Pub Date : 2019-09-01DOI: 10.1109/AiDAS47888.2019.8970690
S. Mishra, R. K. Patnaik, P. K. Dash, R. Bisoi, J. Naik
This paper proposes low complexity Empirical mode decomposition trained by Kernel based (KEMD) algorithm for wind power prediction for various time horizon such as ten minutes to five hours interval for California wind farm. For a comparative performance analysis, another two forecasting model named as Empirical mode decomposition with pseudo inverse neural network and Pseudo Inverse neural network with Legendre functions and RBF units, which is further optimized by Firefly algorithm (FFA) is described here. Kernel based pseudo inverse algorithm is proposed because it eliminates the involvement of the hidden layers in each iteration, which helps in return to reduce the computational complexity and generates more precise response in prediction purpose. In the other two models the weights which are used between the hidden layer and the output neuron are obtained by PINN which is also known as Moore-Penrose pseudo inverse algorithm. This proposed KEMD trained by kernel based pseudo inverse algorithm has a very good and precise prediction of wind power. This model has been proved by doing several observations for various seasons which has been demonstrated in the results and simulation section.
本文提出了一种基于Kernel based (KEMD)算法训练的低复杂度经验模态分解方法，用于加州风电场10分钟至5小时间隔等不同时间范围内的风电预测。为了进行性能对比分析，本文描述了另外两种预测模型，分别是基于伪逆神经网络的经验模态分解模型和基于Legendre函数和RBF单元的伪逆神经网络模型，并通过Firefly算法(FFA)进行了进一步优化。提出了基于核的伪逆算法，因为它在每次迭代中消除了隐藏层的介入，从而有助于降低计算复杂度，在预测目的上产生更精确的响应。在另外两种模型中，隐含层与输出神经元之间的权值由PINN(也称为Moore-Penrose伪逆算法)获得。本文提出的基于核的伪逆算法训练的KEMD具有很好的风电预测精度。该模式已通过对不同季节的多次观测得到证实，结果和模拟部分已对此进行了论证。
{"title":"An EMD Based Polynomial Kernel Methodology for superior Wind Power Prediction.","authors":"S. Mishra, R. K. Patnaik, P. K. Dash, R. Bisoi, J. Naik","doi":"10.1109/AiDAS47888.2019.8970690","DOIUrl":"https://doi.org/10.1109/AiDAS47888.2019.8970690","url":null,"abstract":"This paper proposes low complexity Empirical mode decomposition trained by Kernel based (KEMD) algorithm for wind power prediction for various time horizon such as ten minutes to five hours interval for California wind farm. For a comparative performance analysis, another two forecasting model named as Empirical mode decomposition with pseudo inverse neural network and Pseudo Inverse neural network with Legendre functions and RBF units, which is further optimized by Firefly algorithm (FFA) is described here. Kernel based pseudo inverse algorithm is proposed because it eliminates the involvement of the hidden layers in each iteration, which helps in return to reduce the computational complexity and generates more precise response in prediction purpose. In the other two models the weights which are used between the hidden layer and the output neuron are obtained by PINN which is also known as Moore-Penrose pseudo inverse algorithm. This proposed KEMD trained by kernel based pseudo inverse algorithm has a very good and precise prediction of wind power. This model has been proved by doing several observations for various seasons which has been demonstrated in the results and simulation section.","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":"121144577","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/AiDAS47888.2019.8970867
Sitti Munirah Abdul Razak, Muhamad Sadry Abu Seman, Wan Ali, Wan Yusoff Wan, Noor Hasrul Nizan, Mohammad Noor
Natural Language Processing (NLP) is a vital field of artificial intelligence that automates the study of human language. However for Malay manuscripts (MM) written in old jawi, its exposure on such field is limited. Besides, most of the studies related to MM studies and NLP were focused on rule based or rule based machine transliteration (RBMT). Hence the objective of this study is to propose a statistical approach for old jawi to modern jawi transliteration of Malay manuscript contents using Phrase Based Statistical Machine Translation (PBSMT) as its model. In order to achieve such purpose, quality score of Word Error Rate (WER) was computed on the transliteration output. Besides, the issues formerly encountered by rule based approach such as vocals limitation and homograph, reduplication, letters error and combination of multiple words were observed in the implementation. Moreover, this paper utilized exploratory approach as its research strategy and mixed method as its research method. The data for the analysis were extracted from a MM titled Bidāyat al-Mubtadī bi-Fālillah al-Muhdī. Quality score of WER was computed for the evaluation of SMT output. Afterwards, related issues were identified and assessed. The research found that quality score of PBSMT for old jawi to modern jawi transliteration was high in terms of WER, however the issues of rule based were generally addressed by PBSMT except homograph. The research is however limited to the approach of SMT that solely focused on PBSMT as its model. Moreover, the corpus size was limited to one manuscript while SMT relies on corpus size. Nevertheless the research contributes to the wider coverage on Malay language as one of the under resource languages in NLP, in form of old and modern jawi. Besides, to the best of the researcher’s knowledge, it is also the first to apply SMT (PBSMT) approach on old jawi transliteration. Most importantly, the study is to contribute on MM’s.
