TF-LIME : Interpretation Method for Time-Series Models Based on Time–Frequency Features by Jiazhan Wang, Ruifeng Zhang, Qiang Li

“…The TFHS algorithm achieves precise homogeneous segmentation of the time–frequency matrix through peak detection and clustering analysis, incorporating the distribution characteristics of signals in both frequency and time dimensions. …”
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Frequency hopping modulation recognition based on time-frequency energy spectrum texture feature by Hongguang LI, Ying GUO, Ping SUI, Zisen QI

“…For frequency hopping modulation identification,a novel method based on time-frequency energy spectrum texture feature was proposed.Firstly,the time-frequency diagram of the frequency hopping signal was obtained by smoothed pseudo Wigner-Ville distribution,and the background noise of the time-frequency diagram was removed by two-dimensional Wiener filtering to improve the resolution of the time-frequency diagram under low SNR conditions.Then,the connected-domain detection algorithm was used to extract the time-frequency energy spectrum of each hop signal and convert it into a time-frequency gray-scale image.The histogram statistical features and the gray-scale co-occurrence matrix feature were combined to form a 22-dimensional eigenvector.Finally,the feature set was trained,classified and identified by optimized support vector machine classifier.Simulation experiments show that the multi-dimensional feature vector extracted by the algorithm has strong representation ability and avoids the misjudgment caused by the similarity of single features.The average recognition accuracy of the six modulation methods of frequency hopping signals BPSK,QPSK,SDPSK,QASK,64QAM and GMSK is 91.4% under the condition of -4 dB SNR.…”
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Detection of water surface targets based on improved Deformable DETR by Pengjiu WANG, Junbin Gong, Wei LUO, Xiao HUANG, Junjie GUO

“…However, conventional detection methods encounter several challenges, and existing deep-learning-based algorithms have limitations in this field, including limited datasets and insufficient detection speed even after improvement. …”
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Sysmon event logs for machine learning-based malware detection by Riki Mi’roj Achmad, Dyah Putri Nariswari, Baskoro Adi Pratomo, Hudan Studiawan

“…In this research, we employed various machine learning algorithms, both classification (supervised learning) and outlier detection (unsupervised learning) approaches, such as Naive Bayes, Decision Tree, Random Forest, Support Vector Machine (SVM) for supervised learning, and Isolation Forest, Local Outlier Factor (LOF), and One-Class SVM for unsupervised learning. …”
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Feature-Based Classification of Amino Acid Substitutions outside Conserved Functional Protein Domains by Branislava Gemovic, Vladimir Perovic, Sanja Glisic, Nevena Veljkovic

“…There are more than 500 amino acid substitutions in each human genome, and bioinformatics tools irreplaceably contribute to determination of their functional effects. We have developed feature-based algorithm for the detection of mutations outside conserved functional domains (CFDs) and compared its classification efficacy with the most commonly used phylogeny-based tools, PolyPhen-2 and SIFT. …”
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Advanced heart disease classification based on multi-channel heart sound coupling features. by Yu Fang, Dongbo Liu, Zijian Guo, Hongxia Leng, Xing Liu, Xiaochen Wu

“…The ReliefF algorithm is then used to evaluate feature importance, retaining the top 20% of features to construct a coupling feature set. …”
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Deepfake detection method based on patch-wise lighting inconsistency by Wenxuan WU, Wenbo ZHOU, Weiming ZHANG, Nenghai YU

“…The rapid development and widespread dissemination of deepfake techniques has caused increased concern.The malicious application of deepfake techniques also poses a potential threat to the society.Therefore, how to detect deepfake content has become a popular research topic.Most of the previous deepfake detection algorithms focused on capturing subtle forgery traces at pixel level and have achieved some results.However, most of the deepfake algorithms ignore the lighting information before and after generation, resulting in some lighting inconsistency between the original face and the forged face, which provided the possibility of using lighting inconsistency to detect deepfake.A corresponding algorithm was designed from two perspectives: introducing lighting inconsistency information and designing a network structure module for a specific task.For the introduction of lighting task, a new network structure was derived by designing the corresponding channel fusion method to provide more lighting inconsistency information to the network feature extraction layer.In order to ensure the portability of the network structure, the process of feature channel fusion was placed before the network extraction information, so that the proposed method can be fully planted to common deepfake detection networks.For the design of the network structure, a deepfake detection method was proposed for lighting inconsistency based on patch-similarity from two perspectives: network structure and loss function design.For the network structure, based on the characteristic of inconsistency between the forged image tampering region and the background region, the extracted features were chunked in the network feature layer and the feature layer similarity matrix was obtained by comparing the patch-wise cosine similarity to make the network focus more on the lighting inconsistency.On this basis, based on the feature layer similarity matching scheme, an independent ground truth and loss function was designed for this task in a targeted manner by comparing the input image with the untampered image of this image for patch-wise authenticity.It is demonstrated experimentally that the accuracy of the proposed method is significantly improved for deepfake detection compared with the baseline method.…”
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A novel transformer-based approach for cardiovascular disease detection by Nimra Noor, Muhammad Bilal, Saadullah Farooq Abbasi, Omid Pournik, Theodoros N. Arvanitis

