Integrating Learning-Driven Model Behavior and Data Representation for Enhanced Remaining Useful Life Prediction in Rotating Machinery by Tarek Berghout, Eric Bechhoefer, Faycal Djeffal, Wei Hong Lim

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Bayesian model parameter learning in linear inverse problems: application in EEG focal source imaging by Alexandra Koulouri, Ville Rimpiläinen

“…Even though supervised machine learning techniques offer promising avenues to improve the robustness of the solutions, we have to rely on model-based learning when there is no access to ground truth for the training. …”
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Benchmarking vision-language models for diagnostics in emergency and critical care settings by Christoph F. Kurz, Tatiana Merzhevich, Bjoern M. Eskofier, Jakob Nikolas Kather, Benjamin Gmeiner

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Effectiveness of generative AI-large language models’ recognition of veteran suicide risk: a comparison with human mental health providers using a risk stratification model by Sean A. Lauderdale, Randee Schmitt, Breanna Wuckovich, Natashaa Dalal, Hela Desai, Shealyn Tomlinson

“…BackgroundWith over 6,300 United States military veterans dying by suicide annually, the Veterans Health Administration (VHA) is exploring innovative strategies, including artificial intelligence (AI), for suicide risk assessment. Machine learning has been predominantly utilized, but the application of generative AI-large language models (GAI-LLMs) remains unexplored.ObjectiveThis study evaluates the effectiveness of GAI-LLMs, specifically ChatGPT-3.5, ChatGPT-4o, and Google Gemini, in using the VHA’s Risk Stratification Table for identifying suicide risks and making treatment recommendations in response to standardized veteran vignettes.MethodsWe compared the GAI-LLMs’ assessments and recommendations for both acute and chronic suicide risks to evaluations by mental health care providers (MHCPs). …”
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A synergistic approach for enhanced eye blink detection using wavelet analysis, autoencoding and Crow-Search optimized k-NN algorithm by M. Chandralekha, N. Priyadharshini Jayadurga, Thomas M. Chen, Mithileysh Sathiyanarayanan, Kasif Saleem, Mehmet A. Orgun

“…This proves the advantage of optimized traditional Machine Learning models over the Deep Learning models in realistic EEG-based eye blink detection.…”
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Predictive modelling of graphene-enhanced greases using classical feedback control and quantum kernel regression by Ethan Stefan-Henningsen, Amirkianoosh Kiani

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Black carbon derived PET plastic bottle waste and rice straw for sorption of Acid Red 27 dye: Machine learning approaches, kinetics, isotherm and thermodynamic studies. by Tapos Kumar Chakraborty, Lamia Tammim, Khandakar Rashedul Islam, Md Simoon Nice, Baytune Nahar Netema, Md Sozibur Rahman, Sujoy Sen, Samina Zaman, Gopal Chandra Ghosh, Asadullah Munna, Ahsan Habib, Khadiza Tul-Coubra, Himel Bosu, Monishanker Halder, Md Aliur Rahman

“…High coefficient value [PETWBC (R2 = 0.94), and RSBC (R2 = 0.97)] of process optimization model suggesting that this model was significant, where pH and adsorbent dose expressively stimulus removal efficiency including 99.88, and 99.89% for PETWBC, and RSBC at pH (2). …”
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Predicting Software Perfection Through Advanced Models to Uncover and Prevent Defects by Tariq Shahzad, Sunawar Khan, Tehseen Mazhar, Wasim Ahmad, Khmaies Ouahada, Habib Hamam

“…The models were trained and tested on preprocessed and feature-selected data, followed by optimization through hyperparameter tuning. …”
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CFS-MOES Ensemble Model on Metaheuristic Search-Based Feature Selection by Santosini Bhutia, Bichitrananda Patra, Mitrabinda Ray

“…The features chosen by our proposed CFS-MOES method significantly improve the accuracy of the proposed model. Moreover, the proposed model has also been compared with the other ensemble models generated using CFS-ES (correlation-based feature selection —evolutionary search), CFS-PSOS (correlation-based feature selection—particle swarm optimization search), CFS-GS (correlation-based feature selection—genetic search), and CFS-HS (correlation-based feature selection—harmony search) feature selection methods, ensuring better classification accuracy with a reduced feature subset. …”
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Transformer Fault Diagnosis Utilizing Feature Extraction and Ensemble Learning Model by Gonglin Xu, Mei Zhang, Wanli Chen, Zhihui Wang

