Ensemble Machine Learning Model for Inner Speech Recognition: A Subject-Specific Investigation | Paper Citation Record

Observation 54ce2cba-2b83-4b5b-984d-a68a8c6f020c · outbound

This paper cites up," "down,.

Ensemble Machine Learning Model for Inner Speech Recognition: A Subject-Specific Investigation up," "down,

Reference 1

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Observation eb4b7318-e2ed-4ba4-abce-1fa8bfddbabe · outbound

This paper cites Thinking Out Loud Dataset.

Ensemble Machine Learning Model for Inner Speech Recognition: A Subject-Specific Investigation Thinking Out Loud Dataset

Reference 2

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Ensemble Machine Learning Model for Inner Speech Recognition: A Subject-Specific Investigation Unresolved cited work

Reference 3

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This paper cites an unresolved cited work.

Ensemble Machine Learning Model for Inner Speech Recognition: A Subject-Specific Investigation Unresolved cited work

Reference 4

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Ensemble Machine Learning Model for Inner Speech Recognition: A Subject-Specific Investigation Unresolved cited work

Reference 5

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Ensemble Machine Learning Model for Inner Speech Recognition: A Subject-Specific Investigation Unresolved cited work

Reference 6

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This paper cites The Origin of Speech,.

Ensemble Machine Learning Model for Inner Speech Recognition: A Subject-Specific Investigation The Origin of Speech,

Reference 7

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This paper cites selecting K best features instead of best K features.

Ensemble Machine Learning Model for Inner Speech Recognition: A Subject-Specific Investigation selecting K best features instead of best K features

Reference 8

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T19:29:28.423718Z

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This paper cites It expresses the accuracy as the ratio of accurate predictions to the total number of predictions.

Ensemble Machine Learning Model for Inner Speech Recognition: A Subject-Specific Investigation It expresses the accuracy as the ratio of accurate predictions to the total number of predictions

Reference 9

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T19:29:28.410487Z

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This paper cites The F1 score is calculated as the harmonic mean of precision and recall, providing a well-rounded evaluation of a model's performance.

Ensemble Machine Learning Model for Inner Speech Recognition: A Subject-Specific Investigation The F1 score is calculated as the harmonic mean of precision and recall, providing a well-rounded evaluation of a model's performance

Reference 10

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T19:29:28.396630Z

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This paper cites Arriba” and “Izquierda.

Ensemble Machine Learning Model for Inner Speech Recognition: A Subject-Specific Investigation Arriba” and “Izquierda

Reference 11

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T19:29:28.382494Z

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This paper cites Data were collected from 10 healthy subjects, and four words were uttered by each of them during inner speech tasks.

Ensemble Machine Learning Model for Inner Speech Recognition: A Subject-Specific Investigation Data were collected from 10 healthy subjects, and four words were uttered by each of them during inner speech tasks

Reference 12

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T19:29:28.362911Z

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This paper cites The Margins of the Language Network in the Brain,.

Ensemble Machine Learning Model for Inner Speech Recognition: A Subject-Specific Investigation The Margins of the Language Network in the Brain,

Reference 13

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T19:29:28.349495Z

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This paper cites The Organization of Language and the Brain,.

Ensemble Machine Learning Model for Inner Speech Recognition: A Subject-Specific Investigation The Organization of Language and the Brain,

Reference 14

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T19:29:28.337691Z

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This paper cites Monkey to human comparative anatomy of the frontal lobe association tracts,.

Ensemble Machine Learning Model for Inner Speech Recognition: A Subject-Specific Investigation Monkey to human comparative anatomy of the frontal lobe association tracts,

Reference 15

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T19:29:28.321876Z

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This paper cites Evolution of the neural language network,.

Ensemble Machine Learning Model for Inner Speech Recognition: A Subject-Specific Investigation Evolution of the neural language network,

Reference 16

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T19:29:28.308245Z

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This paper cites Words in the brain's language,.

Ensemble Machine Learning Model for Inner Speech Recognition: A Subject-Specific Investigation Words in the brain's language,

Reference 17

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T19:29:28.294066Z

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This paper cites A review and synthesis of the first 20 years of PET and fMRI studies of heard speech, spoken language and reading,.

