Pub Date : 2023-03-01DOI: 10.11591/ijai.v12.i1.pp478-487
N. Chaudhary, R. Mahajan
Increased use of wireless technologies and in turn more utilization of available spectrum is subsequently leading to the increasing demand for wireless spectrum. This research work incorporates spectrum sensing detection consisting of a double dynamic threshold followed by cooperative type spectrum sensing. The performance has been analyzed using two modulation schemes, quadrature-amplitude-modulation (QAM) & binary-phase-shift-keying (BPSK). Improved probability of detection has been witnessed using the double dynamic threshold where a comparison of average values of local decision (LD) and the observed value of energy (EO) has been considered instead of using direct values of local decisions and energy. Further, the probability-of-detection ( ) is found to be better with QAM as compared to the BPSK. From the results, it has been observed that the detection of primary users is also affected by the number of samples. The simulation environment considered for this work is MATLAB and the performance of cooperative spectrum sensing for 500 and 1000 samples with -9db and -12 SNR by considering different false alarm values i. e 0.1,0.3 and 0.5 has been analyzed. The further scope shall be to enhance the primary user detection by considering different QAM schemes and different SNRs.
{"title":"Performance analysis of cooperative spectrum sensing using double dynamic threshold","authors":"N. Chaudhary, R. Mahajan","doi":"10.11591/ijai.v12.i1.pp478-487","DOIUrl":"https://doi.org/10.11591/ijai.v12.i1.pp478-487","url":null,"abstract":"Increased use of wireless technologies and in turn more utilization of available spectrum is subsequently leading to the increasing demand for wireless spectrum. This research work incorporates spectrum sensing detection consisting of a double dynamic threshold followed by cooperative type spectrum sensing. The performance has been analyzed using two modulation schemes, quadrature-amplitude-modulation (QAM) & binary-phase-shift-keying (BPSK). Improved probability of detection has been witnessed using the double dynamic threshold where a comparison of average values of local decision (LD) and the observed value of energy (EO) has been considered instead of using direct values of local decisions and energy. Further, the probability-of-detection ( ) is found to be better with QAM as compared to the BPSK. From the results, it has been observed that the detection of primary users is also affected by the number of samples. The simulation environment considered for this work is MATLAB and the performance of cooperative spectrum sensing for 500 and 1000 samples with -9db and -12 SNR by considering different false alarm values i. e 0.1,0.3 and 0.5 has been analyzed. The further scope shall be to enhance the primary user detection by considering different QAM schemes and different SNRs.","PeriodicalId":52221,"journal":{"name":"IAES International Journal of Artificial Intelligence","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"47060685","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-03-01DOI: 10.11591/ijai.v12.i1.pp220-231
M. Rahman, Rayan Abbas Ahmed Alharazi, Muhammad Khairul Imban b Zainal Badri
This paper introduced an Intelligent Salat Monitoring and Training System based on machine vision and image processing. In Islam, prayer (i.e. salat) is the second pillar of Islam. It is the most important and fundamental worshipping activity that believers have to perform five times a day. From gestures’ perspective, there are predefined human postures that must be performed in a precise manner. There are lots of materials on the internet and social media for training and correction purposes. However, some people do not perform these postures correctly due to being new to salat or even having learned prayers incorrectly. Furthermore, the time spent in each posture has to be balanced. To address these issues, we propose to develop an assistive intelligence framework that guides worshippers to evaluate the correctness of their prayer’s postures. Image comparison and pattern matching are used to study the system’s effectiveness by using several combining algorithms, such as Euclidean distance, template matching and grey-level correlation, to compare the images of the user and the database. The experiments’ results, both correct and incorrect salat performances, are shown via pictures and graph for each of the postures of salat.
