SD-YOLOv8: SAM-Assisted Dual-Branch YOLOv8 Model for Tea Bud Detection on Optical Images by Xintong Zhang, Dasheng Wu, Fengya Xu

“…This demonstrates its superior capability in efficiently detecting tea buds against complex backgrounds. …”
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Early Detection of Alzheimer’s Disease: An Extensive Review of Advancements in Machine Learning Mechanisms Using an Ensemble and Deep Learning Technique by Renjith Prabhavathi Neelakandan, Ramesh Kandasamy, Balasubramani Subbiyan, Mariya Anto Bennet

“…The findings of this study contribute significantly to the field of AD diagnoses and pave the way for more precise and efficient early detection strategies.…”
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Underwater Magnetic Sensors Network by Arkadiusz Adamczyk, Maciej Klebba, Mariusz Wąż, Ivan Pavić

“…This study explores the design and performance of an underwater magnetic sensor network (UMSN) tailored for intrusion detection in complex environments such as riverbeds and areas with dense vegetation. …”
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A Scalable Framework for Real-Time Network Security Traffic Analysis and Attack Detection Using Machine and Deep Learning by Zineb Maasaoui, Mheni Merzouki, Abdella Battou, Ahmed Lbath

“…This dual approach of real-time and static analysis provides a robust foundation for developing scalable, efficient, and automated security solutions, essential for managing the evolving threats in modern networks.…”
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Evaluating model generalization for cow detection in free-stall barn settings: Insights from the COw LOcalization (COLO) dataset by Mautushi Das, Gonzalo Ferreira, C.P. James Chen

“…This study investigates the generalization capabilities of object detection models for cow detection in indoor free-stall barn settings, focusing on varying training data characteristics such as view angles and lighting, and model complexities. …”
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FED-GEM-CN: A federated dual-CNN architecture with contrastive cross-attention for maritime radar intrusion detection by Md. Alamgir Hossain

“…These findings substantiate the efficacy of the proposed system in delivering robust, energy-efficient, and privacy-aware intrusion detection tailored to the constraints of maritime radar networks. …”
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Multi-Modal Dynamic Fusion for Defect Detection in Electronic Products: A Novel Approach Based on Energy and Deep Learning by Yulin Liu, Yang Gao

“…Conventional defect detection approaches, which typically depend on a single modality, often fall short in both efficiency and reliability. …”
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A hybrid machine learning model for intrusion detection in wireless sensor networks leveraging data balancing and dimensionality reduction by Md. Alamin Talukder, Majdi Khalid, Nasrin Sultana

“…The model employs classifiers such as Decision Tree Classifier, Random Forest Classifier (RFC), and gradient boosting techniques like XGBoost (XGBC) to enhance detection accuracy and efficiency. The proposed hybrid (KMS + PCA + RFC) approach achieves remarkable performance, with an accuracy of 99.94% and an f1-score of 99.94% on the WSN-DS dataset. …”
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Enhanced framework for credit card fraud detection using robust feature selection and a stacking ensemble model approach by Rahul Kumar Gupta, Asmaul Hassan, Samir Kumar Majhi, Nikhat Parveen, Abu Taha Zamani, Raju Anitha, Binayak Ojha, Abhinav Kumar Singh, Debendra Muduli

“…With the increase in online payment volumes, conventional fraud detection technologies are increasingly confronted by the complexity of fraudulent strategies that require intelligent and scalable alternatives. …”
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Golden Chip-Free Hardware Trojan Detection Using Attention-Based Non-Local Convolution With Simple Recurrent Unit by Rama Devi Maddineni, Deepak Ch

“…The emergence of machine learning and deep learning models has enhanced the feasibility of hardware Trojan detection, as these models can learn complex patterns and representations from extensive datasets. …”
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A Small-Sample Target Detection Method of Side-Scan Sonar Based on CycleGAN and Improved YOLOv8 by Ye Zheng, Jun Yan, Junxia Meng, Ming Liang

