scholarly journals A Novel Location Prediction Algorithm of Mobile Users For Cellular Networks

2015 ◽  
pp. 58
Author(s):  
Giang Minh Duc ◽  
Le Manh ◽  
Do Hong Tuan
Keyword(s):  
Execution Time ◽  
Mobile Network ◽  
Mobile User ◽  
Location Based Services ◽  
Prediction Algorithm ◽  
Second Phase ◽  
Location Prediction ◽  
Mobility Patterns ◽  
Two Phase ◽  
Computing Systems

Predicting the location of a mobile user is one of  the  important  issues  in  mobile  computing  systems. Applications of the location prediction include adjusting the bandwidth of the mobile network, the location based services  (LSB),  smart  handover,  etc.  However,  the applications  require  the  execution  time  of  the  User Mobility  Patterns  Mining  (UMPMining)  algorithm  be instantaneous.  In  this  paper,  we  propose  a  new algorithm named Find_UMP for mining next location of a  mobile  user.  Our  algorithm  includes  two  phase  as follows.  In  the  first  phase  (Find_UMP_1),  we  make  to reduce the complexity of the UMPMining algorithm. In the second phase (Find_UMP_2), we perform to reduce the  number  of  transactions  of  the  paths  database. Results  of  our  experiments  show  that  our  proposed algorithm  outperforms  the  UMPMining  algorithm  in terms of the execution time.

scholarly journals Bayesian Learning Neural Network Techniques to Forecast Mobile Phone User Location

2019 ◽  
Vol 8 (9) ◽  
pp. 1233-1237
Keyword(s):  
Neural Network ◽  
Mobile Phone ◽  
Bayesian Learning ◽  
Mobile Network ◽  
Prediction Algorithm ◽  
Location Prediction ◽  
Bayesian Neural Network ◽  
Network Location ◽  
Network Methods ◽  
Reality Mining

In the computerized period Location Based Service is a significant pretended in computing frameworks. Aside from the present area, knowing the area of the person's next spot ahead of time that can likewise empower numerous cell phone applications and its overhaul [3].Mobile network location prediction is by and large widely analyzed for use with regards to mobile network location and wireless network communication concerning more effectual mobile network location source administration patterns. Mobile network location extrapolation consents the mobile network and amenities to auxiliary heighten the excellence of provision stages for the mobile phone users. In the present-day a mobile network location prediction algorithm is used feats mobile phone users practises. In this studies the prediction of the location is carried out and the individual’s location are stored and encounters. We introduce an innovative crossbreed Bayesian neural network prototypical for foretelling mobile network locations. We scrutinize diverse analogous execution practises on cell phones of the projected loom and contrast with numerous typical neural network system procedures. In this investigation the outcomes of the projected Bayesian Neural Network through some typical neural network methods in foretelling together subsequent mobile network location and subsequent facility to demand. The Neural Networks of Bayesian learning foresees together mobile Network location and also enhanced provision than typical neural network methods meanwhile this one routines fine originated probability structure to signify vagueness around the associations are erudite. The consequence of training Bayesian learning is a subsequent dissemination through network weights. In this research MCMC method is used to trial N assessments commencing the later weights dissemination [1]. Using reality mining dataset, we exhibit that the proposed methodology can understand the smooth redesign of the expectation execution and perform dynamically [3]. The Simulations algorithms are achieved by means of an Accurate Movement Patterns and confirmation improved forecast accurateness.

scholarly journals Location-Dependent Query Processing: Semantic Cache for Real-Time Smart City Analytics

2021 ◽  
Vol 2021 ◽  
pp. 1-9
Author(s):  
Rabia Hasan ◽  
Waseem Shehzad ◽  
Ejaz Ahmed ◽  
Hasan Ali Khattak ◽  
Ahmed S. AlGhamdi ◽  
...  
Keyword(s):  
Query Processing ◽  
Research Work ◽  
Processing System ◽  
Mobile User ◽  
Location Based Services ◽  
Prediction Algorithm ◽  
User Query ◽  
Novel Approach ◽  
Deep Integration ◽  
Location Dependent Query

With the advent of wireless sensor networks and their deep integration with the world have enabled users worldwide to achieve benefits from location-based services through mobile applications, the problems such as low bandwidth, high network traffic, and disconnections issues are normally extracted from mobile services. An efficient database system is required to manage mentioned problems. Our research work finds the probability of user’s next locations. A mobile user (query issuer) changes its position when performing a specific mobile search, where these queries change and repeat the search with the issuer position. Moreover, the query issuer can be static and may perform searches with varying conditions of queries. Data is exchanged with mobile devices and questions that are formulated during searching for query issuer locations. An aim of the research work is achieved through effectively processing of queries in terms of location-dependent, originated by mobile users. Significant studies have been performed in this field in the last two decades. In this paper, our novel approach comprise of usage of semantic caches with the Bayesian networks using a prediction algorithm. Our approach is unique and distinct from the traditional query processing system especially in mobile domain for the prediction of future locations of users. Consequently, a better search is analyzed using the response time of data fetch from the cache.

