Pub Date : 2019-09-01DOI: 10.1109/AiDAS47888.2019.8970916
S. Masrom, Thuraiya Mohd, Nur Syafiqah Jamil, Abdullah Sani Abdul Rahman, N. Baharun
Designing an effective machine learning model for prediction or classification problem is a tedious endeavor. Significant time and expertise are needed to customize the model for a specific problem. A significant way to reduce the complicated design is by using Automated Machine Learning (AML) that can intelligently optimize the best pipeline suitable for a problem or dataset. This paper demonstrates the utilization of an AML that has been developed with a meta-heuristic algorithm namely Genetic Programming (GP). Empirical experiment has been conducted to test the performances of AML on a real dataset of house prices in the area of Petaling Jaya, Selangor. The results show that the AML with GP able to produce the best pipeline of machine learning with high score of accuracy and minimal error. (Abstract)
{"title":"Automated Machine Learning based on Genetic Programming: a case study on a real house pricing dataset","authors":"S. Masrom, Thuraiya Mohd, Nur Syafiqah Jamil, Abdullah Sani Abdul Rahman, N. Baharun","doi":"10.1109/AiDAS47888.2019.8970916","DOIUrl":"https://doi.org/10.1109/AiDAS47888.2019.8970916","url":null,"abstract":"Designing an effective machine learning model for prediction or classification problem is a tedious endeavor. Significant time and expertise are needed to customize the model for a specific problem. A significant way to reduce the complicated design is by using Automated Machine Learning (AML) that can intelligently optimize the best pipeline suitable for a problem or dataset. This paper demonstrates the utilization of an AML that has been developed with a meta-heuristic algorithm namely Genetic Programming (GP). Empirical experiment has been conducted to test the performances of AML on a real dataset of house prices in the area of Petaling Jaya, Selangor. The results show that the AML with GP able to produce the best pipeline of machine learning with high score of accuracy and minimal error. (Abstract)","PeriodicalId":227508,"journal":{"name":"2019 1st International Conference on Artificial Intelligence and Data Sciences (AiDAS)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2019-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114290442","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 : 2019-09-01DOI: 10.1109/AiDAS47888.2019.8970755
Ayush Banka, Naman Juneja, Arushi Shrimal, Samiksha Agrawal, Dr. Lalit Purohit
The problem of web service selection is an important problem from engineering perspective. Quality of Service (QoS) based selection of web services is a popular technique. However, the QoS based selection techniques have their own limitations. Therefore, the Classification of web services before selection can be useful. Two datasets are used for analyzing and obtaining the results. In this paper, we have compared various web service classification techniques and found that stacking is most suitable technique to be applied for classification of web services. The accuracy of stacking is found to be 86.53.
{"title":"Web Service Classification using Stacking","authors":"Ayush Banka, Naman Juneja, Arushi Shrimal, Samiksha Agrawal, Dr. Lalit Purohit","doi":"10.1109/AiDAS47888.2019.8970755","DOIUrl":"https://doi.org/10.1109/AiDAS47888.2019.8970755","url":null,"abstract":"The problem of web service selection is an important problem from engineering perspective. Quality of Service (QoS) based selection of web services is a popular technique. However, the QoS based selection techniques have their own limitations. Therefore, the Classification of web services before selection can be useful. Two datasets are used for analyzing and obtaining the results. In this paper, we have compared various web service classification techniques and found that stacking is most suitable technique to be applied for classification of web services. The accuracy of stacking is found to be 86.53.","PeriodicalId":227508,"journal":{"name":"2019 1st International Conference on Artificial Intelligence and Data Sciences (AiDAS)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2019-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124695993","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 : 2019-09-01DOI: 10.1109/AiDAS47888.2019.8970823
M. F. Misman, A. A. Samah, Farah Aqilah Ezudin, Hairuddin Abu Majid, Z. A. Shah, H. Hashim, Muhamad Farhin Harun
Autism Spectrum Disorder (ASD) is a developmental brain disorder that causes deficits in linguistic, communicative, and cognitive skills as well as social skills. Various application of Machine Learning has been applied apart from the clinical tests available, which has increased the performance in the diagnosis of this disorder. In this study, we applied the Deep Neural Network (DNN) architecture, which has been a popular method in recent years and proved to improve classification accuracy. This study aims to analyse the performance of DNN model in the diagnosis of ASD in terms of classification accuracy by using two datasets of adult ASD screening data. The results are then compared with the previous Machine Learning method by another researcher, which is Support Vector Machine (SVM). The accuracy achieved by the DNN model in the classification of ASD diagnosis is 99.40% on the first dataset and achieved 96.08% on the second dataset. Meanwhile, the SVM model achieved an accuracy of 95.24% and 95.08% using the first and second data, respectively. The results show that ASD cases can be accurately identified by implementing the DNN classification method using ASD adult screening data.
