scholarly journals Increasing the Prediction Power of Moodle Machine Learning Models with Self-defined Indicators

2021 ◽  
Vol 16 (24) ◽  
pp. 23-39
Author(s):  
Tibor Fauszt ◽  
László Bognár ◽  
Ágnes Sándor
Keyword(s):  
Development Process ◽  
Analysis Of Algorithms ◽  
Learning Models ◽  
Calculation Algorithm ◽  
Students At Risk ◽  
The Core ◽  
New Models ◽  
The One ◽  
Further Development ◽  
Machine Learning Models

Starting with version 3.4 of Moodle, it has been possible to build educational ML models using predefined indicators in the Analytics API. These models can be used primarily to identify students at risk of failure. Our research shows that the goodness and predictability of models built using predefined core indicators in the API lags far behind the generally acceptable level. Moodle is an open-source system, which on the one hand allows the analysis of algorithms, and on the oth-er hand its modification and further development. Utilizing the openness of the system, we examined the calculation algorithm of the core indicators, and then, based on the experience, we built new models with our own indicators. Our re-sults show that the goodness of models built on a given course can be significant-ly improved. In the article, we discuss the development process in detail and pre-sent the results achieved.

Boosting the Competitiveness of Organizations With the Use of Software Engineering

2021 ◽  
pp. 198-216
Author(s):  
Mirna Muñoz
Keyword(s):  
Software Engineering ◽  
Software Development ◽  
Software Quality ◽  
Development Process ◽  
Research Work ◽  
Software Development Process ◽  
The Core ◽  
Development Organizations ◽  
Technological Base ◽  
The One

Software has become the core of organizations in different domains because the capacity of their products, systems, and services have an increasing dependence on software. This fact highlights the research challenges to be covered by computer science, especially in the software engineering (SE) area. On the one way, SE is in charge of covering all the aspects related to the software development process from the early stages of software development until its maintenance and therefore is closely related to the software quality. On the other hand, SE is in charge of providing engineers able to provide technological-base solutions to solve industrial problems. This chapter provides a research work path focused on helping software development organizations to change to a continuous software improvement culture impacting both their software development process highlighting the human factor training needs. Results show that the implementation of best practices could be easily implemented if adequate support is provided.

Machine learning models for Hg prospecting in the Almadén mining district

2021 ◽  
Author(s):  
Julio Alberto López-Gómez ◽  
Daniel Carrasco Pardo ◽  
Pablo Higueras ◽  
Jose María Esbrí ◽  
Saturnino Lorenzo
Keyword(s):  
Machine Learning ◽  
Learning Model ◽  
Mining District ◽  
Learning Models ◽  
Geological Features ◽  
Machine Learning Model ◽  
Mineral Prospectivity ◽  
Data Point ◽  
The One ◽  
Machine Learning Models

