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Heart Disease Prediction: A Comparative Study of Optimisers Performance in Deep Neural Networks
Chisom Chibuike, Adeyinka Ogunsanya
https://arxiv.org/abs/2509.08499 https://

Heart Disease Prediction: A Comparative Study of Optimisers Performance in Deep Neural Networks
Optimization has been an important factor and topic of interest in training deep learning models, yet less attention has been given to how we select the optimizers we use to train these models. Hence, there is a need to dive deeper into how we select the optimizers we use for training and the metrics that determine this selection. In this work, we compare the performance of 10 different optimizers in training a simple Multi-layer Perceptron model using a heart disease dataset from Kaggle. We se…

Federated Learning for ICD Classification with Lightweight Models and Pretrained Embeddings
Binbin Xu, G\'erard Dray
https://arxiv.org/abs/2507.03122 h…

Federated Learning for ICD Classification with Lightweight Models and Pretrained Embeddings
This study investigates the feasibility and performance of federated learning (FL) for multi-label ICD code classification using clinical notes from the MIMIC-IV dataset. Unlike previous approaches that rely on centralized training or fine-tuned large language models, we propose a lightweight and scalable pipeline combining frozen text embeddings with simple multilayer perceptron (MLP) classifiers. This design offers a privacy-preserving and deployment-efficient alternative for clinical NLP app…

First Experience with Real-Time Control Using Simulated VQC-Based Quantum Policies
Yize Sun, Mohamad Hagog, Marc Weber, Daniel Hein, Steffen Udluft, Volker Tresp, Yunpu Ma
https://arxiv.org/abs/2508.01690

First Experience with Real-Time Control Using Simulated VQC-Based Quantum Policies
This paper investigates the integration of quantum computing into offline reinforcement learning and the deployment of the resulting quantum policy in a real-time control hardware realization of the cart-pole system. Variational Quantum Circuits (VQCs) are used to represent the policy. Classical model-based offline policy search was applied, in which a pure VQC with trainable input-output weights is used as a policy network instead of a classical multilayer perceptron. The goal is to evaluate t…

Robust Anomaly Detection in Network Traffic: Evaluating Machine Learning Models on CICIDS2017
Zhaoyang Xu, Yunbo Liu
https://arxiv.org/abs/2506.19877 https…

Robust Anomaly Detection in Network Traffic: Evaluating Machine Learning Models on CICIDS2017
Identifying suitable machine learning paradigms for intrusion detection remains critical for building effective and generalizable security solutions. In this study, we present a controlled comparison of four representative models - Multi-Layer Perceptron (MLP), 1D Convolutional Neural Network (CNN), One-Class Support Vector Machine (OCSVM) and Local Outlier Factor (LOF) - on the CICIDS2017 dataset under two scenarios: detecting known attack types and generalizing to previously unseen threats. O…

Machine Learning based Radio Environment Map Estimation for Indoor Visible Light Communication
Helena Serpi (Tanya), Christina (Tanya), Politi
https://arxiv.org/abs/2507.19149

Machine Learning based Radio Environment Map Estimation for Indoor Visible Light Communication
An innovative method for radio map estimation in optical wireless communications is proposed that is based on Machine Learning rather than simulation techniques. Multi-Layer Perceptron (MLP) representation of indoor Visible Light Communication (VLC) systems is suggested, and signal propagation is estimated. The simulation and performance predictions are accurate, fast and require a reduced set of training sample size with respect to other counterparts, making this solution very suitable for rea…

Advanced For-Loop for QML algorithm search
FuTe Wong
https://arxiv.org/abs/2506.18260 https://arxiv.org/pdf/2506.18260

Advanced For-Loop for QML algorithm search
This paper introduces an advanced framework leveraging Large Language Model-based Multi-Agent Systems (LLMMA) for the automated search and optimization of Quantum Machine Learning (QML) algorithms. Inspired by Google DeepMind's FunSearch, the proposed system works on abstract level to iteratively generates and refines quantum transformations of classical machine learning algorithms (concepts), such as the Multi-Layer Perceptron, forward-forward and backpropagation algorithms. As a proof of conc…

