Advanced Neural Classifier-Based Effective Human Assistance Robots Using Comparable Interactive Input Assessment Technique

Research output: Journal contributionsJournal articlesResearchpeer-review

Standard

Advanced Neural Classifier-Based Effective Human Assistance Robots Using Comparable Interactive Input Assessment Technique. / Albekairi, Mohammed; Kaaniche, Khaled; Abbas, Ghulam et al.
In: Mathematics, Vol. 12, No. 16, 2500, 13.08.2024.

Research output: Journal contributionsJournal articlesResearchpeer-review

Harvard

APA

Vancouver

Albekairi M, Kaaniche K, Abbas G, Mercorelli P, Alanazi MD, Almadhor A. Advanced Neural Classifier-Based Effective Human Assistance Robots Using Comparable Interactive Input Assessment Technique. Mathematics. 2024 Aug 13;12(16):2500. doi: 10.3390/math12162500

Bibtex

@article{61da43349ef34732be37cccdc977148e,
title = "Advanced Neural Classifier-Based Effective Human Assistance Robots Using Comparable Interactive Input Assessment Technique",
abstract = "The role of robotic systems in human assistance is inevitable with the bots that assist with interactive and voice commands. For cooperative and precise assistance, the understandability of these bots needs better input analysis. This article introduces a Comparable Input Assessment Technique (CIAT) to improve the bot system{\textquoteright}s understandability. This research introduces a novel approach for HRI that uses optimized algorithms for input detection, analysis, and response generation in conjunction with advanced neural classifiers. This approach employs deep learning models to enhance the accuracy of input identification and processing efficiency, in contrast to previous approaches that often depended on conventional detection techniques and basic analytical methods. Regardless of the input type, this technique defines cooperative control for assistance from previous histories. The inputs are cooperatively validated for the instruction responses for human assistance through defined classifications. For this purpose, a neural classifier is used; the maximum possibilities for assistance using self-detected instructions are recommended for the user. The neural classifier is divided into two categories according to its maximum comparable limits: precise instruction and least assessment inputs. For this purpose, the robot system is trained using previous histories and new assistance activities. The learning process performs comparable validations between detected and unrecognizable inputs with a classification that reduces understandability errors. Therefore, the proposed technique was found to reduce response time by 6.81%, improve input detection by 8.73%, and provide assistance by 12.23% under varying inputs.",
keywords = "interactive classification, machine learning, neural networks, optimal control, robot systems, Engineering",
author = "Mohammed Albekairi and Khaled Kaaniche and Ghulam Abbas and Paolo Mercorelli and Alanazi, {Meshari D.} and Ahmad Almadhor",
note = "Publisher Copyright: {\textcopyright} 2024 by the authors.",
year = "2024",
month = aug,
day = "13",
doi = "10.3390/math12162500",
language = "English",
volume = "12",
journal = "Mathematics",
issn = "2227-7390",
publisher = "MDPI AG",
number = "16",

}

RIS

TY - JOUR

T1 - Advanced Neural Classifier-Based Effective Human Assistance Robots Using Comparable Interactive Input Assessment Technique

AU - Albekairi, Mohammed

AU - Kaaniche, Khaled

AU - Abbas, Ghulam

AU - Mercorelli, Paolo

AU - Alanazi, Meshari D.

AU - Almadhor, Ahmad

N1 - Publisher Copyright: © 2024 by the authors.

PY - 2024/8/13

Y1 - 2024/8/13

N2 - The role of robotic systems in human assistance is inevitable with the bots that assist with interactive and voice commands. For cooperative and precise assistance, the understandability of these bots needs better input analysis. This article introduces a Comparable Input Assessment Technique (CIAT) to improve the bot system’s understandability. This research introduces a novel approach for HRI that uses optimized algorithms for input detection, analysis, and response generation in conjunction with advanced neural classifiers. This approach employs deep learning models to enhance the accuracy of input identification and processing efficiency, in contrast to previous approaches that often depended on conventional detection techniques and basic analytical methods. Regardless of the input type, this technique defines cooperative control for assistance from previous histories. The inputs are cooperatively validated for the instruction responses for human assistance through defined classifications. For this purpose, a neural classifier is used; the maximum possibilities for assistance using self-detected instructions are recommended for the user. The neural classifier is divided into two categories according to its maximum comparable limits: precise instruction and least assessment inputs. For this purpose, the robot system is trained using previous histories and new assistance activities. The learning process performs comparable validations between detected and unrecognizable inputs with a classification that reduces understandability errors. Therefore, the proposed technique was found to reduce response time by 6.81%, improve input detection by 8.73%, and provide assistance by 12.23% under varying inputs.

