Development and Investigation of Vision System for a Small-Sized Mobile Humanoid Robot in a Smart Environment by Amer Tahseen Abu-Jassar, Hani Attar, Ayman Amer, Vyacheslav Lyashenko, Vladyslav Yevsieiev, Ahmed Solyman

“…A structure of information interaction between hardware modules is proposed, and a connection scheme is developed, on the basis of which a model of a computer vision system is assembled for research, with the required algorithmic and software for solving the problem. To ensure the high speed of the computer vision system based on the ESP32-CAM module, the neural network was improved by replacing the Visual Geometry Group 16 (VGG-16) network as the base network for extracting the functions of the Single Shot Detector (SSD) network model with the tiny-YOLO lightweight network model, which made it possible to preserve the multidimensional structure of the network model feature graph, resulting in increasing the detection accuracy, while significantly reducing the amount of calculations generated by the network operation, thereby significantly increasing the detection speed, due to a limited set of objects. …”
Get full text

The EyCon Dataset: A Visual Corpus of Early Conflict Photography by Marina Giardinetti, Daniel Foliard, Julien Schuh, Mohamed-Salim Aissi

“…Available online and on Zenodo for download and reuse, the dataset confronts issues in computer vision for heritage photographs, such as degradation from fading, discoloration, scratches and noise, which impair algorithms reliant on visual features. The under-representation of early photographic cultures in datasets introduces bias in applying standard solutions to archival materials.…”
Get full text

Boosting skin cancer diagnosis accuracy with ensemble approach by Priya Natha, Sivarama Prasad Tera, Ravikumar Chinthaginjala, Safia Obaidur Rab, C. Venkata Narasimhulu, Tae Hoon Kim

“…Overall performance was increased by the combined predictions made with the Max Voting technique. Moreover, feature vectors that were optimally produced from image data by a Genetic Algorithm (GA) were given to the ML models. …”
Get full text

Quantitative Prediction of Protein Content in Corn Kernel Based on Near-Infrared Spectroscopy by Chenlong Fan, Ying Liu, Tao Cui, Mengmeng Qiao, Yang Yu, Weijun Xie, Yuping Huang

“…Near-infrared spectral data from different varieties of maize grain powder were collected, and quantitative analysis of protein content was conducted using Partial Least Squares Regression (PLSR), Support Vector Machine (SVM), and Extreme Learning Machine (ELM) models. Feature wavelengths were selected to enhance model accuracy further using the Successive Projections Algorithm (SPA) and Uninformative Variable Elimination (UVE). …”
Get full text

Machine learning for predicting metabolic-associated fatty liver disease including NHHR: a cross-sectional NHANES study. by Liyu Lin, Yirui Xie, Zhuangteng Lin, Cuiyan Lin, Yichun Yang

“…Furthermore, NHHR was identified as the second most important feature in the SHAP analysis, following body mass index (BMI), highlighting its potential in predicting MAFLD.…”
Get full text

Towards full integration of explainable artificial intelligence in colon capsule endoscopy’s pathway by Esmaeil S. Nadimi, Jan-Matthias Braun, Benedicte Schelde-Olesen, Smith Khare, Vinay C. Gogineni, Victoria Blanes-Vidal, Gunnar Baatrup

“…We developed a family of algorithms based on explainable deep neural networks (DNN) that detect polyps within a sequence of images, feed only those images containing polyps into two parallel independent networks to characterize, and estimate the size of important findings. …”
Get full text

Deep-Learning and Dynamic Time Warping-Based Approaches for the Diagnosis of Reactor Systems by Hoejun Jeong, Jihyun Kim, Doyun Jung, Jangwoo Kwon

“…Previous studies have concentrated on the detection of clamping force degradation but have been constrained in their ability to identify the precise size and position. …”
Get full text

A Method of Modal Parameter Identification for Wind Turbine Blade Based on Binocular Dynamic Photogrammetry by Wenyun Wang, Xuejun Li, Anhua Chen

“…Image feature extraction and target point tracking (PT) are carried out to acquire the displacement of labeled targets on the wind turbine blade. …”
Get full text

Parallel Implementation of Gaussian Filter Image Processing on a Cluster of Single Board Computer by Achmad Nurul Fauzie, Setyawan Purnomo Sakti, Rahmadwati

“…Gaussian filters are widely used in image processing applications, such as edge detection, segmentation, and feature extraction. However, computationally intensive computations can take a long time to process large images. …”
Get full text

A bearing fault diagnosis method based on hybrid artificial intelligence models. by Lijie Sun, Xin Tao, Yanping Lu

“…The process employs Maximum Second-order Cyclostationary Blind Deconvolution (CYCBD) to filter out noise from the vibration signals emitted by bearings; secondly, considering the issue with the conventional Harris Hawks Optimization (HHO) algorithm which tends to prematurely converge to local optima, the differential evolution mutation operator is introduced and the escape energy factor is improved from linear to nonlinear in IHHO; then, a double-layer network model based on DBN-ELM is proposed, to avoid the number of hidden layer nodes of DBN from human experience interference, and IHHO is used to optimize DBN structure, which is denoted as IHHO-DBN-ELM method; with the optimal structure is obtained by using a combined IHHO optimized DBN and ELM; in conclusion, the proposed IHHO-DBN-ELM approach is applied to the bearing fault detection using the Western Reserve University's bearing fault dataset. …”
Get full text

