From Artificial Intelligence to Dependability: Modeling and Analysis with Bayesian Networks (original) (raw)

Reliability in preprocessing-Bayes rules SIESTA

Medical and Biological Engineering and Computing, Supplement 2, Proceedings of EMBEC’99, 1999

Abstract: The S ESTª pro® ect aims at de¹ning a new description o¿ human sleep. ªs suchÄ the S ESTª sleep analyÆer is a diagnostic tool applied to biosignals o¿ humans. ªlthough the risk that wrong decisions harm people is lowÄ it is still there. Ëence introducing a reliability measure that Îags decisions that are probably wrong is a ma® or aim o¿ the pro® ect. This paper introduces a method¿ or preprocessing within the Ñayesian¿ ramework. Òe show that Ñayesian belie¿ s can be used to Îag segments where reliable decisions about ...

Pengembangan Buku Ajar Analisis Laporan Keuangan Berbasis Problem Based Learning

JURNAL EKONOMI PENDIDIKAN DAN KEWIRAUSAHAAN

Permasalahan yang dirumuskan adalah bagaimanakah validitas, kepratisan, dan efektivitas buku ajar analisis laporan keungan berbasis problem based learning (berbasis masalah) . Adapun yang menjadi tujuan penelitian ini yaitu untuk mendeskripsikan tingkat validitas, kepraktisan, dan efektivitas buku ajar analisis laporan keungan berbasis masalah. Jenis penelitian ini yaitu penelitian Research & Development (R&D) oleh Borg & Gall. Populasi dalam penelitian ini seluruh mahasiswa pendidikan ekonomi semester genap tahun akademik 2017-2018. Dengan sampel penelitian yaitu mahasiswa yang mengikuti mata kuliah analisis laporan keuangan kelas Indralaya yang berjumlah 38 mahasiswa. Teknik pengumpulan data yang digunakan yaitu angket. Dengan uji kevalidan diperoleh 3,5 dari ahli materi terkategori snagat valid dan 2,89 dari ahli media yang terkategori valid. Kemudian dari uji kepaktisan diperoleh hasil yang menyatakan bahwa bahan ajar ini praktis digunakan untuk memahami materi analisis lapora...

Sample Complexity for Function Learning Tasks Through Linear Neural Networks

International Journal on Artificial Intelligence Tools, 2002

We find new sample complexity bounds for real function learning tasks in the uniform distribution by means of linear neural networks. These bounds, tighter than the distribution-free ones reported elsewhere in the literature, are applicable to simple functional link networks and radial basis neural networks.

A Design Method of Scalable Fuzzy Rule-Based Systems for Solving Regression Problems

2021

Received: 27/7/2021 This paper proposes an approach for handling linguistic words directly to develop an evolutionary method for designing fuzzy rulebased systems interpretable in Tarski et al.’s sense and scalable to solve dataset regression problems. This interpretability requires that the constructed fuzzy multi-granularity structures representing the currently used word sets of dataset’s attributes must be the isomorphic images of their respective semantic word sets’ structures. Furthermore, in practice, human domain knowledge are accumulated and grown over time, leading to the requrements of expanding the currently used word sets to solve their encountered problems more effectively. It suggests studying behaviors of fuzzy rule-based systems when allowing the currently used word sets of dataset’s attributes to grow while requiring the already constructed fuzzy sets based semantics of the existing linguistic words are reused. Experiments were conducted with 15 regression datasets...

Applying statistical analysis for assessing the reliability of input data to improve the quality of short-term load forecasting for a Ho Chi Minh City distribution network

Science & Technology Development Journal - Engineering and Technology, 2020

Short-term load forecasting has an extremely important role in the design, operation and planning of power system, especially on a power grid of Ho Chi Minh City (HCMC) - an active city has the highest power demand in Vietnam. Through the data survey, the load power in the HCMC area changes suddenly so that it causes disturbances in the load data. Accordingly, the reliability assessment of the load data will be essential in the processing stage of data-filtering before implementing load forecasting models. This study introduces a novel statistical data-filtering method that takes into account the reliability of the input-data source by analyzing many different confidence levels. Results of the proposed data-filtering method will be compared to previous data -iltering methods (such as Kalman, DBSCAN, Wavelet Transform and SSA filtering methods). The data source used in this study was collected from more than 50 substations uisng the SCADA system in Ho Chi Minh City's distribution...