Atmospheric Modeling for Wildfire Prediction by Fathima Nuzla Ismail, Brendon J. Woodford, Sherlock A. Licorish

“…Our study focuses on developing wildfire prediction models using one-class classification algorithms. These include Support Vector Machine, Isolation Forest, AutoEncoder, Variational AutoEncoder, Deep Support Vector Data Description, and Adversarially Learned Anomaly Detection. …”
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Robust Bi-CBMSegNet framework for advancing breast mass segmentation in mammography with a dual module encoder-decoder approach by Yu Wang, Mudassar Ali, Tariq Mahmood, Amjad Rehman, Tanzila Saba

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Revolutionizing Water Quality Monitoring with Artificial Intelligence: A Systematic Review by Mahmoud Saleh Al-Khafaji, Layth Abdulameer, Muthanna M. A. AL-Shammari, Najah M. L. Al Maimuri, Anmar Dulaimi, Dhiya Al‑Jumeily

“…This systematic review addresses these gaps by evaluating the transformative role of artificial intelligence (AI) in revolutionizing monitoring practices through two novel mechanisms: (1) enhanced multivariate data fidelity via Internet of Things (IoT)-sensor networks and satellite remote sensing, and (2) predictive modeling precision using machine learning (ML) algorithms. …”
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From Neural Networks to Emotional Networks: A Systematic Review of EEG-Based Emotion Recognition in Cognitive Neuroscience and Real-World Applications by Evgenia Gkintoni, Anthimos Aroutzidis, Hera Antonopoulou, Constantinos Halkiopoulos

“…Further, related ethical issues with respect to privacy, data security, and machine learning model biases need to be much more proclaimed to responsibly apply research on emotions to areas such as healthcare, human–computer interaction, and marketing. …”
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Bridging the Gap Between Computational Efficiency and Segmentation Fidelity in Object-Based Image Analysis by Fernanda Pereira Leite Aguiar, Irenilza de Alencar Nääs, Marcelo Tsuguio Okano

“…This study introduces a novel preprocessing algorithm to bridge the gap between computational efficiency and segmentation fidelity in object-based image analysis for machine learning applications. …”
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New Perspectives on Lung Cancer Screening and Artificial Intelligence by Leonardo Duranti, Luca Tavecchio, Luigi Rolli, Piergiorgio Solli

“…This review explores the potential of AI and biomarker-driven methods, particularly liquid biopsy, in enhancing early lung cancer detection. We report the findings of major randomized controlled trials, cohort studies, and research on AI algorithms that use multi-modal imaging (e.g., CT and PET scans) and liquid biopsy to identify early molecular alterations. …”
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Automatic Segmentation of the Infraorbital Canal in CBCT Images: Anatomical Structure Recognition Using Artificial Intelligence by Ismail Gumussoy, Emre Haylaz, Suayip Burak Duman, Fahrettin Kalabalık, Muhammet Can Eren, Seyda Say, Ozer Celik, Ibrahim Sevki Bayrakdar

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Node-Based Graph Convolutional Network With SLIC Method for Breast Cancer Ultrasound Images Classification by Kien Trang, Fung Fung Ting, Bao Quoc Vuong, Chee-Ming Ting

“…The graph-structured data is processed through a multi-layer GCN, which effectively aggregates and learns from complex node interrelations to classify the nodes as benign, malignant, or normal. …”
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Hybrid Paddy disease classification using optimized statistical feature based transformation technique with explainable AI by M Amudha, Brindha K

“…To enhance predictive performance by transforming features, an improved Owl Search Optimization (IOSO) algorithm is used. This method not only yields consistent outcomes, but it also provides insights into its decisions, which helps the user better understand and trust the data. …”
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A comprehensive review of remote sensing techniques for monitoring Ulva prolifera green tides by Xiaomeng Geng, Xiaomeng Geng, Xiaomeng Geng, Huiru Li, Huiru Li, Le Wang, Weidong Sun, Yize Li

