Simple Yet Powerful: Machine Learning-Based IoT Intrusion System With Smart Preprocessing and Feature Generation Rivals Deep Learning by Kazim Kivanc Eren, Kerem Kucuk, Fatih Ozyurt, Omar H. Alhazmi

“…Here we propose a classical machine learning system, built around a Random Forest classifier paired with a novel feature extraction algorithm adapted from Explainable Boosted Linear Regression (EBLR). …”
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Design of Enhanced License Plate Information Recognition Algorithm Based on Environment Perception by Zilu Wang, Limin Zheng, Gang Li

“…The effectiveness of our proposed perceptual enhancement algorithm is further confirmed by the fact that when validating the China City Parking Dataset 2019 (CCPD 2019), it improves the detection rate by 24.53% compared to traditional image enhancement methods. …”
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A Learning Emotion Recognition Model Based on Feature Fusion of Photoplethysmography and Video Signal by Xiaoliang Zhu, Zili He, Chuanyong Wang, Zhicheng Dai, Liang Zhao

“…The ability to recognize learning emotions facilitates the timely detection of students’ difficulties during the learning process, supports teachers in modifying instructional strategies, and allows for personalized student assistance. …”
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Determined the Edges Using the ant Colony Algorithm and Apply them to Medical Images by Maha Rahman Hasso, Aseel Ali

“…The results show high accuracy in edge detection of different biomedical images with different neighbors, the proposed algorithm is implemented in C Sharp 2008 language which provides high-efficiency software visible language and speed. …”
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Optimal azimuth positioning algorithm for base station antenna based on Gaussian distribution by Songbai LIANG, Feng CHEN, Haiping SONG, Wensheng LI

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Algorithm for Cloud Particle Phase Identification Based on Bayesian Random Forest Method by Fu Tao, Yang Zhipeng, Tao Fa, Hu Shuzhen, Lu Yuxiang, Fu Changqing

“…Millimeter-wave cloud radar is widely used for identifying phase states of cloud particles due to its ability to detect internal cloud structures and microphysical characteristics. …”
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Deep multi-view feature fusion with data augmentation for improved diabetic retinopathy classification by Boualleg Yaakoub, Daouadi Kheir Eddine, Guehairia Oussama, Djeddi Chawki, Cheddad Abbas, Siddiqi Imran, Bouderah Brahim

“…Diabetic retinopathy (DR) is a leading cause of blindness worldwide, necessitating early detection to prevent severe visual impairment. Despite numerous proposed classification techniques, challenges persist due to the high parameter count of deep learning algorithms, imbalanced datasets, and limited performance. …”
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Mutual information-based radiomic feature selection with SHAP explainability for breast cancer diagnosis by Oladosu Oyebisi Oladimeji, Hamail Ayaz, Ian McLoughlin, Saritha Unnikrishnan

“…Selected features were assessed using machine learning algorithms, with Random Forest achieving 92% accuracy in lesion classification. …”
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Research on algorithm for improving imaging accuracy of CFRP low speed impact damage by WU Xiangnan, CHENG Xiaojin, LI Qixin, SHANG Jianhua

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A new aggregation and riming discrimination algorithm based on polarimetric weather radars by A. Blanke, M. Gergely, S. Trömel, S. Trömel

“…Considering balanced accuracy, the algorithm is able to correctly predict 74 % of the observed riming features; thus, the feasibility of reliable riming detection with national radar networks has been successfully demonstrated.…”
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Music genre classification with parallel convolutional neural networks and capuchin search algorithm by Yuxin Zhang, Teng Li

“…To do this, each model’s hyperparameters are generated using the capuchin search algorithm (CapSA). Preprocessing the original signals, feature description utilizing DWT, MFCC, and STFT signal matrices, CNN model optimization to extract signal features, and music genre identification based on combined features make up the four main components of the technique. …”
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Development of a multi-level feature fusion model for basketball player trajectory tracking by Tao Wang

“…The study first optimized the current object detection algorithm in basketball, utilized a hybrid attention mechanism to extract object features, and improved the non-maximum suppression strategy. …”
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An Empirical Evaluation of Supervised Learning Methods for Network Malware Identification Based on Feature Selection by C. Manzano, C. Meneses, P. Leger, H. Fukuda

“…The selected features were used to train the algorithms using binary and multiclass classification. …”
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Deep Belief Network-Based Multifeature Fusion Music Classification Algorithm and Simulation by Tianzhuo Gong

“…In this paper, the multifeature fusion music classification algorithm and its simulation results are studied by deep confidence networks, the multifeature fusion music database is established and preprocessed, and then features are extracted. …”
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Structural Parameter Identification Using Multi-Objective Modified Directional Bat Algorithm by LIU Li-jun, LIN Ying-hai, SU Yong-hui, LEI Ying

“…By integrating static and dynamic features， the algorithm ensured accurate model updating even under noisy conditions. …”
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Detection of Sentence Boundaries in Polish Based on Acoustic Cues by Magdalena IGRAS, Bartosz ZIÓŁKO

“…In the first stage, the algorithm detects pauses and divides a speech signal into segments. …”
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Good Apples / Bad Apples: Detection and classification of damage by Angel Star, Conor Pommer, Marius Silaghi

“…Parameters of the segment anything algorithm are evaluated for this problem domain and appropriateness of algorithm features is discussed for the real problem, obtaining directions for algorithmic improvement.…”
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Diabetes Mellitus Disease Prediction Using Machine Learning Classifiers with Oversampling and Feature Augmentation by B. Shamreen Ahamed, Meenakshi S. Arya, Auxilia Osvin V. Nancy

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Leveraging Graph Neural Networks for IoT Attack Detection by Mevlüt Uysal, Erdal Özdoğan, Onur Ceran

“…To address this challenge, researchers propose a novel hybrid approach combining Graph Neural Networks and XGBoost algorithm for robust intrusion detection in IoT ecosystems. …”
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Singular-Value-Based Cluster Number Detection Method by Yating Li, Jianghui Cai, Haifeng Yang, Jie Wang, Chenhui Shi, Bo Liang, Xujun Zhao, Yaling Xun

“…Based on this, a singular-value-based cluster number detection method is proposed. First, the transferred K-nearest neighbors (TKNN) density formula is proposed to address the limitation of the DPC algorithm in failing to identify centroids in sparse clusters of unbalanced datasets. …”
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