Attention-driven hybrid deep learning and SVM model for early Alzheimer’s diagnosis using neuroimaging fusion by Arjun Kidavunil Paduvilan, Godlin Atlas Lawrence Livingston, Sampath Kumar Kuppuchamy, Rajesh Kumar Dhanaraj, Muthuvel Subramanian, Amal Al-Rasheed, Masresha Getahun, Ben Othman Soufiene

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Efficient deep learning model for classifying lung cancer images using normalized stain agnostic feature method and FastAI-2 by Pranshu Saxena, Sanjay Kumar Singh, Mamoon Rashid, Sultan S. Alshamrani, Mrim M. Alnfiai

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Real-Time Evaluation of Ground Insulation Degradation and Fault Warning Method Under Multiple Operating Conditions for Train Traction Drive System by Zhenglin Cheng, Kan Liu, Xueming Li, Shaolong Xu, Zhiwen Chen, Fengbing Jiang

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Comparison of EfficientNet CNN models for multi-label chest X-ray disease diagnosis by Murat Ucan, Buket Kaya, Osman Aygun, Mehmet Kaya, Reda Alhajj

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An eXplainable deep learning model for multi-modal MRI grading of IDH-mutant astrocytomas by Hamail Ayaz, Oladosu Oladimeji, Ian McLoughlin, David Tormey, Thomas C. Booth, Saritha Unnikrishnan

“…This is followed by an eXplainable AI approach that uses SHapley Additive exPlanations (SHAP) to interpret model decisions and identify key contributing features. …”
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Evaluating the change and trend of construction land in Changsha City based GeoSOS-FLUS model and machine learning methods by Zuopeng Zhang, Zhe Li, Zhirong Li

“…Abstract This study systematically analyzes the land use changes in Changsha City from 2000 to 2023. Three classification models—Random Forest (RF), Gradient Boosting Decision Tree (GBDT), and Artificial Neural Network (ANN) were employed to evaluate the accuracy of land use classification. …”
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Conformal deep forest for uncertainty-aware classification by Jing Zhang, Yunfei Qiu, Libo Dong

“…Extensive experiments demonstrate that CDForest significantly outperforms state-of-the-art models in terms of classification accuracy, robustness against noise, and decision reliability, further confirming its effectiveness as a highly competitive solution for uncertainty-aware classification tasks.…”
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Anemia Classification System Using Machine Learning by Jorge Gómez Gómez, Camilo Parra Urueta, Daniel Salas Álvarez, Velssy Hernández Riaño, Gustavo Ramirez-Gonzalez

“…We built a supervised learning approach and trained three models (Linear Discriminant Analysis, Decision Trees, and Random Forest) using an anemia dataset from a previous study by Sabatini in 2022. …”
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An Explainable Fuzzy Framework for Assessing Preeclampsia Classification by Matías Salinas, Daira Velandia, Leondry Mayeta-Revilla, Ayleen Bertini, Marvin Querales, Fabian Pardo, Rodrigo Salas

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Automated diagnosis of respiratory diseases from lung ultrasound videos ensuring XAI: an innovative hybrid model approach by Arefin Ittesafun Abian, Mohaimenul Azam Khan Raiaan, Asif Karim, Sami Azam, Nur Mohammad Fahad, Niusha Shafiabady, Kheng Cher Yeo, Friso De Boer

“…This study focuses on the difficulties associated with identifying and categorizing respiratory diseases, including COVID-19, influenza, and pneumonia.MethodsWe propose a novel method that combines three dimensional (3D) models, model explainability (XAI), and a Decision Support System (DSS) that utilizes lung ultrasound (LUS) videos. …”
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Integrating explainable artificial intelligence and light gradient boosting machine for glioma grading by Teuku Rizky Noviandy, Ghalieb Mutig Idroes, Irsan Hardi

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Enhancing Bottleneck Concept Learning in Image Classification by Xingfu Cheng, Zhaofeng Niu, Zhouqiang Jiang, Liangzhi Li

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A comprehensive transplanting of black-box adversarial attacks from multi-class to multi-label models by Zhijian Chen, Qi Zhou, Yujiang Liu, Wenjian Luo

“…Therefore, existing multi-class attack algorithms cannot directly attack multi-label classification models. In this paper, we study the transplantation methods of multi-class black-box attack algorithms to multi-label classification models and propose the multi-label versions for eight classic black-box attack algorithms, which include three score-based attacks and five decision-based (label-only) attacks, for the first time. …”
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Developing a hybrid feature selection method to detect botnet attacks in IoT devices by Alshaeaa H.Y., Ghadhban Z.M., Ministry of Education, Iraq

“…The UNSW-NB15 dataset is used to assess the proposed system. Several classification models including decision tree (DT), random forest (RF), k-nearest neighbors (KNN), adaptive boosting (AdaBoost), and bagging are utilized for the classification purpose. …”
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Monitoring land-use changes and predicting their spatio-temporal trends in Hamedan City by Naser Shafiei Sabet, Faranak Feyzbabaei cheshmeh sefidi

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Uncertainty-aware automatic TNM staging classification for [18F] Fluorodeoxyglucose PET-CT reports for lung cancer utilising transformer-based language models and multi-task learni... by Stephen H. Barlow, Sugama Chicklore, Yulan He, Sebastien Ourselin, Thomas Wagner, Anna Barnes, Gary J.R. Cook

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Classification of urbanized areas for public administration purposes by K.M., A.Ye.

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A Hybrid Network Analysis and Machine Learning Model for Enhanced Financial Distress Prediction by Saba Taheri Kadkhoda, Babak Amiri

“…The results underscore the efficacy of network-based strategies in refining financial distress prediction models, providing valuable insights for decision-makers.…”
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Analysis of Driver Decisions at the Onset of Yellow at Signalized Intersections by Juan Li, Boyu Jiang, Chunjiao Dong, Jue Wang, Xuan Zhang

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CausalCervixNet: convolutional neural networks with causal insight (CICNN) in cervical cancer cell classification—leveraging deep learning models for enhanced diagnostic accuracy by Zahra Taghados, Zohreh Azimifar, Malihezaman Monsefi, Mojgan Akbarzadeh Jahromi

“…This ensures greater consistency, comprehensibility, and transparency in medical decision-making. This study introduces CausalCervixNet, a Convolutional Neural Network with Causal Insight (CICNN) tailored for cervical cancer cell classification. …”
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