EHA-YOLOv5: An Efficient and Highly Accurate Improved YOLOv5 Model for Workshop Bearing Rail Defect Detection Application by Jiyong Hu, Hongfei Yang, Jiatang He, Dongxu Bai, Hongda Chen

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Deep Learning-Driven Multi-Temporal Detection: Leveraging DeeplabV3+/Efficientnet-B08 Semantic Segmentation for Deforestation and Forest Fire Detection by Joe Soundararajan, Andrew Kalukin, Jordan Malof, Dong Xu

“…This study presents a deep learning framework that combines the DeepLabV3+ architecture with an EfficientNet-B08 backbone to address both deforestation and wildfire detection using satellite imagery. …”
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FECI-RTDETR a Lightweight Unmanned Aerial Vehicle Infrared Small Target Detector Algorithm Based on RT-DETR by Renzheng Xue, Shijie Hua, Haiqiang Xu

“…Addressing the challenges of small target detection in aerial infrared images from a drone’s perspective, such as diverse target scales, complex backgrounds, the clustering of small targets, and limited computational resources of the drone platform. …”
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FGS-YOLOv8s-seg: A Lightweight and Efficient Instance Segmentation Model for Detecting Tomato Maturity Levels in Greenhouse Environments by Dongfang Song, Ping Liu, Yanjun Zhu, Tianyuan Li, Kun Zhang

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SAM-Based Efficient Feature Integration Network for Remote Sensing Change Detection: A Case Study on Macao Sea Reclamation by Junqing Huang, Junqi Bao, Min Xia, Xiaochen Yuan

“…To utilize the visual recognition capabilities of SAM for improving RSCD, we propose an SAM-based efficient feature integration network (EFI-SAM). Random fourier features adaptor (RFFA) is utilized to enhance Mobile SAM’s capability for extracting general features from complex RSIs, resulting in adaptive Mobile SAM, which serves as the feature extractor of EFI-SAM. …”
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Assessment of Doppler Wind Lidar Detection Efficiency and Influencing Factors at Plateau Airport: A Case Study of Lhasa Gonggar Airport by Junjie Wu, Hongyu Du, Chunjiong Xia, Xiaoyuan Huang

“…The influence of different underlying surface types on detection efficiency was minimal, with detection efficiency at a 270° azimuth slightly better than at a 90° azimuth. …”
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Small Target Detection Algorithm for UAV Aerial Images Based on Improved YOLOv7-tiny by ZHANG Guanghua, LI Congfa, LI Gangying, LU Weidang

“…ObjectiveUAVs provide advantages such as easy control, low cost, and good performance, and efficiently perform tasks in diverse sites and complex environments. …”
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AppleLeafNet: a lightweight and efficient deep learning framework for diagnosing apple leaf diseases by Muhammad Umair Ali, Majdi Khalid, Majed Farrash, Hassan Fareed M. Lahza, Amad Zafar, Seong-Han Kim

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Efficient Switched-Beam Detection for Dynamic Spectrum Sharing in 6G Wireless Networks With Full Duplex Technology at the THz Band by Andrea Tani, Dania Marabissi

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Para-YOLO: An Efficient High-Parameter Low-Computation Algorithm Based on YOLO11n for Remote Sensing Object Detection by Hang Chen, Qi Cao, Yongqiang Wang, Shang Wang, Haisheng Fu, Zhenjiao Chen, Feng Liang

“…Moreover, the increasing demand for real-time onboard image processing imposes stringent requirements on both algorithmic complexity and detection accuracy. To overcome these challenges, we propose Para-YOLO, an efficient and cost-effective algorithm consisting of a novel feature fusion network and three innovative modules. …”
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Optimizing Security in IoT Ecosystems Using Hybrid Artificial Intelligence and Blockchain Models: A Scalable and Efficient Approach for Threat Detection by William Villegas-Ch, Jaime Govea, Rommel Gutierrez, Aracely Mera-Navarrete