A Comparison of Tem and Apfim to the Interpretation of Modulated Microstructures

1985 ◽  
Vol 43 ◽  
pp. 70-71
Author(s):  
M.G. Burke ◽  
M.K. Miller
Keyword(s):  
Low Temperature ◽  
Microstructural Characterization ◽  
Superlattice Reflection ◽  
High Volume ◽  
Atom Probe ◽  
Dark Field ◽  
Miscibility Gap ◽  
Second Phase ◽  
Two Phase ◽  
Probe Field

Interpretation of fine-scale microstructures containing high volume fractions of second phase is complex. In particular, microstructures developed through decomposition within low temperature miscibility gaps may be extremely fine. This paper compares the morphological interpretations of such complex microstructures by the high-resolution techniques of TEM and atom probe field-ion microscopy (APFIM).The Fe-25 at% Be alloy selected for this study was aged within the low temperature miscibility gap to form a <100> aligned two-phase microstructure. This triaxially modulated microstructure is composed of an Fe-rich ferrite phase and a B2-ordered Be-enriched phase. The microstructural characterization through conventional bright-field TEM is inadequate because of the many contributions to image contrast. The ordering reaction which accompanies spinodal decomposition in this alloy permits simplification of the image by the use of the centered dark field technique to image just one phase. A CDF image formed with a B2 superlattice reflection is shown in fig. 1. In this CDF micrograph, the the B2-ordered Be-enriched phase appears as bright regions in the darkly-imaging ferrite. By examining the specimen in a [001] orientation, the <100> nature of the modulations is evident.

THE DYNAMIC FRACTURE OF TWO PHASE ALLOYS WITH SECOND PHASE INCLUSIONS

1985 ◽  
Vol 46 (C5) ◽  
pp. C5-251-C5-255
Author(s):  
S. Pytel ◽  
L. Wojnar
Keyword(s):  
Dynamic Fracture ◽  
Second Phase ◽  
Two Phase

Behaviour of air injection nozzles in dissolved air flotation

1995 ◽  
Vol 31 (3-4) ◽  
pp. 25-35 ◽  
Author(s):  
E. M. Rykaart ◽  
J. Haarhoff
Keyword(s):  
Bubble Coalescence ◽  
Second Phase ◽  
Initial Growth ◽  
Two Phase ◽  
Dissolved Air Flotation ◽  
Nozzle Orifice ◽  
Pressure Release ◽  
Air Volume ◽  
Air Flotation ◽  
Dissolved Air

A simple two-phase conceptual model is postulated to explain the initial growth of microbubbles after pressure release in dissolved air flotation. During the first phase bubbles merely expand from existing nucleation centres as air precipitates from solution, without bubble coalescence. This phase ends when all excess air is transferred to the gas phase. During the second phase, the total air volume remains the same, but bubbles continue to grow due to bubble coalescence. This model is used to explain the results from experiments where three different nozzle variations were tested, namely a nozzle with an impinging surface immediately outside the nozzle orifice, a nozzle with a bend in the nozzle channel, and a nozzle with a tapering outlet immediately outside the nozzle orifice. From these experiments, it is inferred that the first phase of bubble growth is completed at approximately 1.7 ms after the start of pressure release.

scholarly journals A novel two-phase cycle algorithm for effective cyber intrusion detection in edge computing

2021 ◽  
Vol 2021 (1) ◽  
Author(s):  
Yiguang Gong ◽  
Yunping Liu ◽  
Chuanyang Yin
Keyword(s):  
Intrusion Detection ◽  
Optimal Parameter ◽  
Edge Computing ◽  
Cloud Services ◽  
Second Phase ◽  
Pareto Optimal ◽  
Two Phase ◽  
Multi Objective ◽  
Phase Cycle ◽  
Positive Rate

