Bi-directional Pre-trained Network for Single-station Seismic Waveform Analysis by Yuqi CAI, Ziye YU, Weitao WANG, Yanru AN, Lu LI

“…However, most neural network models in seismology currently focus on single tasks. Based on the CSNCD dataset released by the China Earthquake Networks Center, we have developed a bi-directional neural network pre-trained model for single-station data analysis. …”
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Rectified Adam Optimizer and LSTM with Attention Mechanism for ECG-Based Multi-class Classification of Cardiac Arrhythmia by T. Sivaranjani, B. Sasikumar, G. Sugitha

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An integrated strategy based on radiomics and quantum machine learning: diagnosis and clinical interpretation of pulmonary ground-glass nodules by Xianzhi Huang, Fangyi Xu, Wenchao Zhu, Lin Yao, Jiahuan He, Junhao Su, Wending Zhao, Hongjie Hu

“…The CT images was randomly divided into training and testing cohorts (80:20), with radiomic features extracted from the training cohort. …”
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Recurrent Neural Network Optimized by Grasshopper for Accurate Audio Data-Based Diagnosis of Parkinson's Disease by Saif Wali Ali Alsudani, Ghassan Khudair Saud

“…The LSTM neural network serves as the core deep learning model for this framework. It is trained on the speech signals produced by both healthy individuals and those with PD, allowing it to detect the class of a given speech signal based on the conditions necessary for successful classification. …”
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Patch-Wise-Based Self-Supervised Learning for Anomaly Detection on Multivariate Time Series Data by Seungmin Oh, Le Hoang Anh, Dang Thanh Vu, Gwang Hyun Yu, Minsoo Hahn, Jinsul Kim

“…The proposed approach comprises four key components: (i) maintaining continuous features through patching, (ii) incorporating various temporal information by learning channel dependencies and adding relative positional bias, (iii) achieving feature representation learning through self-supervised learning, and (iv) supervised learning based on anomaly augmentation for downstream tasks. …”
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Diabetic Retinopathy Classification Using Hybrid Color-Based CLAHE and Blood Vessel in Deep Convolution Neural Network by Ammar Jawad Kadhim, Hadi Seyedarabi, Reza Afrouzian, Fadhil Sahib Hasan

“…Variant models, especially VGG19 and InceptionV3, are trained using a transfer learning approach on the proposed extracted features for DR grading. …”
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Effective classification of android malware families through dynamic features and neural networks by Gianni D'Angelo, Francesco Palmieri, Antonio Robustelli, Arcangelo Castiglione

“…Therefore, the main aim of this paper is proposing a new dataset called Unisa Malware Dataset (UMD) available on http://antlab.di.unisa.it/malware/, which is based on the extraction of static and dynamic features characterising the malware activities. …”
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A New Approach for Clustered MCs Classification with Sparse Features Learning and TWSVM by Xin-Sheng Zhang

“…We formulate this classification problem as sparse feature learning based classification on behalf of the test samples with a set of training samples, which are also known as a “vocabulary” of visual parts. …”
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Methodological features of the development of the communicative competence of local general practitioner in preventive counseling by S. Yu. Astanina, A. M. Kalinina, R. N. Shepel, O. M. Drapkina

“…There is a contradiction between the need of teachers of internal medicine departments in the methodology for communicative competence (CC) of general practitioners in brief preventive counseling and the lack of evidence-based classes on CC formation in brief preventive counseling.The existing contradiction made it possible to identify the research problem — the need to develop a methodology for the development of CC of general practitioners in brief preventive counseling.In the context of the problem, the study aim was determined — to define the methodological features of CC formation in the general practitioner in brief preventive counseling.To achieve this goal, the following research methods were used: theoretical: theoretical analysis of philosophical, pedagogical, psychological and methodological literature; designing the educational process; experimental: direct and indirect pedagogical observation, pedagogical experiment, questioning, control sections.The methodological features of CC formation are the simultaneous development of the intellectual and emotional fields of the doctor’s personality (communication skills in conducting all stages of brief preventive counseling) and the volitional field of the doctor’s personality (belief in the need to master the CC of doctor-patient interaction).The method of doctor’s CC development is based on the technology of educational training. …”
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PFVnet, a feature enhancement network for low recognition coal and rock images by Cai Han, Zhenwen Liu, Shenglei Zhao, Yubo Li, Yanwei Duan, Xinzhou Yang, Chuanbo Hao

