An Optimal Ride Sharing Recommendation Framework for Carpooling Services

Qadir, Hajra; Khalid, Osman; Khan, Muhammad Usman Shahid; Khan, Atta ur Rehman; Nawaz, Raheel

doi:10.1109/access.2018.2876595

Cited by 40 publications

(32 citation statements)

References 12 publications

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“…An electrical grid comprising of renewable energy sources, smart appliances, smart meters, and energy efficient resources is called the smart grid [24][25][26]. Smart grid domains include bulk and non-bulk generation, customers, service provider, distribution, transmission, foundation support system, markets, and operations.…”

Section: A System Modelmentioning

confidence: 99%

Machine Learning Based Energy Management Model for Smart Grid and Renewable Energy Districts

et al. 2020

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The combination of renewable energy sources and prosumer-based smart grid is a sustainable solution to cater to the problem of energy demand management. A pressing need is to develop an efficient Energy Management Model (EMM) that integrates renewable energy sources with smart grids. However, the variable scenarios and constraints make this a complex problem. Machine Learning (ML) methods can often model complex and non-linear data better than the statistical models. Therefore, developing an ML algorithm for the EMM is a suitable option as it reduces the complexity of the EMM by developing a single trained model to predict the performance parameters of EMM for multiple scenarios. However, understanding latent correlations and developing trust in highly complex ML models for designing EMM within the stochastic prosumer-based smart grid is still a challenging task. Therefore, this paper integrates ML and Gaussian Process Regression (GPR) in the EMM. At the first stage, an optimization model for Prosumer Energy Surplus (PES), Prosumer Energy Cost (PEC), and Grid Revenue (GR) is formulated to calculate base performance parameters (PES, PEC, and GR) for the training of the ML-based GPR model. In the second stage, stochasticity of renewable energy sources, load, and energy price, same as provided by the Genetic Algorithm (GA) based optimization model for PES, PEC, and GR, and base performance parameters act as input covariates to produce a GPR model that predicts PES, PEC, and GR. Seasonal variations of PES, PEC, and GR are incorporated to remove hitches from seasonal dynamics of prosumers energy generation and prosumers energy consumption. The proposed adaptive Service Level Agreement (SLA) between energy prosumers and the grid benefits both these entities. The results of the proposed model are rigorously compared with conventional optimization (GA and PSO) based EMM to prove the validity of the proposed model.

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Section: A System Modelmentioning

confidence: 99%

Machine Learning Based Energy Management Model for Smart Grid and Renewable Energy Districts

et al. 2020

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“…To optimize the autoencoder neural network, cost function in conjunction with a sparsity parameter is implemented to drive the neural network for learning the correlation when different inputs are given [30]. In addition to the these parameters, the cost function can be expressed as follows:…”

Section: Autoencoder For Scalogram Classificationmentioning

confidence: 99%

Privacy-Preserving Non-Wearable Occupancy Monitoring System Exploiting Wi-Fi Imaging for Next-Generation Body Centric Communication

et al. 2020

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Nano-scaled structures, wireless sensing, wearable devices, and wireless communications systems are anticipated to support the development of new next-generation technologies in the near future. Exponential rise in future Radio-Frequency (RF) sensing systems have demonstrated its applications in areas such as wearable consumer electronics, remote healthcare monitoring, wireless implants, and smart buildings. In this paper, we propose a novel, non-wearable, device-free, privacy-preserving Wi-Fi imaging-based occupancy detection system for future smart buildings. The proposed system is developed using off-the-shelf non-wearable devices such as Wi-Fi router, network interface card, and an omnidirectional antenna for future body centric communication. The core idea is to detect presence of person along its activities of daily living without deploying a device on person’s body. The Wi-Fi signals received using non-wearable devices are converted into time–frequency scalograms. The occupancy is detected by classifying the scalogram images using an auto-encoder neural network. In addition to occupancy detection, the deep neural network also identifies the activity performed by the occupant. Moreover, a novel encryption algorithm using Chirikov and Intertwining map-based is also proposed to encrypt the scalogram images. This feature enables secure storage of scalogram images in a database for future analysis. The classification accuracy of the proposed scheme is 91.1%.

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“…In [15] the authors proposed a recommender system for Carpooling services which allows the drivers to share rides with other passengers. This reduces the passengers' fares and time, as well as traffic congestion and increases the income for drivers.…”

Section: Recurrent Neural Network For Recommendationmentioning

confidence: 99%

An Intelligent Context Aware Recommender System for Real-Estate

Rehman

Masood

Ul-Hasan³

et al. 2019

Communications in Computer and Information Science

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Finding products and items in large online space that meet the user needs is difficult. Users may spend a considerable amount of time before finding item relevant to their needs. Like many other economic branches, growing Internet usage also change user behavior in the real-estate market. Advancement in virtual reality offers a virtual tour, interactive maps, floor plans that make an online rental website popular among users. With an abundance of information, recommender systems become more than ever important to suggest the user with relevant property and reduce search time. A sophisticated recommender in this domain can assist the need of a real-estate agent. Session-based user behavior, lack of user profile leads to the use of traditional recommendation methods. In this research, we proposed an approach for real-estate recommendation based on Gated Orthogonal Recurrent Unit (GORU) and Weighted Cosine Similarity. GORU captures the user searching context and weighted cosine similarity improves the rank of pertinent property. To conduct this research, we have used the data of an online public real estate web portal 4. The factual data represents the original behavior of the user on an online portal. We have used Recall, User coverage and Mean Reciprocal Rank (MRR) metric for the evaluation of our system against other state-of-the-art techniques. Proposed solution outperforms various baselines and state-of-the-art RNN based solutions.

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An Optimal Ride Sharing Recommendation Framework for Carpooling Services

Cited by 40 publications

References 12 publications

Machine Learning Based Energy Management Model for Smart Grid and Renewable Energy Districts

Machine Learning Based Energy Management Model for Smart Grid and Renewable Energy Districts

Privacy-Preserving Non-Wearable Occupancy Monitoring System Exploiting Wi-Fi Imaging for Next-Generation Body Centric Communication

An Intelligent Context Aware Recommender System for Real-Estate

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