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29,922 Article Results

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

Grid search of exponential smoothing method: a case study of Ho Chi Minh City load demand

10.11591/ijeecs.v19.i3.pp1121-1130
Ngoc Thanh Tran , Le Van Dai
The exponential smoothing method is one of the widely used methods for load forecasting. The taxonomy of exponential smoothing method shows that its trend and seasonal component affect the results of exponential smoothing method. This paper proposed a framework for grid search with the optimal model of exponential smoothing method based on math formulas. The training process will specify the optimal models which satisfy requirement of minimum of akaike information criterion, accuracy scores of the root mean square error, mean absolute percentage error, and mean absolute error. The testing process will evaluate the accuracy scores between the optimal models and all other ones. The results indicated that the optimal models have accuracy scores near the minimum values. The load demand data collected in Ho Chi Minh City were used to verify the accuracy and reliability of the grid search framework.
Volume: 19
Issue: 3
Page: 1121-1130
Publish at: 2020-09-01

Development process and testing of partial discharge detection device on medium voltage XLPE cable

10.11591/ijeecs.v19.i3.pp1297-1305
Mohamad Izmir Farhan Mohamad Radzi , N H Nik Ali , Azrul Mohd Ariffin , Muhamad Safwan Abd Rahman , Norhidayu Rameli , Mohamad Radzi Ahmad , Ali Syari’ati Mohd Salleh
High voltage assets play a vital role in providing uninterrupted power to the consumers and any slight problems experienced by the assets may cause losses in millions of dollars to businesses. Therefore it is of utmost importance to monitor the health of high voltage assets. This research presents the development process of a partial discharge (PD) device that is able to detect PD acoustic waves for monitoring high voltage assets purposes. Medium voltage cross-linked polyethylene (XLPE) cable was used which was introduced with spherical void defects at the joints of the cable that functioned to produce PD acoustic waves. Outcome of the development processes provides the finished design of the PD sensing device, known as partial discharge detection (PDD) device. The functionality of the PDD device was also assessed through controlled experimentations, and they proved to be successful. Pure PD waveform captured by the ultrasonic sensor was similar when compared to a HFCT sensor’s pure PD waveform. The PDD device is a small and affordable, and is opened to various improvements such as integrating artificial intelligence (AI) unto the device, and one day may replace most existing bulky and expensive PD sensing devices that are readily available in the market.
Volume: 19
Issue: 3
Page: 1297-1305
Publish at: 2020-09-01

On the review of image and video-based depression detection using machine learning

10.11591/ijeecs.v19.i3.pp1677-1684
Arselan Ashraf , Teddy Surya Gunawan , Bob Subhan Riza , Edy Victor Haryanto , Zuriati Janin
Machine learning has been introduced in the sphere of the medical field to enhance the accuracy, precision, and analysis of diagnostics while reducing laborious jobs. With the mounting evidence, machine learning has the capability to detect mental distress like depression. Since depression is the most prevalent mental disorder in our society at present, and almost the majority of the population suffers from this issue. Hence there is an extreme need for the depression detection models, which will provide a support system and early detection of depression. This review is based on the image and video-based depression detection model using machine learning techniques. This paper analyses the data acquisition techniques along with their databases. The indicators of depression are also reviewed in this paper. The evaluation of different researches, along with their performance parameters, is summarized. The paper concludes with remarks about the techniques used and the future scope of using the image and video-based depression prediction. 
Volume: 19
Issue: 3
Page: 1677-1684
Publish at: 2020-09-01

Development of marine observation system using LPWA communication system for marine IoT service

10.11591/ijeecs.v19.i3.pp1556-1563
Se-Hoon Kim , Min-Ho Jeon , Yeon-Ju Jo , Chang-Heon Oh
Because oceans account for 70.8% of the Earth's surface, various internet of things (IoT) applications for the marine industry are being researched and developed. The success of an IoT technology in the maritime industry depends on the location tracking technology used and how far it can communicate. In this study, we implemented a marine observation system using low power wide area (LPWA) communication technology to provide maritime IoT services. The proposed marine observation system consists of a marine IoT module and an ocean observation monitoring system. The information collected is GPS information and communication signal reception strength. As a result of an actual field test, we were able to obtain the distance to the gateway by measuring the position through the marine IoT module, and we found that the intensity of communication was proportional to the change in sea level.
Volume: 19
Issue: 3
Page: 1556-1563
Publish at: 2020-09-01

