An Efficient Prediction System for Diabetes Disease Based on Deep Neural Network by Tawfik Beghriche, Mohamed Djerioui, Youcef Brik, Bilal Attallah, Samir Brahim Belhaouari

“…Moreover, they can be combined with medical knowledge to improve decision-making effectiveness, adaptability, and transparency. A performance comparison between the DNN algorithm and some well-known machine learning techniques as well as the state-of-the-art methods is presented. …”
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Decentralized Nonstationary Fuzzy Neural Network with Meta-Learning-Net by Zhen Zhang, Meiling Yu, Hui Jia

“…Additionally, we demonstrate improved computational efficiency and establish the linear convergence of the proposed decentralized algorithm. By integrating a meta-learning network, we further enhance the output strategy of the NFNN, enabling it to adaptively determine the contribution of individual sub-networks. …”
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Supervised Reinforcement Learning-Based Collaborative Master–Slave Harvest Control Study in Wheat by Zhikai Ma, Chao Zhang, Wei Wang, Hao Wang, Helong Yu, Chunjiang Zhao

“…Secondly, in order to improve the effect of agricultural machine operation, considering the actual grain unloading operation scene and combining the smoothness of operation and the safety of unloading, a new reward function in the supervised reinforcement learning algorithm is designed. …”
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Hybrid model integration with explainable AI for brain tumor diagnosis: a unified approach to MRI analysis and prediction by D. Vamsidhar, Parth Desai, Sagar Joshi, Shrikrishna Kolhar, Nilkanth Deshpande, Shilpa Gite

“…A combination of image processing, vision transformer (ViT), and machine learning algorithms is the first approach that focuses on analyzing medical images. …”
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Advances in UAV Path Planning: A Comprehensive Review of Methods, Challenges, and Future Directions by Wenlong Meng, Xuegang Zhang, Lvzhuoyu Zhou, Hangyu Guo, Xin Hu

“…Innovative methods utilizing artificial intelligence, particularly machine learning and neural networks, are emphasized for their promise in facilitating adaptive responses to intricate, evolving environments. …”
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Assessing Physiological Stress Responses in Student Nurses Using Mixed Reality Training by Kamelia Sepanloo, Daniel Shevelev, Young-Jun Son, Shravan Aras, Janine E. Hinton

“…The simulation consists of six segments, during which critical events like hypotension and hypoxia occur, and the patient’s condition changes based on the nurse’s clinical decisions. Machine learning algorithms were then used to analyze the nurse’s physiological data and to classify different levels of stress. …”
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Advanced Applications of Artificial Intelligence in Pharmacovigilance: Current Trends and Future Perspectives by Yogesh S Ahire, Jayesh H Patil, Harshita N Chordiya, Rashmi A Deore, Vinod A Bairagi

“…They are adept at applying sophisticated algorithms, machine learning models, and natural language processing to glean insights from unstructured data sources like clinical notes, patient narratives, and regulatory reports. …”
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Detecting Unbalanced Network Traffic Intrusions With Deep Learning by S. Pavithra, K. Venkata Vikas

“…To overcome these challenges, this project proposes a novel hybrid Intrusion Detection System using machine learning algorithms, which includes XGBoost, Long Short-Term Memory (LSTM), Mini-VGGNet, and AlexNet, which is used to handle the unbalanced network traffic data. …”
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Recent Advances in In Situ 3D Surface Topographical Monitoring for Additive Manufacturing Processes by Vignesh Suresh, Badrinath Balasubramaniam, Li-Hsin Yeh, Beiwen Li

“…Furthermore, the article discusses the state-of-the-art advancements in closed-loop feedback control systems, sensor fusion, and machine learning algorithms to integrate 3D surface data with various process signatures to dynamically adjust laser parameters and scan strategies. …”
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Integrating artificial intelligence in nanomembrane systems for advanced water desalination by Anbarasu Krishnan, Thanigaivel Sundaram, Beemkumar Nagappan, Yuvarajan Devarajan, Bhumika

“…AI algorithms embedded in these nanomembrane systems enable real-time monitoring, adaptive responses to changing conditions, and proactive maintenance strategies. …”
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Handling Class Imbalanced Data in Sarcasm Detection with Ensemble Oversampling Techniques by Ya-Han Hu, Ting-Hsuan Liu, Chih-Fong Tsai, Yu-Jung Lin

