DFTD-YOLO: Lightweight Multi-Target Detection From Unmanned Aerial Vehicle Viewpoints by Yuteng Chen, Zhaoguang Liu

“…Due to the low detection accuracy of small and dense target objects in multi-target detection tasks from the unmanned aerial vehicle (UAV) perspective and the deployment of deep learning models for UAVs as embedded devices, these models must be lightweight. …”
Get full text

Spatial-Temporal Fusion Graph Neural Networks With Mixed Adjacency for Weather Forecasting by Ang Guo, Yanghe Liu, Shiyu Shao, Xiaowei Shi, Zhenni Feng

“…Many existing approaches based on deep learning models, e.g., recurrent neutral networks and graph neural networks, have been proposed for weather forecasting. …”
Get full text

RMGANets: reinforcement learning-enhanced multi-relational attention graph-aware network for anti-money laundering detection by Qianyu Wang, Wei-Tek Tsai, Bowen Du

“…Abstract Given the anonymity and complexity of illegal transactions, traditional deep-learning methods struggle to establish correlations between transaction addresses, cash flows, and physical users. …”
Get full text

Temporal-Spatial Traffic Flow Prediction Model Based on Prompt Learning by Siteng Cai, Gang Liu, Jing He, Yulun Du, Zhichao Si, Yunhao Jiang

“…Existing studies utilizing deep learning for traffic flow prediction often suffer from distribution shift issues, leading to poor generalization capabilities when dealing with data that has different spatiotemporal distributions. …”
Get full text

DCWM-LSTM: A Novel Attack Detection Framework for Robotic Arms by Esra Nergis Yolacan, Hande Cavsi Zaim

“…In response to these challenges, this study proposes a new approach that leverages deep learning techniques for attack detection. We present a new framework, LSTM-based Dynamic Compound Weight Mechanism (DCWM), designed to identify cyberattacks targeting robotic arms effectively. …”
Get full text

Smartphone image dataset for radish plant leaf disease classification from BangladeshMendeley Data by Mahamudul Hasan, Raiyan Gani, Mohammad Rifat Ahmmad Rashid, Maherun Nessa Isty, Raka Kamara, Taslima Khan Tarin

“…Utilizing this robust dataset, deep learning models can be trained to identify the leaf diseases which helps to detect the diseases in order to reduce the harm of the cultivation of radish. …”
Get full text

Chemical Process Fault Diagnosis Based on Improved ResNet Fusing CBAM and SPP by Xiaochen Yan, Yang Zhang, Qibing Jin

“…The Tennessee-Eastman (TE) process is used as the experimental object to compare the improved ResNet with several other deep learning models. The experimental results show that the improved ResNet model achieves the best fault diagnosis results. …”
Get full text

Research on YOLOv5 Oracle Recognition Algorithm Based on Multi-Module Fusion by Xinhang Zhang, Zhenhua Ma, Yaru Zhang, Huiying Ru

“…However, traditional methods and some deep learning models have limited ability to capture the complex forms and fine details of oracle bone script, which makes it difficult to fully detect subtle differences between characters. …”
Get full text

Application of IoT-Based Drones in Precision Agriculture for Pest Control by Mohamad Reda. A. Refaai, Vinjamuri SNCH Dattu, N. Gireesh, Ekta Dixit, CH. Sandeep, David Christopher

“…The presented study takes into account the used pretrained deep learning model for extracting features and fine-tuning. …”
Get full text

A Cross-Regional Load Forecasting Method Based on a Pseudo-Distributed Federated Learning Strategy by Jinsong Deng, Shaotang Cai, Weinong Wu, Rong Jiang, Hongyu Deng, Jinhua Ma, Yonghang Luo

“…Experiments demonstrated that this strategy exhibited exceptional generalization ability across various deep learning models, federated learning methods, and datasets.…”
Get full text

Rethinking Exploration and Experience Exploitation in Value-Based Multi-Agent Reinforcement Learning by Anatolii Borzilov, Alexey Skrynnik, Aleksandr Panov

