Large language models for depression recognition in spoken language integrating psychological knowledge by Yupei Li, Shuaijie Shao, Manuel Milling, Manuel Milling, Björn W. Schuller, Björn W. Schuller, Björn W. Schuller, Björn W. Schuller

“…We extract the audio features using the pre-trained model Wav2Vec, and map them to text-based LLMs for further processing. …”
Get full text

Design of experiments for the stochastic unit commitment with economic dispatch models by Nahal Sakhavand, Jay Rosenberger, Victoria C.P. Chen, Harsha Gangammanavar

“…To address this challenge, we develop a novel DACE approach that exploits the structure of the first-stage unit commitment decision space in a design of experiments, uses features based upon solar generation, and trains a multivariate adaptive regression splines model to approximate the second stage of the stochastic unit commitment-economic dispatch problem. …”
Get full text

Ensemble Learning with Multiclassifiers on Pediatric Hand Radiograph Segmentation for Bone Age Assessment by Rui Liu, Yuanyuan Jia, Xiangqian He, Zhe Li, Jinhua Cai, Hao Li, Xiao Yang

“…We utilized the normalized histogram, mean value, and variance of each image as input features to train the classification model, based on ensemble learning with multiple classifiers. 600 left-hand radiographs with the bone age ranging from 1 to 18 years old were included in the dataset. …”
Get full text

Enhanced Wheat Head Detection in Images Using Fourier Domain Adaptation and Random Guided Filter: Détection améliorée des têtes de blé dans les images à l’aide de l’adaptation du d... by Sylvester C. Okafor, Linjing Wei, Solomon Boamah, Le Zhang, Mamadou B. Diallo

“…An EfficientDet model was trained on the publicly available wheat dataset and the results were analyzed and compared to a baseline model. …”
Get full text

The Cart-Pole Application as a Benchmark for Neuromorphic Computing by James S. Plank, Charles P. Rizzo, Chris A. White, Catherine D. Schuman

“…Spiking neural networks are the basis of brain-based, or neuromorphic computing. They are attractive, especially as agents for control applications, because of their very low size, weight and power requirements. …”
Get full text

Field Measurement and Numerical Simulation of Sound Insulation Characteristics of Buildings Along Elevated Urban Rail Lines by Teng Wang, Wei He, Dongyang Wang, Jialiang Chen

“…The noise pollution issue along elevated urban rail transit lines has attracted considerable attention; however, research on the sound insulation characteristics of buildings during operational train noise remains limited. In this study, a field testing campaign was conducted in a building featuring facades constructed from aerated concrete blocks embedded with single-layer glass sliding windows along an elevated rail transit line. …”
Get full text

The Role of Education in Building National Soft Power: An Empirical Analysis From a Global Perspective Using Deep Neural Networks by Yun Bai

“…First, we preprocess the data by standardizing the features and handling missing values. Secondly, we build and train a DNN with multiple hidden layers to capture nonlinear relationships between educational factors and soft power scores. …”
Get full text

Computed tomography enterography radiomics and machine learning for identification of Crohn’s disease by Qiao Shi, Yajing Hao, Huixian Liu, Xiaoling Liu, Weiqiang Yan, Jun Mao, Bihong T. Chen

“…A machine learning classification system was constructed by combining six selected radiomics features with eight classification algorithms. The models were trained using leave-one-out cross-validation and evaluated for accuracy. …”
Get full text

A deep learning model for fault detection in distribution networks with high penetration of electric vehicle chargers by Seyed Amir Hosseini, Behrooz Taheri, Seyed Hossein Hesamedin Sadeghi, Adel Nasiri

“…These features are then used to train a deep learning model based on black widow optimization bi-directional long short-term memory (BWO-BiLSTM) technique. …”
Get full text

Lncident: A Tool for Rapid Identification of Long Noncoding RNAs Utilizing Sequence Intrinsic Composition and Open Reading Frame Information by Siyu Han, Yanchun Liang, Ying Li, Wei Du

