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Identification of important features and data mining classification techniques in predicting employee absenteeism at work

10.11591/ijece.v11i5.pp4587-4596
Amal Al-Rasheed
Employees absenteeism at the work costs organizations billions a year. Prediction of employees’ absenteeism and the reasons behind their absence help organizations in reducing expenses and increasing productivity. Data mining turns the vast volume of human resources data into information that can help in decision-making and prediction. Although the selection of features is a critical step in data mining to enhance the efficiency of the final prediction, it is not yet known which method of feature selection is better. Therefore, this paper aims to compare the performance of three well-known feature selection methods in absenteeism prediction, which are relief-based feature selection, correlation-based feature selection and information-gain feature selection. In addition, this paper aims to find the best combination of feature selection method and data mining technique in enhancing the absenteeism prediction accuracy. Seven classification techniques were used as the prediction model. Additionally, cross-validation approach was utilized to assess the applied prediction models to have more realistic and reliable results. The used dataset was built at a courier company in Brazil with records of absenteeism at work. Regarding experimental results, correlationbased feature selection surpasses the other methods through the performance measurements. Furthermore, bagging classifier was the best-performing data mining technique when features were selected using correlation-based feature selection with an accuracy rate of (92%).
Volume: 11
Issue: 5
Page: 4587-4596
Publish at: 2021-10-01

Convolution neural network and histogram equalization for COVID-19 diagnosis system

10.11591/ijeecs.v24.i1.pp420-427
Bashra Kadhim Oleiwi Chabor Alwawi , Layla H. Abood
The coronavirus disease-2019 (COVID-19) is spreading quickly and globally as a pandemic and is the biggest problem facing humanity nowadays. The medical resources have become insufficient in many areas. The importance of the fast diagnosis of the positive cases is increasing to prevent further spread of this pandemic. In this study, the deep learning technology for COVID-19 dataset expansion and detection model is proposed. In the first stage of proposed model, COVID-19 dataset as chest X-ray images were collected and pre-processed, followed by expanding the data using data augmentation, enhancement by image processing and histogram equalization techniuque. While in the second stage of this model, a new convolution neural network (CNN) architecture was built and trained to diagnose the COVID-19 dataset as a COVID-19 (infected) or normal (uninfected) case. Whereas, a graphical user interface (GUI) using with Tkinter was designed for the proposed COVID-19 detection model. Training simulations are carried out online on using Google colaboratory based graphics prossesing unit (GPU). The proposed model has successfully classified COVID-19 with accuracy of the training model is 93.8% for training dataset and 92.1% for validating dataset and reached to the targeted point with minimum epoch’s number to train this model with satisfying results.
Volume: 24
Issue: 1
Page: 420-427
Publish at: 2021-10-01

Blockchain based voting system for Jordan parliament elections

10.11591/ijece.v11i5.pp4325-4335
Mohammad Malkawi , Muneer Bani Yassein , Asmaa Bataineh
Covid-19 pandemic has stressed more than any-time before the necessity for conducting election processes in an electronic manner, where voters can cast their votes remotely with complete security, privacy, and trust. The different voting schema in different countries makes it very difficult to utilize a one fits all system. This paper presents a blockchain based voting system (BBVS) applied to the Parliamentary elections system in the country of Jordan. The proposed system is a private and centralized blockchain implemented in a simulated environment. The proposed BBVS system implements a hierarchical voting process, where a voter casts votes at two levels, one for a group, and the second for distinct members within the group. This paper provides a novel blockchain based e-Voting system, which proves to be transparent and yet secure. This paper utilizes synthetic voter benchmarks to measure the performance, accuracy and integrity of the election process. This research introduced and implemented new algorithms and methods to maintain acceptable performance both at the time of creating the blockchain(s) for voters and candidates as well as at the time of casting votes by voters.
Volume: 11
Issue: 5
Page: 4325-4335
Publish at: 2021-10-01

