Approaches for Localizing the Origin of PVCs in the Outflow Tract: Future Perspectives and Challenges by Baiping Sui, Yingjie Ji, Ying Wang, Ling Gao, Minghao Li

“…To decrease human error and achieve more efficient PVC localization, algorithmic analysis and anatomical modeling with computer-based methods have emerged as promising new approaches. …”
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Filtering Techniques To Reduce Speckle Noise And Image Quality Enhancement Methods On Porous Silicon Images Layers by Tifouti Issam, Rahmouni Salah, Meriane Brahim

“…In the case of digital images, noise can produce difficulties in the diagnosis of images details, such as edges and limits, should be preserved. Most algorithms can reduce or remove speckle noise, but they do not consider the conservation of these details. …”
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SUMMARIA — A XAI Support Methodology by Generating Composite Linguistic Summaries of Qualitative Data by Carlos Rafael Rodríguez-Rodríguez, Yeleny Zulueta-Veliz, Dainys Gainza-Reyes

“…Abstract The main stream of Linguistic Data Summarization involves modeling numerical attributes using linguistic variables, which makes it difficult addressing real-world problems with qualitative or mixed data. …”
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Lentil plant disease and quality assessment: A detailed dataset of high-resolution images for deep learning researchMendeley Data by Eram Mahamud, Md Assaduzzaman, Shayla Sharmin

“…The dataset aims to support the development of machine-learning models for precise disease detection and quality assessment in lentil cultivation. …”
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Mammography reporting dataset with BI-RADS system for natural language processing applications: Addressing public data gaps in SpanishZENODO by José Luis Vázquez Noguera, Alejandro Torres-Hurtado, Helena Gómez-Adorno, Julio César Mello-Román, Enrique Javier Fleitas-Alvarez, Federico Fernando Espinola Schulze, Miguel Garcia-Torres, Carlos Domingo Méndez Gaona, Pedro Esteban Gardel Sotomayor, Silvia Vázquez Noguera, Norma Elizabeth Zaracho Amarilla, Oxades Wilfrido Gamarra Esquivel

“…It provides real-world data for developing and testing NLP algorithms and machine learning models, specifically for automating BI-RADS classification and analyzing mammography reports.…”
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One-Class Anomaly Detection for Industrial Applications: A Comparative Survey and Experimental Study by Davide Paolini, Pierpaolo Dini, Ettore Soldaini, Sergio Saponara

“…The article concludes with a discussion of additional strategies to enhance the runtime analysis of these algorithms, offering insights into potential future applications and improvement directions.…”
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Anomaly detection using machine learning and adopted digital twin concepts in radio environments by Mohamed Hussien Moharam, Omar Hany, Ahmed Hany, Amenah Mahmoud, Mariam Mohamed, Sohila Saeed

“…This work presents an anomaly detection framework using Digital Twin (DT) technology to simulate and monitor dynamic radio environments. By modeling network conditions and attack scenarios, the DT enables accurate identification of anomalies, particularly security threats. …”
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Prediction of time averaged wall shear stress distribution in coronary arteries’ bifurcation varying in morphological features via deep learning by Mohammad Hossein Sarkhosh, Mohammad Hossein Sarkhosh, Hadis Edrisnia, Mohammad Reza Raveshi, Mahkame Sharbatdar

“…To overcome this challenge, we integrate a deep learning (DL) method to improve efficiency and responsiveness. This study presents a DL approach for predicting Time-Averaged Wall Shear Stress (TAWSS) values in coronary arteries’ bifurcation.ResultsTo prepare the dataset, 1800 idealized models with varying morphological parameters are created. …”
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Optimization of Adaptive Observation Strategies for Multi-AUVs in Complex Marine Environments Using Deep Reinforcement Learning by Jingjing Zhang, Weidong Zhou, Xiong Deng, Shuo Yang, Chunwang Yang, Hongliang Yin

“…This paper explores the application of Deep Reinforcement Learning (DRL) to optimize adaptive observation strategies for multi-AUV systems in complex marine environments. Traditional algorithms struggle with the strong coupling between environmental information and observation modeling, making it challenging to derive optimal strategies. …”
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A Novel Logarithmic Approach to General Relativistic Hydrodynamics in Dynamical Spacetimes by Mario Imbrogno, Rita Megale, Luca Del Zanna, Sergio Servidio

