This presentation is a case study taken from the travel and holiday industry. Paxport/Multicom, based in UK and Sweden, have recently adopted a recommendation system for holiday accommodation bookings. Machine learning techniques such as Collaborative Filtering have been applied using Python (3.5.1), with Jupyter (4.0.6) as the main framework. Data scale and sparsity present significant challenges in the case study, and so the effectiveness of various techniques are described as well as the performance of Python-based libraries such as Python Data Analysis Library (Pandas), and Scikit-learn (built on NumPy, SciPy and matplotlib). The presentation is suitable for all levels of programmers.

Machine learning with Python

Deep learning has been proved to be an advanced technology for big data analysis with a large number of successful cases in image processing, speech recognition, object detection, and so on. Recently, it has also been introduced in food science and engineering. To our knowledge, this review is the first in the food domain. In this paper, we provided a brief introduction of deep learning and detailedly described the structure of some popular architectures of deep neural networks and the approaches for training a model. We surveyed dozens of articles that used deep learning as the data analysis tool to solve the problems and challenges in food domain, including food recognition, calories estimation, quality detection of fruits, vegetables, meat and aquatic products, food supply chain, and food contamination. The specific problems, the datasets, the preprocessing methods, the networks and frameworks used, the performance achieved, and the comparison with other popular solutions of each research were investigated. We also analyzed the potential of deep learning to be used as an advanced data mining tool in food sensory and consume researches. The result of our survey indicates that deep learning outperforms other methods such as manual feature extractors, conventional machine learning algorithms, and deep learning as a promising tool in food quality and safety inspection. The encouraging results in classification and regression problems achieved by deep learning will attract more research efforts to apply deep learning into the field of food in the future.

https://onlinelibrary.wiley.com/doi/epdf/10.1111/1541-4337.12492

Application of Deep Learning in Food: A Review.

Modern hyperspectral imaging systems produce huge datasets potentially conveying a great abundance of information; such a resource, however, poses many challenges in the analysis and interpretation of these data. Deep learning approaches certainly offer a great variety of opportunities for solving classical imaging tasks and also for approaching new stimulating problems in the spatial–spectral domain. This is fundamental in the driving sector of Remote Sensing where hyperspectral technology was born and has mostly developed, but it is perhaps even more true in the multitude of current and evolving application sectors that involve these imaging technologies. The present review develops on two fronts: on the one hand, it is aimed at domain professionals who want to have an updated overview on how hyperspectral acquisition techniques can combine with deep learning architectures to solve specific tasks in different application fields. On the other hand, we want to target the machine learning and computer vision experts by giving them a picture of how deep learning technologies are applied to hyperspectral data from a multidisciplinary perspective. The presence of these two viewpoints and the inclusion of application fields other than Remote Sensing are the original contributions of this review, which also highlights some potentialities and critical issues related to the observed development trends.

Deep Learning Meets Hyperspectral Image Analysis: A Multidisciplinary Review.

Deep learning for vibrational spectral analysis: Recent progress and a practical guide.

Non-destructive testing techniques have gained importance in monitoring food quality over the years. Hyperspectral imaging is one of the important non-destructive quality testing techniques which provides both spatial and spectral information. Advancement in machine learning techniques for rapid analysis with higher classification accuracy have improved the potential of using this technique for food applications. This paper provides an overview of the application of different machine learning techniques in analysis of hyperspectral images for determination of food quality. It covers the principle underlying hyperspectral imaging, the advantages, and the limitations of each machine learning technique. The machine learning techniques exhibited rapid analysis of hyperspectral images of food products with high accuracy thereby enabling robust classification or regression models. The selection of effective wavelengths from the hyperspectral data is of paramount importance since it greatly reduces the computational load and time which enhances the scope for real time applications. Due to the feature learning nature of deep learning, it is one of the most promising and powerful techniques for real time applications. However, the field of deep learning is relatively new and need further research for its full utilization. Similarly, lifelong machine learning paves the way for real time HSI applications but needs further research to incorporate the seasonal variations in food quality. Further, the research gaps in machine learning techniques for hyperspectral image analysis, and the prospects are discussed.

Machine learning techniques for analysis of hyperspectral images to determine quality of food products: A review.

A deep learning based feature extraction method on hyperspectral images for nondestructive prediction of TVB-N content in Pacific white shrimp (Litopenaeus vannamei)

In this study, deep learning method coupled with near-infrared (NIR) hyperspectral imaging (HSI) technique was used for nondestructively determining total viable count (TVC) of peeled Pacific white shrimp. Firstly, stacked auto-encoders (SAE) was conducted as a big data analytical method to extract 20 deep hyperspectral features from NIR hyperspectral image (900–1700 nm) of peeled shrimp stored at 4 °C, and the extracted features were used to predict TVC by fully-connected neural network (FNN). The SAE–FNN method obtained high prediction accuracy for determining TVC, with R
 P
 2
  = 0.927. Additionally, TVC spatial distribution of peeled shrimp during storage could be visualized via applying the established SAE–FNN model. The results demonstrate that SAE–FNN combined with HSI technique has a potential for non-destructive prediction of TVC in peeled shrimp, which supply a novel method for the hygienic quality and safety inspections of shrimp product.

Using deep learning and hyperspectral imaging to predict total viable count (TVC) in peeled Pacific white shrimp

In the cloud service community, cloud service providers typically limit component cloud services to perform a specific and limited function to maximize the versatility of cloud services. However, the business needs of consumers are often very complex, requiring multiple component cloud services to be combined to complete the business. In the process of cloud service composition, the question of how to find the right combination of partner cloud services is very challenging. This paper starts with a brief history of cloud service execution and it then analyzes the historical information of the combined cloud service execution. Based on the invocation relationship of the cloud service, the community discovery algorithm is used to divide the component cloud service into multiple cooperative communities. This paper will provide appropriate guidance for subsequent combinations of cloud services to improve the efficiency of cloud service portfolios and improve user satisfaction for combined cloud service execution. The experimental results show that the proposed method can divide the combined cloud service cooperation community in effective time and can effectively improve the success rate of the combined cloud service execution.

/pdf/enhancing-service-composition-by-discovering-cloud-services-1cl1ubw2vi.pdf

Jinqiu Yang

Papers

A deep learning based feature extraction method on hyperspectral images for nondestructive prediction of TVB-N content in Pacific white shrimp (Litopenaeus vannamei)

Using deep learning and hyperspectral imaging to predict total viable count (TVC) in peeled Pacific white shrimp

Enhancing Service Composition by Discovering Cloud Services Community