Optimal integration of photovoltaic distributed generation in electrical distribution network using hybrid modified PSO algorithms

10.11591/ijeecs.v24.i1.pp50-60
Nasreddine Belbachir , Mohamed Zellagui , Adel Lasmari , Claude Ziad El-Bayeh , Benaissa Bekkouche
The satisfaction of electricity customers and environmental constraints imposed have made the trend towards renewable energies making them more essential due to their advantages as reducing power losses and ameliorating system’s voltage profiles and reliability. This article addresses the optimal location and size of multiple distributed generations (DGs) based on solar photovoltaic panels (PV) connected to electrical distribution network (EDN) using the various proposed hybrid particle swarm optimization (PSO) algorithms based on chaotic maps and adaptive acceleration coefficients. These algorithms are implemented to optimally allocate the DGs based PV (PV-DG) into EDN by minimizing the multi-objective function (MOF), which is represented as the sum of three technical parameters of the total active power loss (TAPL), total voltage deviation (TVD), and total operation time (TOT) of overcurrent relays (OCRs). The effectiveness of the proposed PSO algorithms were validated on both standards IEEE 33-bus, and 69-bus. The optimal integrating of PV-DGs into EDNs reduced the TAPL percentage by 56.94 % for the IEEE 33-bus and by 61.17 % for the IEEE 69-bus test system, enhanced the voltage profiles while minimizing the TVD by 37.35 % and by 32.27 % for two EDNs, respectively.
Volume: 24
Issue: 1
Page: 50-60
Publish at: 2021-10-01

Methods and instruments for stray current verification in DC rapid transit and railway systems

10.11591/ijece.v11i5.pp3727-3735
Andrea Mariscotti
Modern electrified transportation systems feature increasing installed power and performance and correspondingly stray current phenomena and corrosion are receiving more attention in terms of contractual specifications and request of final validation of proposed solutions, as well as a maintenance program that can actively tackle stray current issues. The problem is complex for the system physical extension, difficult measurement conditions and variability of electrical parameters. This work considers validation of stray current protection performance, in terms of track voltage, track leakage and collected current, and impressed potential on structures along the right-ofway. The exemplification of the specifications of a hypothetical instrument and setup support the discussion.
Volume: 11
Issue: 5
Page: 3727-3735
Publish at: 2021-10-01

Lifetime centric load balancing mechanism in wireless sensor network based IoT environment

10.11591/ijece.v11i5.pp4183-4193
Veerabadrappa Veerabadrappa , Booma Poolan Marikannan
Wireless sensor network (WSN) is a vital form of the underlying technology of the internet of things (IoT); WSN comprises several energy-constrained sensor nodes to monitor various physical parameters. Moreover, due to the energy constraint, load balancing plays a vital role considering the wireless sensor network as battery power. Although several clustering algorithms have been proposed for providing energy efficiency, there are chances of uneven load balancing and this causes the reduction in network lifetime as there exists inequality within the network. These scenarios occur due to the short lifetime of the cluster head. These cluster head (CH) are prime responsible for all the activity as it is also responsible for intra-cluster and inter-cluster communications. In this research work, a mechanism named lifetime centric load balancing mechanism (LCLBM) is developed that focuses on CH-selection, network design, and optimal CH distribution. Furthermore, under LCLBM, assistant cluster head (ACH) for balancing the load is developed. LCLBM is evaluated by considering the important metrics, such as energy consumption, communication overhead, number of failed nodes, and one-way delay. Further, evaluation is carried out by comparing with ES-Leach method, through the comparative analysis it is observed that the proposed model outperforms the existing model.
Volume: 11
Issue: 5
Page: 4183-4193
Publish at: 2021-10-01