“…Traditional security solutions, based on centralized architectures, are neither scalable nor efficient enough to handle the increasing complexity and number of IoT devices, leading to high latencies, increased energy consumption, and inadequate intrusion detection. …”
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Technical study on the efficiency and models of weed control methods using unmanned ground vehicles: A review by Evans K. Wiafe, Kelvin Betitame, Billy G. Ram, Xin Sun

“…Also, there is a shift from using traditional machine learning (ML) algorithms to deep learning neural networks, including convolutional neural networks (CNNs) and recurrent neural networks (RNNs), for weed detection algorithm development due to their potential to work in complex environments. …”
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Potato plant disease detection: leveraging hybrid deep learning models by Jackson Herbert Sinamenye, Ayan Chatterjee, Raju Shrestha

“…This study proposes a novel approach to improve potato plant disease detection and identification using a hybrid deep-learning model, EfficientNetV2B3+ViT. …”
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Pyridine–Quinoline and Biquinoline-Based Ruthenium <i>p</i>-Cymene Complexes as Efficient Catalysts for Transfer Hydrogenation Studies: Synthesis and Structural Characterization by Nikolaos Zacharopoulos, Gregor Schnakenburg, Eleni I. Panagopoulou, Nikolaos S. Thomaidis, Athanassios I. Philippopoulos

“…Searching for new and efficient transfer hydrogenation catalysts, a series of new organometallic ruthenium(II)-arene complexes of the formulae [Ru(η⁶-<i>p</i>-cymene)(L)Cl][PF₆] (<b>1</b>–<b>8</b>) and [Ru(η⁶-<i>p</i>-cymene)(L)Cl][Ru(η⁶-<i>p</i>-cymene)Cl₃] (<b>9</b>–<b>11</b>) were synthesized and fully characterized. …”
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Using U-Net models in deep learning for brain tumor detection from MRI scans by Minh Khiem Nguyen, Phuoc Huy Tran, Tan Tai Phan

“… Tumor diseases in the nervous system are both dangerous and complex. Magnetic Resonance Imaging (MRI) is crucial for detecting brain disease; however, identifying the presence of tumors from these is time-consuming and requires a professional doctor. …”
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Using U-Net models in deep learning for brain tumor detection from MRI scans by Minh Khiem Nguyen, Phuoc Huy Tran, Tan Tai Phan

“… Tumor diseases in the nervous system are both dangerous and complex. Magnetic Resonance Imaging (MRI) is crucial for detecting brain disease; however, identifying the presence of tumors from these is time-consuming and requires a professional doctor. …”
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MEFA-Net: Multilevel Feature Extraction and Fusion Attention Network for Infrared Small-Target Detection by Jingcui Ma, Nian Pan, Dengyu Yin, Di Wang, Jin Zhou

“…Infrared small-target detection encounters significant challenges due to a low image signal-to-noise ratio, limited target size, and complex background noise. …”
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A Lightweight Semantic- and Graph-Guided Network for Advanced Optical Remote Sensing Image Salient Object Detection by Jie Liu, Jinpeng He, Huaixin Chen, Ruoyu Yang, Ying Huang

“…To further efficiently aggregate multi-level features and preserve the integrity and complexity of overall object shape, we introduce a Graph-Based Region Awareness Module (GRAM). …”
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PVLF: point-voxel local feature fusion for 3D detection by Haowei Zhao, Zhuolei Xiao

“…PVLF explores local spatial features to improve accuracy regarding tiny object detection. To address the potential complex computational issues in the convolution process, we have designed an innovative Adaptive Sparse Convolution (ASC) module that effectively eliminates redundant information in the feature layer. …”
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Hybrid convolutional neural network and bi-LSTM model with EfficientNet-B0 for high-accuracy breast cancer detection and classification by Umesh Kumar Lilhore, Yogesh Kumar Sharma, Brajesh Kumar Shukla, Muniraju Naidu Vadlamudi, Sarita Simaiya, Roobaea Alroobaea, Majed Alsafyani, Abdullah M. Baqasah

“…The model further enhances performance by incorporating Bi-LSTM, which allows for processing temporal dependencies in the data, which is crucial for accurately detecting complex patterns in breast cancer images. …”
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