Main keyword comparison based on document analysis system

10.11591/ijeecs.v19.i3.pp1533-1539
Jongwon Lee , Jaeseung Lee , Hoekyung Jung
Existing document analysis systems list words in the document using a morpheme analyzer. Such a structural feature is difficult to help users to understand the document. To understand a document, you need to analyze the keyword in the document and extract the paragraphs including the keyword. The proposed system retrieves keywords from documents written in XML format, extracts them, and displays them to the user. In addition, it extracts the paragraphs including the keyword entered by the user and maintains paragraph sequence and delete for duplicate paragraphs. Then, the frequency and weight of the keyword are calculated, and the number of paragraphs is reduced by removing the paragraphs including the keyword having a weight less than other keywords weighed. This method may reduce the time and effort required for the user to understand the document as compared to the existing document analysis systems.
Volume: 19
Issue: 3
Page: 1533-1539
Publish at: 2020-09-01

A novel academic performance estimation model using two stage feature selection

10.11591/ijeecs.v19.i3.pp1610-1619
Pamela Chaudhury , Hrudaya Kumar Tripathy
Educational data mining has gained tremendous interest from researchers across the globe. Using data mining techniques in the field of education several significant findings have been made. Accurate academic performance estimation has been a challenging task due to the variety of students’ attributes involved. In this study we have developed a novel framework to estimate the academic performance of students. Our proposed model outperformed existing models of students’ academic performance determination and gives a new direction to academic performance estimation. The proposed model can help not only to reduce the number of academic failures but also help to comprehend the factors contributing to a students’ academic performance (poor, average or outstanding). Some of the techniques used were conversion of categorical attributes into dummy variables, instance segregation, classification using an optimised and improved differential evolutionary algorithm.
Volume: 19
Issue: 3
Page: 1610-1619
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

Development of solar panel cleaning robot using Arduino

10.11591/ijeecs.v19.i3.pp1245-1250
Faridah Hanim Binti Mohd Noh , Muhamad Faizal Yaakub , Ili Najaa Aimi Mohd Nordin , Norain Sahari , Nor Aira Zambri , Sim Sy Yi , Muhammad Syukri Mohd Saibon
Solar power is mainly harnessed from photovoltaic (PV) panels which are arranged in multiple arrays in a solar farm or solar system. Though, power generation from PV solar system is characterised by uncertain efficiency, many countries with high insolation prefer solar as an alternative way of generating clean energy. However, the efficiency of energy generated from PV panels is affected by the accumulation of dust and debris, even on one panel in an array. This condition leads to the need for regular cleaning of the surface of PV panels. Current labour-based cleaning methods for photovoltaic arrays are costly in time, water and energy usage as well as lacking in automation capabilities. To overcome this problem, a fully automatic solar panel cleaning system with/without water is proposed. Hence, in this paper, the design of a robot for automated cleaning of the surface of PV panel is presented. The design utilizes an Arduino controller system to control the robot movement during the cleaning process. In addition, it is equipped with two rough sponge and a water pump system that can be used to clean dust or debris found on PV panel surfaces. The efficiency of the PV panels before and after the cleaning process is also observed. The result shows that the developed solar panel cleaning robot is able to clean the panel effectively and increase back the output current as well as the maximum power of the panel by 50%, after the dust on the PV panel is cleaned.
Volume: 19
Issue: 3
Page: 1245-1250
Publish at: 2020-09-01

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

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

Optimal economic dispatch of power generation solution using lightning search algorithm

10.11591/ijai.v9.i3.pp371-378
Murad Yahya Nassar , Mohd Noor Abdullah , Asif Ahmed Rahimoon
Economic dispatch (ED) is the power demand allocating process for the committed units at minimum generation cost while satisfying system and operational constraints. Increasing cost of fuel price and electricity demand can increase the cost of thermal power generation. Therefore, robust and efficient optimization algorithm is required to determine the optimal solution for ED problem in power system operation and planning. In this paper the lightning search algorithm (LSA) is proposed to solve the ED problem. The system constraints such as power balance, generator limits, system transmission losses and valve-points effects (VPE) are considered in this paper. To verify the effectiveness of LSA in terms of convergence characteristic, robustness, simulation time and solution quality, the two case studies consists of 6 and 13 units have been tested. The simulation results show that the LSA can provide optimal cost than many methods reported in literature. Therefore, it has potential to solve many optimization problems in power dispatch and power system applications.
Volume: 9
Issue: 3
Page: 371-378
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

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
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