Wearable Internet-of-Things platform for human activity recognition and health care by Asif Iqbal, Farman Ullah, Hafeez Anwar, Ata Ur Rehman, Kiran Shah, Ayesha Baig, Sajid Ali, Sangjo Yoo, Kyung Sup Kwak

“…These measurements and their statistical are then represented in features vectors that used to train and test supervised machine learning algorithms (classifiers) for activity recognition. …”
Get full text

MMPred: a tool to predict peptide mimicry events in MHC class II recognition by Filippo Guerri, Filippo Guerri, Valentin Junet, Valentin Junet, Judith Farrés, Xavier Daura, Xavier Daura, Xavier Daura

“…However, the tool is easily extendable to MHC class I predictions by incorporating pre-trained models from CNN-PepPred and NetMHCpan. To evaluate MMPred’s ability to produce biologically meaningful insights, we conducted a comprehensive assessment involving i) predicting associations between known HLA class II human autoepitopes and microbial-peptide mimicry, ii) interpreting these predictions within a systems biology framework to identify potential functional links between the predicted autoantigens and pathophysiological pathways related to autoimmune diseases, and iii) analyzing illustrative cases in the context of SARS-CoV-2 infection and autoimmunity. …”
Get full text

Forced Oscillation Detection via a Hybrid Network of a Spiking Recurrent Neural Network and LSTM by Xiaomei Yang, Jinfei Wang, Xingrui Huang, Yang Wang, Xianyong Xiao

“…The proposed hybrid network is trained using the backpropagation-through-time (BPTT) optimization algorithm, with adjustments made to address the discontinuous gradient in the SRNN. …”
Get full text

Panel Temperature Dependence on Atmospheric Parameters of an Operative Photovoltaic Park in Semi-Arid Zones Using Artificial Neural Networks by Sonia Montecinos, Carlos Rodríguez, Andrea Torrejón, Jorge Cortez, Marcelo Jaque

“…We applied the back-propagation algorithm to train the model by using the atmospheric variables tilted solar radiation (TSR), air temperature, and wind speed measured in the park. …”
Get full text

Automatic recognition of adrenal incidentalomas using a two-stage cascade network: a multicenter study by Xiao Xie, Sheng-Xiao Ma, Xiang-De Luo, De-Ying Liao, Dong Han, Zhi-Peng Huang, Zhi-Hua Chen, Xian-Ping Li, Bo Li, Shi-Di Hu, Yan-Jun Chen, Peng-Fei Liu, De-Zhong Zheng, Hui Xia, Cun-Dong Liu, Shan-Chao Zhao, Ming-Kun Chen

“…The segmentation network was mainly evaluated by the Dice similarity coefficient (DSC), and the classifier was evaluated by the area under the receiver operator characteristic curve (AUC), accuracy, sensitivity, and specificity. …”
Get full text

Machine learning applications to classify and monitor medication adherence in patients with type 2 diabetes in Ethiopia by Ewunate Assaye Kassaw, Ewunate Assaye Kassaw, Ashenafi Kibret Sendekie, Ashenafi Kibret Sendekie, Bekele Mulat Enyew, Biruk Beletew Abate, Biruk Beletew Abate

“…Eight widely used ML algorithms were employed to develop the models, and their performance was evaluated using metrics such as accuracy, precision, recall, and F1 score. …”
Get full text

Machine-learning model for predicting left atrial thrombus in patients with paroxysmal atrial fibrillation by Wanli Xiong, Qiqi Cao, Lu Jia, Min Chen, Tao Liu, Qingyan Zhao, Yanhong Tang, Bo Yang, Li Li, Shaobo Shi, He Huang, Congxin Huang, China Atrial Fibrillation Center Project Team

“…Sixty-one variables were initially included to train machine learning models, with the random forest algorithm demonstrating the best predictive performance (AUC 0.833, 95%CI 0.730–0.924). …”
Get full text

Enhancing one-year mortality prediction in STEMI patients post-PCI: an interpretable machine learning model with risk stratification by Wenqiang Li, Wenqiang Li, Dongdong Yan, Dongdong Yan, Dongdong Yan, Dongdong Yan, Wei Hu, Wei Hu, Wei Hu, Wei Hu, Xiaoling Su, Zheng Zhang, Zheng Zhang, Zheng Zhang, Zheng Zhang

“…ML models were trained to predict one-year mortality in STEMI patients post-PCI, with performance evaluated using accuracy, sensitivity, precision, F1-score, area under the receiver operating characteristic curve (AUROC), and the area under the precision-recall curve (AUPRC).ResultsWe analyzed data from 1,274 patients, incorporating 46 clinical and laboratory features. …”
Get full text

A comparative framework to develop transferable species distribution models for animal telemetry data by Joshua A. Cullen, Camila Domit, Margaret M. Lamont, Christopher D. Marshall, Armando J. B. Santos, Christopher R. Sasso, Mehsin Al Ansi, Kristen M. Hart, Mariana M. P. B. Fuentes

“…In predictive modeling, practitioners often use correlative SDMs that only evaluate a single spatial scale and do not account for differences in life stages. …”
Get full text

