An Effective Hybrid Deep Neural Network for Arabic Fake News Detection by Tahseen A. Wotaifi, Ban N. Dhannoon

“…As such, this study aims to use deep learning techniques to detect Arabic fake news based on Arabic dataset called the AraNews dataset. …”
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A bearing fault diagnosis method for hydrodynamic transmissions integrating few-shot learning and transfer learning by Dong Sun, Xudong Yang, Hai Yang

“…First, a Siamese Wide Convolutional Neural Network (Siamese-WDCNN) is constructed based on public bearing datasets to extract essential features of vibration signals through few-shot contrastive learning. …”
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Learning Deceptive Strategies in Adversarial Settings: A Two-Player Game with Asymmetric Information by Sai Krishna Reddy Mareddy, Dipankar Maity

“…We develop a suite of progressively complex grid-based environments featuring dynamic goals, fake targets, and navigational obstacles. …”
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Optimized Deep Neural Network for High-Precision Psoriasis Classification from Dermoscopic Images by Charu Bolia, Sunil Joshi

“…The models are trained and tested on a publicly benchmark dataset comprising 7 distinct classes using the publically available benchmark dataset by Dermnet and BFL-NTU. …”
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Spatiotemporal upscaling of sparse air-sea pCO2 data via physics-informed transfer learning by Siyeon Kim, Juan Nathaniel, Zhewen Hou, Tian Zheng, Pierre Gentine

“…Several approaches, especially based on direct learning, have been used to upscale and extrapolate sparse point data to dense estimates using globally available input features. …”
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SwinVNETR: Swin V-net Transformer with non-local block for volumetric MRI Brain Tumor Segmentation by Maria Nancy A, K. Sathyarajasekaran

“…Unfortunately, most existing brain tumour segmentation algorithms use inadequate 2D picture segmentation methods and fail to capture the spatial correlation between features. In this study, we propose a segmentation model (SwinVNETR) Swin V-NetTRansformer-based architecture with a non-local block. …”
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Harnessing multimodal approaches for depression detection using large language models and facial expressions by Misha Sadeghi, Robert Richer, Bernhard Egger, Lena Schindler-Gmelch, Lydia Helene Rupp, Farnaz Rahimi, Matthias Berking, Bjoern M. Eskofier

“…We evaluate three approaches: text-based features, facial features, and a combination of both. …”
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DEVELOPING YOUTH RESILIENCE IN CONDITIONS OF SOCIAL INSTABILITY AND WAR by Vira Syniakova, Yuliia Bokhonkova, Mykyta Minin

“…The research is based on a systemic approach, which views resilience as a multidimensional phenom- enon that integrates cognitive, emotional, motivational, behavioural, and social components. …”
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scCamAge: A context-aware prediction engine for cellular age, aging-associated bioactivities, and morphometrics by Vishakha Gautam, Subhadeep Duari, Saveena Solanki, Mudit Gupta, Aayushi Mittal, Sakshi Arora, Anmol Aggarwal, Anmol Kumar Sharma, Sarthak Tyagi, Rathod Kunal Pankajbhai, Arushi Sharma, Sonam Chauhan, Shiva Satija, Suvendu Kumar, Sanjay Kumar Mohanty, Juhi Tayal, Nilesh Kumar Dixit, Debarka Sengupta, Anurag Mehta, Gaurav Ahuja

“…We present scCamAge, an advanced context-aware multimodal prediction engine that co-leverages image-based cellular spatiotemporal features at single-cell resolution alongside cellular morphometrics and aging-associated bioactivities such as genomic instability, mitochondrial dysfunction, vacuolar dynamics, reactive oxygen species levels, and epigenetic and proteasomal dysfunctions. scCamAge employed heterogeneous datasets comprising ∼1 million single yeast cells and was validated using pro-longevity drugs, genetic mutants, and stress-induced models. scCamAge also predicted a pro-longevity response in yeast cells under iterative thermal stress, confirmed using integrative omics analyses. …”
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Resolution enhancement of scanning electron micrographs using artificial intelligence by T. Reclik, S. Medghalchi, P. Schumacher, M.A. Wollenweber, T. Al-Samman, S. Korte-Kerzel, U. Kerzel

