Titlebook: Soft Computing in Data Science; 5th International Co Michael W. Berry,Bee Wah Yap,Mario Köppen Conference proceedings 2019 Springer Nature

inspiration

A Hybrid TSR and LSTM for Forecasting NO2 and SO2 in Surabayaity, Indonesia. TSR is one of the linear statistical methods to capture deterministic patterns, both are trend and seasonal, whereas LSTM is a neural network method that has a memory block in its hidden layer to handle the nonlinear pattern. Data about half-hourly NO. and SO. at three SUF stations i

BALE

Evaluation of Pooling Layers in Convolutional Neural Network for Script Recognitionges. A common practice of CNN for object recognition is to position a convolve layer alternately with a pooling layer followed by a few layers of fully connected layers. We re-evaluate this basic principle by examining the position of pooling layer after every convolve layer, reducing and increasing

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Predictive Model of Graduate-On-Time Using Machine Learning Algorithmsaduate-on-time or GOT students achievement will indirectly reduce the university’s annual operation cost per student. Not as trivial as it seems, to ensure most of the students able to GOT is challenging. It may vary in the perspective of university practises, academic programmes, and students’ back

Vsd168

New Hybrid Statistical Method and Machine Learning for PM10 Predictionetwork (FFNN) or Long Short-Term Memory (LSTM) as machine learning for PM. prediction at three SUF stations in Surabaya City, Indonesia. TSR as an individual linear model is used to capture trend and seasonal pattern. Whereas, FFNN or LSTM is employed to handle nonlinear pattern. Thus, this research

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NATTY

Multilevel Logistic Regression and Neural Network-Genetic Algorithm for Modeling Internet Accesss are widely used in the supervised learning and competing to be the best methods in many classifications research. This paper aims to study the performance of both methods using data of youth internet access of East Java Province of Indonesia. The first method used is Multilevel Logistic Regression

gustation

A Case Study on Student Attrition Prediction in Higher Education Using Data Mining Techniqueshin the stipulated period. Student attrition problem relates to the resource’s usage in which dropout students still use the same resources as graduated students though they do not yield any outcomes. Hence, HEI efforts to curb the percentage of student attrition numbers would have positive impact o

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Machine Learning Assisted Medical Diagnosis for Segmentation of Follicle in Ovary Ultrasoundbase on the similarity of follicle shape feature so that it is more accurate in calculating the number and diameter of follicles. The performance of this method is tested on a dataset of ovarian ultrasound images of patients at Sardjito Hospital, Yogyakarta using Probabilistic Rand Index (PRI) and Global Consistency Error (GCE).

启发

Multilevel Logistic Regression and Neural Network-Genetic Algorithm for Modeling Internet Access number of hidden nodes, and the optimal connection weights. The result shows that Multilevel Logistic Regression produced a slightly better accuracy rate of 0.873 compared to Genetic Neural Network Algorithm with an accuracy rate of 0.871.

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Evaluation of Pooling Layers in Convolutional Neural Network for Script Recognition its numbers. Experimental results on MLe2e dataset for script recognition show that a CNN with less number of pooling layers and non-overlapping pooling stride can reach excellent percentage of accuracy compared to alternating convolve layer with pooling layer.