Data Augmentation For Sorani Kurdish News Headline Classification Using Back-Translation And Deep Learning Model by Soran Badawi

“…The proposed BiLSTM model is trained on the augmented data and compared with baseline models SVM (Support-Vector-Machines) and Naïve Bayes an trained on the original data. …”
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Brain Tumor Identification and Classification of MRI Images Using Deep Learning Techniques by Zheshu Jia, Deyun Chen

“…In this paper, a Fully Automatic Heterogeneous Segmentation using Support Vector Machine (FAHS-SVM) has been proposed for brain tumor segmentation based on deep learning techniques. …”
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Can the Mosquito Bite? The Multispecies Transmutation of Wolbachia Mosquitoes as Biotechnologies of Epidemic Control in Rio de Janeiro by Luísa Reis Castro

“…While public health campaigns have historically focused on eliminating this vector species, a project in Rio de Janeiro, Brazil, proposes to release A. aegypti carrying the intracellular bacterium Wolbachia. …”
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Detection of Cumulative Bruising in Prunes Using Vis–NIR Spectroscopy and Machine Learning: A Nonlinear Spectral Response Approach by Lisi Lai, Hui Zhang, Jiahui Gu, Long Wen

“…Spectral data were collected from the equatorial region of each fruit and processed using a hybrid modeling framework comprising continuous wavelet transform (CWT) for spectral enhancement, uninformative variable elimination (UVE) for optimal wavelength selection, and support vector machine (SVM) for classification. …”
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Modern Approaches to Arctic Development in View of Synergy Potential in the New Risks and challenges Environment by N. N. Bondareva

“…The author of the study disclosed the organizational and methodological problems of Arctic management, assessed the limitations for the full-scale launch of synergistic management models.Conclusions and Relevance: the presented results of the analysis set the management vector for maximum synergistic activation of financial, logistical and intellectual resources to achieve sustainable, holistic and safe development of the Arctic. …”
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Power Extraction Control of Variable Speed Wind Turbine Systems Based on Direct Drive Synchronous Generator in All Operating Regimes by Youssef Errami, Abdellatif Obbadi, Smail Sahnoun, Mohammed Ouassaid, Mohamed Maaroufi

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A comparative study of machine learning algorithms for fall detection in technology-based healthcare system: Analyzing SVM, KNN, decision tree, random forest, LSTM, and CNN by Afuan Lasmedi, Isnanto R. Rizal

“…The results show that CNN and LSTM deliver the best performance with accuracies of 94% and 92%, while Random Forest offers a good balance between accuracy and processing time. SVM and KNN exhibit faster processing times but slightly lower accuracies, at 87% and 84%, respectively. …”
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STRUCTURAL SYNTHESIS OF NAVIGATION SUPPORT OF TRIAD INTEGRATED NAVIGATION SYSTEM ON THE BASIS OF INERTIAL AND SATELLITE TECHNOLOGIES by V. S. Maryukhnenko, V. V. Erokhin

“…In connection with the increasing complexity of aircraft navigation support, with growing demands placed on them, it is increasingly necessary to develop integrated navigation data processing system by solving synthesis problems based on optimal filtering methods. …”
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Adaptive Weighted Diversity Ensemble Learning Approach for Fetal Health Classification on Cardiotocography Data by K. Aditya Shastry, Mohan Sellappa Gounder, T. G. Mohan Kumar, D. U. Karthik, V. Sushma, D. Subashree

“…Our proposed Adaptive Weighted Diversity Ensemble Model (AWD) incorporates Random Forest (RF), AdaBoost (AB), Gradient Boost (GB), and Support Vector Classifier (SVC) as base classifiers. The framework includes three modules: Exploratory Data Analysis (EDA), data pre-processing, and an adaptive weighted diversity ensemble module. …”
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Exploring the future of privacy-preserving heart disease prediction: a fully homomorphic encryption-driven logistic regression approach by Vankamamidi S. Naresh, Sivaranjani Reddi

“…Future work will focus on optimizing encryption techniques and exploring parallel processing methods to address these challenges.…”
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Multi-Signal Induction Motor Broken Rotor Bar Detection Based on Merged Convolutional Neural Network by Tianyi Wang, Shiguang Wen, Shaotong Sheng, Huimin Ma

“…The method preprocesses motor currents by Hilbert-Huang Transform (HHT) and Park’s Vector Modulus (PVM) and then uses a merged convolutional neural network (CNN) for classification. …”
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Cross-Modal Collaboration and Robust Feature Classifier for Open-Vocabulary 3D Object Detection by Hengsong Liu, Tongle Duan

