The face expression is the first thing we pay attention to when we want to understand a person’s state of mind. Thus, the ability to recognize facial expressions in an automatic way is a very interesting research field. In this paper, because the small size of available training datasets, we propose a novel data augmentation technique that improves the performances in the recognition task. We apply geometrical transformations and build from scratch GAN models able to generate new synthetic images for each emotion type. Thus, on the augmented datasets we fine tune pretrained convolutional neural networks with different architectures. To measure the generalization ability of the models, we apply extra-database protocol approach, namely we train models on the augmented versions of training dataset and test them on two different databases. The combination of these techniques allows to reach average accuracy values of the order of 85% for the InceptionResNetV2 model.
{"title":"Data Augmentation Techniques and Transfer Learning Approaches Applied to Facial Expressions Recognition Systems","authors":"Enrico Randellini, Leonardo Rigutini, Claudio Saccà","doi":"10.5121/ijaia.2022.13104","DOIUrl":"https://doi.org/10.5121/ijaia.2022.13104","url":null,"abstract":"The face expression is the first thing we pay attention to when we want to understand a person’s state of mind. Thus, the ability to recognize facial expressions in an automatic way is a very interesting research field. In this paper, because the small size of available training datasets, we propose a novel data augmentation technique that improves the performances in the recognition task. We apply geometrical transformations and build from scratch GAN models able to generate new synthetic images for each emotion type. Thus, on the augmented datasets we fine tune pretrained convolutional neural networks with different architectures. To measure the generalization ability of the models, we apply extra-database protocol approach, namely we train models on the augmented versions of training dataset and test them on two different databases. The combination of these techniques allows to reach average accuracy values of the order of 85% for the InceptionResNetV2 model.","PeriodicalId":93188,"journal":{"name":"International journal of artificial intelligence & applications","volume":"1 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2022-01-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"45621233","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 : 2022-01-31DOI: 10.5121/ijaia.2022.13102
Manav Agarwal, S. Venugopal, Rishab Kashyap, R. Bharathi
Movies are among the most prominent contributors to the global entertainment industry today, and they are among the biggest revenue-generating industries from a commercial standpoint. It's vital to divide films into two categories: successful and unsuccessful. To categorize the movies in this research, a variety of models were utilized, including regression models such as Simple Linear, Multiple Linear, and Logistic Regression, clustering techniques such as SVM and K-Means, Time Series Analysis, and an Artificial Neural Network. The models stated above were compared on a variety of factors, including their accuracy on the training and validation datasets as well as the testing dataset, the availability of new movie characteristics, and a variety of other statistical metrics. During the course of this study, it was discovered that certain characteristics have a greater impact on the likelihood of a film's success than others. For example, the existence of the genre action may have a significant impact on the forecasts, although another genre, such as sport, may not. The testing dataset for the models and classifiers has been taken from the IMDb website for the year 2020. The Artificial Neural Network, with an accuracy of 86 percent, is the best performing model of all the models discussed.
{"title":"Movie Success Prediction and Performance Comparison using Various Statistical Approaches","authors":"Manav Agarwal, S. Venugopal, Rishab Kashyap, R. Bharathi","doi":"10.5121/ijaia.2022.13102","DOIUrl":"https://doi.org/10.5121/ijaia.2022.13102","url":null,"abstract":"Movies are among the most prominent contributors to the global entertainment industry today, and they are among the biggest revenue-generating industries from a commercial standpoint. It's vital to divide films into two categories: successful and unsuccessful. To categorize the movies in this research, a variety of models were utilized, including regression models such as Simple Linear, Multiple Linear, and Logistic Regression, clustering techniques such as SVM and K-Means, Time Series Analysis, and an Artificial Neural Network. The models stated above were compared on a variety of factors, including their accuracy on the training and validation datasets as well as the testing dataset, the availability of new movie characteristics, and a variety of other statistical metrics. During the course of this study, it was discovered that certain characteristics have a greater impact on the likelihood of a film's success than others. For example, the existence of the genre action may have a significant impact on the forecasts, although another genre, such as sport, may not. The testing dataset for the models and classifiers has been taken from the IMDb website for the year 2020. The Artificial Neural Network, with an accuracy of 86 percent, is the best performing model of all the models discussed.","PeriodicalId":93188,"journal":{"name":"International journal of artificial intelligence & applications","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2022-01-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"47463314","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 : 2022-01-31DOI: 10.5121/ijaia.2022.13106
Athanasios Sypsas, D. Kalles
Process Mining (PM) emerged from business process management but has recently been applied to educational data and has been found to facilitate the understanding of the educational process. Educational Process Mining (EPM) bridges the gap between process analysis and data analysis, based on the techniques of model discovery, conformance checking and extension of existing process models. We present a systematic review of the recent and current status of research in the EPM domain, focusing on application domains, techniques, tools and models, to highlight the use of EPM in comprehending and improving educational processes.
