Usage of Machine Learning for Strategic Decision Making at Higher Educational Institutions

Nieto, Yuri Vanessa; Gacia-Diaz, Vicente; Montenegro, Carlos; González, Claudio; Crespo, Rubén González

doi:10.1109/access.2019.2919343

Cited by 72 publications

(32 citation statements)

References 49 publications

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“…Concurrently, machine-learning based methodologies have shown great potential for pattern recognition and predicting results for multiple types of datasets, in spite of the field using supervised algorithms for most of these works. The results of these methods can be incorporated into the decision-making process [19], even for strategic decision making at higher educational institutions [20], predicting the performance of the students in blended learning [21] or prediction of early dropout [22].…”

Section: Cfd Methods In the Learning Contextmentioning

confidence: 99%

Short CFD Simulation Activities in the Context of Fluid-Mechanical Learning in a Multidisciplinary Student Body

et al. 2019

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Simulation activities are a useful tool to improve competence in industrial engineering bachelors. Specifically, fluid simulation allows students to acquire important skills to strengthen their theoretical knowledge and improve their future professional career. However, these tools usually require long training times and they are usually not available in the subjects of B.Sc. degrees. In this article, a new methodology based on short lessons is raised and evaluated in the fluid-mechanical subject for students enrolled in three different bachelor degree groups: B.Sc. in Mechanical Engineering, B.Sc. in Electrical Engineering and B.Sc. in Electronic and Automatic Engineering. Statistical results show a good acceptance in terms of usability, learning, motivation, thinking over, satisfaction and scalability. Additionally, a machine-learning based approach was applied to find group peculiarities and differences among them in order to identify the need for further personalization of the learning activity.

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Section: Cfd Methods In the Learning Contextmentioning

confidence: 99%

Short CFD Simulation Activities in the Context of Fluid-Mechanical Learning in a Multidisciplinary Student Body

et al. 2019

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“…Setelah kategori yang akan digunakan telah ditentukan, langkah selanjutnya yaitu melakukan data collection and preparation [4]. Setelah pengumpulan dan persiapan data selesai, maka dilanjutkan dengan data pre-processing menggunakan tahapan sebagai berikut [3…”

Section: Koleksi Dan Persiapan Dataunclassified

“…Seleksi fitur dilakukan terhadap kedua dataset ("mingguan" dengan 28 fitur dan "bulanan" dengan 29 fitur [4]. Dalam melakukan seleksi fitur, klasifikasi zona yang diwakili oleh tiga data numerik yaitu 0 (Zona Merah), 1 (Zona Kuning) dan 2 (Zona Hijau) menjadi patokan.…”

Section: B Seleksi Fiturunclassified

Deteksi Dini Status Keanggotaan Industri Kebugaran Menggunakan Pendekatan Supervised Learning

Narabel¹,

Budi²

2020

JuTISI

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In the fitness industry, the number of members is a major factor for the sustainability of its business. The ability of managers and trainers to detect members who represent traits to quit membership is critical. Four supervised learning classification methods like Support Vector Machine, Random Forest, K-Nearest Neighbor, and Artificial Neural Network were used to generate early detection using two variants of datasets that have different amounts of data. Classification results are separated into three different zones, which are Green Zone, Yellow Zone, and Red Zone. Artificial Neural Network methods using backpropagation training give 99.90% of accuracy on a dataset which has more amount of data. The evaluation has been done using the confusion matrix and AUC-ROC curves.

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“…Trust in machine learning (ML) models is one of the greatest challenges in real‐life applications of ML [TAC*20]. ML models are now commonplace in many research and application domains, and they are frequently used in scenarios of complex and critical decision‐making [NGDM*19, PWJ06, TKK18]. Medicine, for example, is one of the fields where the use of ML might offer potential improvements and solutions to many difficult problems [KKS*19, SGSG19, SKK*19].…”

Section: Introductionmentioning

confidence: 99%

The State of the Art in Enhancing Trust in Machine Learning Models with the Use of Visualizations

Chatzimparmpas

Martins

Jusufi

et al. 2020

Computer Graphics Forum

119

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Machine learning (ML) models are nowadays used in complex applications in various domains, such as medicine, bioinformatics, and other sciences. Due to their black box nature, however, it may sometimes be hard to understand and trust the results they provide. This has increased the demand for reliable visualization tools related to enhancing trust in ML models, which has become a prominent topic of research in the visualization community over the past decades. To provide an overview and present the frontiers of current research on the topic, we present a State‐of‐the‐Art Report (STAR) on enhancing trust in ML models with the use of interactive visualization. We define and describe the background of the topic, introduce a categorization for visualization techniques that aim to accomplish this goal, and discuss insights and opportunities for future research directions. Among our contributions is a categorization of trust against different facets of interactive ML, expanded and improved from previous research. Our results are investigated from different analytical perspectives: (a) providing a statistical overview, (b) summarizing key findings, (c) performing topic analyses, and (d) exploring the data sets used in the individual papers, all with the support of an interactive web‐based survey browser. We intend this survey to be beneficial for visualization researchers whose interests involve making ML models more trustworthy, as well as researchers and practitioners from other disciplines in their search for effective visualization techniques suitable for solving their tasks with confidence and conveying meaning to their data.

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Usage of Machine Learning for Strategic Decision Making at Higher Educational Institutions

Cited by 72 publications

References 49 publications

Short CFD Simulation Activities in the Context of Fluid-Mechanical Learning in a Multidisciplinary Student Body

Short CFD Simulation Activities in the Context of Fluid-Mechanical Learning in a Multidisciplinary Student Body

Deteksi Dini Status Keanggotaan Industri Kebugaran Menggunakan Pendekatan Supervised Learning

The State of the Art in Enhancing Trust in Machine Learning Models with the Use of Visualizations

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