scholarly journals Resource Allocation using Deep Learning in Mobile Small Cell Networks

2021 ◽  
Author(s):  
Saniya Zafar ◽  
sobia Jangsher ◽  
Arafat Al-Dweik
Keyword(s):  
Resource Allocation ◽  
Deep Learning ◽  
Network Performance ◽  
Short Term Memory ◽  
High Mobility ◽  
Small Cells ◽  
Time Interval ◽  
Performance Simulation ◽  
Interference Graph

The deployment of mobile-Small cells (mScs) is widely adopted to intensify the quality-of-service (QoS) in high mobility vehicles. However, the rapidly varying interference patterns among densely deployed mScs make the resource allocation (RA) highly challenging. In such scenarios, RA problem needs to be solved nearly in real-time, which can be considered as drawback for most existing RA algorithms. To overcome this constraint and solve the RA problem efficiently, we use deep learning (DL) in this work due to its ability to leverage the historical data in RA problem and to deal with computationally expensive tasks offline. More specifically, this paper considers the RA problem in vehicular environment comprising of city buses, where DL is explored for optimization of network performance. Simulation results reveal that RA in a network using Long Short-Term Memory (LSTM) algorithm outperforms other machine learning (ML) and DL-based RA mechanisms. Moreover, RA using LSTM provides less accurate results as compared to existing Time Interval Dependent Interference Graph (TIDIG)-based, and Threshold Percentage Dependent Interference Graph (TPDIG)-based RA but shows improved results when compared to RA using Global Positioning System Dependent Interference Graph (GPSDIG). However, the proposed scheme is computationally less expensive in comparison with TIDIG and TPDIG-based algorithms.

scholarly journals Resource Allocation using Deep Learning in Mobile Small Cell Networks

2021 ◽  
Author(s):  
Saniya Zafar ◽  
sobia Jangsher ◽  
Arafat Al-Dweik
Keyword(s):  
Resource Allocation ◽  
Deep Learning ◽  
Network Performance ◽  
Short Term Memory ◽  
High Mobility ◽  
Small Cells ◽  
Time Interval ◽  
Performance Simulation ◽  
Interference Graph

The deployment of mobile-Small cells (mScs) is widely adopted to intensify the quality-of-service (QoS) in high mobility vehicles. However, the rapidly varying interference patterns among densely deployed mScs make the resource allocation (RA) highly challenging. In such scenarios, RA problem needs to be solved nearly in real-time, which can be considered as drawback for most existing RA algorithms. To overcome this constraint and solve the RA problem efficiently, we use deep learning (DL) in this work due to its ability to leverage the historical data in RA problem and to deal with computationally expensive tasks offline. More specifically, this paper considers the RA problem in vehicular environment comprising of city buses, where DL is explored for optimization of network performance. Simulation results reveal that RA in a network using Long Short-Term Memory (LSTM) algorithm outperforms other machine learning (ML) and DL-based RA mechanisms. Moreover, RA using LSTM provides less accurate results as compared to existing Time Interval Dependent Interference Graph (TIDIG)-based, and Threshold Percentage Dependent Interference Graph (TPDIG)-based RA but shows improved results when compared to RA using Global Positioning System Dependent Interference Graph (GPSDIG). However, the proposed scheme is computationally less expensive in comparison with TIDIG and TPDIG-based algorithms.

scholarly journals DeepLMS: a deep learning predictive model for supporting online learning in the Covid-19 era

2020 ◽  
Vol 10 (1) ◽  
Author(s):  
Sofia B. Dias ◽  
Sofia J. Hadjileontiadou ◽  
José Diniz ◽  
Leontios J. Hadjileontiadis
Keyword(s):  
Online Learning ◽  
Deep Learning ◽  
Predictive Model ◽  
Short Term Memory ◽  
Ground Truth ◽  
Face To Face ◽  
Learning Techniques ◽  
Learning Plans ◽  
First Time

AbstractCoronavirus (Covid-19) pandemic has imposed a complete shut-down of face-to-face teaching to universities and schools, forcing a crash course for online learning plans and technology for students and faculty. In the midst of this unprecedented crisis, video conferencing platforms (e.g., Zoom, WebEx, MS Teams) and learning management systems (LMSs), like Moodle, Blackboard and Google Classroom, are being adopted and heavily used as online learning environments (OLEs). However, as such media solely provide the platform for e-interaction, effective methods that can be used to predict the learner’s behavior in the OLEs, which should be available as supportive tools to educators and metacognitive triggers to learners. Here we show, for the first time, that Deep Learning techniques can be used to handle LMS users’ interaction data and form a novel predictive model, namely DeepLMS, that can forecast the quality of interaction (QoI) with LMS. Using Long Short-Term Memory (LSTM) networks, DeepLMS results in average testing Root Mean Square Error (RMSE) $$<0.009$$ < 0.009 , and average correlation coefficient between ground truth and predicted QoI values $$r\ge 0.97$$ r ≥ 0.97 $$(p<0.05)$$ ( p < 0.05 ) , when tested on QoI data from one database pre- and two ones during-Covid-19 pandemic. DeepLMS personalized QoI forecasting scaffolds user’s online learning engagement and provides educators with an evaluation path, additionally to the content-related assessment, enriching the overall view on the learners’ motivation and participation in the learning process.

