A Damage Detection Approach in the Era of Industry 4.0 Using the Relationship between Circular Economy, Data Mining, and Artificial Intelligence by Meisam Gordan, Saeed-Reza Sabbagh-Yazdi, Khaled Ghaedi, Zubaidah Ismail

“…Several data mining techniques were also employed to compare the performance of the developed model. …”
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Automated detection of craters on the lunar surface using deep learning: A review with insights from Chandrayaan-2 TMC-2 data by Mimansa Sinha, Sanchita Paul

“…This review explores recent advancements in deep learning (DL) and machine learning (ML) techniques applied to lunar crater detection, with a focus on the Chandrayaan-2 Terrain Mapping Camera-2 (TMC-2) data. …”
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Application of Isokinetic Dynamometry Data in Predicting Gait Deviation Index Using Machine Learning in Stroke Patients: A Cross-Sectional Study by Xiaolei Lu, Chenye Qiao, Hujun Wang, Yingqi Li, Jingxuan Wang, Congxiao Wang, Yingpeng Wang, Shuyan Qie

“…Objective: This study aims to investigate the use of sensor-acquired isokinetic muscle strength data, combined with machine learning techniques, to predict the GDI in hemiplegic patients. …”
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A Cloud-Based Framework for Creating Scalable Machine Learning Models Predicting Building Energy Consumption from Digital Twin Data by Elham Mahamedi, Alaeldin Suliman, Martin Wonders

“…Although machine learning (ML) techniques are increasingly used to predict building energy consumption from this DT data, existing approaches often lack scalability in handling data growth (data scalability) and/or adapting to evolving data patterns (model scalability). …”
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A Framework for Real-Time Crop Monitoring and Yield Optimisation: Improving Precision Agriculture with IoT-Driven Sensor Networks and Data Analytics by Shnain Ammar H., Mohammed G., Kumar Yalla Jeevan Nagendra

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Dynamic Localized SNV, Peak SNV, and Partial Peak SNV: Novel Standardization Methods for Preprocessing of Spectroscopic Data Used in Predictive Modeling by Emily Grisanti, Maria Totska, Stefan Huber, Christina Krick Calderon, Monika Hohmann, Dominic Lingenfelser, Matthias Otto

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Effectiveness evaluation of combining SAR and multiple optical data on land cover mapping of a fragmented landscape in a cloud computing platform by Giovanni Romano, Giovanni Francesco Ricci, Francesco Gentile

“…Land use/land cover (LULC) mapping in fragmented landscapes, characterized by multiple and small land uses, is still a challenge. This study aims to evaluate the effectiveness of Synthetic Aperture Radar (SAR) and multispectral optical data in land cover mapping using Google Earth Engine (GEE), a cloud computing platform allowing big geospatial data analysis. …”
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From prediction to practice: mitigating bias and data shift in machine-learning models for chemotherapy-induced organ dysfunction across unseen cancers by Heather Shaw, Pinkie Chambers, Matthew Watson, Luke Steventon, James Harmsworth King, Angelo Ercia, Noura Al Moubayed

“…We demonstrate that data aggregation techniques have unintended consequences on model bias.…”
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Innovative Sentiment Analysis and Prediction of Stock Price Using FinBERT, GPT-4 and Logistic Regression: A Data-Driven Approach by Olamilekan Shobayo, Sidikat Adeyemi-Longe, Olusogo Popoola, Bayode Ogunleye

“…., Finaance Bidirectional Encoder representations from Transsformers (FinBERT), Generatice Pre-trained Transformer GPT-4, and Logistic Regression, for sentiment analysis and stock index prediction using financial news and the NGX All-Share Index data label. By leveraging advanced natural language processing models like GPT-4 and FinBERT, alongside a traditional machine learning model, Logistic Regression, we aim to classify market sentiment, generate sentiment scores, and predict market price movements. …”
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Limited Performance of Machine Learning Models Developed Based on Demographic and Laboratory Data Obtained Before Primary Treatment to Predict Coronary Aneurysms by Mi-Jin Kim, Gi-Beom Kim, Dongha Yang, Yeon-Jin Jang, Jeong-Jin Yu

“…Afterward, unsupervised learning techniques were employed to explore the data’s inherent structure. …”
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Data-driven supervision to optimize the effectiveness of proactive case detection for mental health care among children: a proof-of-concept study by Myrthe van den Broek, M. Claire Greene, Anthony F. Guevara, Sandra Agondeze, Erimiah Kyanjo, Olivier Irakoze, Rosco Kasujja, Brandon A. Kohrt, Mark J. D. Jordans