Ensemble Machine Learning Model for Inner Speech Recognition: A Subject-Specific Investigation A review and synthesis of the first 20 years of PET and fMRI studies of heard speech, spoken language and reading,

Reference 18

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T19:29:28.276253Z

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This paper cites Neural reuse of action perception circuits for language, concepts and communication,.

Ensemble Machine Learning Model for Inner Speech Recognition: A Subject-Specific Investigation Neural reuse of action perception circuits for language, concepts and communication,

Reference 19

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Observation c11d3480-bd24-4e37-9633-69882a2dc23d · outbound

This paper cites Body-part-specific Representations of Semantic Noun Categories,.

Ensemble Machine Learning Model for Inner Speech Recognition: A Subject-Specific Investigation Body-part-specific Representations of Semantic Noun Categories,

Reference 20

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T19:29:28.234862Z

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This paper cites The motor features of action verbs: fMRI evidence using picture naming,.

Ensemble Machine Learning Model for Inner Speech Recognition: A Subject-Specific Investigation The motor features of action verbs: fMRI evidence using picture naming,

Reference 21

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T19:29:28.222710Z

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Observation b850ed13-d653-44c9-b56a-3a799a6fcb5f · outbound

This paper cites Interpersonal processes and brain sciences —A new anthropology,.

Ensemble Machine Learning Model for Inner Speech Recognition: A Subject-Specific Investigation Interpersonal processes and brain sciences —A new anthropology,

Reference 22

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T19:29:28.208039Z

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Observation aec3dec8-4e50-4a35-8938-f35c3065bc84 · outbound

This paper cites Emotion recognition from EEG using higher order crossings,.

Ensemble Machine Learning Model for Inner Speech Recognition: A Subject-Specific Investigation Emotion recognition from EEG using higher order crossings,

Reference 23

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T19:29:28.194877Z

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This paper cites Multiple kernel learning based on three discriminant features for a P300 speller BCI,.

Ensemble Machine Learning Model for Inner Speech Recognition: A Subject-Specific Investigation Multiple kernel learning based on three discriminant features for a P300 speller BCI,

Reference 24

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T19:29:28.182145Z

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This paper cites Sonification and textification: Proposing methods for classifying unspoken words from EEG signals,.

Ensemble Machine Learning Model for Inner Speech Recognition: A Subject-Specific Investigation Sonification and textification: Proposing methods for classifying unspoken words from EEG signals,

Reference 25

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T19:29:28.165859Z

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This paper cites Classifying phonological categories in imagined and articulated speech,.

Ensemble Machine Learning Model for Inner Speech Recognition: A Subject-Specific Investigation Classifying phonological categories in imagined and articulated speech,

Reference 26

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T19:29:28.152694Z

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This paper cites Covert speech vs. motor imagery: a comparative study of class separability in identical environments,.

Ensemble Machine Learning Model for Inner Speech Recognition: A Subject-Specific Investigation Covert speech vs. motor imagery: a comparative study of class separability in identical environments,

Reference 27

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T19:29:28.138860Z

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This paper cites Jahangiri and F.

Ensemble Machine Learning Model for Inner Speech Recognition: A Subject-Specific Investigation Jahangiri and F

Reference 28

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T19:29:28.126433Z

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This paper cites Optimizing layers improves CNN generalization and transfer learning for imagined speech decoding from EEG,.

Ensemble Machine Learning Model for Inner Speech Recognition: A Subject-Specific Investigation Optimizing layers improves CNN generalization and transfer learning for imagined speech decoding from EEG,

Reference 29

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T19:29:28.114115Z

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This paper cites Classification of V owels from Imagined Speech with Convolutional Neural Networks,.

Ensemble Machine Learning Model for Inner Speech Recognition: A Subject-Specific Investigation Classification of V owels from Imagined Speech with Convolutional Neural Networks,

Reference 30

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T19:29:28.099637Z

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This paper cites Learning Patterns in Imaginary Vowels for an Intelligent Brain Computer Interface (BCI) Design.

Ensemble Machine Learning Model for Inner Speech Recognition: A Subject-Specific Investigation Learning Patterns in Imaginary Vowels for an Intelligent Brain Computer Interface (BCI) Design

Reference 31

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This paper cites Multiclass covert speech classification using extreme learning machine,.

