Journal of Automation, Mobile Robotics and Intelligent Systems
type: channel
description: Journal of Automation, Mobile Robotics and Intelligent Systems (JAMRIS) is an international scientific quarterly published in Łukasiewicz Research Network – Industrial Research Institute for Automation and Measurements PIAP located in Warsaw, Poland. JAMRIS publishes since 2007 high-quality technical, position and state-of-the-art papers in the fields of theory, design and applications of broadly perceived intelligent technologies, notably based on artificial intelligence and computational intelligence, particularly for analysing and solving scientific and engineering problems and systems. The articles are published in English, and they are peer-reviewed and open-access. ISSN: 1897-8649 (Print) | ISSN: 2080-2145 (Online) | DOI: 10.14313/JAMRIS/
publisher: Łukasiewicz – Industrial Research Institute for Automation and Measurements PIAP
language: en
publication name: Journal of Automation, Mobile Robotics and Intelligent Systems
issn: 2080-2145
copyright: The authors wishing to publish in JAMRIS journal must sign the license to publish upon paper acceptance. The license governs in detail the commercial and non-commercial use of papers published by our journal and determines user and author rights.
items: Retraction note
items: Finding the Sweet Spot: A Study of Data Augmentation Intensity for Small-Scale Image Classification
items: Design of a Fuzzy PID Controller to Improve Electric Vehicles Performance Based on Regenerative Braking System
items: A Custom Robotic Vehicle Prototype for Disinfecting Workplaces
items: A Kinematic Prototype of a Compliant Artificial Prosthetic Knee Joint
items: Innovation Capacities and Business Model in Colombian Farming Organizations
items: Self-Tuning Controller for Scalar Systems: a Preselected-Time Control Approach
items: Comparison of Computer Vision and Convolutional Neural Networks for Vehicle Parking Control
items: Grey Wolf Optimization Algorithm for a Concurrent Real-Time Optimization Problem in Game Theory
items: Autonomous Goal Following for Quadruped Robot using Fuzzy Proportional Control
items: Mutual Inductance Model of the Mechanical Stress Sensitivity of the Power Transformer Functional Parameters
items: Patterns of Acoustic Emission Changes with Alterations in the Damage Area of a Composite Material According to the Mises Criterion
items: Formation Control of Multi-agent Nonlinear Systems using the State-Dependent Riccati Equation
items: Positivity and Stability of Descriptor Discrete-Time Linear Systems with Interval State Matrices
items: Monocular 3D Object Localization using 2D Estimates for Industrial Robot Vision System
items: Multi-Input Melanoma Classification using MobileNet-V3-Large Architecture
items: Enhancing Efficiency and Security in Healthcare IoT: a Novel Approach for Fog Computing Resource Optimization Using TGA-RNN
items: Partitioning of Complex Discrete Models for Highly Scalable Simulations
items: Optimization of the Nominal Capacity of the Energy Storage System for Ensuring Security of Electrical Energy Supply
items: Health Recommender System for Sleep Apnea using Computational Intelligence
items: Identification of Keywords for Legal Documents Categories using SOM
items: The Design of 3D-Printed Open Bearings for Human Assisting Robots
items: In-Flight Solar Radiation Intensity Measurement Using a Small Unmanned Aerial Vehicle
items: Preface
items: A 2‐Tuple Linguistic Dynamic OWAWA Aggregation Operator and its Application to Multi‐Attribute Decision Making
items: Implementation of Enzyme Family Classification by using Autoencoders in a Study Case with Imbalanced and Underrepresented Classes
items: Construction Automation with Bio-inspired Hierarchical Extremely Modular Systems
items: Controllability, Observability and Transfer Matrix Zeroing of the 2D Roesser Model
items: Comparison of Open Source SDN Controllers and Cloud Platforms in Terms of Performance, Stability, and Infrastructure Flexibility
items: Segmentation of E-Commerce Users on Cart Abandonment and Product Recommendation Using Double Transformer Residual Super-Resolution Network

Retraction note
type: item
subject: Retraction
  topic: section
subject: Retraction
  topic: keywords
rights: Copyright (c) 2025 Journal of Automation, Mobile Robotics and Intelligent Systems https://creativecommons.org/licenses/by-nc-nd/4.0
date: 2025-12-15
publication date: 2025-12-15
starting page: 109
ending page: 109
doi: 10.14313/jamris-2025-040