“…We characterized the grayscale and texture feature patterns of coal-rock media under varying degrees of interference and established a comprehensive multi-element image training sample library. …”
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Comparative Analysis of Feature Selection Methods with XGBoost for Malware Detection on the Drebin Dataset by Ines Aulia Latifah, Fauzi Adi Rafrastara, Jevan Bintoro, Wildanil Ghozi, Waleed Mahgoub Osman

“…Traditional detection methods, such as signature-based detection, are often ineffective against new or polymorphic malware. …”
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Optimizing visual data retrieval using deep learning driven CBIR for improved human machine interaction by Arulmozhi P, Gopi R

“…Abstract Content-based image retrieval (CBIR) systems have formidable obstacles in connecting human comprehension with machine-driven feature extraction due to the exponential expansion of visual data across many areas. …”
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Features of professional activity of an academic staff in the conditions of a digital educational environment by Ludmila Vladimirovna Chistobaeva

“…The purpose of the research is to theoretically substantiate the main areas of professional activity of academic staff in a digital educational environment.The research methodology was based on the study of scientific information and the experience of pedagogical activity in foreign and domestic literature, the use of theoretical and empirical research methods, such as analysis, generalization, deduction.The results of the research are as follows the features of the digital educational environment of a modern university have been substantiated; the structural and content characteristics of the activities of academic staff in the digital space of the university have been disclosed.Key conclusions are: the key role of digital technologies lies in the innovative development of a higher education, namely, in the competence of modern university teachers to work in a dig- ital educational environment; structural and content characteristics of the activity of academic staff in the digital space of a university include a number of areas: interaction with the subjects of education in the electronic information and educational environment of a university, creation of electronic methodological tools for readable disciplines, the use of various forms of organizing training sessions in the electronic educational environment, the creation of a digital portfolio, etc. …”
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Capacity Prognostics of Marine Lithium-Ion Batteries Based on ICPO-Bi-LSTM Under Dynamic Operating Conditions by Qijia Song, Xiangguo Yang, Telu Tang, Yifan Liu, Yuelin Chen, Lin Liu

“…The paper develops a marine lithium-ion battery capacity prognostic method based on ICPO-Bi-LSTM under dynamic operating conditions to address this. …”
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Features of the pre-analytical stage in quantitative determination of TREC/KREC in peripheral blood by M. A. Saitgalina, Yu. V. Ostankova, A. V. Sedykh, A. A. Totolian

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Boosting adversarial transferability in vision-language models via multimodal feature heterogeneity by Long Chen, Yuling Chen, Zhi Ouyang, Hui Dou, Yangwen Zhang, Haiwei Sang

“…To improve transferability, we propose a cross-modal variance aggregation-based multi-domain feature perturbation method, using text-guided image attacks to perturb consistent spatial and frequency features while combining previous gradient momentum, achieving better transferability. …”
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Federated and ensemble learning framework with optimized feature selection for heart disease detection by Olfa Hrizi, Karim Gasmi, Abdulrahman Alyami, Adel Alkhalil, Ibrahim Alrashdi, Ali Alqazzaz, Lassaad Ben Ammar, Manel Mrabet, Alameen E.M. Abdalrahman, Samia Yahyaoui

“…To improve classification performance while protecting data privacy, this study investigated a combined method that uses ensemble learning, feature selection, and federated learning (FL). The ensemble-based approaches proved the most predictive after testing several different machine learning (ML) models, including random forests, the light gradient boosting machine, support vector machines, k-nearest neighbors, convolutional neural networks, and long short-term memory. …”
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AFQSeg: An Adaptive Feature Quantization Network for Instance-Level Surface Crack Segmentation by Shaoliang Fang, Lu Lu, Zhu Lin, Zhanyu Yang, Shaosheng Wang

“…To address these issues, this paper proposes a crack detection model based on adaptive feature quantization, which primarily consists of a maximum soft pooling module, an adaptive crack feature quantization module, and a trainable crack post-processing module. …”
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Probabilistic Forecasting of Provincial Regional Wind Power Considering Spatio-Temporal Features by Gang Li, Chen Lin, Yupeng Li

“…Meanwhile, an efficient channel attention (ECA) mechanism and an improved quantile regression-based loss function are introduced in the training to directly generate prediction intervals. …”
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SPECIFIC FEATURES OF TEACHING THE CONCEPT OF SUSTAINABLE DEVELOPMENT IN EDUCATIONAL ESTABLISHMENTS OF UNTSUKULSKY DISTRICT by Nadira O. Guseynova, Zarema I. Soltanmuradova, Marina Z. Magomedova

“…The aim of the work is to identify the specific features of teaching the concept of sustainable development in the general education establishments of the Untsukul District of Dagestan. …”
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