A quantum machine learning framework for predicting drug sensitivity in multiple myeloma using proteomic data by M. Priyadharshini, B. Deevena Raju, A. Faritha Banu, P. Jagdish Kumar, V. Murugesh, Oleg Rybin

“…Abstract In this paper, we introduce QProteoML, a new quantum machine learning (QML) framework for predicting drug sensitivity in Multiple Myeloma (MM) using high-dimensional proteomic data. …”
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Ethical challenges and solutions in AI-driven medical data management: a focus on distributed machine learning by Martin Hähnel

“…The paper underscores the need for a synergy between technological advancements and ethical considerations to uphold values such as patient autonomy, data privacy, and justice in AI applications. It also provides an in-depth analysis of different DML methods (split learning, federated learning, and swarm learning) and their potential applications and drawbacks in healthcare, stressing the importance of developing ethical, secure, and transparent AI systems to prevent misuse and ensure patient trust.…”
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A multi-data fusion deep learning model for prognostic prediction in upper tract urothelial carcinoma by Hongdi Sun, Siping Chen, Yongxing Bao, Fengyan You, Honghui Zhu, Xin Yao, Lianguo Chen, Lianguo Chen, Jiangwei Miao, Fanggui Shao, Fanggui Shao, Xiaomin Gao, Binwei Lin

“…Patients were divided into a training set (n=103) and a testing set (n=30). A multi-modal deep learning model named Multi-modal Image-Clinical Combination Classifier (MICC) was developed by integrating multi-phase contrast-enhanced CT imaging and clinical data. …”
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Flooded and Irrigation Area Monitoring After the Kakhovka Dam Disaster Based on Machine Learning and Satellite Data by Bohdan Yailymov, Hanna Yailymova, Andrii Kolotii, Andrii Shelestov, Sergii Skakun, Sheila Baber, Inbal Becker-Reshef, Nataliia Kussul

“…Thus, this study shows the utility of satellite remote sensing and machine learning approaches for rapid monitoring and quantification of flood-related natural disaster impacts and the analysis of irrigated areas in conflict-affected regions.…”
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Effects of individuality, education, and image on visual attention: Analyzing eye-tracking data using machine learning by Sangwon Lee, Yongha Hwang, Yan Jin, Sihyeong Ahn, Jaewan Park

“…Machine learning, particularly classification algorithms, constructs mathematical models from labeled data that can predict labels for new data. …”
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Deep Learning for Anomaly Detection in CNC Machine Vibration Data: A RoughLSTM-Based Approach by Rasım Çekik, Abdullah Turan

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Improving polygenic prediction from summary data by learning patterns of effect sharing across multiple phenotypes. by Deborah Kunkel, Peter Sørensen, Vijay Shankar, Fabio Morgante

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Enhanced Broad-Learning-Based Dangerous Driving Action Recognition on Skeletal Data for Driver Monitoring Systems by Pu Li, Ziye Liu, Hangguan Shan, Chen Chen

“…This paper proposes a novel method based on 3D skeletal data, combining Graph Spatio-Temporal Feature Representation (GSFR) with a Broad Learning System (BLS) to overcome these challenges. …”
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Inspection Data-Driven Machine Learning Models for Predicting the Remaining Service Life of Deteriorating Bridge Decks by Gitae Roh, Changsu Shim, Hyunhye Song

“…Environmental zoning was applied based on regional conditions, while structural zoning was performed according to load characteristics, thereby allowing the classification of deck regions into moment zones and cantilever sections. Machine learning models were employed to identify dominant deterioration mechanisms, with the validity of the zoning classification being evaluated via model accuracy and SHAP value analysis. …”
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Design of a Prediction Model to Predict Students’ Performance Using Educational Data Mining and Machine Learning by Jayasree R, Sheela Selvakumari

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Part B: Innovative Data Augmentation Approach to Boost Machine Learning for Hydrodynamic Purposes—Computational Efficiency by Hamed Majidiyan, Hossein Enshaei, Damon Howe, Eric Gubesch

