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29,939 Article Results

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

Detecting African hoofed animals in aerial imagery using convolutional neural network

10.11591/ijra.v10i2.pp133-143
Yunfei Fang , Shengzhi Du , Larbi Boubchir , Karim Djouani
Small unmanned aerial vehicles applications had erupted in many fields including conservation management. Automatic object detection methods for such aerial imagery were in high demand to facilitate more efficient and economical wildlife management and research. This paper aimed to detect hoofed animals in aerial images taken from a quad-rotor in Southern Africa. Objects captured in this way were small both in absolute pixels and from an object-to-image ratio point of view, which were not perfectly suit for general purposed object detectors. We proposed a method based on the iconic Faster region-based convolutional neural networks (R-CNN) framework with atrous convolution layers in order to retain the spatial resolution of the feature map to detect small objects. A good choice of anchors was of prime importance in detecting small objects. The performance of the proposed Faster R-CNN with atrous convolutional filters in the backbone network was proven to be outstanding in our scenario by comparing to other object detection architectures.
Volume: 10
Issue: 2
Page: 133-143
Publish at: 2021-06-01

Comparison of mutual information and its point similarity implementation for image registration

10.11591/ijece.v11i3.pp2613-2620
Wassim El Hajj Chehade , Peter Rogelj
Mutual information (MI) is one of the most popular and widely used similarity measures in image registration. In traditional registration processes, MI is computed in each optimization step to measure the similarity between the reference image and the moving image. The presumption is that whenever MI reaches its highest value, this corresponds to the best match. This paper shows that this presumption is not always valid and this leads to registration error. To overcome this problem, we propose to use point similarity measures (PSM) which in contrast to MI allows constant intensity dependence estimates called point similarity functions (PSF). We compare MI and PSM similarity measures in terms of registration misalignment errors. The result of the comparison confirms that the best alignment is not at the highest value of MI but near to it and it shows that PSM performs better than MI if PSF matches the correct intensity dependence between images. This opens a new direction of research towards the improvement of image registration.
Volume: 11
Issue: 3
Page: 2613-2620
Publish at: 2021-06-01

Missing data handling for machine learning models

10.11591/ijra.v10i2.pp123-132
Karim H. Erian , Pedro H. Regalado , James M. Conrad
This paper discusses a novel algorithm for solving a missing data problem in the machine learning pre-processing stage. A model built to help lenders evaluate home loans based on numerous factors by learning from available user data, is adopted in this paper as an example. If one of the factors is missing for a person in the dataset, the currently used methods delete the whole entry therefore reducing the size of the dataset and affecting the machine learning model accuracy. The novel algorithm aims to avoid losing entries for missing factors by breaking the dataset into multiple subsets, building a different machine learning model for each subset, then combining the models into one machine learning model. In this manner, the model makes use of all available data and only neglects the missing values. Overall, the new algorithm improved the prediction accuracy by 5% from 93% accuracy to 98% in the home loan example.
Volume: 10
Issue: 2
Page: 123-132
Publish at: 2021-06-01

High frequency of low noise amplifier architecture for WiMAX application: A review

10.11591/ijece.v11i3.pp2153-2164
Abu Bakar Ibrahim , Che Zalina Zulkifli , Shamsul Arrieya Ariffin , Nurul Husna Kahar
The low noise amplifier (LNA) circuit is exceptionally imperative as it promotes and initializes general execution performance and quality of the mobile communication system. LNA's design in radio frequency (R.F.) circuit requires the trade-off numerous imperative features' including gain, noise figure (N.F.), bandwidth, stability, sensitivity, power consumption, and complexity. Improvements to the LNA's overall performance should be made to fulfil the worldwide interoperability for microwave access (WiMAX) specifications' prerequisites. The development of front-end receiver, particularly the LNA, is genuinely pivotal for long-distance communications up to 50 km for a particular system with particular requirements. The LNA architecture has recently been designed to concentrate on a single transistor, cascode, or cascade constrained in gain, bandwidth, and noise figure.
Volume: 11
Issue: 3
Page: 2153-2164
Publish at: 2021-06-01

