Advancing Particle Tracking: Self-Organizing Map Hyperparameter Study and Long Short-Term Memory-Based Outlier Detection by Max Klein, Niklas Dormagen, Lukas Wimmer, Markus H. Thoma, Mike Schwarz

“…Combined with automatic hyperparameter calibration, outlier detection and additional computational speed optimization, this work delivers a robust, versatile and efficient framework for PTV analysis.…”
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Development of an Eco-Friendly Method for Iron Extraction and Determination in Pharmaceuticals Using Ciprofloxacin Drug as Chelating agent by Baghdad Science Journal

“…Several variables which impact on the extraction and determination of Fe (III) are optimized in order to maximize the extraction efficiency and improve the sensitivity of the method. …”
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Optimized small object detection in low resolution infrared images using super resolution and attention based feature fusion. by Weilun Wang, Jian Xu, Ruopeng Zhang

“…Infrared (IR) imaging is extensively applied in domains such as object detection, industrial monitoring, medical diagnostics, intelligent transportation due to its robustness in low-light, adverse weather, and complex environments. …”
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A novel YOLOv11-Driven deep learning algorithm for UAV multispectral oil spill detection in Inland lakes by Yu Zhang, Jian Xing, Weida Chen, Haitao Wang, Bingyu Shi, Yang Song, Xiaoou Huang, Zihan Jiang

“…Abstract Lake oil spills are challenging to detect accurately due to complex oil–water interactions resulting from water flow disturbances, vegetation occlusion, and the diffusion behavior of oil films. …”
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Double-Condensing Attention Condenser: Leveraging Attention in Deep Learning to Detect Skin Cancer from Skin Lesion Images by Chi-en Amy Tai, Elizabeth Janes, Chris Czarnecki, Alexander Wong

“…A recent movement for TinyML applications is integrating Double-Condensing Attention Condensers (DC-AC) into a self-attention neural network backbone architecture to allow for faster and more efficient computation. This paper explores leveraging an efficient self-attention structure to detect skin cancer in skin lesion images and introduces a deep neural network design with DC-AC customized for skin cancer detection from skin lesion images. …”
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Detection of low-altitude infrared small targets for UAVs using a density-based artificial bee colony algorithm by Haixia Wang, Hailong Wang, Fen Han

“…This study enriches the application framework of density-driven intelligent optimization strategies in the field of infrared small target detection. Moreover, it provides theoretical support and technical path for low-altitude unmanned aerial vehicles to achieve efficient and stable infrared target detection in tasks such as security patrols, disaster search and rescue, and border monitoring.…”
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LMSOE-Net: lightweight multi-scale small object enhancement network for UAV aerial images by Zhixing Ma, Peidong Luo, Xiaole Shen

“…To tackle this issue, we propose a lightweight multi-scale small object enhancement network (LMSOE-Net) based on the YOLOv8 architecture. To improve both detection performance and model efficiency, we introduce the Efficient Multi Scale Pyramid (EMSP) neck network. …”
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Efficient Design of a Terahertz Metamaterial Dual-Band Absorber Using Multi-Objective Firefly Algorithm Based on a Multi-Cooperative Strategy by Guilin Li, Yan Huang, Yurong Wang, Weiwei Qu, Hu Deng, Liping Shang

“…Terahertz metamaterial dual-band absorbers are used for multi-target detection and high-sensitivity sensing in complex environments by enhancing information that reflects differences in the measured substances. …”
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FD²-YOLO: A Frequency-Domain Dual-Stream Network Based on YOLO for Crack Detection by Junwen Zhu, Jinbao Sheng, Qian Cai

“…However, most existing methods use multi-scale and attention mechanisms to improve on a single backbone, and this single backbone network is often ineffective in detecting slender or variable cracks in complex scenarios. …”
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Synthesis of Rhodamine TEMPO Conjugates via Isonitrile‐Based Multicomponent‐Reactions for Mitochondria‐Targeted ROS‐Detection in Cancer Cells by Haider N. Sultani, Ibrahim M. Morgan, Hidayat Hussain, Haleh H. Haeri, Dariush Hinderberger, Goran N. Kaluđerović, Bernhard Westermann