AbstractEdge computing extends traditional cloud services to the edge of the network, closer to users, and is suitable for network services with low latency requirements. With the rise of edge computing, its security issues have also received increasing attention. In this paper, a novel two-phase cycle algorithm is proposed for effective cyber intrusion detection in edge computing based on a multi-objective genetic algorithm (MOGA) and modified back-propagation neural network (MBPNN), namely TPC-MOGA-MBPNN. In the first phase, the MOGA is employed to build a multi-objective optimization model that tries to find the Pareto optimal parameter set for MBPNN. The Pareto optimal parameter set is applied for simultaneous minimization of the average false positive rate (Avg FPR), mean squared error (MSE) and negative average true positive rate (Avg TPR) in the dataset. In the second phase, some MBPNNs are created based on the parameter set obtained by MOGA and are trained to search for a more optimal parameter set locally. The parameter set obtained in the second phase is used as the input of the first phase, and the training process is repeated until the termination criteria are reached. A benchmark dataset, KDD cup 1999, is used to demonstrate and validate the performance of the proposed approach for intrusion detection. The proposed approach can discover a pool of MBPNN-based solutions. Combining these MBPNN solutions can significantly improve detection performance, and a GA is used to find the optimal MBPNN combination. The results show that the proposed approach achieves an accuracy of 98.81% and a detection rate of 98.23% and outperform most systems of previous works found in the literature. In addition, the proposed approach is a generalized classification approach that is applicable to the problem of any field having multiple conflicting objectives.

scholarly journals Three-Dimensional Analysis of the In Vivo Motion of Implantable Cardioverter Defibrillator Leads

2021 ◽  
Author(s):  
Tamas Szili-Torok ◽  
Jens Rump ◽  
Torsten Luther ◽  
Sing-Chien Yap
Keyword(s):  
Three Dimensional ◽  
Second Phase ◽  
Two Phase ◽  
Maximum Curvature ◽  
Phase Study ◽  
Absolute Curvature ◽  
Icd Leads ◽  
Lead Testing ◽  
Design And Testing

Abstract Better understanding of the lead curvature, movement and their spatial distribution may be beneficial in developing lead testing methods, guiding implantations and improving life expectancy of implanted leads. Objective The aim of this two-phase study was to develop and test a novel biplane cine-fluoroscopy-based method to evaluate input parameters for bending stress in leads based on their in vivo 3D motion using precisely determined spatial distributions of lead curvatures. Potential tensile, compressive or torque forces were not subjects of this study. Methods A method to measure lead curvature and curvature evolution was initially tested in a phantom study. In the second phase using this model 51 patients with implanted ICD leads were included. A biplane cine-fluoroscopy recording of the intracardiac region of the lead was performed. The lead centerline and its motion were reconstructed in 3D and used to define lead curvature and curvature changes. The maximum absolute curvature Cmax during a cardiac cycle, the maximum curvature amplitude Camp and the maximum curvature Cmax@amp at the location of Camp were calculated. These parameters can be used to characterize fatigue stress in a lead under cyclical bending. Results The medians of Camp and Cmax@amp were 0.18 cm−1 and 0.42 cm−1, respectively. The median location of Cmax was in the atrium whereas the median location of Camp occurred close to where the transit through the tricuspid valve can be assumed. Increased curvatures were found for higher slack grades. Conclusion Our results suggest that reconstruction of 3D ICD lead motion is feasible using biplane cine-fluoroscopy. Lead curvatures can be computed with high accuracy and the results can be implemented to improve lead design and testing.

scholarly journals A Decade in Trying to Increase Hand Hygiene—Finally Success

2020 ◽  
Vol 41 (S1) ◽  
pp. s93-s94
Author(s):  
Linda Huddleston ◽  
Sheila Bennett ◽  
Christopher Hermann
Keyword(s):  
Hand Hygiene ◽  
Hand Hygiene Compliance ◽  
Compliance Rate ◽  
Bloodstream Infections ◽  
Central Line ◽  
Second Phase ◽  
Two Phase ◽  
Hygiene Compliance ◽  
Tract Infections ◽  
The Voice

