ENHANCED COASTLINE EXTRACTION AND EROSION ANALYSIS USING UNET AND DEXINED MODELS by Yan Wang, Adisorn Sirikham, Jessada Konpang, Chunguang Li

“…Complex coastal environments with dynamic features make manual digitisation and threshold-based segmentation inefficient and inaccurate. …”
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Enhancing phase change thermal energy storage material properties prediction with digital technologies by Minghao Yu, Jing Liu, Cheng Chen, Mingyue Li

“…Hierarchical feature fusion modules combine low-level atomistic descriptors with high-level continuum features.ResultsBenchmark evaluations show improved performance in predicting elastic modulus, thermal conductivity, and phase transition temperature across five material classes. …”
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ENHANCED COASTLINE EXTRACTION AND EROSION ANALYSIS USING UNET AND DEXINED MODELS by Yan Wang, Adisorn Sirikham, Jessada Konpang, Chunguang Li

“…Complex coastal environments with dynamic features make manual digitisation and threshold-based segmentation inefficient and inaccurate. …”
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Machine learning-based real-time prediction of duodenal stump leakage from gastrectomy in gastric cancer patients by Jae Hun Chung, Jae Hun Chung, Jae Hun Chung, Yushin Kim, Dongjun Lee, Dongwon Lim, Dongwon Lim, Dongwon Lim, Sun-Hwi Hwang, Sun-Hwi Hwang, Sun-Hwi Hwang, Si-Hak Lee, Si-Hak Lee, Si-Hak Lee, Woohwan Jung

“…Six ML algorithms were evaluated: Logistic Regression (LR), K-nearest neighbors (KNN), Support Vector Machine (SVM), Random Forest (RF), Extreme Gradient Boosting (XGB), and Neural Network (NN). …”
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Predicting PD-L1 status in NSCLC patients using deep learning radiomics based on CT images by Jiameng Lu, Xinyi Liu, Xiaoqing Ji, Yunxiu Jiang, Anli Zuo, Zihan Guo, Shuran Yang, Haiying Peng, Fei Sun, Degan Lu

“…Tumor regions of interest (ROI) were semi-automatically segmented based on CT images, and DL features were extracted using Residual Network 50. …”
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MS-YOLOv8: multi-scale adaptive recognition and counting model for peanut seedlings under salt-alkali stress from remote sensing by Fan Zhang, Longgang Zhao, Longgang Zhao, Dongwei Wang, Jiasheng Wang, Igor Smirnov, Juan Li

“…Additionally, the module automatically adjusts the channel weights of each group based on their contribution, improving the feature fusion effect. Second, the neck network structure is reconstructed to enhance recognition capabilities for small objects, and the MPDIoU loss function is introduced to effectively optimize the detection boxes for seedlings with scattered branch growth.ResultsExperimental results demonstrate that the proposed MS-YOLOv8 model achieves an AP50 of 97.5% for peanut seedling detection, which is 12.9%, 9.8%, 4.7%, 5.0%, 11.2%, 5.0%, and 3.6% higher than Faster R-CNN, EfficientDet, YOLOv5, YOLOv6, YOLOv7, YOLOv8, and RT-DETR, respectively.DiscussionThis research provides valuable insights for crop recognition under extreme environmental stress and lays a theoretical foundation for the development of intelligent production equipment.…”
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An Improved YOLOv8 Model for Detecting Four Stages of Tomato Ripening and Its Application Deployment in a Greenhouse Environment by Haoran Sun, Qi Zheng, Weixiang Yao, Junyong Wang, Changliang Liu, Huiduo Yu, Chunling Chen

“…A multi-dimensional feature neck network was integrated to enhance feature fusion, and three Semantic Feature Learning modules (SGE) were added before the detection head to minimize environmental interference. …”
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Machine learning for Internet of Things (IoT) device identification: a comparative study by Hamid Tahaei, Anqi Liu, Hamid Forooghikian, Mehdi Gheisari, Faiz Zaki, Nor Badrul Anuar, Zhaoxi Fang, Longjun Huang

“…One of the major steps in distinguishing IoT devices involves leveraging machine learning (ML) techniques on device network flows known as device fingerprinting. Numerous studies have proposed various solutions that incorporate ML and feature selection (FS) algorithms with different degrees of accuracy. …”
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Limbic system abnormalities in episodic cluster headache: a 7T MRI multimodal study by Xinyu Wang, Luhua Zhang, Yongqin Xiong, Mengmeng Hou, Shuhua Zhang, Caohui Duan, Song Wang, Xiaoyu Wang, Haoxuan Lu, Jiayu Huang, Yan Li, Zhixuan Li, Zhao Dong, Xin Lou

“…Automated volumetry and resting-state functional MRI analyses were performed after adjusting for age, Generalized Anxiety Disorder scale, sex (and intracranial volume when evaluating volumetric measures). Then functional-structural coupling indices were computed to assess network-level relationships. …”
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Application of a Hybrid Model Based on CEEMDAN and IMSA in Water Quality Prediction by GUO Li-jin, WU Hao-tian

