Enhancing the Performance of YOLOv9t Through a Knowledge Distillation Approach for Real-Time Detection of Bloomed Damask Roses in the Field by Farhad Fatehi, Hossein Bagherpour, Jafar Amiri Parian

“…Recent developments in deep learning algorithms, especially in convolutional models, have shown significant promise for object detection, highlighting strong possibilities for improving the efficiency of this process. …”
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Accurate Sugarcane Detection and Row Fitting Using SugarRow-YOLO and Clustering-Based Spline Methods for Autonomous Agricultural Operations by Guiqing Deng, Fangyue Zhou, Huan Dong, Zhihao Xu, Yanzhou Li

“…However, sugarcane leaves and stalks intertwine and overlap at this stage. They can form a complex occlusion structure, which poses a greater challenge to target detection. …”
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SMS Scam Detection Application Based on Optical Character Recognition for Image Data Using Unsupervised and Deep Semi-Supervised Learning by Anjali Shinde, Essa Q. Shahra, Shadi Basurra, Faisal Saeed, Abdulrahman A. AlSewari, Waheb A. Jabbar

“…The rationale behind highlighting these models is their potential to significantly improve smishing detection rates. For instance, the high accuracy of the KNN-Flatten model suggests its applicability in real-time spam detection systems, but its computational complexity might limit scalability in large-scale deployments. …”
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Enhanced TumorNet: Leveraging YOLOv8s and U-net for superior brain tumor detection and segmentation utilizing MRI scans by Wisal Zafar, Ghassan Husnain, Abid Iqbal, Ali Saeed Alzahrani, Muhammad Abeer Irfan, Yazeed Yasin Ghadi, Mohammed S. AL-Zahrani, Ramasamy Srinivasaga Naidu

“…The integration of YOLOv8s and U-Net in Enhanced TumorNet offers a powerful solution for the automated analysis of brain tumors in MRI scans, significantly improving detection and segmentation accuracy. This hybrid approach holds great potential for clinical applications, enhancing the efficiency and effectiveness of brain tumor diagnosis.…”
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A Novel Error Detection due to Joint CRC Aided Denoise-and-Forward Network Coding for Two-Way Relay Channels by Yulun Cheng, Longxiang Yang

“…In wireless two-way (TW) relay channels, denoise-and-forward (DNF) network coding (NC) is a promising technique to achieve spectral efficiency. However, unsuccessful detection at relay severely deteriorates the diversity gain, as well as end-to-end pairwise error probability (PEP). …”
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A novel cluster of improved frilled lizard optimization and multi-ladder gated networks for the detection of cyber-attacks in computer networks by Vandana Dharmapuri, Sushama Rani Dutta

“…The proposed ML-GNUs are used for differentiating diverse attack patterns, and the improved frilled lizard technique is employed to tune the model’s hyper-parameters, reducing computational overhead and enhancing detection performance. The efficiency of the proposed model is assessed utilising both simulated real-time data traffic patterns and the CIC-IDS-2017 benchmark dataset, and standard performance metrics like accuracy, precision, recall, specificity, Matthews correlation coefficient (MCC), and F1-score are analyzed and measured. …”
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Enhancing Pest Detection: Assessing Tuta absoluta (Lepidoptera: Gelechiidae) Damage Intensity in Field Images through Advanced Machine Learning by Edwin Lewıs, Atilla Erdinç, Yavuz Selim Şahin, Alperen Kaan Bütüner, Hilal Erdoğan

“…The integration of DTs in such applications, due to their unique ability to handle complex and indistinct shapes without the need for feature extraction, sets the stage for a new era of efficient and effective pest control strategies.…”
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A Semi-Supervised Diffusion-Based Framework for Weed Detection in Precision Agricultural Scenarios Using a Generative Attention Mechanism by Ruiheng Li, Xuaner Wang, Yuzhuo Cui, Yifei Xu, Yuhao Zhou, Xuechun Tang, Chenlu Jiang, Yihong Song, Hegan Dong, Shuo Yan

“…The development of smart agriculture has created an urgent demand for efficient and accurate weed recognition and detection technologies. …”
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Application of novel oligomeric Co(II) complexes of 4,4′-bipyridine and 1,10-phenanthroline modified glassy carbon electrode for differential pulse voltammetric determination of ci... by Mezgebu Biresaw, Adane Kassa, Getinet Tamiru Tigineh, Atakilt Abebe

