A Study on Partial Discharge Fault Identification in GIS Based on Swin Transformer-AFPN-LSTM Architecture by Jiawei Li, Shangang Ma, Fubao Jin, Ruiting Zhao, Qiang Zhang, Jiawen Xie

“…Aiming at the problem of manual feature extraction and insufficient mining of feature information for partial discharge pattern recognition under different insulation faults in GIS, a deep learning model based on phase and timing features with Swin Transformer-AFPN-LSTM architecture is proposed. …”
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Machine learning models and dimensionality reduction for improving the Android malware detection by Pablo Morán, Antonio Robles-Gómez, Andres Duque, Llanos Tobarra, Rafael Pastor-Vargas

“…They can detect an average of 91.72% malware samples, with a very low false positive rate of 0.13%, and using only 5,000 features. This is just over 9% of the total number of features of DREBIN. …”
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Dilated Convolution and YOLOv8 Feature Extraction Network: An Improved Method for MRI-Based Brain Tumor Detection by Lincy Annet Abraham, Gopinath Palanisamy, Veerapu Goutham

“…Secondly, a dual feature pyramid network (DFPN) is built to provide more discriminative data for dynamic sparse attention mechanism to extract features from the shallow network and top-down routes to direct the following network modules to fuse features. …”
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Employing combined spatial and frequency domain image features for machine learning-based malware detection by Abul Bashar

“…To combat this, numerous efforts have explored automated botnet detection mechanisms, with anomaly-based approaches leveraging machine learning (ML) gaining attraction due to their signature-agnostic nature. However, the problem lies in devising accurate ML models which capture the ever evolving landscape of malwares by effectively leveraging all the possible features from Android application packages (APKs).This paper delved into this domain by proposing, implementing, and evaluating an image-based Android malware detection (AMD) framework that harnessed the power of feature hybridization. …”
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A Binary Superior Tracking Artificial Bee Colony with Dynamic Cauchy Mutation for Feature Selection by Xianghua Chu, Shuxiang Li, Da Gao, Wei Zhao, Jianshuang Cui, Linya Huang

“…Experimental results demonstrate that BSTABC-DCM could obtain the optimal classification accuracy and select the best representative features for the UCI problems.…”
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ASSESSMENT OF SOCIAL AND PSYCHOLOGICAL CONSEQUENCES OF RADIATION DANGER EXPERIENCE AMONG DIFFERENT AGE GROUPS OF THE POPULATION FROM CONTAMINATED AREAS OF RUSSIA by T. A. Marchenko, T. B. Melnitskaya, T. V. Belykh

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Microsimulation framework for urban price-taker markets by Bilal Farooq, Eric J. Miller, Franco Chingcuanco, Martin Giroux-Cook

“…Here, we present a microsimulation framework of a price-taker market that recognizes this generality and develop efficient algorithms for the associated market-clearing problem. By abstracting the problem as a specific graph theoretic problem (i.e., maximum weighted bipartite graph), we are first able to exploit algorithms that are developed in graph theory. …”
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FORMATION OF SOFT SKILLS IN FUTURE SPANISH AND ITALIAN TRANSLATORS IN THE CONTEXT OF CROSS-CULTURAL COMMUNICATION by Taras Pysmennyi, Marina Pavlovich, Yuliia Tiapina

“…In the future, the authors plan to consider each of the flexible skills features in details, to work out a set of methodological and practical exercises for their development…”
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High performance adaptive step size fractional numerical scheme for solving fractional differential equations by Mudassir Shams, Ahmad Alalyani

“…These equations provide a powerful framework for describing phenomena with memory effects and hereditary features that standard integer-order models cannot account for. …”
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NONLINEAR BEHAVIOR CALCULATION ALGORITHM FOR THIN-WALLED SYSTEMS by G. M. Murtazaliev, M. M. Payzulaev

“…Based on an algorithm combining approximate analytical and numerical methods, the article solves the model problem — studying the behavior of a thin-walled spherical shell under load.Method. …”
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Deep-Learning-Based Approach in Imaging Radiometry by Aperture Synthesis: Application to Real SMOS Data by Ali Khazaal, Richard Faucheron, Nemesio J. Rodriguez-Fernandez, Eric Anterrieu

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IMPLEMENTATION OF FEATURE IMPORTANCE XGBOOST ALGORITHM TO DETERMINE THE ACTIVE COMPOUNDS OF SEMBUNG LEAVES (BLUMEA BALSAMIFERA) by Kusnaeni Kusnaeni, Nurul Fuady Adhalia, Abdul Khaliq Zulfattah