scholarly journals Real Time LDR Data Prediction using IoT and Deep Learning Algorithm

2020 ◽  
pp. 158-161
Author(s):  
Chandraprabha S ◽  
Pradeepkumar G ◽  
Dineshkumar Ponnusamy ◽  
Saranya M D ◽  
Satheesh Kumar S ◽  
...  
Keyword(s):  
Deep Learning ◽  
Light Intensity ◽  
Real Time ◽  
Short Term Memory ◽  
Learning Algorithm ◽  
Time Interval ◽  
Deep Learning Algorithm ◽  
Data Prediction ◽  
Solar Plant

This paper outfits artificial intelligence based real time LDR data which is implemented in various applications like indoor lightning, and places where enormous amount of heat is produced, agriculture to increase the crop yield, Solar plant for solar irradiance Tracking. For forecasting the LDR information. The system uses a sensor that can measure the light intensity by means of LDR. The data acquired from sensors are posted in an Adafruit cloud for every two seconds time interval using Node MCU ESP8266 module. The data is also presented on adafruit dashboard for observing sensor variables. A Long short-term memory is used for setting up the deep learning. LSTM module uses the recorded historical data from adafruit cloud which is paired with Node MCU in order to obtain the real-time long-term time series sensor variables that is measured in terms of light intensity. Data is extracted from the cloud for processing the data analytics later the deep learning model is implemented in order to predict future light intensity values.

scholarly journals Discriminative Multi-Stream Postfilters Based on Deep Learning for Enhancing Statistical Parametric Speech Synthesis

Biomimetics ◽  
2021 ◽  
Vol 6 (1) ◽  
pp. 12
Author(s):  
Marvin Coto-Jiménez
Keyword(s):  
Deep Learning ◽  
Hidden Markov Models ◽  
Speech Synthesis ◽  
Short Term Memory ◽  
Markov Models ◽  
Hidden Markov ◽  
Voice Conversion ◽  
Statistical Parametric Speech Synthesis ◽  
Parametric Speech Synthesis

Statistical parametric speech synthesis based on Hidden Markov Models has been an important technique for the production of artificial voices, due to its ability to produce results with high intelligibility and sophisticated features such as voice conversion and accent modification with a small footprint, particularly for low-resource languages where deep learning-based techniques remain unexplored. Despite the progress, the quality of the results, mainly based on Hidden Markov Models (HMM) does not reach those of the predominant approaches, based on unit selection of speech segments of deep learning. One of the proposals to improve the quality of HMM-based speech has been incorporating postfiltering stages, which pretend to increase the quality while preserving the advantages of the process. In this paper, we present a new approach to postfiltering synthesized voices with the application of discriminative postfilters, with several long short-term memory (LSTM) deep neural networks. Our motivation stems from modeling specific mapping from synthesized to natural speech on those segments corresponding to voiced or unvoiced sounds, due to the different qualities of those sounds and how HMM-based voices can present distinct degradation on each one. The paper analyses the discriminative postfilters obtained using five voices, evaluated using three objective measures, Mel cepstral distance and subjective tests. The results indicate the advantages of the discriminative postilters in comparison with the HTS voice and the non-discriminative postfilters.

scholarly journals FORESAM—FOG Paradigm-Based Resource Allocation Mechanism for Vehicular Clouds

Sensors ◽  
2021 ◽  
Vol 21 (15) ◽  
pp. 5028
Author(s):  
Rickson Pereira ◽  
Azzedine Boukerche ◽  
Marco A. C. da Silva ◽  
Luis H. V. Nakamura ◽  
Heitor Freitas ◽  
...  
Keyword(s):  
Resource Allocation ◽  
High Mobility ◽  
Transport Systems ◽  
Management Problem ◽  
Allocation Mechanism ◽  
Network Characteristics ◽  
Intelligent Transport ◽  
Multiple Attribute ◽  
Objective Quality

The Intelligent Transport Systems (ITS) has the objective quality of transportation improvement through transportation system monitoring and management and makes the trip more comfortable and safer for drivers and passengers. The mobile clouds can assist the ITS in handling the resource management problem. However, resource allocation management in an ITS is challenging due to vehicular network characteristics, such as high mobility and dynamic topology. With that in mind, we propose the FORESAM, a mechanism for resources management and allocation based on a set of FOGs which control vehicular cloud resources in the urban environment. The mechanism is based on a more accurate mathematical model (Multiple Attribute Decision), which aims to assist the allocation decision of resources set that meets the period requested service. The simulation results have shown that the proposed solution allows a higher number of services, reducing the number of locks of services with its accuracy. Furthermore, its resource allocation is more balanced the provided a smaller amount of discarded services.

scholarly journals Analysis of Parkinson’s Disease using Deep Learning and Word Embedding Models