AB - The role of robotic systems in human assistance is inevitable with the bots that assist with interactive and voice commands. For cooperative and precise assistance, the understandability of these bots needs better input analysis. This article introduces a Comparable Input Assessment Technique (CIAT) to improve the bot system’s understandability. This research introduces a novel approach for HRI that uses optimized algorithms for input detection, analysis, and response generation in conjunction with advanced neural classifiers. This approach employs deep learning models to enhance the accuracy of input identification and processing efficiency, in contrast to previous approaches that often depended on conventional detection techniques and basic analytical methods. Regardless of the input type, this technique defines cooperative control for assistance from previous histories. The inputs are cooperatively validated for the instruction responses for human assistance through defined classifications. For this purpose, a neural classifier is used; the maximum possibilities for assistance using self-detected instructions are recommended for the user. The neural classifier is divided into two categories according to its maximum comparable limits: precise instruction and least assessment inputs. For this purpose, the robot system is trained using previous histories and new assistance activities. The learning process performs comparable validations between detected and unrecognizable inputs with a classification that reduces understandability errors. Therefore, the proposed technique was found to reduce response time by 6.81%, improve input detection by 8.73%, and provide assistance by 12.23% under varying inputs.

KW - interactive classification

KW - machine learning

KW - neural networks

KW - optimal control

KW - robot systems

KW - Engineering

UR - http://www.scopus.com/inward/record.url?scp=85202529598&partnerID=8YFLogxK

UR - https://www.mendeley.com/catalogue/e9643668-58bd-3cbc-8cb3-b26b6ad7c762/

U2 - 10.3390/math12162500

DO - 10.3390/math12162500

M3 - Journal articles

AN - SCOPUS:85202529598

VL - 12

JO - Mathematics

JF - Mathematics

SN - 2227-7390

IS - 16

M1 - 2500

ER -

DOI

Recently viewed

Activities

  1. Interpreting Strings, Weaving Threads – Structuring Provenance Data with AI
  2. Institutional Proxy Representatives of Future Generations: A Comparative Analysis of Types and Design Features
  3. A conceptual framework on users' digitalisation practices transforming their digital infrastructure for work
  4. Towards a fully-automated adaptive e-learning environment: A predictive model for difficulty generating factors in gap-filling activities that target English tense-aspect-mood
  5. Collaborative modeling in climatic change adaptation and energy transformation.
  6. Institutionalizing transdisciplinary learning on different levels
  7. How stereotypes affect grading and tutorial feedback: Shifting evaluations or shifting standards?
  8. HyperKult IX - Computer als Medium: Augmented Space 2000
  9. International Conference of Mathematical Modelling and Applications - ICTMA 17
  10. Learning to Rate Player Actions on the Example of Soccer
  11. Empirical Insights into Working in Research-Practice Partnerships: New Findings on Motivation, Co-Constructive Collaboration and Learning Effects
  12. Methodenworkshop KuBIn
  13. Are Hybrid Work Models Here to Stay?
  14. Linking national and international Large-Scale Assessments
  15. Configurating the Creation of the New: Spaces of Organising and Entrepreneuring
  16. PhD Masterclass ''Discourse Theoretical Approaches to Politics, Society, Communication and Media" - 2019
  17. Preliminary results of a web-based and mobile stress-management intervention for employees
  18. Unintended Consequences of Field Experiments in Poverty Settings

Publications

  1. A Multilevel CFA-MTMM Model for Nested Structurally Different Methods
  2. Multidimensional Cross-Recurrence Quantification Analysis (MdCRQA)–A Method for Quantifying Correlation between Multivariate Time-Series
  3. Vision-Based Deep Learning Algorithm for Detecting Potholes
  4. Lyapunov stability analysis to set up a PI controller for a mass flow system in case of a non-saturating input
  5. Analyzing math teacher students' sensitivity for aspects of the complexity of problem oriented mathematics instruction
  6. Automated Invoice Processing: Machine Learning-Based Information Extraction for Long Tail Suppliers
  7. Identification of conductive fiber parameters with transcutaneous electrical nerve stimulation signal using RLS algorithm
  8. Continuous and Discrete Concepts for Detecting Transport Barriers in the Planar Circular Restricted Three Body Problem
  9. Can measurement errors explain variance in the relationship between muscle- and tendon stiffness and range of motion?—a blinded reliability and objectivity study
  10. Using EEG movement tagging to isolate brain responses coupled to biological movements
  11. Exploring the Unknown
  12. A luenberger observer for a quasi-static disturbance estimation in linear time invariant systems
  13. Dynamic capabilities and routinization
  14. Comparing Web-Based and Blended Training for Coping With Challenges of Flexible Work Designs
  15. Test of advanced hyperfine structure theory by precision radio-frequency and laser spectroscopy in molybdenum
  16. Sharing in Christ's rule
  17. Effect of erbium modification on the microstructure, mechanical and corrosion characteristics of binary Mg-Al alloys
  18. Approximate tree kernels
  19. Collaborative design prototyping in transdisciplinary research
  20. End-to-End Active Speaker Detection
  21. Reconfiguring Desecuritization
  22. Individual differences and cognitive load theory
  23. Assessing authenticity in modelling test items: deriving a theoretical model
  24. Properties of some overlapping self-similar and some self-affine measures
  25. General Patterns and Conclusions

Press / Media

  1. Orchester ohne Dirigent