Optimized decomposition and identification method for multiple power quality disturbances by Wang Zhaoqing, Chang Yanzhao, Chen Jianlei, Bao Weiyu

“…The OSVM strategically employs distinct kernel functions to augment the discriminability of the feature set. The synergy of IVMD and OSVM enables the detection of multiple PQDs, remarkably even with a minimal amount of training data. …”
Get full text

A Short-Term Risk Prediction Method Based on In-Vehicle Perception Data by Xinpeng Yao, Nengchao Lyu, Mengfei Liu

“…The proposed approach provides an effective solution for short-term risk prediction and offers algorithmic support for future ADAS applications.…”
Get full text

Application of optical flow method in metro train speed measurement by LYU Hongqiang, HUANG Tao, NIE Xingjia, CHEN Keyu, CAI Zhengkai, SHAN Qi

“…The image pyramid was introduced to solve the optical flow of large displacement motion through the method of coarse to fine and layer by layer refinement, which solved the defect that LK optical flow method could not calculate large displacement motion; GPU was used in optical flow calculation to realize the parallelization of image feature point detection and calculation, which significantly improved the speed of optical flow calculation; The line simulation software was designed for speed measurement simulation experiment. …”
Get full text

Multitarget Tracking of Pedestrians in Video Sequences Based on Particle Filters by Hui Li, Shengwu Xiong, Pengfei Duan, Xiangzhen Kong

“…In order to improve the efficiency and accuracy of the detection, the algorithm firstly obtains target regions in training frames by combining the methods of background subtraction and Histogram of Oriented Gradient (HOG) and then establishes discriminative appearance model by generating patches and constructing codebooks using superpixel and Local Binary Pattern (LBP) features in those target regions. …”
Get full text

Machine learning with the body roundness index and associated indicators: a new approach to predicting metabolic syndrome by Yaxuan He, Zekai Chen, Zhaohui Tang, Yuexiang Qin, Fang Wang

“…Developing a non-invasive, accurate prediction model is clinically significant for early MetS detection and prevention. Objective To develop a non-invasive prediction model for MetS by integrating body roundness index (BRI), gender, age, height, and waist circumference with machine learning algorithms, and to validate its generalizability across different ethnic groups. …”
Get full text

Human Action Recognition Method Based on Multi-channel Fusion by Zhiyong TAO, Xijun GUO, Xiaokui REN, Ying LIU, Zemin WANG

“…This mechanism must overcome the multipath effect in multi-channel transmission to ensure accuracy and consistency when extracting action features. In this challenging context, innovative algorithms and model designs are crucial for addressing differences in CSI features between various actions and enhancing the model’s generalization ability and robustness.Method This study explores human actions’ physical and MIMO transmission characteristics, proposing a deep learning-based human action recognition method that employs a dual attention mechanism and a multi-channel, multi-scale fusion temporal convolution network to address the above challenges. …”
Get full text

Machine Learning for Chinese Corporate Fraud Prediction: Segmented Models Based on Optimal Training Windows by Chang Chuan Goh, Yue Yang, Anthony Bellotti, Xiuping Hua

“…The dissimilarities between the top features set of the general and segmented models suggest that segmented models are useful in providing a better understanding of fraud occurrence.…”
Get full text

Enhancing wheat flour origin traceability by using laser-induced breakdown spectroscopy and Raman spectroscopy by Minghui Gu, Chao Liu, Hansong Huang, Xin Zhang, Jiguo Li, Qingbin Jiao, Liang Xu, Mingyu Yang, Xin Tan

“…First, a hybrid feature selection method, termed ANOVA-Sine Cosine Algorithm (AVSCA), was developed by integrating analysis of variance (ANOVA) with the Sine Cosine Algorithm (SCA) to extract spectral fingerprint information of wheat flour. …”
Get full text

RADAI: A Deep Learning-Based Classification of Lung Abnormalities in Chest X-Rays by Hanan Aljuaid, Hessa Albalahad, Walaa Alshuaibi, Shahad Almutairi, Tahani Hamad Aljohani, Nazar Hussain, Farah Mohammad

“…The RadAI model can accurately detect four types of lung abnormalities in chest X-rays and generate a report on each identified abnormality. …”
Get full text

Explainable Machine Learning in the Prediction of Depression by Christina Mimikou, Christos Kokkotis, Dimitrios Tsiptsios, Konstantinos Tsamakis, Stella Savvidou, Lillian Modig, Foteini Christidi, Antonia Kaltsatou, Triantafyllos Doskas, Christoph Mueller, Aspasia Serdari, Kostas Anagnostopoulos, Gregory Tripsianis

“…Additionally, a genetic algorithm (GA) was utilized for feature selection and SHAP (SHapley Additive exPlanations) for interpreting the contributions of each employed feature. …”
Get full text