The UX of Interactive Machine Learning

Ghajargar, Maliheh; Persson, Jan; Bardzell, Jeffrey; Holmberg, Lars; Tegen, Agnes

doi:10.1145/3419249.3421236

Cited by 8 publications

(10 citation statements)

References 12 publications

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“…Dove et al (2017) investigated how to make UX design and ML experts collaborate. At the same time, the study by [34] identifying interesting directions for the application of ML to UX. As [36] points out involves the iterative generation of design artefacts and experiential ways that assist designers in the growth of their knowledge abilities through applying concepts with ML.…”

Section: ) General Characteristics Of Rq1mentioning

confidence: 98%

“…In total, 35 of the 49 survey respondents believe that the interaction between the two disciplines will grow in the future. In addition, [34] interviewed 13 UX experts from industry and academia in semi-structured interviews to learn how they envision ML technologies enhancing or influencing their UX processes.…”

Section: ) General Characteristics Of Rq1mentioning

confidence: 99%

“…Another area of focus was assisting design decisions by analyzing and recommending UI solutions based on previous user behavior or preferences [36]. While [34] proposed term is called Interaction Design. Interaction Design develops mappings between factors of UX and those of IML, which contains four elements that influence UX (Artifact, context, epistemology, and collaboration).…”

Section: ) General Characteristics Of Rq1mentioning

confidence: 99%

“…Therefore, a UX designer's use of ML techniques during the design process can substantially impact the UX [35]. A UX designer's profession and education entail using ML algorithms to investigate links between various design solutions, including different design materials [34].…”

Section: ) General Characteristics Of Rq2mentioning

confidence: 99%

See 3 more Smart Citations

User Experience Design Using Machine Learning: A Systematic Review

2022

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User experience (UX) is the key to increased productivity by enhancing the usability and interactivity of the product. Machine learning (ML) solutions have raised user and academic awareness of technical innovation. As a result, ML is becoming increasingly popular to improve the quality of UX. Several investigations have highlighted a potential lack of studies on the overall challenges and recommendations for UX using ML. Therefore, more attention should be paid to ML's existence and potential applications across various applications to get the most out of ML techniques to improve the UX design process. To this objective, a systematic review of the literature was performed as to determine the challenges faced by UX designers when incorporating ML in their design process. Recommendations that help UX designers incorporate ML into UX design will be highlighted. Furthermore, the PRISMA approach is used (a process that has been established in the literature), to restrict the chance of bias at the selection stage. Relevant articles in the following four databases were searched: IEEE Xplore, Scopus, Web of Science, and ACM. The findings revealed that the number of publications on issues linked to UX with ML had advanced exponentially. This review highlights the challenges, recommendations, tools, algorithms, techniques and datasets used in different studies. In addition, suggestions are given for future investigations.

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Section: ) General Characteristics Of Rq1mentioning

confidence: 98%

Section: ) General Characteristics Of Rq1mentioning

confidence: 99%

Section: ) General Characteristics Of Rq1mentioning

confidence: 99%

Section: ) General Characteristics Of Rq2mentioning

confidence: 99%

See 2 more Smart Citations

User Experience Design Using Machine Learning: A Systematic Review

2022

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“…Experimental attributes such as the chemical nature of the columns through McReynolds coefficients (x', y', z', u', and s'), column geometry (length [L, m], internal diameter [ID, mm] and film thickness [PE, μm]) and other operational aspects of the chromatographic methods, such as the modulation period (PM, s), the carrier gas flow rate (mL min −1 ), carrier gas viscosities at the oven temperatures (GV, Po) and heating rates (A, • C min −1 ) were combined to predict retention times in the first and second dimensions (t1D, min and t2D, s). For this, two DNN-based models were developed: one specialized in 1 The architecture of the networks is presented in Figure 1. Briefly, the input data and outputs were range scaled, and the constructed neural networks had 18 neurons in the input layer (representing the variables under investigation), four or six neurons in the first hidden layer, three or four neurons in the second hidden layer (representing the processing units), and one in the output layer (representing the results predicted by the models).…”

Section: Database and Neural Network Developmentmentioning

confidence: 99%

A deep learning‐based simulator for comprehensive two‐dimensional GC applications

Minho

Cardeal

Menezes

2023

J of Separation Science

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Among the main approaches for predicting the spatial positions of eluates in comprehensive two-dimensional gas chromatography, the still under-explored computational models based on deep learning algorithms emerge as robust and reliable options due to their high adaptability to the structure and complexity of the data. In this work, an open-source program based on deep neural networks was developed to optimize chromatographic methods and simulate operating conditions outside the laboratory. The deep neural networks models were fit to convenient experimental predictors, resulting in scaled losses (mean squared error) equivalent to 0.006 (relative average deviation = 8.56%, R 2 = 0.9202) and 0.014 (relative average deviation = 1.67%, R 2 = 0.8009) in the prediction of the first-and second-dimension retention times, respectively. Good compliance was observed for the main chemical classes, such as environmental contaminants: volatile, semivolatile organic compounds, and pesticides; biochemistry molecules: amino acids and lipids; pharmaceutical industry and personal care products and residues: drugs and metabolites; among others. On the other hand, there is a need for continuous database updates to predict retention times of less common compounds accurately. Thus, forming a collaborative database is proposed, gathering voluntary findings from other users.

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