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All Journal International Journal of Electrical and Computer Engineering IAES International Journal of Artificial Intelligence (IJ-AI)
Espitia-Cubillos, Anny Astrid
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Computer Science & IT Electrical & Electronics Engineering Engineering

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Learning assistance module based on a small language model Jinete, Marco Antonio; Jiménez-Moreno, Robinson; Espitia-Cubillos, Anny Astrid
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 14, No 5: October 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijai.v14.i5.pp4202-4210

Abstract

This paper presents the development of a low-cost learning assistant embedded in an NVIDIA Jetson Xavier board that uses speech and gesture recognition, together with a long language model for offline work. Using the large language model (LLM) Phi-3 Mini (3.8B) model and the Whisper (model base) model for automatic speech recognition, a learning assistant is obtained under a compact and efficient design based on extensive language model architectures that give a general answer set of a topic. Average processing times of 0.108 seconds per character, a speech transcription efficiency of 94.75%, an average accuracy of 9.5/10 and 8.5/10 in the consistency of the responses generated by the learning assistant, a full recognition of the hand raising gesture when done for at least 2 seconds, even without fully extending the fingers, were obtained. The prototype is based on the design of a graphical interface capable of responding to voice commands and generating dynamic interactions in response to the user's gesture detection, representing a significant advance towards the creation of comprehensive and accessible human-machine interface solutions.
Robotic product-based manipulation in simulated environment Guacheta-Alba, Juan Camilo; Espitia-Cubillos, Anny Astrid; Jimenez-Moreno, Robinson
International Journal of Electrical and Computer Engineering (IJECE) Vol 15, No 6: December 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v15i6.pp5894-5903

Abstract

Before deploying algorithms in industrial settings, it is essential to validate them in virtual environments to anticipate real-world performance, identify potential limitations, and guide necessary optimizations. This study presents the development and integration of artificial intelligence algorithms for detecting labels and container formats of cleaning products using computer vision, enabling robotic manipulation via a UR5 arm. Label identification is performed using the speeded-up robust features (SURF) algorithm, ensuring robustness to scale and orientation changes. For container recognition, multiple methods were explored: edge detection using Sobel and Canny filters, Hopfield networks trained on filtered images, 2D cross-correlation, and finally, a you only look once (YOLO) deep learning model. Among these, the custom-trained YOLO detector provided the highest accuracy. For robotic control, smooth joint trajectories were computed using polynomial interpolation, allowing the UR5 robot to execute pick-and-place operations. The entire process was validated in the CoppeliaSim simulation environment, where the robot successfully identified, classified, and manipulated products, demonstrating the feasibility of the proposed pipeline for future applications in semi-structured industrial contexts.
Image segmentation using fuzzy clustering for industrial applications Jiménez-Moreno, Robinson; Vargas Duanca, Laura María; Espitia-Cubillos, Anny Astrid
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 14, No 6: December 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijai.v14.i6.pp4636-4642

Abstract

This paper presents a fuzzy logic clustering algorithm oriented to image segmentation and the procedure designed to evaluate its performance by varying two parameters: the number of clusters (c) and the diffusivity parameter (m), which leads to the conclusion that an adjusted number of clusters is sufficient to recognize main elements of the image, but a more detailed reconstruction requires a higher number of clusters. Also, the diffusivity parameter influences the smoothness of the boundaries between clusters, low values generate a segmentation with more abrupt transitions and sharper contours, high values smooth the segmentation, its excessive increase may cause the elements to merge, losing details. In general, the balance between these two parameters is key to obtaining an effective segmentation. Three validation scenarios were used, the first two allowed to establish the most appropriate parameters for segmentation, regulating the clusters to a maximum of 4 and keeping the diffusivity level at 2.0, the third scenario validated the algorithm with real images of industrial cleaning products, all with noise, establishing the computational cost and processing times for images of 350×350 and 2000×3000 pixels resolution. In conclusion, applications of the algorithm are foreseen in automatic quality control and inventory control of finished products and raw materials, thanks to its high efficiency and low response time, even in scenarios involving noisy and large images.
Co-Authors Guacheta-Alba, Juan Camilo Jimenez-Moreno, Robinson Jinete, Marco Antonio Vargas Duanca, Laura María