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Non-destructive Identification of Moldy Walnuts by Fusing X-Ray and Visual Image Features by NING Xinyue, ZHANG Hui, JI Shuai, LAI Lisi

“…Subsequently, using competitive adaptive reweighted sampling (CARS) and successive projection algorithm (SPA), the extracted features were optimized to construct a walnut feature set sensitive to different degrees of moldiness. …”
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The immunomorphological features of cervical intraepithelial neoplasia associated with HPV infection depending on the type of infertility by Е. О. Kindrativ

“…It was established that for infertility the cervical intraepithelial neoplasia (CIN) associated with HPV infection (PVI) is characterized by different individual potential for the development of cervical carcinoma. For early detection, verification of the severity degree and prognosis of CIN, especially during the tubular, hormonal and combined forms of infertility it is advisable to include the immunomorphological study of tissue of the cervix using monoclonal antibodies (Ki-67, r63, pl6ink4a, ER, PR, VEGF) in the algorithm of complex inspection.…”
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An Effective Detection Approach for Phishing URL Using ResMLP by S. Remya, Manu J. Pillai, Kajal K. Nair, Somula Rama Subbareddy, Yong Yun Cho

“…Traditional blacklists struggle to identify dynamic URLs, necessitating advanced detection mechanisms. In this study, we propose an effective approach utilizing residual pipelining for phishing URL detection. …”
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Early Detection of Parkinson's Disease: Ensemble Learning for Improved Diagnosis by Raut Komal, Balpande Vijaya

“…This paper proposed several machine learning algorithms such as Decision Tree, Random Forest, Logistic Regression and Support Vector Machine and design an ensemble of these models to detect and classify Parkinson's disease. …”
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Acoustic Features for Identifying Suicide Risk in Crisis Hotline Callers: Machine Learning Approach by Zhengyuan Su, Huadong Jiang, Ying Yang, Xiangqing Hou, Yanli Su, Li Yang

“…Given the complicated features of sound, adopting artificial intelligence models to analyze callers’ acoustic features is promising. …”
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TBM Enclosure Rock Grade Prediction Method Based on Multi-Source Feature Fusion by Yong Huang, Xiewen Hu, Shilong Pang, Wei Fu, Shuaipeng Chang, Bin Gao, Weihua Hua

“…Aiming to mitigate engineering risks such as tunnel face collapse and equipment jamming caused by poor geological conditions during the construction of tunnel boring machines (TBMs), this study proposes a TBM surrounding rock grade prediction method based on multi-source feature fusion. Firstly, a multi-source dataset is established by systematically integrating TBM tunnelling parameters, horizontal acoustic profile (HSP) detection data and three-dimensional geological spatial information. …”
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Detection of Fake Instagram Accounts via Machine Learning Techniques by Stefanos Chelas, George Routis, Ioanna Roussaki

“…After making the necessary feature additions to and removals from these data, they are fed into machine learning algorithms with the aim of detecting fake Instagram accounts. …”
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Precision Detection of Infrared Small Target in Ground-to-Air Scene by Xiaona Dong, Huilin Jiang, Yansong Song, Keyan Dong

“…In recent years, most target detection methods usually use the statistical features of a rectangular window to represent the contrast between the target and the background. …”
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Detection of axonal synapses in 3D two-photon images. by Cher Bass, Pyry Helkkula, Vincenzo De Paola, Claudia Clopath, Anil Anthony Bharath

“…To find the most appropriate techniques for this task, we compared several well-known algorithms for interest point detection and feature descriptor generation. …”
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Weighted Hybrid Random Forest Model for Significant Feature prediction in Alzheimer’s Disease Stages by M. Rohini, D. Surendran

“…As a consequence of this challenge discussed, whether all the mild cognitively impaired people change to AD cohorts or remain in normal cognition and identification of the structural and functional features remains underexplored. Thus, the proposed Weighted Hybrid Random Forest algorithm (WHBM) utilized the 63 features that comprise the whole brain volume. …”
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Study on the Lightweighting Strategy of Target Detection Model with Deep Learning by Junli Hu

“…Aiming at the high miss detection and false detection rate of traditional SSD (single shot multibox detector) target detection algorithm in target detection, this paper proposes a lightweight detection algorithm for deep learning target detection model in order to improve the detection accuracy. …”
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Combining Global Features and Local Interoperability Optimization Method for Extracting and Connecting Fine Rivers by Jian Xu, Xianjun Gao, Zaiai Wang, Guozhong Li, Hualong Luan, Xuejun Cheng, Shiming Yao, Lihua Wang, Sunan Shi, Xiao Xiao, Xudong Xie

“…Subsequently, the improved GA-OTSU thresholding algorithm is applied for feature segmentation, yielding the initial results. …”
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