Polar fox optimization algorithm: a novel meta-heuristic algorithm
The proposed paper introduces a new optimization algorithm inspired by nature called the
polar fox optimization algorithm (PFA). This algorithm addresses the herd life of polar foxes …
polar fox optimization algorithm (PFA). This algorithm addresses the herd life of polar foxes …
Antisocial Behavior Identification from Twitter Feeds Using Traditional Machine Learning Algorithms and Deep Learning.
Antisocial behavior (ASB) is one of the ten personality disorders included in 'The Diagnostic
and Statistical Manual of Mental Disorders (DSM-5) and falls in the same cluster as …
and Statistical Manual of Mental Disorders (DSM-5) and falls in the same cluster as …
Region-based evaluation particle swarm optimization with dual solution libraries for real-time traffic signal timing optimization
Traffic signal timing optimization (TSTO) is a significant topic in the smart city. However,
there are two challenges when solving TSTO. Firstly, it often uses time-consuming …
there are two challenges when solving TSTO. Firstly, it often uses time-consuming …
A multi-source heterogeneous medical data enhancement framework based on lakehouse
M Sheng, S Wang, Y Zhang, R Hao, Y Liang… - … Information Science and …, 2024 - Springer
Obtaining high-quality data sets from raw data is a key step before data exploration and
analysis. Nowadays, in the medical domain, a large amount of data is in need of quality …
analysis. Nowadays, in the medical domain, a large amount of data is in need of quality …
A Dataset of Sudoku Puzzles with Difficulty Metrics Experienced by Human Players
SW Wang - IEEE Access, 2024 - ieeexplore.ieee.org
Sudoku is a popular logic-based puzzle game. Difficulty rating of a Sudoku puzzle is a
difficult problem and a large number of research works have been proposed to tackle the …
difficult problem and a large number of research works have been proposed to tackle the …
A Privacy-Preserving Evolutionary Computation Framework for Feature Selection
Feature selection is a crucial process in data science that involves selecting the most
effective subset of features. Evolutionary computation (EC) is one of the most commonly …
effective subset of features. Evolutionary computation (EC) is one of the most commonly …
A heterogeneous graph-based semi-supervised learning framework for access control decision-making
For modern information systems, robust access control mechanisms are vital in
safeguarding data integrity and ensuring the entire system's security. This paper proposes a …
safeguarding data integrity and ensuring the entire system's security. This paper proposes a …
Supervised Learning for Insider Threat Detection
P Manoharan - 2024 - vuir.vu.edu.au
Cyberattacks cause havoc in the digital world, but the most significant threat might be from
those who appear to be trustworthy: insiders. Insider threats pose a significant and evolving …
those who appear to be trustworthy: insiders. Insider threats pose a significant and evolving …
Sudoku solutions: a comparative analysis of breadth-first search, depth-first search, and human approaches
Sudoku is a puzzle that has a unique solution. No matter how many methods are used, the
result will always be the same. The player thought that the number of givens or clues, the …
result will always be the same. The player thought that the number of givens or clues, the …
Optimising Insider Threat Prediction: Exploring BiLSTM Networks and Sequential Features
Insider threats pose a critical risk to organisations, impacting their data, processes,
resources, and overall security. Such significant risks arise from individuals with authorised …
resources, and overall security. Such significant risks arise from individuals with authorised …