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29,939 Article Results

Adaptation and parameters studies of CS algorithm for flow shop scheduling problem

10.11591/ijece.v11i3.pp2266-2274
Driss Belbachir , Fatima Boumediene , Ahmed Hassam , Ltéfa Ghomri
Scheduling concerns the allocation of limited resources overtime to perform tasks to fulfill certain criterion and optimize one or several objective functions. One of the most popular models in scheduling theory is that of the flow-shop scheduling. During the last 40 years, the permutation flow-shop sequencing problem with the objective of makespan minimization has held the attraction of many researchers. This problem characterized as Fm/prmu/Cmax in the notation of Graham, involves the determination of the order of processing of n jobs on m machines. In addition, there was evidence that m-machine permutation flow-shop scheduling problem (PFSP) is strongly NP-hard for m ≥3. Due to this NP-hardness, many heuristic approaches have been proposed, this work falls within the framework of the scientific research, whose purpose is to study Cuckoo search algorithm. Also, the objective of this study is to adapt the cuckoo algorithm to a generalized permutation flow-shop problem for minimizing the total completion time, so the problem is denoted as follow: Fm | | Cmax. Simulation results are judged by the total completion time and algorithm run time for each instance processed.
Volume: 11
Issue: 3
Page: 2266-2274
Publish at: 2021-06-01

A hybrid objective function with empirical stability aware to improve RPL for IoT applications

10.11591/ijece.v11i3.pp2350-2359
Abdelhadi Eloudrhiri Hassani , Aicha Sahel , Abdelmajid Badri , El Mourabit Ilham
The diverse applications of the internet of things (IoT) require adaptable routing protocol able to cope with several constraints. Thus, RPL protocol was designed to meet the needs for IoT networks categorized as low power and lossy networks (LLN). RPL uses an objective function based on specific metrics for preferred parents selection through these packets are sent to root. The single routing metric issue generally doesn’t satisfy all routing performance requirements, whereas some are improved others are degraded. In that purpose, we propose a hybrid objective function with empirical stability aware (HOFESA), implemented in the network layer of the embedded operating system CONTIKI, which combines linearly three weighty metrics namely hop count, RSSI and node energy consumption. Also, To remedy to frequent preferred parents changes problems caused by taking into account more than one metric, our proposal relies on static and empirical thresholds. The designed HOFESA, evaluated under COOJA emulator against Standard-RPL and EC-OF, showed a packet delivery ratio improvement, a decrease in the power consumption, the convergence time and DIO control messages as well as it gives network stability through an adequate churn.
Volume: 11
Issue: 3
Page: 2350-2359
Publish at: 2021-06-01

Soil moisture index estimation from landsat 8 images for prediction and monitoring landslide occurrences in Ulu Kelang, Selangor, Malaysia

10.11591/ijece.v11i3.pp2101-2108
Noraisyah Tajudin , Norsuzila Ya'acob , Darmawaty Mohd Ali , Nor Aizam Adnan
Soil moisture is one of the contributing factors that accelerates soil erosion and landslide events due to the increase in pore pressure which eventually reduces the soil strength. For landslide prediction and monitoring purposes, large-scale measurement involves estimating the soil moisture. However, estimation of soil moisture usually involves point-based measurements at a particular site and time, which is difficult to capture the spatial and temporal soil moisture dynamics. This paper presents the estimation of the SMI using Landsat 8 images for prediction and monitoring of landslide events in Ulu Kelang, Selangor. The selected SMI map for dry, moist, and wet seasons are obtained from climatology rainfall analysis over 20-year periods (1998-2017). SMI is assessed based on remote sensing data which are land surface temperature (LST) and normalized difference vegetation index (NDVI) using GIS software. Overall results indicated that rainfall distribution is high during inter-monsoon (IM), followed by northeast monsoon (NEM) and southwest monsoon (SWM) season. High rainfall distribution is a direct contributor towards SMI condition. Results from simulation show that April 2017 is known to have the highest SMI estimation season and selected to be the best SMI mapping parameter to be applied for prediction and monitoring of landslide events.
Volume: 11
Issue: 3
Page: 2101-2108
Publish at: 2021-06-01

Power systems automation, communication, and information technologies for smart grid: A technical aspects review