{"title":"Intelligent system for Islamic prayer (salat) posture monitoring","authors":"M. Rahman, Rayan Abbas Ahmed Alharazi, Muhammad Khairul Imban b Zainal Badri","doi":"10.11591/ijai.v12.i1.pp220-231","DOIUrl":"https://doi.org/10.11591/ijai.v12.i1.pp220-231","url":null,"abstract":"This paper introduced an Intelligent Salat Monitoring and Training System based on machine vision and image processing. In Islam, prayer (i.e. salat) is the second pillar of Islam. It is the most important and fundamental worshipping activity that believers have to perform five times a day. From gestures’ perspective, there are predefined human postures that must be performed in a precise manner. There are lots of materials on the internet and social media for training and correction purposes. However, some people do not perform these postures correctly due to being new to salat or even having learned prayers incorrectly. Furthermore, the time spent in each posture has to be balanced. To address these issues, we propose to develop an assistive intelligence framework that guides worshippers to evaluate the correctness of their prayer’s postures. Image comparison and pattern matching are used to study the system’s effectiveness by using several combining algorithms, such as Euclidean distance, template matching and grey-level correlation, to compare the images of the user and the database. The experiments’ results, both correct and incorrect salat performances, are shown via pictures and graph for each of the postures of salat.","PeriodicalId":52221,"journal":{"name":"IAES International Journal of Artificial Intelligence","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"47106289","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-03-01DOI: 10.11591/ijai.v12.i1.pp124-136
Tanatorn Tanantong, Patcharajak Yongwattana
In the digital world, the Internet and online media especially video media are convenient and easy to access. It leads to problems of inappropriate content media consumption among children and youths. However, measures or methods to control the inappropriate content for children and young people are still a challenge for management. In this research, an automated model was developed and presented to classify the content on online video media using a deep learning technique namely convolution neural networks (CNN). For data collection and preparation, the researchers collected video clips from movies and television (TV) series from websites that distribute the clips online. It consists of different types of content: i) sexually inappropriate content; ii) violently inappropriate content; and iii) general content. The collected video clip data was then extracted into frames and then used for developing the automatically-content-classifying model with algorithm CNN, analyzing and comparing the result of CNN model performance. For enhancing the model performance, a transfer learning approach and different regularization techniques were adopted in order to find the most suitable method to create high-performance modeling to classify content in video clips, movies and TV series published online.
{"title":"A convolutional neural network framework for classifying inappropriate online video contents","authors":"Tanatorn Tanantong, Patcharajak Yongwattana","doi":"10.11591/ijai.v12.i1.pp124-136","DOIUrl":"https://doi.org/10.11591/ijai.v12.i1.pp124-136","url":null,"abstract":"In the digital world, the Internet and online media especially video media are convenient and easy to access. It leads to problems of inappropriate content media consumption among children and youths. However, measures or methods to control the inappropriate content for children and young people are still a challenge for management. In this research, an automated model was developed and presented to classify the content on online video media using a deep learning technique namely convolution neural networks (CNN). For data collection and preparation, the researchers collected video clips from movies and television (TV) series from websites that distribute the clips online. It consists of different types of content: i) sexually inappropriate content; ii) violently inappropriate content; and iii) general content. The collected video clip data was then extracted into frames and then used for developing the automatically-content-classifying model with algorithm CNN, analyzing and comparing the result of CNN model performance. For enhancing the model performance, a transfer learning approach and different regularization techniques were adopted in order to find the most suitable method to create high-performance modeling to classify content in video clips, movies and TV series published online.","PeriodicalId":52221,"journal":{"name":"IAES International Journal of Artificial Intelligence","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"47814091","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-03-01DOI: 10.11591/ijai.v12.i1.pp415-421
Rania R. Kadhim, Mohammed Y. Kamil
Breast cancer is the most common cause of death among women worldwide. Breast cancer can be detected early, and the death rate can be reduced. Machine learning techniques are a hot topic for study and have proved influential in cancer prediction and early diagnosis. This study's objective is to predict and diagnose breast cancer using machine learning models and evaluate the most effective based on six criteria: specificity, sensitivity, precision, accuracy, F1-score and receiver operating characteristic curve. All work is done in the anaconda environment, which uses Python's NumPy and SciPy numerical and scientific libraries, and pandas and matplotlib. This study used the Wisconsin diagnostic breast cancer dataset to test ten machine learning algorithms: decision tree, linear discriminant analysis, forests of randomized trees, gradient boosting, passive aggressive, logistic regression, naïve Bayes, nearest centroid, support vector machine, and perceptron. After collecting the findings, we performed a performance evaluation and compared these various classification techniques. Gradient boosting model outperformed all other algorithms, scoring 96.77% on the F1-score.
