Emerging technologies for detecting antibiotics in aquaculture wastewater: A critical review by Xinyu Chang, Junchi Cui, Guihua Wang, Shujuan Meng, Lingling Chen, Meng Zhang

“…Consequently, accurate and efficient detection methods for antibiotics, particularly within complex environmental matrices, are essential. …”
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Obstacle detection and avoidance system based on layered costmaps for robot tractors by Ricardo Ospina, Kota Itakura

“…This paper presents a novel obstacle detection and avoidance system based on layered costmaps, designed to enhance the safety and efficiency of robot tractors navigating farm roads. …”
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Multiscale Information Fusion Based on Large Model Inspired Bacterial Detection by Zongduo Liu, Yan Huang, Jian Wang, Genji Yuan, Junjie Pang

“…Accurate and efficient bacterial detection is essential for public health and medical diagnostics. …”
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Ensemble Deep Learning Models for Enhanced Brain Tumor Classification by Leveraging ResNet50 and EfficientNet-B7 on High-Resolution MRI Images by Retinderdeep Singh, Sheifali Gupta, Salil Bharany, Ahmad Almogren, Ayman Altameem, Ateeq Ur Rehman

“…Brain tumors are among the most significant and challenging medical conditions because of their sometimes-fatal outcomes and complexity of treatment options. Early, exact detection of brain tumors is essential for both the most effective treatment programs and much higher patient survival rates. …”
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PhishingAgent: an agentic workflow method for advanced phishing email detection by JIN Jiandong, HUANG Zheng, HU Zhanyu, ZOU Yuanxin, QIN Huidong, LAI Qingnan, YANG Jia, ZHOU Changling

“…PhishingAgent integrated multi-source knowledge bases and security tools to fully leverage the reasoning capabilities of large language model (LLM), enhancing the precision and depth of identifying complex phishing email attacks. The agentic workflow was built on a dual-system reasoning framework, a rapid detection system facilitates efficient preliminary threat identification, followed by a deep reasoning system that conducted detailed semantic analysis and contextual inference, significantly improving the interpretability of results. …”
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Detecting DDoS attack based on compensation non-parameter CUSUM algorithm by YAN Fen1~3, CHEN Yi-qun3, HUANG Hao1, YIN Xin-chun3

“…t,calculated the ratio of the number of unacknowledged segments and the number of all segments.Then,the statistical sequence based on time came into being.After that,an improved non-parameter recursive CUSUM algorithm was used to detect attack effi-ciently on line.In this procedure,the suspicious packets were also recorded.Experiments prove that this algorithm is fast and efficient.It has low false-positive rate and could adapt to more complex network environments.In addition,it is helpful to attack analysis and tracing.…”
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Fast and Accurate Detection of Forty Types of Fruits and Vegetables: Dataset and Method by Xiaosheng Bu, Yongfeng Wu, Hongtai Lv, Youling Yu

“…However, existing detection methods typically focus on identifying a single type of fruit or vegetable and are not equipped to handle complex and diverse environments. …”
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Structural Fault Detection and Diagnosis for Combine Harvesters: A Critical Review by Haiyang Wang, Liyun Lao, Honglei Zhang, Zhong Tang, Pengfei Qian, Qi He

“…Combine harvesters, as essential equipment in agricultural engineering, frequently experience structural faults due to their complex structure and harsh working conditions, which severely affect their reliability and operational efficiency, leading to significant downtime and reduced agricultural productivity during critical harvesting periods. …”
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SWMD-YOLO: A Lightweight Model for Tomato Detection in Greenhouse Environments by Quan Wang, Ye Hua, Qiongdan Lou, Xi Kan

“…The accurate detection of occluded tomatoes in complex greenhouse environments remains challenging due to the limited feature representation ability and high computational costs of existing models. …”
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Research on Fire Smoke Detection Algorithm Based on Improved YOLOv8 by Tianxin Zhang, Fuwei Wang, Weimin Wang, Qihao Zhao, Weijun Ning, Haodong Wu