10.12928/telkomnika.v19i3.16428
Vikram; SVKM’s NMIMS University Kulkarni , Sarat Kumar; Parala Maharaja Engineering College Sahoo , Sudhakar Babu; Universiti Tenaga Nasional Thanikanti , Srikanth; Kakatiya Institute of Technology & Science Velpula , Dharmesh Ishwerlal; Kakatiya Institute of Technology & Science Rathod
Smart grid (SG) introduced proven power system, based on modernized power delivery system with introduction of advanced data-information and communication technologies (ICT). SGs include improved quality of power transmission/distribution from power generation to end-users with optimized power flow and efficiency. In addition to above modern automation, two-way communications, advanced monitoring, and control to optimize power quality issues are the classic features of SGs. This ensures the efficiency and reliability of all its interconnected power system elements against potential threats and life time cycle. By integrating ICT into the power system SGs improved the working capabilities of the utility companies. Resultant of ICT with SG leads to better management of assets and ensure energy management for end users. This review article presents the different areas of communication and information technology areas involved in SG automation.
Volume: 19
Issue: 3
Page: 1017-1029
Publish at: 2021-06-01

Deep fingerprint classification network

10.12928/telkomnika.v19i3.18771
Abdulsattar M.; Northern Technical University Ibrahim , Abdulrahman K.; Northern Technical University Eesee , Raid Rafi Omar; Northern Technical University Al-Nima
Fingerprint is one of the most well-known biometrics that has been used for personal recognition. However, faked fingerprints have become the major enemy where they threat the security of this biometric. This paper proposes an efficient deep fingerprint classification network (DFCN) model to achieve accurate performances of classifying between real and fake fingerprints. This model has extensively evaluated or examined parameters. Total of 512 images from the ATVS-FFp_DB dataset are employed. The proposed DFCN achieved high classification performance of 99.22%, where fingerprint images are successfully classified into their two categories. Moreover, comparisons with state-of-art approaches are provided.
Volume: 19
Issue: 3
Page: 893-901
Publish at: 2021-06-01

Contrast-distorted image quality assessment based on curvelet domain features

10.11591/ijece.v11i3.pp2595-2603
Ismail Taha Ahmed , Chen Soong Der , Baraa Tareq Hammad , Norziana Jamil
Contrast is one of the most popular forms of distortion. Recently, the existing image quality assessment algorithms (IQAs) works focusing on distorted images by compression, noise and blurring. Reduced-reference image quality metric for contrast-changed images (RIQMC) and no reference-image quality assessment (NR-IQA) for contrast-distorted images (NR-IQA-CDI) have been created for CDI. NR-IQA-CDI showed poor performance in two out of three image databases, where the pearson correlation coefficient (PLCC) were only 0.5739 and 0.7623 in TID2013 and CSIQ database, respectively. Spatial domain features are the basis of NR-IQA-CDI architecture. Therefore, in this paper, the spatial domain features are complementary with curvelet domain features, in order to take advantage of the potent properties of the curvelet in extracting information from images such as multiscale and multidirectional. The experimental outcome rely on K-fold cross validation (K ranged 2-10) and statistical test showed that the performance of NR-IQA-CDI rely on curvelet domain features (NR-IQA-CDI-CvT) significantly surpasses those which are rely on five spatial domain features.
Volume: 11
Issue: 3
Page: 2595-2603
Publish at: 2021-06-01

Reducing depressive symptoms and increasing positive feelings with expressive writing

10.11591/ijphs.v10i2.20797
Chen Sung Wong , Melissa Jane Chua , Kususanto Ditto Prihadi
This experimental study examined the effect of expressive writing on depressive symptoms. 86 undergraduate students were recruited from a private university to perform online positive experience writing (PEW) (n=31), negative experience (NEW) (n=32) and control topic (CW) (n=23) for three sessions in three consecutive weeks. The depressive symptoms of participants were measured at pre-treatment and post-treatment. It was hypothesized that PEW has significant greater reduction in depressive symptoms compared to NEW and CW based on broaden and build model. Nevertheless, results showed that PEW had significantly greater symptoms reduction than CW, yet there was no significant difference between PEW and NEW. The findings suggested that PEW might be potentially useful in reducing depressive symptoms among undergraduates.
Volume: 10
Issue: 2
Page: 433-444
Publish at: 2021-06-01

Comparison some of kernel functions with support vector machines classifier for thalassemia dataset

10.11591/ijai.v10.i2.pp430-437
Ilsya Wirasati , Zuherman Rustam , Jane Eva Aurelia , Sri Hartini , Glori Stephani Saragih
In the medical field, accurate classification of medical data is really important because of its impact on disease detection and patient’s treatment. Technology, machine learning, is needed to help medical staff to improve accuracy to classify disease. This research discussed some kernel functions, such as gaussian radial basis function (RBF) kernel, Polynomial kernel, and linear kernel with support vector machine (SVM) to classify thalassemia data. Thalassemia is a genetic blood disorder which is also one of the major public health problems. In this paper, there is an explanation about thalassemia, SVM, and some of the kernel functions that serve as a comprehensive source for the next research about this topic. Furthermore, there is a comparison result from three kernel functions to find out which one has the best performance. The result is gaussian RBF kernel with SVM is the best method with an average of accuracy 99,63%.
Volume: 10
Issue: 2
Page: 430-437
Publish at: 2021-06-01

