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29,939 Article Results

Spark plug failure detection using Z-freq and machine learning

10.12928/telkomnika.v19i6.22027
Nor Azazi; Universiti Teknikal Malaysia Melaka Ngatiman , Mohd Zaki; Universiti Kebangsaan Malaysia Nuawi , Azma; Universiti Teknikal Malaysia Melaka Putra , Isa; Bahrain Society of Engineers S. Qamber , Tole; Universitas Ahmad Dahlan Sutikno , Mohd Hatta; Universiti Teknikal Malaysia Melaka Jopri
Preprogrammed monitoring of engine failure due to spark plug misfire can be traced using a method called machine learning. Unluckily, a challenge to get a high-efficiency rate because of a massive volume of training data is required. During the study, these failure-generated were enhanced with a novel statistical signal-based analysis called Z-freq to improve the exploration. This study is an exploration of the time and frequency content attained from the engine after it goes under a specific situation. Throughout the trial, the misfire was formed by cutting the voltage supplied to simulate the actual outcome of the worn-out spark plug. The failure produced by fault signals from the spark plug misfire were collected using great sensitivity, space-saving and a robust piezo-based sensor named accelerometer. The achieved result and analysis indicated a significant pattern in the coefficient value and scattering of Z-freq data for spark plug misfire. Lastly, the simulation and experimental output were proved and endorsed in a series of performance metrics tests using accuracy, sensitivity, and specificity for prediction purposes. Finally, it confirmed that the proposed technique capably to make a diagnosis: fault detection, fault localization, and fault severity classification.
Volume: 19
Issue: 6
Page: 2020-2029
Publish at: 2021-12-01

A performance evaluation of convolutional neural network architecture for classification of rice leaf disease

10.11591/ijai.v10.i4.pp1069-1078
Afis Julianto , Andi Sunyoto
Plant disease is a challenge in the agricultural sector, especially for rice production. Identifying diseases in rice leaves is the first step to wipe out and treat diseases to reduce crop failure. With the rapid development of the convolutional neural network (CNN), rice leaf disease can be recognized well without the help of an expert. In this research, the performance evaluation of CNN architecture will be carried out to analyze the classification of rice leaf disease images by classifying 5932 image data which are divided into 4 disease classes. The comparison of training data, validation, and testing are 60:20:20. Adam optimization with a learning rate of 0.0009 and softmax activation was used in this study. From the experimental results, the InceptionV3 and InceptionResnetV2 architectures got the best accuracy, namely 100%, ResNet50 and DenseNet201 got 99.83%, MobileNet 99.33%, and EfficientNetB3 90.14% accuracy.
Volume: 10
Issue: 4
Page: 1069-1078
Publish at: 2021-12-01

Disturbance observer-based controller for inverted pendulum with uncertainties: Linear matrix inequality approach

10.11591/ijece.v11i6.pp4907-4921
Van-Phong Vu , Minh-Tam Nguyen , Anh-Vu Nguyen , Vi-Do Tran , Tran Minh Nguyet Nguyen
A new approach based on linear matrix inequality (LMI) technique for stabilizing the inverted pendulum is developed in this article. The unknown states are estimated as well as the system is stabilized simultaneously by employing the observer-based controller. In addition, the impacts of the uncertainties are taken into consideration in this paper. Unlike the previous studies, the uncertainties in this study are unnecessary to satisfy the bounded constraints. These uncertainties will be converted into the unknown input disturbances, and then a disturbance observer-based controller will be synthesized to estimate the information of the unknown states, eliminate completely the effects of the uncertainties, and stabilize inverted pendulum system. With the support of lyapunov methodology, the conditions for constructing the observer and controller under the framework of linear matrix inequalities (LMIs) are derived in main theorems. Finally, the simulations for system with and without uncertainties are exhibited to show the merit and effectiveness of the proposed methods.
Volume: 11
Issue: 6
Page: 4907-4921
Publish at: 2021-12-01

An LMI approach to Mixed H_∞/H_- fault detection observer design for linear fractional-order systems

10.12928/telkomnika.v19i6.18741
Mohammad; Shahrood University of Technology Azimi , Heydar Toossian; Ferdowsi University of Mashhad Shandiz
This study deals with the problem of robust fault detection for linear time-invariant fractional-order systems (FOSs) assumed to be affected by sensor, actuator and process faults as well as disturbances. The observer-based method was employed to solve the problem, where the detector is an observer. The problem was transformed into the mixed  robust optimization problem to make the system disturbance-resistant on one hand and fault-sensitive on the other hand. Then, sufficient conditions were obtained to solve the problem in the linear matrix inequality (LMI) mode. Finally, the effectiveness and superiority of the method were demonstrated by simulating the solutions on a single-input multi-output thermal testing bench.
Volume: 19
Issue: 6
Page: 1948-1961
Publish at: 2021-12-01