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DGS-Yolov7-Tiny: a lightweight pest and disease target detection model suitable for edge computing environments by Ping Yu, Baoshu Zong, Xiaozhong Geng, Hui Yan, Baijin Liu, Cheng Chen, Hupeng Liu, Xiaoqing Xu

“…However, traditional object detection models are often computationally intensive and complex, rendering them unsuitable for real-time applications in edge computing. …”
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A Novel Approach for Rapid Detection of Forest Degradation and Diseases Through Anomaly Analysis of Sentinel-2 Spectral Data by Sofiia Drozd, Nataliia Kussul, Hanna Yailymova

“…Forest degradation is an ongoing global issue, with significant environmental impacts that necessitate efficient monitoring and management. This paper presents a simple yet effective method for detecting forest degradation using freely available Sentinel-2 satellite data and an anomaly detection approach. …”
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DMF-YOLO: Dynamic Multi-Scale Feature Fusion Network-Driven Small Target Detection in UAV Aerial Images by Xiaojia Yan, Shiyan Sun, Huimin Zhu, Qingping Hu, Wenjian Ying, Yinglei Li

“…The method exhibits superior robustness in dense scenarios, complex backgrounds, and long-range target detection. …”
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Air-ground collaborative multi-source orbital integrated detection system: Combining 3D imaging and intrusion recognition. by Mengyuan Yan, Xingyu Yang, Wei Gao, Lifan Rong, Shengbo Li, Yuan Xiong

“…With the rapid expansion of railway networks globally, ensuring rail infrastructure safety through efficient detection methods has become critical. Traditional inspection systems face limitations in flexibility, adaptability to adverse weather, and multifunctional integration. …”
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Adaptive Congestion Detection and Traffic Control in Software-Defined Networks via Data-Driven Multi-Agent Reinforcement Learning by Kaoutar Boussaoud, Abdeslam En-Nouaary, Meryeme Ayache

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Morphological Analysis and Subtype Detection of Acute Myeloid Leukemia in High-Resolution Blood Smears Using ConvNeXT by Mubarak Taiwo Mustapha, Dilber Uzun Ozsahin

“…(1) Background: Acute Myeloid Leukemia (AML) is a complex hematologic malignancy where accurate subtype classification is crucial for targeted treatment and improved patient outcomes. …”
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A Comparative Study of Machine Learning and Deep Learning Models for Automatic Parkinson’s Disease Detection from Electroencephalogram Signals by Sankhadip Bera, Zong Woo Geem, Young-Im Cho, Pawan Kumar Singh

“…Therefore, there is an urgent need for a more reliable and efficient technique for early detection of PD. Using the potential of electroencephalogram (EEG) signals, this study introduces an innovative method for the detection or classification of PD patients through machine learning, as well as a more accurate deep learning approach. …”
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RFCS-YOLO: Target Detection Algorithm in Adverse Weather Conditions via Receptive Field Enhancement and Cross-Scale Fusion by Gang Liu, Yingzheng Huang, Shuguang Yan, Enxiang Hou

“…It addresses challenges like complex backgrounds and problems of missing and mis-detecting traffic targets in bad weather. …”
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A Comprehensive Approach to Intrusion Detection in IoT Environments Using Hybrid Feature Selection and Multi-Stage Classification Techniques by G. Logeswari, J. Deepika Roselind, K. Tamilarasi, V. Nivethitha

“…The rapid expansion of Internet of Things (IoT) devices has led to an increasingly complex threat landscape, challenging traditional Intrusion Detection Systems (IDS) to effectively handle the vast and diverse data generated by IoT networks. …”
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YOLO-SW: A Real-Time Weed Detection Model for Soybean Fields Using Swin Transformer and RT-DETR by Yizhou Shuai, Jingsha Shi, Yi Li, Shaohao Zhou, Lihua Zhang, Jiong Mu

“…The research stands out for its novel integration of three key advancements: the Swin Transformer backbone, which leverages local window self-attention to achieve linear O(N) computational complexity for efficient global context capture; the CARAFE dynamic upsampling operator, which enhances small target localization through context-aware kernel generation; and the RTDETR encoder, which enables end-to-end detection via IoU-aware query selection, eliminating the need for complex post-processing. …”
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