“…These complexes, designated as [Co21(phen)42(bipy)21]Cl42 (C2) and [Co100(phen)200(bipy)100]Cl200 (C3), were employed for the modification of a glassy carbon electrode (GCE) to detect ciprofloxacin (CPF) in tablet formulations and human urine samples. …”
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Enhanced detection of surface deformations in LPBF using deep convolutional neural networks and transfer learning from a porosity model by Muhammad Ayub Ansari, Andrew Crampton, Samer Mohammed Jaber Mubarak

“…Our approach demonstrates the power of transfer learning in adapting a model known for porosity detection in LPBF to identify surface deformations with high accuracy (94%), matching the performance of the best existing models but with significantly less complexity. …”
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Improving machine learning detection of Alzheimer disease using enhanced manta ray gene selection of Alzheimer gene expression datasets by Zahraa Ahmed, Mesut Çevik

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Crack Detection, Classification, and Segmentation on Road Pavement Material Using Multi-Scale Feature Aggregation and Transformer-Based Attention Mechanisms by Arselan Ashraf, Ali Sophian, Ali Aryo Bawono

“…Detection precision increased from 88.7% to 94.3%, and IoU improved from 78.8% to 93.2%. …”
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Hyperspectral imaging-driven deep learning approach: Asymptomatic stage detection and severity grading of tomato yellow leaf curl disease by Junzhi Chen, Wenshan Zhong, Xuejun Yue, Ziyu Ding, Mengdan Du, Xuantian Li, Biao Chen, Haifeng Li, ZiFu He, Xiaoman She, Yafei Tang

“…This study proposes a spectral-spatial dual-branch residual network (SSDBRN) designed to fully extract and leverage complex features within hyperspectral images, aiming to achieve detection of TYLCD during its asymptomatic stage and classification of disease severity levels. …”
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A Wireless Sensor Network-Based Combustible Gas Detection System Using PSO-DBO-Optimized BP Neural Network by Min Zhou, Sen Wang, Jianming Li, Zhe Wei, Lingqiao Shui

“…The model exhibits strong robustness in handling nonlinear responses and cross-sensitivity effects across multiple sensors, demonstrating its effectiveness in complex detection scenarios under laboratory conditions within embedded wireless sensor networks.…”
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Anomaly Detection and Remaining Useful Life Prediction for Turbofan Engines with a Key Point-Based Approach to Secure Health Management by Yuntao Duan, Tao Zhang, Dunhuang Shi

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Enhanced YOLOv7-Tiny for Small-Scale Fire Detection via Multi-Scale Channel Spatial Attention and Dynamic Upsampling by Shihao Wu, Yi Xia

“…Fire detection remains a challenging task, particularly in distinguishing small smoke and flames from complex backgrounds. …”
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One-Step Rapid Detection of Histamine in Fish Using a Colorimetric Aptasensor Based on Magnetic Metal Organic Framework Nanozymes by QIU Yixing, LIU Xin, SONG Yufan, DING Yuting, XU Xia

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A reagent-free, fouling-resistant electrochemical multifunctional peptide sensor for highly sensitive and rapid detection of Alzheimer's disease biomarkers by Yajing Ji, Xueqin Li, Xiumin Li, Xiaochun Hu, Wei Cong, Honggang Hu, Siyao Liu, Yan Chen

“…This study presents a novel, efficient, and rapid strategy for detecting Aβ aggregates in human serum without complex pre-processing and provides practical support for the early clinical diagnosis of AD.…”
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Research on damage detection technology for wind turbine blade acoustic signals by fusion of sparse representation, compressive sensing and deep learning by Liang Wang, Chun Yang, Chao Yuan, Yanan Liu, Yanqing Chen

“…The sparse representation method is used to effectively encode the voiceprint signal and extract representative signal features; the compressed sensing technology is applied to efficiently reconstruct the signal using a small amount of sampled data, significantly reducing the data collection amount and storage requirements; deep feature learning and damage pattern classification based on convolutional neural network further improve the accuracy and intelligence level of detection.The research results show that the proposed method effectively reduces the computational complexity and greatly improves the detection accuracy. …”
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T-blocker: a simple and robust probe-free quantitative PCR assay to detect somatic mutations down to 0.1% frequency by Hanyoup Kim, Aaron E Ruby, Harini G Shandilya, Arvind K Virmani, Nandita Rahman, Christina M Strange, Jarkko Huuskonen

“…Highly efficient and specific mutant amplification in conjunction with selective wild-type suppression by the T-blocker concept enabled 0.1% detection sensitivity using the intercalating dye-based qPCR chemistry instead of more complex target-specific dye-labeled probes. …”
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