Optimizing Powerline Infrastructure Inspection, Monitoring And Asset Management Using UAVs And Artificial Intelligence Techniques by Faustin A. S. Banda, Chilombo Sakahundu, Penjani Hopkins Nyimbili, Erastus Misheng’u Mwanaumo, Wellington Didibhuku Thwala

“…Additionally, the asset management framework enabled proactive maintenance scheduling, reducing downtime by 40%. …”
Get full text

Evaluation of CNN-Based Approaches to Adverse Weather Image Classification for Autonomous Driving Systems by Viktoria Afxentiou, Tanya Vladimirova

“…This paper introduces a novel evaluation methodology for classifying AWC images using Convolutional Neural Network (CNN) models, with the goal of assessing their effectiveness for use in ADSs. …”
Get full text

Anomaly Detection of Acoustic Signals in Ultra-High Voltage Converter Valves Based on the FAVAE-AS by Shuyan Pan, Mingzhu Tang, Na Li, Jiawen Zuo, Xingpeng Zhou

“…It proposes an extensible framework for industrial intelligent maintenance.…”
Get full text

Implicit Is Not Enough: Explicitly Enforcing Anatomical Priors inside Landmark Localization Models by Simon Johannes Joham, Arnela Hadzic, Martin Urschler

Get full text

DP-FWCA: A Prompt-Enhanced Model for Named Entity Recognition in Educational Domains by Zhenkai Qin, Dongze Wu, Jiajing He, Jingming Xie, Aimin Wei

“…To address these challenges, we propose the Domain-adaptive Prompt Feature-Weighted CNN-Attention-CRF (DP-FWCA), a novel framework specifically designed for educational NER. …”
Get full text

Federated Learning-Based CNN Models for Orthodontic Skeletal Classification and Diagnosis by Demet Süer Tümen, Mehmet Nergiz

“…Models are evaluated on the ISBI and Dicle datasets using accuracy, sensitivity, and specificity metrics, with performance gains benchmarked across CL, LL, and FL frameworks. <b>Results:</b> Accuracy improvements exceed 26% compared to the baseline model on FL framework. …”
Get full text

Forecasting Day-Ahead Electricity Demand in Australia Using a CNN-LSTM Model with an Attention Mechanism by Laial Alsmadi, Gang Lei, Li Li

“…The primary contribution of this paper lies in the novel integration of CDD and HDD data within the CNN-LSTM framework, which has not been extensively explored in prior studies. …”
Get full text

Spatial Orientation Relation Recognition for Water Surface Targets by Peiyong Gong, Kai Zheng, Yi Jiang, Huixuan Zhao, Xiao Liang, Zhiwen Feng, Wenbin Huang

“…The WST-SOVF algorithm encodes the spatial orientation relation into the learning framework of a new deep convolutional neural network model, which comprises two distinct branches: the T-branch and the S-branch, both designed for the spatial feature extraction. …”
Get full text

Predicting housing price, housing density, and green area coverage from combined satellite and street view imagery using deep learning by Ana Beatriz de Figueiredo Oliveira, Mauro Castelli, Esra Suel

Get full text

Susceptibility evaluation of valley debris flow based on dual-channel network with fusion attention mechanism by Yumeng LUO, Baoyun WANG, Ruohao YUAN, Xu WANG, Cunxi LIU, Kuayue CHEN

“…The main contributions of this paper are as follows: Firstly, based on historical debris flow records and using Digital Elevation Maps (DEMs) and remote sensing images as data sources, a dual-channel network structure is designed as the basic technical framework. Within the DEM image feature extraction channel, a channel attention mechanism is introduced to emphasize the channel weights of the image features, while in the remote sensing image feature extraction channel, 3D convolutional blocks are employed to extract the surface information of the gullies. …”
Get full text

Construction of crown profile prediction model of Pinus yunnanensis based on CNN-LSTM-attention method by Longfeng Deng, Jianming Wang, Jiting Yin, Yuling Chen, Baoguo Wu

