Quantum-Inspired Data Embedding for Unlabeled Data in Sparse Environments: A Theoretical Framework for Improved Semi-Supervised Learning without Hardware Dependence by Shawn Ray

“…In contrast to conventional quantum machine learning methodologies that often rely on quantum hardware, this framework is fully realizable within classical computational architectures, thus bypassing the practical limitations of quantum hardware. …”
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Decision Tree Ensembles to Predict Coronavirus Disease 2019 Infection: A Comparative Study by Amir Ahmad, Ourooj Safi, Sharaf Malebary, Sami Alesawi, Entisar Alkayal

“…The machine learning algorithms were applied on a Covid-19 dataset based on commonly taken laboratory tests to predict Covid-19 positive cases. …”
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Feature selection in single-cell RNA sequencing data: a comprehensive evaluation by Petros Paplomatas, Konstantinos Lazaros, Georgios N. Dimitrakopoulos, Aristidis Vrahatis

“…We developed the GenesRanking package, which offers 20 techniques for dimensionality reduction, including filter-based and embedding machine learning–based methods. By integrating feature selection methods from both statistics and machine learning, we provide a robust framework for improving data interpretation. …”
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Sentiment Analysis from Face Expressions Based on Image Processing Using Deep Learning Methods by Orhan Emre Aksoy, Selda Güney

“…In this study, while comparing classical machine learning methods and deep learning architectures, real-time and non-real-time applications were also compared with two different applications. …”
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Towards Transparent Diabetes Prediction: Combining AutoML and Explainable AI for Improved Clinical Insights by Raza Hasan, Vishal Dattana, Salman Mahmood, Saqib Hussain

“…This study integrates Automated Machine Learning (AutoML) with Explainable Artificial Intelligence (XAI) to improve diabetes risk prediction and enhance model interpretability for healthcare professionals. …”
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Learning-based page replacement scheme for efficient I/O processing by Hwajung Kim

“…Abstract Recent improvements in machine learning techniques offer new opportunities for addressing challenges across various domains. …”
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The Use of Neural Networks in the Diagnosis of Heart Failure Via the Analysis of Medical Data by Valentino Blanco, Aitana Iglesias

“…The three models, namely "k-means, support vector machine, and neural network," are extensively used classification methods in the domains of data mining and machine learning. These models have been applied to forecast cardiac disease, and their predictive performance has been evaluated and compared. …”
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Modelling Customs Revenue in Ghana Using Novel Time Series Methods by Diana Ayorkor Agbenyega, John Andoh, Samuel Iddi, Louis Asiedu

“…The Neural Network Autoregression model of the form NNAR (1, 3) provided the best forecasts with the least Mean Squared Error (MSE) of 53.87 and relatively lower Mean Absolute Percentage Error (MAPE) of 0.08. Generally, the machine learning models (NNAR (1, 3) and BSTS) outperformed the traditional time series models (ARIMA and ARIMAX models). …”
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Teaching a neural network modeling socio-economic development of the region by S. V. Romanchukov, O. G. Berestneva, L. A. Petrova

“…At the same time, however, it should be noted that the resources of an individual research team may be (and most likely will be) insufficient to create their own software solution for the implementation of machine learning algorithms from scratch. The use of third-party cloud-based software platforms (primarily IBM and Google infrastructures) allows to bypass the problem of the research team’s lack of expensive material and technical base, however they impose a number of limitations dictated by the requirements of the existing machine learning algorithms and the specific architecture provided platforms This puts the research team in front of the need to prepare the accumulated data set for processing: reducing the dimension, checking the data for compliance with the platform requirements and eliminating potential problem areas: “data leaks”, “learning distortions” and others. …”
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Enhanced analysis of tabular data through Multi-representation DeepInsight by Alok Sharma, Yosvany López, Shangru Jia, Artem Lysenko, Keith A. Boroevich, Tatsuhiko Tsunoda

“…While traditional machine learning methods can be used for feature engineering and dimensionality reduction, they often struggle to capture the intricate relationships and dependencies within real-world datasets. …”
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Discovering novel lead-free solder alloy by multi-objective Bayesian active learning with experimental uncertainty by Qinghua Wei, Yuanhao Wang, Guo Yang, Tianyuan Li, Shuting Yu, Ziqiang Dong, Tong-Yi Zhang