Identification of Strongly Lensed Gravitational Wave Events Using Squeeze-and-Excitation Multilayer Perceptron Data-efficient Image Transformer
Dejiang Li, Tonghua Liu, Ao Liu, Cuihong Wen, Jieci Wang, Kai Liao, Jiaxing Cui
https://arxiv.org/abs/2508.19311

Identification of Strongly Lensed Gravitational Wave Events Using Squeeze-and-Excitation Multilayer Perceptron Data-efficient Image Transformer
With the advancement of third-generation gravitational wave detectors, the identification of strongly lensed gravitational wave (GW) events is expected to play an increasingly vital role in cosmology and fundamental physics. However, traditional Bayesian inference methods suffer from combinatorial computational overhead as the number of events grows, making real-time analysis infeasible. To address this, we propose a deep learning model named Squeeze-and-Excitation Multilayer Perceptron Data-ef…

Deep Learning for Short Term Equity Trend Forecasting: A Behavior Driven Multi Factor Approach
Yuqi Luan
https://arxiv.org/abs/2508.14656 https://arxiv.org…

Deep Learning for Short Term Equity Trend Forecasting: A Behavior Driven Multi Factor Approach
This study proposes a behaviorally-informed multi-factor stock selection framework that integrates short-cycle technical alpha signals with deep learning. We design a dual-task multilayer perceptron (MLP) that jointly predicts five-day future returns and directional price movements, thereby capturing nonlinear market behaviors such as volume-price divergence, momentum-driven herding, and bottom reversals. The model is trained on 40 carefully constructed factors derived from price-volume pattern…

Classification by Separating Hypersurfaces: An Entropic Approach
Argimiro Arratia, Mahmoud El Daou, Henryk Gzyl
https://arxiv.org/abs/2507.02732 https://…

Classification by Separating Hypersurfaces: An Entropic Approach
We consider the following classification problem: Given a population of individuals characterized by a set of attributes represented as a vector in ${\mathbb R}^N$, the goal is to find a hyperplane in ${\mathbb R}^N$ that separates two sets of points corresponding to two distinct classes. This problem, with a history dating back to the perceptron model, remains central to machine learning. In this paper we propose a novel approach by searching for a vector of parameters in a bounded $N$-dimensi…

Pushing the Performance of Synthetic Speech Detection with Kolmogorov-Arnold Networks and Self-Supervised Learning Models
Tuan Dat Phuong, Long-Vu Hoang, Huy Dat Tran
https://arxiv.org/abs/2506.14153

Pushing the Performance of Synthetic Speech Detection with Kolmogorov-Arnold Networks and Self-Supervised Learning Models
Recent advancements in speech synthesis technologies have led to increasingly advanced spoofing attacks, posing significant challenges for automatic speaker verification systems. While systems based on self-supervised learning (SSL) models, particularly the XLSR-Conformer model, have demonstrated remarkable performance in synthetic speech detection, there remains room for architectural improvements. In this paper, we propose a novel approach that replaces the traditional Multi-Layer Perceptron …

Machine Learning-Based Estimation of Monthly GDP
Yonggeun Jung
https://arxiv.org/abs/2506.14078 https://arxiv.org/pdf/2506.14078

Machine Learning-Based Estimation of Monthly GDP
This paper proposes a scalable framework to estimate monthly GDP using machine learning methods. We apply Multi-Layer Perceptron (MLP), Long Short-Term Memory networks (LSTM), Extreme Gradient Boosting (XGBoost), and Elastic Net regression to map monthly indicators to quarterly GDP growth, and reconcile the outputs with actual aggregates. Using data from China, Germany, the UK, and the US, our method delivers robust performance across varied data environments. Benchmark comparisons with prior U…