“…This method employs a “3 + 1” decomposition of spacetime, complemented by the “1 + log” slicing condition and Gamma-driver shift conditions, which have been shown to improve numerical stability in spacetime evolution. …”
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Strategies and Challenges for Unmanned Aerial Vehicle-Based Continuous Inspection and Predictive Maintenance of Solar Modules by Ghulam E. Mustafa Abro, Amjad Ali, Sufyan Ali Memon, Tayab Din Memon, Faheem Khan

“…The choice to prioritize thermal and visual imaging above mathematical modeling or optimization algorithms arises from the necessity of delivering practical, scalable inspection solutions that can be readily implemented in the field. …”
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A Hybrid Automata-Driven Machine Learning Framework for Real-Time Energy Optimization in Smart Buildings by Rikame Rajashri, Ranjan Mritunjay Kr., Jadhav Monali, Wankhede Shrushti, Bankar Sakshi, Bachhav Pranjal

“…These inputs train the predictive models like Random Forest, XGBoost, LightGBM, and Neural Networks to predict energy requirements. …”
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Machine Learning-Based Detection of Archeological Sites Using Satellite and Meteorological Data: A Case Study of Funnel Beaker Culture Tombs in Poland by Krystian Kozioł, Natalia Borowiec, Urszula Marmol, Mateusz Rzeszutek, Celso Augusto Guimarães Santos, Jerzy Czerniec

“…The machine learning models, including logistic regression and decision tree-based algorithms, demonstrated strong potential for predicting site visibility. …”
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Development of a deep learning system for predicting biochemical recurrence in prostate cancer by Lu Cao, Ruimin He, Ao Zhang, Lingmei Li, Wenfeng Cao, Ning Liu, Peisen Zhang

“…Finally, patient-level artificial intelligence models were developed by integrating deep learning -generated pathology features with several machine learning algorithms. …”
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Challenges of manufacturing for energy efficiency: towards a systematic approach through applications of machine learning by Elaheh Gholamzadeh Nabati, Maria Teresa Alvela Nieto, Dennis Bode, Thimo Florian Schindler, André Decker, Klaus-Dieter Thoben

“…Essential applications and technical challenges for data mapping, integrating, modelling, implementing, and deploying machine learning algorithms in manufacturing processes for increasing energy efficiency are presented. …”
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On the issue of analysis of legal documents on information security of automated systems of internal affairs bodies of the Russian Federation to assess the level of their security by E. A. Rogozin, I. G. Drovnikova, A. O. Yefimov, V. R. Romanova

“…An analysis of international, Russian, as well as departmental legal documents on information security of the ATS of the Russian Federation showed that the documents on the protection of information of the ATS of the Russian Federation, as well as the methodology for quantifying the level of security of these systems, have been developed insufficiently, in particular, they are absent a system of indicators, as well as mathematical models and algorithms for assessing the level of security of the ATS of the Russian Federation, which requires significant improvement of these documents.…”
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Smart diabetes management: remote monitoring and predictive health insights by K.S. Smelyakov, I.A. Lurin, K.V. Misiura, A.S. Chupryna, T.V. Tyzhnenko, O.D. Dolhanenko, V.M. Repikhov

“…A key innovation discussed is GluComp, a modular digital health platform designed to improve diabetes management. GluComp integrates CGM systems with personalized machine learning models to deliver real-time alerts, predictive insights, and adaptive care. …”
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Towards representation learning of radar altimeter waveforms for sea ice surface classification by L. Happ, L. Happ, S. Patil, S. Hendricks, R. Fellegara, L. Kaleschke, A. Gerndt, A. Gerndt

“…Moreover, machine learning models for sea ice classification often depend on supervised training, which is vulnerable to uncertainties in labeled data, especially in polar regions. …”
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Key metrics for monitoring performance variability in edge computing applications by Panagiotis Giannakopoulos, Bart van Knippenberg, Kishor Chandra Joshi, Nicola Calabretta, George Exarchakos

“…We deploy a set of single-particle analysis algorithms, designed for an electron microscopy use case, running on a Kubernetes cluster monitored by Prometheus. …”
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Advancing cardiovascular care through actionable AI innovation by Giuseppe Biondi-Zoccai, Arjun Mahajan, Dylan Powell, Mariangela Peruzzi, Roberto Carnevale, Giacomo Frati

“…Indeed, offline RL refers to a class of ML algorithms that learn optimal decision-making policies from a fixed dataset of previously collected experiences—such as electronic health records or registries—without the need for active, real-time interaction with the clinical environment. …”
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