AgriSage: Android‐Based Application for Empowering Farmers With E‐Commerce and AI‐Driven Disease Detection by Shabeena Naveed, Mujeeb Ur Rehman, Mumtaz Ali Shah, Shahid Sultan, Zafar Ullah Khan, Syed Zarak Shah, Mansoor Iqbal, Muhammad Ahsan Amjed

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FruVeg_MultiNet: A hybrid deep learning-enabled IoT system for fresh fruit and vegetable identification with web interface and customized blind glasses for visually impaired indivi... by Khondokar Oliullah, Md. Reazul Islam, Jahirul Islam Babar, M.A. Nur Quraishi, Md. Mahbubur Rahman, Md. Mahbub-Or-Rashid, T.M. Amir-Ul-Haque Bhuiyan

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DAViT: A Domain-Adapted Vision Transformer for Automated Pneumonia Detection and Explanation Using Chest X-Ray Images by Michael Fu, Chakkrit Tantithamthavorn, Trung Le

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Biomarker discovery and development of prognostic prediction model using metabolomic panel in breast cancer patients: a hybrid methodology integrating machine learning and explaina... by Fatma Hilal Yagin, Yasin Gormez, Fahaid Al-Hashem, Irshad Ahmad, Fuzail Ahmad, Luca Paolo Ardigò

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Brain Tumor Detection and Prediction in MRI Images Utilizing a Fine-Tuned Transfer Learning Model Integrated Within Deep Learning Frameworks by Deependra Rastogi, Prashant Johri, Massimo Donelli, Lalit Kumar, Shantanu Bindewari, Abhinav Raghav, Sunil Kumar Khatri

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Two stage malware detection model in internet of vehicles (IoV) using deep learning-based explainable artificial intelligence with optimization algorithms by Manal Abdullah Alohali, Sultan Alahmari, Mohammed Aljebreen, Mashael M. Asiri, Achraf Ben Miled, Sami Saad Albouq, Othman Alrusaini, Ali Alqazzaz

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Large Language Models’ Accuracy in Emulating Human Experts’ Evaluation of Public Sentiments about Heated Tobacco Products on Social Media: Evaluation Study by Kwanho Kim, Soojong Kim

“…The majority of the labels from these responses were assigned as a model’s decision for the message. The models’ classification decisions were then compared with those of human evaluators. …”
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A Deep Learning Model for Identifying the Risk of Mesenteric Malperfusion in Acute Aortic Dissection Using Initial Diagnostic Data: Algorithm Development and Validation by Zhechuan Jin, Jiale Dong, Chengxiang Li, Yi Jiang, Jian Yang, Lei Xu, Ping Li, Zhun Xie, Yulin Li, Dongjin Wang, Zhili Ji

“…This model may serve as a valuable tool for early risk identification, facilitating timely therapeutic decision-making. …”
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Development and validation of an in-hospital major adverse cardiovascular events risk model for young patients with acute coronary syndrome: a retrospective cohort study by Jia Zheng, Junyang Li, Tingting Li, Fang Hu, Degang Cheng, Chengzhi Lu

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Closing the gap in the clinical adoption of computational pathology: a standardized, open-source framework to integrate deep-learning models into the laboratory information system by Miriam Angeloni, Davide Rizzi, Simon Schoen, Alessandro Caputo, Francesco Merolla, Arndt Hartmann, Fulvia Ferrazzi, Filippo Fraggetta

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Development of a clinical prediction model for sensitivity to combination therapy of Bcl-2 inhibitors and hypomethylating agents in elderly/unfit patients with acute myeloid leukem... by Yufeng Du, Chunhong Li, Yonghong Chen, Fang Xie, Jinsong Yan

“…The FAB classification, AML type, AML status, prior HMAs exposure, chromosomal karyotype, and mutations in ASXL1, FLT3, IDH, NPM1, and CEBPA were screened to develop the nomogram. …”
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Clinical, radiological, and radiomics feature-based explainable machine learning models for prediction of neurological deterioration and 90-day outcomes in mild intracerebral hemor... by Weixiong Zeng, Jiaying Chen, Linling Shen, Genghong Xia, Jiahui Xie, Shuqiong Zheng, Zilong He, Limei Deng, Yaya Guo, Jingjing Yang, Yijun Lv, Genggeng Qin, Weiguo Chen, Jia Yin, Qiheng Wu