{"title":"Reviewing Process Mining Applications and Techniques in Education","authors":"Athanasios Sypsas, D. Kalles","doi":"10.5121/ijaia.2022.13106","DOIUrl":"https://doi.org/10.5121/ijaia.2022.13106","url":null,"abstract":"Process Mining (PM) emerged from business process management but has recently been applied to educational data and has been found to facilitate the understanding of the educational process. Educational Process Mining (EPM) bridges the gap between process analysis and data analysis, based on the techniques of model discovery, conformance checking and extension of existing process models. We present a systematic review of the recent and current status of research in the EPM domain, focusing on application domains, techniques, tools and models, to highlight the use of EPM in comprehending and improving educational processes.","PeriodicalId":93188,"journal":{"name":"International journal of artificial intelligence & applications","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2022-01-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"46984888","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 : 2022-01-31DOI: 10.5121/ijaia.2022.13107
Eduardo Calo, Gabriel Vaca, Cristina Sánchez, David Jines, Giovanny Amancha, Ángel Flores, A. Santana G, Fernanda Oñate
The principal objective of this work is to be able to use artificial intelligence techniques to be able to design a predictive model of the performance of a third-generation mobile phone base radio, using the analysis of KPIs obtained in a statistical data set of the daily behaviour of an RBS. For the realization of these models, various techniques such as Decision Trees, Neural Networks and Random Forest were used. which will allow faster progress in the deep analysis of large amounts of data statistics and get better results. In this part of the work, data was obtained from the behaviour of a third-party mobile phone base radio generation of the Claro operator in Ecuador, it should be noted that. To specify this practical case, several models were generated based on in various artificial intelligence technique for the prediction of performance results of a mobile phone base radio of third generation, the same ones that after several tests were creation of a predictive model that determines the performance of a mobile phone base radio. As a conclusion of this work, it was determined that the development of a predictive model based on artificial intelligence techniques is very useful for the analysis of large amounts of data in order to find or predict complex results, more quickly and trustworthy. The data are KPIs of the daily and hourly performance of a radio base of third generation mobile telephony, these data were obtained through the operator's remote monitoring and management tool Sure call PRS.