“…Because of their low cost and ease of deployment, side-scan sonars is one of the most widely used underwater survey instruments. However, the complexity of the marine environment and the difficulty in target acquisition limit the detection accuracy of side-scan sonars. …”
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Proposal for an Intelligent Methodology to Manage Energy in Buildings and Detect Anomalies as a Compass Towards Zero Energy Buildings (ZEB) by Irati Prol-Godoy, Roberto Santana, Francisco-Javier Rey-Martinez, Ana Picallo-Perez

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High-resolution accurate mass- mass spectrometry based- untargeted metabolomics: Reproducibility and detection power across data-dependent acquisition, data-independent acquisition... by Hanane El Boudlali, Laura Lehmicke, Uta Ceglarek

“…Here, we compare three acquisition modes—Data-Dependent Acquisition (DDA), Data-Independent Acquisition (DIA), and AcquireX —to evaluate performance and reproducibility in detecting low-abundance metabolites in a complex matrix. …”
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Non-Destructive Detection of Fillet Fish Quality Using MQ135 Gas Sensor and Neutrosophic Logic-Enhanced System by M. Y. Shams, M. R. Darwesh, Roheet Bhatnagar, N. S. A. Al-Sattary, A. A. Salama, M. S. Ghoname

“…This integration enhances decision-making by accurately modeling complex real-world uncertainties. These methods, combining electronic nose technology, artificial intelligence, and neutrosophic inference, provide a robust, non-destructive, and cost-effective approach to detecting spoilage in fillet fish. …”
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BlockDroid: detection of Android malware from images using lightweight convolutional neural network models with ensemble learning and blockchain for mobile devices by Emre Şafak, İbrahim Alper Doğru, Necaattin Barışçı, İsmail Atacak

“…Because these methods can automatically extract meaningful features from data and efficiently learn complex relationships, they can achieve better performance in malware detection as well as in solving many other problems. …”
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A Novel Convolutional Neural Network–Long Short-term Memory Model for Interplanetary Coronal Mass Ejection Detection by Junpei Li, Hong Chen, Jingjing Wang, Yanhong Chen, Bingxian Luo, Hao Deng

“…This study presents a convolutional neural network–long short-term memory (CNN-LSTM) model with dynamic loss function, designed to efficiently process complex multidimensional spatiotemporal data. …”
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Protocol-Agnostic and Packet-Based Intrusion Detection Using a Multi-Layer Deep-Learning Architecture at the Network Edge by Rodolphe Picot, Felipe Gohring de Magalhaes, Ahmad Shahnejat Bushehri, Maroua Ben Atti, Gabriela Nicolescu, Alejandro Quintero

“…Its protocol-agnostic design and customized preprocessing method enable it to efficiently detect network attacks across multiple protocols while preserving the original packet structure. …”
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Secure Quantum Communication With Multi-Users in Quantum Networks by As'Ad Mahmoud As'Ad Alnaser, Hazem Moh'D Said Hatamleh, Nawaf Abdualziz Almolhis, Salahaldeen Duraibi, Yahya Alqahtani

“…The performance of the system has been analyzed with metrics like detection accuracy of eavesdrop attack (97%), communication complexity (95%), effective key rate (590 b/s), communication efficiency (96%), and computational overhead (98%). …”
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Towards precision diagnosis: a novel hybrid DC-CAD model for lung disease detection leveraging multi-scale capsule networks and temporal dynamics by Esther Stacy E. B. Aggrey, Qin Zhen, Seth Larweh Kodjiku, Linda Delali Fiasam, Collins Sey, Chiagoziem C. Ukwuoma, Evans Aidoo, Emmanuel Osei-Mensah

“…Abstract The early detection of lung diseases, including cancer, is essential for improving patient outcomes. …”
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Identifying All Matches of a Rigid Object in an Input Image Using Visible Triangles by Abdullah N. Arslan

“…It has been suggested that for objects identifiable by their corners, every triangle formed by these corner points can serve as a reference for detecting other corner points. This approach enables effective rigid object detection, including partial matches. …”
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