<p>Traditionally, prospectivity models were designed using approaches mainly based on expert judgement. These models have been widely applied and they are also known as knowledge-driven prospectivity models (see Harris et al. (2015)). Currently, artificial intelligence approaches, especially machine learning models, are being applied to build prospectivity models since they have been proven to be successful in many other domains (see Sun et al., 2019 and Guerra Prado et al., 2020). They are also known as data-driven prospectivity models. Machine learning models allow to learn from data repositories in order to extract and detect relationships from the data to predict new instances.</p><p>In this work, a geological dataset was collected by a team of expert geologists. The data collected includes the geographical coordinates as well as several geological features of points belonged to seventy-seven different mercury deposits in the Almadén mining district. The resulting dataset is composed by a total of 24798 points and 24 attributes for each point. In particular, we have collected geological and mining-related data regarding the Almadén mercury (Hg) mining district; these data include the location of the several Hg mineralizations, including their typology, size, mineralogy, and stratigraphic position, as well as other information associated to the metallogenetic model set up by Hernández et al. (1999).</p><p>Later, few machine learning models are built to select the one which offers the best results. The aim of this work is twofold: on the one hand, it is intended to build a machine learning model capable of, given the geological features of a data point, to determine the mercury deposit to which it belongs. On the other hand, the aim is to build a machine learning model capable of, given the geological features of a data point, to identify the kind of deposit to which it belongs. The experiments conducted in this work have been properly designed, validating the results obtained using statistical techniques.</p><p>Finally, the models built in this work will allow to generate mercury prospectivity maps. The final aim of this process is to get and train a system able to perform antimony prospection in the nearby Guadalmez syncline.</p><p>This work was funded by the ANR (ANR-19-MIN2-0002-01), the AEI (MICIU/AEI/REF.: PCI2019-103779) and author’s institutions in the framework of the ERA-MIN2 AUREOLE project.</p><p><strong>References</strong></p><p>Guerra Prado E.M.; de Souza Filho C.R.; Carranza E.M.; Motta J.G. (2020). Modeling of Cu-Au prospectivity in the Carajás mineral province (Brasil) through machine learning: Dealing with embalanced training data.</p><p>Harris, J.R.; Grunsky, E.; Corrigan, D. (2015). Data- and knowledge-driven mineral prospectivity maps for Canda’s North.</p><p>Hernández, A.; Jébrak, M.; Higueras, P.; Oyarzun, R.; Morata, D.; Munhá, J. (1999). The Almadén mercury mining district, Spain. Mineralium Deposita, 34: 539-548.</p><p>Sun, T.; Chen, F.; Zhong, L.; Liu, W.; Wang, Y. (2019). GIS-based mineral prospectivity mapping using machine learning methods: A case study from Tongling ore district, eastern China.</p>

scholarly journals Machine Learning Models and Their Development Process as Learning Affordances for Humans

2021 ◽  
pp. 228-240
Author(s):  
Carmel Kent ◽  
Muhammad Ali Chaudhry ◽  
Mutlu Cukurova ◽  
Ibrahim Bashir ◽  
Hannah Pickard ◽  
...  
Keyword(s):  
Machine Learning ◽  
Development Process ◽  
Learning Models ◽  
Machine Learning Models

scholarly journals Data Augmentation and Pretraining for Template-Based Retrosynthetic Prediction in Computer-Aided Synthesis Planning

2020 ◽  
Author(s):  
Michael Fortunato ◽  
Connor W. Coley ◽  
Brian Barnes ◽  
Klavs F. Jensen
Keyword(s):  
Neural Network ◽  
Machine Learning ◽  
Data Augmentation ◽  
Machine Learning Algorithms ◽  
Learning Models ◽  
The Neural Network ◽  
Computer Aided ◽  
Synthesis Planning ◽  
The One ◽  
Machine Learning Models

This work presents efforts to augment the performance of data-driven machine learning algorithms for reaction template recommendation used in computer-aided synthesis planning software. Often, machine learning models designed to perform the task of prioritizing reaction templates or molecular transformations are focused on reporting high accuracy metrics for the one-to-one mapping of product molecules in reaction databases to the template extracted from the recorded reaction. The available templates that get selected for inclusion in these machine learning models have been previously limited to those that appear frequently in the reaction databases and exclude potentially useful transformations. By augmenting open-access datasets of organic reactions with artificially calculated template applicability and pretraining a template relevance neural network on this augmented applicability dataset, we report an increase in the template applicability recall and an increase in the diversity of predicted precursors. The augmentation and pretraining effectively teaches the neural network an increased set of templates that could theoretically lead to successful reactions for a given target. Even on a small dataset of well curated reactions, the data augmentation and pretraining methods resulted in an increase in top-1 accuracy, especially for rare templates, indicating these strategies can be very useful for small datasets.

scholarly journals The Process of Becoming Multilingual: Individual Language Biographies of Poles in Bukovina