{"title":"Artificial Intelligence Techniques for the Modeling of a 3G Mobile Phone Base Radio","authors":"Eduardo Calo, Gabriel Vaca, Cristina Sánchez, David Jines, Giovanny Amancha, Ángel Flores, A. Santana G, Fernanda Oñate","doi":"10.5121/ijaia.2022.13107","DOIUrl":"https://doi.org/10.5121/ijaia.2022.13107","url":null,"abstract":"The principal objective of this work is to be able to use artificial intelligence techniques to be able to design a predictive model of the performance of a third-generation mobile phone base radio, using the analysis of KPIs obtained in a statistical data set of the daily behaviour of an RBS. For the realization of these models, various techniques such as Decision Trees, Neural Networks and Random Forest were used. which will allow faster progress in the deep analysis of large amounts of data statistics and get better results. In this part of the work, data was obtained from the behaviour of a third-party mobile phone base radio generation of the Claro operator in Ecuador, it should be noted that. To specify this practical case, several models were generated based on in various artificial intelligence technique for the prediction of performance results of a mobile phone base radio of third generation, the same ones that after several tests were creation of a predictive model that determines the performance of a mobile phone base radio. As a conclusion of this work, it was determined that the development of a predictive model based on artificial intelligence techniques is very useful for the analysis of large amounts of data in order to find or predict complex results, more quickly and trustworthy. The data are KPIs of the daily and hourly performance of a radio base of third generation mobile telephony, these data were obtained through the operator's remote monitoring and management tool Sure call PRS.","PeriodicalId":93188,"journal":{"name":"International journal of artificial intelligence & applications","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2022-01-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"47192335","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 : 2021-11-30DOI: 10.5121/ijaia.2021.12603
Prudhvi Parne
Digital disruption is redefining industries and changing the way business function. Artificial Intelligence is the future of banking as it brings the power of advanced data analytics to combat fraudulent transactions and improve compliance. Financial services are the economical backbone of any nation in the world. There are billions of financial transactions which are taking place and all this data is stored and can be considered as a gold mine of data for many different organizations. No human intelligence can dig in this amount of data to come up with something valuable. This is the reason financial organizations are employing artificial intelligence to come up with new algorithms which can change the way financial transactions are being carried out. Artificial Intelligence can complete the task in a very short period. Artificial intelligence can be used to detect frauds, identify possible attacks, and any other kind of anomalies that may be detrimental for the institution. This paper discusses the role of artificial intelligence and machine learning in the finance sector. Additionally, the paper will provide the necessary strategies that any banking organization can follow when digitizing its operations when implementing Artificial Intelligence, Machine learning and Cloud Computing.
{"title":"Digital Transformation of Financial Services using Artificial Intelligence, Machine Learning, and Cloud Computing","authors":"Prudhvi Parne","doi":"10.5121/ijaia.2021.12603","DOIUrl":"https://doi.org/10.5121/ijaia.2021.12603","url":null,"abstract":"Digital disruption is redefining industries and changing the way business function. Artificial Intelligence is the future of banking as it brings the power of advanced data analytics to combat fraudulent transactions and improve compliance. Financial services are the economical backbone of any nation in the world. There are billions of financial transactions which are taking place and all this data is stored and can be considered as a gold mine of data for many different organizations. No human intelligence can dig in this amount of data to come up with something valuable. This is the reason financial organizations are employing artificial intelligence to come up with new algorithms which can change the way financial transactions are being carried out. Artificial Intelligence can complete the task in a very short period. Artificial intelligence can be used to detect frauds, identify possible attacks, and any other kind of anomalies that may be detrimental for the institution. This paper discusses the role of artificial intelligence and machine learning in the finance sector. Additionally, the paper will provide the necessary strategies that any banking organization can follow when digitizing its operations when implementing Artificial Intelligence, Machine learning and Cloud Computing.","PeriodicalId":93188,"journal":{"name":"International journal of artificial intelligence & applications","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2021-11-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"44541852","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 : 2021-11-30DOI: 10.5121/ijaia.2021.12604
R. Ando, Yoshiyasu Takefuji
This paper gives complete guidelines for authors submitting papers for the AIRCC Journals. A sliding puzzle is a combination puzzle where a player slides pieces along specific routes on a board to reach a certain end configuration. In this paper, we propose a novel measurement of the complexity of 100 sliding puzzles with paramodulation, which is an inference method of automated reasoning. It turned out that by counting the number of clauses yielded with paramodulation, we can evaluate the difficulty of each puzzle. In the experiment, we have generated 100 * 8 puzzles that passed the solvability checking by countering inversions. By doing this, we can distinguish the complexity of 8 puzzles with the number generated with paramodulation. For example, board [2,3,6,1,7,8,5,4, hole] is the easiest with score 3008 and board [6,5,8,7,4,3,2,1, hole] is the most difficult with score 48653.Besides, we have succeeded in obverse several layers of complexity (the number of clauses generated) in 100 puzzles. We can conclude that the proposed method can provide a new perspective of paramodulation complexity concerning sliding block puzzles.