“…In this study, we employ five oversampling algorithms: Synthetic Minority Oversampling TEchnique (SMOTE), Adaptive Synthetic Sampling (ADASYN), polynom-fit-SMOTE, Proximity Weighted Synthetic Sampling (ProWSyn), and SMOTE with Instance Prioritization and Filtering (SMOTE_IPF). …”
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Soft sensor modeling method for Pichia pastoris fermentation process based on substructure domain transfer learning by Bo Wang, Jun Wei, Le Zhang, Hui Jiang, Cheng Jin, Shaowen Huang

“…Finally, based on the source and target domain data after substructure domain adaptation, the least squares support vector machine algorithm is used to establish the prediction model. …”
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The future of critical care: AI-powered mortality prediction for acute variceal gastrointestinal bleeding and acute non-variceal gastrointestinal bleeding patients by Zhou Liu, Guijun Jiang, Liang Zhang, Palpasa Shrestha, Yugang Hu, Yi Zhu, Guang Li, Yuanguo Xiong, Liying Zhan

“…As many as 12 machine learning (ML) algorithms, namely, logistic regression (LR), decision tree (DT), random forest (RF), gradient boosting (GB), AdaBoost, XGBoost, Naive Bayes (NB), support vector machine (SVM), light gradient-boosting machine (LightGBM), K-nearest neighbors (KNN), extremely randomized trees (ET), and voting classifier (VC), were performed. …”
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Sound-Based Unsupervised Fault Diagnosis of Industrial Equipment Considering Environmental Noise by Jeong-Geun Lee, Kwang Sik Kim, Jang Hyun Lee

“…The influence of environmental noise is generally excluded during research on machine fault diagnosis using acoustic signals. This study proposes a fault diagnosis method using a variational autoencoder (VAE) and domain adaptation neural network (DANN), both of which are based on unsupervised learning, to address this problem. …”
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Poisson random measure noise-induced coherence in epidemiological priors informed deep neural networks to identify the intensity of virus dynamics by Saima Rashid, Ayesha Siddiqa, Fekadu Tesgera Agama, Nazeran Idrees, Mohammed Shaaf Alharthi

“…Compartmental models have estimates of parameter complications, whereas machine learning algorithms struggle to understand MPV’s progression and lack elucidation. …”
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Risk assessment of water inrush from coal floor based on enhanced samples with class distribution by Shiwei Liu, Jiaxin Zhao, Hao Yu, Jiaqi Chen

“…A prediction model of the water inrush risk for the coal seam floor was established using a coupled algorithm of extreme learning machines, self-adaptive differential evolution, and CDMTD (PCA-CDMTD-SaDE-ELM) and was used to evaluate the water inrush risk in the 19,105 working face of the Yunjialing Mine. …”
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Unleashing the power of intelligence: revolutionizing malaria outbreak preparedness with an advanced warning system in Benin, West Africa by Gouvidé Jean Gbaguidi, Nikita Topanou, Walter Leal Filho, Komi Agboka, Guillaume K. Ketoh

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The octoPus: An open-source software for supporting farmers in the control of grapevine downy mildew by Simone Bregaglio, Eleonora Del Cavallo, Lorenzo Ascari, Eugenio Rossi

“…Here, we introduce the octoPus, a free digital tool featuring an ensemble of models predicting grapevine downy mildew outbreaks, enhanced by a machine learning algorithm and a large language model, aimed at providing science-based and easy-to-interpret decision support. …”
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Tree networks of real-world data: Analysis of efficiency and spatiotemporal scales by Davide Cipollini, Lambert Schomaker

“…Within the class of hierarchical self-organized systems, we investigate the interplay of structure and function associated with the emergence of complex tree structures in disordered environments. Using an algorithm that creates and searches trees of real-world patterns, our work stands at the intersection of statistical physics, machine learning, and network theory. …”
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Analysis of the capabilities of an information system for improving sleep quality based on biometric data analysis by M.S. Graf, A.V. Yakoniuk, D.V. Krant, I.I. Golovach

“…The system is able to function independently thanks to the use of machine learning algorithms, including LSTM for prediction, Kalman filter for data cleaning, Isolation Forest for anomaly detection, and K-means for clustering sleep patterns.…”
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