“…The XGBoost algorithm can calculate the feature importance score that affects the goal variable so that it does not have to include all variables in the modeling, this can overcome problems in high-dimensional data. …”
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PSYCHOSOCIAL ILLNESS IN CHILDREN WITH THALASSEMIA: A CASE-CONTROL STUDY by Erum Afzal, Muhammad Aslam Sheikh, Sajjad Hussain Bhaba, Tanveer Ahmed, Imran Iqbal, Muhammad Khalid Iqbal

“…Regarding characteristics of thalassemia 74 %( n=37) patients were diagnosed within 1st year of life, while 26 %( n=13) after 1st year.64 %( n=32) had well controlled and 36 %( n=18) poor controlled disease. 20%(n=10) had developed Diabetes mellitus,2%(n=1) heart failure,74%(37) growth failure,76%(n=38) hemolytic facial features and 72%(n=36)skin discoloration. Psychosocial problems were statistically significant in children with Thalassemia as compared to healthy ones (p-value<0.001).Poorly controlled thalassemia and complications of heart and growth failure were found statistically significant risk factors.  …”
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A Multimodal Machine Learning Model in Pneumonia Patients Hospital Length of Stay Prediction by Anna Annunziata, Salvatore Cappabianca, Salvatore Capuozzo, Nicola Coppola, Camilla Di Somma, Ludovico Docimo, Giuseppe Fiorentino, Michela Gravina, Lidia Marassi, Stefano Marrone, Domenico Parmeggiani, Giorgio Emanuele Polistina, Alfonso Reginelli, Caterina Sagnelli, Carlo Sansone

“…Specifically, our approach uses the following: (i) feature extraction from chest CT scans via a convolutional neural network (CNN), (ii) their integration with clinically relevant tabular data from patient exams, refined through a feature selection system to retain only significant predictors. …”
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A New Feature Extraction Algorithm Based on Orthogonal Regularized Kernel CCA and Its Application by Xinchen Guo, Xiuling Fan, Xiantian Xi, Fugeng Zeng

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Multi-user physical layer authentication mechanism based on lightweight CNN and channel feature assistance by Yankun WANG, Dengke GUO, Dongtang MA, Jun XIONG, Xiaoying ZHANG

“…To address the problems of poor robustness and high complexity of current physical layer user authentication algorithms, a lightweight convolutional neural network (CNN) channel feature extraction algorithm was proposed to reduce the channel state response required for training by changing the form of network input, and a multi-user physical layer channel feature-assisted authentication mechanism was established based on this algorithm to design a detailed process from user registration to authentication, and multi-user authentication and network parameter update online were completed.Simulation results show that the proposed algorithm can complete multi-user authentication, obtain good detection performance with smaller training rounds, and require fewer training samples than existing multi-user authentication algorithms.…”
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WT-HMFF: Wavelet Transform Convolution and Hierarchical Multi-Scale Feature Fusion Network for Detecting Infrared Small Targets by Siyu Li, Jingsi Huang, Qingwu Duan, Zheng Li

“…To tackle this problem, we introduce WT-HMFF, an innovative network architecture that combines the Wavelet Transform Convolution (WTConv) module with the Hierarchical Multi-Scale Feature Fusion (HMFF) module to enhance the ISTD algorithm’s performance. …”
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An AI-based automatic leukemia classification system utilizing dimensional Archimedes optimization by Warda M. Shaban

“…This improves both the precision and efficiency of convergence while reducing the likelihood of the “two steps forward, one step back” phenomenon. This problem offers a more precise solution. Finally, these selected features are fed to the proposed classification model. …”
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A novel similarity-constrained feature selection method for epilepsy detection via EEG signals by Chunlei Shi, Jun Gao, Jian Yu, Lingzhi Zhao, Faxian Jia

“…Then, an optimization problem for feature selection is formulated by enhancing intra-class similarity and reducing inter-class similarity. …”
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Analysis of Data and Feature Processing on Stroke Prediction using Wide Range Machine Learning Model by Untari Novia Wisesty, Tjokorda Agung Budi Wirayuda, Febryanti Sthevanie, Rita Rismala

“…Then, data sampling techniques are used to handle data imbalance problems in the stroke dataset, which include Random Undersampling, Random Oversampling, and SMOTE techniques. …”
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