{"title":"A New Perspective of Paramodulation Complexity by Solving 100 Sliding Block Puzzles","authors":"R. Ando, Yoshiyasu Takefuji","doi":"10.5121/ijaia.2021.12604","DOIUrl":"https://doi.org/10.5121/ijaia.2021.12604","url":null,"abstract":"This paper gives complete guidelines for authors submitting papers for the AIRCC Journals. A sliding puzzle is a combination puzzle where a player slides pieces along specific routes on a board to reach a certain end configuration. In this paper, we propose a novel measurement of the complexity of 100 sliding puzzles with paramodulation, which is an inference method of automated reasoning. It turned out that by counting the number of clauses yielded with paramodulation, we can evaluate the difficulty of each puzzle. In the experiment, we have generated 100 * 8 puzzles that passed the solvability checking by countering inversions. By doing this, we can distinguish the complexity of 8 puzzles with the number generated with paramodulation. For example, board [2,3,6,1,7,8,5,4, hole] is the easiest with score 3008 and board [6,5,8,7,4,3,2,1, hole] is the most difficult with score 48653.Besides, we have succeeded in obverse several layers of complexity (the number of clauses generated) in 100 puzzles. We can conclude that the proposed method can provide a new perspective of paramodulation complexity concerning sliding block puzzles.","PeriodicalId":93188,"journal":{"name":"International journal of artificial intelligence & applications","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2021-11-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"42759253","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 : 2021-11-30DOI: 10.5121/ijaia.2021.12602
Pauline Sherly Jeba P, Manju Kiran, A. Sharma, Divakar Venkatesh
Sales forecasting became crucial for industries in past decades with rapid globalization, widespread adoption of information technology towards e-business, understanding market fluctuations, meeting business plans, and avoiding loss of sales. This research precisely predicts the automotive industry sales using a bag of multiple machine learning and time series algorithms coupled with historical sales and auxiliary features. Three-year historical sales data (from 2017 till 2020) were used for the model building or training, and one-year (2020-2021) predictions were computed for 900 unique SKU's (stock-keeping units). In the present study, the SKU is a combination of sales office, core business field, and material customer group. Various data cleaning and exploratory data analysis algorithms were implemented over raw datasets before use for modeling. Mean absolute percentage error (mape) were estimated for individual predictions from time series and machine learning models. The best model was selected for unique SKU's as per the most negligible mape value.
{"title":"Automation of Best-Fit Model Selection using a Bag of Machine Learning Libraries for Sales Forecasting","authors":"Pauline Sherly Jeba P, Manju Kiran, A. Sharma, Divakar Venkatesh","doi":"10.5121/ijaia.2021.12602","DOIUrl":"https://doi.org/10.5121/ijaia.2021.12602","url":null,"abstract":"Sales forecasting became crucial for industries in past decades with rapid globalization, widespread adoption of information technology towards e-business, understanding market fluctuations, meeting business plans, and avoiding loss of sales. This research precisely predicts the automotive industry sales using a bag of multiple machine learning and time series algorithms coupled with historical sales and auxiliary features. Three-year historical sales data (from 2017 till 2020) were used for the model building or training, and one-year (2020-2021) predictions were computed for 900 unique SKU's (stock-keeping units). In the present study, the SKU is a combination of sales office, core business field, and material customer group. Various data cleaning and exploratory data analysis algorithms were implemented over raw datasets before use for modeling. Mean absolute percentage error (mape) were estimated for individual predictions from time series and machine learning models. The best model was selected for unique SKU's as per the most negligible mape value.","PeriodicalId":93188,"journal":{"name":"International journal of artificial intelligence & applications","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2021-11-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"45765844","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 : 2021-11-30DOI: 10.5121/ijaia.2021.12601
Erwei Bai, J. Xia
In radiation therapy, preventing treatment plan errors is of paramount importance. In this paper, an alert system is proposed and developed for checking if the pending cancer treatment plan is consistent with the intended use. A key step in the development of the paper is characterization of various treatment plan fingerprints by three-dimension vectors taken from possibly thousands of variables in each treatment plan. Then three machine learning based algorithms are developed and tested in the paper. The first algorithm is a knowledge-based support vector machine method. If an incorrect treatment plan were offered, the algorithm would tell that the pending treatment plan is inconsistent with the intended use and provide a red flag. The algorithm is tested on the actual patient data sets with 100% successful rate and 0% failure rate. In addition, two algorithms based on the well-known k-nearest neighbour and Bayesian approach respectively are developed. Similar to the support vector machine algorithm, these two algorithms are also tested with 100% success rate and 0% failure rate. The key seems to pick up the right features.