Finding the Sweet Spot: A Study of Data Augmentation Intensity for Small-Scale Image Classification
type: item
description: Finding the optimal level of data augmentation intensity remains one of the most challenging aspects of training deep learning models on small-scale datasets. While data augmentation is universally recognized as essential for preventing overfitting and improving generalization, excessive augmentation can paradoxically harm model performance by introducing too much variability in the training data. This research investigates the "sweet spot" of augmentation intensity through a comprehensive study of six distinct augmentation strategies on CIFAR-10, a representative small-scale image classification benchmark. We designed a controlled experiment comparing: No Augmentation (baseline), Basic torchvision transforms, Light Advanced albumentations, Moderate Advanced geometric-photometric combinations, Strong Advanced with noise injection and dropout, and AutoAugment Style with complex transformations. Our findings reveal a clear quadratic relationship between augmentation intensity and model performance (R² = 0.85), with peak performance achieved at moderate intensity levels (0.21 on our normalized scale). The Basic augmentation strategy wins with 79.04% validation accuracy, significantly outperforming both minimal augmentation (77.15%) and excessive augmentation (72.17%). Through statistical analysis including effect size calculations, confidence intervals, and correlation studies, we demonstrate that the "sweet spot" lies in balanced augmentation that provides regularization benefits without overwhelming the learning process. These findings offer evidence-based guidelines for practitioners working with small-scale image datasets.
subject: Articles
  topic: section
subject: augmentation intensity
  topic: keywords
subject: small-scale dataset
  topic: keywords
subject: sweet spot optimization
  topic: keywords
subject: CIFAR-10
  topic: keywords
subject: regularization
  topic: keywords
creator: Windra Swastika
rights: Copyright (c) 2025 Journal of Automation, Mobile Robotics and Intelligent Systems https://creativecommons.org/licenses/by-nc-nd/4.0
date: 2025-12-15
publication date: 2025-12-15
starting page: 94
ending page: 101
doi: 10.14313/jamris-2025-038

Design of a Fuzzy PID Controller to Improve Electric Vehicles Performance Based on Regenerative Braking System
type: item
description: This study aims to improve energy storage in electric vehicle applications using a regenerative braking system. A brushless DC motor (BLDC) was chosen to power the vehicle due to its inherent advantages and suitability for electric vehicles. These vehicles require a low rate of error and stable and transient responses to make wheel acceleration smoother. The main objective of this study is to improve response time and stability under different operating conditions and overcome the drawbacks of traditional control techniques. Traditional PID controllers suffer from several problems, including transient overshoots, load fluctuations, and non-linear response, which lead to poor performance in electric vehicle propulsion systems. In this study, a fuzzy PID controller and a hysteresis current control loop are designed for the BLDC motor to address the above issues. The motor speed and battery charge state are verified using the MATLAB/Simulink environment in different situations to measure the reliability of the proposed controller. The results show that this proposed controller improves the dynamic performance of the regenerative braking system and enhances other operating characteristics such as maximum overshoot and settling time.
subject: Articles
  topic: section
subject: Brushless direct current motor
  topic: keywords
subject: Electric vehicles
  topic: keywords
subject: Fuzzy logic system
  topic: keywords
subject: Hysteresis current control loop
  topic: keywords
subject: Regenerative braking system
  topic: keywords
creator: Hayder Abdulabbas Abdulameer
creator: Mustafa A Khamis
creator: Mohammed Joudah Zaiter
creator: Karin Ennser
rights: Copyright (c) 2025 Journal of Automation, Mobile Robotics and Intelligent Systems https://creativecommons.org/licenses/by-nc-nd/4.0
date: 2025-12-15
publication date: 2025-12-15
starting page: 102
ending page: 108
doi: 10.14313/jamris-2025-039

