Hybrid neural network method for damage localization in structural health monitoring by Fatahlla Moreh, Yusuf Hasan, Zarghaam Haider Rizvi, Sven Tomforde, Frank Wuttke

“…Many existing methods for crack detection rely on deep learning algorithms or traditional approaches that typically use image data. …”
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Data-driven decoding of quantum error correcting codes using graph neural networks by Moritz Lange, Pontus Havström, Basudha Srivastava, Isak Bengtsson, Valdemar Bergentall, Karl Hammar, Olivia Heuts, Evert van Nieuwenburg, Mats Granath

“…Accurate, maximum likelihood, decoders are computationally very expensive whereas decoders based on more efficient algorithms give sub-optimal performance. In addition, the accuracy will depend on the quality of models and estimates of error rates for idling qubits, gates, measurements, and resets, and will typically assume symmetric error channels. …”
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Enhancing surface detection: A comprehensive analysis of various YOLO models by G. Deepti Raj, B. Prabadevi

“…Version 5 of You Only Look Once (YOLO), a precisely optimized learning model, has undergone extensive testing on steel strip datasets, providing effective solutions for recognition and detection in industry environments. …”
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Long Short-Term Memory-Based Computerized Numerical Control Machining Center Failure Prediction Model by Jintak Choi, Zuobin Xiong, Kyungtae Kang

“…When rolling pins are machining with CNC equipment, a sensor system is installed to collect acoustic data, analyze failure patterns, and apply RUL prediction algorithms. The AI-based predictive maintenance system developed ensures the reliability and operational efficiency of CNC equipment, while also laying the foundation for a smart factory monitoring platform, thus enhancing competitiveness in intelligent manufacturing environments.…”
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The Analysis of the Possibility to Conduct Orbital Manoeuvres of Nanosatellites in the Context of the Maximisation of a Specific Operational Task by Magdalena Lewinska, Michal Kedzierski

“…For example, slight adjustments to the altitude of the orbit with the use of Hohmann transfer proved to be optimal in terms of fuel costs. On the other hand, changes in inclination, although they are definitely energy-consuming, may significantly improve the coverage of the defined area. …”
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AI-driven biomarker discovery: enhancing precision in cancer diagnosis and prognosis by Esther Ugo Alum

“…This commentary aims to clarify how AI is improving the identification of novel biomarkers for optimal early diagnosis, focused treatment, and improved clinical outcomes, while also addressing certain obstacles and ethical issues related to the application of artificial intelligence in oncology. …”
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Challenges and Applications of Artificial Intelligence in Education: A Systematic Review by Saeid Arifnejad, Asadolah khadivi, Farhad Alipour

“…Additionally, it facilitates efficient teaching methodologies, automates grading and assessment, and optimizes resource allocation. The integration of AI can significantly improve the quality of education.Value: This research contributes to bridging the knowledge gap regarding AI applications in education while identifying key challenges for educators and policymakers.…”
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Understanding visitor flow and behaviour in developing tourism-service-oriented villages by space syntax methodologies: a case study of Tabian Rural Section of Qingshan Village, Ha... by Shuqin Wang, Yan Huang, Tianjie Li

“…The methodologies employed in this study are based on two space syntax algorithms: 3-D isovist analysis and agent-based models (ABMs). …”
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Architectural Gaps in Generative AI: Quantifying Cognitive Risks for Safety Applications by He Wen, Pingfan Hu

“…A methodological framework is developed to examine core AI mechanisms—vectorization, positional encoding, attention scoring, and optimization functions—focusing on how these introduce quantifiable “distances” from human semantic understanding. …”
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Energy-Efficient Cooperative Transmission in Ultra-Dense Millimeter-Wave Network: Multi-Agent Q-Learning Approach by Seung-Yeon Kim, Haneul Ko

“…The results show that our scheme can achieve optimal transmission power and significantly improved network energy efficiency compared with conventional algorithms such as no transmit power control and random control. …”
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The Method of Images Revisited: Approximate Solutions in Wedge‐Shaped Aquifers of Arbitrary Angle by I. A. Nikoletos, K. L. Katsifarakis