2019 ◽  
Vol 2 (3) ◽  
pp. 786-797
Author(s):  
Feyza Cevik ◽  
Zeynep Hilal Kilimci
Keyword(s):  
Neural Networks ◽  
Social Media ◽  
Deep Learning ◽  
Short Term Memory ◽  
Learning Algorithms ◽  
Economic Effects ◽  
Word Embedding ◽  
Social Media Platforms ◽  
Accuracy Performance

Parkinson&apos;s disease is a common neurodegenerative neurological disorder, which affects the patient&apos;s quality of life, has significant social and economic effects, and is difficult to diagnose early due to the gradual appearance of symptoms. Examining the discussion of Parkinson&amp;rsquo;s disease in social media platforms such as Twitter provides a platform where patients communicate each other in both diagnosis and treatment stage of the Parkinson&amp;rsquo;s disease. The purpose of this work is to evaluate and compare the sentiment analysis of people about Parkinson&apos;s disease by using deep learning and word embedding models. To the best of our knowledge, this is the very first study to analyze Parkinson&apos;s disease from social media by using word embedding models and deep learning algorithms. In this study, Word2Vec, GloVe, and FastText are employed as word embedding models for the purpose of enriching tweets in terms of semantic, context, and syntax. Convolutional Neural Networks (CNNs), Recurrent Neural Networks (RNNs), and Long Short-Term Memory Networks (LSTMs) are implemented for the classification task. This study demonstrates the efficiency of using word embedding models and deep learning algorithms to understand the needs of patients&amp;rsquo; and provide a valuable contribution to the treatment process by analyzing sentiments of them with 93.63% accuracy performance.

scholarly journals OPTIMIZATION OF WIRELESS NETWORK PERFORMANCE USING THE HIERARCHICAL TOKEN BUCKET (CASE STUDY: MUHAMMADIYAH UNIVERSITY OF PALEMBANG)

2019 ◽  
Vol 1 (1) ◽  
pp. 49-59
Author(s):  
Yukos Pratama ◽  
Heri Suroyo
Keyword(s):  
Quality Of Service ◽  
Packet Loss ◽  
Network Performance ◽  
High Mobility ◽  
Bandwidth Management ◽  
Token Bucket ◽  
Very High ◽  
Tree Method

Computer networks have penetrated into various fields including education for the learning process which is used as a medium for delivering scientific concepts to be more attractive and easily accepted. The Muhammadiyah University of Palembang currently has very high mobility, both used for browsing information, downloading data and using other facilities. For the need for bandwidth management to manage each passing data so that the distribution of bandwidth becomes evenly distributed by using the queue tree method that is applied to the proxy. To evaluate internet bandwidth analyze QoS (Quality of Service) using typhoid standardization in terms of measurement of throughput, delay, and packet loss. The results of this study show that the quality of the network with the hierarchical token bucket method is more optimal, this is because the bandwidth will be divided according to the rules applied to bandwidth management and does not cause clients to fight over bandwidth

scholarly journals To investigate the effect of Mobile IP on the performance of VANET through realistic channel conditions.

2015 ◽  
Vol 14 (7) ◽  
pp. 5911-5918
Author(s):  
Komal Sharma
Keyword(s):  
Traffic Safety ◽  
Ad Hoc Network ◽  
Large Scale ◽  
Network Performance ◽  
Ad Hoc ◽  
Mobile Ip ◽  
High Mobility ◽  
Comparative Performance ◽  
Urban Scenario

  Abstract Vehicular Ad hoc Network (VANET) is a specialized Ad hoc Network, which provides safety and comfort for passengers [1]. Due to the specific characteristic of VANET like high mobility and large scale node population [1], providing Quality of Service (QoS) in this type of wireless network is a challenging issue. As a result, we combine Mobile IP and VANET to improve QoS in terms of packet loss and throughput for traffic safety and entertainment applications. Comparative performance evaluation is done in terms of QOS parameters to show the network performance using different traffic types and by varying speed of the vehicles under urban scenario.

scholarly journals OPTIMIZATION OF WIRELESS NETWORK PERFORMANCE USING THE HIERARCHICAL TOKEN BUCKET (CASE STUDY: MUHAMMADIYAH UNIVERSITY OF PALEMBANG)

2019 ◽  
Vol 1 (1) ◽  
pp. 49-59
Author(s):  
Yukos Pratama ◽  
Usman Ependi ◽  
Heri Suroyo
Keyword(s):  
Quality Of Service ◽  
Packet Loss ◽  
Network Performance ◽  
High Mobility ◽  
Bandwidth Management ◽  
Token Bucket ◽  
Very High ◽  
Tree Method

Computer networks have penetrated into various fields including education for the learning process which is used as a medium for delivering scientific concepts to be more attractive and easily accepted. The Muhammadiyah University of Palembang currently has very high mobility, both used for browsing information, downloading data and using other facilities. For the need for bandwidth management to manage each passing data so that the distribution of bandwidth becomes evenly distributed by using the queue tree method that is applied to the proxy. To evaluate internet bandwidth analyze QoS (Quality of Service) using typhoid standardization in terms of measurement of throughput, delay, and packet loss. The results of this study show that the quality of the network with the hierarchical token bucket method is more optimal, this is because the bandwidth will be divided according to the rules applied to bandwidth management and does not cause clients to fight over bandwidth

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