Discrimination of neutron-$\gamma$ in the low energy regime using machine learning for an EJ-276D plastic scintillator
S. Panda (a,b), P. K. Netrakanti (a), S. P. Behera (a,b), R. R. Sahu (a), K. Kumar (a,b), R. Sehgal (a), D. K. Mishra (a,b), V. Jha (a,b)
https://arxiv.org/abs/2506.13802

Discrimination of neutron-$γ$ in the low energy regime using machine learning for an EJ-276D plastic scintillator
In this work, we present results for discrimination of neutron and $γ$ events using a plastic scintillator detector with pulse shape discrimination capabilities. Machine learning (ML) algorithms are used to improve the discriminatory power between neutron and $γ$ events at lower energy ranges which otherwise are not addressed by the conventional pulse shape discrimination techniques. The use of a multilayer perceptron with Bayesian inference (MLPBNN) and support vector machine (SVM) algorithm…

Learning Internal Biological Neuron Parameters and Complexity-Based Encoding for Improved Spiking Neural Networks Performance
Zofia Rudnicka, Janusz Szczepanski, Agnieszka Pregowska
https://arxiv.org/abs/2508.11674

Learning Internal Biological Neuron Parameters and Complexity-Based Encoding for Improved Spiking Neural Networks Performance
This study introduces a novel approach by replacing the traditional perceptron neuron model with a biologically inspired probabilistic meta neuron, where the internal neuron parameters are jointly learned, leading to improved classification accuracy of spiking neural networks (SNNs). To validate this innovation, we implement and compare two SNN architectures: one based on standard leaky integrate-and-fire (LIF) neurons and another utilizing the proposed probabilistic meta neuron model. As a sec…

HAWC Performance Enhanced by Machine Learning in Gamma-Hadron Separation
R. Alfaro, C. Alvarez, A. Andr\'es, E. Anita-Rangel, M. Araya, J. C. Arteaga-Vel\'azquez, D. Avila Rojas, H. A. Ayala Solares, R. Babu, P. Bangale, E. Belmont-Moreno, A. Bernal, T. Capistr\'an, A. Carrami\~nana, F. Carre\'on, S. Casanova, U. Cotti, E. De la Fuente, D. Depaoli, P. Desiati, N. Di Lalla, R. Diaz Hernandez, M. A. DuVernois, J. C. D\'iaz-V\'elez, K. Engel, T. Ergin, C. Espinoza,…

HAWC Performance Enhanced by Machine Learning in Gamma-Hadron Separation
Improving gamma-hadron separation is one of the most effective ways to enhance the performance of ground-based gamma-ray observatories. With over a decade of continuous operation, the High-Altitude Water Cherenkov (HAWC) Observatory has contributed significantly to high-energy astrophysics. To further leverage its rich dataset, we introduce a machine learning approach for gamma-hadron separation. A Multilayer Perceptron shows the best performance, surpassing traditional and other Machine Learni…

Enhanced Image Recognition Using Gaussian Boson Sampling
Si-Qiu Gong, Ming-Cheng Chen, Hua-Liang Liu, Hao Su, Yi-Chao Gu, Hao-Yang Tang, Meng-Hao Jia, Yu-Hao Deng, Qian Wei, Hui Wang, Han-Sen Zhong, Xiao Jiang, Li Li, Nai-Le Liu, Chao-Yang Lu, Jian-Wei Pan
https://arxiv.org/abs/2506.19707

Enhanced Image Recognition Using Gaussian Boson Sampling
Gaussian boson sampling (GBS) has emerged as a promising quantum computing paradigm, demonstrating its potential in various applications. However, most existing works focus on theoretical aspects or simple tasks, with limited exploration of its capabilities in solving real-world practical problems. In this work, we propose a novel GBS-based image recognition scheme inspired by extreme learning machine (ELM) to enhance the performance of perceptron and implement it using our latest GBS device, J…