MRI Delta Radiomics to Track Early Changes in Tumor Following Radiation: Application in Glioblastoma Mouse Model by Mohammed S. Alshuhri, Haitham F. Al-Mubarak, Abdulrahman Qaisi, Ahmad A. Alhulail, Abdullah G. M. AlMansour, Yahia Madkhali, Sahal Alotaibi, Manal Aljuhani, Othman I. Alomair, A. Almudayni, F. Alablani

“…A machine learning model was developed to classify irradiated tumors based on delta radiomic features, and statistical analyses were conducted to evaluate feature feasibility, stability, and predictive performance. …”
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Science and technology indicators by eyes of a librarian by V. S. Lazarev

“…The main directions of the International Conference «STI 2017: Open indicators: innovation, participation and actor-based STI indicators» (Paris, 6-8 September 2017) were the following according to the author's opinion: survey of geographical features, patterns of the science development and a choice of adequate indicators to evaluate these features and patterns; as well as development of new methods for data processing, sharing, analysis and use to assess science and technology. …”
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Ascetical and mystical experience in Chinese Buddhism by Dabetić Ilija

“…The permeation of indigenous Chinese and newly arrived religious patterns will lead to the emergence of an unusual authentic form of ascetism and mysticism in religious life of the Far East. …”
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A comprehensive survey of the machine learning pipeline for wildfire risk prediction and assessment by Naveed Ejaz, Salimur Choudhury

“…It highlights the integration of diverse data sources, including remote sensing, in-situ measurements, geospatial layers, and historical fire records and outlines pre-processing and feature engineering techniques to represent climatic, topographic, vegetation, anthropogenic, and temporal fire patterns. …”
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A Novel Fuzzy Kernel Extreme Learning Machine Algorithm in Classification Problems by Asli Kaya Karakutuk, Ozer Ozdemir

“…The method is enhanced with various feature extraction methods. On the JAFFE dataset, the algorithm achieved an average classification accuracy of 94.55% when supported with local binary patterns and 94.27% with a histogram of oriented gradients. …”
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A performance-driven hybrid text-image classification model for multimodal data by Swati Gupta, Bal Kishan

“…Roberta is very good at extracting valuable information from both textual embeddings and at capturing complex patterns across many modalities. By using multi-modal feature extraction layers, which guarantee that different kinds of data are compatible, image representations attain this. …”
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Decoding a Huge Success: the Two Faces of C’è ancora domani (There’s Still Tomorrow) by Marco Cucco, Raoul Spiccia

“…From this perspective, C’è ancora domani demonstrates how, in order to valorized films, it is always important to develop ad hoc strategies, and that the prototypal nature of films also concerns the promotional patterns.…”
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Machine Learning-Assisted NIR Spectroscopy for Dynamic Monitoring of Leaf Potassium in Korla Fragrant Pear by Mingyang Yu, Weifan Fan, Junkai Zeng, Yang Li, Lanfei Wang, Hao Wang, Feng Han, Jianping Bao

“…Potassium (K), a critical macronutrient for the growth and development of Korla fragrant pear (Pyrus sinkiangensis Yu), plays a pivotal regulatory role in sugar-acid metabolism. …”
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Distributed denial-of-service (DDoS) on the smart grids based on VGG19 deep neural network and Harris Hawks optimization algorithm by Abdurahim Alhashmi, H. Idwaib, Selçuk Alparslan Avci, Javad Rahebi, Raheleh Ghadami

“…The suggested approach uses the robust feature extraction capability of VGG19-DNN for network traffic pattern analysis to detect abnormal traffic flows indicative of DDoS attacks. …”
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The effect of motivation on movement: a study of bradykinesia in Parkinson's disease. by Tamara Shiner, Ben Seymour, Mkael Symmonds, Peter Dayan, Kailash P Bhatia, Raymond J Dolan

“…<h4>Background</h4>Bradykinesia is a cardinal feature of Parkinson's disease (PD). Despite its disabling impact, the precise cause of this symptom remains elusive. …”
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iMESc – an interactive machine learning app for environmental sciences by Danilo Cândido Vieira, Danilo Cândido Vieira, Fabiana S. Paula, Luciana Erika Yaginuma, Gustavo Fonseca

“…As environmental sciences increasingly rely on complex datasets, machine learning (ML) has become crucial for identifying patterns and relationships. However, the integration of ML into workflows can pose challenges due to technical barriers or the time-intensive nature of coding. …”
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International Natural Uranium Price Prediction Based on TF-CNN-BiLSTM Model by YANG Jingzhe, XUE Xiaogang