{"title":"Comparison of machine learning models for breast cancer diagnosis","authors":"Rania R. Kadhim, Mohammed Y. Kamil","doi":"10.11591/ijai.v12.i1.pp415-421","DOIUrl":"https://doi.org/10.11591/ijai.v12.i1.pp415-421","url":null,"abstract":"Breast cancer is the most common cause of death among women worldwide. Breast cancer can be detected early, and the death rate can be reduced. Machine learning techniques are a hot topic for study and have proved influential in cancer prediction and early diagnosis. This study's objective is to predict and diagnose breast cancer using machine learning models and evaluate the most effective based on six criteria: specificity, sensitivity, precision, accuracy, F1-score and receiver operating characteristic curve. All work is done in the anaconda environment, which uses Python's NumPy and SciPy numerical and scientific libraries, and pandas and matplotlib. This study used the Wisconsin diagnostic breast cancer dataset to test ten machine learning algorithms: decision tree, linear discriminant analysis, forests of randomized trees, gradient boosting, passive aggressive, logistic regression, naïve Bayes, nearest centroid, support vector machine, and perceptron. After collecting the findings, we performed a performance evaluation and compared these various classification techniques. Gradient boosting model outperformed all other algorithms, scoring 96.77% on the F1-score.","PeriodicalId":52221,"journal":{"name":"IAES International Journal of Artificial Intelligence","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"49615611","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-03-01DOI: 10.11591/ijai.v12.i1.pp262-270
Hastuadi Harsa, Anistia Malinda Hidayat, Adi Mulsandi, Bambang Suprihadi, Roni Kurniawan, Muhammad Najib Habibie, Thahir Daniel Hutapea, Yunus S. Swarinoto, Erwin Eka Syahputra Makmur, Welly Fitria, Rahayu Sapta Sri Sudewi, Alfan Sukmana Praja
In Indonesia, rainfall is one crucial triggering factor for landslides. This paper aims to build landslide event prediction models using several machine learning and artificial intelligence algorithms. The algorithms were trained with two different methods. The input of the algorithms was precipitation data obtained from the global satellite mapping of precipitation satellite observation, and the target was landslide event occurrence data obtained from the Indonesian National Board for Disaster Management. Each algorithm provided some model candidates with different parameter settings for each method. As a result, there were 52 and 72 model candidates for both methods. The best model was then chosen from each method. The result shows that the model generated by generalized linear model was the best model for the first method and deep learning for the second one. Furthermore, the best models at each method gained 0.828 and 0.836 for the area under receiver operating characteristics curve, and their log-loss were 0.156 and 0.154. The second method, which used input data transformation, provided better performance.
{"title":"Machine learning and artificial intelligence models development in rainfall-induced landslide prediction","authors":"Hastuadi Harsa, Anistia Malinda Hidayat, Adi Mulsandi, Bambang Suprihadi, Roni Kurniawan, Muhammad Najib Habibie, Thahir Daniel Hutapea, Yunus S. Swarinoto, Erwin Eka Syahputra Makmur, Welly Fitria, Rahayu Sapta Sri Sudewi, Alfan Sukmana Praja","doi":"10.11591/ijai.v12.i1.pp262-270","DOIUrl":"https://doi.org/10.11591/ijai.v12.i1.pp262-270","url":null,"abstract":"<span lang=\"EN-US\">In Indonesia, rainfall is one crucial triggering factor for landslides. This paper aims to build landslide event prediction models using several machine learning and artificial intelligence algorithms. The algorithms were trained with two different methods. The input of the algorithms was precipitation data obtained from the global satellite mapping of precipitation satellite observation, and the target was landslide event occurrence data obtained from the Indonesian National Board for Disaster Management. Each algorithm provided some model candidates with different parameter settings for each method. As a result, there were 52 and 72 model candidates for both methods. The best model was then chosen from each method. The result shows that the model generated by generalized linear model was the best model for the first method and deep learning for the second one. Furthermore, the best models at each method gained 0.828 and 0.836 for the area under receiver operating characteristics curve, and their log-loss were 0.156 and 0.154. The second method, which used input data transformation, provided better performance.</span>","PeriodicalId":52221,"journal":{"name":"IAES International Journal of Artificial Intelligence","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"136132079","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-03-01DOI: 10.11591/ijai.v12.i1.pp34-40
M. Rahman, R. Ikeura
This paper describes the impedance characteristics of the human arm during passive movement. The arm was moved in the desired trajectory. The motion was actuated by a 1-degree-of-freedom robot system. Trajectories used in the experiment were minimum jerk (the rate of change of acceleration) trajectories, which were found during a human and human cooperative task and optimum for muscle movement. As the muscle is mechanically analogous to a spring-damper system, a second-order equation was considered as the model for arm dynamics. In the model, inertia, stiffness, and damping factor were considered. The impedance parameters were estimated from the position and torque data obtained from the experiment and based on the “Estimation of Parametric Model”. It was found that the inertia is almost constant over the operational time. The damping factor and stiffness were high at the starting position and became near zero after 0.4 seconds.