Background: Over the past 10 years, a rural health system has tried 10 different interventions to reduce hospital-associated infections (HAIs), and only 1 intervention has led to a reduction in HAIs. Reducing HAIs is a goal of nearly all hospitals, and improper hand hygiene is widely accepted as the main cause of HAIs. Even so, improving hand hygiene compliance is a challenge. Methods: Our facility implemented a two-phase longitudinal study to utilize an electronic hand hygiene reminder system to reduce HAIs. In the first phase, we implemented an intervention in 2 high-risk clinical units. The second phase of the study consisted of expanding the system to 3 additional clinical areas that had a lower incidence of HAIs. The hand hygiene baseline was established at 45% for these units prior to the voice reminder being turned on. Results: The system gathered baseline data prior to being turned on, and our average hand hygiene compliance rate was 49%. Once the voice reminder was turned on, hand hygiene improved nearly 35% within 6 months. During the first phase, there was a statistically significant 62% reduction in the average number of HAIs (catheter associated urinary tract infections (CAUTI), central-line–acquired bloodstream infections (CLABSIs), methicillin-resistant Staphylococcus aureus (MRSA), multidrug-resistant organisms (MDROs), and Clostridiodes difficile experienced in the preliminary units, comparing 12 months prior to 12 months after turning on the voice reminder. In the second phase, hand hygiene compliance increased to >65% in the following 6 months. During the second phase, all HAIs fell by a statistically significant 60%. This was determined by comparing the HAI rates 6 months prior to the voice reminder being turned on to 6 months after the voice reminder was turned on. Conclusions: The HAI data from both phases were aggregated, and there was a statistically significant reduction in MDROs by 90%, CAUTIs by 60%, and C. difficile by 64%. This resulted in annual savings >$1 million in direct costs of nonreimbursed HAIs.Funding: NoneDisclosures: None

CLIL as a pathway for cross-curricular and translingual classroom practices: A comparative quantitative study on Scottish and Spanish teachers’ views

2021 ◽  
pp. 136216882110324
Author(s):  
Xabier San Isidro
Keyword(s):  
Conceptual Framework ◽  
Quantitative Study ◽  
Classroom Practice ◽  
Classroom Practices ◽  
Research Tool ◽  
Second Phase ◽  
Integrated Learning ◽  
Two Phase ◽  
Pilot Testing ◽  
Final Questionnaire

Despite the numerous attempts to characterize Content and Language Integrated Learning (CLIL), the specialized literature has shown a dearth of cross-contextual studies on how stakeholders conceptualize classroom practice. This article presents the results of a two-phase comparative quantitative study on teachers’ views on CLIL design, implementation and results in two different contexts, Scotland ( n = 127) and Spain ( n = 186). The first phase focused on the creation, pilot-testing and validation of the research tool. The second phase consisted in administering the final questionnaire and analysing the results. The primary goals were (1) to ascertain whether practitioners’ perceptions on CLIL effects and classroom practices match the topics addressed by research; and (2) to analyse and compare teachers’ views in the two contexts. The study offers interesting insights into the main challenges in integrating language and content. Besides providing a conceptual framework for identifiable classroom practice, findings revealed that both cohorts shared broadly similar perceptions, although the Spanish respondents showed more positive views and significantly higher support for this approach.

scholarly journals XCOVNet: Chest X-ray Image Classification for COVID-19 Early Detection Using Convolutional Neural Networks

2021 ◽  
Author(s):  
Vishu Madaan ◽  
Aditya Roy ◽  
Charu Gupta ◽  
Prateek Agrawal ◽  
Anand Sharma ◽  
...  
Keyword(s):  
Image Classification ◽  
Second Phase ◽  
Two Phase ◽  
X Ray ◽  
The Neural Network ◽  
Rapid Spread ◽  
Chest X Ray ◽  
Polymerase Chain ◽  
Two Phases ◽  
Active Patients

AbstractCOVID-19 (also known as SARS-COV-2) pandemic has spread in the entire world. It is a contagious disease that easily spreads from one person in direct contact to another, classified by experts in five categories: asymptomatic, mild, moderate, severe, and critical. Already more than 66 million people got infected worldwide with more than 22 million active patients as of 5 December 2020 and the rate is accelerating. More than 1.5 million patients (approximately 2.5% of total reported cases) across the world lost their life. In many places, the COVID-19 detection takes place through reverse transcription polymerase chain reaction (RT-PCR) tests which may take longer than 48 h. This is one major reason of its severity and rapid spread. We propose in this paper a two-phase X-ray image classification called XCOVNet for early COVID-19 detection using convolutional neural Networks model. XCOVNet detects COVID-19 infections in chest X-ray patient images in two phases. The first phase pre-processes a dataset of 392 chest X-ray images of which half are COVID-19 positive and half are negative. The second phase trains and tunes the neural network model to achieve a 98.44% accuracy in patient classification.

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