{"title":"Malay Manuscripts Transliteration Using Statistical Machine Translation (SMT)","authors":"Sitti Munirah Abdul Razak, Muhamad Sadry Abu Seman, Wan Ali, Wan Yusoff Wan, Noor Hasrul Nizan, Mohammad Noor","doi":"10.1109/AiDAS47888.2019.8970867","DOIUrl":"https://doi.org/10.1109/AiDAS47888.2019.8970867","url":null,"abstract":"Natural Language Processing (NLP) is a vital field of artificial intelligence that automates the study of human language. However for Malay manuscripts (MM) written in old jawi, its exposure on such field is limited. Besides, most of the studies related to MM studies and NLP were focused on rule based or rule based machine transliteration (RBMT). Hence the objective of this study is to propose a statistical approach for old jawi to modern jawi transliteration of Malay manuscript contents using Phrase Based Statistical Machine Translation (PBSMT) as its model. In order to achieve such purpose, quality score of Word Error Rate (WER) was computed on the transliteration output. Besides, the issues formerly encountered by rule based approach such as vocals limitation and homograph, reduplication, letters error and combination of multiple words were observed in the implementation. Moreover, this paper utilized exploratory approach as its research strategy and mixed method as its research method. The data for the analysis were extracted from a MM titled Bidāyat al-Mubtadī bi-Fālillah al-Muhdī. Quality score of WER was computed for the evaluation of SMT output. Afterwards, related issues were identified and assessed. The research found that quality score of PBSMT for old jawi to modern jawi transliteration was high in terms of WER, however the issues of rule based were generally addressed by PBSMT except homograph. The research is however limited to the approach of SMT that solely focused on PBSMT as its model. Moreover, the corpus size was limited to one manuscript while SMT relies on corpus size. Nevertheless the research contributes to the wider coverage on Malay language as one of the under resource languages in NLP, in form of old and modern jawi. Besides, to the best of the researcher’s knowledge, it is also the first to apply SMT (PBSMT) approach on old jawi transliteration. Most importantly, the study is to contribute on MM’s.","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":"123751503","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/AiDAS47888.2019.8970980
Masurah Mohamad, A. Selamat, K. Salleh
Big data sets are mainly derived from social media as well as stock market exchange. It is commonly described according to its main characteristics the 3Vs, which refers to Volume, Velocity and Variety. Big data sets often contributed to difficulties faced by the back end groups such as data analyst, system developer, programmer, and network analyst due to its complexity issue. To overcome this issue, many researchers and professionals have proposed and initiated various solutions, for instance; algorithm, software, hardware and framework related to big data. One beneficial and popularly known approach in dealing with big data is deep learning. It is an extension of neural network that is able to analyze huge data sets without assistance from any parameterization methods. To make use of this advantage, this paper aimed to evaluate the capability of deep learning in analyzing big data sets. Several data sets were selected and support vector machine (SVM) was chosen as a benchmark method for the experimental work. The results obtained revealed that deep learning has outperformed SVM in classifying big data set. As a conclusion, deep learning can be categorized as one of the best machine learning approaches to be used in decision analysis process. It can also be used as an alternative approach to other traditional approaches such as Naive Bayes or SVM which require more data processing phases.
