Search Results - signal data processing and classification

MSFF-Net: Multi-Sensor Frequency-Domain Feature Fusion Network with Lightweight 1D CNN for Bearing Fault Diagnosis by Miao Dai, Hangyeol Jo, Moonsuk Kim, Sang-Woo Ban

“…Ablation studies confirm that multi-sensor fusion improves all classification metrics over single-sensor setups. Under few-shot conditions with 20 samples per class, the model retains 94.69% accuracy, highlighting its strong generalization in data-limited scenarios. …”
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Research Progress on Vehicle Status Information Perception Based on Distributed Acoustic Sensing by Wenqiang Dong, Xin Cheng, Jingmei Zhou, Wei Liu, Jianjin Gao, Chuan Hu, Xiangmo Zhao

“…This study further examines the principles, advantages, limitations, and application scenarios of various DAS signal processing algorithms. Traditional methods are becoming less effective in handling massive data generated by numerous distributed nodes. …”
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EEG-SymNet: multi-channel EEG signal-based schizophrenia diagnosis using channel recalibration and symmetric spatial temporal transformer network by Naif Alsharabi, Rakesh Kumar Mahendran, Gharbi Alshammari

“…The pre-processed signals are then fed into our model, which consists of four main modules. …”
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A Multi-Modal Approach Using a Hybrid Vision Transformer and Temporal Fusion Transformer Model for Stock Price Movement Classification by Ibanga Kpereobong Friday, Sarada Prasanna Pati, Debahuti Mishra

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Spectral Components of Honey Bee Sound Signals Recorded Inside and Outside the Beehive: An Explainable Machine Learning Approach to Diurnal Pattern Recognition by Piotr Książek, Urszula Libal, Aleksandra Król-Nowak

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PROPER: Personality Recognition Based on Public Speaking Using Electroencephalography Recordings by Muhammad Majid, Amna Rauf Butt, Imran Fareed Nizami, Aamir Arsalan, Jihyoung Ryu

“…The personality recognition process involves data acquisition, pre-processing, feature extraction and selection, and classification. …”
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Introducing a Deep Neural Network Model with Practical Implementation for Polyp Detection in Colonoscopy Videos by Hajar Keshavarz, Zohreh Ansari, Hossein Abootalebian, Babak Sabet, Mohammadreza Momenzadeh

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AI-enabled OSA screening using EEG data analysis and English listening comprehension insights by Zhihong Gu, Yanbing Fan

“…Furthermore, these methods rarely incorporate insights from cognitive and auditory processing frameworks that could deepen diagnostic precision.MethodsTo address these gaps, we propose an AI-enabled screening methodology that utilizes EEG signals in conjunction with insights from English listening comprehension models. …”
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Improved Liquefaction Hazard Assessment via Deep Feature Extraction and Stacked Ensemble Learning on Microtremor Data by Oussama Arab, Soufiana Mekouar, Mohamed Mastere, Roberto Cabieces, David Rodríguez Collantes

“…Regarding this challenge, our research proposes a new approach in the signal processing chain and feature extraction from microtremor data that focuses mainly on the Horizontal-to-Vertical Spectral Ratio (HVSR) so as to assess liquefaction potential as a natural hazard using AI. …”
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Are Artificial Intelligence Models Listening Like Cardiologists? Bridging the Gap Between Artificial Intelligence and Clinical Reasoning in Heart-Sound Classification Using Explain... by Sami Alrabie, Ahmed Barnawi

“…This study is motivated by two key concerns in the field of heart-sound classification. First, we observed that automatic heart-sound segmentation algorithms—commonly used for data augmentation—produce varying outcomes, raising concerns about the accuracy of both the segmentation process and the resulting classification performance. …”
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Combined Method for Informative Feature Selection for Speech Pathology Detection by D. S. Likhachov, M. I. Vashkevich, N. A. Petrovsky, E. S. Azarov

