Influenza virus genotype to phenotype predictions through machine learning: a systematic review by Laura K. Borkenhagen, Martin W. Allen, Jonathan A. Runstadler

“…We, therefore, offer guidance to overcome these limitations, aid in improving predictive models of previously studied influenza A virus phenotypes, and extend those models to unexplored phenotypes in the ultimate pursuit of tools to enable the characterization of virus isolates across surveillance laboratories.…”
Get full text

Predicting emergency department visits for non-traumatic dental-related conditions among Medicaid beneficiaries by Sung Eun Choi, Rindala Fayyad, Daniel Nolte, Christine Riedy, Sharon-Lise Normand

“…We sought to predict future “ED users” for non-traumatic dental-related conditions using machine learning algorithms and identify associated risk factors to help Medicaid outreach programs better target populations in most need of improvement in access to dental care. …”
Get full text

Exosomal microRNA signatures in youth at clinical high risk for bipolar disorder by Xinyu Meng, Shengmin Zhang, Yingzhen Xu, Zaohui Ma, Shuzhe Zhou, Yantao Ma, Hong Ma, Xin Yu, Lili Guan

“…Using machine-learning algorithms, target miRNAs were selected from differentially expressed candidates. …”
Get full text

Cross-Scene Multi-Object Tracking for Drones: Leveraging Meta-Learning and Onboard Parameters with the New MIDDTD by Chenghang Wang, Xiaochun Shen, Zhaoxiang Zhang, Chengyang Tao, Yuelei Xu

“…Finally, a novel dataset, termed the Multi-Information Drone Detection and Tracking Dataset (MIDDTD), is introduced, containing rich drone-related information and diverse scenes, thereby providing a solid foundation for the validation of cross-scene multi-object tracking algorithms. Experimental results demonstrate that the proposed method improves the IDF1 tracking metric by 1.92% compared to existing state-of-the-art methods, showcasing strong cross-scene adaptability and offering an effective solution for multi-object tracking from a drone’s perspective, thereby advancing theoretical and technical support for related fields.…”
Get full text

A comparative analysis of emotion recognition from EEG signals using temporal features and hyperparameter-tuned machine learning techniques by Rabita Hasan, Sheikh Md. Rabiul Islam

“…A five-fold cross-validation procedure was applied to estimate the model's performance and hyperparameter tuning was conducted to optimize classifier efficiency. …”
Get full text

Securing Communication Channels: An Advanced Steganography Approach With Orthogonal Frequency Division Multiplexing (OFDM) by Ammar Mohammedali Fadhil, Ali Y. Jaber

“…In this work, a robust model is proposed to combine OFDM during information hiding. …”
Get full text

The Impact of AI-Personalized Recommendations on Clicking Intentions: Evidence from Chinese E-Commerce by Jiwang Yin, Xiaodong Qiu, Ya Wang

“…In study 3, based on the SOR theory, we constructed a model and formulated hypotheses and then conducted empirical analysis using 1097 valid questionnaires. …”
Get full text

Kalman Filter-Based Fusion of LiDAR and Camera Data in Bird’s Eye View for Multi-Object Tracking in Autonomous Vehicles by Loay Alfeqy, Hossam E. Hassan Abdelmunim, Shady A. Maged, Diaa Emad

“…Single-modality 3D MOT algorithms often face limitations due to sensor constraints, resulting in unreliable tracking. …”
Get full text

Prediction of the Ultimate Impact Response of Concrete Strengthened with Polyurethane Grout as the Repair Material by Sadi I. Haruna, Yasser E. Ibrahim, Sani I. Abba

“…The SHAP (SHapley Additive exPlanations) analysis showed that the thickness of the grouting materials and maximum load were the most significant parameters affecting the ultimate capacity of the composite U-shaped specimen. The RF model showed moderate improvements, with RF-M3 performing better than RF-M1 and RF-M2. …”
Get full text

Myoelectric Control in Rehabilitative and Assistive Soft Exoskeletons: A Comprehensive Review of Trends, Challenges, and Integration with Soft Robotic Devices by Alejandro Toro-Ossaba, Juan C. Tejada, Daniel Sanin-Villa

