A toolkit for quantifying individual response to herbal extracts in metabolic and inflammatory stress by Soo-yeon Park, Oran Kwon, Tim van den Broek, Jildau Bouwman, Ji Yeon Kim

“…This study integrated a health space model and machine learning to quantify and visualize the impact of herbal extracts on inflammatory and metabolic health at the individual level. …”
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Estimating Compressive Strength of High Performance Concrete with Gaussian Process Regression Model by Nhat-Duc Hoang, Anh-Duc Pham, Quoc-Lam Nguyen, Quang-Nhat Pham

“…This machine learning approach is utilized to establish the nonlinear functional mapping between the compressive strength and HPC ingredients. …”
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An algorithm to Detect Overlapping Red Blood Cells for Sickle Cell Disease Diagnosis. by Mabirizi, Vicent, Kawuma, Simon, Safari, Yonasi

“…To facilitate early detection of sickle cell anemia, medical experts employ machine learning algorithms to detect sickle cell abnormality. …”
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PCA and PSO based optimized support vector machine for efficient intrusion detection in internet of things by Mutkule Prasad Raghunath, Shyam Deshmukh, Poonam Chaudhari, Sunil L. Bangare, Kishori Kasat, Mohan Awasthy, Batyrkhan Omarov, Rajesh R. Waghulde

“…Evaluating the veracity, exactness, and retrieval rate of different machine learning algorithms is crucial for choosing the most effective ones. …”
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Baseline [18F]FDG PET/CT radiomics for predicting interim efficacy in follicular lymphoma treated with first-line R-CHOP by Zeying Wen, Xiaohe Gao, Qingxia Wu, Jianwei Yang, Jian Sun, Keliu Wu, Hongfei Zhao, Ruihua Wang, Yanmei Li

“…Abstract Objective To investigate the predictive value of machine learning-based PET/CT radiomics and clinical risk factors in predicting interim efficacy in patients with follicular lymphoma (FL). …”
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Toward Integrating ChatGPT Into Satellite Image Annotation Workflows: A Comparison of Label Quality and Costs of Human and Automated Annotators by Jacob Beck, Lukas Malte Kemeter, Konrad Durrbeck, Mohamed Hesham Ibrahim Abdalla, Frauke Kreuter

“…High-quality annotations are a critical success factor for machine learning (ML) applications. To achieve this, we have traditionally relied on human annotators, navigating the challenges of limited budgets and the varying task-specific expertise, costs, and availability. …”
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An Interpretable Model for Salinity Inversion Assessment of the South Bank of the Yellow River Based on Optuna Hyperparameter Optimization and XGBoost by Xia Liu, Yu Hu, Xiang Li, Ruiqi Du, Youzhen Xiang, Fucang Zhang

“…It has been a mainstream trend to use machine-learning methods to achieve monitoring of large-scale salinized soil quickly. …”
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Bioinformatics insights into mitochondrial and immune gene regulation in Alzheimer's disease by Tian Meng, Yazhou Zhang, Yuan Ye, Hui Li, Yongsheng He

“…Conclusions Five mitochondrial and immune biomarkers, i.e., TSPO, HIGD1A, NDUFAB1, NT5DC3, and MRPS30, with diagnostic value in Alzheimer's disease, were screened by machine-learning algorithmic models, which will be a guide for future clinical research of Alzheimer's disease in the mitochondria–immunity-related direction.…”
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Intricacies of human–AI interaction in dynamic decision-making for precision oncology by Dipesh Niraula, Kyle C. Cuneo, Ivo D. Dinov, Brian D. Gonzalez, Jamalina B. Jamaluddin, Jionghua Judy Jin, Yi Luo, Martha M. Matuszak, Randall K. Ten Haken, Alex K. Bryant, Thomas J. Dilling, Michael P. Dykstra, Jessica M. Frakes, Casey L. Liveringhouse, Sean R. Miller, Matthew N. Mills, Russell F. Palm, Samuel N. Regan, Anupam Rishi, Javier F. Torres-Roca, Hsiang-Hsuan Michael Yu, Issam El Naqa