Effect of filter sizes on image classification in CNN: a case study on CFIR10 and Fashion-MNIST datasets

10.11591/ijai.v10.i4.pp872-878
Owais Mujtaba Khanday , Samad Dadvandipour , Mohd Aaqib Lone
Convolution neural networks (CNN or ConvNet), a deep neural network class inspired by biological processes, are immensely used for image classification or visual imagery. These networks need various parameters or attributes like number of filters, filter size, number of input channels, padding stride and dilation, for doing the required task. In this paper, we focused on the hyperparameter, i.e., filter size. Filter sizes come in various sizes like 3×3, 5×5, and 7×7. We varied the filter sizes and recorded their effects on the models' accuracy. The models' architecture is kept intact and only the filter sizes are varied. This gives a better understanding of the effect of filter sizes on image classification. CIFAR10 and FashionMNIST datasets are used for this study. Experimental results showed the accuracy is inversely proportional to the filter size. The accuracy using 3×3 filters on CIFAR10 and Fashion-MNIST is 73.04% and 93.68%, respectively.
Volume: 10
Issue: 4
Page: 872-878
Publish at: 2021-12-01

Virtual education and student perception of teacher performance in the distance learning environment

10.11591/ijeecs.v24.i3.pp1638-1646
Richard Flores-Cáceres , Cesar León-Velarde , Teodoro Díaz-Leyva , Frank Escobedo-Bailón , Orlando Ortega-Galicio , Antuanet Chirinos-Mendoza , Abel Tasayco-Jala
This article aims to carry out a descriptive analysis of the performance of teachers qualified as researchers, in the distance education environment according to the student's perspective. The results will be a frame of reference for university authorities on the path of continuous improvement of virtual education. When carrying out the research, a general qualification of the teaching performance of 14.20 was determined, established the highest grade equal to 20, it can be indicated that there is a good performance of the teacher in the virtual education environment. In addition, the results show that the highest evaluation corresponds to the indicator management of the group and fulfillment of the objectives, which is directly related to the administration of the class, while the lowest rating is for the indicator "Teacher effectiveness so that their students acquire relevant knowledge, skills and attitudes”, which is directly related to the didactic strategies used, that is, to the use of technological tools that today are more than just an option. Finally, it can be noted that of the total of 17 teaching, 23.5% present a very good performance, 35.3% present a good performance and 41.2% present a regular performance.
Volume: 24
Issue: 3
Page: 1638-1646
Publish at: 2021-12-01

Lifestyle breast cancer patients among Indonesian women: A nationwide survey

10.11591/ijphs.v10i4.20913
Solikhah Solikhah , Khairunnisaa Nuur Aliifah Setyawati , Monthida Sangruangake
Recently, cancer is a major health problem in the world. Lifestyle changes and growing urbanization likely led to increasing breast cancer incidence in such in Indonesia. Therefore, this study aimed to explore lifestyle breast cancer patients among Indonesian women. The investigation was a cross-sectional study distributed among 3,392 females drawn from 13 out of 27 provinces in Indonesia. Multiple binary logistic regressions were conducted to investigate breast cancer risk among Indonesian. A significance level of 0.05 was employed in all analysis. Of the 3,392 respondents included in the analysis, more than half (52.71%; n=1,788) was aged 40–49 years old. The most common marital status of the participants was married (98.20%; n=3,331), followed by no smoking (94.69%; n=3,212) and active exercise (62.12%; n=2,107). Education level was significantly associated with breast cancer (AdjOR_Junior high school=0.21; 95%CI=0.06 to 0.70; p<0.01 and AdjOR_senior high school=0.60; 95%CI=0.15 to 2.26; p<0.05). Education level was significantly related to breast cancer. Lifestyle such as smoking and physical activity was suspected to affect breast cancer indirectly.
Volume: 10
Issue: 4
Page: 730-734
Publish at: 2021-12-01

A MATLAB model for diagnosing sickle cells and other blood abnormalities using image processing

