Curriculum-Guided Adversarial Learning for Enhanced Robustness in 3D Object Detection by Jinzhe Huang, Yiyuan Xie, Zhuang Chen, Ye Su

“…The pursuit of robust 3D object detection has emerged as a critical focus within the realm of computer vision. This paper presents a curriculum-guided adversarial learning (CGAL) framework, which significantly enhances the adversarial robustness and detection accuracy of the LiDAR-based 3D object detector PointPillars. …”
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Deep learning for smartphone-aided detection system of Helicobacter Pylori in gastric biopsy by Guanmeng Gao, Zihan Wei, Fei Pei, Yajie Du, Beiying Liu

“…Finally, the diagnoses of different pathologists with/without AI assistance were compared. As results, we present a deep learning framework (the Faster-R-CNN with ResNet 50) which can automatically detect HP of gastric biopsies and achieve 89.23% accuracy. …”
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Explainable deep learning approach for recognizing “Egyptian Cobra” bite in real-time by Elhoseny Mohamed, Hassan Ahmed, Shehata Marwa H., Kayed Mohammed

“…This research employs Internet of things (IoT) and deep learning methods to precisely recognize bites of Egyptian cobra, in the real-time, by analyzing images of the bite marks. …”
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Explaining the Earnings Management Prediction Model Using the Hybrid of Machine Learning Methods by Hassan Hassani, Esfandiar Malekian Kallehbasti, Yahya Kamyabi

“…The purpose of this research is to use machine learning methods such as decision tree, support vector machine, k-nearest neighbor, and deep learning to predict earnings management. …”
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Computational modelling of immunological mechanisms: From statistical approaches to interpretable machine learning by María Rodríguez Martínez, Matteo Barberis, Anna Niarakis

“…This large amount of data has facilitated the emergence of statistical and machine-learning models focused on unravelling the intricate complexities of the immune system. …”
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IMPROVING AGRICULTURAL YIELDS IN THE DEMOCRATIC REPUBLIC OF CONGO USING MACHINE LEARNING ALGORITHMS by Rodolphe Nsimba Malumba, Mardochee Longo Kayembe, Fiston Chrisnovic Balanganayi Kabutakapua, Bopatriciat Boluma Mangata, Trésor MAZAMBI KILONGO, Rufin Tabiaki Tandele, Emmanuel Ntanyungu Ndizieye, Parfum Bukanga Christian

“…This article presents an analysis of agricultural yields in the Democratic Republic of Congo (DRC) using machine learning algorithms. …”
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A Comparative Study of Machine Learning Models for Accurate E-Waste Prediction by Mohammed Algafri, Mohammed Sayad, Mohammad A.M. Abdel-Aal, Ahmed M. Attia

“…The rapid growth of electrical and electronic equipment waste (e-waste) presents a major environmental challenge. Traditional linear production models fail to optimize resource recovery, while circular economy (CE) strategies remain underutilized due to inadequate forecasting methods. …”
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Probabilistic machine learning-based phytoplankton abundance using hyperspectral remote sensing by Do Hyuck Kwon, Jung Min Ahn, Jong Cheol Pyo, Jiye Lee, Ather Abbas, Sanghyun Park, Kyunghyun Kim, Hyuk Lee, Kyung Hwa Cho

“…To address this gap, this study employed airborne remote sensing using hyperspectral imagery and a deep-learning approach to directly estimate phytoplankton cell concentrations across extensive water bodies. …”
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DART-Vetter: A Deep Learning Tool for Automatic Triage of Exoplanet Candidates by Stefano Fiscale, Laura Inno, Alessandra Rotundi, Angelo Ciaramella, Alessio Ferone, Christian Magliano, Luca Cacciapuoti, Veselin Kostov, Elisa V. Quintana, Giovanni Covone, Maria Teresa Muscari Tomajoli, Vito Saggese, Luca Tonietti, Antonio Vanzanella, Vincenzo Della Corte

“…To further improve the robustness of these models, it is necessary to exploit the complementarity of data collected from different transit surveys such as NASA’s Kepler, Transiting Exoplanet Survey Satellite (TESS), and, in the near future, the ESA Planetary Transits and Oscillation of stars mission. In this work, we present a deep learning model, named DART-Vetter , that is able to distinguish planetary candidates from false positives signals detected by any potential transiting survey. …”
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Collaborative Estimation of Tropical Cyclone Wind Radii With Multitask Learning and Multiteacher Distillation by Jia Liu, Yinlei Yue, Yongjun Jin, Kaijun Ren, Xiang Wang, Kefeng Deng, Chongjiu Deng, Ke Deng

