Article Per Year (5 Year)
p-Index From 2021 - 2026
0.444
P-Index
This Author published in this journals
All Journal International Transactions on Artificial Intelligence (ITALIC) Research of Scientia Naturalis
Schmidt, Daniel
Unknown Affiliation
Agriculture, Biological Sciences & Forestry Chemistry Computer Science & IT Control & Systems Engineering Library & Information Science Physics

Published : 2 Documents Claim Missing Document
Claim Missing Document
Check
Articles

Found 2 Documents
Search

 1 
The Role of Machine Learning in Improving Robotic Perception and Decision Making Chen, Shih-Chih; Pamungkas, Ria Sari; Schmidt, Daniel
International Transactions on Artificial Intelligence Vol. 3 No. 1 (2024): International Transactions on Artificial Intelligence
Publisher : Pandawan Sejahtera Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33050/italic.v3i1.661

Abstract

Machine learning, specifically through Convolutional Neural Networks (CNNs) and Reinforcement Learning (RL), significantly enhances robotic perception and decision-making capabilities. This research explores the integration of CNNs to improve object recognition accuracy and employs sensor fusion for interpreting complex environments by synthesizing multiple sensory inputs. Furthermore, RL is utilized to refine robots real-time decision-making processes, which reduces task completion times and increases decision accuracy. Despite the potential, these advanced methods require extensive datasets and considerable computational resources for effective real-time applications. The study aims to optimize these machine learning models for better efficiency and address the ethical considerations involved in autonomous systems. Results indicate that machine learning can substantially advance robotic functionality across various sectors, including autonomous vehicles and industrial automation, supporting sustainable industrial growth. This aligns with the United Nations Sustainable Development Goals, particularly SDG 9 (Industry, Innovation, and Infrastructure) and SDG 8 (Decent Work and Economic Growth), by promoting technological innovation and enhancing industrial safety. The conclusion suggests that future research should focus on improving the scalability and ethical application of these technologies in robotics, ensuring broad, sustainable impact.
ADAPTIVE COMPLEXITY IN LIVING SYSTEMS: INTEGRATING ECOLOGICAL DYNAMICS WITH NONLINEAR MATHEMATICAL MODELING Sharma, Aarav; Lim, Sofia; Schmidt, Daniel
Research of Scientia Naturalis Vol. 3 No. 1 (2026)
Publisher : Yayasan Adra Karima Hubbi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.70177/scientia.v3i1.3541

Abstract

Adaptive complexity is a defining feature of living systems, where nonlinear interactions, feedback mechanisms, and environmental variability shape dynamic behaviors that cannot be adequately explained through linear models. Ecological research increasingly recognizes the limitations of equilibrium-based approaches, yet a coherent integration of ecological dynamics with nonlinear mathematical modeling remains underdeveloped. This study aims to develop an integrative framework that captures adaptive complexity by combining empirical ecological data with nonlinear dynamical systems analysis. The research employs a mixed-methods design, incorporating secondary ecological datasets, computational modeling, and techniques such as bifurcation and sensitivity analysis to examine system behavior under varying conditions. Results demonstrate that ecological systems exhibit multi-stability, threshold effects, and chaotic dynamics, with environmental variability and interaction intensity significantly influencing system transitions. Nonlinear models successfully capture emergent behaviors and reveal critical tipping points that are not identifiable through linear approaches. These findings highlight that adaptive complexity operates as an organizing principle rather than a peripheral characteristic of living systems. The study concludes that integrating ecological dynamics with nonlinear mathematical modeling enhances both theoretical understanding and practical predictive capacity, offering a robust framework for analyzing resilience and transformation in ecological systems.
Co-Authors Chen, Shih-Chih Lim, Sofia Pamungkas, Ria Sari Sharma, Aarav