A harmonic current detection algorithm for aviation active power filter based on generalized delayed signal superposition by Yong Lu, Bohan Li, Guofei Teng, Zhen Zhang, Xianfeng Xu

“…Then, the complexity of the operator was analyzed. Due to the symmetry of the coefficients, the operation time of the operators can be reduced. …”
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A deep Reinforcement learning-based robust Intrusion Detection System for securing IoMT Healthcare Networks by Jamshed Ali Shaikh, Chengliang Wang, Muhammad Wajeeh Us Sima, Muhammad Arshad, Muhammad Owais, Dina S. M. Hassan, Reem Alkanhel, Mohammed Saleh Ali Muthanna

“…The system integrates Convolutional Neural Networks (CNN), Long Short-Term Memory (LSTM) networks, and Reinforcement Learning (RL) techniques, namely Deep Q-Network (DQN) and Proximal Policy Optimization (PPO), to enhance the detection of evolving threats. The methodology begins with Enhanced Mutual Information Feature Selection (MIFS) to preprocess the CICIoMT2024 dataset, selecting the most relevant features while reducing noise and computational complexity. …”
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Portable Fluorescence Microarray Reader-Enabled Biomarker Panel Detection System for Point-of-Care Diagnosis of Lupus Nephritis by Aygun Teymur, Iftak Hussain, Chenling Tang, Ramesh Saxena, David Erickson, Tianfu Wu

“…However, many POC systems lack the sensitivity or multiplexing capability required for complex diseases. This study introduces an LED-based fluorescence reader designed for POC applications, enabling multiplex detection of lupus nephritis (LN) biomarkers using a biomarker microarray (BMA) slide. …”
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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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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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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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Zn-3, a Tris[3-(2-pyridyl)-pyrazolyl]borate zinc(II) complex, shows preferential antiproliferative effects on breast cancer rather than normal breast cells via oxidative stress by Jun-Ping Shiau, Min-Yu Lee, Sodio C.N. Hsu, Sih-Ruei Tseng, Hsueh-Wei Chang

“…This investigation evaluated the antiproliferative efficiency of Zn-3 in breast cancer and normal cells. …”
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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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SPL-YOLOv8: A Lightweight Method for Rape Flower Cluster Detection and Counting Based on YOLOv8n by Yue Fang, Chenbo Yang, Jie Li, Jingmin Tu

“…To address these challenges, this study proposes a lightweight rape flower clusters detection model, SPL-YOLOv8. First, the model introduces StarNet as a lightweight backbone network for efficient feature extraction, significantly reducing computational complexity and parameter counts. …”
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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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LCD-Net: A Lightweight Remote Sensing Change Detection Network Combining Feature Fusion and Gating Mechanism by Wenyu Liu, Jindong Li, Haoji Wang, Run Tan, Yali Fu, Qichuan Tian

“…While traditional CNN-based methods have improved detection accuracy, they often suffer from high computational complexity and large parameter counts, limiting their use in resource-constrained environments. …”
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Emergence of concurrently transmissible mcr-9 and carbapenemase genes in bloodborne colistin-resistant Enterobacter cloacae complex isolated from ICU patients in Kolkata, India by Gourab Halder, Bhaskar Narayan Chaudhury, Priyanka Denny, Mandira Chakraborty, Subhranshu Mandal, Shanta Dutta

“…ABSTRACT Colistin resistance in carbapenem-resistant Enterobacter cloacae complex (CR-ECC) infections has grown expeditiously but detecting the underlying mechanism of resistance is often challenging in clinical settings. …”
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Design and Research on a Reed Field Obstacle Detection and Safety Warning System Based on Improved YOLOv8n by Yuanyuan Zhang, Zhongqiu Mu, Kunpeng Tian, Bing Zhang, Jicheng Huang

“…Unmanned agricultural machinery can significantly reduce labor intensity while substantially enhancing operational efficiency and production benefits. However, the presence of various obstacles in complex farmland environments is inevitable. …”
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Adaptive Hierarchical Multi-Headed Convolutional Neural Network With Modified Convolutional Block Attention for Aerial Forest Fire Detection by Md. Najmul Mowla, Davood Asadi, Shamsul Masum, Khaled Rabie

“…Effective detection and classification of forest fire imagery are critical for timely and efficient wildfire management. …”
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Ghost-Attention-YOLOv8: Enhancing Rice Leaf Disease Detection with Lightweight Feature Extraction and Advanced Attention Mechanisms by Thanh Dang Bui, Tra My Do Le

“…In agricultural research, effective and efficient disease detection in crops is crucial for enhancing yield and sustainability. …”
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TIMA-Net: A Lightweight Remote Sensing Image Change Detection Network Based on Temporal Interaction Enhancement and Multi-Scale Aggregation by Zhijun Zhou, Xuejie Zhang, Xiaoliang Luo, Lvchun Wang, Wei Yu, Shufang Xu, Longbao Wang

“…To address these issues, this paper proposes a novel network architecture, TIMA-Net, which is designed for efficient remote sensing image change detection. By introducing the timing interaction enhancement module and the progressive decoder based on multi-scale fusion, TIMA-Net improves the accuracy of change detection while ensuring efficient computing performance. …”
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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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A Modified MobileNetv3 Coupled With Inverted Residual and Channel Attention Mechanisms for Detection of Tomato Leaf Diseases by Rubina Rashid, Waqar Aslam, Romana Aziz, Ghadah Aldehim

“…This study introduces a feasible lightweight model optimized for an Android application to detect diseased areas within images. This research focuses on enhancing the efficiency and accuracy of tomato leaf disease detection by modifying mobile-based Convolutional Neural Networks (CNNs). …”
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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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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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