An FPGA Prototype for Parkinson’s Disease Detection Using Machine Learning on Voice Signal by Mujeev Khan, Abdul Moiz, Gani Nawaz Khan, Mohd Wajid, Mohammed Usman, Jabir Ali

“…This paper proposes an efficient machine learning model for PD detection using voice-based features, which offer a non-invasive, cost-effective, and accessible alternative to complex imaging methods. …”
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Deep learning to evaluate seismic-induced soil liquefaction and modified transfer learning between various data sources by Hongwei Guo, Chao Zhang, Hongyuan Fang, Timon Rabczuk, Xiaoying Zhuang

“…Also, the deep learning model is compared with several classical machine learning and ensemble learning models and the modified transfer learning model is formulated by comparing different feature transformation techniques integrated with various feature-based and instance-based transfer learning methods. …”
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Mamba and cross-channel aggregation for efficient multispectral image compression by Jingang Wang, Qizhi Fang, Jiahui Liu, Lili Zhang

“…Abstract In multispectral image compression, Convolutional Neural Network (CNN) effectively capture local features but are limited in modeling global context. …”
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AI-Driven Non-Invasive Diagnosis of Diverse Medical Conditions Through Nail Image Analysis with High-Performance Ensemble Classifier by Abeer Alshiha, Wai Woo

“…Feature extraction was performed using VGG16, ResNet50, and EfficientNetB0, and three machine learning classifiers, AdaBoost, LightGBM, and a Meta Classifier, were applied. …”
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Analyzing Optimal Wearable Motion Sensor Placement for Accurate Classification of Fall Directions by Sokea Teng, Jung-Yeon Kim, Seob Jeon, Hyo-Wook Gil, Jiwon Lyu, Euy Hyun Chung, Kwang Seock Kim, Yunyoung Nam

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Conversion of a Small-Size Passenger Car to Hydrogen Fueling: Evaluation of Boosting Potential and Peak Performance During Lean Operation by Adrian Irimescu, Simona Silvia Merola, Bianca Maria Vaglieco

“…The approach combined measurements performed with gasoline and predictive 0D/1D models for correctly including fuel chemistry effects; the experimental data were used for calibration purposes. …”
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Comparative Analysis of AI Models for Atypical Pigmented Facial Lesion Diagnosis by Alessandra Cartocci, Alessio Luschi, Linda Tognetti, Elisa Cinotti, Francesca Farnetani, Aimilios Lallas, John Paoli, Caterina Longo, Elvira Moscarella, Danica Tiodorovic, Ignazio Stanganelli, Mariano Suppa, Emi Dika, Iris Zalaudek, Maria Antonietta Pizzichetta, Jean Luc Perrot, Gabriele Cevenini, Ernesto Iadanza, Giovanni Rubegni, Harald Kittler, Philipp Tschandl, Pietro Rubegni

“…This work aimed to evaluate and compare the effectiveness of machine learning (logistic regression of lesion features and patient metadata) and deep learning (CNN analysis of images) models in dermoscopy diagnosis and the management of aPFLs. …”
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Protein structural domain-disease association prediction based on heterogeneous networks by Jingpu Zhang, Lianping Deng, Lei Deng

“…The results show that the binary classification model using the XGBOOST algorithm performs significantly better than models using other machine learning algorithms, achieving an AUC (Area Under Curve) score of 0.94 in the leave-one-out cross-validation experiment. …”
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Insect Bio-inspired Neural Network Provides New Evidence on How Simple Feature Detectors Can Enable Complex Visual Generalization and Stimulus Location Invariance in the Miniature... by Mark Roper, Chrisantha Fernando, Lars Chittka

“…We produced several simple models inspired by the known anatomical structures and neuronal responses within the bee brain and subsequently compared their ability to generalize achromatic patterns to the observed behavioural performance of honeybees on these cues. …”
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Prediction of formation energy for oxides in ODS steels by machine learning by Tian-Xing Yang, Peng Dou

“…The method developed in this study can establish an accurate prediction model for the formation energy of oxides. The knowledge obtained in this work guides the future development of high-performance ODS alloys.…”
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An early warning system for oil wells based on improved long short-term memory network by Jinman Li, Jinman Li, Chunsheng Zhang, Yang Lin, Yimeng Liu, Qingshuang Jin, Tianhao Xiao, Xiaoqi Liu, Ying Zhang

