KANDiff: Kolmogorov–Arnold Network and Diffusion Model-Based Network for Hyperspectral and Multispectral Image Fusion by Wei Li, Lu Li, Man Peng, Ran Tao

“…To address these problems, we propose a new model, KanDiff, for hyperspectral and multispectral image fusion. …”
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Anodapter: A Unified Framework for Generating Aligned Anomaly Images and Masks Using Diffusion Models by Minkyoung Shin, Seonggyun Jeong, Yong Seok Heo

“…These identifiers assist the model in learning the features of anomalous regions and the overall structure of the image. …”
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WHA-Net: A Low-Complexity Hybrid Model for Accurate Pseudopapilledema Classification in Fundus Images by Junpeng Pei, Yousong Wang, Mingliang Ge, Jun Li, Yixing Li, Wei Wang, Xiaohong Zhou

“…On the test set, the model achieved outstanding performance, with 97.79% accuracy, 95.55% precision, 95.69% recall, and 98.53% specificity. …”
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A novel image captioning model with visual-semantic similarities and visual representations re-weighting by Alaa Thobhani, Beiji Zou, Xiaoyan Kui, Asma A. Al-Shargabi, Zaid Derea, Amr Abdussalam, Mohammed A. Asham

“…Furthermore, the integration between semantic attributes and visual representations in previous works is considered superficial and ineffective, leading to the neglection of the rich visual-semantic connections affecting the captioning model’s performance. To address the limitations of previous models, we introduced a novel framework that adapts attribute usage based on contextual relevance and effectively utilizes the similarities between visual features and semantic attributes. …”
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The relationship between epigenetic biomarkers and the risk of diabetes and cancer: a machine learning modeling approach by Shiqi Zhang, Shiqi Zhang, Jianan Jin, Benfeng Xu, Qi Zheng, Haibo Mou

“…In the diabetes model, the top three contributing features were logA1Mort, family income-to-poverty ratio, and marital status. …”
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Development of an optimized deep learning model for predicting slope stability in nano silica stabilized soils by Ishwor Thapa, Sufyan Ghani, Prabhu Paramasivam, Mitiku Adare Tufa

“…Furthermore, XAI and SHAP techniques were employed to enhance model interpretability, revealing that features c, NS%, and β are the most influential factors governing slope stability. …”
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DeepOmicsSurv: a deep learning-based model for survival prediction of oral cancer by Deepali, Neelam Goel, Padmavati Khandnor

“…Additionally, SHapley Additive Explanations (SHAP) was used to analyze the impact of clinical features on survival predictions. Results DeepOmicsSurv achieved a C-index of 0.966, MSE of 0.0138, RMSE of 0.1174, MAE of 0.0795, and MedAE of 0.0515, outperforming other deep learning models. …”
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Dual-Model Synergy for Fingerprint Spoof Detection Using VGG16 and ResNet50 by Mohamed Cheniti, Zahid Akhtar, Praveen Kumar Chandaliya

“…While existing methods often rely on a single feature extraction model, they may struggle with generalization across diverse spoofing materials and sensor types. …”
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Recognizing American Sign Language gestures efficiently and accurately using a hybrid transformer model by Mohammed Aly, Islam S. Fathi

“…Central to this success is our feature fusion strategy using element-wise multiplication, which helps the model focus on important gesture details while suppressing background noise. …”
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Zero-shot instance segmentation for plant phenotyping in vertical farming with foundation models and VC-NMS by Qin-Zhou Bao, Yi-Xin Yang, Qing Li, Hai-Chao Yang

“…IntroductionImage instance segmentation is essential for plant phenotyping in vertical farms, yet the diversity of plant types and limited annotated image data constrain the performance of traditional supervised techniques. These challenges necessitate a zero-shot approach to enable segmentation without relying on specific training data for each plant type.MethodsWe present a zero-shot instance segmentation framework combining Grounding DINO and the Segment Anything Model (SAM). …”
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Benchmarking 21 Open-Source Large Language Models for Phishing Link Detection with Prompt Engineering by Arbi Haza Nasution, Winda Monika, Aytug Onan, Yohei Murakami

“…While traditional methods rely heavily on handcrafted features and supervised machine learning, recent advances in large language models (LLMs) provide promising alternatives. …”
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Development of machine learning models to predict clinical outcome and recovery time in dogs with parvovirus enteritis by Negin Sanaei, Mohamad Zamani-Ahmadmahmudi, Seyed Mahdi Nassiri

“…The performance of this model was evaluated using two criteria: mean square error (MSE) and root mean square error (RMSE). …”
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Predicting postoperative nausea and vomiting using machine learning: a model development and validation study by Maxim Glebov, Teddy Lazebnik, Maksim Katsin, Boris Orkin, Haim Berkenstadt, Svetlana Bunimovich-Mendrazitsky

“…Therefore, prognostic models for the prediction of early and delayed PONV were developed in this study to achieve satisfactory predictive performance. …”
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Assessment of photovoltaic system in existence of nanomaterial cooling flow by Mashhour A. Alazwari, Ali Basem, Hussein A.Z. AL-bonsrulah, Khalid H. Almitani, Nidal H. Abu-Hamdeh, Mahmood Shaker Albdeiri, Turki AlQemlas, Ria H. Egami

“…The optimal design, featuring eight shorter fins arranged around an eight-lobed pipe, resulted in a 4.3 % improvement in thermal performance. …”
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Leveraging Vision Foundation Model via PConv-Based Fine-Tuning with Automated Prompter for Defect Segmentation by Yifan Jiang, Jinshui Chen, Jiangang Lu

“…The Segment Anything Model (SAM) has shown exceptional performance across various downstream tasks, owing to its vast semantic knowledge and strong generalization capabilities. …”
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Development and validation of machine learning models for predicting low muscle mass in patients with obesity and diabetes by Jiaying Ge, Siqi Sun, Jiangping Zeng, Yujie Jing, Huihui Ma, Chunhua Qian, Ran Cui, Shen Qu, Hui Sheng

“…Feature importance analysis highlighted BRI, creatinine, race, age, and HbA1c as key contributors to the model’s predictive performance. …”
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Diagnosis of parotid gland tumors using a ternary classification model based on ultrasound radiomics by Xiaoling Liu, Weihan Xiao, Chen Yang, Zhihua Wang, Dong Tian, Gang Wang, Xiachuan Qin

“…Two-step LASSO-BNB and voting ensemble learning modeling algorithm with recursive feature elimination feature selection method (RFE-Voting) models were then applied for classification. …”
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Fast algorithms for a linear system with infinitesimal generator structure of a Markovian queueing model by Jiaqi Qu, Yunlan Wei, Yanpeng Zheng, Zhaolin Jiang

“…To evaluate the algorithm's performance, we presented two examples in numerical simulations.…”
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Computed Tomography-Based Radiomics Diagnostic Model for Fat-Poor Small Renal Tumor Subtypes by Seokhwan Bang, Heehwan Wang, Hoyoung Bae, Sung-Hoo Hong, Jiook Cha, Moon Hyung Choi

“…These results outperformed other single-phase CT radiomic feature-based machine learning models trained with 20% of principal components. …”
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Artificial Intelligence and Machine Learning Models for Predicting Drug-Induced Kidney Injury in Small Molecules by Mohan Rao, Vahid Nassiri, Sanjay Srivastava, Amy Yang, Satjit Brar, Eric McDuffie, Clifford Sachs

“…These models were then combined into an ensemble model for enhanced performance. …”
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