Feasibility of EfficientDet-D3 for Accurate and Efficient Void Detection in GPR Images by Sung-Pil Shin, Sang-Yum Lee, Tri Ho Minh Le

“…This study presents a novel approach using the EfficientDet-D3 deep learning model for automated void detection in GPR images. The model combines advanced feature extraction and compound scaling to balance accuracy and computational efficiency, making it suitable for real-time applications. …”
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Attack resilient IoT security framework using multi head attention based representation learning with improved white shark optimization algorithm by Jawhara Aljabri

“…Besides, the sand cat swarm optimization (SCSO) model is used for the feature selection (FS) process. The proposed MHAID-IWSOA model employs the bidirectional gated recurrent unit with multi-head attention (BiGRU-MHA) technique for attack detection and classification. …”
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Comprehensive analysis of noise in Macau Science Satellite-1 vector magnetometer data by SiShan Song, Fan Yin, Qin Yan, Hermann Lühr, Chao Xiong, Yi Jiang, PengFei Liu

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Automated detection of submarine pipelines in the Yellow River Estuary: a deep learning approach for side-scan sonar data in dynamic deltaic systems by Min Wei, Min Wei, Yongqing Yu, Xing Du, Yupeng Song, Lifeng Dong, Qikun Zhou, Linfeng Wang, Longying Zhang, Yamei Wang

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Machine learning and discriminant analysis model for predicting benign and malignant pulmonary nodules by Zhi Li, Wenjing Zhang, Jinyi Huang, Ling Lu, Dongming Xie, Jinrong Zhang, Jiamin Liang, Yuepeng Sui, Linyuan Liu, Jianjun Zou, Ao Lin, Lei Yang, Fuman Qiu, Zhaoting Hu, Mei Wu, Yibin Deng, Xin Zhang, Jiachun Lu

“…The GMU_D model constructed by discriminative analysis based on machine learning screening features had an excellent discriminative performance (AUC = 0.866, 95% CI: 0.858–0.874), and higher accuracy than the PKUPH model (AUC = 0.559, 95% CI: 0.552–0.567) and the Block model (AUC = 0.823, 95% CI: 0.814–0.833). …”
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PCA and PSO based optimized support vector machine for efficient intrusion detection in internet of things by Mutkule Prasad Raghunath, Shyam Deshmukh, Poonam Chaudhari, Sunil L. Bangare, Kishori Kasat, Mohan Awasthy, Batyrkhan Omarov, Rajesh R. Waghulde

“…This article presents the development of an intrusion detection system for the Internet of Things using machine learning and feature selection techniques. …”
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Classification of Flying Drones Using Millimeter-Wave Radar: Comparative Analysis of Algorithms Under Noisy Conditions by Mauro Larrat, Claudomiro Sales

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Sentiment analysis for deepfake X posts using novel transfer learning based word embedding and hybrid LGR approach by Madiha Khalid, Muhammad Faheem Mushtaq, Urooj Akram, Mejdl Safran, Sultan Alfarhood, Imran Ashraf

“…This study proposes a hybrid deep learning (DL) approach and novel transfer learning (TL)-based feature extraction approach for deepfake posts’ sentiment analysis. …”
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Electroencephalography-Based Pain Detection Using Kernel Spectral Connectivity Network with Preserved Spatio-Frequency Interpretability by Santiago Buitrago-Osorio, Julian Gil-González, Andrés Marino Álvarez-Meza, David Cardenas-Peña, Alvaro Orozco-Gutierrez

“…(iii) Further, to account for subject variability, we conduct cross-subject analysis and grouping, clustering individuals based on similar pain detection performance, functional connectivity patterns, sex, and age. …”
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Iterative PolInSAR Target Decomposition for Scattering Characterization and Building Detection by Di Zhuang, Lamei Zhang, Bin Zou

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GENDER FEATURES OF THE POPULATION'S ATTITUDE TO MEDICINES by N. B. Dremova, N. P. Yaroshenko, N. I. Afanaseva, S. V. Solomka

“…The study of gender features of attitudes visitors of pharmacies to medicines.Materials and Methods. …”
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FP-Deeplab: A Novel Face Parsing Network for Fine-Grained Boundary Detection and Semantic Understanding by Borui Zeng, Can Shu, Ziqi Liao, Jingru Yu, Zhiyu Liu, Xiaoyan Chen

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ECG-based cardiac arrhythmia classification using fuzzy encoded features and deep neural networks by Kiruthika Balakrishnan, Durgadevi Velusamy, Karthikeyan Ramasamy, Lisiane Pruinelli

“…Compared to conventional deep learning models that rely on raw ECG signals, our method enhances interpretability and feature extraction by incorporating time–frequency analysis and fuzzy feature encoding. …”
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Features of vertebral arteries pathology at trauma by V. V. Shchedrenok, T. V. Zakhmatova, O. V. Moguchaya

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Accurate detection of low concentrations of microplastics in soils via short-wave infrared hyperspectral imaging by Huan Chen, Taesung Shin, Bosoon Park, Kyoung Ro, Changyoon Jeong, Hwang-Ju Jeon, Pei-Lin Tan

“…Using indium gallium arsenide (InGaAs; 800–1600 ​nm) and mercury cadmium telluride (MCT; 1000–2500 ​nm) sensors, we applied logistic regression and support vector machines by employing both linear and nonlinear kernels to analyze spectral features extracted via principal component analysis and partial least squares. …”
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Postinfectious epilepsy: clinical and diagnostical features by А. V. Vasilenko, А. Yu. Ulitin, L. S. Onishchenko, N. I. Ananyeva, R. V. Grebenshchikova, О. N. Gaykova, А. V. Ivanenko, S. S. Kolosov, S. А. Turanov, S. N. Chudievich

“…Gross and marked diffuse disturbances in brain bioelectrical activity were most often detected (58% and 31%, respectively) during video-EEG monitoring in Group 1, whereas moderate alterations were recorded less frequently (11% of observations). …”
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Large Pretrained Foundation Model for Key Performance Indicator Multivariate Time Series Anomaly Detection by Xu Wang, Qisheng Xu, Kele Xu, Ting Yu, Bo Ding, Dawei Feng, Yong Dou

“…Additionally, we leverage frequency domain information to design a feature enhancement method, further boosting the model's detection accuracy. …”
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Machine learning and facial recognition for down syndrome detection: A comprehensive review by Khosro Rezaee

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CUNet-CLSTM: A Novel Fusion of CUNet and CLSTM for Superior Liver Cancer Detection in CT Scans by K. Vijayaprabakaran, Padmanaban Ramalingam, Rajakumar Ramalingam, A. Ilavendhan, R. Vedhapriyavadhana

“…Despite the triumph of convolutional neural networks in medical image analysis, challenges such as overfitting, limited labeled data, and complex tumor morphology hinder accurate detection of liver cancer. …”
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COVID-19 Diagnosis Using Spectral and Statistical Analysis of Cough Recordings Based on the Combination of SVD and DWT by Thabit Sultan Mohammed, Awni Ismail Sultan, Khattab M. Ali Alheeti, Karim Mohammed Aljebory, Hasan Ismail Sultan, Muzhir Shaban Al-Ani

“…As a cough recognition approach, we found it well-performing, as it generates and utilizes an efficient minimum number of features. Mean and median frequencies, which are known to be the most useful features in the frequency domain, are applied to generate an effective statistical measure to compare the results. …”
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