Real-Time Forest Fire Detection with Lightweight CNN Using Hierarchical Multi-Task Knowledge Distillation by Ismail El-Madafri, Marta Peña, Noelia Olmedo-Torre

“…The novelty of this approach lies in leveraging knowledge distillation to transfer the deeper insights learned by the DenseNet201 teacher model—specifically, the auxiliary task of recognizing the confounding elements responsible for false positives—into a lightweight student model, enabling it to achieve a similar robustness without the need for complex architectures. Using this distilled knowledge, we trained a MobileNetV3-based student model, which was designed to operate efficiently in real-time while maintaining a low computational overhead. …”
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Detecting Anomalies in Attributed Networks Through Sparse Canonical Correlation Analysis Combined With Random Masking and Padding by Wasim Khan, Mohammad Ishrat, Ahmad Neyaz Khan, Mohammad Arif, Anwar Ahamed Shaikh, Mousa Mohammed Khubrani, Shadab Alam, Mohammed Shuaib, Rajan John

“…Our approach is the first of its kind to provide a novel remedy to the fundamental problems preventing efficient and accurate anomaly identification, thereby establishing a new standard in this field. …”
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Employing CNN mobileNetV2 and ensemble models in classifying drones forest fire detection images by Dima Suleiman, Ruba Obiedat, Rizik Al-Sayyed, Shadi Saleh, Wolfram Hardt, Yazan Al-Zain

“… In recent years, the adoption of advanced machine learning techniques has revolutionized approaches to solving complex problems, such as identifying occurrences of forest fires. …”
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Aerial image segmentation of embankment dams based on multispectral remote sensing: a case study in the Belo Monte Hydroelectric Complex, Pará, Brazil by Carlos André de Mattos Teixeira, Thabatta Moreira Alves de Araujo, Evelin Cardoso, Marcos Antonio Costantin Filho, João Weyl Costa, Carlos Renato Lisboa Frances

“…Recently, multispectral remote sensing data and machine learning techniques have been applied to develop methodologies that enable automatic vegetation analysis and anomaly detection based on computer vision. As a first step toward this automation, this study introduces a methodology for land cover segmentation of earth-rock embankment dam structures within the Belo Monte Hydroelectric Complex, located in the state of Pará, northern Brazil. …”
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Detection of Crack Sealant in the Pretreatment Process of Hot In-Place Recycling of Asphalt Pavement via Deep Learning Method by Kai Zhao, Tianzhen Liu, Xu Xia, Yongli Zhao

“…They often appear as wide black patches that overlap with cracks and potholes, and complex background noise further complicates detection. …”
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Assessing Temperature-Dependent Deltamethrin Toxicity in Various <i>kdr</i> Genotypes of <i>Aedes aegypti</i> Mosquitoes by Joshua Kalmouni, Brook M. Jensen, Joshua Ain, Krijn P. Paaijmans, Silvie Huijben

“…Insecticide resistance surveillance systems for vector-borne diseases are crucial for early detection of resistance and the implementation of evidence-based resistance management strategies. …”
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Tomato yellow leaf curl virus detection based on cross-domain shared attention and enhanced BiFPN by Henghui Mo, Linjing Wei

“…This model exhibits exceptional performance in detecting TYLCV in complex environments, providing robust technical support for tomato growth monitoring and offering insights for the detection of other crop diseases. …”
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Integrating Stride Attention and Cross-Modality Fusion for UAV-Based Detection of Drought, Pest, and Disease Stress in Croplands by Yan Li, Yaze Wu, Wuxiong Wang, Huiyu Jin, Xiaohan Wu, Jinyuan Liu, Chen Hu, Chunli Lv

“…This study demonstrates that the integration of lightweight attention mechanisms with multimodal UAV remote sensing imagery enables efficient, accurate, and scalable agricultural disaster detection under complex field conditions.…”
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CD-STMamba: Toward Remote Sensing Image Change Detection With Spatio-Temporal Interaction Mamba Model by Shanwei Liu, Shuaipeng Wang, Wei Zhang, Tao Zhang, Mingming Xu, Muhammad Yasir, Shiqing Wei