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Network Anomaly Detection Using Quantum Neural Networks on Noisy Quantum Computers by Alon Kukliansky, Marko Orescanin, Chad Bollmann, Theodore Huffmire

“…The escalating threat and impact of network-based attacks necessitate innovative intrusion detection systems. Machine learning has shown promise, with recent strides in quantum machine learning offering new avenues. …”
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Shipyard Manpower Digital Recruitment: A Data-Driven Approach for Norwegian Stakeholders by Bogdan Florian Socoliuc, Andrei Alexandru Suciu, Mădălina Ecaterina Popescu, Doru Alexandru Plesea, Florin Nicolae

“…The application of machine learning algorithms provides predictive insights that support real-time adjustments to job postings, optimizing recruitment strategies. …”
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Modified Particle Swarm Optimization on Feature Selection for Palm Leaf Disease Classification by Veri Julianto, Ahmad Rusadi Arrahimi, Oky Rahmanto, Mohammad Sofwat Aldi

“…This study explores the application of artificial intelligence, specifically computer vision and machine learning, for disease detection. Various machine learning techniques, including Local Binary Pattern (LBP), K-Nearest Neighbors (KNN), and Support Vector Machine (SVM), have been used in different studies with varying accuracy. …”
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A Secure Object Detection Technique for Intelligent Transportation Systems by Jueal Mia, M. Hadi Amini

“…Federated Learning is a decentralized machine learning technique that creates a global model by aggregating local models from multiple edge devices without a need to access the local data. …”
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Prioritize Effective Factors of Scheduling in Tehran Metro Station with Fuzzy TOPSIS Model by Vinh Phuc Dung, Minh Tien Nhung

“…This research tries to provide an optimal data mining approach and machine learning principles to predict the route and select the optimal path in metro lines with minimum time, best speed, and minor errors in routing. …”
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Enhancing real estate price prediction using optimized least squares moment balanced machine by Radian Khasani Riqi

“…The OLSMBM was benchmarked against five other machine learning models, including LSSVM, BPNN, ELSIM, Decision Tree, and Linear Regression. …”
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Hypertension Detection Using Passive-Aggressive Algorithm With The PA-I And PA-II Methods by M. Hafidz Ariansyah, Sri Winarno

“…Researchers use machine learning that can explore large amounts of data sets to produce knowledge that is beneficial to science. …”
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Explainable Artificial Intelligence (XAI) to Enhance Trust Management in Intrusion Detection Systems Using Decision Tree Model by Basim Mahbooba, Mohan Timilsina, Radhya Sahal, Martin Serrano

“…Despite the growing popularity of machine learning models in the cyber-security applications (e.g., an intrusion detection system (IDS)), most of these models are perceived as a black-box. …”
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Analisis Kinerja Algoritma Klasifikasi Teks Bert dalam Mendeteksi Berita Hoaks by Assyfa Rasida Hanum, Ivykaeyla Adriana Zetha, Salwa Cahyani Putri, Rafifah Ayud Wulandari, Sherla Puspa Andina, Julia Nur Fajrina, Novanto Yudistira

“…Hasil evaluasi menunjukkan bahwa model klasifikasi BERT memiliki akurasi sebesar 76% pada data validasi dalam mengklasifikasikan berita hoaks, yang menunjukkan performa atau kinerja model Machine Learning dalam melakukan klasifikasi berita hoaks. …”
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Application of ensemble learning techniques to model the atmospheric concentration of SO2 by A. Masih

“…In general, it demonstrates that the performance of ensemble classifiers random forest, bagging and voting can outperform single base traditional statistical and machine learning algorithms such as linear regression, support vector machine for regression and multilayer perceptron to model the atmospheric concentration of sulphur dioxide.…”
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Dynamic Hierarchical Optimization for Train-to-Train Communication System by Haifeng Song, Mingxuan Xu, Yu Cheng, Xiaoqing Zeng, Hairong Dong

“…The DHA combines the stability of traditional algorithms with the flexibility of machine learning to adapt to changing network topologies. …”
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