A Federated Learning Model for Detecting Cyberattacks in Internet of Medical Things Networks by Abdallah Ghourabi, Adel Alkhalil

“…Unlike traditional FL systems that rely on computationally intensive neural networks, our approach leverages XGBoost—a lightweight yet powerful algorithm—to train detection models locally on devices. …”
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Stunting Prediction Modeling in Toddlers Using a Machine Learning Approach and Model Implementation for Mobile Application by Eko Abdul Goffar, Rosa Eliviani, Lili Ayu Wulandhari

“…The models were trained and assessed using public datasets and the most effective algorithm was integrated into a mobile application for practical use. …”
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Determination of agricultural land use: incidence of atmospheric corrections and the implementation in multi-sensor and multi-temporal images by E. Willington, J. P. Clemente, M. Bocco

“…The objectives of this work were to determinate algorithm and images combination that produces the best results to classify agricultural land and, simultaneously, evaluate the need of making atmospheric corrections over them, when classifying multi-temporal/multi-sensor series. …”
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Prediction of Parkinson Disease Using Long-Term, Short-Term Acoustic Features Based on Machine Learning by Mehdi Rashidi, Serena Arima, Andrea Claudio Stetco, Chiara Coppola, Debora Musarò, Marco Greco, Marina Damato, Filomena My, Angela Lupo, Marta Lorenzo, Antonio Danieli, Giuseppe Maruccio, Alberto Argentiero, Andrea Buccoliero, Marcello Dorian Donzella, Michele Maffia

“…A dataset comprising 81 voice samples (41 from healthy individuals and 40 from PD patients) was utilized to train and evaluate common machine learning (ML) models using various types of features, including long-term (jitter, shimmer, and cepstral peak prominence (CPP)), short-term features (Mel-frequency cepstral coefficient (MFCC)), and non-standard measurements (pitch period entropy (PPE) and recurrence period density entropy (RPDE)). …”
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Classification of diabetic retinopathy stages based on neural networks by M. M. Lukashevich, Y. I. Golub

“…Neural network models were trained and results were evaluated with class imbalance taken into account.…”
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Advancing Breast Cancer Diagnosis: A Comprehensive Machine Learning Approach for Predicting Malignant and Benign Cases with Precision and Insight in a Neutrosophic Environment usin... by Nihar Ranjan Panda, R. Rajalakshmi, Surapati Pramanik, Mana Donganont, Prasanta Kumar Raut

“…Four top machine learning algorithms are trained and evaluated with a series of performance measures such as accuracy, positive predictive value (PPV), negative predictive value (NPV), F1-score, etc. …”
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Aircraft Skin Machine Learning-Based Defect Detection and Size Estimation in Visual Inspections by Angelos Plastropoulos, Kostas Bardis, George Yazigi, Nicolas P. Avdelidis, Mark Droznika

“…Aircraft maintenance is a complex process that requires a highly trained, qualified, and experienced team. The most frequent task in this process is the visual inspection of the airframe structure and engine for surface and sub-surface cracks, impact damage, corrosion, and other irregularities. …”
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Improving resectable gastric cancer prognosis prediction: A machine learning analysis combining clinical features and body composition radiomics by Gianni S.S. Liveraro, Maria E.S. Takahashi, Fabiana Lascala, Luiz R. Lopes, Nelson A. Andreollo, Maria C.S. Mendes, Jun Takahashi, José B.C. Carvalheira

“…Body composition radiomics were integrated with clinicopathological factors using machine learning (ML) algorithms trained for patient outcome prediction. We compared results using Random Forest, Logistic Regression and Boosted Decision Tree algorithms. …”
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An automated hip fracture detection, classification system on pelvic radiographs and comparison with 35 clinicians by Abdurrahim Yilmaz, Kadir Gem, Mucahit Kalebasi, Rahmetullah Varol, Zuhtu Oner Gencoglan, Yegor Samoylenko, Hakan Koray Tosyali, Guvenir Okcu, Huseyin Uvet

“…The images were preprocessed using the Contrast Limited Adaptive Histogram Equalization (CLAHE) algorithm. The YOLOv5 architecture was employed for the object detection model, while three different pre-trained deep neural network (DNN) architectures were used for classification, applying transfer learning. …”
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A Multi-Epiphysiological Indicator Dog Emotion Classification System Integrating Skin and Muscle Potential Signals by Wenqi Jia, Yanzhi Hu, Zimeng Wang, Kai Song, Boyan Huang