{"title":"An Analysis on Deep Learning Approach Performance in Classifying Big Data Set","authors":"Masurah Mohamad, A. Selamat, K. Salleh","doi":"10.1109/AiDAS47888.2019.8970980","DOIUrl":"https://doi.org/10.1109/AiDAS47888.2019.8970980","url":null,"abstract":"Big data sets are mainly derived from social media as well as stock market exchange. It is commonly described according to its main characteristics the 3Vs, which refers to Volume, Velocity and Variety. Big data sets often contributed to difficulties faced by the back end groups such as data analyst, system developer, programmer, and network analyst due to its complexity issue. To overcome this issue, many researchers and professionals have proposed and initiated various solutions, for instance; algorithm, software, hardware and framework related to big data. One beneficial and popularly known approach in dealing with big data is deep learning. It is an extension of neural network that is able to analyze huge data sets without assistance from any parameterization methods. To make use of this advantage, this paper aimed to evaluate the capability of deep learning in analyzing big data sets. Several data sets were selected and support vector machine (SVM) was chosen as a benchmark method for the experimental work. The results obtained revealed that deep learning has outperformed SVM in classifying big data set. As a conclusion, deep learning can be categorized as one of the best machine learning approaches to be used in decision analysis process. It can also be used as an alternative approach to other traditional approaches such as Naive Bayes or SVM which require more data processing phases.","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":"129060314","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/AiDAS47888.2019.8970785
Akshay Ratnakar, Prerna Sharma, Shruti Gupta, Dr. Lalit Purohit
Due to ease of development, service oriented applications have replaced traditional web based applications. Web Services have made development easier and secure. But it is always a tough task to select the best service. Thus, web services clustering prior to selection can be useful. Before performing clustering on web services, it is desired to first determine appropriate clustering technique. In this paper, an in-depth analysis of various clustering techniques is performed. Two quality evaluation parameters, internal and stability are used. To conduct various experiments, dataset based on real world web services and dataset generated using standard available dataset generators are used.
{"title":"Web Service Clustering on the Basis of QoS Parameters","authors":"Akshay Ratnakar, Prerna Sharma, Shruti Gupta, Dr. Lalit Purohit","doi":"10.1109/AiDAS47888.2019.8970785","DOIUrl":"https://doi.org/10.1109/AiDAS47888.2019.8970785","url":null,"abstract":"Due to ease of development, service oriented applications have replaced traditional web based applications. Web Services have made development easier and secure. But it is always a tough task to select the best service. Thus, web services clustering prior to selection can be useful. Before performing clustering on web services, it is desired to first determine appropriate clustering technique. In this paper, an in-depth analysis of various clustering techniques is performed. Two quality evaluation parameters, internal and stability are used. To conduct various experiments, dataset based on real world web services and dataset generated using standard available dataset generators are used.","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":"129281984","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/AiDAS47888.2019.8970698
M. Saad, M. Mohsin, H. Hamid, Z. Muda
Recent technology evolution has emerged many applications that consumed data in extremely highly dimensional. For medical images, outsourcing the computation of image feature extraction to the cloud has become common method in order to alleviate the heavy computation workload for local devices. However, unlike other images, the medical images content cannot be easily manipulated because they exist in visual presentation that cannot be explored with textual data in order to capture the visual structure of the image. Hence, appropriate features are required to classify these images. Feature extraction for medical images based on image shape, color and texture using machine learning can improve the performance to categorize image features into homogeneous group. Feature extraction automatically learn and recognize complex patterns and make intelligent decisions based on features attributes. Therefore, this proposed a multi-level feature extraction model for high dimensional medical image features. By applying the multi-level model, features from medical images are extracted from general image features into specific features category. Later, a specified features categories are assigned to the image so that the image presentation can become more meaningful and assist the performance of image classification. We expect the findings derived from our method provides new approaches for extracting medical image features from big data source. It also improve the relevance and quality of image classification, thus enhance performance of medical imaging in the radiology service.
