CfP: MLA 2024 - Special Session @ ACIIDS 2024
16th Asian Conference on Intelligent Information and Database Systems
15-18 April 2024, Ras Al Khaimah, UAE
MLA 2024: Special Session on Machine Learning Algorithms in Theory and Practice
at the 16th Asian Conference on Intelligent Information and Database Systems (ACIIDS 2024)15-18 April 2024, Ras Al Khaimah, UAE
Conference website: http://www.aciids.pwr.edu.pl
Special Session Organizers
Prof. dr hab. Urszula Boryczka
University of Silesia in Katowice, Poland
E-mail: urszula.boryczka@us.edu.pl
Dr Rafał Skinderowicz
University of Silesia in Katowice, Poland
E-mail: rafal.skinderowicz@us.edu.pl
University of Silesia in Katowice, Poland
E-mail: urszula.boryczka@us.edu.pl
Dr Rafał Skinderowicz
University of Silesia in Katowice, Poland
E-mail: rafal.skinderowicz@us.edu.pl
Objectives and topics
Machine learning algorithms are inspired by the way living beings learn.
Most often, we are dealing with a simple mechanism of reinforcing
desirable behaviour and ignoring inappropriate behaviour, combined with
providing the necessary time to develop its own mode of action. The IT
equivalent of such a process is called machine learning, which is a subset
of artificial intelligence solutions. In the nutshell, a Machine Learning
algorithm is given input, a goal and feedback if it is moving in the right
direction. It is also given time to work out for itself the best way to achieve
its goal.
In the business sphere, machine learning allows companies to understand
the data they have and extract relevant management information from it.
A company can use this technology in a wide variety of applications, the
goal of which can be, for example, to increase sales, to create an effective
product search engine with hints and suggestions for the buyer, or even to
launch an autonomous car.
ML algorithms have proven their usefulness wherever traditional
solutions based on lists of instructions have proved useless. Their
outstanding capabilities can be seen, for example, in recognising objects
in photographs, recognising speech and translating it into other languages,
and in analysing and searching for correlations in large Big Data datasets
Artificial intelligence and machine learning are increasingly being used to
create works of art or simulate creative processes. From computational
intelligence algorithms to sophisticated neural network architectures , this
interesting discipline of computer science is revolutionising the fields of
art, poetry and music. The explanation of these processes and their correct
interpretability may contribute to the discovery of completely new areas of
application, at a completely different level of abstraction...
Most often, we are dealing with a simple mechanism of reinforcing
desirable behaviour and ignoring inappropriate behaviour, combined with
providing the necessary time to develop its own mode of action. The IT
equivalent of such a process is called machine learning, which is a subset
of artificial intelligence solutions. In the nutshell, a Machine Learning
algorithm is given input, a goal and feedback if it is moving in the right
direction. It is also given time to work out for itself the best way to achieve
its goal.
In the business sphere, machine learning allows companies to understand
the data they have and extract relevant management information from it.
A company can use this technology in a wide variety of applications, the
goal of which can be, for example, to increase sales, to create an effective
product search engine with hints and suggestions for the buyer, or even to
launch an autonomous car.
ML algorithms have proven their usefulness wherever traditional
solutions based on lists of instructions have proved useless. Their
outstanding capabilities can be seen, for example, in recognising objects
in photographs, recognising speech and translating it into other languages,
and in analysing and searching for correlations in large Big Data datasets
Artificial intelligence and machine learning are increasingly being used to
create works of art or simulate creative processes. From computational
intelligence algorithms to sophisticated neural network architectures , this
interesting discipline of computer science is revolutionising the fields of
art, poetry and music. The explanation of these processes and their correct
interpretability may contribute to the discovery of completely new areas of
application, at a completely different level of abstraction...
- Regression
- Classification
- Clustering
- Time-series forecasting
- Anomaly detection
- Ranking
- Recommendation
- Data generation
- Optimization
- DEEP LEARNING ALGORITHMS
- Convolutional Neural Network (CNN)
- Recurrent Neural Networks (RNNs)
- Long Short-Term Memory Networks (LSTMs)
- Stacked Auto-Encoders
- Deep Boltzmann Machine (DBM)
- Deep Belief Networks (DBN)
- DIMENSIONALITY REDUCTION ALGORITHMS
- Principal Component Analysis (PCA)
- Principal Component Regression (PCR)
- Partial Least Squares Regression (PLSR)
- Sammon Mapping
- Multidimensional Scaling (MDS)
- Projection Pursuit
- Linear Discriminant Analysis (LDA)
- Mixture Discriminant Analysis (MDA)
- Quadratic Discriminant Analysis (QDA)
- Flexible Discriminant Analysis (FDA)
- t-distributed Stochastic Neighbor Embedding (t-SNE)
- Uniform Manifold Approximation and Projection for Dimension Reduction (UMAP)
- ENSEMBLE ALGORITHMS
- Boosting
- Bootstrapped Aggregation (Bagging)
- AdaBoost
- Weighted Average (Blending)
- Stacked Generalization (Stacking)
- Gradient Boosting Machines (GBM)
- Gradient Boosted Regression Trees (GBRT)
- Random Forest
- OTHER MACHINE LEARNING ALGORITHMS
- Feature selection algorithms
- Algorithm accuracy evaluation
- Performance measures
- Optimization algorithms
- ALGORITHMS FROM SPECIALTY SUBFIELDS OF MACHINE LEARNING, SUCH AS:
- Computational intelligence (evolutionary algorithms, etc.)
- Computer Vision (CV)
- Natural Language Processing (NLP)
- Recommender Systems
- Reinforcement Learning
- Graphical Models
- And more...
ACIIDS 2024 important dates
Paper submission: January 15, 2024 (hard deadline)
Notification of acceptance: February 28, 2024
Camera-ready papers: March 15, 2024
Registration & payment: March 15, 2024
Conference date: April 15-18, 2024
Notification of acceptance: February 28, 2024
Camera-ready papers: March 15, 2024
Registration & payment: March 15, 2024
Conference date: April 15-18, 2024