Pixel-Based Change Detection in Moving-Camera Videos Using Twin Convolutional Features on a Data-Constrained Scenario by Luiz G. C. Tavares, Allan F. Da Silva, Rafael Padilla, Lucas A. Thomaz, Sergio L. Netto, Eduardo A. B. Da Silva

“…This work proposes the Pixel-Based Change Detection using a Moving Camera (PBCD-MC) algorithm for detecting anomalies in a cluttered industrial site. …”
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A quantum machine learning framework for predicting drug sensitivity in multiple myeloma using proteomic data by M. Priyadharshini, B. Deevena Raju, A. Faritha Banu, P. Jagdish Kumar, V. Murugesh, Oleg Rybin

“…Abstract In this paper, we introduce QProteoML, a new quantum machine learning (QML) framework for predicting drug sensitivity in Multiple Myeloma (MM) using high-dimensional proteomic data. …”
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Disassembly of Distribution Transformers Based on Multimodal Data Recognition and Collaborative Processing by Li Wang, Feng Chen, Yujia Hu, Zhiyao Zheng, Kexin Zhang

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Comparative Analysis of Machine Learning and Deep Learning Models for Lung Cancer Prediction Based on Symptomatic and Lifestyle Features by Bireswar Dutta

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Suicide Risk Screening in Jails: Protocol for a Pilot Study Leveraging the Mental Health Research Network Algorithm and Health Care Data by Erin B Comartin, Grant Victor, Athena Kheibari, Brian K Ahmedani, Bethany Hedden-Clayton, Richard N Jones, Ted R Miller, Jennifer E Johnson, Lauren M Weinstock, Sheryl Kubiak

“…This study will use administrative data and ML modeling to improve suicide risk detection at jail booking. …”
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Prediabetes risk classification algorithm via carotid bodies and K-means clustering technique by Rafael F. Pinheiro, Maria P. Guarino, Marlene Lages, Rui Fonseca-Pinto

“…In the search for methods to support early diagnosis, this article introduces a novel prediabetes risk classification algorithm (PRCA) for type-2 diabetes mellitus (T2DM), utilizing the chemosensitivity of carotid bodies (CB) and K-means clustering technique from the field of machine learning. …”
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Gaze cluster analysis reveals heterogeneity in attention allocation and predicts learning outcomes by Nathalie John, Sebastian P. Korinth, Mareike Kunter, Franziska Baier-Mosch

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A novel approach based on XGBoost classifier and Bayesian optimization for credit card fraud detection by Mohammed Tayebi, Said El Kafhali

“…This study proposes an enhanced XGBoost algorithm for detecting fraudulent transactions using an intelligent technique that tunes the hyperparameters of the algorithm through Bayesian optimization. …”
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SARE: an optimized method for detection functional regulatory elements in genome by Mohammadjavad Hosseinpoor

“…Abstract Background Metaheuristic algorithms have been widely employed to solve optimization problems, but their application in analyzing health-related data remains limited. …”
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XGBoost Algorithm for Cervical Cancer Risk Prediction: Multi-dimensional Feature Analysis by Sudi Suryadi, Masrizal

“…Future research directions include prospective validation across diverse populations, integration of longitudinal data, and further exploration of explainable AI techniques to bridge the gap between algorithmic predictions and clinical implementation.…”
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A stacked ensemble approach with resampling techniques for highly effective fraud detection in imbalanced datasets by Idongesit E. Eteng, Udeze L. Chinedu, Ayei E. Ibor

“… In several earlier studies, machine learning (ML) has been widely explored for fraud detection. …”
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Cnidaria herd optimized fuzzy C-means clustering enabled deep learning model for lung nodule detection by R. Hari Prasada Rao, Agam Das Goswami

“…Further, the implemented methods have certain limitations including scalability, robustness, data availability, and false detection rate.MethodsTo overcome the limitations in the existing techniques, this research proposes the Cnidaria Herd Optimization (CHO) algorithm-enabled Bi-directional Long Short-Term Memory (CHSTM) model for effective lung nodule detection. …”
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Utilization of Remote Sensing Dataset and a Deep Learning Object Detection Model to Map Siam Weed Infestations by Zulfadli Mawardi, Deepak Gautam, Timothy G. Whiteside

“…This article introduces an innovative algorithm for mapping Siam weed infestations using a bounding box approach from a deep learning object detection model. …”
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Scalable Detection of Underground Water Leaks in Dense Urban Environments Using L-Band SAR and Machine Learning by E. Ali, E. Ali, L. Xie, A. Sani-Mohammed, W. Xu, T. Zayed

“…This study proposes the use of L-band SAR imagery from ALOS-2 combined with machine learning techniques to address these challenges. A robust leak detection framework was developed using six dual-polarized SAR images (HH and VV modes) alongside historical leak data from the Hong Kong Water Supplies Department (WSD). …”
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Blockchain-Enabled Federated Learning to Enhance Security and Privacy in Internet of Medical Things (IoMT) by zahra eskandari

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CGD-CD: A Contrastive Learning-Guided Graph Diffusion Model for Change Detection in Remote Sensing Images by Yang Shang, Zicheng Lei, Keming Chen, Qianqian Li, Xinyu Zhao

“…Self-supervised methods (SSL) for remote sensing image change detection (CD) can effectively address the issue of limited labeled data. …”
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Advanced intrusion detection in internet of things using graph attention networks by Aamir S. Ahanger, Sajad M. Khan, Faheem Masoodi, Ayodeji Olalekan Salau

“…In this paper, we present a novel perspective to IoT security by using a Graph-based (GB) algorithm to construct a graph that is evaluated with a graph-based learning Intrusion Detection System (IDS) incorporating a Graph Attention Network (GAT). …”
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Algorithm for Extraction of Reflection Waves in Single-Well Imaging Based on MC-ConvTasNet by Wanting Lin, Jiaqi Xu, Hengshan Hu

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An effectiveness of deep learning with fox optimizer-based feature selection model for securing cyberattack detection in IoT environments by Mimouna Abdullah Alkhonaini

“…This paper presents a Fox Optimizer-Based Feature Selection with Deep Learning for Securing Cyberattack Detection (FOFSDL-SCD) model. …”
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Development and validation of computer-aided detection for colorectal neoplasms using deep learning incorporated with computed tomography colonography by Shungo Endo, Koichi Nagata, Kenichi Utano, Satoshi Nozu, Takaaki Yasuda, Ken Takabayashi, Michiaki Hirayama, Kazutomo Togashi, Hiromasa Ohira

“…Abstract Objectives Computed tomography (CT) colonography is increasingly recognized as a valuable modality for diagnosing colorectal lesions, however, the interpretation workload remains challenging for physicians. Deep learning-based artificial intelligence (AI) algorithms have been employed for imaging diagnoses. …”
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