One of the challenges practitioners face when applying structure learning algorithms to their data involves determining a set of hyperparameters; otherwise, a set of hyperparameter defaults is assumed. The optimal hyperparameter configuration …

The use of mobile devices for web browsing has increased extraordinarily in recent years, becoming the main source of information. Unfortunately, developers cannot meet the needs of all users. As a result, users have been forced to adapt web …

Subevent relation identification (SRI) is a challenging natural language processing task of great value for knowledge acquisition and reasoning. Given an event pair, previous work mainly defines SRI as a classification task and usually relies on …

The increasing vulnerability of systems and the rise in malicious events have sparked concerns about network security. In order to address these threats, network intrusion detection systems (NIDSs) play a role in protecting against malicious …

Credit scoring models are crucial tools for lenders to assess credit risks. Researchers from academia and the financial industry have shown intense interest in these models. However, real credit datasets often have high dimensionality and class …

Road safety remains a critical issue in contemporary society, where the sudden deterioration of road conditions due to weather-related natural phenomena poses significant risks. These abrupt changes can lead to severe safety hazards on the roads …

To address the problem of sparse data and cold-start when facing new users and items in the single-domain recommendation, cross-domain recommendation has gradually become a hot topic in the recommendation system. This method enhances target domain …

Recently, the impact of product or service reviews on customers' purchasing decisions has become increasingly significant in online businesses. Consequently, manipulating reviews for fame or profit has become prevalent, with some businesses …

Alzheimer’s detection is a challenging task for physicians. There are subtle differences in the bio-marker characteristics of Alzheimers and mild cognitive impairment patients which is very difficult to detect by a physician. Machine learning …

Object ratings in recommendation algorithms are used to represent the extent to which a user likes an object. Most existing recommender systems use these ratings to recommend the top-K objects to a target user. To improve the accuracy and …

As deep neural networks for visual recognition gain momentum, many studies have modified the loss function to improve the classification performance on long-tailed data. Typical and effective improvement strategies are to assign different weights …

Many time series data mining algorithms work by reasoning about the relationships the conserved shapes of subsequences. To facilitate this, the Matrix Profile is a data structure that annotates a time series by recording each subsequence’s …

Large language models (LLMs) have transformed the interpretation and creation of human language in the rapidly developing field of computerized language processing. These models, which are based on deep learning techniques like transformer …

Ensemble learning has gotten a lot of interest because of its capacity to increase predictive accuracy by merging numerous models. However, redundant data and a high level of computing complexity frequently plague ensembles. To choose a subset of …

Sentiment Summarization is an automated technology that extracts important features of sentences and then reorganizes selected words or sentences by their aspect class and sentiment polarity. This emerging research area wields considerable …

Few-shot knowledge graph completion (FKGC) refers to the task of inferring missing facts in a knowledge graph by utilizing a limited number of reference entities. Most FKGC methods assume a single similarity metric, which leads to a single feature …

This paper identifies trends in the application of big data in the transport sector and categorizes research work across scientific subfields. The systematic analysis considered literature published between 2012 and 2022. A total of 2671 studies …

The identification of both closed frequent high average utility itemsets (CFHAUIs) and generators of frequent high average utility itemsets (GFHAUIs) has substantial significance because they play an essential and concise role in representing …

Different embeddings capture various linguistic aspects, such as syntactic, semantic, and contextual information. Taking into account the diverse linguistic facets, we propose a novel hybrid model. This model hinges on the amalgamation of multiple …

We study the certification of stability properties, such as robustness and individual fairness, of the k-nearest neighbor algorithm (kNN). Our approach leverages abstract interpretation, a well-established program analysis technique that has been …