“…[Objectives] To enhance water quality prediction accuracy, this study aims to address the following challenges: (1) traditional prediction methods often rely on simple, elementary decomposition techniques, limiting their ability to extract meaningful data features. (2) Single models and basic optimization algorithms result in low prediction accuracy. (3) Most approaches fail to leverage the advantages of different networks to analyze components of varying complexity, leading to inefficient model utilization. (4) Few studies incorporate error correction after prediction. …”
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mHealth Apps Available in Italy to Support Health Care Professionals in Antimicrobial Stewardship Implementation: Systematic Search in App Stores and Content Analysis by Giuseppa Russo, Annachiara Petrazzuolo, Marino Trivisani, Giuseppe Virone, Elena Mazzolini, Davide Pecori, Assunta Sartor, Sergio Giuseppe Intini, Stefano Celotto, Rossana Roncato, Roberto Cocconi, Luca Arnoldo, Laura Brunelli

“…After downloading the apps, they were evaluated using an 86-item checklist containing expert-validated criteria aggregated in the domains of pathogens and etiological agents, diagnosis and therapy support, AMR, dashboard function, antimicrobial stewardship (AMS), notes and recordings, network, and technical characteristics of the app. …”
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Advancing Hematopoietic Stem Cell Transplantation Typing: Harnessing Hyperledger Fabric’s Blockchain Architecture by Meng Wu, Feng Xu, Geetha Subramaniam, Zeyu Li, Liyun Chen, Wenjuan Zhu

“…Performance metrics, including block size, CPU utilization, network throughput, and response latency, were evaluated on Ubuntu 20.04.2 operating systems using VMware Workstation 16 Pro and Docker containerization. …”
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A hybrid explainable federated-based vision transformer framework for breast cancer prediction via risk factors by Aymen M. Al-Hejri, Archana Harsing Sable, Riyadh M. Al-Tam, Mugahed A. Al-antari, Sultan S. Alshamrani, Kaled M. Alshmrany, Wedad Alatebi

“…This paper addresses this challenge by introducing a comprehensive explainable federated learning framework for breast cancer prediction. We evaluate three deep learning approaches in both centralized and federated scenario settings: (1) individual artificial intelligence (AI) models, (2) high-level feature space ensemble models, and (3) a hybrid model combining global Vision Transformer (ViT) and local convolutional neural network (CNN) features. …”
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Automatic Quality Assessment of Speech-Driven Synthesized Gestures by Zhiyuan He

“…We noticed that recurrent neural networks (RNN) have advantages in modeling advanced spatiotemporal feature sequences, which are very suitable for use in the processing of synthetic gesture video data. …”
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Predictive modeling for rework detection in sustainable building projects by AbdulLateef Olanrewaju, Kafayat Shobowale

“…The dataset consisted of 75 responses, with 17 rework predictors. Feature scaling and normalisation were performed across the dataset to standardise the features. …”
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Paraphrase detection for Urdu language text using fine-tune BiLSTM framework by Muhammad Ali Aslam, Khairullah Khan, Wahab Khan, Sajid Ullah Khan, Abdullah Albanyan, Shabbab Ali Algamdi

“…We provide insights into the underlying linguistic features and patterns that contribute to the robustness of our framework. …”
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Fusing satellite imagery and ground-based observations for PM2.5 air pollution modeling in Iran using a deep learning approach by Zohreh Sohrabi, Jamshid Maleki

“…We utilized satellite data, ground-based observations, and meteorological parameters as input features. The models were evaluated using Root Mean Square Error (RMSE) and the coefficient of determination (R2). …”
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Dynamic cross-domain transfer learning for driver fatigue monitoring: multi-modal sensor fusion with adaptive real-time personalizations by S. S. Aravinth, G. Muni Nagamani, Chanumolu Kiran Kumar, Ayodele Lasisi, Quadri Noorulhasan Naveed, A. Bhowmik, Wahaj Ahmad Khan

“…Firstly, the domain adversarial neural network in EEG, ECG, and video inputs ensures cross-domain invariance of features. …”
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Design and Development of Diabetes Management System Using Machine Learning by Robert A. Sowah, Adelaide A. Bampoe-Addo, Stephen K. Armoo, Firibu K. Saalia, Francis Gatsi, Baffour Sarkodie-Mensah

“…The food recognition model was evaluated with cross-entropy metrics that support validation using Neural networks with a backpropagation algorithm. …”
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360 Using machine learning to analyze voice and detect aspiration by Cyril Varghese, Jianwei Zhang, Sara A. Charney, Abdelmohaymin Abdalla, Stacy Holyfield, Adam Brown, Hunter Stearns, Michelle Higgins, Julie Liss, Nan Zhang, David G. Lott, Victor E. Ortega, Visar Berisha

“…While bedside swallow evaluations are not sensitive/specific, gold standard tests for aspiration are invasive, uncomfortable, expose patients to radiation, and are resource intensive. …”
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