An End-to-End Rumor Detection Model Based on Feature Aggregation by Aoshuang Ye, Lina Wang, Run Wang, Wenqi Wang, Jianpeng Ke, Danlei Wang

“…It is crucial to identify rumors automatically. Machine learning technology is widely implemented in the identification and detection of misinformation on social networks. …”
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Review of communication optimization methods in federated learning by YANG Zhikai, LIU Yaping, ZHANG Shuo, SUN Zhe, YAN Dingyu

“…Federated learning, as a distributed machine learning paradigm with privacy protection capabilities, exchanges model parameters through frequent communication between clients and parameter servers, training a joint model without the raw data leaving the local area. …”
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A data-consistent model of the last glaciation in the Alps achieved with physics-driven AI by Tancrède P. M. Leger, Guillaume Jouvet, Sarah Kamleitner, Jürgen Mey, Frédéric Herman, Brandon D. Finley, Susan Ivy-Ochs, Andreas Vieli, Andreas Henz, Samuel U. Nussbaumer

“…We tackle this issue by applying the Instructed Glacier Model, a three-dimensional model enhanced with physics-informed machine learning. This approach allows us to produce 100 Alps-wide and 17 thousand-year-long simulations at 300 m resolution. …”
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Enhancing Orthopedic Surgery and Treatment Using Artificial Intelligence and Its Application in Health and Dietary Welfare by D. Rubidha Devi, S. K. UmaMaheswaran, Sandhya Tarar, Abu Sarwar Zamani, Durgaprasad Gangodkar, A. Prabhu Chakkaravarthy, IssahAbubakari Samori

“…The current decade has seen an increased usage of high-end digital technologies like machine learning in the field of health care services which enable in supporting and performing different functions with less or no human interventions. …”
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Shallow Foundation Settlement Quantification: Application of Hybridized Adaptive Neuro-Fuzzy Inference System Model by Mariamme Mohammed, Ahmad Sharafati, Nadhir Al-Ansari, Zaher Mundher Yaseen

“…Overall, ANFIS-PSO model showed a robust machine learning for settlement prediction.…”
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Metallicity and α-abundance for 48 Million Stars in Low-extinction Regions in the Milky Way by Kohei Hattori

“…We further investigate how our machine learning models extract information on ([M/H], [ α /M]). …”
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Comparison of Feature Selection and Feature Extraction Role in Dimensionality Reduction of Big Data by Haidar Khalid Malik, Nashaat Jasim Al-Anber, Fuad AbdoEsmail Al- Mekhlafi

“…The importance of Dimensionality Reduction technology lies in several fields, including “data processing, patterns recognition, machine learning, and data mining”. This paper compares two essential methods of dimensionality reduction, Feature Extraction and Feature Selection Which Machine Learning models frequently employ. …”
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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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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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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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