Leveraging pre-trained models within a semi-supervised and explainable AI RealTime framework: A pioneering paradigm for betel leaf disease detection by Md Tahsin, Maksura Binte Rabbani Nuha, Sumaiya Akter, Al Hossain, Mohammad Rifat Ahmmad Rashid, Raiha Ul Islam, Mohammad Shahadat Hossain

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NIRS and machine learning algorithms as a non-invasive technique to discriminate and classify cooked broiler and duck meat by Kamrunnahar Khan Bristy, Dip Ghosh, Md. Abul Hashem

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Machine learning models for prediction of lymph node metastasis in patients with gastric cancer: a Chinese single-centre study with external validation in an Asian American populat... by Qian Li, Yuan Tian, Wei Peng, Shangcheng Yan, Weiran Yang, Zhuan Du, Ming Cheng, Renwei Chen, Qiankun Shao, Mengchao Sheng, Yongyou Wu

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Proactive Detection of Malicious Webpages Using Hybrid Natural Language Processing and Ensemble Learning Techniques by Althaf Ali A, Rama Devi K, Syed Siraj Ahmed N, Ramchandran P, Parvathi S

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Light attenuation as a substitute for nutrient supply for maximizing protein content in Gracilaria cornea (Rhodophyta): Modeling nitrogen and phosphorus supplementation using a pha... by Niva Tadmor-Shalev, Dan Tchernov, Andrea Ghermandi, Eli Shemesh, Álvaro Israel, Anna Brook

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Machine learning-based prediction of Sasang constitution types using comprehensive clinical information and identification of key features for diagnosis by Sa-Yoon Park, Musun Park, Won-Yung Lee, Choong-Yeol Lee, Ji-Hwan Kim, Siwoo Lee, Chang-Eop Kim

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From pixels to planning: scale-free active inference by Karl Friston, Karl Friston, Conor Heins, Tim Verbelen, Lancelot Da Costa, Lancelot Da Costa, Tommaso Salvatori, Dimitrije Markovic, Dimitrije Markovic, Alexander Tschantz, Magnus Koudahl, Christopher Buckley, Christopher Buckley, Thomas Parr

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Developing a predictive model for neoadjuvant therapy in HER2 overexpression breast cancer using multi-parameter MRI radiomics: two-center retrospective study by Lingling Wang, Jingru Yang, Li Yang, Yun Zhu, Xiaomin Tang, Xinyu Cao, Wenbo Kang, Haitao Sun, Zongyu Xie, Zongyu Xie, Zongyu Xie

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Artificial intelligence in resuscitation: a scoping review by Drieda Zace, Federico Semeraro, Sebastian Schnaubelt, Jonathan Montomoli, Giuseppe Ristagno, Nino Fijačko, Lorenzo Gamberini, Elena G. Bignami, Robert Greif, Koenraad G. Monsieurs, Andrea Scapigliati

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Estimating the plausible projections of land use/land cover dynamics in Jhelum and Chenab River basins using satellite imageries and machine learning models in Google Earth Engine by Syeda Maria Zafar, Junaid Aziz Khan, Ammara Mobeen, Wan Shafrina Wan Mohd Jaafar, Ricky Anak Kemarau

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Preoperative lymph node metastasis risk assessment in invasive micropapillary carcinoma of the breast: development of a machine learning-based predictive model with a web-based cal... by Yan Zhang, Nan Wang, Yuxin Qiu, Yingxiao Jiang, Peiyan Qin, Xiaoxiao Wang, Yang Li, Xiangdi Meng, Furong Hao

“…Thirteen ML algorithms were trained and compared to determine the optimal model. Model performance was evaluated using the area under the curve (AUC), calibration plots, and decision curve analysis. …”
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Cost-effectiveness of expanding the target population of biennial screening for breast cancer from ages 50–69 to 45 and/or 74: A cohort modelling study in the Finnish setting by Filip Siegfrids, Sirpa Heinävaara, Tytti Sarkeala, Laura Niinikoski, Juha Laine

“…The model, using a Markov cohort simulation approach, was adapted to the cancer stage classification system used by the Finnish Cancer Registry (FCR) and calibrated to observed metrics in the Finnish female population. …”
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A model predicting the 6-year all cause mortality of patients with advanced schistosomiasis after discharge: Derived from a large population-based cohort study. by Lanyue Pan, Chunmei Wu, Ping Li, Jiaquan Huang, Yizhi Wu, Guo Li

“…Using these variables, we developed a ten-variable model and three simpler models. In the derivation cohort, the ten-variable model showed the highest C statistic (0.759; 95% CI, 0.739-0.778) and the lowest AIC (2834.2). …”
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Coupling Coordination and Influencing Factors of Urbanization and Rural Revitalization in Nine Border Provinces of China by Qin Qiuyan, Lu Rucheng, Duan Lian, Wu Bin, Liu Shaokun, Zhang Liguo

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Multi-modal MRI for objective diagnosis and outcome prediction in depression by Jesper Pilmeyer, Rolf Lamerichs, Sjir Schielen, Faroeq Ramsaransing, Vivianne van Kranen-Mastenbroek, Jacobus F.A. Jansen, Marcel Breeuwer, Svitlana Zinger

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Advancing CVD Risk Prediction with Transformer Architectures and Statistical Risk Factor Filtering by Parul Dubey, Pushkar Dubey, Pitshou N. Bokoro

“…This research addresses the need for a clinically meaningful and computationally efficient prediction model. The study utilizes three real-world datasets comprising demographic, clinical, and lifestyle-based risk factors relevant to CVD. …”
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Mapping invasive noxious weed species in the alpine grassland ecosystems using very high spatial resolution UAV hyperspectral imagery and a novel deep learning model by Fei Xing, Ru An, Xulin Guo, Xiaoji Shen

“…The results indicate that the proposed 3D&2D-INWS-CNN model applied to the collected imagery for mapping INWS and native species with small ground truth training samples is robust and sufficient, with an overall classification accuracy exceeding 95% and a kappa value of 98.67%. …”
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Few-shot hotel industry site selection prediction method based on meta learning algorithms and transportation accessibility by Na Li, Huaishi Wu

“…The results show that: (1) The meta-model achieves a classification accuracy of 90.45% for star-rated hotels, with a location fitting degree of 91.90%, an improvement of approximately 11% compared to the baseline model; (2) Transportation conditions play a crucial role in the distribution of star-rated hotels, contributing 45% of the classification information; (3) It is recommended that future investments in star-rated hotels focus on areas around Xiaobailou Street, Dawangzhuang Street, and Wudadao Street. …”
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Machine Learning and Deep Learning for Loan Prediction in Banking: Exploring Ensemble Methods and Data Balancing by Eslam Hussein Sayed, Amerah Alabrah, Kamel Hussein Rahouma, Muhammad Zohaib, Rasha M. Badry

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Multicenter external validation of a nomogram predicting conversion to open cholecystectomy during laparoscopic surgery for acute calculous cholecystitis: a cross-sectional study by Hongsheng Wu, Keqiang Ma, Biling Liao, Tengfei Ji, Zongmin Zheng, Yong Yan, Jiongbiao Yu, Haitao Yu, Yue Liu, Yanyuan Zhou, Guangrong Huang, Weili Gu, Tiansheng Cao

“…LASSO regression analysis identified five optimal variables from a total of twenty-nine for model development: preoperative C-reactive protein (CRP) level, anesthesia American Society of Anesthesiologists (ASA) classification, calculus location, Tokyo Guidelines 2018 (TG18) classification, and surgeon seniority. …”
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