Prediction of Enthalpy of Mixing of Binary Alloys Based on Machine Learning and CALPHAD Assessments by Shuangying Huang, Guangyu Wang, Zhanmin Cao

“…Using pure element properties and Miedema’s model parameters as descriptors, we trained and evaluated four machine learning algorithms, finding LightGBM to perform best (R² = 92.2%, MAE = 3.5 kJ/mol). …”
Get full text

A high-throughput ResNet CNN approach for automated grapevine leaf hair quantification by Nagarjun Malagol, Tanuj Rao, Anna Werner, Reinhard Töpfer, Ludger Hausmann

“…As final validation, 10,120 input images from a segregating F1 biparental population were used to evaluate the algorithm performance. ResNet CNN-based phenotypic results compared to ground truth data received by two experts revealed a strong correlation with R values of 0.98 and 0.92 and root-mean-square error values of 8.20% and 14.18%, indicating that the model performance is consistent with expert evaluations and outperforms the traditional manual rating. …”
Get full text

A lightweight YOLO network using temporal features for high-resolution sonar segmentation by Sen Gao, Sen Gao, Wei Guo, Wei Guo, Gaofei Xu, Gaofei Xu, Ben Liu, Ben Liu, Yu Sun, Bo Yuan

“…The model was trained and evaluated on a high-resolution sonar dataset collected using an AUV-mounted Oculus MD750d multibeam forward-looking sonar in two distinct underwater environments.ResultsImplementation on Nvidia Jetson TX2 demonstrated significant performance improvements. (1) Processing latency reduced to 87.4 ms (keyframes) and 35.3 ms (non-keyframes)(2)Maintained competitive segmentation accuracy compared to conventional methods and achieved low latency.DiscussionThe proposed architecture successfully addresses the speed-accuracy trade-off in sonar image segmentation through its innovative temporal feature utilization and computational skipping mechanism. …”
Get full text

Enhanced Occupational Safety in Agricultural Machinery Factories: Artificial Intelligence-Driven Helmet Detection Using Transfer Learning and Majority Voting by Simge Özüağ, Ömer Ertuğrul

“…A transfer learning approach was employed, utilizing nine pre-trained neural networks for the extraction of deep features. …”
Get full text

Simulation-Based Design and Machine Learning Optimization of a Novel Liquid Cooling System for Radio Frequency Coils in Magnetic Hyperthermia by Serhat Ilgaz Yöner, Alpay Özcan

“…A dataset of 300 simulation cases was generated to train a Gaussian Process Regression-based machine learning model. …”
Get full text

Airport-FOD3S: A Three-Stage Detection-Driven Framework for Realistic Foreign Object Debris Synthesis by Hanglin Cheng, Yihao Li, Ruiheng Zhang, Weiguang Zhang

“…The image quality of different blending methods was quantitatively evaluated using metrics such as structural similarity index and peak signal-to-noise ratio, as well as Depthanything. …”
Get full text

Development of a machine learning-based model to predict urethral recurrence following radical cystectomy: a multicentre retrospective study and updated meta-analysis by Bo Fan, Luxin Zhang, Hepeng Cui, Shanshan Bai, Haifeng Gao, Shengxiang Xiang, Yuchao Wang, Zhuwei Song, Jiaqiang Chen, Guanghai Yu, Jianbo Wang, Liang Wang, Zhiyu Liu

“…We developed UR predictive models using ten machine learning algorithms and evaluated the model performance by the area under the ROC curve (AUC), accuracy, sensitivity, and other metrics (F1 score, Brier score, C-index). …”
Get full text

Deep Learning-Driven Throughput Maximization in Covert Communication for UAV-RIS Cognitive Systems by Van Nhan Vo, Nguyen Quoc Long, Viet-Hung Dang, Tu Dac Ho, Hung Tran, Symeon Chatzinotas, Dinh-Hieu Tran, Surasak Sanguanpong, Chakchai So-In

“…For this system, the secrecy performance is evaluated on the basis of the concept of covert communication. …”
Get full text

CriSALAD: Robust Visual Place Recognition Using Cross-Image Information and Optimal Transport Aggregation by Jinyi Xu, Yuhang Ming, Minyang Xu, Yaqi Fan, Yuan Zhang, Wanzeng Kong

“…Additionally, we employ the Sinkhorn Algorithm for Locally Aggregated Descriptors (SALAD) as a global descriptor to enhance place recognition accuracy. …”
Get full text

A Data-Driven Approach for Predicting Remaining Useful Life of Semiconductor Devices Based on Machine Learning and Synthetic Data Generation: A Review and Case Study on SiC MOSFETs by Yarens J. Yarenscruz, Fernando Castano, Alberto Villalonga, Madhav Mishra, Rodolfo E. Haber

“…The proposed approach was evaluated on a silicon carbide metal-oxide-semiconductor field-effect transistor dataset. …”
Get full text

Enhancing skin lesion classification: a CNN approach with human baseline comparison by Deep Ajabani, Zaffar Ahmed Shaikh, Amr Yousef, Karar Ali, Marwan A. Albahar

“…A CNN model utilizing the EfficientNetB3 backbone is trained on datasets from the ISIC-2019 and ISIC-2020 SIIM-ISIC melanoma classification challenges and evaluated on a 150-image test set. …”
Get full text