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Artificial Intelligence and Machine Learning in the Aviation Industry

Abstract

Machine learning is seen as the process through which Artificial intelligence is manipulated using machines and computer systems to forecast future occurrences without involving human inputs. Machines help computer software applications to make informed decisions about future outcomes with precision and accuracy (Khan & Al-Badi, 2020). Huge dataset pose challenges to the typical machine learning process since most of them were not designed to handle large data. The emergence of Artificial intelligence has rendered the traditional data algorithms because of their inability to efficiently process the needed information effectively.

Technology is advancing each day in the contemporary world and so are the dynamics associated to technology. Data is widely used in the modern world in different sectors such as aviation industry, social media, mobile phone sector, tracking and sensing devices, web technology, as well as communication infrastructure (Khan & Al-Badi, 2020). These modern technologies handle huge data on a daily basis that could be of immense economic use from a business perspective.

Artificial Intelligence and Machine Learning in the Aviation Industry

Artificial intelligence and machine learning enable a smooth communication which enhances efficiency in air operations. Machine learning is the process by which people collect and analyze huge volumes of data that is essential in decision making and forecasting air operations. The research will explore the available opportunities that the aviation industry will leverage through utilization of artificial intelligence and machine in its day-to-day operations to enhance customer satisfaction. The paper will review the available literature on the challenges facing artificial intelligence and machine learning utilization in aviation sector and providing meaningful insights to the sectoral stakeholders on the most feasible approaches from to benefit from the technology explosion (Khan & Al-Badi, 2020). Artificial intelligence takes control of the large volume of data, the various types of data, as well as the speed at which the data is processes and retrieved.

Artificial Intelligence Background

Technology is growing each day and has revolutionized the modern-day air travel operations. There is a need to research on the current trends in utilization of artificial intelligence in the aviation sector to uncover the underlying opportunities and challenges relate to the use of the technology. Artificial intelligence requires modern manipulation techniques using computer applications and algorithms to derive meaningful information from the data. Traditional data handling systems are struggling to handle the voluminous data sets to make meaningful informational that can be digested by ordinary consumers in the decision-making processes (Khan & Al-Badi, 2020). Processing the raw huge data provides useful insights about the phenomena under study for decision making and process optimization processes.

Artificial intelligence is expected to affect the way we work, live, and think in our day-to-day encounters as a result of its potential. Artificial intelligence enhances knowledge discovery as well as optimization of the decision-making strategy (Hamdan, Hassanien, Razzaque & Alareeni, 2021). Data analysis involves the use of various techniques, technologies, and data handling tools that include statistical analysis, visual devices, business modelling tools, and text analytics. Artificial intelligence analysis provide data driven forecasts depending on the type of data collected and analyzed.

Literature Review

Historical Background

Artificial intelligence and machine learning in aviation industry includes challenges related to deep learning and the idea of unstructured data format, multi-source data types, streaming data, noisy and low-quality data, and the scalability of data algorithms. Studies have identified three main challenges regarding machine learning that include modelling flexible and high scalability topographies, designing ability to function with huge datasets, and the issue of understanding statistical data formats before commanding the algorithms (Cummings, 2017).

Contemporary Context

Research findings have identified the following critical issues related to machine learning; uncertain and incomplete data, high speed of data, and data with minimal value intensity, varying data formats, and large-scale data sets.

Challenges Related to Machine Learning

Artificial intelligence is categorized according to its dimension, volume, variety, and velocity. The modern definition is based on the four Vs (volume, variety, veracity, and velocity).

Volume

It is a common characteristic that is widely talked about in regard to Artificial intelligence. Volume includes the size, amount, and data scale. According to the machine learning context, size may be described as being vertical in terms of the number of samples or records available in a data set or being horizontal in terms of number of attributes contained (Khan & Al-Badi, 2020).

Processing Challenge

Scale or volumes presents instances of computation difficulties when handling Artificial intelligence. An increase in the volume of the data to the process translates to a proportional increase in the trivial operation. An increase in the size of the data will subsequently affect the memory and time required in the case of a support vector machine (Khan & Al-Badi, 2020). An increase in size triggers the performance of the algorithms to depend on the architecture employed to store and retrieve data

Curse of Modularity

The majority of the ML algorithms depend on the assumption that the processing of data is entirely held in the memory or the mono file on a computer disk. However, many classes of algorithms are designed in a way that the building block depend on this assumption, data size causes the failure of its applicability (Cummings, 2017). The challenge is known as the curse of modularity.

Class Imbalance

An increase in the size of the dataset overlap is the assumption that data are evenly distributed across tiers. It creates a challenge known as class imbalance which affects the performance of a machine (Hamdan et al., 2021). Class imbalance has been a subject of discussion in many other fields apart from the Artificial intelligence scenario.

Curse of Dimensionality

It refers to the complexities experienced when handling high dimensional data space. Dimensionality refers to the number of attributes available in a dataset. According to the Hughes effect, the training set of static magnitude, the effectiveness and the predictive capability of an algorithm reduces as the dimension of the dataset increase.

Feature Engineering

It is closely associated to high dimensionality and is the process of creating attributes by use of domain knowledge to enhance a ML perform better. Selection of the most effective attribute is both time consuming as well as a very expensive pre-processing stage in the machine learning (Khan & Al-Badi, 2020).