{"title":"Classification of Adults with Autism Spectrum Disorder using Deep Neural Network","authors":"M. F. Misman, A. A. Samah, Farah Aqilah Ezudin, Hairuddin Abu Majid, Z. A. Shah, H. Hashim, Muhamad Farhin Harun","doi":"10.1109/AiDAS47888.2019.8970823","DOIUrl":"https://doi.org/10.1109/AiDAS47888.2019.8970823","url":null,"abstract":"Autism Spectrum Disorder (ASD) is a developmental brain disorder that causes deficits in linguistic, communicative, and cognitive skills as well as social skills. Various application of Machine Learning has been applied apart from the clinical tests available, which has increased the performance in the diagnosis of this disorder. In this study, we applied the Deep Neural Network (DNN) architecture, which has been a popular method in recent years and proved to improve classification accuracy. This study aims to analyse the performance of DNN model in the diagnosis of ASD in terms of classification accuracy by using two datasets of adult ASD screening data. The results are then compared with the previous Machine Learning method by another researcher, which is Support Vector Machine (SVM). The accuracy achieved by the DNN model in the classification of ASD diagnosis is 99.40% on the first dataset and achieved 96.08% on the second dataset. Meanwhile, the SVM model achieved an accuracy of 95.24% and 95.08% using the first and second data, respectively. The results show that ASD cases can be accurately identified by implementing the DNN classification method using ASD adult screening data.","PeriodicalId":227508,"journal":{"name":"2019 1st International Conference on Artificial Intelligence and Data Sciences (AiDAS)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2019-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130636660","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 : 2019-09-01DOI: 10.1109/aidas47888.2019.8970872
{"title":"[AiDAS 2019 Back Cover]","authors":"","doi":"10.1109/aidas47888.2019.8970872","DOIUrl":"https://doi.org/10.1109/aidas47888.2019.8970872","url":null,"abstract":"","PeriodicalId":227508,"journal":{"name":"2019 1st International Conference on Artificial Intelligence and Data Sciences (AiDAS)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2019-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134216105","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 : 2019-09-01DOI: 10.1109/aidas47888.2019.8970807
{"title":"AiDAS 2019 Reviewers","authors":"","doi":"10.1109/aidas47888.2019.8970807","DOIUrl":"https://doi.org/10.1109/aidas47888.2019.8970807","url":null,"abstract":"","PeriodicalId":227508,"journal":{"name":"2019 1st International Conference on Artificial Intelligence and Data Sciences (AiDAS)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2019-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126423167","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 : 2019-09-01DOI: 10.1109/AiDAS47888.2019.8970706
Muhammad Umair, M. Hashmani, M. H. Hasan
Sea has always played a pivotal role in human life. It formulates the weather, provides transportation medium, food, natural resources like oil and gas, and much more. Countless commercial and industrial activities take place on the surface of the sea, thus understanding, classifying and predicting the sea surface wave is a topic of great interest. Many numerical models (NM) have been proposed to model the behavior of sea waves, however, they are complex and costly for site-specific studies. On the other hand, data-driven machine learning (ML) models have recently proved to be an effective solution for site-specific classification, real-time or near-future prediction problems. The ML approach utilizes marine datasets to train, test and validate the model. In this paper, we present a survey of ML studies on the topic of classification and prediction of sea wave parameters. We hope that this paper provides a holistic model-based view to new researchers and pave the path for future research.
