Design of an Electronic Nose System with Automatic End-Tidal Breath Gas Collection for Enhanced Breath Detection Performance by Dongfu Xu, Pu Liu, Xiangming Meng, Yizhou Chen, Lei Du, Yan Zhang, Lixin Qiao, Wei Zhang, Jiale Kuang, Jingjing Liu

“…At the same time, the control module realizes automation of the experiment process, including control of the gas path state, signal transmission, and data storage. Finally, the system is used for breath detection. …”
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Securing the IoT System of Smart City against Cyber Threats Using Deep Learning by Tanzila Saba, Amjad Rehman Khan, Tariq Sadad, Seng-phil Hong

“…The threads concerned with IoT need to be reduced for efficient Intrusion Detection Systems (IDSs). As a result, machine learning algorithms generate correct outputs from a large and complicated dataset. …”
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Machine learning identifies 6-gene signature in peripheral blood for pancreatic cancer diagnosis by Francisco Carrillo-Perez, Octavio Caba, Cristina Jiménez-Luna, Francisco Ortuño, Daniel Castillo-Secilla, Luis Javier Herrera, Jose Prados, Ignacio Rojas

“…Feature selection using the minimum Redundancy Maximum Relevance (mRMR) algorithm, followed by support vector machine (SVM) classification, identified a 15-gene signature derived from the exLR data. …”
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Improved machine learning framework for prediction of phases and crystal structures of high entropy alloys by Debsundar Dey, Suchandan Das, Anik Pal, Santanu Dey, Chandan Kumar Raul, Pritam Mandal, Arghya Chatterjee, Soumya Chatterjee, Manojit Ghosh

“…Among all these algorithms, XGBoost recorded the highest detection accuracy of 94.05 % for phases and LightGBM yielded the highest detection accuracy of 90.07 % for crystal structure. …”
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A Learning Emotion Recognition Model Based on Feature Fusion of Photoplethysmography and Video Signal by Xiaoliang Zhu, Zili He, Chuanyong Wang, Zhicheng Dai, Liang Zhao

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Developing a machine learning model for predicting varicocelectomy outcomes: a pilot study by Coşkun Kaya, Mehmet Erhan Aydın, Özer Çelik, Aykut Aykaç, Mustafa Sungur

“…Despite the increasing interest in Machine Learning (ML) in urology, there have been limited studies on the detection and prediction of varicocelectomy using artificial intelligence. …”
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The role of advanced machine learning in COVID-19 medical imaging: A technical review by Abdul Muiz Fayyaz, Said Jadid Abdulkadir, Shahab Ul Hassan, Safwan Mahmood Al-Selwi, Ebrahim Hamid Sumiea, Lareib Fatima Talib

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Analyzing mental stress in Indian students through advanced machine learning and wearable technologies by Shruti Gedam, Sandip Dutta, Ritesh Jha

“…This study aims to compare various machine learning (ML) algorithms for detecting mental stress using wearable physiological signal data and proposes a novel model that is automatic, high-performing, low-cost, and with lower time and computation complexity. …”
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Preliminary Electroencephalography-Based Assessment of Anxiety Using Machine Learning: A Pilot Study by Katarzyna Mróz, Kamil Jonak

“…<b>Background</b>: Recent advancements in machine learning (ML) have significantly influenced the analysis of brain signals, particularly electroencephalography (EEG), enhancing the detection of complex neural patterns. …”
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An interpretable machine learning approach for predicting and grading hip osteoarthritis using gait analysis by Qing Yang, Xinyu Ji, Yuyan Zhang, Shaoyi Du, Bing Ji, Wei Zeng

“…Purpose The purpose of this study is to evaluate the efficacy of lower extremity kinematic gait data for detecting and rating the severity of unilateral hip OA using machine learning algorithms. …”
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Recovering NDVI over lake surfaces: Initial insights from CYGNSS data enhanced by ERA-5 inputs by Yinqing Zhen, Qingyun Yan

“…In this paper, we propose a new method to recover lake-surface NDVI on cloudy days, utilizing GNSS-R observables and auxiliary meteorological data in conjunction with a machine learning regression algorithm called Bagging Tree. …”
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A Resilient Deep Learning Approach for State Estimation in Distribution Grids With Distributed Generation by Ronald Kfouri, Harag Margossian

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Comparison of Transfer Learning Model Performance for Breast Cancer Type Classification in Mammogram Images by Cahya Bagus Sanjaya, Muhammad Imron Rosadi, Moch. Lutfi, Lukman Hakim

“…For genBased on test results, the best accuracy was obtained using EfficientNetb2 with an accuracy value of 94% for training data and 98% for test data on mammogram images.…”
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Deep learning and hyperspectral features for seedling stage identification of barnyard grass in paddy field by Siqiao Tan, Qiang Xie, Wenshuai Zhu, Yangjun Deng, Lei Zhu, Xiaoqiao Yu, Zheming Yuan, Zheming Yuan, Yuan Chen, Yuan Chen

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A novel deep learning model for predicting marine pollution for sustainable ocean management by Michael Onyema Edeh, Surjeet Dalal, Musaed Alhussein, Khursheed Aurangzeb, Bijeta Seth, Kuldeep Kumar

“…The classification was done using two types of machine learning algorithms: two-step clustering and a fully convolutional network (FCN). …”
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Development and validation of machine learning models for young-onset colorectal cancer risk stratification by Junhai Zhen, Jiao Li, Fei Liao, Jixiang Zhang, Chuan Liu, Huabing Xie, Cheng Tan, Weiguo Dong

“…The performance of fecal immunochemical test in detecting YOCRC is unsatisfactory. Using routine clinical data, we aimed to develop machine learning (ML) models to identify individuals with high-risk YOCRC who require further colonoscopy. …”
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Early prediction of colorectal adenoma risk: leveraging large-language model for clinical electronic medical record data by Xiaoyu Yang, Jinjian Xu, Hong Ji, Jun Li, Bingqing Yang, Liye Wang

“…Several classical machine learning algorithms were applied in combination with the BGE-M3 large-language model (LLM) for enhanced semantic feature extraction. …”
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Advanced Interpretation of Bullet-Affected Chest X-Rays Using Deep Transfer Learning by Shaheer Khan, Nirban Bhowmick, Azib Farooq, Muhammad Zahid, Sultan Shoaib, Saqlain Razzaq, Abdul Razzaq, Yasar Amin

“…Special deep learning algorithms went through a process of optimization before researchers improved their ability to detect and place objects. …”
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Applying Machine Learning for Sustainable Farm Management: Integrating Crop Recommendations and Disease Identification by P. Ankit Krishna, Neelamadhab Padhy, Archana Patnaik

“…To suggest appropriate crops and detect pertinent plant diseases, cutting-edge machine learning and deep learning algorithms were used. …”
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Snow depth estimation in Northeast China based on space-borne scatterometer data and ML model with optimal features by Wenfei Chen, Lingjia Gu, Xiaofeng Li, Xintong Fan

“…This study indicates that spaceborne scatterometer data hold promise for SD estimation in snow monitoring applications.…”
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