“…Change detection (CD) is a critical Earth observation task. …”
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Achieving Excellence in Cyber Fraud Detection: A Hybrid ML+DL Ensemble Approach for Credit Cards by Eyad Btoush, Xujuan Zhou, Raj Gururajan, Ka Ching Chan, Omar Alsodi

“…The rapid advancement of technology has increased the complexity of cyber fraud, presenting a growing challenge for the banking sector to efficiently detect fraudulent credit card transactions. …”
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Detection of Invasive Species (Siam Weed) Using Drone-Based Imaging and YOLO Deep Learning Model by Deepak Gautam, Zulfadli Mawardi, Louis Elliott, David Loewensteiner, Timothy Whiteside, Simon Brooks

“…We specifically examined the effects of input training images, solar illumination, and model complexity on the model’s detection performance and investigated the sources of false positives. …”
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Comparative evaluation of voltage conversion and encoder pulse detection methods for controlling a model high-bay warehouse by Anas Abu Al-Hija’a, B. J. Szekeres, M. Andó

“…The presented evaluation enhances control strategies for industrial automation, emphasizing efficient voltage conversion and precise encoder pulse detection to enhance system performance and reliability, laying the groundwork for future advancements in remote control capabilities and complex manufacturing systems.…”
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MDFusion: Multi-Dimension Semantic–Spatial Feature Fusion for LiDAR–Camera 3D Object Detection by Renzhong Qiao, Hao Yuan, Zhenbo Guan, Wenbo Zhang

“…Extensive experiments on the KITTI and ONCE datasets demonstrate that our method achieves competitive performance in complex scenes, significantly improving the multi-modal fusion quality and detection accuracy while maintaining computational efficiency.…”
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Application of Improved YOLOv8 Image Model in Urban Manhole Cover Defect Management and Detection: Case Study by Yanqiong Ding, Baojiang Han, Hua Jiang, Hao Hu, Lei Xue, Jiasen Weng, Zhili Tang, Yuzhang Liu

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Designing and analysis of flexible four legged-loaded metamaterial absorber sensor for permittivity detection of a solid substrate by Shihabun Sakib, Mohammad Tariqul Islam, Ahasanul Hoque, Abdulmajeed M. Alenezi, Mohamed S. Soliman, Haitham Alsaif

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A multiple biomolecules-based rapid life detection protocol embedded in a rover scientific subsystem for soil sample analysis by Akib Zaman, Fardeen Ashraf, Haseena Khan, Faria Noshin Ahona, Oliullah Samir, Asif Mahmud Rayhan, Sadia Nur Nazifa, Hafsah Mahzabin Chowdhury, Md. Mahbubur Rahman

“…This research focuses on the objective of the extraterrestrial life detection domain and seeks to provide an efficient protocol that can produce life detection decisions based on empirical data obtained through chemical analysis under time and resource-constrained conditions. …”
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Research on UAV aerial imagery detection algorithm for Mining-Induced surface cracks based on improved YOLOv10 by Jiayong An, Siyuan Dong, Xuanli Wang, Chenlei Li, Wenpei Zhao

“…However, detection is challenged by small crack size, complex morphology, large scale variation, and uneven spatial distribution, further exacerbated by UAVs’ limited onboard computational capacity. …”
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Federated learning with LSTM for intrusion detection in IoT-based wireless sensor networks: a multi-dataset analysis by Raja Waseem Anwar, Mohammad Abrar, Abdu Salam, Faizan Ullah

“…Traditional centralized intrusion detection systems (IDS) face significant challenges in data privacy, computational efficiency, and scalability, particularly in resource-constrained IoT environments. …”
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RFAG-YOLO: A Receptive Field Attention-Guided YOLO Network for Small-Object Detection in UAV Images by Chengmeng Wei, Wenhong Wang

“…The YOLO series of object detection methods have achieved significant success in a wide range of computer vision tasks due to their efficiency and accuracy. …”
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Optimization of VA-DLLME methodology for the determination of beta-cyfluthrin in blood and urine sample of human by TLC-image analysis: Validated by AGREE and complex GAPI tools by Sakshi Pandey, Mahak Malviya, Lalit P. Chandravanshi

“…Overall, this developed method for detecting beta-cyfluthrin in human blood and urine samples is efficient, cost-effective, and adheres to the principles of green analytical chemistry(GAC). …”
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