“…Additionally, it identifies the limitations and unresolved challenges in current approaches, such as constraints on data resolution, algorithmic biases, and environmental variability. …”
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Harnessing Artificial Intelligence for the Diagnosis, Treatment and Research of Multiple Sclerosis by Manisha S. Patil, Linda Y. Lin, Rachel K. Ford, Elizaveta J. James, Stella Morton, Felix Marsh-Wakefield, Simon Hawke, Georges E. Grau

“…AI is revolutionising the diagnosis and treatment of MS by enhancing the accuracy and efficiency of both processes. AI algorithms, particularly those based on machine learning, are being used to analyse medical imaging data, such as MRI scans, to detect early signs of MS, monitor disease progression and assess patient treatment response with greater precision. …”
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Acute kidney disease in hospitalized pediatric patients: risk prediction based on an artificial intelligence approach by Lingyu Xu, Siqi Jiang, Chenyu Li, Xue Gao, Chen Guan, Tianyang Li, Ningxin Zhang, Shuang Gao, Xinyuan Wang, Yanfei Wang, Lin Che, Yan Xu

“…Predictive models were constructed using eight machine learning algorithms and two ensemble algorithms, with the optimal model identified through AUROC. …”
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Multimodal prediction of the need of clozapine in treatment resistant schizophrenia; a pilot study in first-episode psychosis by Jonatan M. Panula, Athanasios Gotsopoulos, Jussi Alho, Jaana Suvisaari, Maija Lindgren, Tuula Kieseppä, Tuukka T. Raij

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Harnessing artificial intelligence of things for cardiac sensing: current advances and network-based perspectives by Hao Ren, Hao Ren, Hao Ren, Fengshi Jing, Yongcong Ma, Ruining Wang, Chaocheng He, Yufan Wang, Jiandong Zhou, Yu Sun

“…A total of 2,128 papers were included in the analysis.ConclusionFrom our perspective, current advancements in AI-powered IoT cardiac sensors primarily focus on optimizing AI algorithms, such as deep learning techniques, and enhancing the functionality of smart wearable devices for precision medicine. …”
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Key Vital Signs Monitor Based on MIMO Radar by Michael Gottinger, Nicola Notari, Samuel Dutler, Samuel Kranz, Robin Vetsch, Tindaro Pittorino, Christoph Würsch, Guido Piai

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Research on Optimization of Magnetic Ranging by Mechanism & Intelligence by QIAN Hongyu, CHE Yang, WANG Xiufang, FENG Fuping, JIANG Chunlei

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SFMattingNet: A Trimap-Free Deep Image Matting Approach for Smoke and Fire Scenes by Shihui Ma, Zhaoyang Xu, Hongping Yan

“…While deep learning-based methods offer promise using aerial images and surveillance images, the scarcity and limited diversity of smoke-and-fire-related image data hinder model accuracy and generalization. …”
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Improved identification and tracking of three-dimensional eddies in the Southern Ocean utilizing 3D-U-Res-Net by Chang Liu, Xiayan Lin, Xiayan Lin, Guangjun Xu, Guangjun Xu, Guoqing Han, Yu Liu

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Artificial Intelligence: The Strategy of Financial Risk Management by A. Kumar, A Kumar, S. Kumari, S. Kumar, N. Kumari, A. K. Behura

“…The paper presents the most promising AI technologies and techniques such as RPA, Data Management, Blockchain, MRL, MRC, CRU, Deep Learning, OML, Modelling and Stress Testing, Machine Learning and Algorithms, Neural Networks, Decision Trees, CPM, CRA, Black Box, etc. to improve “Financial Risk Management (FRM)”.…”
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Zero-Shot Prediction of Conversational Derailment With Large Language Models by Kenya Nonaka, Mitsuo Yoshida

“…Previous studies have trained machine learning algorithms to detect conversational derailment using supervised methods. …”
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