2020 ◽  
Author(s):  
Helena Krasowska
Keyword(s):  
Cultural Identity ◽  
Cultural Values ◽  
Heritage Language ◽  
Development Process ◽  
Roman Catholic ◽  
Language Awareness ◽  
The Core ◽  
Catholic Religion ◽  
Polish Language ◽  
The One

The Process of Becoming Multilingual: Individual Language Biographies of Poles in BukovinaThis article focuses on multilingual female speakers born in Bukovina in the 1920s using the language biography method. Analysing selected language biographies of Poles living in southern and northern Bukovina entails focusing on a heritage language. The language biography method shows the development process of individual language awareness. The cases analysed in the study indicate that it is difficult to preserve the linguistic and cultural identity of an individual in mixed-language marriages. For Bukovinian Poles, the Polish language and the Roman Catholic religion are factors of identification and indigenous values symbolizing their belonging to the culture of their ancestors. These two elements are at the core of their identity and are fundamental cultural values which are passed on to children. All the language biographies presented in the article show the speakers’ multilingualism and the way and time in which they learned subsequent languages. Their acquisition was voluntary on the one hand, but imposed on the other. Proces stawania się wielojęzycznym. Indywidualne biografie językowe Polaków na BukowiniePrzedmiotem analiz są wielojęzyczne rozmowy prowadzone przez rozmówców urodzonych na Bukowinie w latach dwudziestych XX wieku. W badaniach zastosowano metodę biografii językowej. Analiza wybranych biografii językowych Polaków mieszkających w Południowej i Północnej Bukowinie wiąże się z skupieniem się na języku dziedzictwa kulturowego. Metoda biografii językowej pokazuje proces rozwoju indywidualnej świadomości językowej. Przypadki analizowane w badaniach wskazują, że trudno jest zachować tożsamość językową i kulturową jednostki w małżeństwach mieszanych. Dla bukowińskich Polaków język polski i religia rzymskokatolicka są czynnikami identyfikacji i wartościami rodzimymi, symbolizującymi ich przynależność do kultury przodków. Te dwa czynniki identyfikacji są podstawowymi wartościami kulturowymi, przekazywanymi dzieciom. Wszystkie biografie językowe przedstawione w artykule pokazują wielojęzyczność mówców oraz sposób i czas, w jaki nauczyli się kolejnych języków.

Impact of monetary policy on economic growth in developing countries for 1990-2015 (Malaysia model)

2019 ◽  
pp. 16
Author(s):  
Fadhel Jawid Awad
Keyword(s):  
Economic Growth ◽  
Monetary Policy ◽  
Economic Policy ◽  
Development Process ◽  
The Core ◽  
Key Indicator ◽  
The One ◽  
Effect Of Monetary Policy ◽  
The Impact ◽  
Do So

Monetary policy is an important part in the general economic policy, most countries seek various economic doctrines to make the tools of monetary policy leads compatible with its objectives, including economic policy and the adequacy of work to do so.  The economic growth highlights the importance of a key indicator of economic activity in the country and whether it was in favor of the recession or prosperity and that the basic outcome of the development process, it is important that the study of the effect of monetary policy in the economic growth achieved. Perceived from the facts that there was a relationship between the nature of the monetary policy adopted in the country and the economic growth achieved by it, the core of the problem of research is the following question: is there an effect of monetary policy in the growth performance and the nature of the impact, if any.             In the same subject, Malaysia is one of those states that seek to achieve development and economic growth. And there is a strong correlation between the success of monetary policy in the use of tools to achieve its objectives on the one hand and between economic growth and development, on the other.

scholarly journals Data Augmentation and Pretraining for Template-Based Retrosynthetic Prediction in Computer-Aided Synthesis Planning

2020 ◽  
Author(s):  
Michael Fortunato ◽  
Connor W. Coley ◽  
Brian Barnes ◽  
Klavs F. Jensen
Keyword(s):  
Neural Network ◽  
Machine Learning ◽  
Data Augmentation ◽  
Machine Learning Algorithms ◽  
Learning Models ◽  
The Neural Network ◽  
Computer Aided ◽  
Synthesis Planning ◽  
The One ◽  
Machine Learning Models