“…The task of detecting vocal abnormalities is characterized by a small amount of available data for training, as a consequence of which classification systems that use low-dimensional data are the most relevant. …”
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IT Diagnostics of Parkinson’s Disease Based on the Analysis of Voice Markers and Machine Learning by U. A. Vishniakou, Xia YiWei

“…Then the parameters of the processed spectrum of speech data were determined: average value, maximum and minimum, peak, wavelet coefficients, MFCC and TQWT. …”
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Multi-ancestry genome- and phenome-wide association studies of diverticular disease in electronic health records with natural language processing enriched phenotyping algorithm. by Yoonjung Yoonie Joo, Jennifer A Pacheco, William K Thompson, Laura J Rasmussen-Torvik, Luke V Rasmussen, Frederick T J Lin, Mariza de Andrade, Kenneth M Borthwick, Erwin Bottinger, Andrew Cagan, David S Carrell, Joshua C Denny, Stephen B Ellis, Omri Gottesman, James G Linneman, Jyotishman Pathak, Peggy L Peissig, Ning Shang, Gerard Tromp, Annapoorani Veerappan, Maureen E Smith, Rex L Chisholm, Andrew J Gawron, M Geoffrey Hayes, Abel N Kho

“…Our aim was to identify genetic risk variants and clinical phenotypes associated with DD, leveraging multiple electronic health record (EHR) data sources of 91,166 multi-ancestry participants with a Natural Language Processing (NLP) technique.…”
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Multimodal Knowledge Distillation for Emotion Recognition by Zhenxuan Zhang, Guanyu Lu

“…Among various physiological signals, EEG signals and EOG data are highly valued for their complementary strengths in emotion recognition. …”
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Discriminant analysis of variational pulsometry parameters by U. I. Silkina, V. A. Balandin

“…The durations of the RR cardio intervals of patients at the age of 60-70 years are the initial data in the research. The data were taken from the databases of medical signals of the open international resource Physionet. …”
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Multisensor Fault Diagnosis of Rolling Bearing with Noisy Unbalanced Data via Intuitionistic Fuzzy Weighted Least Squares Twin Support Higher-Order Tensor Machine by Shengli Dong, Yifang Zhang, Shengzheng Wang

“…At the global level, the class contribution is assessed based on the relative position of the samples to the classification boundary; at the local level, the topological structural features of the sample distribution are captured by <i>K</i>-nearest neighbor analysis; this mechanism significantly improves the recognition of noisy samples and the handling of class-imbalanced data. …”
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Local Transmissibility-Based Identification of Structural Damage Utilizing Positive Learning Strategies by Oguz Gunes, Burcu Gunes

“…Recent advances in sensor technology, data acquisition, and signal processing have enabled the development of data-driven structural health monitoring (SHM) strategies, offering a powerful alternative or complement to traditional model-based approaches. …”
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Automatic detection of sleep spindles by neural networks algorithms by Jan Rychlík, Roman Mouček

“…This research aims to introduce, formulate, execute, and assess diverse machine/deep learning methodologies tailored for the processing of EEG signals geared explicitly towards identifying sleep spindles. …”
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Understanding and Addressing Variability Through Recalibration in a Brain&#x2013;Computer Interface for Intelligent Vehicle Control Using Long-Term Implanted Human Neural Data: A C... by Yicong Xiao, Spencer Kellis, Christopher. F Reiche, Florian Solzbacher

“…Error from both sources may increase over time with changes in the neural signal or recording process. In practice, systematic error could be addressed through recalibration of model parameters, where the frequency of recalibration is based on the rate of performance loss. …”
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Assessment of transportation capacity balance of railway network polygons by A. F. Borodin, V. V. Panin, M. A. Ageeva, A. Yu. Sokolov, S. Yu. Kirillova, E. O. Dmitriev, A. A. Kravchenko

“…The article presented the results of the substantiation of the normative restrictions on the daily time budget for the passage of trains, depending on the classification and specialisation of railway lines. The procedure for calculating the traffic capacity of sections with automatic locomotive signalling is determined as an independent means of interval control of train traffic with movable boundaries of block sections that change depending on the speed and mass of the train. …”
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