“…To further improve prediction accuracy, we introduce a Long Short-Term Memory (LSTM) model that integrates both dynamic and static well features, overcoming the limitations of traditional approaches. …”
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Augmenting Multimodal Content Representation with Transformers for Misinformation Detection by Jenq-Haur Wang, Mehdi Norouzi, Shu Ming Tsai

“…Finally, Multi-Cell Bi-GRUs with self-attention are used to train the credibility assessment model for misinformation detection. From the experimental results on tweets, the best performance with an accuracy of 0.904 and an F1-score of 0.921 can be obtained when applying feature fusion of augmented embeddings with sentiment classification results. …”
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BIMSSA: enhancing cancer prediction with salp swarm optimization and ensemble machine learning approaches by Pinakshi Panda, Sukant Kishoro Bisoy, Amrutanshu Panigrahi, Abhilash Pati, Bibhuprasad Sahu, Zheshan Guo, Haipeng Liu, Prince Jain

“…Microarray data are high dimensional, which can degrade the performance of the ML-based models. For this, feature selection becomes essential.MethodsSwarm Optimization Algorithm (SSA), Improved Maximum Relevance and Minimum Redundancy (IMRMR), and Boruta form the basis of this work’s ML-based model BIMSSA. …”
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A Detection Method for Sweet Potato Leaf Spot Disease and Leaf-Eating Pests by Kang Xu, Yan Hou, Wenbin Sun, Dongquan Chen, Danyang Lv, Jiejie Xing, Ranbing Yang

“…Finally, in the model architecture, only the structure of the backbone network and the decoupling head combination was retained, and the traditional backbone network was replaced by an efficient feature extraction module, which greatly reduced the model complexity. …”
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Good Practices Preceding the Implementation of the System of Management of Environment, on Small and Medium Enterprises by Anca Atanase, Ion Schileru, Smaranda Vişan

“…The good practices in environment management become more and more necessary and, fortunately, are supplied more and more often by organisations in SMEs category, which encounter highly valuable solutions in this context charged with convictions. This work features an exemplary performance of a small organisation that managed to settle an informal system of management of environment, built up with much rigour, involvement, respect for society and environment. …”
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A Pipeline for Multivariate Time Series Forecasting of Gas Consumption in Pelletization Process by Thadeu Pezzin Melo, Jefferson Andrade, Karin Satie Komati

“…Although AutoML did not outperform the statistical model in terms of RMSE values, regarding training time, AutoML models were significantly more efficient than the statistical approach, optimizing computational resource usage and enabling faster model adjustments. …”
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DFN-YOLO: Detecting Narrowband Signals in Broadband Spectrum by Kun Jiang, Kexiao Peng, Yuan Feng, Xia Guo, Zuping Tang

“…The DFN-YOLO model incorporates a deformable channel feature fusion network (DCFFN), replacing the concatenate-to-fusion (C2f) module to enhance the extraction and integration of channel features. …”
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ALPD-Net: a wild licorice detection network based on UAV imagery by Jing Yang, Huaibin Qin, Jianguo Dai, Guoshun Zhang, Miaomiao Xu, Yuan Qin, Jinglong Liu

“…Traditional licorice resource survey methods are unsuitable for complex terrain and do not meet the requirements of large-scale monitoring.MethodsIn order to solve this problem, this study constructs a new dataset of wild licorice that was gathered using Unmanned Aerial Vehicle (UAV) and proposes a novel detection network named ALPD-Net for identifying wild licorice. To improve the model’s performance in complex backgrounds, an Adaptive Background Suppression Module (ABSM) was designed. …”
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Research Progress on Modulation Format Recognition Technology for Visible Light Communication by Shengbang Zhou, Weichang Du, Chuanqi Li, Shutian Liu, Ruiqi Li

“…It also explores the advancements brought by deep learning (DL) technology, particularly in enhancing noise robustness, classification accuracy, and cross-scenario adaptability through automatic feature extraction and nonlinear mapping. The findings indicate that DL-based MFR substantially enhances recognition performance in intricate channels via multi-dimensional feature fusion, lightweight architectures, and meta-learning paradigms. …”
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Improving forest above-ground biomass estimation using genetic-based feature selection from Sentinel-1 and Sentinel-2 data (case study of the Noor forest area in Iran) by Armin Moghimi, Ava Tavakoli Darestani, Nikrouz Mostofi, Mahdiyeh Fathi, Meisam Amani

“…Furthermore, the GA-RF model selected a wider variety of features including spectral bands, indices, and SAR features compared to GA-MLR, resulting in accurate AGB estimation in the Noor forest.…”
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