“…Four machine learning algorithms—Neural Networks (NN), Support Vector Machines (SVM), Gradient Boosting Decision Trees (GBDT), and XGBoost—were trained and evaluated, with XGBoost achieving the highest classification accuracy of 90.54%. …”
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PM2.5 IoT Sensor Calibration and Implementation Issues Including Machine Learning by Wacharapong Srisang, Krisanadej Jaroensutasinee, Mullica Jaroensutasinee, Chonthicha Khongthong, John Rex P. Piamonte, Elena B. Sparrow

“…The data from the Plantower PMS3003 sensor were then compared to the Davis AirLink values using calibration curves created by machine learning algorithms. Calibration curves were generated using machine learning algorithms trained on sensor measurements collected in two Thai cities (Nakhon Si Thammarat and Phuket). …”
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Rice Plant Disease Detection using Convolutional Neural Networks by A. Bala Ayyappan, T. Gobinath, M. Kumar, A. Sivaramakrishnan

“…In this paper, we use Convolutional Neural Networks (CNNs) and deep learning approaches to identify various rice plant diseases like blast, brown spot and bacterial blight. The CNN model is trained on images of different plant diseases, and various models are evaluated to determine the most effective one for disease identification. …”
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Prediction of Rheological Parameters of Polymers by Machine Learning Methods by T. N. Kondratieva, A. S. Chepurnenko

“…Thus, the k-nearest neighbor algorithm and SVM can be used to predict the rheological parameters of polymers as an alternative to artificial neural networks and the CatBoost algorithm, requiring less effort to preset adjustment. …”
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Medical Specialty Classification Using Large Language Models (LLMs) by Surya Kathirvel, Lenin Mookiah

“… This study evaluates the performance of Large Language Model (LLM)-based classifiers, including BERT, Bio-BERT, and Distil-BERT, in comparison to traditional Machine Learning algorithms to classify the medical transcription reports into various specialties. …”
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Soft Sensor Modeling of Key Effluent Parameters in Wastewater Treatment Process Based on SAE-NN by Yousuf Babiker M. Osman, Wei Li

“…Moreover, stochastic gradient descent (SGD) is used to train each layer of SAE to optimize weight parameters, while a strategy of genetic algorithms to identify the number of neurons in each hidden layer is developed. …”
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Code vulnerability detection method based on graph neural network by Hao CHEN, Ping YI

“…The schemes of using neural networks for vulnerability detection are mostly based on traditional natural language processing ideas, processing the code as array samples and ignoring the structural features in the code, which may omit possible vulnerabilities.A code vulnerability detection method based on graph neural network was proposed, which realized function-level code vulnerability detection through the control flow graph feature of the intermediate language.Firstly, the source code was compiled into an intermediate representation, and then the control flow graph containing structural information was extracted.At the same time, the word vector embedding algorithm was used to initialize the vector of basic block to extract the code semantic information.Then both of above were spliced to generate the graph structure sample data.The multilayer graph neural network model was trained and tested on graph structure data features.The open source vulnerability sample data set was used to generate test data to evaluate the method proposed.The results show that the method effectively improves the vulnerability detection ability.…”
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Solar Sail Transfers under Uncertainties: A Deep Reinforcement Learning Approach by Christian Bianchi, Lorenzo Niccolai, Giovanni Mengali

“…Two distinct scenarios are analyzed, each incorporating the aforementioned sources of uncertainty. The trained control policies are then tested through Monte Carlo simulations to evaluate their effectiveness and robustness. …”
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Diagnostic Models for Differentiating COVID-19-Related Acute Ischemic Stroke Using Machine Learning Methods by Eylem Gul Ates, Gokcen Coban, Jale Karakaya

“…Various feature selection algorithms were applied to identify the most relevant features, which were then used to train and evaluate machine learning classification models. …”
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Multi-Fidelity Machine Learning for Identifying Thermal Insulation Integrity of Liquefied Natural Gas Storage Tanks by Wei Lin, Meitao Zou, Mingrui Zhao, Jiaqi Chang, Xiongyao Xie

“…Three machine learning algorithms—Multilayer Perceptron, Random Forest, and Extreme Gradient Boosting—were evaluated to determine the optimal implementation. …”
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Predictive modeling for rework detection in sustainable building projects by AbdulLateef Olanrewaju, Kafayat Shobowale

“…This research pursues two key objectives: first, to prioritise the primary predictors of rework in sustainable buildings; second, to evaluate the most suitable machine learning algorithms for accurately modelling rework occurrences by classifying the extent of rework in the sustainable buildings. …”
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