Toward Automatic Streetside Building Identification With an Integrated YOLO Model for Building Detection and a Vision Transformer for Identification by Ossama Krawi, Lavdie Rada

“…Utilizing Google Street View datasets that cover entire urban landscapes, the proposed method offers efficiency and scalability, simplifying data acquisition and avoiding logistical complexities of manual interaction for targeted collections. …”
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Lightweight YOLOv8 for tongue teeth marks and fissures detection based on C2f_DCNv3 by Chunyang Jin, Delong Zhang, Xiyuan Cao, Zhidong Zhang, Chenyang Xue, Yanjun Zhang

“…Additionally, the model reduces computational cost by approximately one-third in terms of FLOPS, maintaining high accuracy while greatly decreasing the number of parameters, thus offering a more robust and resource-efficient solution. For tongue crack detection, the mAP increases to 91.34%, with notable improvements in F1 score, precision, and recall. …”
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Semi-Supervised Anomaly Detection for the Identification of Damages in an Aerospace Sandwich Structure Based on Synthetically Generated Strain Data by Florian Forsthuber, Christoph Kralovec, Martin Schagerl

“…In this field, machine learning (ML) techniques are attracting growing attention due to their capability to recognize complex patterns, making them very suitable for the identification of damages in operating mechanical structures. …”
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A High Accuracy and Large-Scale Detection for Fixed-Wing UAV Autonomous Ground Landing With GNSS-Denied by Bin Fang, Zhiwei Zhong, Xiangjun Jiang

“…To further facilitate large-scale range detection in GNSS-denied conditions, a landing guidance window is developed, enabling the efficient search for and tracking of UAVs. …”
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SDKU-Net: A Novel Architecture with Dynamic Kernels and Optimizer Switching for Enhanced Shadow Detection in Remote Sensing by Gilberto Alvarado-Robles, Isac Andres Espinosa-Vizcaino, Carlos Gustavo Manriquez-Padilla, Juan Jose Saucedo-Dorantes

“…To address these issues, this study proposes the Supervised Dynamic Kernel U-Net (SDKU-Net), a novel architecture designed to enhance shadow detection in complex remote sensing scenarios. SDKU-Net integrates dynamic kernel adjustment, a combined loss function incorporating Focal and Tversky Loss, and optimizer switching to effectively tackle class imbalance and improve segmentation quality. …”
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YOLO-AMM: A Real-Time Classroom Behavior Detection Algorithm Based on Multi-Dimensional Feature Optimization by Yi Cao, Qian Cao, Chengshan Qian, Deji Chen

“…However, the existing models are still deficient in detail feature capture capability, multi-layer feature correlation, and multi-scale target adaptability, making it challenging to realize high-precision real-time detection in complex scenes. This paper proposes an improved classroom behavior detection algorithm, YOLO-AMM, to solve these problems. …”
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Effective DDoS attack detection in software-defined vehicular networks using statistical flow analysis and machine learning. by Himanshi Babbar, Shalli Rani, Maha Driss

“…The ML models, especially complex ones like Random Forest, require substantial processing power, which necessitates efficient data handling and possibly leveraging edge computing resources to reduce latency. …”
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Vowel classification with combining pitch detection and one‐dimensional convolutional neural network based classifier for gender identification by Chia‐Hung Lin, Hsiang‐Yueh Lai, Ping‐Tzan Huang, Pi‐Yun Chen, Chien‐Ming Li

“…The proposed PD‐based extractor and voice classifier could reduce complexity and improve classification efficiency. Acoustic datasets were selected from the Hillenbrand database for experimental tests on 12 vowels classifications, and K‐fold cross‐validations were performed. …”
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Deep learning for rice leaf disease detection: A systematic literature review on emerging trends, methodologies and techniques by Chinna Gopi Simhadri, Hari Kishan Kondaveeti, Valli Kumari Vatsavayi, Alakananda Mitra, Preethi Ananthachari

“…Machine learning techniques are used to detect diseases based on the extracted features. In contrast, deep learning techniques learn complex patterns from large datasets without explicit feature extraction techniques and are well-suited for disease detection tasks. …”
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A deep learning approach to detect diseases in pomegranate fruits via hybrid optimal attention capsule network by P. Sajitha, A. Diana Andrushia, N. Anand, M.Z. Naser, Eva Lubloy

