Submissions
Submission Preparation Checklist
As part of the submission process, authors are required to check off their submission's compliance with all of the following items, and submissions may be returned to authors that do not adhere to these guidelines.- The submission has not been previously published, nor is it before another journal for consideration (or an explanation has been provided in Comments to the Editor).
- The submission file is produced using the LaTeX file format provided by the journal (https://github.com/matthias-da/ajs-public)
- All illustrations, figures, and tables are placed within the text at the appropriate points, rather than at the end. The figures are included in vector-graphics such as PDF and not as pixel-graphics such as JPG or PNG.
-
The text adheres to the stylistic and bibliographic requirements outlined in the Author Guidelines, which is found in About the Journal.
Title of the manuscript and references in title case style, section headers in case style.
- Optinally, the names and adresses of at least three potential reviewers are submitted together with the manuscript.
Statistical Applications and Methods in Phenological Research (closed)
In consistency with the aims and scope of the Austrian Journal of Statistics among the
contributions to the European Geosciences Union (EGU) General Assembly 2016, up
to 4–8 papers will be selected. Authors of the selected papers will be invited to prepare
an extended peer-reviewed version for this special issue of the journal.
In addition to full length papers, we also encourage the submission of short articles.
All submissions must contain original unpublished work not being considered for pub-
lication elsewhere.
We cordially invite you to submit your manuscript to this special issue under the
section “pheno”. Please see the Guidelines for Authors located on the journal’s web page
www.ajs.or.at
for further details. Authors are strongly encouraged to produce
their papers using LaTeX, but standard Office formats might be accepted as we
iCMS 2019
Only papers from the iCMS 2019 conference are accepted to submit
Machine Learning and Statistical Modeling for Real-World Data Applications and Artificial Intelligence
Machine learning and statistical modeling have become indispensable tools in the
field of artificial intelligence (AI) and real-world data applications. As the volume and
complexity of data continue to grow exponentially, these techniques enable us to extract
valuable insights, make accurate predictions and derive actionable intelligence from diverse
data. Machine learning algorithms uses statistical models to automatically learn patterns,
relationships and dependencies within data. This enables AI systems to make data-driven
decisions by adapting to changing environments and to uncover hidden information. On the
other hand, real-world data applications encompass a wide range of domains, including
healthcare, finance, marketing, cybersecurity, transportation and many others. Supervised
learning algorithms such as decision trees, support vector machines (SVMs) and neural
networks, learn from labeled training data to make predictions. Similarly, unsupervised
learning algorithms including clustering and dimensionality reduction methods, reveal
patterns and groupings within data without prior knowledge of class labels. In
Reinforcement learning, algorithms learn optimal decision-making policies through trial and
error interactions with an environment. Usually, the success of machine learning and
statistical modeling relies on several factors like data quality, feature engineering, model
selection, hyperparameter tuning and validation techniques. It is crucial to pre-process and
clean the data, handle missing values, normalize features appropriately and split the data
into training, validation and test sets. Proper model selection involves considering the
problem at hand, the available data, computational resources and the interpretability or
complexity requirements.
With the constant advancements in these fields, we can continue to unlock the
potential of AI in diverse domains, improving healthcare outcomes, enhancing financial
decision-making, optimizing business processes, bolstering cybersecurity and transforming
transportation systems. In this Special Issue, we aim to analyse this machine learning and
statistical modelling for real-world data applications and artificial intelligence. We welcome
research articles that focus on developing interpretable and explainable machine learning
models, addressing data quality and availability issues, and exploring ethical considerations
in the development and deployment of AI systems.
SPECIAL ISSUE TOPICS INCLUDE, BUT ARE NOT LIMITED TO THE FOLLOWING:
Transfer learning in real-world applications: Leveraging pre-trained models for
domain adaptation.
Active learning strategies for efficient data annotation in complex tasks.
Handling imbalanced datasets: Methods for addressing class imbalance in real-world
data.
Fairness-aware machine learning: Approaches to mitigate bias and ensure equitable
outcomes.
Robustness and reliability of machine learning models in dynamic environments.
Real-time anomaly detection using statistical modeling and machine learning.
Reinforcement learning for decision-making in complex real-world scenarios.
Bayesian modeling and inference for uncertainty quantification in machine learning.
Integration of machine learning and Internet of Things (IoT) for intelligent systems.
Deep learning architectures for natural language processing and understanding.
Machine learning in personalized medicine: Predictive modeling for patient-specific
treatment.
Time series analysis and forecasting using statistical modeling and machine learning.
Human-centered AI: Designing AI systems that incorporate user preferences and
feedback.
