Covering Covers: Characterization Of Visual Elements Regarding Sleeves

Evaluating the Performance of Different Models for Children’s Book Recommendations

Blockchain-empowered federated learning based solutions for Internet of Things security, privacy, and performance

Improving accuracy of sound reflection estimation using neural networks

Optimisation of subwoofer placement using a finite-difference time-domain acoustic simulation

Improving security and efficiency in IoT data management using BC based solutions.

Robustness of optimal randomized decision trees with dynamic programming

Trait Analysis to Facilitate Children’s Books Recommender Systems: Extracting perspective and sentiment traits from book fulltext and descriptions

Optimal decision tree using dynamic programming: for the algorithm selection problem

Metadata, the undiscovered treasure box for book recommender systems for children: What traits can be inferred from book’s descriptive and structural metadata which could support the designers of books recommender systems for children?

Spatio-temporal embedding in deep learning for algal bloom forecasting

How do different explanation presentation strategies of feature and data attribution techniques affect non-expert understanding?: Explaining Deep Learning models for Fact-Checking

Evaluating Data Distribution Based Concept Drift Detectors

Evaluating Feature Attribution Methods: an Usecase on a Neural Fact-checking Model

A Comparison of Instance Attribution Methods: Comparing Instance Attribution Methods to Baseline k-Nearest Neighbors Method

Finding Shortcuts to a black-box model using Frequent Sequence Mining: Explaining Deep Learning models for Fact-Checking

How Well do Clustering Similarities-Based Concept Drift Detectors Identify Drift in case of Synthetic/Real-World Data?

Privacy Protection and Performance Enhancement in IoT Applications using Blockchain and Machine Learning Techniques

Cost-sensitive optimal decision trees dealing with delays

The Words are not Enough: An Investigation into the Viability of Textual Complexity as a Feature for Recommendation Systems