DT for three datasets. However, it is important to further study current available preprocessing techniques that help us to improve results of various classifiers.
Volume: 9
Issue: 3
Page: 473-479
Publish at: 2020-09-01

A multiband and wideband frequency reconfigurable slotted bowtie antenna

10.11591/ijeecs.v19.i3.pp1399-1406
Izni Husna Idris , Mohamad Rijal Hamid , Kamilia Kamardin , Mohamad Kamal A. Rahim , Huda A. Majid
A multiband and wideband frequency reconfigurable antenna is presented. A wideband from 3.5 GHz to 9.0 GHz is achieved by introducing one stripline in the middle of a slotted bowtie antenna, whereas multiband is obtained by integrating an additional two slotted arms at the end of bowtie-shaped. As a result, the antenna operated at multiband mode (1.7 GHz and 2.6 GHz) and wideband mode (3.5 GHz to 9.0 GHz) simultaneously. The reconfigurability of the antenna is attained through switches. Five states are achieved with three  pairs of switches configurations. All results are presented and discussed, including S11, current distribution, radiation pattern, and gain. The antenna is suitable to be used in multimode communication systems.
Volume: 19
Issue: 3
Page: 1399-1406
Publish at: 2020-09-01

Knowledge and attitudes on sexually transmitted infections and HIV among undergraduates in the state universities

10.11591/ijphs.v9i3.20431
Upuli Amaranganie Pushpakumari Perera , Chrishantha Abeysena
This study aimed to describe knowledge and attitudes on sexually transmitted infections (STI) and HIV among undergraduates in state universities of Western province, Sri Lanka. A descriptive cross-sectional study was conducted among second and third year undergraduates in 2014. The stratified cluster sampling method was applied to select 1575 undergraduates. A pre-tested self-administered questionnaire was administered to assess knowledge and attitudes on STI and HIV. The associations of knowledge and attitude categories with selected variables were assessed. Most (42.3%, n=667) of the undergraduates belonged to poor knowledge category, 41% (n=646) satisfactory knowledge on STI. Only 16.6% (n=262) had good overall knowledge on STI. Undergraduates who had studied in bioscience stream (36.7%, n=91) were better knowledge than others (12.6%, n=171) (p<0.001). A majority (62.6%, n=976) of undergraduates had overall good knowledge on HIV, 27.7%, (n=432) satisfactory knowledge and 9.7% (n=151) had poor knowledge. Males who had studied in bioscience stream and those who had studied at non-mixed schools were better knowledge on HIV than the counterparts. A majority (56.5%, n=883) of undergraduates had undesirable attitudes and 43.5% (n=681) had desirable attitudes towards HIV. Males (45.9%, n=294) had more desirable attitudes than females (42%, n=386) (p>0.05). Knowledge on STI was low and HIV was higher. About half of the undergraduates had desirable attitudes towards HIV.
Volume: 9
Issue: 3
Page: 155-161
Publish at: 2020-09-01

Age, intellectual functions and activity contributions to elderly nutritional status

10.11591/ijphs.v9i3.20464
Isnani Nurhayati , Anas Rahmad Hiadayat
This research aimed to examine contribution of age, mental and activity to the status of elderly nutrition. The research conducted in Boyolali, Indonesia in December 2019-January 2020. This type of research is quantitative with a cross sectional approach; determination of sampling with purposive random sampling employed 70 respondents. Data analysis was using Path Analysis with linear regression rate of significance is 0.05.The results showed that contributions of the age, intellectual functions and simultaneous activities that directly affect the nutritional status of R2square=0.821 =0.674 or 67% while the remaining 33% are contributed from other variables. The amount of simultaneous age and intellectual functions contributions that directly affect elderly activities is R2square=0.0327 or 3.27%, the remaining 96.73% is influenced by other factors. Indirect contributions of age through activity to the status of elderly nutrition and there is direct contribution of elderly intellectual functions condition through activity to the status of elderly nutrition. <w:LsdException Locked="false" Priority="49" Na
Volume: 9
Issue: 3
Page: 216-223
Publish at: 2020-09-01

Intelligent cluster connectionist recommender system using implicit graph friendship algorithm for social networks

10.11591/ijai.v9.i3.pp497-506
Arnold Adimabua Ojugo , Debby Oghenevwede Otakore
Implicit clusters are formed as a result of the many interactions between users and their contacts. Online social platforms today provide special link-types that allows effective communication. Thus, many users can hardly categorize their contacts into groups such as “family”, “friends” etc. However, such contact clusters are easily represented via implicit graphs. This has arisen the need to analyze users’ implicit social graph and enable the automatic add/delete of contacts from/to a group via a suggestion algorithm – making the group creation process dynamic (instead of static, where users are manually added or removed). The study implements the friend suggest algorithm, which analyzes a user’s implicit social graph to create custom contact group using an interaction-based metric to estimate a user’s affinity to his contacts and groups. The algorithm starts with a small seed set of contacts – already categorized by a user as friends/groups; And, then suggest other contacts to be added to a group. The result inherent demonstrates the importance of both the implicit group relationships and the interaction-based affinity in suggesting friends.
Volume: 9
Issue: 3
Page: 497-506
Publish at: 2020-09-01

Model to assess the factors of 10-year future risk of coronary heart disease among people of Framingham, Massachusetts