“…To address these issues and improve both the accuracy and efficiency of wind radii estimation, this article presents a novel collaborative TC wind radii estimation method with multitask learning and multiteacher knowledge distillation. …”
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Model-informed deep-learning photoacoustic reconstruction for low-element linear array by Souradip Paul, S. Alex Lee, Shensheng Zhao, Yun-Sheng Chen

“…However, model matrix inversion during adjoint transformations presents computational challenges in model-based deep learning. …”
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Advanced Multi-Level Ensemble Learning Approaches for Comprehensive Sperm Morphology Assessment by Abdulsamet Aktas, Taha Cap, Gorkem Serbes, Hamza Osman Ilhan, Hakkı Uzun

“…By leveraging ensemble learning and multi-level fusion, the model provides a reliable and scalable solution for clinical decision-making in male fertility assessment.…”
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Targeted nano-energetic material exploration through active learning algorithm implementation by Leandro Carreira, Lea Pillemont, Yasser Sami, Nicolas Richard, Alain Esteve, Matthieu Jonckheere, Carole Rossi

“…This paper presents an active learning-based method designed to guide experiments towards user-defined specific regions, termed ”regions of interest,” within vast and multi-dimensional thermite design spaces. …”
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Predicting diabetic retinopathy based on routine laboratory tests by machine learning algorithms by Xiaohua Wan, Ruihuan Zhang, Yanan Wang, Wei Wei, Biao Song, Lin Zhang, Yanwei Hu

“…Abstract Objectives This study aimed to identify risk factors for diabetic retinopathy (DR) and develop machine learning (ML)-based predictive models using routine laboratory data in patients with type 2 diabetes mellitus (T2DM). …”
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Deep Learning for Predicting the Difficulty Level of Removing the Impacted Mandibular Third Molar by Vorapat Trachoo, Unchalisa Taetragool, Ploypapas Pianchoopat, Chatchapon Sukitporn-udom, Narapathra Morakrant, Kritsasith Warin

“…Methods: The study included 1367 LM3 images from 784 patients who presented from 2021–2023 to the University Dental Hospital; images were collected retrospectively. …”
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A Meta-Learning-Based Ensemble Model for Explainable Alzheimer’s Disease Diagnosis by Fatima Hasan Al-bakri, Wan Mohd Yaakob Wan Bejuri, Mohamed Nasser Al-Andoli, Raja Rina Raja Ikram, Hui Min Khor, Zulkifli Tahir, The Alzheimer’s Disease Neuroimaging Initiative

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Multimodal mobile brain and body imaging for quantification of dance motor sequence learning by Russell W. Chan, Victoria Lakomski, Johannes V.R. Pannermayr, Emma Wiechmann, Jan-Willem J.R. van ‘t Klooster, Willem B. Verwey

“…Understanding motor learning in naturalistic settings presents a key challenge in neuroscience. …”
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Multivariate machine learning algorithms for energy demand forecasting and load behavior analysis by Farhan Hussain, M. Hasanuzzaman, Nasrudin Abd Rahim

“…This article presents deep learning frameworks for predicting electricity demand in the Western region of Bangladesh, utilizing Artificial Neural Network (ANN) and Adaptive Neuro-Fuzzy Inference System (ANFIS) algorithms. …”
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Sulfur-Fumigated Ginger Identification Method Based on Meta-Learning for Different Devices by Tianshu Wang, Jiawang He, Hui Yan, Kongfa Hu, Xichen Yang, Xia Zhang, Jinao Duan

“…Next, the recognition model is generated based on the features. Finally, meta-learning parameters are introduced to enable the model to learn and adapt to new tasks, thereby improving the adaptability of the model. …”
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USING ARTIFICIAL INTELLIGENCE (AI) AND DEEP LEARNING TECHNIQUES IN FINANCIAL RISK MANAGEMENT by Joseph Olorunfemi AKANDE

“…Moreover, federated learning systems present a promising solution for ensuring privacy and security when dealing with sensitive financial data. …”
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