The effect of automated swab robot: new technology drives new behavior

10.11591/ijeecs.v24.i1.pp99-107
Jonalyn Mae E. Aranda , Jasper Rae Zeus A. Antonio
The world is now faced with a devastating pandemic outbreak coronavirus disease-2019 (COVID-19). The latest coronavirus infected almost all continents and witnessed sharp rises in cases diagnosed. The engineers tend to eliminate the matter and have solutions, one in every utilizing technical innovation. Researchers from Singapore, Taiwan, and Denmark have developed a fully automated robot that may take coronavirus swabs in order for health care professionals don’t seem to be exposed to the chance of infection. The objective of this study is to present the potential effects of robotics to help healthcare professionals on getting specimens and testing for COVID-19. These possible consequences include positive and negative outcomes and as a result, the overall impact on the profit or loss to society is far from obvious. The paper discusses two theoretical scenarios, distinguished fundamentally by the different behavioral responses of the automated swab robot and the selection of results in line with policy interventions.
Volume: 24
Issue: 1
Page: 99-107
Publish at: 2021-10-01

Active tremor control in human-like hand tremor using fuzzy logic

10.11591/ijeecs.v24.i1.pp108-115
Hafiz Bin Jamaludin , Azizan As'arry , R. Musab , Khairil Anas Md Rezali , Raja Mohd Kamil Bin Raja Ahmad , Mohd Zarhamdy Bin Md. Zain
Tremoris the vibration in sinusoidal orientation that is experienced regularly by a person with Parkinson’s disease (PD), which disturbs their daily activities. One solution that may be used to counter this tremor effect is by developing an active tremor control system in LabVIEW for linear voice coil actuator (LVCA), where the system uses proportional (P) controller and various types of fuzzy logic controller (FLC) as a hybrid controller to reduce tremor vibration. From this research, it can be concluded that the best controller for tremor reduction is the P+FLC 1st set of rules, compared to P+FLC 2nd set of rules, and P controller only, with the highest percentage of 88.39% of tremor reduction with the actual tremor vibration of PD patients as the reference result. The P+FLC 2nd set of rules has the highest percentage of tremor reduction with a value of 86.81%, whereas P controller only has the highest tremor reduction percentage of 67.10%. This percentage of tremor reduction is based on the power spectral density (PSD) values, in which it represents the intensity of the tremor vibration. This experimental study can be used as an initial step for researchers and engineers to design and develop an anti-tremor device in the future.
Volume: 24
Issue: 1
Page: 108-115
Publish at: 2021-10-01

Design of microstrip patch antenna to deploy unmanned aerial vehicle as UE in 5G wireless network

10.11591/ijece.v11i5.pp4202-4213
Abu Zafar Md. Imran , Mohammad Lutful Hakim , Md. Razu Ahmed , Mohammad Tariqul Islam , Elias Hossain
The use of unmanned aerial vehicle (UAV) has been increasing rapidly in the civilian and military applications, because of UAV's high-performance communication with ground clients, especially for its intrinsic properties such as adaptive altitude, mobility, and flexibility. UAV deployment can be monitored and controlled through 5G wireless network as user equipment (UE) along with other devices. A highly directive microstrip patch antenna (MPA) could establish long-distance communication by overcoming air attenuation and reduce co-channel interference in the limited region if UAV uses a specifically dedicated band, which might enhance spatially reuse of the spectrum. Also, MPA is highly recommended for UAV because of its low weight, low cost, compact size, and flat shape. In this paper, we have designed a highly directive single-band 2×2 and 4×4 antenna array for 5.8 GHz and 28 GHz frequency respectively for UAV application in a focus to deploy UAV through 5G wireless network. Here, The Roger RT5880 (lossy) material utilize as a substrate due to its lower dielectric constant which achieves higher directivity and good mechanical stability. Inset feed technique used to feed antenna for lowering input impedance which provides higher antenna efficiency. The results show a wider bandwidth of 702 MHz and 1.596 GHz for 5.8 GHz and 28 GHz antenna array correspondingly with a compact size.
Volume: 11
Issue: 5
Page: 4202-4213
Publish at: 2021-10-01

Improvements in space radiation-tolerant FPGA implementation of land surface temperature-split window algorithm

