A transformer-based embedding approach to developing short-form psychological measures by Se-Jin Jung, Jang-Won Seo

“…However, existing short-form development approaches typically require full-scale administration and rely on factor analysis or machine learning techniques based on response data.MethodsThis study proposes a novel, data-independent method for item reduction using transformer-based semantic embeddings. …”
Get full text

Joint learning equation of state surfaces with uncertainty-aware physically regularized neural networks by Dongyang Kuang, Shiwei Li, Buxuan Wang, Chao Xiong, Shichang Zhang, Yanyao Zhang

“…Abstract The equation of state (EOS) is essential for understanding material behavior under different pressure-temperature-volume (P-T-V) conditions across various disciplines. Traditional models, such as the Mie-Gr $$\ddot{\text {u}}$$ neisen-Debye equation, rely on thermodynamic assumptions and expert knowledge, while classical Gaussian process based machine learning approaches can be sensitive to choice of kernels and are limited by scalability and extrapolability. …”
Get full text

Optimizing Tumor Detection in Brain MRI with One-Class SVM and Convolutional Neural Network-Based Feature Extraction by Azeddine Mjahad, Alfredo Rosado-Muñoz

Get full text

HCCD: A handwritten camera-captured dataset for document enhancement under varied degradation conditionsMendeley Data by K.S. Koushik, Bipin Nair B J, N. Shobha Rani

Get full text

AI-driven diagnosis and health management of autonomous electric vehicle powertrains: An empirical data-driven approach by Hicham El hadraoui, Adila El maghraoui, Oussama Laayati, Erroumayssae Sabani, Mourad Zegrari, Ahmed Chebak

“…The approach leverages vibration signals acquired from accelerometers and employs a hybrid machine learning (ML) framework. The study focuses on identifying the most informative features from time, frequency, and wavelet domains, followed by dimensionality reduction using Principal Component Analysis (PCA) and Correlation Analysis (CA) to enhance classification performance, reduce complexity, and improve model interpretability. …”
Get full text

Zero-Shot Classification of Art With Large Language Models by Tatsuya Tojima, Mitsuo Yoshida

“…Both traditional statistical methods and machine learning methods have been used to predict art prices. …”
Get full text

Deep learning and hyperspectral features for seedling stage identification of barnyard grass in paddy field by Siqiao Tan, Qiang Xie, Wenshuai Zhu, Yangjun Deng, Lei Zhu, Xiaoqiao Yu, Zheming Yuan, Zheming Yuan, Yuan Chen, Yuan Chen

“…Notably, this surpasses the capabilities of other models that rely on amalgamations of machine learning algorithms and feature dimensionality reduction methods. …”
Get full text

Robust Driving Control Design for Precise Positional Motions of Permanent Magnet Synchronous Motor Driven Rotary Machines with Position-Dependent Periodic Disturbances by Syh-Shiuh Yeh, Zhi-Hong Liu

“…However, problems with learning period convergence and rotary machine dynamics significantly affect transient motion, further constraining the overall motion performance. …”
Get full text

A Dual-Strategy Framework for Cyber Threat Detection in Imbalanced, High-Dimensional Data Across Heterogeneous Networks by T. Saranya, S. Indra Priyadharshini

Get full text

Prediction of Air Quality Index Using Ensemble Models by Theresia Herlina Rochadiani

“…This study uses IoT-based air quality data from Kampung Kalipaten, Tangerang to build an AQI prediction model with machine learning, specifically an ensemble model. …”
Get full text

Q-Learning based VM Consolidation Approach for Enhancing Cloud Data Centres Power Efficiency by Baikani Sreenithya, Bharde Hitesh Dutt, Chennamaneni Jashwanth, R Karthikeyan, MA Jabbar, Majjari Sudhakar

“…We have also delved with reinforcement learning algorithm to tackle the virtual machines. …”
Get full text

Energy-aware federated learning for secure edge computing in 5G-enabled IoT networks by Milad Rahmati

“…Abstract The rapid expansion of 5G-enabled IoT networks has intensified the need for efficient, secure, and privacy-preserving machine learning models that can operate in decentralized edge environments. …”
Get full text

Optimizing chemotherapeutic targets in non-small cell lung cancer with transfer learning for precision medicine. by Varun Malik, Ruchi Mittal, Deepali Gupta, Sapna Juneja, Khalid Mohiuddin, Swati Kumari

“…In addition, we design the deep transfer learning (DTransL) model to boost the drug discovery accuracy for NSCLC patients' therapeutic targets. …”
Get full text

Enhancing feature learning of hyperspectral imaging using shallow autoencoder by adding parallel paths encoding by Bibi Noor Asmat, Hafiz Syed Muhammad Bilal, M. Irfan Uddin, Faten Khalid Karim, Samih M. Mostafa, José Varela-Aldás

“…However, this abundance leads to redundant information, posing a computational challenge for deep learning models. Thus, models must effectively extract indicative features. …”
Get full text

Adaptive Learning Algorithms for Low Dose Optimization in Coronary Arteries Angiography: A Comprehensive Review by Komal Tariq, Muhammad Adnan Munir, Hafiza Tooba Aftab, Amir Naveed, Ayesha Yousaf, Sajjad Ul Hassan

“…Results: The extracted data shows a comprehensive data on various techniques that are used for low dose CAA, advancements in image segmentation, noise reduction, and operator dose reduction highlight the potential of machine learning techniques. …”
Get full text

Element-specific estimation of background mutation rates in whole cancer genomes through transfer learning by Farideh Bahari, Reza Ahangari Cohan, Hesam Montazeri

“…Additionally, we provide an extensive analysis of BMR estimation, examining different machine learning models, genomic interval strategies, feature categories, and dimensionality reduction techniques.…”
Get full text

Blockchain-enabled federated learning with edge analytics for secure and efficient electronic health records management by Munusamy S, Jothi K R

“…Abstract The rapid adoption of Federated Learning (FL) in privacy-sensitive domains such as healthcare, IoT, and smart cities underscores its potential to enable collaborative machine learning without compromising data ownership. …”
Get full text

Optimizing MRI Scheduling in High-Complexity Hospitals: A Digital Twin and Reinforcement Learning Approach by Fabián Silva-Aravena, Jenny Morales, Manoj Jayabalan, Paula Sáez

“…Our strategy learns policies that maximize MRI machine utilization, minimize average waiting times, and ensure fairness by prioritizing urgent cases in the patient waitlist. …”
Get full text

Deep Learning-Based Coding Strategy for Improved Cochlear Implant Speech Perception in Noisy Environments by Billel Essaid, Hamza Kheddar, Noureddine Batel, Muhammad E. H. Chowdhury

“…The second strategy builds on this framework by additionally incorporating bidirectional gated recurrent units (Bi-GRU) alongside TCN and MHA layers, further refining sequence modeling and enhancing noise reduction. The optimal model configuration, using TCN-MHA-Bi-GRU with a kernel size of 16, achieved a compact model size of 788K parameters and recorded training, and validation losses of 0.0350 and 0.0446, respectively. …”
Get full text

Hierarchical Multi-Scale Decomposition and Deep Learning Ensemble Framework for Enhanced Carbon Emission Prediction by Yinuo Sun, Zhaoen Qu, Zhuodong Liu, Xiangyu Li

“…Traditional statistical and machine learning methods struggle to capture complex multi-scale temporal patterns and long-range dependencies in emission data. …”
Get full text