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View article: Machine Learning Framework for Automated Transistor-Level Analogue and Digital Circuit Synthesis
Machine Learning Framework for Automated Transistor-Level Analogue and Digital Circuit Synthesis Open
Transistor-level Integrated Circuit (IC) design is fundamental to modern electronics, yet it remains one of the most expertise-intensive and time-consuming stages of chip development. As circuit complexity continues to rise, the need to au…
View article: Meta-learner-based frameworks for interpretable email spam detection
Meta-learner-based frameworks for interpretable email spam detection Open
Introduction With the increasing reliance on digital communication, email has become an essential tool for personal and professional correspondence. However, despite its numerous benefits, digital communication faces significant challenges…
View article: Arab2Vec: An Arabic word embedding model for use in Twitter NLP applications
Arab2Vec: An Arabic word embedding model for use in Twitter NLP applications Open
The analysis of Arabic Twitter data sets is a highly active research topic, particularly since the outbreak of COVID-19 and subsequent attempts to understand public sentiment related to the pandemic. This activity is partially driven by th…
View article: On fitting numerical features into probabilistic distributions to represent data for fuzzy pattern trees
On fitting numerical features into probabilistic distributions to represent data for fuzzy pattern trees Open
Fuzzy Pattern Trees (FPTs) are symbolic tree-based structures whose internal nodes are fuzzy operators, and the leaves are fuzzy features, which enhance interpretability by representing data with meaningful fuzzy terms. However, convention…
View article: GlassBoost: A Lightweight and Explainable Classification Framework for Tabular Datasets
GlassBoost: A Lightweight and Explainable Classification Framework for Tabular Datasets Open
Explainable artificial intelligence (XAI) is essential for fostering trust, transparency, and accountability in machine learning systems, particularly when applied in high-stakes domains. This paper introduces a novel XAI system designed f…
View article: Automatic Generation of Synthesisable Hardware Description Language Code of Multi-Sequence Detector Using Grammatical Evolution
Automatic Generation of Synthesisable Hardware Description Language Code of Multi-Sequence Detector Using Grammatical Evolution Open
Quickly designing digital circuits that are both correct and efficient poses significant challenges. Electronics, especially those incorporating sequential logic circuits, are complex to design and test. While Electronic Design Automation …
View article: Improving Breast Cancer Diagnosis Using Grammatical Evolution-Based Feature Selection
Improving Breast Cancer Diagnosis Using Grammatical Evolution-Based Feature Selection Open
Machine learning has significantly advanced breast cancer diagnosis, yet challenges such as high-dimensional data, severe class imbalance, and limited interpretability persist. To address these issues, we proposed a Grammatical Evolution (…
View article: Self-Adaptive Learning Using Test Case Selection in Grammatical Evolution for Automatic Design of Digital Circuit
Self-Adaptive Learning Using Test Case Selection in Grammatical Evolution for Automatic Design of Digital Circuit Open
In today’s digital age, the demand for increasingly complex digital circuits spans applications ranging from watches to spacecraft. To meet this demand, circuit design automation using Artificial Intelligence (AI) has emerged as a key solu…
View article: A novel ML-driven test case selection approach for enhancing the performance of grammatical evolution
A novel ML-driven test case selection approach for enhancing the performance of grammatical evolution Open
Computational cost in metaheuristics such as Evolutionary Algorithm (EAs) is often a major concern, particularly with their ability to scale. In data-based training, traditional EAs typically use a significant portion, if not all, of the d…
View article: Interpretable Solutions for Breast Cancer Diagnosis with Grammatical Evolution and Data Augmentation
Interpretable Solutions for Breast Cancer Diagnosis with Grammatical Evolution and Data Augmentation Open
Medical imaging diagnosis increasingly relies on Machine Learning (ML) models. This is a task that is often hampered by severely imbalanced datasets, where positive cases can be quite rare. Their use is further compromised by their limited…
View article: Neural Architecture Search for Bearing Fault Classification
Neural Architecture Search for Bearing Fault Classification Open
In this research, we address bearing fault classification by evaluating three neural network models: 1D Convolutional Neural Network (1D-CNN), CNN-Visual Geometry Group (CNN-VGG), and Long Short-Term Memory (LSTM). Utilizing vibration data…
View article: A Novel ML-driven Test Case Selection Approach for Enhancing the Performance of Grammatical Evolution
A Novel ML-driven Test Case Selection Approach for Enhancing the Performance of Grammatical Evolution Open
Computational cost in metaheuristics such as Evolutionary Algorithms (EAs) is often a major concern, particularly with their ability to scale. In data-based training, traditional EAs typically use a significant portion, if not all, of the …
View article: STEM Rebalance: A Novel Approach for Tackling Imbalanced Datasets using SMOTE, Edited Nearest Neighbour, and Mixup
STEM Rebalance: A Novel Approach for Tackling Imbalanced Datasets using SMOTE, Edited Nearest Neighbour, and Mixup Open
Imbalanced datasets in medical imaging are characterized by skewed class proportions and scarcity of abnormal cases. When trained using such data, models tend to assign higher probabilities to normal cases, leading to biased performance. C…
View article: Evolving Multi-Output Digital Circuits Using Multi-Genome Grammatical Evolution
Evolving Multi-Output Digital Circuits Using Multi-Genome Grammatical Evolution Open
Grammatical Evolution is a Genetic Programming variant which evolves problems in any arbitrary language that is BNF compliant. Since its inception, Grammatical Evolution has been used to solve real-world problems in different domains such …
View article: Grammatical Evolution-Driven Algorithm for Efficient and Automatic Hyperparameter Optimisation of Neural Networks
Grammatical Evolution-Driven Algorithm for Efficient and Automatic Hyperparameter Optimisation of Neural Networks Open
Neural networks have revolutionised the way we approach problem solving across multiple domains; however, their effective design and efficient use of computational resources is still a challenging task. One of the most important factors in…
View article: Dynamic Grammar Pruning for Program Size Reduction in Symbolic Regression
Dynamic Grammar Pruning for Program Size Reduction in Symbolic Regression Open
Grammar is a key input in grammar-based genetic programming. Grammar design not only influences performance, but also program size. However, grammar design and the choice of productions often require expert input as no automatic approach e…
View article: Comprehensive Analysis of Learning Cases in an Autonomous Navigation Task for the Evolution of General Controllers
Comprehensive Analysis of Learning Cases in an Autonomous Navigation Task for the Evolution of General Controllers Open
Robotics technology has made significant advancements in various fields in industry and society. It is clear how robotics has transformed manufacturing processes and increased productivity. Additionally, navigation robotics has also been i…
View article: Using active learning and an agent-based system to perform interactive knowledge extraction based on the COVID-19 corpus
Using active learning and an agent-based system to perform interactive knowledge extraction based on the COVID-19 corpus Open
Efficient knowledge extraction from Big Data is quite a challenging topic. Recognizing relevant concepts from unannotated data while considering both context and domain knowledge is critical to implementing successful knowledge extraction.…