{"title":"Survey of Sea Wave Parameters Classification and Prediction using Machine Leaming Models","authors":"Muhammad Umair, M. Hashmani, M. H. Hasan","doi":"10.1109/AiDAS47888.2019.8970706","DOIUrl":"https://doi.org/10.1109/AiDAS47888.2019.8970706","url":null,"abstract":"Sea has always played a pivotal role in human life. It formulates the weather, provides transportation medium, food, natural resources like oil and gas, and much more. Countless commercial and industrial activities take place on the surface of the sea, thus understanding, classifying and predicting the sea surface wave is a topic of great interest. Many numerical models (NM) have been proposed to model the behavior of sea waves, however, they are complex and costly for site-specific studies. On the other hand, data-driven machine learning (ML) models have recently proved to be an effective solution for site-specific classification, real-time or near-future prediction problems. The ML approach utilizes marine datasets to train, test and validate the model. In this paper, we present a survey of ML studies on the topic of classification and prediction of sea wave parameters. We hope that this paper provides a holistic model-based view to new researchers and pave the path for future research.","PeriodicalId":227508,"journal":{"name":"2019 1st International Conference on Artificial Intelligence and Data Sciences (AiDAS)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2019-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116891360","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 : 2019-09-01DOI: 10.1109/AiDAS47888.2019.8970745
Shilei Chen, Z. Ong
Structural health monitoring is of great significance to the maintenance of long-term used structures, as unexpected damage may lead to disasters and economic loss. A new structural damage detection scheme using waveform chain code and clustering is proposed in this work. The waveform chain code features are extracted from the frequency response functions. Compared with the raw frequency response data, these features show the alterations caused by structural damage more evidently. K-means clustering method is used to distinguish the features of intact and damaged states. Unlike supervised learning methods whose training data are labeled, the unsupervised clustering is performed with unlabeled data. An experimental test on a rectangular Perspex plate is carried out for verification. The results show the good performance of the newly proposed scheme and this might suggest its potential application in the real practice.
{"title":"Waveform chain code: a more sensitive feature selection in unsupervised structural damage detection","authors":"Shilei Chen, Z. Ong","doi":"10.1109/AiDAS47888.2019.8970745","DOIUrl":"https://doi.org/10.1109/AiDAS47888.2019.8970745","url":null,"abstract":"Structural health monitoring is of great significance to the maintenance of long-term used structures, as unexpected damage may lead to disasters and economic loss. A new structural damage detection scheme using waveform chain code and clustering is proposed in this work. The waveform chain code features are extracted from the frequency response functions. Compared with the raw frequency response data, these features show the alterations caused by structural damage more evidently. K-means clustering method is used to distinguish the features of intact and damaged states. Unlike supervised learning methods whose training data are labeled, the unsupervised clustering is performed with unlabeled data. An experimental test on a rectangular Perspex plate is carried out for verification. The results show the good performance of the newly proposed scheme and this might suggest its potential application in the real practice.","PeriodicalId":227508,"journal":{"name":"2019 1st International Conference on Artificial Intelligence and Data Sciences (AiDAS)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2019-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"117335359","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 : 2019-09-01DOI: 10.1109/AiDAS47888.2019.8970710
D. S. Dias, T. Fernando
Musical creativity being one of the strong-hold characteristics that differentiate humans from computers in today’s technologically advanced society, algorithmic composition and song writing are the research areas that aim to bridge this gap. With the introduction and development of various neural network-based methodologies that have shown quite a promise in applications to a wide range other fields, it is promising to see how these new technologies can cater to the domain of musical creativity. Even though there has been significant amount of research done focusing on musical composition, it is not the same for musical song writing. The main objective of this research study is to apply Long Short-Term Memory Recurrent Neural Networks in constructing a machine learning model that can generate musical melody notes when it is provided with a lyrical input (musical song writing). In this study, we were able to successfully generate musical melody notes for provided lyrical inputs with consistencies of over 80%. In addition to that, a web-based inference tool was developed as a result of this study, which allows us to easily generate musical melody sheets when we provide with a lyrical input.