A Custom Robotic Vehicle Prototype for Disinfecting Workplaces
type: item
description: The SARS‐CoV‐2 pandemic has heightened the need for advanced and automated disinfection methods to ensure workplace safety and hygiene. This study presents the de‐ sign and implementation of a robotic vehicle capable of autonomously disinfecting high‐risk areas in diverse work environments based on human activity levels. The sys‐ tem integrates a machine vision module using YOLOv5 for real‐time human detection, a Decision Support System to assess contamination risks, an autonomous navigation module for path planning, and a user‐friendly interface for operator control. By leveraging real‐time data, the robot precisely applies disinfectant to identified high‐risk zones, dynamically adjusting the spray volume based on the level of contamination. The system was validated in a real‐world workplace setting, demonstrating its ability to autonomously perform targeted disinfection, offering a scalable solution to support workplace hygiene.
subject: Articles
  topic: section
subject: Robotic Disinfection
  topic: keywords
subject: Mobile Robots
  topic: keywords
subject: Robot Operating System
  topic: keywords
subject: Deep Learning
  topic: keywords
subject: Infection Prevention
  topic: keywords
creator: Georgios Karamitsos
creator: Vasileios Sidiropoulos
creator: Evangelos Syrmos
creator: Athanasios Sidiropoulos
creator: Xenofon Karamanos
creator: Dimitrios Vlachos
creator: Dimitrios Bechtsis
rights: Copyright (c) 2025 Journal of Automation, Mobile Robotics and Intelligent Systems https://creativecommons.org/licenses/by-nc-nd/4.0
date: 2025-09-09
publication date: 2025-09-09
starting page: 1
ending page: 12
doi: 10.14313/jamris-2025-020

A Kinematic Prototype of a Compliant Artificial Prosthetic Knee Joint
type: item
description: This study presents the development and kinematic evaluation of a compliant artificial knee joint prototype fabricated using multi-material 3D printing. The design integrates a rolling-contact compliant mechanism—derived from the CORE and D-CORE concepts—into a prosthetic knee construction, aiming to replicate biological joint behavior while reducing weight, mechanical complexity, and friction. The prototype underwent iterative refinement, including geometric modifications, material selection for flexure bands, and structural asymmetry to mitigate overextension and increase durability. The resulting joint exhibited hybrid kinematic characteristics, blending features of both single-axis and polycentric knee mechanisms, and achieved a functional flexion range of approximately 142 degrees. Initial cyclic tests confirmed satisfactory stiffness and shape recovery of flexure bands with optimized dimensions, although long-term fatigue performance remains a challenge. A basic spring-damper system was also integrated, potentially aligning the prototype with K-1/K-2 prosthetic classification. However, full validation requires further mechanical testing in accordance with ISO 10328 standards, as well as optimization of the damping system for commercial viability. This research demonstrates the feasibility and potential of compliant mechanisms in lower-limb prosthetics while identifying critical areas for future development.
subject: Articles
  topic: section
subject: compliant mechanisms
  topic: keywords
subject: prosthetic limbs
  topic: keywords
subject: knee joint prosthetic
  topic: keywords
subject: 3D printing
  topic: keywords
creator: Michał Kowalik
creator: Erwin Rogoża
creator: Aleksy Figurski
creator: Mateusz Papis
rights: Copyright (c) 2025 Journal of Automation, Mobile Robotics and Intelligent Systems https://creativecommons.org/licenses/by-nc-nd/4.0
date: 2025-09-09
publication date: 2025-09-09
starting page: 82
ending page: 88
doi: 10.14313/jamris-2025-028

Innovation Capacities and Business Model in Colombian Farming Organizations
type: item
description: The main objective of the paper is to analyze the interaction between innovation capabilities and elements of the business model of farming organizations in Boyacá, Colombia. The study used a quantitative methodology, employing the Partial Least Squares Path Modeling (PLS-PM) technique. The theoretical model includes seven hypotheses that outline the relationships between innovation capabilities (sensing, seizing, and transformation) and business model (creation, delivery, and capture), The study revealed a significant correlation between the ability to detect and retain innovation and the development and implementation of business models. This underscores the significance of continuous learning and efficient alliance management in acquiring and utilizing environmental knowledge. The implications of this study are directed towards decision-makers, stakeholders, and policy makers within Colombia's National Agricultural Innovation System (NAIS). The study emphasizes the crucial role of a well-integrated system among its various actors in influencing the success of business models. It examines innovation as a dynamic capability and its interplay with vital components of the business model, thereby enhancing comprehension of business dynamics within this sector.
subject: Articles
  topic: section
subject: Innovation Capabilities
  topic: keywords
subject: Business Model Innovation
  topic: keywords
subject: Farming Organizations
  topic: keywords
creator: Daniela Niño-Amezquita
creator: Jhancarlos Gutiérrez-Ayala
creator: Diana María Dueñas Quintero
creator: Fabio Blanco-Mesa
creator: Eduardo Covarrubias-Audelo
rights: Copyright (c) 2025 Journal of Automation, Mobile Robotics and Intelligent Systems https://creativecommons.org/licenses/by-nc-nd/4.0
date: 2025-12-15
publication date: 2025-12-15
starting page: 7
ending page: 17
doi: 10.14313/jamris-2025-031