Ensemble Machine Learning Model for Inner Speech Recognition: A Subject-Specific Investigation Multiclass covert speech classification using extreme learning machine,

Reference 32

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T19:29:28.085387Z

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This paper cites Inferring imagined speech using EEG signals: a new approach using Riemannian manifold features,.

Ensemble Machine Learning Model for Inner Speech Recognition: A Subject-Specific Investigation Inferring imagined speech using EEG signals: a new approach using Riemannian manifold features,

Reference 33

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T19:29:28.068103Z

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Observation 56e190c3-c3f0-4f5c-a29d-2d98bd7217ca · outbound

This paper cites Thinking out loud, an open-access EEG-based BCI dataset for inner speech recognition,.

Ensemble Machine Learning Model for Inner Speech Recognition: A Subject-Specific Investigation Thinking out loud, an open-access EEG-based BCI dataset for inner speech recognition,

Reference 34

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T19:29:28.054862Z

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This paper cites Nidal and A.

Ensemble Machine Learning Model for Inner Speech Recognition: A Subject-Specific Investigation Nidal and A

Reference 35

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Ensemble Machine Learning Model for Inner Speech Recognition: A Subject-Specific Investigation Unresolved cited work

Reference 36

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This paper cites Motion artifacts correction from single -channel EEG and fNIRS signals using novel wavelet packet decomposition in combination with canonical correlation analysis,.

Ensemble Machine Learning Model for Inner Speech Recognition: A Subject-Specific Investigation Motion artifacts correction from single -channel EEG and fNIRS signals using novel wavelet packet decomposition in combination with canonical correlation analysis,

Reference 37

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This paper cites The empirical mode decomposition and the Hilbert spectrum for nonlinear and non -stationary time series analysis,.

Ensemble Machine Learning Model for Inner Speech Recognition: A Subject-Specific Investigation The empirical mode decomposition and the Hilbert spectrum for nonlinear and non -stationary time series analysis,

Reference 38

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T19:29:27.999292Z

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This paper cites Ensemble empirical mode decomposition: a noise-assisted data analysis method,.

Ensemble Machine Learning Model for Inner Speech Recognition: A Subject-Specific Investigation Ensemble empirical mode decomposition: a noise-assisted data analysis method,

Reference 39

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T19:29:27.985768Z

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This paper cites Singular-spectrum analysis: A toolkit for short, noisy chaotic signals,.

Ensemble Machine Learning Model for Inner Speech Recognition: A Subject-Specific Investigation Singular-spectrum analysis: A toolkit for short, noisy chaotic signals,

Reference 40

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T19:29:27.972044Z

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This paper cites Variational mode decomposition,.

Ensemble Machine Learning Model for Inner Speech Recognition: A Subject-Specific Investigation Variational mode decomposition,

Reference 41

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T19:29:27.957025Z

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This paper cites Novel approaches for the removal of motion artifact from EEG recordings,.

Ensemble Machine Learning Model for Inner Speech Recognition: A Subject-Specific Investigation Novel approaches for the removal of motion artifact from EEG recordings,

Reference 42

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T19:29:27.943244Z

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This paper cites Motion artifacts correction from EEG and fNIRS signals using novel multiresolution analysis,.

Ensemble Machine Learning Model for Inner Speech Recognition: A Subject-Specific Investigation Motion artifacts correction from EEG and fNIRS signals using novel multiresolution analysis,

Reference 43

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T19:29:27.925576Z

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Observation 6f9d8239-5f72-4571-8a9b-75cb0b0371b8 · outbound

This paper cites A novel non -invasive estimation of respiration rate from motion corrupted photoplethysmograph signal using machine learning model,.

Ensemble Machine Learning Model for Inner Speech Recognition: A Subject-Specific Investigation A novel non -invasive estimation of respiration rate from motion corrupted photoplethysmograph signal using machine learning model,

Reference 44

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T19:29:27.909496Z

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Observation 4c31efad-fe8c-4f52-b721-0da5143a39df · outbound

This paper cites Study of stability of time -domain features for electromyographic pattern recognition,.

Ensemble Machine Learning Model for Inner Speech Recognition: A Subject-Specific Investigation Study of stability of time -domain features for electromyographic pattern recognition,

Reference 45

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T19:29:27.893098Z

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Observation 7e708c1f-d1ed-4492-a62b-87bfbdac589d · outbound

This paper cites Comparison of different time and frequency domain feature extraction methods on elbow gesture’s EMG,.

