The Anonymity of Data Essay

The challenge posed by crew pairing, algorithm development for operational research, is the determination of the flight segment sets to be performed by the same crew (Kasirzadeh et al., 2017). The paper we are filing expounds on Artificial Intelligence algorithms used in the optimization of operational research algorithms, achieving faster speeds, and accuracy. The scale of the problem contributes to this work, whereby, the subject matter is tens of thousands of rotations and employees, rather than hundreds identified in the literature. The paper takes into consideration the time of each incoming flight and estimates the probability of two flights being operated by the same crew consecutively. Therefore, the data files to be used include a.AcRouting and p.TmfTrip. The a.AcRouting considers engine rotations for a particular month, whereas, p.TmfTrip considers the crew rotations for a particular month. The p.TmfTrip data file is used in the learning phase and reconstructed during the testing phase. The prediction model for the next flight takes into consideration the following features:

Is your time best spent reading someone else’s essay? Get a 100% original essay FROM A CERTIFIED WRITER!

Save 25%

• Engine and its rotation number.
• The city of origin (departure city).
• The connecting city (arrival city of the previous flight).
• The time taken by the previous flights (duration of the flight).
• The time from which the crew is ready to take a flight arriving at the connecting city, in terms of, day of the year, week, hours, and minutes.
• The information on the next departing flight.

When presenting publicly an example of the dataset used during the phase, we begin by encoding all the available features. These features are encoded from categorical to indexes, which are values provided for by the dictionary. Eventually, we delete all code flights, city names, year and month details, and engine specifications. The rebuilding of the graph can follow even after the deletion has been done. The process of rebuilding uses numerical codes, information about flight pairs, bases reconstruction, counting number of flights per base, counting number of days, counting the number of flights. The culmination of the process is finding the airline company. The use of the embedding layer facilitates this. Technological advancements in the natural language processing in Artificial Intelligence are based on the application of dense vectors for the representation of language relations, that is, embedded words for encoding relations between phrases and words (word2vec and glove, Mikolov et al. 2013).

The function is standard for word vectors, which is an efficient technique for dimension reduction, such that, there is the reduction of a wide sparse matrix into a dense matrix that is as thin as reasonably achievable. The thin dense matrix is then applied in the prediction tasks located downstream. The techniques for embedding are an extension from words to phrases, sentences, and whole documents, for instance (Le and Mikolov, 2014). Therefore, from our perspective, the flight code encoded to an integer, currently is projected to a hyperspace having 25 dimensions or more. There is no application of the initial encoding method, hence not included in the dataset due to be made in the public domain. The inclusion of the data set is, therefore, only features represented in the hyperspaces having 25 dimensions or more. The representations are taught in the learning phase, hence not unique, for instance, encoding a city by two similar methods which are not unique from each other. The technique is significant in ascertaining the anonymity of classified data, consequently, preventing the possibility of a lawsuit by the disclosed company.