Learning From Imbalanced Data Using Triplet Adversarial Samples by Jaesub Yun, Jong-Seok Lee

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Radiomic Features of Mesorectal Fat as Indicators of Response in Rectal Cancer Patients Undergoing Neoadjuvant Therapy by Francesca Treballi, Ginevra Danti, Sofia Boccioli, Sebastiano Paolucci, Simone Busoni, Linda Calistri, Vittorio Miele

“…The LASSO algorithm selected three features (shape_Sphericity, shape_Maximum2DDiameterSlice, and glcm_Imc2) for the construction of the radiomic logistic regression model, and ROC curves were subsequently generated for each model (AUC: 0.76). …”
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Prediction of cardiovascular diseases based on GBDT+LR by Zengxiao Chi, Li Liu, Liqin Yi, Lin Shi

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Artificial intelligence-based risk management for the banking sector: impact and challenges by L.A. Kuanova, A.N. Otegen, G.K. Kenzhegulova, P. Karacsony

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CapsuleThermNet: A CNN-CapsuleNet Architecture for Early Detection of Suicide Risk in Depressed Patients Using Thermal Facial Imaging by Eddy Muntina Dharma, Harjanto Prabowo, Agung Trisetyarso, Tjhin Wiguna

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Enhancing seizure detection with hybrid XGBoost and recurrent neural networks by Santushti Santosh Betgeri, Madhu Shukla, Dinesh Kumar, Surbhi B. Khan, Muhammad Attique Khan, Nora A. Alkhaldi

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A robot process automation based mobile application for early prediction of chronic kidney disease using machine learning by Md. Hasan Imam Bijoy, Md. Jueal Mia, Md. Mahbubur Rahman, Mohammad Shamsul Arefin, Pranab Kumar Dhar, Tetsuya Shimamura

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An integrated strategy based on radiomics and quantum machine learning: diagnosis and clinical interpretation of pulmonary ground-glass nodules by Xianzhi Huang, Fangyi Xu, Wenchao Zhu, Lin Yao, Jiahuan He, Junhao Su, Wending Zhao, Hongjie Hu

“…SHAP analysis was applied to interpret the contribution of radiomic features to the models’ predictions. Results All three QML models outperformed the classical SVM, with the QNN model achieving the highest improvements ( $$p < 0.05$$ ) in classification metrics, including accuracy (89.23 $$\%$$ , 95 $$\%$$ CI: 81.54 $$\%$$ − 95.38 $$\%$$ ), sensitivity (96.55 $$\%$$ , 95 $$\%$$ CI: 89.66 $$\%$$ − 100.00 $$\%$$ ), specificity (83.33 $$\%$$ , 95 $$\%$$ CI: 69.44 $$\%$$ − 94.44 $$\%$$ ), and area under the curve (AUC) (0.937, 95 $$\%$$ CI: 0.871 - 0.983), respectively. …”
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Can Different Cultivars of <i>Panicum maximum</i> Be Identified Using a VIS/NIR Sensor and Machine Learning? by Gelson dos Santos Difante, Gabriela Oliveira de Aquino Monteiro, Juliana Caroline Santos Santana, Néstor Eduardo Villamizar Frontado, Jéssica Gomes Rodrigues, Aryadne Rhoana Dias Chaves, Dthenifer Cordeiro Santana, Izabela Cristina de Oliveira, Luis Carlos Vinhas Ítavo, Fabio Henrique Rojo Baio, Gabriela Souza Oliveira, Carlos Antonio da Silva Junior, Vanessa Zirondi Longhini, Alexandre Menezes Dias, Paulo Eduardo Teodoro, Larissa Pereira Ribeiro Teodoro

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Reducing bias in coronary heart disease prediction using Smote-ENN and PCA. by Xinyi Wei, Boyu Shi

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Resurgence in focus: Covid-19 dynamics and optimal control frameworks by Evans O. Omorogie, Kolade M. Owolabi, Bola T. Olabode, Tunde T. Yusuf, Edson Pindza

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Cloud Computing-Based Security Analysis on Wireless Sensor Nodes Cluster Using Predictive Technique by Muhammed Zaharadeen Ahmed, Aisha Hassan Abdallah Hashim, Othman Omran Khalifa, Aliyu Muhammad Wakil, Zeinab E. Ahmed, Khmaies Ouhada

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An Explainable Artificial Intelligence Text Classifier for Suicidality Prediction in Youth Crisis Text Line Users: Development and Validation Study by Julia Thomas, Antonia Lucht, Jacob Segler, Richard Wundrack, Marcel Miché, Roselind Lieb, Lars Kuchinke, Gunther Meinlschmidt

“…ObjectiveThis study aimed to (1) develop and implement ML methods for predicting SIBs in a real-world crisis helpline dataset, using transformer-based pretrained models as a foundation; (2) evaluate, cross-validate, and benchmark the model against traditional text classification approaches; and (3) train an explainable model to highlight relevant risk-associated features. …”
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Integrating Satellite-Based Precipitation Analysis: A Case Study in Norfolk, Virginia by Imiya M. Chathuranika, Dalya Ismael

“…The additive bias correction (ABC) method overestimated mean monthly precipitation, while the PERSIANN-Cloud Classification System (CCS), adjusted with multiplicative bias correction (MBC), was found to be the most accurate bias-adjusted model. …”
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Cryptographic hardness assumptions identification based on discrete wavelet transform by Ke Yuan, Yu Du, Yizheng Liu, Rongjin Feng, Bowen Xu, Gaojuan Fan, Chunfu Jia

“…To address the challenges posed by high-dimensionality, complex data distributions, and difficulty fitting ciphertext features, an ensemble learning model called MHERF is constructed, which combines four classifiers: Decision Tree, Adaptive Boosting, Support Vector Machines, and Gradient Boosting. …”
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Wearable device for axillary lymph node screening in breast cancer based on infrared thermography and artificial intelligence by Xiaoying Zhong, Jinqiu Deng, Ping Lu, Zhichao Zuo, Yu Zhao, Yidong Zhou, Xuefei Wang

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Strategies for applying interpretable and explainable AI in real world IoT applications by Anber Abraheem Shlash Mohammad, Suleiman Ibrahim Shelash Mohammad, Badrea Al Oraini, Asokan Vasudevan, Ayman Hindieh, Ali Altarawneh, Muhammad Turki Alshurideh, Imad Ali

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GIQ - Revolutionizing Geospatial Ecosystem Development Through Public-Private Partnerships by S. Al Zeidi, P. Marpu, Y. Tarabalka, M. Balgi

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Machine Learning for Health Insurance Prediction in Nigeria by Victor Enemona Ochigbo, Oluwasogo Adekunle Okunade, Emmanuel Gbenga Dada, Oluyemi Mikail Olaniyi, Oluwatoyosi Victoria Oyewande

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Multimodal Emotion Recognition Based on Facial Expressions, Speech, and EEG by Jiahui Pan, Weijie Fang, Zhihang Zhang, Bingzhi Chen, Zheng Zhang, Shuihua Wang

“…Finally, we adopted the strategy of decision-level fusion to integrate the recognition results of the above three modes, resulting in more comprehensive and accurate performance. …”
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