10.11591/ijece.v11i6.pp5060-5065
Mohammed Al-Momin , Ammar Almomin
The conventional method for detecting blood abnormality is time consuming and lacks the high level of accuracy. In this paper a MATLAB based solution has been suggested to tackle the problem of time consumption and accuracy. Three types of blood abnormality have been covered here, namely, anemia which is characterized by low count of red blood cells (RBCs), Leukemia which is depicted by increasing the number of white blood cells (WBCs), and sickle cell blood disorder which is caused by a deformation in the shape of red cells. The algorithm has been tested on different images of blood smears and noticed to give an acceptable level of accuracy. Image processing techniques has been used here to detect the different types of blood constituents. Unlike many other researches, this research includes the blood sickling disorder which is epidemic in certain regions of the world, and offers a more accuracy than other algorithms through the use of detaching overlapped cells strategy.
Volume: 11
Issue: 6
Page: 5060-5065
Publish at: 2021-12-01

Blocking performance of extended pruned vertically stacked optical banyan structure under different link failure conditions

10.12928/telkomnika.v19i6.19008
Sabrina; University of Chittagong Alam , Fahmida Sharmin; Southern University Bangladesh Jui
The blocking performance of extended pruned vertically stacked optical banyan (VSOB) networks under different link failure conditions has been analyzed in this paper. We applied plane fixed routing with linear search and plane fixed routing with random search algorithms to route the optical data through the network in our simulation. Our simulation results show that adding one or two extra planes to the pruned VSOB network reduces the blocking probability significantly. Beyond two extra planes, the decrease of blocking probability is not so significant. A close approximation of the minimum number of planes required to make the extended pruned vertically stacked optical banyan networks nonblocking has been presented.
Volume: 19
Issue: 6
Page: 1787-1794
Publish at: 2021-12-01

A non-negative matrix factorization based clustering to identify potential tuna fishing zones

10.11591/ijece.v11i6.pp5458-5466
Devi Fitrianah , Hisyam Fahmi , Achmad Nizar Hidayanto , Pang Ning-Tan , Aniati Murni Arymurthy
Many nonnegative matrix factorization based clusterings are employed in discovering pattern and knowledge. Considering the sparseness nature of our data set about the daily tuna fishing data, we attempted to utilize a clustering approach, which is based on non-negative matrix factorization. Adding sparseness constraint and assigning good initial value in the modified NMF method, a proposed algorithm Direct-NMFSC yielded better result cluster compared to other methods which are also utilizing sparse constraint to their approaches, SNMF and NMFSC. The result of this study shows that Direct-NMFSC has 5.376 times of iteration number less than NMFSC in average with 531.97 as the CH index result. The determination of potential fishing zones is one of the essential efforts in the potential fishing zone mapping system for tuna fishing. By means of this novel data-driven study to construct the information and to identify the potential tuna fishing zones is done. We also showed that utilizing the Direct-NMFSC can spot and identify the potential tuna fishing zones presented in red cluster that covers both the spatial and temporal information.
Volume: 11
Issue: 6
Page: 5458-5466
Publish at: 2021-12-01

Medical crisis during pandemic: Career preferences change in medical student

10.11591/ijere.v10i4.21897
Dian Natalia , Rizma Adlia Syakurah
The COVID-19 pandemic is a major threat to global education. Incidental emotions of fear and anxiety during pandemic have unconsciously influenced preference and outcome about their future career. This study aimed to assess the effect of the COVID-19 pandemic towards career preference change in medical students. A total of 1,027 responses from all over the medical students in Indonesia were collected from an online questionnaire which was broadcasted through social media from 14th July 2020–21st July 2020. This study was using Fear of COVID-19 Scale (FCV-19S) and Depression Anxiety Stress-Scale-21 (DASS-21) to assess fear of COVID-19, stress, anxiety, and depression. Out of 1,027 respondents, 44.6% had stressed, 47.8% had anxiety, and 18.5% had depression with an average FCV-19S score was 17.1. The result showed that the fear and anxiety of COVID-19 during the pandemic had associated significantly with the career decisions in medical students (p=<0.05). Indonesian policymakers had to keep in mind that the fear of the COVID-19 pandemic in medical students is due to the high mortality COVID-19 cases of health workers in Indonesia. Health workers need adequate working conditions and specific protection, this requires prompt attention from stakeholders.
Volume: 10
Issue: 4
Page: 1255-1261
Publish at: 2021-12-01

An overview of information extraction techniques for legal document analysis and processing