{"title":"Multi-level feature extraction model for high dimensional medical image features","authors":"M. Saad, M. Mohsin, H. Hamid, Z. Muda","doi":"10.1109/AiDAS47888.2019.8970698","DOIUrl":"https://doi.org/10.1109/AiDAS47888.2019.8970698","url":null,"abstract":"Recent technology evolution has emerged many applications that consumed data in extremely highly dimensional. For medical images, outsourcing the computation of image feature extraction to the cloud has become common method in order to alleviate the heavy computation workload for local devices. However, unlike other images, the medical images content cannot be easily manipulated because they exist in visual presentation that cannot be explored with textual data in order to capture the visual structure of the image. Hence, appropriate features are required to classify these images. Feature extraction for medical images based on image shape, color and texture using machine learning can improve the performance to categorize image features into homogeneous group. Feature extraction automatically learn and recognize complex patterns and make intelligent decisions based on features attributes. Therefore, this proposed a multi-level feature extraction model for high dimensional medical image features. By applying the multi-level model, features from medical images are extracted from general image features into specific features category. Later, a specified features categories are assigned to the image so that the image presentation can become more meaningful and assist the performance of image classification. We expect the findings derived from our method provides new approaches for extracting medical image features from big data source. It also improve the relevance and quality of image classification, thus enhance performance of medical imaging in the radiology service.","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":"130591504","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/aidas47888.2019.8970860
{"title":"AiDAS 2019 Organising Committee","authors":"","doi":"10.1109/aidas47888.2019.8970860","DOIUrl":"https://doi.org/10.1109/aidas47888.2019.8970860","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":"126514727","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/AiDAS47888.2019.8970949
Po-Hsuan Hung, Lai, Rayner, Alfred
Public opinion plays an important role in decision making tasks of various fields. Sentiment Analysis is a key task in summarizing sentiment opinions as it classifies opinion documents according to its sentiment group of positive and negative. Machine learning based classification is efficient and versatile. The ensemble concept is used to improve classification accuracy by combining the decision of multiple classifiers. In this work, a framework for sentiment analysis is designed to extend the concept of ensemble upon all subtasks of machine learning classification in order to achieve better analysis. There are 3 subtasks in machine learning based sentiment analysis which are feature extraction, feature selection and classification. The ensemble concept is applied to all 3 tasks by combining different methods to perform the tasks and combine their results. optimization is performed by using Genetic Algorithm to find the combination of methods that could perform better. The proposed framework is tested on 4 different domain datasets and the sentiment analysis accuracy is shown to be very high. Future works includes testing the framework on different domains of classification and different optimization algorithm.
{"title":"An optimized Multi-Layer Ensemble Framework for Sentiment Analysis","authors":"Po-Hsuan Hung, Lai, Rayner, Alfred","doi":"10.1109/AiDAS47888.2019.8970949","DOIUrl":"https://doi.org/10.1109/AiDAS47888.2019.8970949","url":null,"abstract":"Public opinion plays an important role in decision making tasks of various fields. Sentiment Analysis is a key task in summarizing sentiment opinions as it classifies opinion documents according to its sentiment group of positive and negative. Machine learning based classification is efficient and versatile. The ensemble concept is used to improve classification accuracy by combining the decision of multiple classifiers. In this work, a framework for sentiment analysis is designed to extend the concept of ensemble upon all subtasks of machine learning classification in order to achieve better analysis. There are 3 subtasks in machine learning based sentiment analysis which are feature extraction, feature selection and classification. The ensemble concept is applied to all 3 tasks by combining different methods to perform the tasks and combine their results. optimization is performed by using Genetic Algorithm to find the combination of methods that could perform better. The proposed framework is tested on 4 different domain datasets and the sentiment analysis accuracy is shown to be very high. Future works includes testing the framework on different domains of classification and different optimization algorithm.","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":"116962338","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/AiDAS47888.2019.8970725
Z. Zainuddin, Emelia A P Akhir, Norshakirah Aziz
This paper proposed a technique named Recurrent Neural Network-Gated Recurrent Unit (RNN-GRU) to predict the condition of machines by using time series data generated by oil and gas company. The problem raised due to limited research of RNN-GRU in improving the accuracy through hyperparameter tuning. Hence, this paper will provide an optimization method that can improve the accuracy of RNN-GRU in forecasting time series data. The preliminary findings of the experiment conducted shows that RNN-GRU can utilize time series data to predict machine failure with improved high accuracy.