Self-Tuning Controller for Scalar Systems: a Preselected-Time Control Approach
type: item
description: A Single-Input Single-Output (SISO) linear system can represent various physical systems. One of the challenges in controlling practical systems is that the settling point and settling time cannot be prescribed together. In this paper, a novel Self-Tuning Control (STC) algorithm is presented, which ensures system stability while allowing the simultaneous determination of the settling time and settling point. Four possible scenarios will be defined to investigate the effectiveness of the proposed control method. Three different practices will be described in the simulation section for each scenario. Linear Quadratic Regulation (LQR) will be employed for comparison purposes. MATLAB software will be used to test and simulate these practices. The simulations will demonstrate that the STC is an optimal solution for SISO linear systems, comparable to LQR, with the significant advantage of guaranteeing the attainment of the settling point at the predefined settling time.
subject: Articles
  topic: section
subject: Self-tuning
  topic: keywords
subject: Optimization
  topic: keywords
subject: Control
  topic: keywords
subject: Preselected time
  topic: keywords
subject: Hamiltonian
  topic: keywords
creator: Ali Soltani Sharif Abadi
rights: Copyright (c) 2025 Journal of Automation, Mobile Robotics and Intelligent Systems https://creativecommons.org/licenses/by-nc-nd/4.0
date: 2025-09-09
publication date: 2025-09-09
starting page: 25
ending page: 37
doi: 10.14313/jamris-2025-022

Comparison of Computer Vision and Convolutional Neural Networks for Vehicle Parking Control
type: item
description: This study compares two artificial intelligence approaches for parking occupancy detection: computer vision and convolutional neural networks (CNN). A dataset of 1,000 parking images was captured and labeled, using OpenCV in Python for computer vision processing and the YOLO V5 model for CNN. Results showed that the YOLO V5 model achieved 88% precision and 82% sensitivity, outperforming the computer vision method, which achieved 80% precision and 79% sensitivity. The research suggests that while CNNs offer superior performance, computer vision is a more economical option in contexts with limited resources. Future research will focus on evaluating the YOLOv7 version to reduce false positives and combining techniques to achieve a balance between accuracy and efficiency under variable conditions.
subject: Articles
  topic: section
subject: Artificial Intelligence
  topic: keywords
subject: Computer Vision
  topic: keywords
subject: Convolutional Neural Networks
  topic: keywords
subject: Vehicle Parking
  topic: keywords
creator: Jonathan Aguilar Alvarado
creator: Karina Garcia Galarza
creator: Wilmer Rivas Asanza
creator: Bertha Mazón Olivo
rights: Copyright (c) 2025 Journal of Automation, Mobile Robotics and Intelligent Systems https://creativecommons.org/licenses/by-nc-nd/4.0
date: 2025-06-26
publication date: 2025-06-26
starting page: 26
ending page: 33
doi: 10.14313/jamris-2025-011

Grey Wolf Optimization Algorithm for a Concurrent Real-Time Optimization Problem in Game Theory
type: item
description: In this paper we present a grey wolf algorithm for a concurrent real time optimization problem in searching for optimal game solving solution. There are many solutions to solve the game. Each solution can demand different optimal values of different parameters. However some ways in which the players try to solve the game do not lead to success. The optimization problem consists of two phases. Each phase Impacts the second one in real time. The first phase is responsible for the optimization parameters choice. The second phase validates the choice and makes the optimization of the parameters. As a optimizing method we chose grey wolf optimization. At the beginning the algorithm generates some number of solutions. The solution which has the value of the parameters the closest to maximum is a position of an alfa wolf. The rest of solutions are, according to the values of the parameters, split to be the positions of beta, delta and omega wolfs.
subject: Articles
  topic: section
subject: concurrent real-time optimization
  topic: keywords
subject: grey wolf optimization
  topic: keywords
subject: metaheuristics
  topic: keywords
subject: swarm intelligence
  topic: keywords
subject: game theory
  topic: keywords
creator: Adam M. Górski
creator: Maciej Ogorzałek
rights: Copyright (c) 2025 Journal of Automation, Mobile Robotics and Intelligent Systems https://creativecommons.org/licenses/by-nc-nd/4.0
date: 2025-06-26
publication date: 2025-06-26
starting page: 65
ending page: 72
doi: 10.14313/jamris-2025-016