10.11591/ijece.v11i6.pp5450-5457
Ashwini V. Zadgaonkar , Avinash J. Agrawal
In an Indian law system, different courts publish their legal proceedings every month for future reference of legal experts and common people. Extensive manual labor and time are required to analyze and process the information stored in these lengthy complex legal documents. Automatic legal document processing is the solution to overcome drawbacks of manual processing and will be very helpful to the common man for a better understanding of a legal domain. In this paper, we are exploring the recent advances in the field of legal text processing and provide a comparative analysis of approaches used for it. In this work, we have divided the approaches into three classes NLP based, deep learning-based and, KBP based approaches. We have put special emphasis on the KBP approach as we strongly believe that this approach can handle the complexities of the legal domain well. We finally discuss some of the possible future research directions for legal document analysis and processing.
Volume: 11
Issue: 6
Page: 5450-5457
Publish at: 2021-12-01

Implementation three-step algorithm based on signed digit number system by using neural network

10.11591/ijeecs.v24.i3.pp1832-1839
Qabeela Q. Thabit , Alyaa Ibrahim Dawood , Bayadir A. Issa
The need for a simple and effective system that works with high efficiency features such as high processing speed, the ability to solve problems by learning method and accomplish the largest amount of data processing accurately and in little time produces that system, which attracted the efforts of the researcher to employ neural networks in computing away from the complexities that burden traditional computers. We presented a model for the design of the arithmetic circuit for the process of addition the sign digit numbers in a new way to deal with the arithmetic operations, which employment of the use of neural networks, this model includes a theoretical and practical simulation of them. The model relied on the implementation of the addition process based on a three-step algorithm adopted by the signed systems. Which is characterized by the possibility of execution in a parallel way, and therefore it provides the advantage of completion of arithmetic operation regardless of the length of their operands, or in other words, whatever the number of bits in the operands. The simulation of the model is done by entering operands for 6 addition operations (each one has operands are 15-bit length) to be executed simultaneously.
Volume: 24
Issue: 3
Page: 1832-1839
Publish at: 2021-12-01

An assisting model for the visually challenged to detect bus door accurately

10.12928/telkomnika.v19i6.19811
Sreenu; Vishnu Institute of Technology Ponnada , Praveen Kumar; GITAM University Sekharamantry , Abhinav; Vishnu Institute of Technology Dayal , Srinivas; GITAM University Yarramalle , Nagesh; MVGR College of Engineering Vadaparthi , Jude; Karunya Institute of Technology and Sciences Hemanth
Visually impaired individuals are increasing and as per global statistics, around 39 million are blind, and 246 million are affected by low vision. Even in India, as per the recent reviews, over 5 million visually challenged people are present. Authors performed a survey of some critical problems the visually challenged people faced in India from the centre for visually challenged (CVC) School established by UVSM Hospitals. Among the major problems identified through survey, most of these persons prefer carrying out their tasks independently, and depend on public transport buses for migration. However, critical sub-problems being faced include; bus door identification and identifying the bus route number accurately. This article aims to provide solutions in helping visually challenged individuals to identify exact bus that drives them to their destination, its door, bus number, and the path for boarding bus. A video sequence of current scenario would be sent to mobile, in which the actual processing of image is carried out. After the video sequence processing, generated output is a voice message that specifies the bus's location, door, and exact information of the bus number along the road path directly to the user using a wireless device aiming foa a low-cost solution.
Volume: 19
Issue: 6
Page: 1924-1934
Publish at: 2021-12-01

Review of social media intervention in adult population during COVID-19 pandemic based on Protection Motivation Theory

10.11591/ijphs.v10i4.20510
Muhammad Prima Cakra Randana , Rizma Adlia Syakurah
During COVID-19 pandemic, social media has become a basis for information deployment, it has the potential to change people opinion and solve many issues in this situation. Based on Protection Motivation Theory (PMT), threat and coping appraisal were predictors to behavioral responses in pandemics. This study aimed to analyze the impact of social media intervention in adult population during COVID-19 pandemic based on PMT. This review was created using Preferred Reporting Items for Systematic Reviews and Meta-Analysis (PRISMA) and data collection from electronic databases such as Pubmed, Mendeley app, Europe PMC, Cochrane Databases, Science Direct, and Wiley Online Library. Inclusion criteria consists of English studies, studies related to the topic and match with required variables. There are five cross-sectional studies involving a total of 2.448 participants that were published in 2020. Among all categories based on included studied, it was shown that cyberchondria, perceived severity and perceived vulnerability are predictors in social media, related to behavioral responses during COVID-19 pandemic. Reducing information overload, related to cyberchondria, via the clear structuring and communication of reliable health information is needed. Hence, educating people on responsible and healthy social media use could help alleviate the observed negative consequences from perceived severity and vulnerability.
Volume: 10
Issue: 4
Page: 843-849
Publish at: 2021-12-01
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