Simple Single-Person Fall Detection Model Using 3D Pose Estimation Mechanisms by Jinmo Yang, R. Young Chul Kim

“…Therefore, we propose the simple fall detection model (SFDM) that is computationally efficient with moderate accuracy. …”
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Signal Detection for Enhanced Spatial Modulation-Based Communication: A Block Deep Neural Network Approach by Shaopeng Jin, Yuyang Peng, Fawaz AL-Hazemi, Mohammad Meraj Mirza

“…In ESM, traditional signal detection methods such as maximum likelihood (ML) have the drawback of high complexity. …”
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A Low Complexity Near-Optimal Detector Based on Teaching-Learning Algorithm for Massive MIMO by Hamid Amiriara, Mohammadreza Zahabi

“…In this paper, a low-complexity receiver is proposed using a Teaching-Learning based optimization (TLBO) heuristic algorithm for a large-scale system. …”
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SIC based RL for massive MIMO NOMA signal detection for different modulation schemes under diverse channel conditions by Arun Kumar, Aziz Nanthaamornphong, Mohammed H. Alsharif, Mehedi Masud

“…Abstract Massive-multiple input and Multiple Outputs Non orthogonal multiple access (M-MIMO–NOMA) systems require efficient signal detection techniques to mitigate interference and enhance spectral efficiency, especially under diverse channel conditions and varying modulation schemes. …”
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Fresh Tea Leaf-Grading Detection: An Improved YOLOv8 Neural Network Model Utilizing Deep Learning by Zejun Wang, Yuxin Xia, Houqiao Wang, Xiaohui Liu, Raoqiong Che, Xiaoxue Guo, Hongxu Li, Shihao Zhang, Baijuan Wang

“…The incorporation of an Efficient Multi-Scale Attention Module with Cross-Spatial Learning serves to attenuate the influence of irrelevant features in complex backgrounds, which in turn, elevates the model’s detection Precision. …”
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Optimized Wireless Sensor Network Architecture for AI-Based Wildfire Detection in Remote Areas by Safiah Almarri, Hur Al Safwan, Shahd Al Qisoom, Soufien Gdaim, Abdelkrim Zitouni

“…Wildfires are complex natural disasters that significantly impact ecosystems and human communities. …”
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YOLOv8-Scm: an improved model for citrus fruit sunburn identification and classification in complex natural scenes by Guoxun Cong, Guoxun Cong, Xinghong Chen, Xinghong Chen, Zongyu Bing, Zongyu Bing, Wenhuan Liu, Wenhuan Liu, Xiangling Chen, Qun Wu, Zheng Guo, Zheng Guo, Yongqiang Zheng, Yongqiang Zheng

“…Three key enhancements are introduced: (1) DSConv module replaces the standard convolution for a more efficient and lightweight design, (2) Global Attention Mechanism (GAM) improves feature extraction for multi-scale and occluded targets, and (3) EIoU loss function enhances detection precision and generalization. …”
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Reduced Complexity Initial Synchronization for 5G NR Multibeam LEO-Based Non-Terrestrial Networks by Ashish Kumar Meshram, Sumit Kumar, Jorge Querol, Stefano Andrenacci, Symeon Chatzinotas

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Improved YOLOv10 for Visually Impaired: Balancing Model Accuracy and Efficiency in the Case of Public Transportation by Rio Arifando, Shinji Eto, Tibyani Tibyani, Chikamune Wada

“…The model also exhibits reduced computational complexity and storage requirements, highlighting its efficiency. …”
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Hybrid convolutional neural network and bi-LSTM model with EfficientNet-B0 for high-accuracy breast cancer detection and classification by Umesh Kumar Lilhore, Yogesh Kumar Sharma, Brajesh Kumar Shukla, Muniraju Naidu Vadlamudi, Sarita Simaiya, Roobaea Alroobaea, Majed Alsafyani, Abdullah M. Baqasah

“…The model further enhances performance by incorporating Bi-LSTM, which allows for processing temporal dependencies in the data, which is crucial for accurately detecting complex patterns in breast cancer images. …”
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HFEF²-YOLO: Hierarchical Dynamic Attention for High-Precision Multi-Scale Small Target Detection in Complex Remote Sensing by Yao Lu, Biyun Zhang, Chunmin Zhang, Yifan He, Yanqiang Wang

“…However, achieving high-precision detection of small objects against complex backgrounds remains challenging due to insufficient feature representation and background interference. …”
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Progressive Self-Prompting Segment Anything Model for Salient Object Detection in Optical Remote Sensing Images by Xiaoning Zhang, Yi Yu, Daqun Li, Yuqing Wang

“…With the continuous advancement of deep neural networks, salient object detection (SOD) in natural images has made significant progress. …”
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Quantum-Inspired Multi-Scale Object Detection in UAV Imagery: Advancing Ultra-Small Object Accuracy and Efficiency for Real-Time Applications by Muhammad Muzammul, Muhammad Assam, Ayman Qahmash

“…Ultra-small object detection in UAV imagery presents significant challenges due to scale variation, environmental complexity, and computational constraints. …”
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An Efficient Model for Real-Time Traffic Density Analysis and Management Using Visual Graph Networks by Nikhil Nigam, Dhirendra Pratap Singh, Jaytrilok Choudhary, Surendra Solanki

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Face Detection Using Hybrid SNN-ANN to Process Neuromorphic Event Stream by Waseem Shariff, Paul Kielty, Joe Lemley, Peter Corcoran

“…This paper tackles the challenges of face detection, a vital computer vision task with wide-ranging applications, particularly in driver monitoring systems, where both accuracy and computational efficiency are crucial. …”
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Full-dimensional dynamic convolution and progressive learning strategy for strawberry recognition based on YOLOv8 by Liping Bai, Chenglei Xia, Fei Liu, Xing Yang, Tai Zhang

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AppleLeafNet: a lightweight and efficient deep learning framework for diagnosing apple leaf diseases by Muhammad Umair Ali, Majdi Khalid, Majed Farrash, Hassan Fareed M. Lahza, Amad Zafar, Seong-Han Kim

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Analysis of China’s High-Speed Railway Network Using Complex Network Theory and Graph Convolutional Networks by Zhenguo Xu, Jun Li, Irene Moulitsas, Fangqu Niu

“…First, complex network analysis was applied to provide insights into the network’s fundamental characteristics, such as small-world properties, efficiency, and robustness. …”
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Fast forward modeling and response analysis of extra-deep azimuthal resistivity measurements in complex model by Pan Zhang, Shaogui Deng, Xiyong Yuan, Fen Liu, Weibiao Xie

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Technical study on the efficiency and models of weed control methods using unmanned ground vehicles: A review by Evans K. Wiafe, Kelvin Betitame, Billy G. Ram, Xin Sun

“…Also, there is a shift from using traditional machine learning (ML) algorithms to deep learning neural networks, including convolutional neural networks (CNNs) and recurrent neural networks (RNNs), for weed detection algorithm development due to their potential to work in complex environments. …”
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