Get full text

Hybrid CNN-Based Transfer Learning Enhances Brain Tumor Classification on MRI Images by Rizal Dwi Prayogo, Nur Hamid, Hidetaka Nambo

“…We propose a hybrid transfer learning framework based on CNN architectures for enhanced multiclass classification of brain tumors. …”
Get full text

Cross-survey Image Transformation: Enhancing SDSS and DECaLS Images to Near-HSC Quality for Advanced Astronomical Analysis by Zhijian Luo, Shaohua Zhang, Jianzhen Chen, Zhu Chen, Liping Fu, Hubing Xiao, Wei Du, Chenggang Shu

Get full text

A novel multi-task learning model based on Transformer-LSTM for wind power forecasting by Rongquan Zhang, Siqi Bu, Yuxia Zheng, Gangqiang Li, Xiupeng Wan, Qiangqiang Zeng, Min Zhou

“…The integration of multi-task learning into multi-step deterministic and probabilistic prediction frameworks plays a pivotal role in augmenting the accuracy of wind power forecasts and mitigating associated operational uncertainties. …”
Get full text

A Multi-Class Intrusion Detection System for DDoS Attacks in IoT Networks Using Deep Learning and Transformers by Sheikh Abdul Wahab, Saira Sultana, Noshina Tariq, Maleeha Mujahid, Javed Ali Khan, Alexios Mylonas

“…These results underscore the potential of integrating advanced DL models into IDS frameworks, thereby providing a scalable and effective solution to secure IoT networks against evolving DDoS threats. …”
Get full text

EfficientNet-b0-Based 3D Quantification Algorithm for Rectangular Defects in Pipelines by Di Wu, Yong Hong, Jie Wang, Shaojun Wu, Zhihao Zhang, Yizhang Liu

“…This research introduces EffiTriDimNet (ETDN), a multi-task convolutional neural network that combines one-dimensional pipeline defect leakage detection data into a unified feature map while simultaneously measuring the three-dimensional characteristics of the defects. …”
Get full text

Ultra-lightweight tomatoes disease recognition method based on efficient attention mechanism in complex environment by Wenbin Sun, Zhilong Xu, Kang Xu, Lin Ru, Ranbing Yang, Rong Wang, Jiejie Xing

Get full text

Wind Power Forecasting Based on Multi-Graph Neural Networks Considering External Disturbances by Xiaoyin Xu, Zhumei Luo, Menglong Feng

“…Our innovation lies in the physically informed architecture that explicitly models the mathematical relationship: <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><mi>P</mi><mrow><mo>(</mo><mi>t</mi><mo>)</mo></mrow><mo>=</mo><msub><mi>P</mi><mi>inherent</mi></msub><mrow><mo>(</mo><mi>t</mi><mo>)</mo></mrow><mo>+</mo><mi>EIF</mi><mrow><mo>(</mo><mi>t</mi><mo>)</mo></mrow></mrow></semantics></math></inline-formula>. The framework adopts a three-component architecture consisting of (1) a multi-graph convolutional network using both geographical proximity and power correlation graphs to capture heterogeneous spatial dependencies between wind farms, (2) an attention-enhanced LSTM network that weights temporal features differentially based on their predictive significance, and (3) a specialized Conv2D mechanism to identify and isolate external disturbance patterns. …”
Get full text

Underwater image enhancement using hybrid transformers and evolutionary particle swarm optimization by Ajay Kumar, Gagandeep Berar, Manmohan Sharma, Sakshi, Ajit Noonia, Gunjan Verma

“…The HTN-PSO framework combines the strengths of convolutional neural networks and transformer models to effectively capture low-level features and model long-range dependencies. …”
Get full text

Deep learning based bio-metric authentication system using a high temporal/frequency resolution transform by Sajjad Maleki Lonbar, Akram Beigi, Nasour Bagheri, Nasour Bagheri, Pedro Peris-Lopez, Carmen Camara

“…The proposed framework, leveraging the Wigner-Ville distribution and GoogleNet architecture, demonstrates the potential of deep learning techniques in biometric authentication. …”
Get full text