Improved spectral clustering algorithm and its application in MCI detection by Jie XIANG, Dong-qin ZHAO

“…In order to detect mild cognitive impairment (MCI) using functional magnetic resonance imaging (fMRI)，a method based on fMRI clustering was proposed fMRI data were clustered to obtain the blood oxygen level dependence( BOLD) change model of MCI patients，then abnormal patterns were used to detect disease.The traditional spectral clustering algorithm needs to calculate all of the eigenvalue and eigenvector，so time and space complexity is higher.An improved spectral clustering method was proposed which modified the similar matrix construction method and the setting method of σ and k，and then this method was applied to clustering and detection of MCI patients.To verify the performance of the proposed method，the comparison of the clustering result，classification accuracy using traditional algorithm and Nyström is also done.The comparative experimental results show that the proposed method can get BOLD pattern more accurately，the accuracy of MCI detection is higher than the other two algorithms，and the time and space complexity are less than the traditional algorithm.…”
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Improved spectral clustering algorithm and its application in MCI detection by Jie XIANG, Dong-qin ZHAO

“…In order to detect mild cognitive impairment (MCI) using functional magnetic resonance imaging (fMRI)，a method based on fMRI clustering was proposed fMRI data were clustered to obtain the blood oxygen level dependence( BOLD) change model of MCI patients，then abnormal patterns were used to detect disease.The traditional spectral clustering algorithm needs to calculate all of the eigenvalue and eigenvector，so time and space complexity is higher.An improved spectral clustering method was proposed which modified the similar matrix construction method and the setting method of σ and k，and then this method was applied to clustering and detection of MCI patients.To verify the performance of the proposed method，the comparison of the clustering result，classification accuracy using traditional algorithm and Nyström is also done.The comparative experimental results show that the proposed method can get BOLD pattern more accurately，the accuracy of MCI detection is higher than the other two algorithms，and the time and space complexity are less than the traditional algorithm.…”
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Copy-Move Forgery Verification in Images Using Local Feature Extractors and Optimized Classifiers by S. B. G. Tilak Babu, Ch Srinivasa Rao

“…The paper aims to present copy-move forgery detection algorithms with the help of advanced feature descriptors, such as local ternary pattern, local phase quantization, local Gabor binary pattern histogram sequence, Weber local descriptor, and local monotonic pattern, and classifiers such as optimized support vector machine and optimized NBC. …”
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Novel Multimodal Fusion Algorithm for Non-Intrusive Anxiety Detection by Mahir Shadid, Mushfiqus Salehin Afnan, Rashed Mustafa, M. Jamshed Alam Patwary

“…Early detection of anxiety disorders in a non-intrusive manner is crucial, as these conditions can profoundly impact an individual’s health and daily functioning. …”
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Early detection of risky spatio-temporal congestion in urban traffic by Shaobo Sui, Dan Xu, Mingyang Bai, Xiaoke Zhang, Zhaojun Mao, Daqing Li

“…However, large-scale congestion in city exhibits an unique spatio-temporal growth pattern. In this article, we develop a detection method for risky congestion based on its spatio-temporal evolution feature, which can detect risky spatio-temporal congestion clusters (SCCs) when they are small. …”
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Detecting clusters in spatially repetitive point event data sets by Allan Brimicombe

“…The analysis of point event patterns has a long tradition. The patterns of particular interest are patterns of clustering or ‘hot spots’ and such cluster detection lies at the heart of spatial data mining. …”
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An influence of a modulating signal starting phase on the modulation detection by A. P. SĘK, E. B. SKRODZKA

“…Data shown in this paper are consistent with predictions of models based on excitation pattern changes and with a concept according to which AM/FM detection at low modulation rates depends mainly on maximum and minimum values of signal amplitude/frequency. …”
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An optimal federated learning-based intrusion detection for IoT environment by A. Karunamurthy, K. Vijayan, Pravin R. Kshirsagar, Kuan Tak Tan

“…Analyzing and detecting intrusions by analyzing diverse attack patterns is complex for machine learning algorithms. …”
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Detecting and Analysing Possible Outliers in Global Stock Market Returns by Ali A. Shehadeh, Sadam M. Alwadi, Mohammad I. Almaharmeh