“…Here, we present a novel method for rapid identification of lncRNAs utilizing sequence intrinsic composition features and open reading frame information based on support vector machine model, named as Lncident (LncRNAs identification). …”
Get full text

Efficient Moving Object Segmentation in LiDAR Point Clouds Using Minimal Number of Sweeps by Zoltan Rozsa, Akos Madaras, Tamas Sziranyi

“…The model is trained on a dataset of LiDAR point clouds and related camera images. …”
Get full text

Improving microvascular brain analysis with adversarial learning for OCT–TPM vascular domain translation by Nadia Badawi, Jaloliddin Rustamov, Zahiriddin Rustamov, Frederic Lesage, Nazar Zaki, Rafat Damseh

“…We investigate the use of 2D and 3D cycle generative adversarial networks (CycleGANs) trained on unpaired image samples. We evaluate the generated TPM vascular structures based on image similarity and signal-to-noise ratio. …”
Get full text

Predicting the diffusion coefficients of rejuvenators into bitumens using molecular dynamics, machine learning, and force field atom types by Eli I. Assaf, Xueyan Liu, Peng Lin, Shisong Ren, Sandra Erkens

“…The manuscript presents three case studies to illustrate how the model can be used for the iterative design of rejuvenators by optimizing molecular structures based on critical chemical features, such as rejuvenator oxygen content, bitumen sulfur content, and molecular weights. …”
Get full text

Enhancing Brain Tumor Diagnosis with L-Net: A Novel Deep Learning Approach for MRI Image Segmentation and Classification by Lehel Dénes-Fazakas, Levente Kovács, György Eigner, László Szilágyi

“…These two units are coupled such a way that the CNN classifies the MRI images based on the features extracted by the U-net while segmenting the tumor, instead of relying on the original input images. …”
Get full text

Point Defect Detection and Classification in MoS₂ Scanning Tunneling Microscopy Images: A Deep Learning Approach by Shiru Wu, Guoyang Chen, Si Shen, Jiaxu Yan

“…This deep learning pipeline is trained on augmented STM image data and evaluated against manual annotations. …”
Get full text

Annotated intraoral image dataset for dental caries detection by Syed Muhammad Faizan Ahmed, Muhammad Huzaifa Ghori, Aamna Khalid, Ayesha Nooruddin, Niha Adnan, Abhishek Lal, Fahad Umer

“…This work advances AI-based dental diagnostics and sets a benchmark for caries detection. …”
Get full text

A robust deep learning approach for segmenting cortical and trabecular bone from 3D high resolution µCT scans of mouse bone by Amine Lagzouli, Peter Pivonka, David M. L. Cooper, Vittorio Sansalone, Alice Othmani

“…DBAHNet’s hierarchical structure combines transformers and convolutional neural networks to capture long-range dependencies and local features for improved contextual representation. We trained DBAHNet on a limited dataset of 3D µCT scans of mouse tibiae and evaluated its performance on a diverse dataset collected from seven different research studies. …”
Get full text

Speech as a Biomarker for Disease Detection by Catarina Botelho, Alberto Abad, Tanja Schultz, Isabel Trancoso

“…The classification strategy explored is based on Neural Additive Models, a type of glass-box neural network, which enables interpretability. …”
Get full text

MCAUnet: a deep learning framework for automated quantification of body composition in liver cirrhosis patients by Jiening Wang, Shuqi Xia, Jie Zhang, Xinyi Wang, Cai Zhao, Wen Zheng

“…A total of 11,362 L3-level lumbar CT slices were collected to train and validate the segmentation model. The proposed model incorporates an attention mechanism from the channel perspective, enabling adaptive fusion of critical channel features. …”
Get full text

An Unsupervised Learning Approach for Coal Spontaneous Combustion Warning Level Classification Using t-SNE and k-Means Clustering by Pengyu Zhang, Xiaokun Chen

“…The indicator gases and warning levels were input into a trained Support Vector Classifier (SVC) to establish a classification model for coal spontaneous combustion warning levels in goaf areas. …”
Get full text