{"title":"Impedance characteristic of the human arm during passive movements","authors":"M. Rahman, R. Ikeura","doi":"10.11591/ijai.v12.i1.pp34-40","DOIUrl":"https://doi.org/10.11591/ijai.v12.i1.pp34-40","url":null,"abstract":"This paper describes the impedance characteristics of the human arm during passive movement. The arm was moved in the desired trajectory. The motion was actuated by a 1-degree-of-freedom robot system. Trajectories used in the experiment were minimum jerk (the rate of change of acceleration) trajectories, which were found during a human and human cooperative task and optimum for muscle movement. As the muscle is mechanically analogous to a spring-damper system, a second-order equation was considered as the model for arm dynamics. In the model, inertia, stiffness, and damping factor were considered. The impedance parameters were estimated from the position and torque data obtained from the experiment and based on the “Estimation of Parametric Model”. It was found that the inertia is almost constant over the operational time. The damping factor and stiffness were high at the starting position and became near zero after 0.4 seconds.","PeriodicalId":52221,"journal":{"name":"IAES International Journal of Artificial Intelligence","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"44171224","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-03-01DOI: 10.11591/ijai.v12.i1.pp41-47
Arif Ridho Lubis, M. K. Nasution, O. S. Sitompul, E. M. Zamzami
The data generated from social media is very large, while the use of data from social media has not been fully utilized to become new knowledge. One of the things that can become new knowledge is user habits on social media. Searching for user habits on Twitter by using user tweets can be done by using modeling, the use of modeling lies when the data has been preprocessed, and the ranking will then be checked in the dictionary, this is where the role of the model is carried out to get a chance that the words that have been ranked will perform check the word in the dictionary. The benefit of the model in general is to get an understanding of the mechanism in the problem so that it can predict events that will arise from a phenomenon which in this case is user habits. So that with the availability of this model, it can be a model in getting opportunities for user habits on Twitter social media.