“…The active learning strategy demonstrates that a machine learning model will have high generalizability if experimental data uncertainty is included, which greatly improves the model prediction or the material design accuracy. …”
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Rede Neural Fuzzy Autoexpansível baseada na Teoria da Ressonância Adaptativa para detecção de sites de Phishing by Gustavo Henrique Santiago da Silva, Reginaldo José da Silva, Angela Leite Moreno

“…Consequentemente, abordagens utilizando Machine Learning vem sendo amplamente propostas, pois apresentam a capacidade de detectar Phishing em tempo real e com performance. …”
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Assessing bias and computational efficiency in vision transformers using early exits by Seth Nixon, Pietro Ruiu, Marinella Cadoni, Andrea Lagorio, Massimo Tistarelli

“…The carbon footprint of machine learning is a concern. A real push is developing to reduce the energy consumption of machine learning as we strive for a more eco-friendly society. …”
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Classifiability Analysis of Spectroscopic Profiling Datasets in Food Safety-related Discriminative Tasks by Yinsheng Zhang, Xudong Yang, Zhengyong Zhang, Haiyan Wang

“…In recent years, spectroscopic profiling combined with machine learning is becoming popular for food-related discriminative tasks, but finding an appropriate classification model can be challenging. …”
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Self-Supervised Chinese Ontology Learning from Online Encyclopedias by Fanghuai Hu, Zhiqing Shao, Tong Ruan

“…In order to avoid the errors in encyclopedias and enrich the learnt ontology, we also apply some machine learning based methods. First, we proof that the self-supervised machine learning method is practicable in Chinese relation extraction (at least for synonymy and hyponymy) statistically and experimentally and train some self-supervised models (SVMs and CRFs) for synonymy extraction, concept-subconcept relation extraction, and concept-instance relation extraction; the advantages of our methods are that all training examples are automatically generated from the structural information of encyclopedias and a few general heuristic rules. …”
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Hyoid bone-based sex discrimination among Egyptians using a multidetector computed tomography: discriminant function analysis, meta-analysis, and artificial intelligence-assisted s... by Afaf Abdelkader, Susan A. Ali, Ahmed Abdeen, Ehab S. Taher, Asmaa Y. A. Hussein, Mamdouh Eldesoqui, Mohamed Abdo, Liana Fericean, Bănăţean-Dunea Ioan, Samah F. Ibrahim, Ashraf M. Said, Darine Amin, Elturabi E. Ebrahim, Amany M. Allam, Mihaela Ostan, Khaled A. Bayoumi, Tabinda Hasan, Ekramy M. Elmorsy

“…The accuracies of machine learning models ranged from 0.8667 to 0.933 with precision, recall, and F1-scores also showing improvements. …”
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Automating airborne pollen classification: Identifying and interpreting hard samples for classifiers by Manuel Milling, Simon D.N. Rampp, Andreas Triantafyllopoulos, Maria P. Plaza, Jens O. Brunner, Claudia Traidl-Hoffmann, Björn W. Schuller, Athanasios Damialis

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Critical biomarkers for responsive deep brain stimulation and responsive focal cortex stimulation in epilepsy field by Zhikai Yu, Binghao Yang, Penghu Wei, Hang Xu, Yongzhi Shan, Xiaotong Fan, Huaqiang Zhang, Changming Wang, Jingjing Wang, Shan Yu, Guoguang Zhao

“…The Linear Discriminant Analysis model demonstrates the highest accuracy among the three basic machine learning models, whereas the Naive Bayesian model necessitates the least amount of computational and storage space. …”
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Deep learning captures the effect of epistasis in multifactorial diseases by Vladislav Perelygin, Alexey Kamelin, Alexey Kamelin, Nikita Syzrantsev, Layal Shaheen, Layal Shaheen, Anna Kim, Nikolay Plotnikov, Anna Ilinskaya, Valery Ilinsky, Alexander Rakitko, Alexander Rakitko, Maria Poptsova

“…Penetrance tables were generated using PyTOXO package. For machine learning methods we used multilayer perceptron (MLP), convolutional neural network (CNN) and recurrent neural network (RNN), Lasso regression, random forest and gradient boosting models. …”
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Attention-enhanced corn disease diagnosis using few-shot learning and VGG16 by Ruchi Rani, Jayakrushna Sahoo, Sivaiah Bellamkonda, Sumit Kumar

“…With the advent of AI, Machine Learning and Deep Learning methods are used to detect and categorize plant diseases. …”
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