Enhancing Performance of Credit Card Model by Utilizing LSTM Networks and XGBoost Algorithms by Kianeh Kandi, Antonio García-Dopico

“…This paper conducts an extensive literature review, comparing various machine learning methods, and proposes an innovative framework that compares LSTM with XGBoost to improve fraud detection accuracy. LSTM, a recurrent neural network renowned for its ability to capture temporal dependencies within sequences of transactions, is compared with XGBoost, a formidable ensemble learning algorithm that enhances feature-based classification. …”
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Robust Optical and SAR Image Registration Using Weighted Feature Fusion by Ao Luo, Anxi Yu, Yongsheng Zhang, Wenhao Tong, Huatao Yu

“…Subsequently, a dual-modal feature description is developed, constructing both gradient-based descriptors and local standard deviation (LSD) descriptors for the neighborhoods surrounding the detected feature points. …”
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Multi-Domain Feature Analysis and Application Research of GPR Aliased Signals by Chuan Li, Yawei Wang, Qibing Ma, Xiaorong Wan

“…The Hilbert transform effectively extracts instantaneous phase inversions, and STFT provides an intuitive time–frequency distribution, facilitating the extraction and analysis of signal features. Additionally, this study includes the design and implementation of an aliasing peak point extraction algorithm with a relative error of less than 10% in practical applications.…”
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Temporal Community Detection and Analysis with Network Embeddings by Limengzi Yuan, Xuanming Zhang, Yuxian Ke, Zhexuan Lu, Xiaoming Li, Changzheng Liu

“…We derive the updating rules and provide rigorous theoretical proofs for the algorithm’s validity and convergence. Extensive experiments on synthetic and real-world social networks, including email and phone call networks, demonstrate the superior performance of our model in community detection and tracking temporal network evolution. …”
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A Feature-Selective Independent Component Analysis Method for Functional MRI by Yi-Ou Li, Tülay Adali, Vince D. Calhoun

“…We also compare the task-related sources estimated from true fMRI data by a feature-selective ICA algorithm versus an ICA algorithm and show evidence that the feature-selective scheme helps improve the estimation of the sources in both spatial activation patterns and the time courses.…”
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Prognostic, oncogenic roles, and pharmacogenomic features of AMD1 in hepatocellular carcinoma by Youliang Zhou, Yi Zhou, Jiabin Hu, Yao Xiao, Yan Zhou, Liping Yu

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Exploring the Key Features of Repeating Fast Radio Bursts with Machine Learning by Wan-Peng Sun, Ji-Guo Zhang, Yichao Li, Wan-Ting Hou, Fu-Wen Zhang, Jing-Fei Zhang, Xin Zhang

“…We find that the spectral morphology parameters, specifically spectral running ( r ), represent the key features for identifying repeaters from the nonrepeaters. …”
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PTCDet: advanced UAV imagery target detection by Jia Su, Yichang Qin, Ze Jia, Yanli Hou

“…Based on the multiscale feature fusion structure, an enhanced scale fusion detection (ESFD) module is used to improve small object detection by generating larger scale feature maps. …”
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Rapid video copy detection on compressed domain by ZHANG Yong-dong, ZHANG Dong-ming, GUO Jun-bo, TANG Sheng

“…To reduce the detection time efficiency under large scale data environment, a rapid algorithm was proposed on compressed domain using a two-level hierarchical detection scheme.The ordinal measures of DCT coefficients were adopted as visual features for similarity-matching in order to reduce the computational load in video decoding.Inverted indexing structure was used to accelerate the first level detection process.The experiment results show, compared with the previous algorithm, the algorithm can improve the detection speed obviously with the similar detection precision.…”
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Systematic Review: Malware Detection and Classification in Cybersecurity by Sebastian Berrios, Dante Leiva, Bastian Olivares, Héctor Allende-Cid, Pamela Hermosilla

“…These studies cover a variety of detection techniques, including machine learning, deep learning and hybrid models, with a focus on feature extraction, malware behavior analysis and the application of advanced algorithms to improve detection accuracy. …”
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Ensemble Method for Anomaly Detection On the Internet of Things by Kurniabudi Kurniabudi, Eko Arip Winanto, Lola Yorita Astri, Sharipuddin Sharipuddin

“…High data dimensions and unbalanced data are one of the challenges in detecting attacks. To overcome the large data dimensions, Chi-square was chosen as a feature selection technique. …”
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MRP-YOLO: An Improved YOLOv8 Algorithm for Steel Surface Defects by Shuxian Zhu, Yajie Zhou

“…The existing detection algorithms are unable to achieve a suitable balance between detection accuracy and inference speed. …”
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Detecting anomalies in graph networks on digital markets. by Agata Skorupka

“…It compares different graph algorithms to extract feature sets for anomaly detection models. …”
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Unsupervised detection of semantic correlations in big data by Santiago Acevedo, Alex Rodriguez, Alessandro Laio

“…Abstract In real-world data, information is stored in extremely large feature vectors. These variables are typically correlated due to complex interactions involving many features simultaneously. …”
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Complex Indoor Human Detection with You Only Look Once: An Improved Network Designed for Human Detection in Complex Indoor Scenes by Yufeng Xu, Yan Fu

“…However, the complex indoor environment and background pose challenges to the detection task. The YOLOv8 algorithm is a cutting-edge technology in the field of object detection, but it is still affected by indoor low-light environments and large changes in human scale. …”
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Parkinson’s Disease Detection Based on Transfer Learning by Mohammad Talal Ghazal

“…In this research, a method was developed to detect Parkinson's disease using machine learning,  learning transfer techniques were relied upon to extract features from handwriting images that we obtained from the NewHandPD database, and then these images were classified into two categories (Parkinson's disease and non-Parkinson's disease) by KNN classification algorithm, for being accurate and fast in calculations, the results  of the  training of the INCEPTION-V4 model showed  a detection accuracy of up to 93%, as well as an area under the curve of 0.89 with a loss of only 0.2 , where this model can be relied on to diagnose and detect Parkinson's disease with high accuracy. …”
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Defect identification method for overhead transmission lines based on SIFT algorithm by Qiang Liu, Xi Zheng, Qiuhan Zhang, Hongjie Sun, Jun Yan

“…The system uses a Scale-Invariant Feature Transform (SIFT) algorithm to precisely identify defect markers by initially extracting the texture features of standard wires and subsequently identifying variations that indicate faults. …”
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An advanced method for surface damage detection of concrete structures in low-light environments based on image enhancement and object detection networks by Tianyong Jiang, Lin Liu, Chunjun Hu, Lingyun Li, Jianhua Zheng

“…Abstract Surface damage detection in concrete structures is critical for maintaining structural integrity, yet current object detection algorithms often struggle in low-light environments. …”
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Dynamic primary user spectrum sensing algorithm based on deep learning by Li Xinyu, Zhao Zhijin

“…The proposed algorithm can maintain a high detection probability for the primary users who randomly arrive or leave following different distributions.…”
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Attention-Based Lightweight YOLOv8 Underwater Target Recognition Algorithm by Shun Cheng, Zhiqian Wang, Shaojin Liu, Yan Han, Pengtao Sun, Jianrong Li

“…The detection frame rate reaches 189 frames per second on the ROUD dataset, thus meeting the high accuracy requirements for underwater object detection algorithms and facilitating lightweight and fast edge deployment.…”
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