“…Despite being a relatively new area of research, most MARL methods are based on well-established approaches used in single-agent deep learning tasks due to their proven effectiveness. …”
Get full text

Attention-enhanced corn disease diagnosis using few-shot learning and VGG16 by Ruchi Rani, Jayakrushna Sahoo, Sivaiah Bellamkonda, Sumit Kumar

“…With the advent of AI, Machine Learning and Deep Learning methods are used to detect and categorize plant diseases. …”
Get full text

Traffic Flow Parameters Collection under Variable Illumination Based on Data Fusion by Shaojie Jin, Ying Gao, Shoucai Jing, Fei Hui, Xiangmo Zhao, Jianzhen Liu

“…In complicated lighting conditions at night, however, neither classical nor deep-learning-based image processing algorithms can provide adequate detection results. …”
Get full text

CLOUD COMPUTING AND FINANCIAL PERFORMANCE OF LISTED DEPOSIT MONEY BANKS IN NIGERIA by Hakeem Olayinka Hakeem Olayinka, Adetumilara Adepeju Dedire-Ampitan

“…The analysis includes variables such as Automated Chatbot Banking Services (ACBS), Deep Learning Machine in Credit Risk Assessment (DLM), Machine Learning Solutions (MLS), and Return on Asset (ROA). …”
Get full text

Machine Learning-Based Offloading Strategy for Lightweight User Mobile Edge Computing Tasks by Shuchen Zhou, Waqas Jadoon, Junaid Shuja

“…The anomaly detection method based on a decision tree combined with deep learning proposed in this paper, unlike traditional IoT attack detection methods, overcomes the drawbacks of rule-based security detection methods and enables them to adapt to both established and unknown hostile environments. …”
Get full text

MalHAPGNN: An Enhanced Call Graph-Based Malware Detection Framework Using Hierarchical Attention Pooling Graph Neural Network by Wenjie Guo, Wenbiao Du, Xiuqi Yang, Jingfeng Xue, Yong Wang, Weijie Han, Jingjing Hu

“…While deep learning techniques have been extensively employed in malware detection, there is a notable challenge in effectively embedding malware features. …”
Get full text

Data-Driven Technology in Event-Based Vision by Ruolin Sun, Dianxi Shi, Yongjun Zhang, Ruihao Li, Ruoxiang Li

“…Thanks to the preponderance of deep learning techniques and the compatibility between bio-inspired spiking neural networks and event-based sensors, data-driven approaches have become a hot spot, which along with the dedicated hardware and datasets constitute an emerging field named event-based data-driven technology. …”
Get full text

Vehicle-to-Vehicle Communication Channel Estimator Based on Gate Recurrent Unit by Jun-Han Wang, He He, Kosuke Tamura, Shun Kojima, Jaesang Cha, Chang-Jun Ahn

“…On the other hand, the frame structure of the IEEE 802.11p standard contains a few number of pilots and a large pilot interval, which is not sufficient to track the rapidly changing channel environment accurately. In recent years, deep learning has been widely used for channel estimation. …”
Get full text

UAlpha40: A comprehensive dataset of Urdu alphabet for Pakistan sign languageMendeley Data by Sameena Javaid, Shahood Sajid, Yusra Khan Baloch

“…This newly developed dataset can be utilized to train vision-based deep learning models, which in turn can aid in the development of sign language translators and finger-spelling systems for USL.…”
Get full text

Knowledge Graph Entity Similarity Calculation under Active Learning by Lianhuan Li, Zheng Zhang, Shaoda Zhang

“…To address the objectives of the adaptive learning platform, the requirements of the system in terms of business, functionality, and performance are mainly analysed, and the design of functions and database is completed; then, an updatable learner model is constructed based on the cognitive diagnosis model and resource preference attributes; then, the construction of the knowledge map is completed based on embedding to achieve knowledge point alignment, and based on this, the target knowledge points of learners are located with the help of deep learning; at the same time, the target knowledge points are taken as the starting point to generate the best learning path by traversing the knowledge map, and the corresponding learning resources and test questions are recommended for them with the help of the architecture; finally, the adaptive learning platform is developed in the environment using the architecture. …”
Get full text