Predicting the Likelihood of Operational Risk Occurrence in the Banking Industry Using Machine Learning Algorithms by Hamed Naderi, Mohammad Ali Rastegar Sorkhe, Bakhtiar Ostadi, Mehrdad Kargari

“…Machine learning models can process complex datasets, identify hidden patterns, and facilitate early risk detection, enabling banks to implement preventive measures before risks materialize. …”
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Anomaly detection research using Isolation Forest in Machine Learning by A. S. Kechedzhiev, O. L. Tsvetkova

“…To implement this research, the Python programming language and the scikit-learn library were chosen to implement the Isolation Forest, as well as Pandas for working with data.Result. Evaluating the applicability of the Isolation Forest method on unstructured data revealed its potential for identifying anomalous patterns without the need for extensive labeling. …”
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Multichannel convolutional transformer for detecting mental disorders using electroancephalogrpahy records by Mamadou Dia, Ghazaleh Khodabandelou, Syed Muhammad Anwar, Alice Othmani

“…Our proposed model, the multichannel convolutional transformer, integrates the strengths of both convolutional neural networks and transformers. Before feeding the model as low-level features, the input is pre-processed using a common spatial pattern filter, a signal space projection filter, and a wavelet denoising filter. …”
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Design and Analysis of Plasma-Based Reconfigurable Maxwell Fish-Eye-Lens Antennas by Jafar Bazrafshan, Fatemeh Sadeghikia, Mohamed Himdi, Ali Karami Horestani, Hajar Binti Ja'afar

“…Specifically, the enhanced signal quality and dynamic beam-forming capabilities make it ideal for satellite communication, deep space missions, and interplanetary networks. The ability to dynamically adjust the radiation pattern and beam direction provides significant advantages in improving communication efficiency and reliability in challenging space environments.…”
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Trends in progress test performance of medical students at a university in Peru. by Franco Romaní-Romaní, César Gutiérrez

“…This study aimed to evaluate longitudinal trends in PT scores at a Peruvian medical school.…”
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Integrating brain-inspired computation with big-data analytics for advanced diagnostics in neuroradiology by Senthil Kumar, J. Ramprasath, V. Kalpana, Manikandan Rajagopal, Maheswaran S, Rupesh Gupta

“…BDA-D discovers important patterns from high-dimensional neuroimaging data through biologically inspired neural networks, reaching a remarkable diagnosis accuracy of 97.18%. …”
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Serum testosterone partially mediates the adverse effects of organophosphate pesticides exposure on growth indicators of children and adolescents by Zhenao Zhang, Xiaoya Sun, Jingyi Ren, Yadong Zhang, Rui Wen, Huanting Pei, Siqi Zhu, Bowen Yin, Ziyi Wang, Yuxia Ma

“…The role of total testosterone (TT) as a mediator was assessed. Network pharmacology methods were used to identify key targets and therapeutic drugs. …”
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Cyber security Enhancements with reinforcement learning: A zero-day vulnerabilityu identification perspective. by Muhammad Rehan Naeem, Rashid Amin, Muhammad Farhan, Faisal S Alsubaei, Eesa Alsolami, Muhammad D Zakaria

“…For the real-time zero-day vulnerabilities detection, we bring out a novel reinforcement learning (RL) methodology with the help of Deep Q-Networks (DQN). It works by learning the vulnerabilities without any prior knowledge of vulnerabilities, and it is evaluated using rigorous statistical metrics. …”
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VGGBM-Net: A Novel Pixel-Based Transfer Features Engineering for Automated Coffee Bean Diseases Classification by Muhammad Shadab Alam Hashmi, Azam Mehmood Qadri, Ali Raza, Saleem Ullah, Aseel Smerat, Changgyun Kim, Muhammad Syafrudin, Norma Latif Fitriyani

“…The performance of multiple machine learning classifiers, including Random Forest (RF), Logistic Regression (LoR), LightGBM (LGBM), and K-Nearest Neighbor Classifier (KNC), is evaluated alongside neural network techniques such as Convolutional Neural Networks (CNN) and VGG-19. …”
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MIMO Lyapunov Theory-Based RBF Neural Classifier for Traffic Sign Recognition by King Hann Lim, Kah Phooi Seng, Li-Minn Ang

