Interpretability in deep learning for finance: A case study for the Heston model by Damiano Brigo, Xiaoshan Huang, Andrea Pallavicini, Haitz Sáez de Ocáriz Borde

“…In this study, we focused on the calibration of a stochastic volatility model, a subject recently tackled by deep-learning algorithms. We analyzed the Heston model in particular, as this model’s properties are well known, resulting in an ideal benchmark case. …”
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Tracking Sumatran Tiger (Panthera tigris sumatrae Pocock, 1929) distribution in Gunung Leuser National Park: The influence of prey presence and environmental variables on habitat s... by M Hadi Saputra, Salmah Widyastuti, Wanda Kuswanda, R. Taufiq Purna Nugraha, Hendra Helmanto, Nunu Anugrah, U Mamat Rahmat, Rudianto Saragih Napitu, Andrinaldi Adnan, Iskandarrudin, Agus Purwoko, Pindi Patana

“…The algorithms used are maximum entropy and random forest. …”
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Optimizing Energy Supply for Full Electric Vehicles in Smart Cities: A Comprehensive Mobility Network Model by Victor Fernandez, Virgilio Pérez, Rosa Roig

“…This study presents a comprehensive mobility network model designed to predict and optimize the energy supply for FEVs within smart cities. …”
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Employing Data Mining Techniques and Machine Learning Models in Classification of Students’ Academic Performance. by Hussein, Alkattan, Alhumaima, Ali Subhi, Oluwaseun, Adelaja A., Abotaleb, Mostafa, Mijwil, Maad M., Pradeep, Mishra, Sekiwu, Denis, Bamwerinde, Wilson, Turyasingura, Benson

“…Special emphasis was placed on the use of algorithms such as decision trees. The study includes an analysis of factors that affect students' academic performance such as previous academic achievement in educational activities, as well as social and psychological factors. …”
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Data-Driven Approach for Intelligent Classification of Tunnel Surrounding Rock Using Integrated Fractal and Machine Learning Methods by Junjie Ma, Tianbin Li, Roohollah Shirani Faradonbeh, Mostafa Sharifzadeh, Jianfeng Wang, Yuyang Huang, Chunchi Ma, Feng Peng, Hang Zhang

“…The proposed GBDT-BO model shows promise for predicting surrounding rock quality and guiding dynamic tunnel excavation and support.…”
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Characterization of Hazelnut Trees in Open Field Through High-Resolution UAV-Based Imagery and Vegetation Indices by Maurizio Morisio, Emanuela Noris, Chiara Pagliarani, Stefano Pavone, Amedeo Moine, José Doumet, Luca Ardito

“…Three supervised binary classification algorithms were used to build models capable of predicting the status of the tree quadrant, using the VIs as predictors. …”
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Integrative in Silico modeling for mTOR inhibition: From ridge classifiers to descriptor-free deep neural networks by Seyed Alireza Khanghahi, Hadi Kamkar, Seyedehsamaneh Shojaeilangari, Abdollah Allahverdi, Parviz Abdolmaleki

“…Unlike prior studies that focused on specific algorithms or limited descriptors, we benchmark a wide range of models, from traditional models like Random Forest, logistic regression, and SVM to modern algorithms like CNNs, GRUs, and LSTMs, on both descriptor-based features obtained by Dragon and descriptor-free inputs, including raw SMILES string, and Morgan fingerprints. …”
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Analysis of Blockchain-Technology by Danylo Dvorchuk, Iryna Shpinareva

“…The study explores the comparative advantages and limitations of different blockchain architectures and evaluates emerging optimization techniques such as hybrid consensus algorithms and artificial intelligence-based enhancements. …”
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The diagnostic and prognostic value of C1orf174 in colorectal cancer by Elham Nazari, Ghazaleh Khalili-Tanha, Ghazaleh Pourali, Fatemeh Khojasteh-Leylakoohi, Hanieh Azari, Mohammad Dashtiahangar, Hamid Fiuji, Zahra Yousefli, Alireza Asadnia, Mina Maftooh, Hamed Akbarzade, Mohammadreza Nassiri, Seyed Mahdi Hassanian, Gordon A Ferns, Godefridus J Peters, Elisa Giovannetti, Jyotsna Batra, Majid Khazaei, Amir Avan

“…Survival analysis was evaluated using Kaplan–Meier analysis to identify prognostic biomarkers. Predictive biomarkers were determined by machine learning algorithms such as Deep learning, Decision Tree, and Support Vector Machine. …”
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Vibration, current, torque, RPM dataset for multiple fault conditions in industrial-scale electric motors under randomized speed and load variationsMendeley DataMendeley DataMendel... by Wonho Jung, Junho Kim, Kangmin Jang, Sung-Hyun Yun, Daeguen Lim, Minje Jin, Yong-Hwa Park