10.11591/ijece.v11i5.pp3844-3854
Assaad El Makhloufi , Nisrine Chekroun , Noha Tagmouti , Samir El Adib , Naoufal Raissouni
The trend in satellite remote sensing assignments has continuously been concerning using hardware devices with more flexibility, smaller size, and higher computational power. Therefore, field programmable gate arrays (FPGA) technology is often used by the developers of the scientific community and equipment for carrying out different satellite remote sensing algorithms. This article explains hardware implementation of land surface temperature split window (LST-SW) algorithm based on the FPGA. To get a high-speed process and real-time application, VHSIC hardware description language (VHDL) was employed to design the LST-SW algorithm. The paper presents the benefits of the used Virtex-4QV of radiation tolerant series FPGA. The experimental results revealed that the suggested implementation of the algorithm using Virtex4QV achieved higher throughput of 435.392 Mbps, and faster processing time with value of 2.95 ms. Furthermore, a comparison between the proposed implementation and existing work demonstrated that the proposed implementation has better performance in terms of area utilization; 1.17% reduction in number of Slice used and 1.06% reduction in of LUTs. Moreover, the significant advantage of area utilization would be the none use of block RAMs comparing to existing work using three blocks RAMs. Finally, comparison results show improvements using the proposed implementation with rates of 2.28% higher frequency, 3.66 x higher throughput, and 1.19% faster processing time.
Volume: 11
Issue: 5
Page: 3844-3854
Publish at: 2021-10-01

Classification of hand gestures from forearm electromyogram signatures from support vector machine

10.11591/ijeecs.v24.i1.pp260-268
Diaa Albitar , R. Jailani , Megat Syahirul Amin Megat Ali , Anwar P. P. Abdul Majeed
Robotic prosthetics is increasingly adopted as an enabling technology for amputees. These are vital not only for activities of daily living but to display expression and affection. A vital element to this system is an intelligent model that can identify signatures from the remaining limb that can be mapped to specific effector movements. Therefore, the study proposes the use of forearm electromyogram to classify between different types of hand gestures; fingers spread, wave out, wave in, fist, double tap, and relaxed state. Data are acquired from 32 subjects using Myo armband. Initially, a total of 248 time-and frequency-domain features are extracted from the eightchannel device. Neighborhood component analysis has reduced them to a total of fourteen features. A hand gesture classification model based on electromyogram signal has been successfully developed using support vector machine with overall accuracy of 97.4% for training, and 88.0% for testing.
Volume: 24
Issue: 1
Page: 260-268
Publish at: 2021-10-01

Malicious vehicle detection based on beta reputation and trust management for secure communication in smart automotive cars network

10.12928/telkomnika.v19i5.19358
Dilshad Ara; International Islamic University Malaysia (IIUM) Hossain , S. M. Salim; The National University of Malaysia Reza
High reliance on wireless network connectivity makes the vehicular ad hoc network (VANET) vulnerable to several kinds of cyber security threats. Malicious vehicles accessing the network can lead to hazardous situation by disseminating misleading information or data in the network or by performing cyber-attacks. It is a requirement that the information must be originated from the authentic and authorized vehicle and confidentiality must be maintained. In these circumstances, to protect the network from malicious vehicles, reputation system based on beta probability distribution with trust management model has been proposed to differentiate trustworthy vehicles from malicious vehicles. The trust model is based on adaptive neuro fuzzy inference system (ANFIS) which takes trust metrics as input to evaluate the trustworthiness of the vehicles. The simulation platform for the model is in MATLAB. Simulation results show that the vehicles need at least 80% trustworthiness to be considered as a trusted vehicle in the network.
Volume: 19
Issue: 5
Page: 1688-1696
Publish at: 2021-10-01

Self-doped carrier as a performance limiting factor of perovskite solar cells: study on tandem-junction cells with SCAPS

