scholarly journals Internet Connection Control based on Idle Time Using User Behavior Pattern Analysis

2014 ◽  
Vol 2 (2) ◽  
Author(s):  
Fadilah Fahrul Hardiansyah ◽  
Joko Lianto Buliali ◽  
Waskitho Wibisono
Keyword(s):  
Power Consumption ◽  
Pattern Analysis ◽  
User Behavior ◽  
Behavior Pattern ◽  
Power Level ◽  
Idle Time ◽  
The Internet ◽  
Time Interval ◽  
Time Duration ◽  
Internet Connection

The increase of smartphone ability is rapidly increasing the power consumption. Many methods have been proposed to reduce smartphone power consumption. Most of these methods use the internet connection control based on the availability of the battery power level regardless of when and where a waste of energy occurs. This paper proposes a new approach to control the internet connection based on idle time using user behavior pattern analysis. User behavior patterns are used to predict idle time duration. Internet connection control performed during idle time. During idle time internet connection periodically switched on and off by a certain time interval. This method effectively reduces a waste of energy. Control of the internet connection does not interfere the user because it is implemented on idle time.Keywords: Smartphone, User Behavior, Pattern Recognition, Idle Time, Internet Connection Control

scholarly journals Sistem Pemantau Gas di Tempat Pembuangan Sampah Akhir Berbasis Internet of Things

2018 ◽  
Vol 6 (3) ◽  
pp. 100-105
Author(s):  
Fathur Zaini Rachman
Keyword(s):  
Carbon Dioxide ◽  
Social Media ◽  
Internet Of Things ◽  
Environmental Data ◽  
The Internet ◽  
Time Interval ◽  
Data Delivery ◽  
Internet Connection ◽  
Carbon Dioxide Co2 ◽  
The Internet Of Things

This research developed a gas monitoring system in the final waste disposal. The system has implemented the Internet of Things (IoT) using the ESP8266 Wi-Fi module to transmit methane (CH4) and carbon dioxide (CO2) data concentration, as well as temperature and humidity to the ThingSpeak server. Users can monitor and access these environmental data through social media Twitter and websites from anywhere. The fastest data delivery can be obtained with a time interval of 16 seconds on each data packet sent when there is an Internet connection.

scholarly journals Personalized project recommendations: using reinforcement learning

2019 ◽  
Vol 2019 (1) ◽  
Author(s):  
Faxin Qi ◽  
Xiangrong Tong ◽  
Lei Yu ◽  
Yingjie Wang
Keyword(s):  
Reinforcement Learning ◽  
User Behavior ◽  
Collaborative Work ◽  
Recursive Least Squares ◽  
The Internet ◽  
Dynamic Impact ◽  
Rls Algorithm ◽  
Trust Value ◽  
Q Learning ◽  
Actual Evaluation

AbstractWith the development of the Internet and the progress of human-centered computing (HCC), the mode of man-machine collaborative work has become more and more popular. Valuable information in the Internet, such as user behavior and social labels, is often provided by users. A recommendation based on trust is an important human-computer interaction recommendation application in a social network. However, previous studies generally assume that the trust value between users is static, unable to respond to the dynamic changes of user trust and preferences in a timely manner. In fact, after receiving the recommendation, there is a difference between actual evaluation and expected evaluation which is correlated with trust value. Based on the dynamics of trust and the changing process of trust between users, this paper proposes a trust boost method through reinforcement learning. Recursive least squares (RLS) algorithm is used to learn the dynamic impact of evaluation difference on user’s trust. In addition, a reinforcement learning method Deep Q-Learning (DQN) is studied to simulate the process of learning user’s preferences and boosting trust value. Experiments indicate that our method applied to recommendation systems could respond to the changes quickly on user’s preferences. Compared with other methods, our method has better accuracy on recommendation.

P1-559: TOWARD PERCEPTUAL DEEP LEARNING-BASED ABNORMAL BEHAVIOR PATTERN ANALYSIS FOR PATIENTS WITH ALZHEIMER'S DISEASE

2006 ◽  
Vol 14 (7S_Part_10) ◽  
pp. P548-P548
Author(s):  
Jeonghwan Gwak ◽  
Jong-In Song ◽  
Kiseon Kim ◽  
Moongu Jeon ◽  
Cheolbin Park ◽  
...  
Keyword(s):  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Deep Learning ◽  
Pattern Analysis ◽  
Behavior Pattern ◽  
Abnormal Behavior

scholarly journals Coupling Disease-Progress-Curve and Time-of-Infection Functions for Predicting Yield Loss of Crops

2000 ◽  
Vol 90 (8) ◽  
pp. 788-800 ◽  
Author(s):  
L. V. Madden ◽  
G. Hughes ◽  
M. E. Irwin
Keyword(s):  
Yield Loss ◽  
Numerical Solutions ◽  
Disease Incidence ◽  
Time Interval ◽  
Short Time Interval ◽  
Time Duration ◽  
Plant Yield ◽  
Disease Progress ◽  
Time Of Infection ◽  
Over Time

A general approach was developed to predict the yield loss of crops in relation to infection by systemic diseases. The approach was based on two premises: (i) disease incidence in a population of plants over time can be described by a nonlinear disease progress model, such as the logistic or monomolecular; and (ii) yield of a plant is a function of time of infection (t) that can be represented by the (negative) exponential or similar model (ζ(t)). Yield loss of a population of plants on a proportional scale (L) can be written as the product of the proportion of the plant population newly infected during a very short time interval (X′(t)dt) and ζ(t), integrated over the time duration of the epidemic. L in the model can be expressed in relation to directly interpretable parameters: maximum per-plant yield loss (α, typically occurring at t = 0); the decline in per-plant loss as time of infection is delayed (γ; units of time-1); and the parameters that characterize disease progress over time, namely, initial disease incidence (X0), rate of disease increase (r; units of time-1), and maximum (or asymptotic) value of disease incidence (K). Based on the model formulation, L ranges from αX0 to αK and increases with increasing X0, r, K, α, and γ-1. The exact effects of these parameters on L were determined with numerical solutions of the model. The model was expanded to predict L when there was spatial heterogeneity in disease incidence among sites within a field and when maximum per-plant yield loss occurred at a time other than the beginning of the epidemic (t > 0). However, the latter two situations had a major impact on L only at high values of r. The modeling approach was demonstrated by analyzing data on soybean yield loss in relation to infection by Soybean mosaic virus, a member of the genus Potyvirus. Based on model solutions, strategies to reduce or minimize yield losses from a given disease can be evaluated.

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