An Improved Unmanned Aerial Vehicle Forest Fire Detection Model Based on YOLOv8 by Bensheng Yun, Xiaohan Xu, Jie Zeng, Zhenyu Lin, Jing He, Qiaoling Dai

“…Taking into account efficiency and cost-effectiveness, deep-learning-driven UAV remote sensing fire detection algorithms have emerged as a favored research trend and have seen extensive application. …”
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A Dual-Branches Multiscale Dynamic Partial Convolutional Attention Network for Remote Sensing Change Detection by Wenbin Tang, Shuli Cheng, Anyu Du

“…In addition, a bottom-up training strategy is applied to strengthen the completeness of change detection. Extensive experiments on three publicly available datasets demonstrate that, compared to other methods, our proposed approach achieves superior performance and detection results, while reducing parameters and computational complexity.…”
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TomaFDNet: A multiscale focused diffusion-based model for tomato disease detection by Rijun Wang, Rijun Wang, Yesheng Chen, Fulong Liang, Xiangwei Mou, Xiangwei Mou, Guanghao Zhang, Hao Jin

“…Current tomato leaf disease detection methods, however, encounter challenges in extracting multi-scale features, identifying small targets, and mitigating complex background interference. …”
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YOLO-WAS: A Lightweight Apple Target Detection Method Based on Improved YOLO11 by Xinwu Du, Xiaoxuan Zhang, Tingting Li, Xiangyu Chen, Xiufang Yu, Heng Wang

“…To overcome the limitations of existing apple target detection methods, including low recognition accuracy of multi-species apples in complex orchard environments and a complex network architecture that occupies large memory, a lightweight apple recognition model based on the improved YOLO11 model was proposed, named YOLO-WAS model. …”
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Towards precision agriculture tea leaf disease detection using CNNs and image processing by Irfan Sadiq Rahat, Hritwik Ghosh, Suresh Dara, Shashi Kant

“…Our model’s architecture is not just a testament to the sophistication of modern deep learning techniques but also highlights the novelty of applying such complex structures to the challenges of agricultural disease detection. …”
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Target detection of helicopter electric power inspection based on the feature embedding convolution model. by Dakun Liu, Wei Zhou, Linzhen Zhou, Wen Guan

“…In addition, this study further optimizes the model with reinforcement learning technology, conducts a comparative analysis of different flight environments and facilities, and reveals the diversity and complexity of inspection objectives. The performance of the optimized model in fault detection is increased by more than 36%. …”
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A lightweight model for echo trace detection in echograms based on improved YOLOv8 by Jungang Ma, Jianfeng Tong, Minghua Xue, Junfan Yao

“…However, current detection models are too parameter-heavy to embed in echosounders and struggle with noisy, irregular, and dense echograms. …”
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YOLOv8-LSW: A Lightweight Bitter Melon Leaf Disease Detection Model by Shuang Liu, Haobin Xu, Ying Deng, Yixin Cai, Yongjie Wu, Xiaohao Zhong, Jingyuan Zheng, Zhiqiang Lin, Miaohong Ruan, Jianqing Chen, Fengxiang Zhang, Huiying Li, Fenglin Zhong

“…Bitter melon, an important medicinal and edible economic crop, is often threatened by diseases such as downy mildew, powdery mildew, viral diseases, anthracnose, and blight during its growth. Efficient and accurate disease detection is of significant importance for achieving sustainable disease management in bitter melon cultivation. …”
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Research on anomaly detection model for traffic time series data integrating multiple mechanisms by Peipei ZHANG, Jiaqi LIU

“…The results show that adding each module on the basis of LSTM significantly improves the model's prediction and anomaly detection ability; Compared with the general hybrid model Transformer-Bi-LSTM, the proposed model has stronger prediction ability and lower computational complexity. …”
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Use of Business Intelligence as a Strategic Information Technology in Banking: Farud Discovery & Detection by علی محقر, کارو لوکس, ، فرید حسینی, آصف علی منشی