“…We find a sequential pattern in outlier occurrence within individual return series, and a concurrent pattern across stock markets. …”
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Detection of Gene Localization in a Large Concomitant Strabismus Family by Kadriye Erkan Turan, Emin Cumhur Şener, Ali Şefik Sanaç

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MLAD: A Multi-Task Learning Framework for Anomaly Detection by Kunqi Li, Zhiqin Tang, Shuming Liang, Zhidong Li, Bin Liang

“…While existing methods often utilize sequence modeling or graph neural networks to capture global sensor relationships, they typically treat all sensors uniformly—potentially overlooking the benefit of grouping sensors with similar temporal patterns. To this end, we propose a novel framework called Multi-task Learning Anomaly Detection (MLAD), which leverages clustering techniques to group sensors based on their temporal characteristics, and employs a multi-task learning paradigm to jointly capture both shared patterns across all sensors and specialized patterns within each cluster. …”
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An Artificial Intelligence QRS Detection Algorithm for Wearable Electrocardiogram Devices by Zihao Li, Wenliang Zhu, Yiheng Xu, Yunbo Guo, Junbo Li, Peng Song, Ying Liang, Binquan You, Lirong Wang

“…It achieves an excellent F1 score of 99.83 on the MITBIHA database and 99.77 on the INCART database, specifically in the inter-patient pattern. In the cross-database pattern, our approach maintains a strong performance with an F1 score of 99.22 on the INCART database and an F1 score of 99.09 on the MITBIHA database. …”
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The application and research of machine learning in malicious encrypted traffic detection by Tian Rui, Zhang Yaqin, Dong Wei, Li Zhicheng, Feng Zhi

“…The findings indicate that machine learning significantly improves the accuracy of malicious behavior detection, particularly in complex feature extraction and the identification of new attack patterns.…”
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Cooperator and Defector-Based Dynamic Community Detection in Social Networks by Hui Jiang, Chenlin Zhao, Zhexi Guo, Tangyu Wang

“…Dynamic community detection in social networks requires advanced methods to capture the intricate patterns of user interactions. …”
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No genetic structure detected in multiple Brazilian marine fish species by Julia Tovar Verba, Adam Stow, Priscila F. M. Lopes, Maria Grazia Pennino, Antonio Sole-Cava, Sergio Maia Queiroz Lima

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Financial phishing detection method based on sensitive characteristics of webpage by Xiang-dong HU, Ke LIU, Feng ZHANG, Jia-fu LIN, Jun FU, Zhi-hui GUO

“…The method matches number of sensitive text using multiple pattern matching algorithm AC_SC (AC suitable for Chinese). …”
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Enhancing Federated Intrusion Detection with Class-Specific Dynamic Sampling by Sungkwan Youm, Taeyoon Kim

“…Notably, DS-FedIDS excels in detecting minority attack classes and adapting to client-specific normal traffic patterns, making it ideal for real-world intrusion detection scenarios with inherently imbalanced and heterogeneous data distributions.…”
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Enhancing Security of Databases through Anomaly Detection in Structured Workloads by Charanjeet Dadiyala, Faijan Qureshi, Kritika Anil Bhattad, Sourabh Thakur, Nida Tabassum Sharif Sheikh, Kushagra Anil Kumar Singh

“…Anomaly detection methods can identify patterns that are unusual, an indication of malicious activity, or a data security breach. …”
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Research on intrusion detection model based on improved MLP algorithm by Qihao Zhao, Fuwei Wang, Weimin Wang, Tianxin Zhang, Haodong Wu, Weijun Ning

“…The multi-layer perceptron (MLP) offers distinct advantages for intrusion detection, as attack patterns often follow complex, nonlinear relationships. …”
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Enhancing Security of Databases through Anomaly Detection in Structured Workloads by Charanjeet Dadiyala, Faijan Qureshi, Kritika Anil Bhattad, Sourabh Thakur, Nida Tabassum Sharif Sheikh, Kushagra Anil Kumar Singh

“…Anomaly detection methods can identify patterns that are unusual, an indication of malicious activity, or a data security breach. …”
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