Metacognition researches in Turkey, Japan and Singapore

10.11591/ijere.v10i2.20790
Ayşe Elitok Kesici , Derya Güvercin , Hızır Küçükakça
In this study, fundamental researches on “metacognition” in Turkey, Japan and Singapore between the years of 2010 and 2020 were examined and conclusions were made in terms of comparative education. For this purpose, the data of the research was collected by document scanning method and the data were analyzed using the document review technique, which is one of the qualitative research method techniques. Years of studies, countries, objectives, research methods, sample working group, data analysis methods and results; it has been examined according to comparative education approaches and data collection techniques. As a result, quantitative research methods are seen to be frequently used in researches on metacognition in these three countries. It has been determined that experimental studies are the main research patterns of the metacognition studies conducted in three countries. Metacognitive awareness scales are the most used data collection tools in all three countries. Considered in general; the research made about metacognition in Singapore Turkey and Japan shows that the studies investigating the relationship between students' problem-solving skills and metacognition are in majority. Researches examining the relationship between metacognition and foreign language teaching are also widely discussed.
Volume: 10
Issue: 2
Page: 535-544
Publish at: 2021-06-01

Gamification in e-learning: The mitigation role in technostress

10.11591/ijere.v10i2.21199
Faridiah Aghadiati Fajri , RY. Kun Haribowo P. , Nurisqi Amalia , Dina Natasari
The digital world demands graduates who are accustomed to deal with technology. Blended learning is one of the strategies by combining online media with face-to-face classes. It cannot be denied that students who interact with technology experience stress and tension. This condition have an impact on the learning process so that a way out is needed to bring it down. Gamification is a gaming technique that is applied to non-game applications to increase pleasure when interacting with these applications. This feature has been implemented in business applications, social media, e-commerce, and e-learning. However, the impact of playfulness in mitigating technostress has not been studied. This research examined the role of feedback mechanism and presentation mechanism in giving pleasure in LMS. Furthermore, this playfulness is expected to reduce the stress experienced by users. The research was conducted using a quasi-experimental method by giving participants time to follow the course with the gamification feature. The results showed that the gamification mechanism is able to provide pleasure which in turn will reduce the user's stress level. Based on the user-perceived of playfulness, gamification can reduce stress levels so it will reduce user resistance and increase the effectiveness of technology implementation.
Volume: 10
Issue: 2
Page: 606-614
Publish at: 2021-06-01

A smart method for spark using neural network for big data

10.11591/ijece.v11i3.pp2525-2534
Md. Armanur Rahman , J. Hossen , Aziza Sultana , Abdullah Al Mamun , Nor Azlina Ab. Aziz
Apache spark, famously known for big data handling ability, is a distributed open-source framework that utilizes the idea of distributed memory to process big data. As the performance of the spark is mostly being affected by the spark predominant configuration parameters, it is challenging to achieve the optimal result from spark. The current practice of tuning the parameters is ineffective, as it is performed manually. Manual tuning is challenging for large space of parameters and complex interactions with and among the parameters. This paper proposes a more effective, self-tuning approach subject to a neural network called Smart method for spark using neural network for big data (SSNNB) to avoid the disadvantages of manual tuning of the parameters. The paper has selected five predominant parameters with five different sizes of data to test the approach. The proposed approach has increased the speed of around 30% compared with the default parameter configuration.
Volume: 11
Issue: 3
Page: 2525-2534
Publish at: 2021-06-01