{"title":"Predictive Analytics For Machine Failure Using optimized Recurrent Neural Network-Gated Recurrent Unit (GRU)","authors":"Z. Zainuddin, Emelia A P Akhir, Norshakirah Aziz","doi":"10.1109/AiDAS47888.2019.8970725","DOIUrl":"https://doi.org/10.1109/AiDAS47888.2019.8970725","url":null,"abstract":"This paper proposed a technique named Recurrent Neural Network-Gated Recurrent Unit (RNN-GRU) to predict the condition of machines by using time series data generated by oil and gas company. The problem raised due to limited research of RNN-GRU in improving the accuracy through hyperparameter tuning. Hence, this paper will provide an optimization method that can improve the accuracy of RNN-GRU in forecasting time series data. The preliminary findings of the experiment conducted shows that RNN-GRU can utilize time series data to predict machine failure with improved high accuracy.","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":"128622743","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/AiDAS47888.2019.8971002
Muhammad Yusry Bin Ishak, Samsiah Ahmad, Zalikha Zulkifli
In the last few decades, Internet of things (IOT) is one of the key elements in industrial revolution 4.0 that used mart phones as one of the best technological advances’ intelligent device. It allows us to have power over devices without people intervention, either remote or voice control. Therefore, the “Smart Radar Door “system uses a microcontroller and mobile Bluetooth module as an automation of smart door lock system. It is describing the improvement of a security system integrated with an Android mobile phone that uses Bluetooth as a wireless connection protocol and processing software as a tool in order to detect any object near to the door. The mob ile device is required a password as authentication method by using microcontroller to control lock and unlock door remotely. The Bluetooth protocol was chosen as a method of communication between microcontroller and mobile devices which integrated with many Android devices in secured protocol.
{"title":"Iot Based Bluetooth Smart Radar Door System Via Mobile Apps","authors":"Muhammad Yusry Bin Ishak, Samsiah Ahmad, Zalikha Zulkifli","doi":"10.1109/AiDAS47888.2019.8971002","DOIUrl":"https://doi.org/10.1109/AiDAS47888.2019.8971002","url":null,"abstract":"In the last few decades, Internet of things (IOT) is one of the key elements in industrial revolution 4.0 that used mart phones as one of the best technological advances’ intelligent device. It allows us to have power over devices without people intervention, either remote or voice control. Therefore, the “Smart Radar Door “system uses a microcontroller and mobile Bluetooth module as an automation of smart door lock system. It is describing the improvement of a security system integrated with an Android mobile phone that uses Bluetooth as a wireless connection protocol and processing software as a tool in order to detect any object near to the door. The mob ile device is required a password as authentication method by using microcontroller to control lock and unlock door remotely. The Bluetooth protocol was chosen as a method of communication between microcontroller and mobile devices which integrated with many Android devices in secured protocol.","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":"129344201","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/aidas47888.2019.8970863
{"title":"AiDAS 2019 Author Index","authors":"","doi":"10.1109/aidas47888.2019.8970863","DOIUrl":"https://doi.org/10.1109/aidas47888.2019.8970863","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":"130175709","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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