“…We previously highlighted the sensitivity of trained models to noise, the importance of computational efficiency, and the need for feature engineering/compactness in hydrodynamic models due to the stochastic nature of waves. A novel data analysis framework was introduced with two purposes to augment data for machine learning (ML) models: transferring features from high-fidelity to low-fidelity surrogates and enhancing simulation data and increasing computational efficiency. …”
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A machine learning approach for the diagnosis of obstructive sleep apnoea using oximetry, demographic and anthropometric data by Zhou Hao Leong, Shaun Ray Han Loh, Leong Chai Leow, Thun How Ong, Song Tar Toh

“…Seven commonly used supervised learning algorithms were trained with the data. Sensitivity (recall), specificity, positive predictive value (PPV) (precision), negative predictive value, area under the receiver operating characteristic curve (AUC) and F1 measure were reported for each model. …”
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A human-on-the-loop approach for labelling seismic recordings from landslide site via a multi-class deep-learning based classification model by Jiaxin Jiang, David Murray, Vladimir Stankovic, Lina Stankovic, Clement Hibert, Stella Pytharouli, Jean-Philippe Malet

Subjects: “…Seismic signal analysis…”
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Explainable machine learning for predicting ICU mortality in myocardial infarction patients using pseudo-dynamic data by Munib Mesinovic, Peter Watkinson, Tingting Zhu

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Advancing EEG-based biometric identification through multi-modal data fusion and deep learning techniques by Touseef Ur Rehman, Madallah Alruwaili, Muhammad Hameed Siddiqi, Yousef Alhwaiti, Sajid Anwar, Zahid Halim, Maaz Alam

“…By leveraging the synergy of multi-modal data analysis and deep learning, this work contributes to the broader objective of developing self-organizing systems capable of adapting to diverse data sources. …”
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Estimating Nitrogen Dioxide Levels Using Open Data and Machine Learning: A Comparative Modeling Study by D. Varam, R. Mitra, F. Kamran, D. A. Abuhani, H. Sulieman, I. Zualkernan

“…Specifically, predictions for urban, rural, and mixed cities demonstrated that urban areas exhibited higher NO₂ concentrations, while rural regions showed comparatively lower levels. The analysis underscores the importance of tailoring models to regional and temporal contexts, affirming that open-source data, combined with machine learning techniques, can effectively estimate NO₂ pollution levels across diverse environments.…”
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Integrating Multi-Source Urban Data with Interpretable Machine Learning for Uncovering the Multidimensional Drivers of Urban Vitality by Yuchen Xie, Jiaxin Zhang, Yunqin Li, Zehong Zhu, Junye Deng, Zhixiu Li

“…It utilizes urban multi-source data to explore how these variables influence different dimensions of street vitality. …”
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Privacy-Preserving Poisoning-Resistant Blockchain-Based Federated Learning for Data Sharing in the Internet of Medical Things by Xudong Zhu, Hui Li

“…This study proposes a privacy-preserving poisoning-resistant blockchain-based federated learning (PPBFL) scheme for secure IoMT data sharing. …”
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Method for Preprocessing Video Data for Training Deep-Learning Models for Identifying Behavioral Events in Bio-Objects by Marina Barulina, Alexander Andreev, Ilya Kovalenko, Ilya Barmin, Eduard Titov, Danil Kirillov

“…Monitoring moving bio-objects is currently of great interest for both fundamental and practical research. The advent of deep-learning algorithms has made it possible to automate the qualitative and quantitative analysis of the behavior of bio-objects recorded in video format. …”
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Enabling Federated Learning Services Using OPC UA, Linked Data and GAIA-X in Cognitive Production by Christian Friedrich, Stefan Vogt, Franziska Rudolph, Paul Patolla, Jossy Milagros Grützmann, Orlando Hohmeier, Martin Richter, Ken Wenzel, Dirk Reichelt, Steffen Ihlenfeldt

“…GAIA-X connectors transfer the service relevant data through a shared data space. The solution enables intelligent analysis and decision-making under the prioritization of data sovereignty and transparency and, therefore, acts as an enabler for future collaborative, data-driven manufacturing applications.…”
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