“…The performance comparisons between the proposed classifier and some existing conventional techniques are evaluated using traffic sign patterns. Simulation results reveal that our proposed system achieved better performance with lower number of training iterations.…”
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Automatic detection and prediction of epileptic EEG signals based on nonlinear dynamics and deep learning: a review by Shixiao Tan, Zhen Tang, Qiang He, Ying Li, Yuliang Cai, Jiawei Zhang, Di Fan, Zhenkai Guo

“…In recent years, nonlinear dynamics methods such as chaos theory, fractal analysis, and entropy computation have provided new perspectives for EEG signal analysis, while deep learning approaches like convolutional neural networks and long short-term memory networks further enhance the robustness of dynamical pattern recognition through end-to-end nonlinear feature extraction. …”
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Predicting sport event outcomes using deep learning by Jianxiong Gao, Yi Cheng, Jianwei Gao

“…In this study, we present a deep learning framework that combines a one-dimensional convolutional neural network (1D CNN) with a Transformer architecture to improve prediction accuracy. …”
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Lightweight Anomaly-Based Detection Using Cuckoo Search Algorithm and Decision Tree to Mitigate Man-in-the-Middle Attacks in DNS by Ramahlapane Lerato Moila, Mthulisi Velempini

“…The proposed model utilizes machine learning algorithms and statistical analysis techniques to ensure that the analysis of DNS query patterns can efficiently detect anomalies associated with the MitM. …”
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TACO: Adversarial Camouflage Optimization on Trucks to Fool Object Detectors by Adonisz Dimitriu, Tamás Vilmos Michaletzky, Viktor Remeli

“…Adopting Unreal Engine 5, TACO integrates differentiable rendering with a Photorealistic Rendering Network to optimize adversarial textures targeted at YOLOv8. …”
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Abnormal arm swing movements in Parkinson’s disease: onset, progression and response to L-Dopa by M. Patera, A. Zampogna, L. Pietrosanti, F. Asci, M. Falletti, G. Pinola, E. Bianchini, G. Di Lazzaro, V. Rosati, P. Grillo, F. Giannini, F. Fattapposta, G. Costantini, A. Pisani, G. Saggio, Antonio Suppa

“…Methods Twenty healthy subjects (HS) and 40 PD patients, including 20 early-stage and 20 mid-advanced subjects, underwent a 6-m Timed Up and Go test while monitored through a network of wearable inertial sensors. Arm swing movements were objectively evaluated in both hemibodies and different upper limb joints (shoulder and elbow), using specific time-domain (range of motion and velocity) and frequency-domain measures (harmonics and total harmonic distortion). …”
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Dynamic Threshold-Based Resource Management for Fog Computing Environments by Jui-Pin Yang

“…Fog computing extends cloud services to the network edge, thereby reducing latency and bandwidth usage for time-sensitive applications. …”
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Bioinformatics‑Based Analysis Reveals Diagnostic Biomarkers and Immune Landscape in Atopic Dermatitis by Yang M, Zhang X, Zhou C, Du Y, Zhou M, Zhang W

“…Weighted gene co-expression network analysis (WGCNA) was employed to identify gene modules. …”
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Transcriptome analysis revealed that ischemic post-conditioning suppressed the expression of inflammatory genes in lung ischemia-reperfusion injury by Liangen Lin, Congcong Sun, Yuanwen Ye, Peng Zhu, Keyue Pan, Linglong Chen

“…Additionally, protein-protein interaction (PPI) network analysis was carried out to examine associations among the DEGs. …”
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Genetic Spectrum of Lithuanian Familial Hypercholesterolemia Patients by Urte Aliosaitiene, Rimante Cerkauskiene, Aleksandras Laucevicius, Migle Vilniskyte, Viktoras Sutkus, Antanas Mainelis, Birute Burnyte, Jurate Barysiene, Zaneta Petrulioniene

“…The subjects were divided into four groups according to the Dutch Lipid Clinic Network (DLCN) criteria (definite, probable, possible, and unlikely). …”
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Wearable Sensors and Computational Intelligence in Alpine Skiing Analysis by Ales Prochazka, Hana Charvatova

“…Machine learning results indicate differences in energy distribution before and after ski turns and demonstrate the feasibility of classifying associated motion patterns with accuracies of 98.1% and 90.7%, respectively, using a two-layer neural network. …”
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