{"title":"Komposer – Automated Musical Note Generation based on Lyrics with Recurrent Neural Networks","authors":"D. S. Dias, T. Fernando","doi":"10.1109/AiDAS47888.2019.8970710","DOIUrl":"https://doi.org/10.1109/AiDAS47888.2019.8970710","url":null,"abstract":"Musical creativity being one of the strong-hold characteristics that differentiate humans from computers in today’s technologically advanced society, algorithmic composition and song writing are the research areas that aim to bridge this gap. With the introduction and development of various neural network-based methodologies that have shown quite a promise in applications to a wide range other fields, it is promising to see how these new technologies can cater to the domain of musical creativity. Even though there has been significant amount of research done focusing on musical composition, it is not the same for musical song writing. The main objective of this research study is to apply Long Short-Term Memory Recurrent Neural Networks in constructing a machine learning model that can generate musical melody notes when it is provided with a lyrical input (musical song writing). In this study, we were able to successfully generate musical melody notes for provided lyrical inputs with consistencies of over 80%. In addition to that, a web-based inference tool was developed as a result of this study, which allows us to easily generate musical melody sheets when we provide with a lyrical input.","PeriodicalId":227508,"journal":{"name":"2019 1st International Conference on Artificial Intelligence and Data Sciences (AiDAS)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2019-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133341654","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 : 2019-09-01DOI: 10.1109/AiDAS47888.2019.8970974
P. Uruthiran, L. Ranathunga
Classification of music instrument is a challenging but important problem in music information retrieval. In music instrument identification, multimedia signal processing is heavily utilized. In this work, we have presented a sequential forward feature selection method to select a suitable feature set for the classification. We have used a reduced number of input data for the classification. Spectral domain and Time domain features are used for this study. Music instrument signals are identified as belonging to one of the three families namely string, brass, and woodwimt Decision tree, k-Nearest Neighbor (kNN) and Support Vector Machines (SVM) have been used as classifiers. The average accuracy achieved from SVM classifier has recorded the highest value as 93.37%. Therefore, it is concluded that the SVM classifier is the best classifier among the other classifiers for the derived feature vector.
{"title":"Optimization of Feature Selection and Classification of Oriental Music Instruments Identification","authors":"P. Uruthiran, L. Ranathunga","doi":"10.1109/AiDAS47888.2019.8970974","DOIUrl":"https://doi.org/10.1109/AiDAS47888.2019.8970974","url":null,"abstract":"Classification of music instrument is a challenging but important problem in music information retrieval. In music instrument identification, multimedia signal processing is heavily utilized. In this work, we have presented a sequential forward feature selection method to select a suitable feature set for the classification. We have used a reduced number of input data for the classification. Spectral domain and Time domain features are used for this study. Music instrument signals are identified as belonging to one of the three families namely string, brass, and woodwimt Decision tree, k-Nearest Neighbor (kNN) and Support Vector Machines (SVM) have been used as classifiers. The average accuracy achieved from SVM classifier has recorded the highest value as 93.37%. Therefore, it is concluded that the SVM classifier is the best classifier among the other classifiers for the derived feature vector.","PeriodicalId":227508,"journal":{"name":"2019 1st International Conference on Artificial Intelligence and Data Sciences (AiDAS)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2019-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131558408","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 : 2019-09-01DOI: 10.1109/AiDAS47888.2019.8971010
Ying-Mei Leong, Chockalingam Letchumanan
Internet of Things (IoT) is among the future intelligent spaces primary drivers. It allows for fresh operating techniques and provides essential economic and environmental advantages. With IoT, rooms evolve to become intelligent and linked from being just ’smart’. This paper focuses on the way to leverage IoT tools in teaching to create a standard approach to introduce IoT courses in the Computer or Information Sciences curricula. The paper classifies the key advantages and motivation behind the promotion of IoT course. Next, it delivers an exhaustive and complete perspective of general varieties of IoT tools for teaching and learning. Finally, four challenges in implementing IoT tools in teaching as well as introducing IoT courses in Computer or Information Science curricula were identified.
{"title":"Effective Learning in Higher Education in Malaysia by Implementing Internet of Things related Tools in Teaching and Introducing IoT courses in Curriculum","authors":"Ying-Mei Leong, Chockalingam Letchumanan","doi":"10.1109/AiDAS47888.2019.8971010","DOIUrl":"https://doi.org/10.1109/AiDAS47888.2019.8971010","url":null,"abstract":"Internet of Things (IoT) is among the future intelligent spaces primary drivers. It allows for fresh operating techniques and provides essential economic and environmental advantages. With IoT, rooms evolve to become intelligent and linked from being just ’smart’. This paper focuses on the way to leverage IoT tools in teaching to create a standard approach to introduce IoT courses in the Computer or Information Sciences curricula. The paper classifies the key advantages and motivation behind the promotion of IoT course. Next, it delivers an exhaustive and complete perspective of general varieties of IoT tools for teaching and learning. Finally, four challenges in implementing IoT tools in teaching as well as introducing IoT courses in Computer or Information Science curricula were identified.","PeriodicalId":227508,"journal":{"name":"2019 1st International Conference on Artificial Intelligence and Data Sciences (AiDAS)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2019-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132838584","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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