A new method and information system based on artificial intelligence for black flight identification by Arwin Datumaya Wahyudi Sumari, Rosa Andrie Asmara, Ika Noer Syamsiana

“…Military radar units that have the task of monitoring airspace are generally equipped with Primary Surveillance Radar that detects the presence of aircraft in their operating area and Secondary Surveillance Radar which functions to identify the aircraft. In the case of black flight, data from the radar in the form of airspeed, altitude, and position are not able to help identify the identity of the black flight. …”
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Hybrid 3D CNN-LSTM Model for rs-fMRI-Based Parkinson’s Prediction by Salman Ibne Eunus, Sakib Rokoni, Mitheela Das Armisha, Jasia Hossain Omi, Asika Islam, Farah Binta Haque

“…Three-dimensional structural and functional brain scans from both healthy controls and patients with Parkinson’s disease form our dataset. …”
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MultiGNN: A Graph Neural Network Framework for Inferring Gene Regulatory Networks from Single-Cell Multi-Omics Data by Dongbo Liu, Hao Chen, Jianxin Wang, Yeru Wang

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CTSS in the tumor microenvironment links immune escape and immunotherapy sensitivity in kidney renal clear cell carcinoma by Hanjing Zhou, Jun Ying, Xuchun Xu, Jian Huang

“…Functional analyses revealed significant enrichment of CTSS in immune-regulatory pathways, including B/T cell activation and inflammatory activity. …”
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A comprehensive review on the integration of artificial intelligence in friction stir welding for monitoring, modelling, and process optimization by Mostafa Akbari, Ezatollah Hassanzadeh, Yaghuob Dadgar Asl, Amirhossein Moghanian

“…The second section examines process control, emphasizing how AI systems enable real-time monitoring and adaptive control of the welding process. This functionality allows for immediate parameter adjustments, thus significantly improving weld consistency and quality by minimizing defects. …”
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Implementing HoLEP in an Academic Department With Multiple Surgeons in Training: Mentoring Is the Key for Success by Clément Klein, Thibault Marquette, Grégoire Capon, Eric Alezra, Peggy Blanc, Vincent Estrade, Jean-Christophe Bernhard, Franck Bladou, Grégoire Robert

“…In April 2012, 2 surgeons were trained by an experienced urologist. The 29 others learned the technique progressively with the help of the first 2 surgeons (surgical mentoring).ResultsA total of 1259 patients were included. …”
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The inconvenient truth of ground truth errors in automotive datasets and DNN-based detection by Pak Hung Chan, Boda Li, Gabriele Baris, Qasim Sadiq, Valentina Donzella

“…Assisted and automated driving functions will rely on machine learning algorithms, given their ability to cope with real-world variations, e.g. vehicles of different shapes, positions, colors, and so forth. …”
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Automatic Filtering of Sugarcane Yield Data by Eudocio Rafael Otavio da Silva, José Paulo Molin, Marcelo Chan Fu Wei, Ricardo Canal Filho

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Application of artificial neural networks in the drilling processes: Can equivalent circulation density be estimated prior to drilling? by Husam H. Alkinani, Abo Taleb T. Al-Hameedi, Shari Dunn-Norman, David Lian

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Comparative Analysis of Neural Network Models for Predicting Battery Pack Safety in Frontal Collisions by Jun Wang, Ouyang Chen, Zhenfei Zhan, Zhiwei Zhao, Huanhuan Bao

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Examining the relationship between emotion regulation, sleep quality, and anxiety disorder diagnosis by Bryana L. Schantz, Emma R. Toner, Mackenzie L. Brown, Nikki Kaiser, Alan Chen, Samrachana Adhikari, Susanne S. Hoeppner, Eric Bui, Naomi M. Simon, Kristin L. Szuhany

“…For those with better emotion regulation, poor sleep quality and worse daytime functioning due to sleep were important predictors of anxiety diagnosis. …”
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Cellpose as a reliable method for single-cell segmentation of autofluorescence microscopy images by Jeremiah M. Riendeau, Amani A. Gillette, Emmanuel Contreras Guzman, Mario Costa Cruz, Aleksander Kralovec, Shirsa Udgata, Alexa Schmitz, Dustin A. Deming, Beth A. Cimini, Melissa C. Skala

“…Overall, the Cellpose ATM provides a fast, reliable, reproducible, and accurate method to segment single cells in autofluorescence microscopy images such that functional changes in cells are accurately captured in both 2D and 3D culture.…”
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Research on screening key proteins related to the pathogenesis of sepsis associated encephalopathy based on DIA proteomics technology by Wu Shuhui, Hu Hongjie, Lu Yuru, Zhu Wei

“…The differential proteins between the two patient groups were identified and functionally analyzed using bioinformatics methods. Key differential proteins were further screened using machine learning models such as Extreme Gradient Boosting （XGBoost） and Least Absolute Shrinkage and Selection Operator （LASSO） regression to construct a nomogram and evaluate its diagnostic performance. …”
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Using linguistic-informed analysis to assess model texts for EFL reading and writing by Monaliza Hernandez Mamac, Lungguh Ariang Bangga

“…In Thai basic education, learning in content areas is typically done in Thai. …”
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Training a Minesweeper Agent Using a Convolutional Neural Network by Wenbo Wang, Chengyou Lei

“…The Minesweeper game is modeled as a sequential decision-making task, for which a neural network architecture, state encoding, and reward function were herein designed. Both a Deep Q-Network (DQN) and supervised learning methods were successfully applied to optimize the training of the game. …”
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Mitigating Impact of Data Poisoning Attacks on CPS Anomaly Detection with Provable Guarantees by Sahar Abedzadeh, Shameek Bhattacharjee

“…To improve the robustness of time series learning for anomaly detection, we propose a framework by modifying design choices such as regression error type and loss function type while learning the thresholds for an anomaly detection framework during the training phase. …”
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Application of machine learning and neural network models based on experimental evaluation of dissimilar resistance spot-welded joints between grade 2 titanium alloy and AISI 304 s... by Marwan T. Mezher, Alejandro Pereira, Rusul Ahmed Shakir, Tomasz Trzepieciński

“…The best prediction model was found to be the ANN model when training the conjugate gradient with the Polak-Ribiere updates (Traincgp) training function with the hyperbolic tangent sigmoid transfer function (Tansig) with the mean squared error (MSE) and correlation coefficient (R2) values recorded as 0.01886 and 0.94973, respectively. …”
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Explainable machine learning prediction of internet addiction among Chinese primary and middle school children and adolescents: a longitudinal study based on positive youth develop... by Jiahe Liu, Lang Chen, Yuxin Chen, Jingsong Luo, Kexin Yu, Linlin Fan, Chan Yong, Huiyu He, Simei Liao, Zongyuan Ge, Lihua Jiang, Lihua Jiang

“…Key predictors included grade level, delinquent behavior, anxiety, family function, and depression scores. SHAP analysis revealed consistent and interpretable feature contributions across individuals.ConclusionDepression, anxiety, and family dynamics are significant factors influencing IA in children. …”
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Holistic education – a model based on three pillars from cognitive science. An example from science education by Eliza Rybska, Maciej Błaszak

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Effect of individual variations in genes related to dopamine brain transmission on performance with and without rewards during motor sequence and probabilistic learning tasks in ch... by Barrett Dryden, Jesse Matsubara, Eric Wassermann, Hans Forssberg, Diane L Damiano

“…Children with cerebral palsy (CP) often participate in training to improve mobility, hand function and other motor abilities. However, responses to these interventions vary considerably across individuals even those with similar brain injuries, ages and functional levels. …”
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