{"title":"A new approach to achieve the users’ habitual opportunities on social media","authors":"Arif Ridho Lubis, M. K. Nasution, O. S. Sitompul, E. M. Zamzami","doi":"10.11591/ijai.v12.i1.pp41-47","DOIUrl":"https://doi.org/10.11591/ijai.v12.i1.pp41-47","url":null,"abstract":"The data generated from social media is very large, while the use of data from social media has not been fully utilized to become new knowledge. One of the things that can become new knowledge is user habits on social media. Searching for user habits on Twitter by using user tweets can be done by using modeling, the use of modeling lies when the data has been preprocessed, and the ranking will then be checked in the dictionary, this is where the role of the model is carried out to get a chance that the words that have been ranked will perform check the word in the dictionary. The benefit of the model in general is to get an understanding of the mechanism in the problem so that it can predict events that will arise from a phenomenon which in this case is user habits. So that with the availability of this model, it can be a model in getting opportunities for user habits on Twitter social media.","PeriodicalId":52221,"journal":{"name":"IAES International Journal of Artificial Intelligence","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"44413825","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-03-01DOI: 10.11591/ijai.v12.i1.pp171-179
Erlinda Ratnasari Putri, A. Zarkasi, P. Prajitno, Djarwani Soeharso Soejoko
Cervical cancer is the second deadliest after breast cancer in Indonesia. Sundry diagnostic imaging modalities had been used to decide the location and severity of cervical cancer, one among those is computed tomography (CT) Scan. This study handles a CT image dataset consisting of two categories, abnormal cervical images of cervical cancer patients and normal cervix images of patients with other diseases. It focuses on the ability of segmentation and classification programs to localize cervical cancer areas and classify images into normal and abnormal categories based on the features contained in them. We conferred a novel methodology for the contour detection round the cervical organ classified with artificial neural network (ANN) which was employed to categorize the image data. The segmentation algorithm used was a region-based snake model. The texture features of the cervical image area were arranged in the form of gray level co-occurrence matrix (GLCM). Support vector machine (SVM) had been added to determine which algorithm was better for comparison. Experimental results show that ANN model has better receiver operating characteristic (ROC) parameter values than SVM model’s and existing approach’s regarding 96.2% of sensitivity, 95.32% of specificity, and 95.75% of accuracy.
{"title":"Artificial neural network for cervical abnormalities detection on computed tomography images","authors":"Erlinda Ratnasari Putri, A. Zarkasi, P. Prajitno, Djarwani Soeharso Soejoko","doi":"10.11591/ijai.v12.i1.pp171-179","DOIUrl":"https://doi.org/10.11591/ijai.v12.i1.pp171-179","url":null,"abstract":"Cervical cancer is the second deadliest after breast cancer in Indonesia. Sundry diagnostic imaging modalities had been used to decide the location and severity of cervical cancer, one among those is computed tomography (CT) Scan. This study handles a CT image dataset consisting of two categories, abnormal cervical images of cervical cancer patients and normal cervix images of patients with other diseases. It focuses on the ability of segmentation and classification programs to localize cervical cancer areas and classify images into normal and abnormal categories based on the features contained in them. We conferred a novel methodology for the contour detection round the cervical organ classified with artificial neural network (ANN) which was employed to categorize the image data. The segmentation algorithm used was a region-based snake model. The texture features of the cervical image area were arranged in the form of gray level co-occurrence matrix (GLCM). Support vector machine (SVM) had been added to determine which algorithm was better for comparison. Experimental results show that ANN model has better receiver operating characteristic (ROC) parameter values than SVM model’s and existing approach’s regarding 96.2% of sensitivity, 95.32% of specificity, and 95.75% of accuracy. ","PeriodicalId":52221,"journal":{"name":"IAES International Journal of Artificial Intelligence","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"47934787","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-03-01DOI: 10.11591/ijai.v12.i1.pp23-33
I. K. Wardani, Phakkharawat Sittiprapaporn, Djohan Djohan, Fortunata Tyasinestu
The rapid advancement of music studies has resulted in a plethora of multidisciplinary participants. Rather than distinguishing between musicians and non-musicians’ brain activity, the current study indicated differences in brain activity while musicians listened to music based on their musical experience. In Go/NoGo response task reaction times, it showed that effects between treatments and visits were different across periods of cognitive function tests. The cognitive function at post-listening assessment out-performed the pre-listening in terms of reaction times (531.94 (±24.70) msec for post-listening assessment; and 557.13 (±37.15) msec for pre-listening assessment. The results of using electroencephalography (EEG) recording in an experimental manner with Karawitan musicians (N=20) revealed that listening to unknown cultural music, Mozart's Piano Sonata in C Major, and western music resulted in increased brain activity. Furthermore, while Karawitan musicians were listening to Mozart's Piano Sonata in C Major, the major brain activity occurred in the frontal lobe. This outcome will elicit additional consideration of music's integration, such as neuroscience of music.