“…The final step involves optimizing the Light Gradient Boosting Machine (LightGBM) model using IAOS algorithm for transformer fault classification; this model is an ensemble learning model. …”
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Sustainable Cold Chain Management: An Evaluation of Predictive Waste Management Models by Hajar Fatorachian, Kulwant Pawar

“…The integration of advanced predictive models is pivotal for optimizing demand forecasting and inventory management in cold chain logistics. …”
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Automated and Enhanced Classification of Persea Americana Using Optimized Deep Convolutional Neural Networks With Improved Training Strategies for Agro-Industrial Settings by Oscar Vera, Jose Cruz, Severo Huaquipaco, Wilson Mamani, Victor Yana-Mamani, Saul Huaquipaco

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Wind energy system fault classification and detection using deep convolutional neural network and particle swarm optimization‐extreme gradient boosting by Chun‐Yao Lee, Edu Daryl C. Maceren

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The applications of CT with artificial intelligence in the prognostic model of idiopathic pulmonary fibrosis by Zeyu Chen, Zheng Lin, Zihan Lin, Qi Zhang, Haoyun Zhang, Haiwen Li, Qing Chang, Jianqi Sun, Feng Li

“…Artificial intelligence (AI) algorithms, including principal component analysis, support vector machine, random survival forest, and convolutional neural network, could be applied to the procedure of IPF prognostic model, that is, region of interest extraction, image feature selection, clinical feature selection, and model construction. …”
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Attack resilient IoT security framework using multi head attention based representation learning with improved white shark optimization algorithm by Jawhara Aljabri

“…Besides, the sand cat swarm optimization (SCSO) model is used for the feature selection (FS) process. …”
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GA-Attention-Fuzzy-Stock-Net: An optimized neuro-fuzzy system for stock market price prediction with genetic algorithm and attention mechanism by Burak Gülmez

“…Genetic algorithms optimize the hyperparameters, including learning rates and network architectures, while the attention mechanism enhances the model's ability to focus on relevant temporal patterns. …”
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Optimized customer churn prediction using tabular generative adversarial network (GAN)-based hybrid sampling method and cost-sensitive learning by I Nyoman Mahayasa Adiputra, Paweena Wanchai, Pei-Chun Lin

“…However, these methods have not performed well with classical machine learning algorithms. Methods To optimize the performance of classical machine learning on customer churn prediction tasks, this study introduces an extension framework called CostLearnGAN, a tabular generative adversarial network (GAN)-hybrid sampling method, and cost-sensitive Learning. …”
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A fuzzy multi-criteria decision-making for optimizing supply chain aggregate production planning based on cost reduction and risk mitigation by Noppasorn Sutthibutr, Kunihiko Hiraishi, Navee Chiadamrong

“…A Multi-Objective Fuzzy Linear Programming model was proposed by integrating the Chance-Constraint Programming and Zimmermann’s approach designed to assist decision makers in optimizing the production plan, material flows and resource allocation across the entire supply chain network. …”
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HCAP: Hybrid cyber attack prediction model for securing healthcare applications. by Mohanad Faeq Ali, Mohammed Shakir Mohmood, Ban Salman Shukur, Rex Bacarra, Jamil Abedalrahim Jamil Alsayaydeh, Masrullizam Mat Ibrahim, Safarudin Gazali Herawan

“…The extracted features are fed into the lion-optimization technique to fine-tune the hyperparameters of the recurrent neural networks, enhancing the model's ability to efficiently predict cybersecurity threats with a maximum recognition rate in IoMT environments. …”
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Computational Linguistics Applications in AI-Based Investment and Cost Structuring Models by Miralieva Dilafruz, Ramatov Jumaniyoz, Azimova Lola, Khusamiddinova Malika, Alimbaeva Shahlo, Omonova Laylo

“…AI-enabled linguistics modeling promotes scalable optimization and context-aware applications of financial analytics, and realizes cost transparency improvements in automated investment systems. …”
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