“…The main advantages of the proposed methodology are the following: (a) The model needs only finite number of terms compared to conventional analytical and numerical solutions that involve infinite series, (b) The computational load is low, so it can be easily used in conjunction with meta‐heuristic algorithms to solve groundwater resources optimization problems, (c) Stream depletion rate can be calculated rather accurately and (d) The method is applicable to related flow problems.…”
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Credit Rating Model Based on Improved TabNet by Shijie Wang, Xueyong Zhang

“…Subsequently, the model architecture is refined through the integration of a multi-head attention mechanism to extract both global and local feature representations. Bayesian optimization is then employed to accelerate hyperparameter selection and automate a parameter search for TabNet. …”
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Faster? Softer? Or More Formal? A Study on the Methods of Enterprises’ Crisis Response on Social Media by Yongtian Yu, Weiming Ye, Kaihang Zhang

“…Algorithmic recommendation mechanisms of social media platforms, viral diffusion of user-generated content (UGC), and real-time public opinion pressures are fundamentally deconstructing the traditional corporate crisis response paradigm that used to rely on one-way statements and delayed reactions. …”
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Machine Learning Approaches for Fault Detection in Internal Combustion Engines: A Review and Experimental Investigation by A. Srinivaas, N. R. Sakthivel, Binoy B. Nair

“…Fault diagnostics in internal combustion engines (ICEs) is vital for optimal operation and avoiding costly breakdowns. …”
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Quantitative and Spatially Explicit Clustering of Urban Grocery Shoppers in Montreal: Integrating Loyalty Data with Synthetic Population by Duo Zhang, Laurette Dubé, Antonia Gieschen, Catherine Paquet, Raja Sengupta

“…This segmentation also provides valuable insights for retailers optimizing store locations and marketing strategies and for policymakers aiming to enhance urban accessibility. …”
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Prototyping a graph-based approach for efficient vehicle tracking in a cluster controlled CCTV surveillance systems by Apurva NARAYAN, Charumathi PARIVALLAL, Olirva MAHESWARAN, Sarath CHANDRAN KALATHILPARAMBIL RAMACHANDRAN

“…The optical character recognition (OCR) enables a detailed number plate recognition, while the multimodal data fusion enhances the robustness of vehicle tracking in dynamic conditions. …”
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Simulating the Deterioration Behavior of Tunnel Elements Using Amalgamation of Regression Trees and State-of-the-Art Metaheuristics by Eslam Mohammed Abdelkader, Abobakr Al-Sakkaf, Moaaz Elkabalawy, Abdelhady Omar, Ghasan Alfalah

“…Leveraging metaheuristic algorithms’ strengths, the developed method jointly optimizes critical regression tree hyperparameters and identifies the most relevant features for deterioration prediction. …”
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Energy-Efficient model for integrated berth allocation and quay crane management by Saeedeh Khalilpoor, Mehdi A. Kamran, Reza Babazadeh, Reza Kia

“…Several numerical experiments in different dimensions are defined and implemented with both algorithms. The results analysis gives an overall perspective, while both algorithms show their value and strong points and weaknesses in meeting demanding requirements of modern container terminal logistics. …”
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Automated Calibration of SWMM for Improved Stormwater Model Development and Application by Hossein Ahmadi, Durelle Scott, David J. Sample, Mina Shahed Behrouz

“…During multi-objective optimization calibration, the model achieved NSE, PBIAS, and RSR values of 0.73, 17.1, and 0.52, respectively; while the validation period recorded values of 0.86, 13.1, and 0.37, respectively. …”
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IoT-edge anomaly detection for covariate shifted and point time series health data by Partha Pratim Ray, Dinesh Dash

“…We implement the ”IRKernel” to investigate the efficiency of exponentially weighted moving average (EWMA)-based light-weight statistical learning by using three variants e.g. probabilistic (P), shift-detect (SD), and two-stage shift-detect (TS-SD) over the EWMA algorithms. We perform evaluation of execution time of the algorithms in classical, incremental, and optimized modes while following the Kolmogorov–Smirnov hypothesis testing at the two-stage anomaly detection. …”
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