Optimizing Stroke Risk Prediction: A Primary Dataset‐Driven Ensemble Classifier With Explainable Artificial Intelligence by Md. Maruf Hossain, Md. Mahfuz Ahmed, Md. Rakibul Hasan Rakib, Mohammad Osama Zia, Rakib Hasan, Md. Rakibul Islam, Md. Shohidul Islam, Md Shahariar Alam, Md. Khairul Islam

“…A novel ensemble classifier was developed, combining AdaBoost, Gradient Boosting Machine (GBM), Multilayer Perceptron (MLP), and Random Forest (RF) algorithms to enhance predictive accuracy. Additionally, Explainable Artificial Intelligence (XAI) techniques such as SHAP and LIME were integrated to elucidate key features influencing stroke prediction. …”
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DMM-YOLO: A high efficiency soil fauna detection model based on an adaptive dynamic shuffle mechanism by Jiehui Ke, Renbo Luo, Guoliang Xu, Yuna Tan, Zhifeng Wu, Liufeng Xiao

“…Abstract Soil fauna play a critical role in maintaining ecosystem functions and assessing environmental health, making accurate and efficient detection essential. Therefore, this paper proposes an improved algorithm based on You Only Look Once (YOLO) v9, which enhances feature capture capability while reducing parameters by 33.6%. …”
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A Hybrid CNN-LSTM Model With Attention Mechanism for Improved Intrusion Detection in Wireless IoT Sensor Networks by Pendukeni Phalaagae, Adamu Murtala Zungeru, Abid Yahya, Boyce Sigweni, Selvaraj Rajalakshmi

“…Additionally, the improved dynamic PSO algorithm optimizes feature selection in real time, enhancing classification accuracy and adaptability to evolving IoT network threats. …”
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Enhanced deep learning model for apple detection, localization, and counting in complex orchards for robotic arm-based harvesting by Tantan Jin, Xiongzhe Han, Pingan Wang, Zhao Zhang, Jie Guo, Fan Ding

“…This study presents an enhanced deep learning model designed to improve the accuracy and adaptability of recognition algorithms for robotic arm-based harvesting. Specifically, an optimized You Only Look Once (YOLO) v8n model was developed by integrating a dilation-wise residual–dilated re-parameterization block module, a generalized feature pyramid network, and the Scylla Intersection-over-Union loss function. …”
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Detection method on pantograph-catenary arcing of electric locomotives based on multi-frequency-band characteristics of current signals by LUO Yinbei, GE Ting, SUN Zeyong

“…Additionally, a random forest model was established, with feature vectors as inputs and detection results as outputs. …”
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DSCW-YOLO: Vehicle Detection from Low-Altitude UAV Perspective via Coordinate Awareness and Collaborative Module Optimization by Qingqi Zhang, Hao Wang, Xinbo Wang, Jiapeng Shang, Xiaoli Wang, Jie Li, Yan Wang

“…The proposed algorithm enhances the model’s local feature extraction capability through a module collaboration optimization strategy, integrates coordinate convolution to strengthen spatial perception, and introduces a small object detection head to address target size variations caused by altitude changes. …”
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Intelligent deep learning architecture for precision vegetable disease detection advancing agricultural new quality productive forces by Jun Liu, Xuewei Wang, Qian Chen, Peng Yan, Dugang Guo

“…The Multi-granularity Feature Fusion Detection Layer (MFLayer) improves small target localization accuracy through cross-level feature interaction mechanisms. …”
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A cooperative intrusion detection system for internet of things using fuzzy logic and ensemble of convolutional neural networks by Xiongwei Qiu, Lianzhi Shi, Pengtong Fan

“…In the detection process, when each of the participating observer nodes performs its local detection using this algorithm, they exchange the obtained information with each other to determine the final result of intrusion detection based on a voting method. …”
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PEMANFAATAN DIGITAL TERRAIN MODEL HASIL FOTOGRAMETRI UNMANED AERIAL VEHICLE UNTUK ESTIMASI KETINGGIAN GENANGAN AIR BANJIR HASIL DETEKSI CITRA SYNTHETIC APPERTURE RADAR by Hariady Indra Mantong

“…Utilization of The Unmanned Aerial Vehicle (UAV) or Drone has brought revolution in digital photogrammetry. The feature matching on surface reconstruction or Digital Surface Model (DSM) are quickly finished. …”
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An ultrasonic-AI hybrid approach for predicting void defects in concrete-filled steel tubes via enhanced XGBoost with Bayesian optimization by Shuai Wan, Shipan Li, Zheng Chen, Yunchao Tang