Optimization of network traffic anomaly detection using machine learning

10.11591/ijece.v11i3.pp2360-2370
ChoXuan Do , Nguyen Quang Dam , Nguyen Tung Lam
In this paper, to optimize the process of detecting cyber-attacks, we choose to propose 2 main optimization solutions: Optimizing the detection method and optimizing features. Both of these two optimization solutions are to ensure the aim is to increase accuracy and reduce the time for analysis and detection. Accordingly, for the detection method, we recommend using the Random Forest supervised classification algorithm. The experimental results in section 4.1 have proven that our proposal that use the Random Forest algorithm for abnormal behavior detection is completely correct because the results of this algorithm are much better than some other detection algorithms on all measures. For the feature optimization solution, we propose to use some data dimensional reduction techniques such as information gain, principal component analysis, and correlation coefficient method. The results of the research proposed in our paper have proven that to optimize the cyber-attack detection process, it is not necessary to use advanced algorithms with complex and cumbersome computational requirements, it must depend on the monitoring data for selecting the reasonable feature extraction and optimization algorithm as well as the appropriate attack classification and detection algorithms.
Volume: 11
Issue: 3
Page: 2360-2370
Publish at: 2021-06-01

Parallel P-PD controller to achieve vibration and position control of a flexible beam

10.11591/ijra.v10i2.pp149-160
Ammar N. Abbas , Muhammad Asad Irshad
Robotic arms are considered as a cantilever beam fixed at one end and due to the length-to-weight ratio, it has a significant vibration-induced that needs to be controlled to achieve accurate position, speed control and to increase its efficiency. In this project, a discretized Timoshenko beam model is used to discuss the dynamics of the system. Further, to implement the control on the hardware an experimental setup is fabricated to observe the open-loop and closed-loop responses of the beam made of low-density polyethylene. An accelerometer as a feedback sensor is attached at one end of the flexible beam while another end is fixed at the moving cart having DC motor as an actuator. Simulink is used as the programming tool to perform all of the experimentation. Proportional-integral-derivative (PID) tuning is performed. Following that open-loop responses of the deflection of the beam parallel to the motion are observed with different input waveforms. By applying a proportional control scheme, another experiment is performed to demonstrate the disturbance rejection with an accelerometer as a feedback sensor, while ignoring position control. Finally, a PD and P based parallel control scheme is proposed to obtain simultaneously both position control and vibration reduction.
Volume: 10
Issue: 2
Page: 149-160
Publish at: 2021-06-01

Community mobility reports predict the national spread of COVID-19 in Indonesia in the new normal era

10.11591/ijphs.v10i2.20635
Muhammad Syahrul Ramadhan , Rizma Adlia Syakurah
Indonesia government encouraging to new normal life with obeys the health protocol. In Malaysia, the new normal had a significant impact on mobility trends. This study aimed to analyze the community mobility trend (including six categories) and coronavirus disease (COVID-19) daily cases in Indonesia in new normal era. An observational analytic using cross-sectional design. The community mobility data, include mobility trends for six different location categories, were obtained from Google COVID-19 Community Mobility Reports from May 15-July 14, 2020. The Indonesian’s COVID-19 daily cases data were taken from (http://covid19.go.id//) from May 15-July 17, 2020. Time-lag correlation to analyzed community mobility of each location category and COVID-19 daily cases in Indonesia using Pearson Correlation with significance ≤0.05. Recreation, parks, and transit stations have positively strong to very strong, while the residential has negatively strong, and the grocery and pharmacy and workplaces were positively weak to moderate correlations. The community mobility was significantly correlated with the COVID-19 transmission in Indonesia during new normal era, especially in transit stations, retail and recreation. Indonesia government is expected to improve their effort to manage the COVID-19 transmission and consider new policy to curb the COVID-19 transmission.
Volume: 10
Issue: 2
Page: 380-386
Publish at: 2021-06-01

Switched time delay control based on neural network for fault detection and compensation in robot

10.11591/ijra.v10i2.pp91-103
Maincer Dihya , Mansour Moufid , Boudjedir Chemseddine , Bounabi Moussaab
Fault detection in robotic manipulators is necessary for their monitoring and represents an effective support to use them as independent systems. This present study investigates an enhanced method for representation of the faultless system behavior in a robot manipulator based on a multi-layer perceptron (MLP) neural network learning model which produces the same behavior as the real dynamic manipulator. The study was based on generation of residue by contrasting the actual output of the manipulator with those of the neural network; Then, a time delay control (TDC) is applied to compensate the fault, in which a typical sliding mode command is used to delete the time delay estimate produced by the belated signal in order to obtain strong performances. The results of the simulations performed on a model of the SCARA arm manipulator, showed a good trajectory tracking and fast convergence speed in the presence of faults on the sensors. In addition, the command is completely model independent, for both TDC and MLP neural network, which represents a major advantage of the proposed command.
Volume: 10
Issue: 2
Page: 91-103
Publish at: 2021-06-01

