A Framework to Estimate the Nutritional Value of Food in Real Time Using Deep Learning Techniques

Yunus, Raza; Arif, Omar; Afzal, Hammad; Amjad, Muhammad Faisal; Abbas, Haider; Bokhari, Hira Noor; Haider, Syeda Tazeen; Zafar, Nauman; Nawaz, Raheel

doi:10.1109/access.2018.2879117

Cited by 76 publications

(38 citation statements)

References 35 publications

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“…A GA optimizes an objective function (or fitness function) via simulated genetic operators, that is, mutation and crossover. In a GA, a candidate solution is normally encoded by arrays or character of strings to denote chromosomes; however, other representations are also popular [38][39][40][41][42][43]. The GA begins by creating a population of randomly generated solutions; this population is also called the initial generation.…”

Section: G Genetic Algorithm For Optimizationmentioning

confidence: 99%

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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: G Genetic Algorithm For Optimizationmentioning

confidence: 99%

“…ML is a set of techniques enabling software applications to predict future responses of the dependent variables by learning from the training dataset [39]. A given ML method models the inter-relations between covariates and response variables of the training dataset.…”

Section: Machine Learning (Ml) Based Energy Management Model (Emm)mentioning

confidence: 99%

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

et al. 2020

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“…The biggest challenge that researchers face is the classification of RF signals due to the limited amount of data as a huge amount of time is required for data collection. To cope with the small number of observations, we used the autoencoder neural network, which delivers the best classification performance when exposed in such scenarios [27][28][29]. The autoencoder classifier provided the input data at the output, as shown in Figure 6.…”

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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“…Over the last decade, cloud computing has encompassed a large number of applications, e.g., [20]- [25]. Numerous challenges have been faced by the researchers while performing resource allocation in cloud DCs and one of the most critical issues is energy [26].…”

Section: Literature Reviewmentioning

confidence: 99%

CPU and RAM Energy-Based SLA-Aware Workload Consolidation Techniques for Clouds

et al. 2020

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Cloud computing offers hardware and software resources delivered as services. It provides solutions for dynamic as well as ''pay as you go'' provision of resources. Energy consumption of these resources is high which leads to higher operational costs and carbon emissions in data centers. A number of research studies have been conducted on energy efficiency of data centers, but most of them concentrate on single factor energy consumption, i.e., energy consumed by CPU only, and energy consumption by Random Access Memory (RAM) is neglected. However, recently the focus has been turned towards impact of energy consumption by RAM on data centers. Studies have shown that RAM consumes about 25% of joint energy consumed by a server's CPU and RAM. In this paper, two energy-aware virtual machine (VM) consolidation schemes are proposed that take into account a server's capacity in terms of CPU and RAM to reduce the overall energy consumption. The proposed schemes are compared with existing schemes using CloudSim simulator. The results show that the proposed schemes reduce the energy cost with improved Service Level Agreement (SLA).

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A Framework to Estimate the Nutritional Value of Food in Real Time Using Deep Learning Techniques

Cited by 76 publications

References 35 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

CPU and RAM Energy-Based SLA-Aware Workload Consolidation Techniques for Clouds

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