“…The optimization strategy, CCDT+, combined data-driven supervision with motivational interviewing techniques and behavioural nudges for community gatekeepers using the CCDT. …”
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Data Obfuscation Through Latent Space Projection for Privacy-Preserving AI Governance: Case Studies in Medical Diagnosis and Finance Fraud Detection by Mahesh Vaijainthymala Krishnamoorthy

“…ObjectiveThis paper aims to introduce and validate data obfuscation through latent space projection (LSP), a novel privacy-preserving technique designed to enhance AI governance and ensure responsible AI compliance. …”
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Forecasting Weather using Deep Learning from the Meteorological Stations Data : A Study of Different Meteorological Stations in Kaski District, Nepal by Supath Dhital, Kapil Lamsal, Sulav Shrestha, Umesh Bhurtyal

“…The four hourly parameters: Rainfall, Relative Humidity (R.H), Wind Speed, and Air Temperature, were used for modeling the airport station forecast, whereas Rainfall, Relative Humidity (R.H), Maximum and Minimum Temperature were used for modeling the Begnas and Lumle station forecast and only Precipitation data was used for Lamachaur station. Averaging and linear interpolation techniques were used to fill out the missing values and outliers were detected using Box Plot and replaced with threshold value for each parameter. …”
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Soft clustering using real-world data for the identification of multimorbidity patterns in an elderly population: cross-sectional study in a Mediterranean population by Concepción Violán, Quintí Foguet-Boreu, Sergio Fernández-Bertolín, Marina Guisado-Clavero, Margarita Cabrera-Bean, Francesc Formiga, Jose Maria Valderas, Albert Roso-Llorach

“…Objectives The aim of this study was to identify, with soft clustering methods, multimorbidity patterns in the electronic health records of a population ≥65 years, and to analyse such patterns in accordance with the different prevalence cut-off points applied. …”
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Treatment response of patients with tuberculosis and HIV co-infection: a retrospective analysis of secondary data from Shanghai, China, 2010–2020 by Chenyu Dong, Renfang Zhang, Shenyang Li, Jun Chen, Yunhe Liu, Xiaoqiong Xia, Gang Liu, Yinzhong Shen, Lei Liu, Liyan Zeng

“…A total of 7788 valid treatment records corresponding to 17 TB drug compositions and 150 clinical indicators (each > 100 records) were used for analysis. Data mining techniques were employed, including consensus clustering, Fisher’s exact test, stratified analysis, multivariate logistic regression analysis, and three modeling approaches (logistic regression, support vector machine, and random forest). …”
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Remdesivir associated with reduced mortality in hospitalized COVID-19 patients: treatment effectiveness using real-world data and natural language processing by José Ramón Arribas López, María Pilar Ruiz Seco, Francisco Fanjul, Beatriz Díaz Pollán, Patricia González Ruano Pérez, Adrián Ferre Beltrán, Rosa De Miguel Buckley, Laura Portillo Horcajada, Cristina De Álvaro Pérez, Paulo Jorge Barroso Santos Carvalho, Savana Research Group, Melchor Riera Jaume

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AI-Driven LOPCOW-AROMAN Framework and Topological Data Analysis Using Circular Intuitionistic Fuzzy Information: Healthcare Supply Chain Innovation by Muhammad Riaz, Freeha Qamar, Sehrish Tariq, Kholood Alsager

“…Artificial intelligence (AI) stands out as a significant technological innovation, driving progress in diverse areas such as big data analysis, supply chain management, energy efficiency, sustainable development, etc. …”
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Does data privacy influence digital marketing? The mediating role of AI-driven trust: An empirical study of Zain Telecom company in Jordan by Nidal Al Said

“… This research aims to examine how data privacy concerns influence DOI in Digital Marketing and investigates how artificial intelligence trust mechanism integration modulates that effect. …”
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IBCFaiCDR: Auxiliary data-driven item-based collaborative filtering in cross-domain RSs to address user cold start problem by Ronakkumar Patel, Priyank Thakkar

“…Researchers have conducted investigations into the utilization of auxiliary data like social network data, review text, demographic information etc., to augment the RSs ability to learn and understand the user's preferences, with the aim of solving this issue. …”
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Predicting low birth weight risks in pregnant women in Brazil using machine learning algorithms: data from the Araraquara cohort study by Audêncio Victor, Francielly Almeida, Sancho Pedro Xavier, Patrícia H.C. Rondó

“…Methods We analyzed data from 1,579 pregnant women enrolled in the Araraquara Cohort, a population-based longitudinal study. …”
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