“…Unlike existing public datasets that often assume constant speed or isolated fault types, this dataset uniquely incorporates multi-fault, multi-severity conditions under randomized speed/load variations, filling critical gaps in real-world applicability for robust fault diagnosis algorithms. The dataset enables robust evaluation of machine learning models and signal processing algorithms for fault detection, condition monitoring, and predictive maintenance in rotating machinery. …”
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The State of Artificial Intelligence and its Prospects in Pakistan's Medical Sector by Rohail Akhtar Habib, Yumna Sattar Khan

“…One possible result of AI integration is the early detection of diseases, especially in rural locations where access to expert medical care is limited. Predictive analytics using machine learning algorithms has become more popular in Pakistani healthcare. …”
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Linguistic Markers of Pain Communication on X (Formerly Twitter) in US States With High and Low Opioid Mortality: Machine Learning and Semantic Network Analysis by ShinYe Kim, Winson Fu Zun Yang, Zishan Jiwani, Emily Hamm, Shreya Singh

“…Six machine learning algorithms (random forest, k-nearest neighbor, decision tree, naive Bayes, logistic regression, and support vector machine) were applied to predict state-level opioid mortality risk based on linguistic features derived from Linguistic Inquiry and Word Count. …”
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An Automated Image-Based Dietary Assessment System for Mediterranean Foods by Fotios S. Konstantakopoulos, Eleni I. Georga, Dimitrios I. Fotiadis

“…The proposed volume estimation subsystem uses stereo vision techniques and algorithms, and needs the input of two food images to reconstruct the point cloud of the food and to compute its quantity. …”
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AI-powered approaches for enhancing remote sensing-based water contamination detection in ecological systems by Li Yang, Zhang Ziwen, Xinhao Lin, Junmiao Hei, Yixiao Wang, Ang Zhang

“…Recent advancements in artificial intelligence (AI) offer promising solutions to enhance water contamination detection, particularly by leveraging machine learning algorithms and sensor networks for continuous monitoring.MethodsThis paper presents a novel AI-powered approach for improving water contamination detection, which incorporates real-time data processing and predictive modeling to identify contamination events and optimize response strategies. …”
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MAST Kinases’ Function and Regulation: Insights from Structural Modeling and Disease Mutations by Michael C. Lemke, Nithin R. Avala, Michael T. Rader, Stefan R. Hargett, Daniel S. Lank, Brandon D. Seltzer, Thurl E. Harris

“…We also estimate the functional consequences of disease point mutations on protein stability by integrating predictive algorithms and AlphaFold. <b>Results</b>: Higher-order organisms often have multiple MASTs and a single MASTL kinase. …”
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Exploring the ceRNA network involving AGAP2-AS1 as a novel biomarker for preeclampsia by Fan Lu, Ni Zeng, Xiang Xiao, Xingxing Wang, Han Gong, Houkang Lei

“…Then candidate hub genes were obtained through five algorithms by the protein-protein intersection (PPI) network of the mRNAs. …”
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Machine learning analysis of cardiovascular risk factors and their associations with hearing loss by Ali Nabavi, Farimah Safari, Ali Faramarzi, Mohammad Kashkooli, Meskerem Aleka Kebede, Tesfamariam Aklilu, Leo Anthony Celi

“…The National Health and Nutrition Examination Survey (NHANES) 2012–2018 data comprising audiometric tests and cardiovascular risk factors was utilized. Machine learning algorithms were trained to classify hearing impairment thresholds and predict pure tone average values. …”
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iMESc – an interactive machine learning app for environmental sciences by Danilo Cândido Vieira, Danilo Cândido Vieira, Fabiana S. Paula, Luciana Erika Yaginuma, Gustavo Fonseca

“…Finally, a hybrid model combining an unsupervised SOM and followed by the supervised Random Forest model returned an accuracy of 83.47% for the training and 80.77% for the test, with Bathymetry, Chlorophyll, and Coarse Sand as key predictive variables. IMESc permits the customization of plots and saving the workflows into “savepoints” guarantying reproducibility. iMESc bridges the gap between the complexity of machine learning algorithms and the need for user-friendly interfaces in environmental research. …”
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Development and Analysis of Patient-Based Complete Conducting Airways Models. by Rafel Bordas, Christophe Lefevre, Bart Veeckmans, Joe Pitt-Francis, Catalin Fetita, Christopher E Brightling, David Kay, Salman Siddiqui, Kelly S Burrowes

“…Other studies have incorporated an algorithmic approach to extrapolate CT segmented airways in order to obtain a complete conducting airway tree down to the level of the acinus. …”
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Universal multilayer network embedding reveals a causal link between GABA neurotransmitter and cancer by Léo Pio-Lopez, Michael Levin

“…However, drug discovery in the context of complex phenotypes are hampered by the difficulties inherent in producing machine learning algorithms that can integrate molecular-genetic, biochemical, physiological, and other diverse datasets. …”
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