Ensemble Machine Learning Model for Inner Speech Recognition: A Subject-Specific Investigation Comparison of different time and frequency domain feature extraction methods on elbow gesture’s EMG,

Reference 46

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T19:29:27.878498Z

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Observation f969184c-9434-411d-9741-13b9bc88205f · outbound

This paper cites Frequency domain characteristics of ground reaction forces during walking of young and elderly females,.

Ensemble Machine Learning Model for Inner Speech Recognition: A Subject-Specific Investigation Frequency domain characteristics of ground reaction forces during walking of young and elderly females,

Reference 47

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T19:29:27.860246Z

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Observation 2d08aecf-8625-4755-a970-ca32e37a319c · outbound

This paper cites Blind source separation of single-sensor recordings: Application to ground reaction force signals,.

Ensemble Machine Learning Model for Inner Speech Recognition: A Subject-Specific Investigation Blind source separation of single-sensor recordings: Application to ground reaction force signals,

Reference 48

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T19:29:27.846367Z

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This paper cites Machine learning -based classification of healthy and impaired gaits using 3D -GRF signals,.

Ensemble Machine Learning Model for Inner Speech Recognition: A Subject-Specific Investigation Machine learning -based classification of healthy and impaired gaits using 3D -GRF signals,

Reference 49

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T19:29:27.830936Z

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Observation d029273f-599b-414d-b4ea-25871d149560 · outbound

This paper cites DCT domain feature extraction scheme based on motor unit action potential of EMG signal for neuromuscular disease classification,.

Ensemble Machine Learning Model for Inner Speech Recognition: A Subject-Specific Investigation DCT domain feature extraction scheme based on motor unit action potential of EMG signal for neuromuscular disease classification,

Reference 50

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T19:29:27.813985Z

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Observation 1e634345-e194-4326-9a6c-a917acd6b886 · outbound

This paper cites A mother wavelet selection study for vertical ground reaction force signals,.

Ensemble Machine Learning Model for Inner Speech Recognition: A Subject-Specific Investigation A mother wavelet selection study for vertical ground reaction force signals,

Reference 51

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T19:29:27.799798Z

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Observation f2d68751-7559-424c-81d9-59fef76a4eff · outbound

This paper cites Overfitting and undercomputing in machine learning,.

Ensemble Machine Learning Model for Inner Speech Recognition: A Subject-Specific Investigation Overfitting and undercomputing in machine learning,

Reference 52

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T19:29:27.787607Z

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No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation 4fd23db7-704c-4fc7-8434-1d538909f93f · outbound

This paper cites Modular neural -SVM scheme for speech emotion recognition using ANOV A feature selection method,.

Ensemble Machine Learning Model for Inner Speech Recognition: A Subject-Specific Investigation Modular neural -SVM scheme for speech emotion recognition using ANOV A feature selection method,

Reference 53

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T19:29:27.774924Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation 72009b96-44f8-4562-8550-1905d0aec103 · outbound

This paper cites Performance assessment of artificial neural network using chi -square and backward elimination feature selection methods for landslide susceptibility analysis,.

Ensemble Machine Learning Model for Inner Speech Recognition: A Subject-Specific Investigation Performance assessment of artificial neural network using chi -square and backward elimination feature selection methods for landslide susceptibility analysis,

Reference 54

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T19:29:27.759328Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation 0f05eca3-ca5d-4116-bcd1-77703136e758 · outbound

This paper cites Mutual information -based feature selection for multilabel classification,.

Ensemble Machine Learning Model for Inner Speech Recognition: A Subject-Specific Investigation Mutual information -based feature selection for multilabel classification,

Reference 55

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T19:29:27.746879Z

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No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation 3139b32e-7d8c-45d0-9ff5-5703b11c43f9 · outbound

This paper cites Daily activity feature selection in smart homes based on pearson correlation coefficient,.

Ensemble Machine Learning Model for Inner Speech Recognition: A Subject-Specific Investigation Daily activity feature selection in smart homes based on pearson correlation coefficient,

Reference 56

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T19:29:27.733822Z

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No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation 6cf30ccf-16c7-41fb-b231-b331b29a8037 · outbound

This paper cites Predicting breast cancer from risk factors using SVM and extra -trees-based feature selection method,.