Dynamic frequency scheduling for CubeSat's on-board and data handling subsystem

10.11591/ijeecs.v22.i3.pp1672-1678
Sharizal Fadlie Sabri , Noor Azurati Ahmad , Shamsul Sahibuddin , Rudzidatul Dziyauddin
CubeSat is a small-sized satellite that provides a cheaper option for the manufacturer to have a fully operational satellite. Due to its size, CubeSat can only generate limited power, and this will restrict its functionality. This research aims to improve CubeSat’s power consumption by implementing the dynamic voltage and frequency scaling (DVFS) technique to on-board and data handling subsystem (OBDH). DVFS will find the best operating frequency to execute all of OBDH’s task. This paper explains how we determined the task set, representing all routine tasks performed by OBDH during normal operation mode. We have simulated the task set using two DVFS algorithms, static earliest deadline first (EDF) and cycle conserving edf (CC EDF). The result shows that both scheduling algorithms give a similar result to our task set. However, when the scheduler is configured as non-preemptive, the simulator failed to schedule the critical task. It means that the system fails to work as intended. Therefore, we conclude that we need to implement mixed-criticality scheduling to prevent critical tasks from being aborted by the system.
Volume: 22
Issue: 3
Page: 1672-1678
Publish at: 2021-06-01

Enhancing the performance of cancer text classification model based on cancer hallmarks

10.11591/ijai.v10.i2.pp316-323
Noha Ali , Ahmed H. AbuEl-Atta , Hala H. Zayed
Deep learning (DL) algorithms achieved state-of-the-art performance in computer vision, speech recognition, and natural language processing (NLP). In this paper, we enhance the convolutional neural network (CNN) algorithm to classify cancer articles according to cancer hallmarks. The model implements a recent word embedding technique in the embedding layer. This technique uses the concept of distributed phrase representation and multi-word phrases embedding. The proposed model enhances the performance of the existing model used for biomedical text classification. The result of the proposed model overcomes the previous model by achieving an F-score equal to 83.87% using an unsupervised technique that trained on PubMed abstracts called PMC vectors (PMCVec) embedding. Also, we made another experiment on the same dataset using the recurrent neural network (RNN) algorithm with two different word embeddings Google news and PMCVec which achieving F-score equal to 74.9% and 76.26%, respectively.
Volume: 10
Issue: 2
Page: 316-323
Publish at: 2021-06-01

Automated tumor segmentation in MR brain image using fuzzy c-means clustering and seeded region methodology

10.11591/ijai.v10.i2.pp284-290
Mustafa Zuhaer Nayef AL-Dabagh
Automated segmentation of a tumor is still a considerably exciting research topic in the medical imaging processing field, and it plays a considerable role in forming a right diagnosis, to aid effective medical treatment. In this work, a fully automated system for segmentation of the brain tumor in MRI images is introduced. The suggested system consists of three parts: Initially, the image is pre-processed to enhance contrast, eliminate noise, and strip the skull from the image using filtering and morphological operations. Secondly, segmentation of the image happens using two techniques, fuzzy c-means clustering (FCM) and with the application of a seeded region growing algorithm (SGR). Thirdly, this method proposes a post-processing step to smooth segmentation region edges using morphological operations. The testing of the proposed system involved 233 patients, which included 287 MRI images. A comparison of the results ensued, with the manual verification of the traces performed by doctors, which ultimately proved an average Dice Coefficient of 90.13% and an average Jaccard Coefficient of 82.60% also, by comparison with traditional segmentation techniques such as FCM method. The segmentation results and quantitative data analysis demonstrates the effectiveness of the suggested system.
Volume: 10
Issue: 2
Page: 284-290
Publish at: 2021-06-01

Minimize electricity generation cost for large scale wind- thermal systems considering prohibited operating zone and power reserve constraints