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An IoT and Machine Learning-based Neonatal Sleep Stage Classification by Awais Abbas, Hafiz Sheraz Sheikh, SaadUllah Farooq Abbasi

“…After feature extraction, support vector machine was used for sleep stage classification. …”
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Characterization and Automated Classification of Underwater Acoustic Environments in the Western Black Sea Using Machine Learning Techniques by Maria Emanuela Mihailov

“…This paper addresses this by presenting a multi-faceted framework using passive acoustic monitoring (PAM) data from fixed locations (50–100 m depth). Acoustic data are processed using advanced signal processing (broadband Sound Pressure Level (SPL), Power Spectral Density (PSD)) for feature extraction (Mel-spectrograms for deep learning; PSD statistical moments for classical/unsupervised ML). …”
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Detection method of slight bruises of apples based on hyperspectral imaging and RELIEF-extreme learning machine by ZHANG Meng, LI Guanghui

“…The final detection accuracy of apple damage detection algorithm based on the image processing technology was over 94%, which indicates that the algorithm is efficient for identifying slight bruises of apples.…”
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Diagrammatics, pentagon equations, and hexagon equations of topological orders with loop- and membrane-like excitations by Yizhou Huang, Zhi-Feng Zhang, Peng Ye

“…We introduce elementary diagrams for fusion and shrinking processes, treating them as vectors in fusion and shrinking spaces, respectively, and build complex diagrams by combining these elementary diagrams. …”
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Research of methodological principles and financial mechanisms of macro-strategic management of the dynamics of technological innovation systems by B. D. Matrizaev

“…The author proposes a new methodological approach based on system dynamics, which combines two modern concepts of technological innovation systems management: the concept of “innovation engines”, based on the research on new technological innovation systems, and the concept of a “three-vector transition module”. A model of the emergence or decline of technological innovation systems in the context of various transitional processes (changes) in socio-technical systems is identified. …”
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The Property of the <i>Gaia</i> Celestial Reference Frame 3 (<i>Gaia</i>-CRF3) by Guangyi Liu, Sufen Guo

“…Using the Fibonacci grid, approximately 430,000 uniformly distributed sources were selected from the 5-parameter solution of <i>Gaia</i> DR3. After VSH processing, the rotation vector and glide vector were determined as <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><mi mathvariant="bold">R</mi><mo>=</mo><mo>(</mo><mn>10.7</mn><mo>±</mo><mn>3.1</mn><mo>,</mo><mn>2.2</mn><mo>±</mo><mn>2.7</mn><mo>,</mo><mo>−</mo><mn>2.5</mn><mo>±</mo><mn>4.0</mn><mo>)</mo><mspace width="4pt"></mspace></mrow></semantics></math></inline-formula><inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mi mathvariant="sans-serif">μ</mi></semantics></math></inline-formula>as · year⁻¹ and <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><mi mathvariant="bold">G</mi><mo>=</mo><mo>(</mo><mn>0.3</mn><mo>±</mo><mn>3.1</mn><mo>,</mo><mo>−</mo><mn>1.2</mn><mo>±</mo><mn>2.7</mn><mo>,</mo><mo>−</mo><mn>2.5</mn><mo>±</mo><mn>4.0</mn><mo>)</mo><mspace width="4pt"></mspace></mrow></semantics></math></inline-formula><inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mi mathvariant="sans-serif">μ</mi></semantics></math></inline-formula>as · year⁻¹, respectively. …”
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Quantized Auto Encoder-Based Anomaly Detection for Multivariate Time Series Data in 5G Networks by Giovanni Trappolini, Antonio Purificato, Federico Siciliano, Luigi D'Addona, Anna Maria Spagnolo, Domenico Dato, Fabrizio Silvestri

“…This paper introduces <monospace>QAED</monospace> (Quantized Auto Encoder Detector), a novel deep learning approach for anomaly detection in 5G networks with three key innovations: 1) a vector quantization mechanism that effectively captures discrete network states, 2) a kernel density estimation preprocessing step that enables detection of both outliers and distribution shifts, and 3) an integrated architecture that processes multivariate time series data in a unified framework. …”
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Content-Enhanced Network Embedding for Academic Collaborator Recommendation by Jie Chen, Xin Wang, Shu Zhao, Yanping Zhang

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