“…Additionally, we identify persistent challenges such as EMG signal variability, computational complexity, and the real-time adaptability of control algorithms, which limit clinical implementation. By interpreting recent trends, this review highlights the need for improved EMG acquisition techniques, robust adaptive control frameworks, and enhanced real-time learning to optimize human-exoskeleton interaction. …”
Get full text

Machine-learning of medical cannabis chemical profiles reveals analgesia beyond placebo expectations by Adi Hatav, Yelena Vysotski, Anna Shapira, Shiri Procaccia, David Meiri, Dvir Aran

“…We used a Random Forest classifier with nested cross-validation to assess the predictive value of demographics, clinical features, and approximately 200 chemical compounds. Model robustness was evaluated using six additional machine learning algorithms. …”
Get full text

GE-YOLO for Weed Detection in Rice Paddy Fields by Zimeng Chen, Baifan Chen, Yi Huang, Zeshun Zhou

“…Therefore, we propose GE-YOLO to identify three common types of weeds in rice fields in the Hunan province of China and to validate its generalization performance. GE-YOLO is an improvement based on the YOLOv8 baseline model. It introduces the Neck network with the Gold-YOLO feature aggregation and distribution network to enhance the network’s ability to fuse multi-scale features and detect weeds of different sizes. …”
Get full text

Diagnostic Analysis of Crosswalk Safety Hazards in Pedestrian Environments: A SHAP-Enhanced Machine Learning Approach With Street-View Imagery by Caryl Anne M. Barquilla, Jeongwoo Lee

“…It used data from 36,750 crosswalks in Seoul, South Korea, to rigorously evaluate multiple machine learning algorithms for predicting pedestrian crash risk. Among the models assessed, the Random forest (RF) demonstrated the highest precision, aligning with the objective of enhancing pedestrian safety through accurate risk identification. …”
Get full text

Progress in plant genome sequencing: research directions by M. K. Bragina, D. A. Afonnikov, E. A. Salina

“…Since the first plant genome of Arabidopsis thaliana has been sequenced and published, genome sequencing technologies have undergone significant changes. New algorithms, sequencing technologies and bioinformatic approaches were adopted to obtain genome, transcriptome and exome sequences for model and crop species, which have permitted deep inferences into plant biology. …”
Get full text

Enhanced Subspace Iteration Technique for Probabilistic Modal Analysis of Statically Indeterminate Structures by Hongfei Cao, Xi Peng, Bin Xu, Fengjiang Qin, Qiuwei Yang

“…Compared to existing stochastic modal analysis algorithms, the proposed method offers improved computational accuracy and efficiency, particularly in scenarios involving high-discreteness random analyses.…”
Get full text

Personal health data protection and intelligent healthcare applications under generative adversarial network by Xiaoyuan Gao, Wei Mi, Xirui Feng

“…Additionally, the model’s accuracy gradually improves as the privacy budget increases. …”
Get full text

Applications of artificial intelligence in neurosurgical education: a scoping review by Hector Julio Piñera-Castro, Christian Borges-García

“…In board examinations, GPT-4 outperformed prior models and junior neurosurgeons in text-based questions but lagged in image-based tasks. …”
Get full text

Enhancing diabetic retinopathy detection through federated convolutional neural networks: Exploring different stages of progression by Saad Alanazi, Rayan Alanazi

“…This paper presents a comprehensive solution for DR identification utilizing deep learning algorithms. To reduce class imbalance and strengthen this model, we first prepare unbalanced data utilizing the Synthetic Minority Oversampling Technique (SMOTE). …”
Get full text

Cooling Load Forecasting Method for Central Air Conditioning Systems in Manufacturing Plants Based on iTransformer-BiLSTM by Xiaofeng Huang, Xuan Zhou, Junwei Yan, Xiaofei Huang

“…Finally, BiLSTM is applied for temporal modeling, leveraging its bidirectional recurrent structure to capture both forward and backward dependencies in time series, thereby improving forecasting accuracy. …”
Get full text

Use of satellite data for detecting icebergs and evaluating the iceberg threats by I. A. Bychkova, V. G. Smirnov

“…To assess the iceberg threat, we propose to use a model of their drif, one of the input parameters of which is the size of the detected objects. …”
Get full text