10.11591/ijece.v11i3.pp1905-1911
Phan Nguyen Vinh , Bach Hoang Dinh , Van-Duc Phan , Hung Duc Nguyen , Thang Trung Nguyen
Wind power plants (WPs) play a very important role in the power systems because thermal power plants (TPs) suffers from shortcomings of expensive cost and limited fossil fuels. As compared to other renewable energies, WPs are more effective because it can produce electricity all a day from the morning to the evening. Consequently, this paper integrates the optimal power generation of TPs and WPs to absolutely exploit the energy from WPs and reduce the total electricity generation cost of TPs. The target can be reached by employing a proposed method, called one evaluation-based cuckoo search algorithm (OEB-CSA), which is developed from cuckoo search algorithm (CSA). In addition, conventional particle swarm optimization (PSO) is also implemented for comparison. Two test systems with thirty TPs considering prohibited working zone and power reserve constraints are employed. The first system has one wind power plant (WP) while the second one has two WPs. The result comparisons indicate that OEB-CSA can be the best method for the combined systems with WPs and TPs.
Volume: 11
Issue: 3
Page: 1905-1911
Publish at: 2021-06-01

Fire-fighting UAV with shooting mechanism of fire extinguishing ball for smart city

10.11591/ijeecs.v22.i3.pp1320-1326
Nastaran Reza Nazar Zadeh , Ameralden H. Abdulwakil , Mike Joshua R. Amar , Bernadette Durante , Christian Vincent Nico Reblando Santos
With the growth of technology and massive city development, firefighting services have become more challenging to cope with a smart-city concept. One of the challenges that firefighters are facing is reaching the top floors of high-raised buildings. Firefighters need heavy and oversized pieces of equipment to reach top floors, which they sometimes fail to deliver on time due to big cities' traffic. The proposed solution to this global problem is using firefighting unmanned aerial vehicle (UAV) to reach the top floors fast and efficiently; It can also provide a better vision for the firefighting team and slow down the spread of fire using fire extinguishing ball. In this paper, a noble design for a Firefighting UAV with shooting and dropping mechanism of fire extinguishing ball has been developed and successfully tested. A Camera with night vision has been integrated into the UAV to provide a helpful aid for firefighters. The UAV has a controller with a 2.4 GHz radio frequency (RF) signal and video surveillance to regulate the UAV's movement. The controller is also for activating the shooting and dropping mechanism. The researchers examined the behavior of the drone in terms of its stability and functionality.
Volume: 22
Issue: 3
Page: 1320-1326
Publish at: 2021-06-01

Mapping log data activity using heuristic miner algorithm in manufacture and logistics company

10.12928/telkomnika.v19i3.18153
Syafrial Fachri; Politeknik Pos Indonesia Pane , Rolly Maulana; Politeknik Pos Indonesia Awan , M. Amran Hakim; Politeknik Pos Indonesia Siregar , Dinda; Politeknik Pos Indonesia Majesty
Strategies for the procurement of goods and services are essential for companies in Indonesia's manufacturing and logistics sectors. The solution to reducing the existing problem is to make a mapping plan, such as verifying documents from each department, so that it takes a long time, resulting in many issues, such as procedural misuse findings. Heuristics miner algorithms get data to form logs that consist of goods and services procurement activities. Processing log data into XML data (data extraction), which produces a dependency model and business and casual matrix (discovery process), then determines the value of fitness and precision (suitability) called the conformity checking phase process. This phase aims to produce a new business (process enhancement phase), which will create a solution to the risk of delay and procedural abuse. The results of each of these processes rank each stage of the procurement of goods and services sequentially and together to provide time-efficient and accurate decisions, resulting in project implementation comparable to the company's business strategy. Implement the heuristics miner algorithm using the Python programming language.
Volume: 19
Issue: 3
Page: 781-791
Publish at: 2021-06-01

Postgraduate students’ perspective on supporting “learning from home” to solve the COVID-19 pandemic

10.11591/ijere.v10i2.21240
Ihsana El Khuluqo , Abdul Rahman A. Ghani , Arum Fatayan
The objective of this present research was to reveal how the postgraduate student perceive of or respond to the online learning process. Quantitative method was adopted in this present research. The results showed that most students who had experienced of the online learning activities encountered some obstacles because they had never conducted Learning From Home (LFH) activities before. The respondents were 428 postgraduate students who actively joined in the LFH activities. There were 316 students used the platform Zoom as the supporting application in the LFH activities. Respondents filled in Google Form, then the collected data could be quickly and accurately processed. Other respondents preferred Google Classroom, WhatsApp and other applications in following the learning activities according to the agreement and features provided in each platform. There were 408 respondents experienced Two-ways communication between the lecturers and the students during the LFH activities. They stated that the limited internet network hindered the online lecturing. There were 31 respondents declared that technology limitations hampered the online lecturing and 105 students revealed that it is the limitations in using the application that caused the online lecturing to become obstacles. 
Volume: 10
Issue: 2
Page: 615-623
Publish at: 2021-06-01
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