Wood Species Identification Based on Gray Level Co-Occurrence Matrix (GLCM) Features on Macroscopic Images by Muhammad Ghiffaari Ilham Ramadhan, Bambang Sugiarto, Okta Dwi Mulya, Defti Septian Chairulsyah, Adyanto Syahrizal, Gunawan Gunawan, Riffa Haviani Laluma, Rini Nuraini Sukmana, Teguh Wiharko

“…This research aims to propose a method for wood species identification based on Gray Level Co-occurrence Matrix (GLCM) features to extract important information about wood characteristics from macroscopic wood images. …”
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2D–3D Geological features collaborative identification of surrounding rock structural planes in hydraulic adit based on OC-AINet by Yang Kaiji, Zhang Rongchun, Chen Yixiang, Yi Xuefeng, Wang Dongchuan, Zhao Wenchao, Wang Guogang, Liu Lanfa, Han Jiaqi

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Classification and identification of pest, diseases and nutrient deficiency in paddy using layer based EMD phase features with decision tree by A. Pushpa Athisaya Sakila Rani, N. Suresh Singh

“…Multiple layers are then constructed on the leaf image through which features are extracted. The Gray Level Co-occurrence Matrix (GLCM) algorithm and Principal Component Analysis (PCA) are used to extract the global texture features of the leaf image. …”
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Crowd Speaker Identification Methodologies, Datasets And Features: Review by Husam Alasadi, Ghadeer Qasim Ali

“…Our work examines crowd speech identification from four perspectives, including the most commonly used datasets, the most effective features for crowed speaker identification, and the best methodologies employed, and the highest results gained. …”
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Identification of NAPL Contamination Occurrence States in Low-Permeability Sites Using UNet Segmentation and Electrical Resistivity Tomography by Mengwen Gao, Yu Xiao, Xiaolei Zhang

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Feature Extraction and Identification of Rheumatoid Nodules Using Advanced Image Processing Techniques by Azmath Mubeen, Uma N. Dulhare

“…The key steps include image preprocessing with anisotropic diffusion and Retinex enhancement, superpixel segmentation using SLIC, and graph-based feature extraction. Texture analysis was performed using Gray-Level Co-occurrence Matrix (GLCM) metrics, while shape analysis was conducted with Fourier descriptors. …”
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Methodology for Identification of Occupational Hazards Using Their Characteristic Features in Hard Coal Mining by Zbigniew Burtan, Dagmara Nowak-Senderowska, Paweł Szczepański

“…The proposed approach, grounded in the identification of characteristic features of hazards, facilitates the effective selection of preventive measures that can be implemented to reduce risk and improve workplace safety. …”
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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

“…First, the gray-level co-occurrence matrix (GLCM) was used to extract texture features from X-ray and visual images, and the first and second moments of the visual images were computed in different color spaces to comprehensively capture the internal and external moldiness characteristics of walnuts in order to construct an original moldy walnut feature set. …”
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MALDI-TOF Mass Spectrometry as the Tool for the Identification of Features of Polymers Obtained by Inverse Vulcanization by Natalia Tarasova, Efrem Krivoborodov, Diana Kirakosian, Alexey Zanin, Ilya Toropygin, Yaroslav Mezhuev

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Managing flash flood crises with cultural perspectives: A user-centric feature identification study. by Siti Fatimah Abdul Razak, Sumendra Yogarayan, Umar Ali Bukar, Md Shohel Sayeed

“…The study collected 351 responses, primarily targeting adults in flood-prone areas using convenience sampling method with the goal of exploring cultural bias for feature identification of in-vehicle flash flood app. …”
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IDENTIFICATION OF THE FEATURES OF THE INFLUENCE OF HETEROGENEOUS-VELOCITY GROUND LAYERS ON LARGE-EARTHQUAKE EFFECTS IN THE MONGOLIAN-SIBERIAN REGION by V. I. Dzhurik, E. V. Bryzhak, S. P. Serebrennikov, A. A. Kakourova

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New Record of <i>Encarsia protransvena</i> and Confirmed Occurrence of <i>Encarsia hispida</i> (Hymenoptera: Aphelinidae) as Parasitoids of <i>Singhiella simplex</i> (Hemiptera: Al... by Giuliano Cerasa, Luigi Tomasello, Gianluca Melone, Elia Russo, Gaetano Siscaro, Carmelo Cavallaro, Annamaria Ienco, Francesca Laudani, Vincenzo Palmeri, Orlando Campolo, Francesca Garganese, Francesco Porcelli, Paolo A. Pedata, Vittorio Farina, Giovanni Gugliuzza, Roberto Rizzo, Stefania Laudonia, Gabriella Lo Verde

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Research on Early Fault Identification of Cables Based on the Fusion of MTF-GAF and Multi-Head Attention Mechanism Features by Hao Wu, Dan Tang, Yuan Cai, Chaowen Zheng

“…To avoid the cable early faults causing great damage to the power grid operation, in this paper, we propose a research method for cable early fault identification based on the fusion of Markov Transition Field (MTF)-Gramian Angular Field (GAF) and multi-head attention mechanism features to accurately identify the cable early faults. …”
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Identification of lethality-related m7G methylation modification patterns and the regulatory features of immune microenvironment in sepsis by Dan Wang, Rujie Huo, Lu Ye

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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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DeepCNNMed: Enhancing Medicinal Plant Identification Through Deep Convolutional and Hybrid Neural Networks by Rajalakshmi Gurusamy, Parameswari Chandrasekaran, Yamuna Devi Manickam Malu, Madhusudhanan Sampath, Nesarani Abraham

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Enhanced safety assessment on tunnel excavation via refined rock mass parameter identification by Hongwei Huang, Tongjun Yang, Jiayao Chen, Zhongkai Huang, Chen Wu, Jianhong Man

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MetaCGRP is a high-precision meta-model for large-scale identification of CGRP inhibitors using multi-view information by Nalini Schaduangrat, Phisit Khemawoot, Apisada Jiso, Phasit Charoenkwan, Watshara Shoombuatong

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Arabic Handwritten Signature Identification by Alaa Taqa, Hanaa Mahmood

“…The signature image is preprocessed by several operations (Noise removal, Conversion of the signature image to binary image, Finding outer rectangle, Thinning and Size normalization) then the fractal number and co-occurrence matrix are computed to estimate texture features. …”
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Specific emitter identification based on ITD and texture analysis by Dong-fang REN, Tao ZHANG, Jie HAN, Huan-huan WANG

“…To solve the defects of time-frequency analysis and poor separability of extracted features in specific emitter identification (SEI) based on Hilbert-Huang transform (HHT)，a novel SEI method based on intrinsic time-scale decomposition(ITD)was proposed.ITD was utilized to decompose the emitter signals and get the time-frequency energy distribution(TFED)firstly，later the TFED spectrum was transformed into gray image and several image texture features through histogram statistic and gray-level co-occurrence matrix was extracted for identification.Measured ship communication signals and simulated emitter signals were used to test the performance of proposed method.Compared with another two SEI methods based on HHT，the proposed method is proved more effective in identification accuracy.…”
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