Three-Dimensional Outdoor Object Detection in Quadrupedal Robots for Surveillance Navigations by Muhammad Hassan Tanveer, Zainab Fatima, Hira Mariam, Tanazzah Rehman, Razvan Cristian Voicu

“…This paper introduces a pioneering method for 3D object detection, termed viewpoint feature histograms, which leverages the established paradigm of 2D detection in projection. …”
Get full text

Dynamic Adjacency Matrix Learning and Multi-Order Random Walk Aggregation for Alzheimer’s Disease Diagnosis From Resting-State fMRI by Zhaoyang Guan, Jierui Li, Yuxin Liu, Xianrui Shan

“…However, most of the existing graph neural network methods based on static adjacency matrices are difficult to capture individual differences and remote brain region interactions, thus limiting their classification performance and interpretability. In this paper, we propose a Dynamic Multi-Scale Brain Network (DMSBN) model based on resting-state fMRI, which improves the discriminative ability and biological interpretability of the model through dynamic adjacency matrix learning and multistage stochastic wandering aggregation mechanism. …”
Get full text

Predicting hydrocarbon reservoir quality in deepwater sedimentary systems using sequential deep learning techniques by Xiao Hu, Jun Xie, Xiwei Li, Junzheng Han, Zhengquan Zhao, Hamzeh Ghorbani

“…The Adam-LSTM model outperformed the others, achieving a Root Mean Square Error of 0.009 and a correlation coefficient (R2) of 0.9995, indicating excellent predictive performance. …”
Get full text

Deep Layered Network Based on Rotation Operation and Residual Transform for Building Segmentation from Remote Sensing Images by Shuzhe Zhang, Taoyi Chen, Fei Su, Hao Xu, Yan Li, Yaohui Liu

“…C_ASegformer shows more accurate performance, demonstrating the validity and sophistication of the model.…”
Get full text

Design and Analysis of a Sowing Depth Detection and Control Device for a Wheat Row Planter Based on Fuzzy PID and Multi-Sensor Fusion by Yueyue Li, Bing Qi, Encai Bao, Zhong Tang, Yi Lian, Meiyan Sun

“…The model operates by first specifying a target sowing depth, then acquiring real-time sowing depth measurements via a laser range sensor and terrain feature data ahead of the machine via an array-based LiDAR sensor. …”
Get full text

Research on Underwater Target Detection Technology Based on SMV-YOLOv11n by Dongcheng Liao, Yijun Shen, Jinyu Ou, Yanlian Du

“…Firstly, the Swin Transformer is adopted to replace the backbone network, and a novel Multi-scale Spatial and Channel Attention module (MSCA) is designed and integrated into the backbone to enhance its feature extraction capability. Subsequently, the VoVGSCSP module is introduced to replace the C3K2 module, effectively leveraging contextual information to improve global detection performance. …”
Get full text

A Review of Research on Flight Delay Propagation: Current Situation and Prospect by N. Li, H. G. Yao

“…Among the methods for predicting delay propagation, models based on machine learning algorithms account for a large proportion and have shown good prediction performance. …”
Get full text

CareAssist GPT improves patient user experience with a patient centered approach to computer aided diagnosis by Ali Algarni

“…Model performance was evaluated in terms of accuracy, precision, recall, specificity, and response time, alongside patient satisfaction through a structured user feedback survey. …”
Get full text

LSOD-YOLOv8: Enhancing YOLOv8n with New Detection Head and Lightweight Module for Efficient Cigarette Detection by Yijie Huang, Huimin Ouyang, Xiaodong Miao

“…However, detecting such small objects poses significant challenges due to their size and limited feature points. To enhance the accuracy of small target detection, we propose a novel small object detection model, LSOD-YOLOv8 (Lightweight Small Object Detection using YOLOv8). …”
Get full text

Cyber-Secure IoT and Machine Learning Framework for Optimal Emergency Ambulance Allocation by Jonghyuk Kim, Sewoong Hwang

“…To support policy decisions, a Lasso-based simulation tool was developed, enabling dynamic scenario testing for optimal ambulance counts and dispatch time estimates. The model also incorporates the coefficient of variation (CV) of workload intensity as a performance metric to guide long-term capacity planning and equity assessment. …”
Get full text

