A Novel Ensemble Classifier Selection Method for Software Defect Prediction by Xin Dong, Jie Wang, Yan Liang

“…The presence of software defects significantly impacts the quality of software systems and increases development and maintenance costs. To improve system quality and reduce costs, it is necessary to predict software defects in the early stages of the software development lifecycle. …”
Get full text

Explainable deep learning for age and gender estimation in dental CBCT scans using attention mechanisms and multi task learning by Najmeh Pishghadam, Rasool Esmaeilyfard, Maryam Paknahad

“…To improve interpretability, we integrate Convolutional Block Attention Module (CBAM) and Grad-CAM visualization, highlighting relevant craniofacial regions. …”
Get full text

Constraint-aware wind power forecasting with an optimized hybrid machine learning model by Md. Omer Faruque, Md. Alamgir Hossain, S.M. Mahfuz Alam, Muhammad Khalid

“…Accurate prediction of wind power generation (WPG) under real-world scenarios is imperative for achieving optimal costing, ensuring reliable operation, and fortifying the security of power systems. …”
Get full text

Reconstruction, Segmentation and Phenotypic Feature Extraction of Oilseed Rape Point Cloud Combining 3D Gaussian Splatting and CKG-PointNet++ by Yourui Huang, Jiale Pang, Shuaishuai Yu, Jing Su, Shuainan Hou, Tao Han

“…Manual phenotyping not only consumes a lot of labor and time costs, but even the measurement process can cause structural damage to oilseed rape plants. …”
Get full text

SDNet: Sandwich Decoder Network for Waterbody Segmentation in Remote Sensing Imagery by Hao Ni, Jianfeng Li, Chenxu Wang, Zhiquan Zhou, Xinsheng Wang

“…Although self-attention (SA) and convolutional neural networks have demonstrated strong modeling capabilities on RS image recognition, their efficiency and expressiveness still deserve further optimization. …”
Get full text

NPI-WGNN: A Weighted Graph Neural Network Leveraging Centrality Measures and High-Order Common Neighbor Similarity for Accurate ncRNA–Protein Interaction Prediction by Fatemeh Khoushehgir, Zahra Noshad, Morteza Noshad, Sadegh Sulaimany

“…To optimize prediction accuracy, we employ a hybrid GNN architecture that combines graph convolutional network (GCN), graph attention network (GAT), and GraphSAGE layers, each contributing unique advantages: GraphSAGE offers scalability, GCN provides a global structural perspective, and GAT applies dynamic neighbor weighting. …”
Get full text

Enhancing FMCW Radar Gesture Classification With Physically Interpretable Data Augmentation by Alessandra Fusco, Zain Amir Zaman, Souvik Hazra, Lorenzo Servadei, Robert Wille

“…This study introduces a novel, physically interpretable data augmentation framework that improves the robustness and accuracy of hand gesture recognition using Frequency-Modulated Continuous Wave (FMCW) radar and Convolutional Neural Networks (CNN). The proposed reconfigurable and parametric method modifies specific characteristics of five time-series features, namely range, velocity, azimuth and elevation angles, and signal magnitude, to generate synthetic gesture samples with realistic variations. …”
Get full text

CINNAMON-GUI: Revolutionizing Pap Smear Analysis with CNN-Based Digital Pathology Image Classification [version 1; peer review: 2 approved] by Luca Zammataro

“…Background Medical imaging has seen significant advancements through machine learning, particularly convolutional neural networks (CNNs). These technologies have transformed the analysis of pathological images, enhancing the accuracy of diagnosing and classifying cellular anomalies. …”
Get full text

AI-Based Solar Panel Detection and Monitoring Using High-Resolution Drone Imagery by Raheleh Parsaeifar, Mojtaba Valinejadshoubi, Anthony Le Guen, Fernando Valdivieso

“…This is addressed by implementing a deep learning-based model using Mask Region-based Convolutional Neural Networks (Mask RCNN) to automate the detection of solar panels from time series high-resolution drone imagery. …”
Get full text

Enhancing the Performance of YOLOv9t Through a Knowledge Distillation Approach for Real-Time Detection of Bloomed Damask Roses in the Field by Farhad Fatehi, Hossein Bagherpour, Jafar Amiri Parian