10.11591/ijphs.v9i3.20469
Azizur Rahman , Arifa Tabassum
In earlier decade, heart disease was the most common cause of death in the US. Among the many important risk factors such as age, number of cigarettes smoke could help in determining the odds of having corona heart disease (CHD) when modeling with other important factors. We analyzed ongoing cardiovascular study on residents of the town of Framingham, Massachusetts, US to predict the 10-year risk of future CHD. We applied the Binary logistic regression model to assess the strength of the association of factors (such as gender, age, number of cigarettes smoke, total cholesterol level) in predicting the odds of having CHD in study population. Results showed that gender, age, number of cigarette smoke, systolic blood pressure were statistically significant and the increased age and cigarettes per day increase the odds of having 10-year risk of CHD. However, the noticeable finding was that patients with Diabetes at higher glucose level have the higher odds of having 10-year risk of CHD than with low level of glucose concentration among the residents of Framingham study.
Volume: 9
Issue: 3
Page: 259-266
Publish at: 2020-09-01

In vivo study on murine species using Cytarabine magnetic microspheres

10.11591/ijphs.v9i3.20468
Satinder Kakar , Anurekha Jain , Ramandeep Singh
Cytarabine magnetic microspheres were formulated and checked for their antileukemic potential. Leukemia was persuaded in the Wister strain of rat by intravenous injection of benzene. Blood was procured and various hematological parameters were noted and compared. Animals were divided into four groups, antileukemial potential was found to be maximum in case of magnetic microspheres of Cytarabine. The study shows the Antileukemic potential of Cytarabine magnetic microspheres.
Volume: 9
Issue: 3
Page: 255-258
Publish at: 2020-09-01

Design and implementation of fast floating point units for FPGAs

10.11591/ijeecs.v19.i3.pp1480-1489
Mohammed Falih Hassan , Karime Farhood Hussein , Bahaa Al-Musawi
Due to growth in demand for high-performance applications that require high numerical stability and accuracy, the need for floating-point FPGA has been increased. In this work, an open-source and efficient floating-point unit is implemented on a standard Xilinx Sparton-6 FPGA platform. The proposed design is described in a hierarchal way starting from functional block descriptions toward modules level design. Our implementation used minimal resources available on the targeting FPGA board, tested on Sparton-6 FPGA platform and verified on ModelSim. The open-source framework can be embedded or customized for low-cost FPGA devices that do not offer floating-point units.
Volume: 19
Issue: 3
Page: 1480-1489
Publish at: 2020-09-01

Interaction between updated FR-CG algorithms with optimal Cuckoo algorithm

10.11591/ijeecs.v19.i3.pp1497-1504
Rana Z. Alkawaz , Abbas Y. AlBayati , Marwan S. Jameel
In this article we have derived two versions, ξk and ρk were derived from an algorithm based on the first suggested modified Fletcher-Reeves method in the article for the two-term CG method and another term to get a downward search towards the function minimum point with the search for an inaccurate line and we have proved rapprochement. These two algorithms combined with the Cuckoo algorithm to achieve a remarkable performance in reducing the number of repetitions in order to reach the minimization of 10 functions is unconstrained in the numerical results. 
Volume: 19
Issue: 3
Page: 1497-1504
Publish at: 2020-09-01

Risky sexual behavior and associated factors among preparatory school students in Arsi Negelle Town Oromia, Ethiopia

10.11591/ijphs.v9i3.20033
Nega Degefa Megersa , Girum Sebsibe Teshome
Risky sexual behavior remains the major public concern commonly affecting youths. These behaviors lead to undesirable health outcomes including sexually transmitted infections, unplanned pregnancy and abortion for female. This study was intended to assess risky sexual behavior and associated factors among preparatory school students in Arsi Negelle Town. Institution-based cross-sectional study was conducted among 300 preparatory school students. Data was collected using a structured and pre-tested questionnaire. Bivariate and multivariable analysis was conducted to identify the association between variables. Statistical significance was declared at p<0.05. The prevalence of risky sexual behavior was, 32. % (95% CI: 24.3, 40.9). It was significantly associated with students grade level (AOR: 5.77; 95% CI: 1.49, 22.28), having no discussion on sexual and reproductive health (AOR: 11.28; 95% CI: 1.8, 77.49), poor knowledge on HIV/AIDS (AOR: 4.86, 95% CI: 1.38, 17.11), not watching porn movies (AOR: 0.01; 95% CI: 0.001, 0.26), having pocket money (AOR: 0.10; 95% CI: 0.03, 0.39) and having peer influence (AOR: 0.07; 95% CI: 0.02, 0.28). Significant number of students engaged in at least one risky sexual behavior. The behavior was commonly seen among students with poor knowledge about HIV, no discussion on sexual and reproductive health, lower grade level, having pocket money, having peer influence and watching pornographic movies.
Volume: 9
Issue: 3
Page: 162-168
Publish at: 2020-09-01

A computing model for trend analysis in stock data stream classification

10.11591/ijeecs.v19.i3.pp1602-1609
Abdul Razak , Nirmala C. R
For several decades, many statistical and scientific efforts took place for the better analysis or prediction of stock trading. But still it is open to offer new avenues for the scientists to rethink and discover new inferences by adopting latest technological scenarios. In this regard, this paper is trying to apply classification techniques on stock data stream through feature extraction for the trend analysis. The proposed work is involving k-means for clustering samples into two clusters (the stocks in trend as one cluster and another on as stocks not in trend). The trend analysis is done based on density estimation of the stocks with respect to sectors. A well-known data representation method that is histogram is used to represent the sector which is in trend. This work has been implemented and experimented by considering live NSE (India) data using python and its related tools.
Volume: 19
Issue: 3
Page: 1602-1609
Publish at: 2020-09-01
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