Non-Linearity

The application of common methodology employed to evaluate dataset features and algorithm execution are affected by data size. The assumption of data linearity affects the validity of many metrics involving machine learning (Hamdan et al., 2021). It is assumed that the correlation coefficient is a good measure for the strength of association between two datasets or variables.

Variety

The variety of Artificial intelligence is presented in terms of the structural variation in a dataset, the data type composition, as well as the variety of its semantic interpretation, and sources.

Data Locality

Machine learning algorithms assumes that the whole data set is located in the memory or in a mono disk file. It is not the case in Artificial intelligence due to the sheer size. For the Artificial intelligence, the data may not fit in the memory and are distributed over the large number of files present in various physical (Khan & Al-Badi, 2020).

Data Heterogeneity

Artificial intelligence analysts are tasked with integrating varying datasets from different sources. The data may be different in terms of data format, type, model, and as well as semantic. Syntactic heterogeneity is concerned with data encoding, type, model, and format while semantic heterogeneity looks at the variances in the meaning and interpretation of the data (Cummings, 2017).

Noisy and Dirty Data

Artificial intelligence possess unique set of distinct attributes that are characterized according to condition, location, and population. The data is located in different sites in unknown quantities making it to be assumed to be dirty (Khan & Al-Badi, 2020). The data has several types of computation errors, missing values, missing values, and outliers making it to be termed as being noisy.

Velocity

In Artificial intelligence, velocity refers to the speed at which the data is retrieved as well as the rate at which it is computed. The velocity for which the data is availed for the users is of big concern to the data handler due the urgency of information by the modern technological gurus.

Data Availability

ML approaches have always relied on data availability insinuating that before learning the whole dataset was assumed to be available. Technology advancement has rendered this analogy null and void due to the demands for data streaming (Khan & Al-Badi, 2020).

Real-Time Streaming

The traditional ML algorithms were not designed to manage the constant data streams. This attribute leads to a velocity associated challenge which is a real-time streaming model. There is need for a near-real-time or real-time machine learning system that is able to update the fast-arriving data on a real-time platform (Cummings, 2017).

Concept Drift

New data are always arriving at an Artificial intelligence system therefore, accessing the whole dataset before the synthesis was not possible with the traditional machine learning models. Concept drift are varying conditional distribution of the anticipated result given the input while holding the distribution input constant.

Veracity

For Artificial intelligence, veracity points to the reliability of the data making up a dataset and well as the unreliability of the sources from which the data was obtained. The quality and provenance of data in Artificial intelligence model is paramount.

Data Provenance

It is a process by which the origin of data and its movement between different locations is traced and recorded. It helps to identify the source of data errors in a machine learning algorithm.

Data Uncertainty

Different approaches are employed to collect data about the various approaches in life. The method used may present instances of uncertainty thus compromising the veracity of the dataset (Khan & Al-Badi, 2020). Absence of absolute truth or objectivity in a data makes it cumbersome for a ML algorithm to identify it.

Recommendations

I recommend that institutions doing businesses in the air travel sector need to make use of artificial intelligence to leverage from the benefits presented by the technology for institutional sustainability. By undertaking the critical steps, the institutions that are involved in machine learning and Artificial intelligence analysis can have all the needed information at their fingertips, which are consistent with the appropriate design model of training in the process of machine learning (Cummings, 2017). Therefore, businesses and institutions will be able to fulfil their dreams and realize the benefits of Artificial intelligence by focusing on machine learning with the help of skilled and knowledgeable data scientists. It involves human interaction and machine intelligence to come up with an analysis that is appealing to the consumer and steer businesses into profits.

Conclusion

Technology has presented a new face of the modern world in terms of its penetration and applicability. Artificial intelligence is commonly utilized in different aspects of the human world including the education sector, health, financial sector, environmental sector as well as in aviation industry. Data is analyzed through machine learning to provide useful insights for decision making and projecting the future occurrences. The past decade has been characterized by Artificial intelligence analysis and machine learning. The two technologies have enabled businesses to operate at optimum with regards to goods and services provided.

References

Aggour, K. S., Gupta, V. K., Ruscitto, D., Ajdelsztajn, L., Bian, X., Brosnan, K. H., … & Vinciquerra, J. (2019). Artificial intelligence/machine learning in manufacturing and inspection: A GE perspective. MRS Bulletin, 44(7), 545-558.

Cummings, M. (2017). Artificial intelligence and the future of warfare. London: Chatham House for the Royal Institute of International Affairs.

Hamdan, A., Hassanien, A. E., Razzaque, A., & Alareeni, B. (Eds.). (2021). The Fourth Industrial Revolution: Implementation of Artificial Intelligence for Growing Business Success (Vol. 935). Springer Nature.

Khan, A. I., & Al-Badi, A. (2020). Open source machine learning frameworks for industrial internet of things. Procedia Computer Science, 170, 571-577.

Wang, T., Pouyanfar, S., Tian, H., Tao, Y., Alonso, M., Luis, S., & Chen, S. C. (2019, July). A framework for airfare price prediction: A machine learning approach. In 2019 IEEE 20th International Conference on Information Reuse and Integration for Data Science (IRI) (pp. 200-207). IEEE.