{"title":"A Knowledge based Automatic Radiation Treatment Plan Alert System","authors":"Erwei Bai, J. Xia","doi":"10.5121/ijaia.2021.12601","DOIUrl":"https://doi.org/10.5121/ijaia.2021.12601","url":null,"abstract":"In radiation therapy, preventing treatment plan errors is of paramount importance. In this paper, an alert system is proposed and developed for checking if the pending cancer treatment plan is consistent with the intended use. A key step in the development of the paper is characterization of various treatment plan fingerprints by three-dimension vectors taken from possibly thousands of variables in each treatment plan. Then three machine learning based algorithms are developed and tested in the paper. The first algorithm is a knowledge-based support vector machine method. If an incorrect treatment plan were offered, the algorithm would tell that the pending treatment plan is inconsistent with the intended use and provide a red flag. The algorithm is tested on the actual patient data sets with 100% successful rate and 0% failure rate. In addition, two algorithms based on the well-known k-nearest neighbour and Bayesian approach respectively are developed. Similar to the support vector machine algorithm, these two algorithms are also tested with 100% success rate and 0% failure rate. The key seems to pick up the right features.","PeriodicalId":93188,"journal":{"name":"International journal of artificial intelligence & applications","volume":"1 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2021-11-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"41857826","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 : 2021-10-01DOI: 10.5121/ijaia.2021.12605
H. Ghandeharioun
Obstructive sleep apnea (OSA) is one of the most widespread respiratory diseases today. Complete or relative breathing cessations due to upper airway subsidence during sleep is OSA. It has confirmed potential influence on Covid-19 hospitalization and mortality, and is strongly associated with major comorbidities of severe Covid-19 infection. Un-diagnosed OSA may also lead to a variety of severe physical and mental side-effects. To score OSA severity, nocturnal sleep monitoring is performed under defined protocols and standards called polysomnography (PSG). This method is time-consuming, expensive, and requiring professional sleep technicians. Automatic home-based detection of OSA is welcome and in great demand. It is a fast and effective way for referring OSA suspects to sleep clinics for further monitoring. On-line OSA detection also can be a part of a closed-loop automatic control of the OSA therapeutic/assistive devices. In this paper, several solutions for online OSA detection are introduced and tested on 155 subjects of three different databases. The best combinational solution uses mutual information (MI) analysis for selecting out of ECG and SpO2-based features. Several methods of supervised and unsupervised machine learning are employed to detect apnoeic episodes. To achieve the best performance, the most successful classifiers in four different ternary combination methods are used. The proposed configurations exploit limited use of biological signals, have online working scheme, and exhibit uniform and acceptable performance (over 85%) in all the employed databases. The benefits have not been gathered all together in the previous published methods.