“…Additionally, we incorporated the Shapley Additive Explanation (SHAP) method to display key features and visualize the decision-making process of the model for each individual. …”
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What Drives Consumers in Poland and the Czech Republic When Choosing Engine Oil Brand? by Artur Wolak, Kamil Fijorek, Grzegorz Zając, Vojtěch Kumbár

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Predicthor: AI-Powered Predictive Risk Model for 30-Day Mortality and 30-Day Complications in Patients Undergoing Thoracic Surgery for Lung Cancer by Xavier Durand, Msc, Julien Hédou, Msc, Grégoire Bellan, Msc, Pascal-Alexandre Thomas, MD, PhD, Pierre-Benoît Pages, PhD, Xavier-Benoît D’Journo, PhD, Laurent Brouchet, PhD, Caroline Rivera, MD, Pierre-Emmanuel Falcoz, PhD, André Gillibert, MD, PhD, Jean-Marc Baste, MD, PhD

“…A total of 6508 patients who have undergone primary lung cancer surgery via lobectomy or bilobectomy, aged over 18 years, and with anAmerican Society of Anesthesiologists (ASA) physical status classification system score under 4, were selected. In a retrospective analysis using a 3-dataset scheme (training cohort, internal and external validation on 118 other centers), we assessed the predictive performance of Predicthor for 30-day complications and mortality following major pulmonary resections. …”
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Application of satellite images and object-oriented processing in land use/land cover map extraction to model ecosystem services (case study: Lorestan province) by Zahra Asadolahi, Mostafa Keshtkar, Zia Badehian

“…In this regard, the present study aimed to apply satellite images and object-oriented processing in land use/cover mapping and habitat services modeling. Material and methods: The research was carried out using eCognition 9.01 and InVEST3.0 software in four steps including preparing information, object-oriented processing of satellite images, object-oriented classification, and finally, habitat modeling. …”
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Calibration and uncertainty quantification for deep learning-based drought detection by Mengxue Zhang, Miguel-Ángel Fernández-Torres, Kai-Hendrik Cohrs, Gustau Camps-Valls

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Development and validation of an interpretable machine learning model for retrospective identification of suspected infection for sepsis surveillance: a multicentre cohort studyRes... by Renée A.M. Tuinte, Luuk P.J. Smolenaers, Bram T. Knoop, Konstantin Föhse, Tamar J. van der Aart, Hjalmar R. Bouma, Mihai G. Netea, Katrijn Van Deun, Jaap ten Oever, Jacobien J. Hoogerwerf

“…The gradient boosting model outperformed other commonly used proxies for suspected infection in terms of sensitivity, achieving 91.1% (95% CI: 83.4–95.4%), compared to Sepsis-3 with 78.9% (95% CI: 69.4–86.0%), the adapted ASE with 85.6% (95% CI: 76.8–91.4%), and ICD codes with 33.3% (95% CI: 24.5–43.6%). …”
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Diabetes Detection Models in Mexican Patients by Combining Machine Learning Algorithms and Feature Selection Techniques for Clinical and Paraclinical Attributes: A Comparative Eval... by Antonio García-Domínguez, Carlos E. Galván-Tejada, Rafael Magallanes-Quintanar, Hamurabi Gamboa-Rosales, Irma González Curiel, Jesús Peralta-Romero, Miguel Cruz

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Development and validation of a prognostic model for post-surgical overall survival in Asian colon cancer patients: a real-world population-based study by Cheng Liu, Huaide Qiu, Junqiang Wang, Min Yang, Zhixiang Wang

“…The nomogram was validated with good accuracy and clinical utility across three datasets.ConclusionThis study identified several independent prognostic factors influencing the postoperative overall survival of Asian colon cancer patients, including age, gender, marital status, histological type, grade classification, AJCC_T, AJCC_N, and AJCC_M stages, CEA levels, and chemotherapy. …”
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Identification of dominant species of submerged vegetation based on Sentinel-2 red-edge band: A case study of Lake Erhai, China by Xiaohan Wang, Yu Zhang, Yanhong Yu, Yunmei Li, Heng Lyu, Junda Li, Xiaolan Cai, Xianzhang Dong, Gaolun Wang, Jianzhong Li, Mengmeng Song, Lanlan Chen

“…Using the disparities in the red-edge band, red-edge chlorophyll index (CIedge), and red-edge normalized vegetation index (NDVIedge), a carefully designed decision tree model was instituted for remote sensing-driven identification of SAV. …”
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