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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YOLO-LSD: A Lightweight Object Detection Model for Small Targets at Long Distances to Secure Pedestrian Safety by Ming-An Chung, Sung-Yun Chai, Ming-Chun Hsieh, Chia-Wei Lin, Kai-Xiang Chen, Shang-Jui Huang, Jun-Hao Zhang

“…YOLO-LSD attains higher detection accuracy for small and distant objects while maintaining lower computational complexity. …”
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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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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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FASTSeg3D: A Fast, Efficient, and Adaptive Ground Filtering Algorithm for 3D Point Clouds in Mobile Sensing Applications by Daniel Ayo Oladele, Elisha Didam Markus, Adnan M. Abu-Mahfouz

“…Background: Accurate ground segmentation in 3D point clouds is critical for robotic perception, enabling robust navigation, object detection, and environmental mapping. However, existing methods struggle with over-segmentation, under-segmentation, and computational inefficiency, particularly in dynamic or complex environments. …”
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AHN-YOLO: A Lightweight Tomato Detection Method for Dense Small-Sized Features Based on YOLO Architecture by Wenhui Zhang, Feng Jiang

“…When benchmarked against other lightweight models in the field, AHN-YOLO exhibits superior training efficiency and detection accuracy in complex, dense scenarios, demonstrating clear advantages.…”
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Deep Learning Method with Domain-Task Adaptation and Client-Specific Fine-Tuning YOLO11 Model for Counting Greenhouse Tomatoes by Igor Glukhikh, Dmitry Glukhikh, Anna Gubina, Tatiana Chernysheva

“…The large-scale implementation of computer vision systems in greenhouses requires approaches that reduce costs, time and complexity, particularly in creating training data and preparing neural network models. …”
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Attention-Based Dual-Knowledge Distillation for Alzheimer’s Disease Stage Detection Using MRI Scans by Chandita Barman, Sudhanshu Singh, Manob Jyoti Saikia, Shovan Barma

“…This study presents an efficient attention-guided dual-knowledge distillation (D-KD) framework for classifying Alzheimer’s disease (AD) stages using magnetic resonance imaging (MRI) scans based on detection of the subtle anatomical differences. …”
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State-of-the-Art Deep Learning Algorithms for Internet of Things-Based Detection of Crop Pests and Diseases: A Comprehensive Review by Jean Pierre Nyakuri, Celestin Nkundineza, Omar Gatera, Kizito Nkurikiyeyezu

“…Moreover, the research discusses the advantages and limitations of these techniques, emphasizing their architecture design, efficiency and accuracy. The findings demonstrate that there is a tradeoff between robustness and complexity among existing techniques, and authors recommend future trends aimed at creating robust models with fewer parameters that are more accurate and easily implementable on small IoT-based and portable devices suitable for in-field and real-time applications. …”
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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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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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Design of C24 fullerene-based sensors for gamma-butyrolactone detection as advanced tools for biomedical and pharmaceutical applications by Bader Huwaimel, Saad Alqarni

“…These results highlight MgC23 as a promising candidate for sensitive and efficient GBL detection in pharmaceutical and forensic applications.…”
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HSF-YOLO: A Multi-Scale and Gradient-Aware Network for Small Object Detection in Remote Sensing Images by Fujun Wang, Xing Wang

“…These results confirm that HSF-YOLO is a unified and effective solution for small object detection in complex RSI scenarios, offering a good balance between accuracy and efficiency.…”
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Anomaly Detection Model in Network Security Situational Awareness Based on Machine Learning: Limitation, Techniques, and Future Trends by Li Zhen, Nazhatul Hafizah Kamarudin, Ven Jyn Kok, Faizan Qamar

“…Finally, this study deepens the understanding of the anomaly detection problem in NSSA and provides both theoretical and practical guidance for the development of efficient and scalable anomaly detection systems.…”
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Exploring the Application of Large Language Models Based AI Agents in Leakage Detection of Natural Gas Valve Chambers by Qian Wei, Hongjun Sun, Yin Xu, Zisheng Pang, Feixiang Gao

“…The traditional detection methods highly depend on manpower with large workload and are prone to missed and false alarms, which seriously affects the efficiency and safety of oil and gas production and transportation. …”
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A Unified Transformer–BDI Architecture for Financial Fraud Detection: Distributed Knowledge Transfer Across Diverse Datasets by Parul Dubey, Pushkar Dubey, Pitshou N. Bokoro

“…With the growing volume and complexity of digital transactions, the traditional rule-based and shallow learning models often fall short in detecting sophisticated fraud patterns. …”
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Signal Detection in Intelligent Reflecting Surface-Assisted NOMA Network Using LSTM Model: A ML Approach by Haleema Sadia, Hafsa Iqbal, Syed Fawad Hussain, Nasir Saeed

“…In this article, we propose a machine learning (ML)-based approach to perform joint channel estimation and signal detection in an IRS-enabled uplink NOMA network under efficient mitigation of SIC error propagation. …”
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Short and Medium-Term Power Load Anomaly Detection Method Based on Convolutional Neural Network and EL-DCC by Zhipeng Li, Shaobo Liu, Yang Zhang, Zhaowei Wang, Lu Wang, Lu Huang

“…With the increasing complexity of load patterns, it is difficult for traditional methods to meet the requirements of accurate detection. …”
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A new approach of anomaly detection in shopping center surveillance videos for theft prevention based on RLCNN model by Muhammad Sajid, Ali Haider Khan, Kaleem Razzaq Malik, Javed Ali Khan, Ayed Alwadain

“…In this article, we introduce a system for efficient anomaly detection that can function in surveillance networks with a modest level of complexity. …”
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