“…Fire has consistently posed a significant disaster risk worldwide. Current fire detection methods primarily rely on traditional physical sensors such as light, smoke, and temperature detectors, which often struggle in complex environments. …”
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Personalizing Seizure Detection for Individual Patients by Optimal Selection of EEG Signals by Rosanna Ferrara, Martino Giaquinto, Gennaro Percannella, Leonardo Rundo, Alessia Saggese

“…This interpretable, patient-specific method enables the development of personalized, efficient, and compact wearable devices for reliable seizure detection in everyday life.…”
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Detection of Hydrophobicity Grade of Composite Insulators Based on MDC‐YOLO Algorithm by Shaotong Pei, Weiqi Wang, Chenlong Hu, Haichao Sun, Keyu Li, Mianxiao Wu, Bo Lan

“…ABSTRACT In the field of power equipment inspection, the aging condition of composite insulators is often determined by the detection of water repellency. However, the existing detection methods are difficult to effectively extract the water repellency level features in the complex background, and it is difficult to meet the real‐time requirements. …”
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From Misinformation to Insight: Machine Learning Strategies for Fake News Detection by Despoina Mouratidis, Andreas Kanavos, Katia Kermanidis

“…Through extensive experimentation across multiple datasets, our results demonstrate that BERT-based models consistently achieve superior performance, significantly improving detection accuracy in complex misinformation scenarios. …”
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Frequency-informed transformer for real-time water pipeline leak detection by Fengnian Liu, Ding Wang, Junya Tang, Lei Wang

“…Abstract Water pipeline leaks pose significant risks to urban infrastructure, leading to water wastage and potential structural damage. Existing leak detection methods often face challenges, such as heavily relying on the manual selection of frequency bands or complex feature extraction, which can be both labour-intensive and less effective. …”
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Moving Vehicles Detection and Tracking on Highways and Transportation System for Smart Cities by Manoj Kumar, Susmita Ray, Dileep Kumar Yadav

“…In this paper, various approaches related to moving vehicle detection for the transportation system especially for highways are considered. …”
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Lightweight RT-DETR with Attentional Up-Downsampling Pyramid Network by Nan Li, Xiao Han, Xingguo Song, Xu Fang, Mengming Wu, Qiulin Yu

“…To address this, we propose an improved RT-DETR R18 model tailored for companion robots. This lightweight, efficient design integrates YOLOv9’s ADown module, the RepNCSPELAN4 module, and custom attention-based AdaptiveGateUpsample and AdaptiveGateDownsample modules for enhanced multi-scale feature fusion, reducing weight and complexity while optimizing real-time detection. …”
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Artificial intelligence-driven predictive framework for early detection of still birth by Sarah A. Alzakari, Asma Aldrees, Muhammad Umer, Lucia Cascone, Nisreen Innab, Imran Ashraf

“…Predictive modeling is becoming increasingly popular in the context of early disease detection. The use of machine learning approaches for predictive modeling can help early detection of diseases thereby enabling medical experts to appropriate medical treatments. …”
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Curb Detection and Mapping via Robust Iterative Gaussian Process Regression by Di Wang, Si Chen, Zhenni Ma, Jiajia Shi, Fuchun Zhang

“…Curb detection and mapping are of great importance to ensure the safety and efficiency of intelligent vehicles. …”
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An interpretable approach for trustworthy intrusion detection systems against evasion samples by Ngoc Tai Nguyen, Hien Do Hoang, The Duy Phan, Van-Hau Pham

“… In recent years, Deep Neural Networks (DNN) have demonstrated remarkable success in various domains, including Intrusion Detection Systems (IDS). The ability of DNN to learn complex patterns from large datasets has significantly improved IDS performance, leading to more accurate and efficient threat detection. …”
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A Sparse Pooling Adversarial Learning Framework for Anomaly Event Detection by ZHANG, M., HU, H., LI, Z.

“…However, due to factors of complex background, large changes in scale, and the randomness of abnormal events, causing abnormal event detection poses significant challenges. …”
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