{"title":"Karawitans’ musician brain adaptation: standardized low-resolution electromagnetic tomography study","authors":"I. K. Wardani, Phakkharawat Sittiprapaporn, Djohan Djohan, Fortunata Tyasinestu","doi":"10.11591/ijai.v12.i1.pp23-33","DOIUrl":"https://doi.org/10.11591/ijai.v12.i1.pp23-33","url":null,"abstract":"The rapid advancement of music studies has resulted in a plethora of multidisciplinary participants. Rather than distinguishing between musicians and non-musicians’ brain activity, the current study indicated differences in brain activity while musicians listened to music based on their musical experience. In Go/NoGo response task reaction times, it showed that effects between treatments and visits were different across periods of cognitive function tests. The cognitive function at post-listening assessment out-performed the pre-listening in terms of reaction times (531.94 (±24.70) msec for post-listening assessment; and 557.13 (±37.15) msec for pre-listening assessment. The results of using electroencephalography (EEG) recording in an experimental manner with Karawitan musicians (N=20) revealed that listening to unknown cultural music, Mozart's Piano Sonata in C Major, and western music resulted in increased brain activity. Furthermore, while Karawitan musicians were listening to Mozart's Piano Sonata in C Major, the major brain activity occurred in the frontal lobe. This outcome will elicit additional consideration of music's integration, such as neuroscience of music.","PeriodicalId":52221,"journal":{"name":"IAES International Journal of Artificial Intelligence","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"47373842","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-03-01DOI: 10.11591/ijai.v12.i1.pp459-468
Musli Yanto, Yogi Wiyandra, Sarjon Defit
The problem of poverty is a scourge for every developing country coupled with the economic crisis that occurred during the COVID-19 pandemic. The impact of these problems is felt directly by the people in Indonesia, especially in the Province of West Sumatra. This study aims to predict and classify the level of poverty status by developing an analytical model based on the deep learning (DL) approach. The methods used in this study include the K-Means method, artificial neural network (ANN), and support vector Machine (SVM). The analytical model will be optimized using the Pearson Correlation (PC) method to measure the accuracy of the analysis. The variable indicator uses the parameters of population (X1), poverty rate (X2), income (X3), and poverty percentage (X4). The results of the study present prediction and classification output with a validity level of accuracy of 99.8%. Based on these results, it can be concluded that the proposed DL analysis model can present an updated analytical model that is quite effective in carrying out the prediction and classification process. The research findings also contribute to the initial handling of the problem of poverty.
{"title":"Deep learning approach analysis model prediction and classification poverty status","authors":"Musli Yanto, Yogi Wiyandra, Sarjon Defit","doi":"10.11591/ijai.v12.i1.pp459-468","DOIUrl":"https://doi.org/10.11591/ijai.v12.i1.pp459-468","url":null,"abstract":"<span lang=\"EN-US\">The problem of poverty is a scourge for every developing country coupled with the economic crisis that occurred during the COVID-19 pandemic. The impact of these problems is felt directly by the people in Indonesia, especially in the Province of West Sumatra. This study aims to predict and classify the level of poverty status by developing an analytical model based on the deep learning (DL) approach. The methods used in this study include the K-Means method, artificial neural network (ANN), and support vector Machine (SVM). The analytical model will be optimized using the Pearson Correlation (PC) method to measure the accuracy of the analysis. The variable indicator uses the parameters of population (</span><em><span lang=\"EN-US\">X<sub>1</sub></span></em><span lang=\"EN-US\">), poverty rate (</span><em><span lang=\"EN-US\">X<sub>2</sub></span></em><span lang=\"EN-US\">), income (</span><em><span lang=\"EN-US\">X<sub>3</sub></span></em><span lang=\"EN-US\">), and poverty percentage (</span><em><span lang=\"EN-US\">X<sub>4</sub></span></em><span lang=\"EN-US\">). The results of the study present prediction and classification output with a validity level of accuracy of 99.8%. Based on these results, it can be concluded that the proposed DL analysis model can present an updated analytical model that is quite effective in carrying out the prediction and classification process. The research findings also contribute to the initial handling of the problem of poverty.</span>","PeriodicalId":52221,"journal":{"name":"IAES International Journal of Artificial Intelligence","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"49242310","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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