“…Accurate predictions aid nuclear power enterprises in devising operational strategies and hedging against market volatility, while providing data support for policymakers to enhance energy security and formulate sustainable development plans. However, due to the intricate interplay of market supply-demand dynamics, geopolitical events, and economic policies, traditional econometric and statistical models fall short in capturing the temporal dependencies and complex patterns in uranium price data. …”

ECG-LM: Understanding Electrocardiogram with a Large Language Model by Kai Yang, Massimo Hong, Jiahuan Zhang, Yizhen Luo, Suyuan Zhao, Ou Zhang, Xiaomao Yu, Jiawen Zhou, Liuqing Yang, Ping Zhang, Mu Qiao, Zaiqing Nie

“…To address the scarcity of text–ECG data, we generated text–ECG pairs by leveraging detailed ECG pattern descriptions from medical guidelines, creating a robust dataset for pre-training ECG-LM. …”
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Dynamic spatiotemporal graph network for traffic accident risk prediction by Pengcheng Zhang, Wen Yi, Yongze Song, Penggao Yan, Peng Wu, Ammar Shemery, Keith Hampson, Albert P. C. Chan

“…Our model uses channel-wise convolutional neural networks to detect spatial accident patterns across weekly, daily, and hourly time scales with automatic weight learning, simultaneously employing graph convolutional networks to process road network features, population feature while integrating external data like weather and dates. …”
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Spatiotemporal Forecasting of Solar and Wind Energy Production: A Robust Deep Learning Model with Attention Framework by Md. Shadman Abid, Razzaqul Ahshan, Mohammed Al-Abri, Rashid Al Abri

“…In this context, a novel robust deep learning model, termed the Convolutional Neural Network-Bidirectional Long Short-Term Memory model with spatiotemporal attention mechanism (CNN-BiLSTM-STA), is developed in this study. The suggested model integrates the feature extraction expertise of CNNs with the sequence modeling proficiency of BiLSTM networks to capture spatial linkages and temporal interdependence adeptly. …”
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Multimodal machine learning for analysing multifactorial causes of disease—The case of childhood overweight and obesity in Mexico by Rosario Silva Sepulveda, Magnus Boman, Magnus Boman

“…Public health interventions have shown only moderate success, possibly from relying on knowledge extracted using limited types of statistical data analysis methods.PurposeTo explore if multimodal machine learning can enhance identifying predictive features from obesogenic environments and investigating complex disease or social patterns, using the Mexican National Health and Nutrition Survey.MethodsWe grouped features into five data modalities corresponding to paediatric population exogenous factors, in two multimodal machine learning pipelines, against a unimodal early fusion baseline. …”
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Machine learning based insights into cardiomyopathy and heart failure research: a bibliometric analysis from 2005 to 2024 by Muhammad Junaid Akram, Muhammad Junaid Akram, Asad Nawaz, Asad Nawaz, Yuan Yuxing, Yuan Yuxing, Jinpeng Zhang, Jinpeng Zhang, Huang Haixin, Huang Haixin, Lingjuan Liu, Lingjuan Liu, Xu Qian, Jie Tian, Jie Tian

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Enhanced Named Entity Recognition and Event Extraction for Power Grid Outage Scheduling Using a Universal Information Extraction Framework by Wei Tang, Yue Zhang, Xun Mao, Mingqi Shan, Kai Lv, Xun Sun, Zhenhuan Ding

“…Subsequently, a trigger-centric event extraction model is developed through feature learning of outage plan triggers and syntactic pattern entities. …”
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Species diversity of soil animals at different desert habitats in Baijitan region of Ningxia and its relationship with environmental factors by Zhang Jiao, Li Yuecheng, Zhang Dazhi

“…In recent years, desert habitat diversity in Baijitan National Nature Reserve of Lingwu in Ningxia has formed along with the development of control engineering. However, there is lack of the information on the diversity and distribution patterns of soil animals, the correlation between distribution patterns of soil animals and process of habitat change.Knowledge of the temporal variability of soil animal populations and species is crucial to understand the soil community dynamics. …”
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PREDICTORS OF PROCRASTINATION IN THE PROFESSIONAL ACTIVITY OF SPECIALISTS OF SOCIONOMIC PROFESSIONS by Svitlana A. Nakhod

“…It is interconnected with personality traits, features of motivation and has development patterns, associated with age. …”
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