“…Using a tailored dataset of dual-phase steel micrographs, we demonstrate that our trained network outperforms traditional interpolation methods in both quantitative similarity metrics and crucial material-specific features, such as phase boundaries and microstructural voids. …”
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MedAlmighty: enhancing disease diagnosis with large vision model distillation by Yajing Ren, Zheng Gu, Wen Liu

“…Conversely, large vision models, trained on extensive general-domain datasets, provide strong generalization but fall short in specialized medical applications due to domain mismatch and limited medical data availability.MethodsTo bridge the gap between general and specialized performance, we propose MedAlmighty, a knowledge distillation-based framework that synergizes the strengths of both large and small models. …”
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Locomotion Joint Angle and Moment Estimation With Soft Wearable Sensors for Personalized Exosuit Control by Luying Feng, Lianghong Gui, Wenzhu Xu, Xiang Wang, Canjun Yang, Yaochu Jin, Wei Yang

“…Calibrated data were used as static features and trained alongside dynamic features. The model was pre-trained on a large open-source dataset and then fine-tuned for our own data. …”
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Validation of a Swine Cough Monitoring System Under Field Conditions by Luís F. C. Garrido, Gabriel S. T. Rodrigues, Leandro B. Costa, Diego J. Kurtz, Ruan R. Daros

“…Features were evaluated through 10-fold cross-validation based on classification performance and runtime, resulting in eight final features. …”
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Mal-Detect: An intelligent visualization approach for malware detection by Olorunjube James Falana, Adesina Simon Sodiya, Saidat Adebukola Onashoga, Biodun Surajudeen Badmus

“…The generated malware images with original malware and benign files images are pre-processed and trained with Deep Convolutional Neural Networks to extract important features from the dataset. …”
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Nanoparticles Enhanced Laser-Induced Breakdown Spectroscopy for Characterisation and Discrimination of Gemstones by Al-Juboori Haider, Haider Zhaib, Hang Wei, Roslan Sufi

“…Nanoparticle-enhanced LIBS (NELIBS) has proven to improve the effectiveness of LIBS by minimizing sample damage and enhancing the spectral features. Therefore, this study is dedicated to exploring the advantages of NELIBS, for characterising Sapphire and Opal and discrimination based on spectral differences. …”
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FireCLIP: Enhancing Forest Fire Detection with Multimodal Prompt Tuning and Vision-Language Understanding by Shanjunxia Wu, Yuming Qiao, Sen He, Jiahao Zhou, Zhi Wang, Xin Li, Fei Wang

“…This study compiles smoke alarm images from five high-definition surveillance cameras in Foshan City, Guangdong, China, collected over one year, to create a smoke-based early warning dataset. The dataset presents two key challenges: (1) high false positive rates caused by pseudo-smoke interference, including non-fire conditions like cooking smoke and industrial emissions, and (2) significant regional data imbalances, influenced by varying human activity intensities and terrain features, which impair the generalizability of traditional pre-train–fine-tune strategies. …”
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Technology and Emotions: AI-Driven Software Prototyping for the Analysis of Emotional States and Early Detection of Risky Behaviors in University Students by Alba Catherine Alves-Noreña, María-José Rodríguez-Conde, Juan Pablo Hernández-Ramos, José William Castro-Salgado

“…As a result, an educational technological innovation was developed, featuring a chatbot structured with a rule-based dialogue tree, complemented by an ontology for knowledge organization and a pre-trained artificial intelligence (AI) model, enhancing the accuracy and contextualization of user interactions. …”
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General Linear Chirplet Transform and Radar Target Classification by Reza Amiri, Ali Shahzadi

“…Consequently, the obtained features are trained to the k-NN classifier. In the numerical experiments we show the superiority of the proposed method in comparison with the existing recent counterparts, for both performance as well as the computational complexity. …”
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Prediction of Radiation Belts Electron Fluxes at a Low Earth Orbit Using Neural Networks With PROBA‐V/EPT Data by Edith Botek, Viviane Pierrard, Alexandre Winant

“…Abstract We introduce for the first time the PROBA‐V/EPT electron flux data to train a deep learning data‐driven model with the purpose of investigating the Earth’s radiation belts dynamics. …”
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Understanding the Impact of Street Environments on Traffic Crash Risk from the Perspective of Aging People: An Interpretable Machine Learning Approach by Ketong Shen, Jian Liu, Xintao Liu

“…Specifically, we use segmented street view images to train models that predict crash risk levels based on normalized crash frequencies, with separate models developed for older and non-older populations. …”
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