This work presents efforts to augment the performance of data-driven machine learning algorithms for reaction template recommendation used in computer-aided synthesis planning software. Often, machine learning models designed to perform the task of prioritizing reaction templates or molecular transformations are focused on reporting high accuracy metrics for the one-to-one mapping of product molecules in reaction databases to the template extracted from the recorded reaction. The available templates that get selected for inclusion in these machine learning models have been previously limited to those that appear frequently in the reaction databases and exclude potentially useful transformations. By augmenting open-access datasets of organic reactions with artificially calculated template applicability and pretraining a template relevance neural network on this augmented applicability dataset, we report an increase in the template applicability recall and an increase in the diversity of predicted precursors. The augmentation and pretraining effectively teaches the neural network an increased set of templates that could theoretically lead to successful reactions for a given target. Even on a small dataset of well curated reactions, the data augmentation and pretraining methods resulted in an increase in top-1 accuracy, especially for rare templates, indicating these strategies can be very useful for small datasets.

Boosting the Competitiveness of Organizations With the Use of Software Engineering

2022 ◽  
pp. 1838-1856
Author(s):  
Mirna Muñoz
Keyword(s):  
Software Engineering ◽  
Software Development ◽  
Software Quality ◽  
Development Process ◽  
Research Work ◽  
Software Development Process ◽  
The Core ◽  
Development Organizations ◽  
Technological Base ◽  
The One

Software has become the core of organizations in different domains because the capacity of their products, systems, and services have an increasing dependence on software. This fact highlights the research challenges to be covered by computer science, especially in the software engineering (SE) area. On the one way, SE is in charge of covering all the aspects related to the software development process from the early stages of software development until its maintenance and therefore is closely related to the software quality. On the other hand, SE is in charge of providing engineers able to provide technological-base solutions to solve industrial problems. This chapter provides a research work path focused on helping software development organizations to change to a continuous software improvement culture impacting both their software development process highlighting the human factor training needs. Results show that the implementation of best practices could be easily implemented if adequate support is provided.

scholarly journals Loss Reserving Models: Granular and Machine Learning Forms

Risks ◽  
2019 ◽  
Vol 7 (3) ◽  
pp. 82 ◽  
Author(s):  
Taylor
Keyword(s):  
Machine Learning ◽  
Future Development ◽  
Learning Models ◽  
The Future ◽  
Loss Reserving ◽  
Recent Developments ◽  
History Of ◽  
Further Development ◽  
Machine Learning Models

The purpose of this paper is to survey recent developments in granular models and machine learning models for loss reserving, and to compare the two families with a view to assessment of their potential for future development. This is best understood against the context of the evolution of these models from their predecessors, and the early sections recount relevant archaeological vignettes from the history of loss reserving. However, the larger part of the paper is concerned with the granular models and machine learning models. Their relative merits are discussed, as are the factors governing the choice between them and the older, more primitive models. Concluding sections briefly consider the possible further development of these models in the future.

scholarly journals Underwater Image Enhancement with a Deep Residual Framework

2021 ◽  
pp. 200-206
Author(s):  
Anuja Phapale ◽  
Puja Kasture ◽  
Keshav Katkar ◽  
Omkar Karale ◽  
Atal Deshmukh
Keyword(s):  
Image Enhancement ◽  
Development Process ◽  
Learning Models ◽  
User Input ◽  
Python Programming Language ◽  
Underwater Image ◽  
Input Type ◽  
Python Programming ◽  
Machine Learning Models

This paper focuses on framework developed with the goal to enhance the quality of underwater images using machine learning models for the Underwater Image enhancement system. It also covers the various technologies and language used in the development process using Python programming language. The developed system provides two major functionality such as feature to provide input as image or video and returns enhanced image or video depending upon user input type with focus on more image quality, sharpness, colour correctness etc.

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