10.11591/ijeecs.v24.i1.pp81-89
Md. Sazzadur Rahman , Md. Samiur Rahman , Al Jaber , Suman Miah
Doping concentration of the absorber layer plays a vital role in the performance of perovskite solar cells, because not only it has a direct impact on the collection efficiency of the photo generated carriers, but it can also be considered as an indicator of the film quality and aging process for so-called self-doped (unintentionally doped) perovskite absorbers, where the carriers are induced from structural imperfections. To observe its influence on the efficiency of perovskite solar cell, a two-junction solar cell structure MAPbBr3/MAPbI3 is analyzed in this study, employing a novel optoelectrical simulation approach with finite-difference time-domain (FDTD) analysis and solar cell capacitance simulation (SCAPS) program. It is found that, the efficiency of the tandem cell falls from ∼22% to ∼12% as front-cell absorber film degrades from single-crystal quality with low self-doped carrier concentration of the order of 1010cm−3 , to degraded film quality with very high carrier concentration of the order of 1018cm−3 . In contrast, the self-doped carrier concentration of the back-cell absorber illustrates less impact on the efficiency of the cell, especially for thicker front-cell absorber. Thus, this case study gives a simpler but novel insight into the long-term stability of the efficiency of high-performance perovskite solar cells establishing a link between the solar cell performance and the self-doped carrier concentration (doping concentration) of the absorber film.
Volume: 24
Issue: 1
Page: 81-89
Publish at: 2021-10-01

Integrating SiO2 nanoparticles to achieve color uniformity and luminous efficiency enhancement for white light emitting diodes

10.12928/telkomnika.v19i5.20484
Phan Xuan; Van Lang University Le , Phung Ton; Industrial University of Ho Chi Minh City That
A phosphor structure with SiO2 nanoparticles is proposed to achieve the enhancement in the correlated color temperature (CCT) homogeneity and the luminescence performance for white light-emitting diodes (WLEDs). As SiO2 is integrated into the phosphorus compound, the scattering effect of this material contributes to better blue-light utilization. Thus, this innovative packaging design results in a significant increased lumen efficiency, more than 12%, in comparison with that of conventional dispensing ones. Meanwhile, the angular CCT deviation also decreases considerably, from 522 K to 7 K, between the angles of -70 and 700. Moreover, this reduction leads to the diminishment of yellow ring phenomenon effect. In addition, the measurement of haze demonstrates that there is a strong scattering in the visible spectrum when SiO2 is added into the silicone film. Besides that, when increasing the driving current, SiO2 stabilizes the chromaticity coordinate shift, which is a vital requirement for indoor lighting applications. Furthermore, SiO2 nanoparticles own excellent optical features, cost efficiency, and simple production will probably turn this material into a potential material in advancing the optical performance of WLEDs.
Volume: 19
Issue: 5
Page: 1648-1653
Publish at: 2021-10-01

Artificial neural network technique for improving prediction of credit card default: A stacked sparse autoencoder approach

10.11591/ijece.v11i5.pp4392-4402
Sarah A. Ebiaredoh-Mienye , E. Esenogho , Theo G. Swart
Presently, the use of a credit card has become an integral part of contemporary banking and financial system. Predicting potential credit card defaulters or debtors is a crucial business opportunity for financial institutions. For now, some machine learning methods have been applied to achieve this task. However, with the dynamic and imbalanced nature of credit card default data, it is challenging for classical machine learning algorithms to proffer robust models with optimal performance. Research has shown that the performance of machine learning algorithms can be significantly improved when provided with optimal features. In this paper, we propose an unsupervised feature learning method to improve the performance of various classifiers using a stacked sparse autoencoder (SSAE). The SSAE was optimized to achieve improved performance. The proposed SSAE learned excellent feature representations that were used to train the classifiers. The performance of the proposed approach is compared with an instance where the classifiers were trained using the raw data. Also, a comparison is made with previous scholarly works, and the proposed approach showed superior performance over other methods.
Volume: 11
Issue: 5
Page: 4392-4402
Publish at: 2021-10-01
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