“…SHAP (SHapley Additive exPlanations) analysis revealed that sound velocity, sound time, acoustic amplitude, concrete strength, and fly ash content were the most influential features for model predictions. This hybrid approach offers high efficiency and accuracy for void defect detection in CFST, providing a novel solution that leverages the strengths of both traditional ultrasonic methods and artificial intelligence algorithms. …”
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Wi-CL: Low-Cost WiFi-Based Detection System for Nonmotorized Traffic Travel Mode Classification by Runnan Xu, Zilin Huang, Sikai Chen, Jinlong Li, Pan Wu, Yongjie Lin

“…In the feature extraction module, we extract relevant traffic features to construct a mode classification model. …”
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Advanced retinal disease detection from OCT images using a hybrid squeeze and excitation enhanced model. by Gülcan Gencer, Kerem Gencer

“…<h4>Results</h4>The combined features from EfficientNetB0 and Xception are processed via fully connected layers and categorized using the Softmax algorithm. …”
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An intelligent method for detection of small target fungal wheat spores based on an improved YOLOv5 with microscopic images by Zhizhou Ren, Kun Liang, Yingqi Zhang, Jinpeng Song, Xiaoxiao Wu, Chi Zhang, Xiuming Mei, Yi Zhang, Xin Liu

“…Therefore, a YOLO-ASF-MobileViT detection algorithm is proposed to detect pathogenic wheat spores with varying sizes, shapes, and textures. …”
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YED-Net: Yoga Exercise Dynamics Monitoring with YOLOv11-ECA-Enhanced Detection and DeepSORT Tracking by Youyu Zhou, Shu Dong, Hao Sheng, Wei Ke

“…Furthermore, a Parallel Spatial Attention (PSA) mechanism is incorporated to enhance multi-target feature discrimination. These enhancements enable the model to achieve a high detection accuracy of 98.6% mAP@0.5 while maintaining low computational complexity (2.35 M parameters, 3.11 GFLOPs). …”
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A Novel Approach to Pine Nut Classification: Combining Near-Infrared Spectroscopy and Image Shape Features with Soft Voting-Based Ensemble Learning by Yueyun Yu, Xin Huang, Danjv Lv, Benjamin K. Ng, Chan-Tong Lam

“…This study proposes a novel near-infrared (NIR) spectral feature selection algorithm, termed the improved binary equilibrium optimizer with selection probability (IBiEO-SP), which incorporates a dynamic probability adjustment mechanism to achieve efficient feature dimensionality reduction. …”
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Stroke Lesion Prediction by Bille-Viper-Segmentation with Tandem-MU-net Model by Beevi Fathima, N Santhi Dr, N Ramasamy Dr

“…Furthermore, the Focus View Algorithm is suggested, which incorporates features from infarcted regions to improve early detection of emerging lesions. …”
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A hybrid attack detection strategy for cybersecurity using moth elephant herding optimisation‐based stacked autoencoder by Abdullah Shawan Alotaibi

“…Finally, the attack detection is performed using the stacked autoencoder classifier, which is trained using the proposed MEHO algorithm. …”
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Development of an efficient mixed mode heart disease detection model using machine learning and deep learning techniques by Reddy C. Ravi Shankar, A Niranjan, Kumari P.V. Gopi, Rajendra Babui Y.

“…This model combines feature selection techniques with a blend of supervised learning methods, such as k-nearest neighbour algorithm, Support Vector Machine (SVM), and Gradient Boosting, to exploit their complementary strengths. …”
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Label semantics and image features aware remote sensing sample retrieval from multi-source datasets for AI-enabled remote sensing monitoring by XingTao Ren, XingTao Ren, Yan Ma, YiXin Zhou, YiXin Zhou

“…Introduction:With the advent of remote sensing (RS) big data, the evolving deep learning (DL) enabled analysis has shown remarkable potential in uncovering intricate features and detecting environmental changes from the vast influx of remote sensing imagery. …”
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A Dynamic Adaptive Ensemble Learning Framework for Noninvasive Mild Cognitive Impairment Detection: Development and Validation Study by Aoyu Li, Jingwen Li, Yishan Hu, Yan Geng, Yan Qiang, Juanjuan Zhao

“…To address the challenges (eg, the curse of dimensionality and increased model complexity) posed by high-dimensional features, we developed a dynamic adaptive feature selection optimization algorithm to identify the most impactful subset of features for classification performance. …”
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