An In-depth Investigation of OBIA Classification with High-Resolution Imagery: Unravelling the Explanations Behind Deep Learning and Machine Learning by E. O. Yilmaz, T. Kavzoglu

“…The SHAP analysis indicated that the HSI transform was the most influential factor in the XGBoost algorithm’s decision-making process whereas the average DN values of the green band were the most effective feature for the CNN model. Global SHAP analyses elucidated the overarching model decision-making process, whereas class-specific analyses furnished insights into the classification of each land use and land cover (LULC) class.…”
Get full text

A Compact Dual-Band Antenna for Brain–Machine Interface and Skin-Implantable Biotelemetry Applications by Emtiaz Ahmed Mainul, Md. Faruque Hossain

“…Such a shorting pin or slot-free simple structure is an advantageous feature of the proposed antenna to reduce the fabrication complexity. …”
Get full text

Genderly: a data-centric gender bias detection system by Wael Khreich, Jad Doughman

“…Our experiments used diverse preprocessing, feature engineering, and hyperparameter optimization methods for traditional ML models and large language models (LLMs) as gender bias detectors, comparing these results to evaluate model performance. …”
Get full text

An Unsupervised Remote Sensing Image Change Detection Method Based on RVMamba and Posterior Probability Space Change Vector by Jiaxin Song, Shuwen Yang, Yikun Li, Xiaojun Li

“…In the RFCC, we propose an unsupervised remote sensing image segmentation algorithm based on the Mamba model, i.e., RVMamba differentiable feature clustering, which introduces two loss functions as constraints to ensure that RVMamba achieves accurate segmentation results and to supply the CSBN module with high-quality training samples. …”
Get full text

A Novel Efficient Method of Estimating Suspended‐To‐Total Sediment Load Fraction in Natural Rivers by Hyoseob Noh, Yong Sung Park, Il Won Seo

“…The five‐variable SVR model showed the best performance. Clustering analyses using a self‐organizing map and Gaussian mixture model, respectively, identified the underlying relationships between dimensionless variables. …”
Get full text

Smart system for real time monitoring and diagnosis of dengue surfaces in Bangladesh by Md. Shahriar Hossain Apu, Samsuddin Ahmed, Md. Toukir Ahmed

“…The integration of the Convolutional Block Attention Module (CBAM) enhances the model’s feature extraction capabilities, improving its focus on critical regions in the images. …”
Get full text

Ensemble-based sesame disease detection and classification using deep convolutional neural networks (CNN) by Abenet Alazar Hailu, Banchalem Chebudie Kassa, Esubalew Asmare Desta, Fikadu Berie Adugna, Ayodeji Olalekan Salau

“…The results highlight the effectiveness of combining multiple deep learning models, which allows for the extraction of diverse feature representations and decision-making strategies. …”
Get full text

Minima-YOLO: A Lightweight Identification Method for Lithium Mineral Components Under a Microscope Based on YOLOv8 by Zeyang Qiu, Xueyu Huang, Xiangyu Xu

“…Finally, to mitigate the impact of deeper backbone network layers, we introduced the Slim-Neck structure in the Neck, further reducing the model size. Ultimately, Minima-YOLO achieves a 99.4% mAP50 on our self-constructed lithium mineral image dataset, with FLOPs reduced to 2.3 G, parameters to 0.72 M, and a model size of just 1.63 MB, while maintaining an FPS of 103. …”
Get full text

Efficacy of swarm-based neural networks in automated depression detection by Alwan Atta, Dina ElSayad, Doaa Ezzat, Safaa Amin, Mahmoud ElGamal

“…Therefore, the model achieved 0.92 F1 on the CMDC dataset and 0.82 on the MODMA dataset, establishing strong performance across different distributions of data. …”
Get full text

Machine Learning in Biomedical Informatics: Optimizing Resource Allocation and Energy Efficiency in Public Hospitals by Agostino Marengo, Vito Santamato, Massimo Iacoviello

“…By integrating interpretable machine learning with computational optimization, the model contributes to building sustainable and high-performing hospital systems aligned with both operational and environmental objectives.…”
Get full text