IMPORTANT DATES:
Submission Deadline: 10 th November, 2023
Authors Notification: 20 th February, 2024
Revised Version Submission: 25 th April, 2024
Final Decision Notification: 05 th July, 2024
GUEST EDITOR AND CO-GUEST EDITOR INFORMATION:
Dr. Gajendra K. Vishwakarma
Professor
Department of Mathematics & Computing
Indian Institute of Technology Dhanbad
Dhanbad, India
E-mail: [email protected], [email protected]
Scholar Link: https://scholar.google.com/citations?user=i0TdQXgAAAAJ&hl=en
Dr. Atanu Bhattacharjee,
Associate Professor
Real World Evidence Unit,
University of Leicester
Leicester, United Kingdom
E-mail: [email protected], [email protected]
Scholar Link: https://scholar.google.com/citations?user=0aUvGq8AAAAJ&hl=en
Dr. Tahani A. Abushal
Associate Professor
Department of Mathematics
Umm AL-Qura University,
Makkah, Saudi Arabia
E-mail: [email protected]
Scholar Link: https://scholar.google.com/citations?user=YE4aZMAAAAAJ&hl=en
Machine Learning and Statistical Modeling for Real-World Data Applications and Artificial Intelligence
Machine learning and statistical modeling have become indispensable tools in the
field of artificial intelligence (AI) and real-world data applications. As the volume and
complexity of data continue to grow exponentially, these techniques enable us to extract
valuable insights, make accurate predictions and derive actionable intelligence from diverse
data. Machine learning algorithms uses statistical models to automatically learn patterns,
relationships and dependencies within data. This enables AI systems to make data-driven
decisions by adapting to changing environments and to uncover hidden information. On the
other hand, real-world data applications encompass a wide range of domains, including
healthcare, finance, marketing, cybersecurity, transportation and many others. Supervised
learning algorithms such as decision trees, support vector machines (SVMs) and neural
networks, learn from labeled training data to make predictions. Similarly, unsupervised
learning algorithms including clustering and dimensionality reduction methods, reveal
patterns and groupings within data without prior knowledge of class labels. In
Reinforcement learning, algorithms learn optimal decision-making policies through trial and
error interactions with an environment. Usually, the success of machine learning and
statistical modeling relies on several factors like data quality, feature engineering, model
selection, hyperparameter tuning and validation techniques. It is crucial to pre-process and
clean the data, handle missing values, normalize features appropriately and split the data
into training, validation and test sets. Proper model selection involves considering the
problem at hand, the available data, computational resources and the interpretability or
complexity requirements.
With the constant advancements in these fields, we can continue to unlock the
potential of AI in diverse domains, improving healthcare outcomes, enhancing financial
decision-making, optimizing business processes, bolstering cybersecurity and transforming
transportation systems. In this Special Issue, we aim to analyse this machine learning and
statistical modelling for real-world data applications and artificial intelligence. We welcome
research articles that focus on developing interpretable and explainable machine learning
models, addressing data quality and availability issues, and exploring ethical considerations
in the development and deployment of AI systems.
SPECIAL ISSUE TOPICS INCLUDE, BUT ARE NOT LIMITED TO THE FOLLOWING:
Transfer learning in real-world applications: Leveraging pre-trained models for
domain adaptation.
Active learning strategies for efficient data annotation in complex tasks.
Handling imbalanced datasets: Methods for addressing class imbalance in real-world
data.
Fairness-aware machine learning: Approaches to mitigate bias and ensure equitable
outcomes.
Robustness and reliability of machine learning models in dynamic environments.
Real-time anomaly detection using statistical modeling and machine learning.
Reinforcement learning for decision-making in complex real-world scenarios.
Bayesian modeling and inference for uncertainty quantification in machine learning.
Integration of machine learning and Internet of Things (IoT) for intelligent systems.
Deep learning architectures for natural language processing and understanding.
Machine learning in personalized medicine: Predictive modeling for patient-specific
treatment.
Time series analysis and forecasting using statistical modeling and machine learning.
Human-centered AI: Designing AI systems that incorporate user preferences and
feedback.
IMPORTANT DATES:
Submission Deadline: 10 th November, 2023
Authors Notification: 20 th February, 2024
Revised Version Submission: 25 th April, 2024
Final Decision Notification: 05 th July, 2024
GUEST EDITOR AND CO-GUEST EDITOR INFORMATION:
Dr. Gajendra K. Vishwakarma
Professor
Department of Mathematics & Computing
Indian Institute of Technology Dhanbad
Dhanbad, India
E-mail: [email protected], [email protected]
Scholar Link: https://scholar.google.com/citations?user=i0TdQXgAAAAJ&hl=en
Dr. Atanu Bhattacharjee,
Associate Professor
Real World Evidence Unit,
University of Leicester
Leicester, United Kingdom
E-mail: [email protected], [email protected]
Scholar Link: https://scholar.google.com/citations?user=0aUvGq8AAAAJ&hl=en
Dr. Tahani A. Abushal
Associate Professor
Department of Mathematics
Umm AL-Qura University,
Makkah, Saudi Arabia
E-mail: [email protected]
Scholar Link: https://scholar.google.com/citations?user=YE4aZMAAAAAJ&hl=en
Copyright Notice
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