{"title":"Automatic Home-based Screening of Obstructive Sleep Apnea using Single Channel Electrocardiogram and SPO2 Signals","authors":"H. Ghandeharioun","doi":"10.5121/ijaia.2021.12605","DOIUrl":"https://doi.org/10.5121/ijaia.2021.12605","url":null,"abstract":"Obstructive sleep apnea (OSA) is one of the most widespread respiratory diseases today. Complete or relative breathing cessations due to upper airway subsidence during sleep is OSA. It has confirmed potential influence on Covid-19 hospitalization and mortality, and is strongly associated with major comorbidities of severe Covid-19 infection. Un-diagnosed OSA may also lead to a variety of severe physical and mental side-effects. To score OSA severity, nocturnal sleep monitoring is performed under defined protocols and standards called polysomnography (PSG). This method is time-consuming, expensive, and requiring professional sleep technicians. Automatic home-based detection of OSA is welcome and in great demand. It is a fast and effective way for referring OSA suspects to sleep clinics for further monitoring. On-line OSA detection also can be a part of a closed-loop automatic control of the OSA therapeutic/assistive devices. In this paper, several solutions for online OSA detection are introduced and tested on 155 subjects of three different databases. The best combinational solution uses mutual information (MI) analysis for selecting out of ECG and SpO2-based features. Several methods of supervised and unsupervised machine learning are employed to detect apnoeic episodes. To achieve the best performance, the most successful classifiers in four different ternary combination methods are used. The proposed configurations exploit limited use of biological signals, have online working scheme, and exhibit uniform and acceptable performance (over 85%) in all the employed databases. The benefits have not been gathered all together in the previous published methods.","PeriodicalId":93188,"journal":{"name":"International journal of artificial intelligence & applications","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2021-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"43303069","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 : 2021-09-30DOI: 10.5121/ijaia.2021.12503
S. Hadi, Asep K Supriatna, Faishal Wahiduddin, W. Srisayekti, A. Djunaidi, E. Fitriana, A. Abdullah, D. Ekawati
Facial expression recognition is one of the types of non-verbal communication that is not only commons for human but also plays an essential role in everyday lives. The development of science and technology allows the machine to automatically detect human facial expressions based on images and videos. Numerous facial expression detection methods have been proposed in the literature. This paper presents a method to find three basic facial expressions (neutral, happy, and angry) from two parameter values: smile and eyes-open. The analysis involves a preprocessing step using a combination of pre-designed proprietary algorithm and Luxand library. Firstly, the parameters were mapped into two-dimensional space and then grouped into three clusters using K-means, a popular heuristic clustering method. Secondly, more than 50,000 frames for each video were experimented using the proprietary research data. The result shows that the proposed method successfully performed a simple video analysis of facial expressions.
{"title":"Finding Facial Expression Patterns on Videos based on Smile and Eyes-Open Confidence Values","authors":"S. Hadi, Asep K Supriatna, Faishal Wahiduddin, W. Srisayekti, A. Djunaidi, E. Fitriana, A. Abdullah, D. Ekawati","doi":"10.5121/ijaia.2021.12503","DOIUrl":"https://doi.org/10.5121/ijaia.2021.12503","url":null,"abstract":"Facial expression recognition is one of the types of non-verbal communication that is not only commons for human but also plays an essential role in everyday lives. The development of science and technology allows the machine to automatically detect human facial expressions based on images and videos. Numerous facial expression detection methods have been proposed in the literature. This paper presents a method to find three basic facial expressions (neutral, happy, and angry) from two parameter values: smile and eyes-open. The analysis involves a preprocessing step using a combination of pre-designed proprietary algorithm and Luxand library. Firstly, the parameters were mapped into two-dimensional space and then grouped into three clusters using K-means, a popular heuristic clustering method. Secondly, more than 50,000 frames for each video were experimented using the proprietary research data. The result shows that the proposed method successfully performed a simple video analysis of facial expressions.","PeriodicalId":93188,"journal":{"name":"International journal of artificial intelligence & applications","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2021-09-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"44805739","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}
京公网安备 11010802042870号
京ICP备2023020795号-1
扫码关注我们
求助内容：
应助结果提醒方式：