“…It also explains how BI can be effective technology in industries like banking to overcome critical issue like fraud discovery and detection. The methodology, based on BI, in the form of model named BI model for fraud discovery and detection has been devised and suggested at the end of paper along with it its details for overcoming the above mentioned issue in more effective and efficient manner.…”
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Surface anomaly detection on island-based PV panels using edge neural networks by ZHANG Yinxian, ZHANG Zhanyao, ZHANG Xiya

“…Due to the poor accuracy and low efficiency of existing detection methods， the paper proposes a surface anomaly detection method for island-based PV panels using edge neural networks. …”
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Streamlined Bearing Fault Detection Using Artificial Intelligence in Permanent Magnet Synchronous Motors by Javier de las Morenas, Lidia M. Belmonte, Rafael Morales

“…Permanent magnet synchronous motors (PMSMs) are widely used in industrial applications due to their high efficiency and reliability. However, bearing faults remain a critical issue, necessitating robust fault detection strategies. …”
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Leveraging Swarm Intelligence for Invariant Rule Generation and Anomaly Detection in Industrial Control Systems by Yunkai Song, Huihui Huang, Hongmin Wang, Qiang Wei

“…Conventional anomaly detection techniques often lack the ability to provide clear explanations for their detection, and their inherent complexity can impede practical implementation in the resource-constrained environments typical of ICSs. …”
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Detecting command injection attacks in web applications based on novel deep learning methods by Xinyu Wang, Jiqiang Zhai, Hailu Yang

“…Abstract Web command injection attacks pose significant security threats to web applications, leading to potential server information leakage or severe server disruption. Traditional detection methods struggle with the increasing complexity and obfuscation of these attacks, resulting in poor identification of malicious code, complicated feature extraction processes, and low detection efficiency. …”
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Cyberattack Detection Systems in Industrial Internet of Things (IIoT) Networks in Big Data Environments by Abdullah Orman

“…Traditional intrusion detection systems (IDSs) often struggle to effectively identify and mitigate complex cyberthreats, such as denial-of-service (DoS) and distributed denial-of-service (DDoS) attacks. …”
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Combination of Principal Component Analysis and Time-Frequency Representation for P-Wave Arrival Detection by Jacek Wodecki, Justyna Hebda-Sobkowicz, Adam Mirek, Radosław Zimroz, Agnieszka Wyłomańska

“…Due to the significant difference between the spectra of recorded seismic wave and pure noise which precedes the event, time-frequency representation allows for better accuracy of signal change detection. However, with an additional domain, the complexity rises. …”
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Enhancing Software Quality with AI: A Transformer-Based Approach for Code Smell Detection by Israr Ali, Syed Sajjad Hussain Rizvi, Syed Hasan Adil

“…In this study, we introduce Relation-Aware BERT (RABERT), a novel transformer-based model that integrates relational embeddings to enhance automated code smell detection. By modeling interdependencies among software complexity metrics, RABERT surpasses classical machine-learning methods, achieving an accuracy of 90.0% and a precision of 91.0%. …”
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A hybrid approach using support vector machine rule-based system: detecting cyber threats in internet of things by M. Wasim Abbas Ashraf, Arvind R. Singh, A. Pandian, Rajkumar Singh Rathore, Mohit Bajaj, Ievgen Zaitsev

“…Complex threats often go undetected by conventional security measures, requiring more sophisticated, adaptive detection methods. …”
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Research on Target Detection Algorithm for Solder Joint Defects Based on the Improved YOLOv8 by Chengkai Zhang, Xiaocui Feng, Pujun Mao, Fuyan Lv

“…Aiming at the problem of low accuracy in solder joint defect detection caused by the complex background and difficult to extract defect features of circuit boards using through-hole technology (THT), an improved YOLOv8 solder joint defect target detection algorithm was proposed. …”
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LSTM-JSO framework for privacy preserving adaptive intrusion detection in federated IoT networks by Shaymaa E. Sorour, Mohammed Aljaafari, Amany M. Shaker, Ahmed E. Amin

“…This research introduces a novel framework that enhances intrusion detection in IoT systems through a decentralized approach. …”
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