A quantization of deep belief networks for long short-term memory in sleep stage detection

Yulita, Intan Nurma; Purwani, Sri; Rosadi, Rudi; Awangga, Rolly Maulana

doi:10.1109/icaicta.2017.8090999

Cited by 15 publications

(13 citation statements)

References 13 publications

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“…There is a node which is directly connected with the highest degree centrality value and connected with the highest points on the Betweenness Centrality.Closeness Centrality is the average distance (shortest path) between vertices connection. Closeness Centrality formula describes in Equation 3. CC (v) is Closeness centrality for vertex v. dG(v, t) is the distance between the vertex v to t.…”

Section: Methodsmentioning

confidence: 99%

“…Especially multiple sites for communicating between designs in remote team member [1] to avoid miss-configuration of software and mediating mechanism for accessing heterogeneous data and information sources [2]. If machine learning and software framework can solve healthy problem [3] [4]. Data science can solve warehouse [5] and knowledge management problem [6].…”

Section: Introductionmentioning

confidence: 99%

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Ontology Design of Influential People Identification Using Centrality

Awangga

Yusril

Setyawan

2018

J. Phys.: Conf. Ser.

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Abstract. Identifying influential people as a node in a graph theory commonly calculated by social network analysis. The social network data has the user as node and edge as relation forming a friend relation graph. This research is conducting different meaning of every nodes relation in the social network. Ontology was perfect match science to describe the social network data as conceptual and domain. Ontology gives essential relationship in a social network more than a current graph. Ontology proposed as a standard for knowledge representation for the semantic web by World Wide Web Consortium. The formal data representation use Resource Description Framework (RDF) and Web Ontology Language (OWL) which is strategic for Open Knowledge-Based website data. Ontology used in the semantic description for a relationship in the social network, it is open to developing semantic based relationship ontology by adding and modifying various and different relationship to have influential people as a conclusion. This research proposes a model using OWL and RDF for influential people identification in the social network. The study use degree centrality, betweenness centrality, and closeness centrality measurement for data validation. As a conclusion, influential people identification in Facebook can use proposed Ontology model in the Group, Photos, Photo Tag, Friends, Events and Works data.

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Section: Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Ontology Design of Influential People Identification Using Centrality

Awangga

Yusril

Setyawan

2018

J. Phys.: Conf. Ser.

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“…The learning process is based on processing data previously owned. This method has been successful in various aspects of life such as sleep disorders [10], education [11], protein grouping [12], transportation [13], and speech processing [14]. One method in machine learning is neural networks.…”

Section: Introductionmentioning

confidence: 99%

Neural Networks Classification for Breast Cancer Analysis

Putri¹,

Yulita²

2020

Proceedings of the Proceedings of the 7th Mathematics, Science, and Computer Science Education International Seminar, MSCEIS 20

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Cancer is caused by a lump from a collection of cells that grows and attacks the surrounding tissue. Most breast lumps are benign, but benign breast lumps can increase the risk of developing breast cancer. Early detection can provide better handling. The diagnosis of breast cancer by a doctor is done by analyzing several factors. To help the doctor in diagnosing the data efficiently, this study implemented machine learning. The diagnosis was based on the features of digital image computation in the process of fine-needle aspiration (FNA) of a breast mass. The data came from 569 patients. The study used the neural networks classification method with multilayer perceptron algorithms. The results were obtained that the use of neural networks gave higher accuracy if it compared it in the ZeroR method. Their accuracies were 95.96%, and 62.74%, respectively.

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“…It is a process for finding useful knowledge or information from large-scale data [9]. Methods in data mining have been widely implemented for a number of cases such as classification [10], association rules [11], clustering [12], and forecasting [13]. Therefore, this study adopts data mining for the early detection of breast cancer.…”

Section: Introductionmentioning

confidence: 99%

Breast Cancer Classification: Comparison of Bayesian Networks, Multilayer Perceptron, and Boosting Method

Yulita¹,

Nadhiroh²

2020

Proceedings of the Proceedings of the 7th Mathematics, Science, and Computer Science Education International Seminar, MSCEIS 20

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Cancer is a dangerous disease that should not be underestimated. The early stages of this disease are often asymptomatic. Early detection of cancer is an important examination so that the disease does not develop into a serious and dangerous disease. This study detected the presence of cancer through five predictors. This study classified the diagnosis results based on five indicators namely radius, texture, perimeter, area, and smoothness. By using these five indicators, the detection was carried out through a classification mechanism using the boosting method. The result had obtained an accuracy of 93.67%. The accuracy was higher than other classification methods such as Bayesian Networks and multilayer Perceptron. Both of them only obtained an accuracy of 89.63%, and 92.79%, respectively. It showed that the ensemble method mechanism of boosting had proven to be more effective in classifying the presence or absence of breast cancer.

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A quantization of deep belief networks for long short-term memory in sleep stage detection

Cited by 15 publications

References 13 publications

Ontology Design of Influential People Identification Using Centrality

Ontology Design of Influential People Identification Using Centrality

Neural Networks Classification for Breast Cancer Analysis

Breast Cancer Classification: Comparison of Bayesian Networks, Multilayer Perceptron, and Boosting Method

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