“…In post-harvest pomegranate fruit disease detection, deep learning has great potential to extract complex patterns and features from large datasets. …”
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LI-YOLOv8: Lightweight small target detection algorithm for remote sensing images that combines GSConv and PConv. by Pingping Yan, Xiangming Qi, Liang Jiang

“…In the domain of remote sensing image small target detection, challenges such as difficulties in extracting features of small targets, complex backgrounds that easily lead to confusion with targets, and high computational complexity with significant resource consumption are prevalent. …”
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Multi-stream feature fusion of vision transformer and CNN for precise epileptic seizure detection from EEG signals by Qi Li, Wei Cao, Anyuan Zhang

“…Conclusions The CMFViT model provides an efficient, accurate, and innovative solution for complex EEG signal analysis and seizure detection tasks for single and cross-subjects while laying the foundation for developing real-time, accurate seizure detection systems.…”
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Sensitive detection of dengue serotype-4 on lignin-derived graphene nanofibre and manganese oxide composite by DNA-sensing by Sivainesh Devi Remesh, Saravanan Karuppanan, Veeradasan Perumal, Subash C. B. Gopinath, Mark Ovinis, Pandian Bothi Raja, Natarajan Arumugam, Raju Suresh Kumar

“…Abstract This research addresses the critical challenge of achieving precise DNA detection in complex biological samples by developing a novel, eco-friendly dengue virus serotype 4 biosensor. …”
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Dynamic Wireless Power Transfer System for Indoor AGV With a Distance-Measurement-Based Position Detection Technique by Deniss Stepins, Janis Zakis, Aleksandrs Sokolovs, Oleksandr Husev, Ryszard Strzelecki, Rik W. de Doncker

“…However, the cost and level of complexity of the position detection system are moderately lower and the proposed position detection system is immune to optical radiation and magnetic fields. …”
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YOLOv8-OCHD: A Lightweight Wood Surface Defect Detection Method Based on Improved YOLOv8 by Zuxing Chen, Junjie Feng, Xueyan Zhu, Bin Wang

“…To overcome the drawbacks of manual defect detection and the challenges of traditional visual inspection algorithms, such as high missed detection rates, slow detection speeds, and difficulties in deployment on embedded devices for detecting subtle wood surface defects, this study aims to improve the utilization efficiency of wood in enterprises. …”
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An intelligent method for detection of small target fungal wheat spores based on an improved YOLOv5 with microscopic images by Zhizhou Ren, Kun Liang, Yingqi Zhang, Jinpeng Song, Xiaoxiao Wu, Chi Zhang, Xiuming Mei, Yi Zhang, Xin Liu

“…Traditional detection methods are time-consuming and labor-intensive, and difficult to detect small target spores in complex environments. …”
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A Deep Learning-Based Approach for the Detection of Various Internet of Things Intrusion Attacks Through Optical Networks by Nouman Imtiaz, Abdul Wahid, Syed Zain Ul Abideen, Mian Muhammad Kamal, Nabila Sehito, Salahuddin Khan, Bal S. Virdee, Lida Kouhalvandi, Mohammad Alibakhshikenari

“…Furthermore, the model’s architecture leverages the high-speed, low-latency characteristics of optical networks, ensuring the efficient processing of large-scale IoT data streams and supporting real-time detection in diverse IoT ecosystems. …”
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THE USE OF NEURAL NETWORKS FOR THE AUTOMATIC ANALYSIS OF ELECTROCARDIOGRAMS IN DIAGNOSIS OF CARDIOVASCULAR DISEASES by G. I. Kachayeva, A. G. Mustafayev

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Efficient joint resource allocation using self organized map based Deep Reinforcement Learning for cybertwin enabled 6G networks by Nivetha A, Preetha KS

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An explainable AI-driven deep neural network for accurate breast cancer detection from histopathological and ultrasound images by Md. Romzan Alom, Fahmid Al Farid, Muhammad Aminur Rahaman, Anichur Rahman, Tanoy Debnath, Abu Saleh Musa Miah, Sarina Mansor

“…However, traditional diagnostic processes relying on manual analysis of medical images are inherently complex and subject to variability between observers, highlighting the urgent need for robust automated breast cancer detection systems. …”
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