“…Recent developments in deep learning algorithms, especially in convolutional models, have shown significant promise for object detection, highlighting strong possibilities for improving the efficiency of this process. …”
Get full text

Enhanced detection of Mpox using federated learning with hybrid ResNet-ViT and adaptive attention mechanisms by V. Maheskumar, R. Vijayarajeswari

“…Traditional diagnostic methods, including visual examination and PCR tests, face limitations such as misdiagnoses, high costs, and unavailability in resource-limited areas. …”
Get full text

Interpretable multi-instance heterogeneous graph network learning modelling CircRNA-drug sensitivity association prediction by Mengting Niu, Chunyu Wang, Yaojia Chen, Quan Zou, Ximei Luo

“…Using traditional biomedical experiments to discover and confirm sensitivity relationships is not only time-consuming but also costly. Therefore, developing an effective method to accurately predict new associations between circRNAs and drug sensitivity is crucial and urgent. …”
Get full text

Advancing smart communities with a deep learning framework for sustainable resource management. by Yongyan Zhao

“…The framework leverages long short-term memory (LSTM) networks for temporal data, convolutional neural networks (CNNs) for spatial analysis, and autoencoders for anomaly detection. …”
Get full text

Human fall direction recognition in the indoor and outdoor environment using multi self-attention RBnet deep architectures and tree seed optimization by Awais Khan, Jung-Yeon Kim, Chomyong Kim, Muhammad Attique Khan, Hyojin Shin, Jiyoung Woo, Yunyoung Nam

“…Subsequently, we developed four novel residual block and self-attention mechanisms, named residual block-deep convolutional neural network (3-RBNet), 5-RBNet, 7-RBNet, and 9-RBNet self-attention models. …”
Get full text

A Deep Learning-Based Diagnostic Framework for Shaft Earthing Brush Faults in Large Turbine Generators by Katudi Oupa Mailula, Akshay Kumar Saha

“…The proposed framework combines advanced signal processing and convolutional neural networks (CNNs) to automatically recognize fault-related patterns in shaft grounding current and voltage signals. …”
Get full text

GhostConv+CA-YOLOv8n: a lightweight network for rice pest detection based on the aggregation of low-level features in real-world complex backgrounds by Fei Li, Yang Lu, Qiang Ma, Shuxin Yin, Rui Zhao

“…Existing approaches suffer from two critical limitations: (1) inadequate feature representation under occlusion and scale variations, and (2) excessive computational costs for edge deployment. To overcome these limitations, this paper introduces GhostConv+CA-YOLOv8n, a lightweight object detection framework was proposed, which incorporates several innovative features: GhostConv replaces standard convolutional operations with computationally efficient ghost modules in the YOLOv8n’s backbone structure, reducing parameters by 40,458 while maintaining feature richness; a Context Aggregation (CA) module is applied after the large and medium-sized feature maps were output by the YOLOv8n’s neck structure. …”
Get full text

Prediction of 123I-FP-CIT SPECT Results from First Acquired Projections Using Artificial Intelligence by Wadi’ Othmani, Arthur Coste, Dimitri Papathanassiou, David Morland

Get full text

Toward Adaptive Unsupervised and Blind Image Forgery Localization with ViT-VAE and a Gaussian Mixture Model by Haichang Yin, KinTak U, Jing Wang, Wuyue Ma

“…Second, we introduce convolutional neural networks (CNNs) to adaptively estimate the mixing coefficients, enabling an end-to-end architecture while significantly lowering computational costs. …”
Get full text

A novel attention-based deep learning model for improving sentiment classification after the case of the 2023 Kahramanmaras/Turkey earthquake on Twitter by Serpil Aslan, Muhammed Yildirim

“…These vectorized inputs are then processed by a hybrid model integrating convolutional neural networks (CNNs) and recurrent neural networks (RNNs) with a global attention mechanism. …”
Get full text

Severity classification and disposition prediction using ensemble learning for home-based patient management with adequate decision making by Amjad El Khatib, Chaima Ben Abdallah, Ahmed Nait Sidi Moh

“…To further assess performance, we evaluated the individual models alongside deep learning approaches, including Long Short-Term Memory and Deep Convolutional Neural Networks. Model performance was assessed using a real-world dataset with accuracy and the area under the receiver operating characteristic curve (AUROC) as evaluation metrics. …”
Get full text