Using a recurrent artificial neural network for dynamic self-adaptation of cluster-based web-server systems

2017 ◽  
Vol 48 (8) ◽  
pp. 2097-2111 ◽  
Author(s):  
Sanaz Sheikhi ◽  
Seyed Morteza Babamir
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Web Server ◽  
Server Systems ◽  
Self Adaptation ◽  
Artificial Neural

scholarly journals Pengembangan Smart Home System Berbasis Kecerdasan Buatan untuk Memanajemen Konsumsi Energi Rumah Tangga dengan Pendekatan Finansial

2021 ◽  
Vol 4 (1) ◽  
pp. 1-10
Author(s):  
Ihsan Auditia Akhinov ◽  
Muhammad Ridwan Arif Cahyono
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Internet Of Things ◽  
Smart Home ◽  
Web Server ◽  
Black Box ◽  
Raspberry Pi ◽  
Artificial Neural ◽  
Online Web

Teknologi rumah pintar yang dikembangkan saat ini belum sepenuhnya mampu mendukung program konservasi energi yang dicanangkan pemerintah. Selain Saat ini kontrol untuk pengaturan rumah pintar masih dilakukan secara manual, belum sepenuhnya otomatis. Pada penelitian ini akan dibangun sistem rumah pintar yang dikendalikan oleh kecerdasan buatan untuk mengendalikan pemakaian energi berdasarkan besaran nilai tagihan bulanan. ESP32 digunakan sebagai perangkat Internet of Things (IoT) yang berfungsi mendeteksi keberadaan manusia dan mengukur energi listrik yang dikonsumsi. Data-data tersebut disimpan dalam online web server yang dibangun dari Raspberry Pi. Sistem ini dapat dimonitor dan dikendalikan oleh aplikasi berbasis Web. Aplikasi ini sudah diuji dengan menggunakan metode Black Box, hasilnya 100% aplikasi berjalan lancar. Artificial Neural Network diimplementasikan menggunakan bahasa Python, dengan 4 input, 2 layer, dan 4 output dimana masing-masing layer terdiri dari 4 neuron. Variabel masukan yang digunakan dalam ANN yaitu intensitas cahaya, temperatur ruangan, durasi waktu penggunaan ruangan, dan target biaya bulanan, sedangkan keluaran dari ANN ini yaitu durasi penggunaan peralatan listrik, dalam purwarupa ini yaitu durasi penggunaan AC, TV, refrigerator, dan lampu. Sistem sudah mampu berjalan dengan baik, mampu memberikan rekomendasi durasi maksimal penggunaan peralatan listrik dengan tingkat kesalahan sebesar 1,64%.

Autofluorescence spectroscopy of oral leukoplakia using an artificial neural network

2000 ◽  
Vol 25 (4) ◽  
pp. 325-325
Author(s):  
J.L.N. Roodenburg ◽  
H.J. Van Staveren ◽  
N.L.P. Van Veen ◽  
O.C. Speelman ◽  
J.M. Nauta ◽  
...  
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Oral Leukoplakia ◽  
Autofluorescence Spectroscopy ◽  
Artificial Neural

1902: Is Steinstrasse After ESWL of Renal Stones Predictable? Artificial Neural Network Analysis

2004 ◽  
Vol 171 (4S) ◽  
pp. 502-503
Author(s):  
Mohamed A. Gomha ◽  
Khaled Z. Sheir ◽  
Saeed Showky ◽  
Khaled Madbouly ◽  
Emad Elsobky ◽  
...  
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Network Analysis ◽  
Renal Stones ◽  
Neural Network Analysis ◽  
Artificial Neural Network Analysis ◽  
Artificial Neural

Forecasting travel demand: a comparison of logit and artificial neural network methods

1998 ◽  
Vol 49 (7) ◽  
pp. 717-722 ◽  
Author(s):  
M C M de Carvalho ◽  
M S Dougherty ◽  
A S Fowkes ◽  
M R Wardman
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Travel Demand ◽  
Network Methods ◽  
Artificial Neural

Framework for rare event detection using Artificial Neural Network based context free grammar

2020 ◽  
Vol 39 (6) ◽  
pp. 8463-8475
Author(s):  
Palanivel Srinivasan ◽  
Manivannan Doraipandian
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Event Detection ◽  
Performance Metrics ◽  
Rare Events ◽  
Rare Event ◽  
Video Stream ◽  
Context Free Grammar ◽  
Artificial Neural ◽  
Context Free

Rare event detections are performed using spatial domain and frequency domain-based procedures. Omnipresent surveillance camera footages are increasing exponentially due course the time. Monitoring all the events manually is an insignificant and more time-consuming process. Therefore, an automated rare event detection contrivance is required to make this process manageable. In this work, a Context-Free Grammar (CFG) is developed for detecting rare events from a video stream and Artificial Neural Network (ANN) is used to train CFG. A set of dedicated algorithms are used to perform frame split process, edge detection, background subtraction and convert the processed data into CFG. The developed CFG is converted into nodes and edges to form a graph. The graph is given to the input layer of an ANN to classify normal and rare event classes. Graph derived from CFG using input video stream is used to train ANN Further the performance of developed Artificial Neural Network Based Context-Free Grammar – Rare Event Detection (ACFG-RED) is compared with other existing techniques and performance metrics such as accuracy, precision, sensitivity, recall, average processing time and average processing power are used for performance estimation and analyzed. Better performance metrics values have been observed for the ANN-CFG model compared with other techniques. The developed model will provide a better solution in detecting rare events using video streams.

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