Ensemble Machine Learning Model for Inner Speech Recognition: A Subject-Specific Investigation Predicting breast cancer from risk factors using SVM and extra -trees-based feature selection method,

Reference 57

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T19:29:27.720494Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation c20196e9-1ebb-464b-b137-37e62a935d3f · outbound

This paper cites Principal component analysis,.

Ensemble Machine Learning Model for Inner Speech Recognition: A Subject-Specific Investigation Principal component analysis,

Reference 58

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T19:29:27.707874Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation 8965a587-6993-4ca1-8589-a8c439027273 · outbound

This paper cites ReliefF for multi -label feature selection,.

Ensemble Machine Learning Model for Inner Speech Recognition: A Subject-Specific Investigation ReliefF for multi -label feature selection,

Reference 59

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T19:29:27.694330Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation aaeb4496-816a-4bed-a543-c783827b73df · outbound

This paper cites Maximum relevance and minimum redundancy feature selection methods for a marketing machine learning platform,.

Ensemble Machine Learning Model for Inner Speech Recognition: A Subject-Specific Investigation Maximum relevance and minimum redundancy feature selection methods for a marketing machine learning platform,

Reference 60

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T19:29:27.677249Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation a289e652-5650-4575-9d30-0902b6f5af3e · outbound

This paper cites The best two independent measurements are not the two best,.

Ensemble Machine Learning Model for Inner Speech Recognition: A Subject-Specific Investigation The best two independent measurements are not the two best,

Reference 61

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T19:29:27.661150Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation 4962be10-a0e8-469b-a4df-59b2f7d16974 · outbound

This paper cites Mazzanti.

Ensemble Machine Learning Model for Inner Speech Recognition: A Subject-Specific Investigation Mazzanti

Reference 62

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T19:29:27.647868Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation e8eda1d3-9d76-4752-81fb-713edee47421 · outbound

This paper cites Can Ensemble of Classifiers Provide Better Recognition Results in Packaging Activity?,.

Ensemble Machine Learning Model for Inner Speech Recognition: A Subject-Specific Investigation Can Ensemble of Classifiers Provide Better Recognition Results in Packaging Activity?,

Reference 63

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T19:29:27.633237Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation e8112961-13d7-4ced-bc0b-15f5ab9432c2 · outbound

This paper cites Heart Failure Emergency Readmission Prediction Using Stacking Machine Learning Model,.

Ensemble Machine Learning Model for Inner Speech Recognition: A Subject-Specific Investigation Heart Failure Emergency Readmission Prediction Using Stacking Machine Learning Model,

Reference 64

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T19:29:27.619380Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation 843cab8d-09f3-484d-87a8-951e093f78c4 · outbound

This paper cites Machine learning with ensemble stacking model for automated sleep staging using dual -channel EEG signal,.

Ensemble Machine Learning Model for Inner Speech Recognition: A Subject-Specific Investigation Machine learning with ensemble stacking model for automated sleep staging using dual -channel EEG signal,

Reference 65

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T19:29:27.602740Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation c0dea0e2-c206-4b3f-a6fa-3897625ec43a · outbound

This paper cites Deep residual learning for image recognition,.

Ensemble Machine Learning Model for Inner Speech Recognition: A Subject-Specific Investigation Deep residual learning for image recognition,

Reference 66

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T19:29:27.581359Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation b780448f-9dcb-430f-9c8b-abef5e3c4f01 · outbound

This paper cites Very Deep Convolutional Networks for Large-Scale Image Recognition.

Ensemble Machine Learning Model for Inner Speech Recognition: A Subject-Specific Investigation Very Deep Convolutional Networks for Large-Scale Image Recognition

Reference 67

Resolution: unresolved; no resolver link, observed 2026-08-11T19:29:27.341108Z

Source-reported events for the cited work

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Observation 54ebb839-e540-4399-8c36-0e3006d653bb · outbound

This paper cites Inner speech classification using eeg signals: A deep learning approach,.

Ensemble Machine Learning Model for Inner Speech Recognition: A Subject-Specific Investigation Inner speech classification using eeg signals: A deep learning approach,

Reference 68

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T19:29:27.560809Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

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