Analysis of Internet Marketing Forecast Model Based on Parallel K-Means Algorithm by Xiaolei Chen, Sikun Ge

“…Secondly, the weights in the K-means algorithm are mostly only applicable to target detection tasks. …”
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Robust outdoor trajectory mapping using CNN features and loop closure optimization by Kamran Kazi, Arbab Nighat Kalhoro, Farida Memon, Tarique Rafique Memon, Azam Rafique Memon

“…This paper introduces a robust framework for outdoor trajectory mapping that synergizes deep learning-based feature extraction with loop closure detection and optimization to address these limitations. …”
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Prediabetes detection in unconstrained conditions using wearable sensors by Dimitra Tatli, Vasileios Papapanagiotou, Aris Liakos, Apostolos Tsapas, Anastasios Delopoulos

“…Future directions involve expanding the study to a larger, more diverse population and exploring the integration of CGM and smartwatch functionalities into a unified device. Automated eating detecting algorithms can also be used.…”
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Optimizing Gammatone Cepstral Coefficients for Gear Fault Detection by Zrar Kh Abdul, Abdulbasit K. Al-Talabani, Wisam Hazim Gwad, Entisar Alkayal, Halgurd S. Maghdid, Safar Maghdid Asaad

“…Cepstral features, such as Gammatone Cepstral Coefficients (GTCC), have recently been applied in fault detection and diagnosis. …”
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Lightweight UAV Detection Method Based on IASL-YOLO by Huaiyu Yang, Bo Liang, Song Feng, Ji Jiang, Ao Fang, Chunyun Li

“…To address this, we propose the IASL-YOLO algorithm, which optimizes the YOLOv8s model to enhance detection accuracy and lightweight efficiency. …”
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Application of deep learning in malware detection: a review by Yafei Song, Dandan Zhang, Jian Wang, Yanan Wang, Yang Wang, Peng Ding

“…The results of these tests will help researchers make decisions and early detection, effectively defense against malware. This work compares and reports a classification of malware detection work based on deep learning algorithms. …”
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A New Approach for Clustered MCs Classification with Sparse Features Learning and TWSVM by Xin-Sheng Zhang

“…Then we designed the sparse feature learning based MCs classification algorithm using twin support vector machines (TWSVMs). …”
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Multi-Level Foreground Prompt for Incremental Object Detection by Jianwen Mo, Ronghua Zou, Hua Yuan

“…To address this, this paper proposes a multi-level foreground prompt incremental learning algorithm for single-stage detectors like YOLO, including foreground prompts at the image level, feature map level, and knowledge level. …”
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Adaptive Anomaly Detection Framework Model Objects in Cyberspace by Hasan Alkahtani, Theyazn H. H. Aldhyani, Mohammed Al-Yaari

“…The information gain method was applied to select the relevant features from the network dataset. These network features were significant to improve the classification algorithm. …”
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THE METHOD OF ADAPTIVE STATISTICAL CODING TAKING INTO ACCOUNT THE STRUCTURAL FEATURES OF VIDEO IMAGES by Volodymyr Barannik, Dmytro Havrylov, Serhii Pantas, Yurii Tsimura, Tatayna Belikova, Rimma Viedienieva, Vasyl Kryshtal

“…The technology is based on the detection of key information at several stages of video data processing. …”
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A Lightweight Direction-Aware Network for Vehicle Detection by Luxia Yang, Yilin Hou, Hongrui Zhang, Chuanghui Zhang

“…Vehicle detection algorithms, which are essential to intelligent traffic management and control systems, have attracted growing attention. …”
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Research on pedestrian detection technology for mining unmanned vehicles by ZHOU Libing, YU Zhengqian, WEI Jianjian, JIANG Xueli, YE Baisong, ZHAO Yexin, YANG Siliang

“…The working environment of mining unmanned vehicles features complex lighting conditions, leading to frequent occurrences of missed detections in pedestrian detection, which undermines the reliability and safety of these vehicles. …”
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Motor imagery EEG signal classification using novel deep learning algorithm by Sathish Mathiyazhagan, M. S. Geetha Devasena

“…For final feature classification, an adaptive deep belief network (ADBN) is proposed. …”
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Image Based Hair Segmentation Algorithm for the Application of Automatic Facial Caricature Synthesis by Yehu Shen, Zhenyun Peng, Yaohui Zhang

“…In this paper, an automatic hair detection algorithm for the application of automatic synthesis of facial caricature based on a single image is proposed. …”
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Improved printed circuit board defect detection scheme by Lufeng Bai, Wen Hao Xu

“…First, we incorporate the Efficient Channel Attention Network (ECANet) attention mechanism into the backbone network of YOLOv8, which improves the performance of small-target detection by adaptively enhancing the expressiveness of key features, so that the network possesses higher sensitivity and focus on tiny details in PCB images. …”
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Potato late blight leaf detection in complex environments by Jingtao Li, Jiawei Wu, Rui Liu, Guofeng Shu, Xia Liu, Kun Zhu, Changyi Wang, Tong Zhu

“…Abstract Potato late blight is a common disease affecting crops worldwide. To help detect this disease in complex environments, an improved YOLOv5 algorithm is proposed. …”
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Parameter Prediction for Metaheuristic Algorithms Solving Routing Problem Instances Using Machine Learning by Tomás Barros-Everett, Elizabeth Montero, Nicolás Rojas-Morales

“…Furthermore, we present an explainability analysis to detect which features are more relevant for the prediction of suitable parameter values.…”
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Detection of child depression using machine learning methods. by Umme Marzia Haque, Enamul Kabir, Rasheda Khanam

“…The variables of yes/no value of low correlation with the target variable (depression status) have been eliminated. The Boruta algorithm has been utilized in association with a Random Forest (RF) classifier to extract the most important features for depression detection among the high correlated variables with target variable. …”
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Blockchain address risk behavior identification algorithm based on neural differential equations by LIANG Fei, WANG Ruili

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Android malware detection based on improved random forest by Hong-yu YANG, Jin XU

“…Aiming at the defect of vote principle in random forest algorithm which is incapable of distinguishing the differences between strong classifier and weak classifier,a weighted voting improved method was proposed,and an improved random forest classification (IRFCM) was proposed to detect Android malware on the basis of this method.The IRFCM chose Permission information and Intent information as attribute features from AndroidManifest.xml files and optimized them,then applied the model to classify the final feature vectors.The experimental results in Weka environment show that IRFCM has better classification accuracy and classification efficiency.…”
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