Design and development of an irrigation mobile robot

10.11591/ijra.v10i2.pp75-90
Ahmed Hassan , Rao M. Asif , Ateeq Ur Rehman , Zuhaib Nishtar , Mohammed K. A. Kaabar , Khan Afsar
Water plays a significant role among other existing natural resources. The daily demand for water supplies is increasingly on the rise as the population grows. To minimize the consumption of water in irrigation, several proposals were suggested. The currently existing system known as the automated irrigation system for effective water resource use with the prediction of the weather (AISWP) functions with a single farm that lacks the reliability in the precision of weather forecasting. So, a robot-based irrigation system has been proposed to improve the performance of the system. To minimize the water usage for crops, an automated irrigation system has been developed which irrigates the field in acres. An additional characteristic of the system has also been given for the soil pH measurement to allow the use of fertilizers accordingly. The solar-powered robot is managed wirelessly by a designated application. The robot is attached with various sensors and with a high-resolution camera that tests crop conditions and senses the soil state. The application has been created to provide information about the soil’s condition such as temperature level, humidity level, water level, and level of nutrients to the PC/Laptop with the real-time values via the GSM module.
Volume: 10
Issue: 2
Page: 75-90
Publish at: 2021-06-01

Detection of duplicate and non-face images in the eRecruitment applications using machine learning techniques

10.11591/ijra.v10i2.pp114-122
Manjunath K. E. , Yogeen S. Honnavar , Rakesh Pritmani , Sethuraman K.
The objective of this work is to develop methodologies to detect, and report the noncompliant images with respect to indian space research organisation (ISRO) recruitment requirements. The recruitment software hosted at U. R. rao satellite centre (URSC) is responsible for handling recruitment activities of ISRO. Large number of online applications are received for each post advertised. In many cases, it is observed that the candidates are uploading either wrong or non-compliant images of the required documents. By non-compliant images, we mean images which do not have faces or there is not enough clarity in the faces present in the images uploaded. In this work, we attempt to address two specific problems namely: 1) To recognise image uploaded to recruitment portal contains a human face or not. This is addressed using a face detection algorithm. 2) To check whether images uploaded by two or more applications are same or not. This is achieved by using machine learning (ML) algorithms to generate similarity score between two images, and then identify the duplicate images. Screening of valid applications becomes very challenging as the verification of such images using a manual process is very time consuming and requires large human efforts. Hence, we propose novel ML techniques to determine duplicate and non-face images in the applications received by the recruitment portal.
Volume: 10
Issue: 2
Page: 114-122
Publish at: 2021-06-01

Improved incentive pricing-based quasi-linear utility function of wireless networks

10.11591/ijeecs.v22.i3.pp1467-1475
Fitri Maya Puspita , Bella Juwita Rezky , Arden Naser Yustian Simarmata , Evi Yuliza , Yusuf Hartono
The model of the incentive pricing scheme-based quasi-linear utility function in wireless network was designed. Previous research seldom focusses on user’s satisfaction while using network. Therefore, the model is then attempted to be set up that is derived from the modification of bundling and models of reverse charging and maintain the quality of service to users by utilizing quasi-linear utility function. The pricing schemes then are applied to local data server traffic. The model used is known as mathematical programming problem that can be solved by LINGO 13.0 program as optimization tool to get the optimal solution. The optimal results show that the improved incentive pricing can achieve better solution compared to original reverse charging where the models will be obtained in flat fee, usage-based, and two-part tariff strategies for homogeneous consumers.
Volume: 22
Issue: 3
Page: 1467-1475
Publish at: 2021-06-01
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