Autonomous Goal Following for Quadruped Robot using Fuzzy Proportional Control
type: item
description: In this paper a fuzzy Proportional (PD) Controller was designed and implemented for dynamically adapting the velocity and motion parameters in a quadruped robot for autonomously following a goal. The FNK0050 Freenove quadruped robot was utilized for the experiments, which has 12 degrees of freedom and this is why it has higher complexity. Experimental results show that the proposed fuzzy PD controller surpasses the standard PID controller provided as default by the manufacturer of the robot.
subject: Articles
  topic: section
subject: Fuzzy Control
  topic: keywords
subject: Fuzzy PD
  topic: keywords
subject: Quadruped Robot
  topic: keywords
creator: Jose Eduardo Lopez Ramos
creator: Oscar Castillo
creator: Patricia Melin
rights: Copyright (c) 2025 Journal of Automation, Mobile Robotics and Intelligent Systems https://creativecommons.org/licenses/by-nc-nd/4.0
date: 2025-06-26
publication date: 2025-06-26
starting page: 73
ending page: 78
doi: 10.14313/jamris-2025-017

Mutual Inductance Model of the Mechanical Stress Sensitivity of the Power Transformer Functional Parameters
type: item
description: The mutual inductance model enables testing the behavior of power transformers under different operation conditions, especially under the environmental influences on the transformer’s core. This paper presents the investigation results of the stress dependence of the magnetic relative permeability of the power transformer. It was observed that under tensile mechanical stresses up to 98 MPa applied to the core, the transformer input current amplitude increases by almost 67%, whereas the transformer's reactive power increases by 53%. In industrial systems, such changes can potentially lead to unwanted power system shutdowns due to overloading. This effect should be considered during the development of critical power systems.
subject: Articles
  topic: section
subject: mutual inductance model
  topic: keywords
subject: magnetoelastic effect
  topic: keywords
subject: electricl steel
  topic: keywords
creator: Paweł Rękas
creator: Roman Szewczyk
creator: Tadeusz Szumiata
creator: Michał Nowicki
rights: Copyright (c) 2024 Journal of Automation, Mobile Robotics and Intelligent Systems https://creativecommons.org/licenses/by-nc-nd/4.0
date: 2024-12-02
publication date: 2024-12-02
starting page: 13
ending page: 21
doi: 10.14313/JAMRIS/4-2024/28

Patterns of Acoustic Emission Changes with Alterations in the Damage Area of a Composite Material According to the Mises Criterion
type: item
description: In this paper, the use of a highly sensitive acoustic emission method for studying the deformation and failure processes of composite materials is considered, which provides a substantial amount of information about phenomena occurring at the sub-micro, micro, and macro levels. However, the additional influence of various factors leads to the problem of interpreting and identifying the recorded information. Addressing this problem involves determining the influence of different factors on acoustic emission signal parameters and their sensitivity to the influencing factor. In this study, during the failure of a composite material under transverse force according to the Mises criterion, an analysis is conducted on the impact of changes in the number of composite material elements (damage area) on the amplitude-time parameters of the acoustic emission signal based on a developed signal model. The results of the simulation allowed for the identification and description of patterns in the changes of amplitude-time parameters of acoustic emission signals (maximum amplitude, area under the signal curve, and signal duration) with variations in the number of composite material elements. These patterns enable the determination of the sensitivity of acoustic emission signal parameters to the influencing factor. The findings of this study may be of interest in the development of methods for monitoring, diagnosing, and predicting the failure of composite materials and products through the registration and analysis of acoustic emission signals.
subject: Articles
  topic: section
subject: Composite material
  topic: keywords
subject: damage
  topic: keywords
subject: acoustic emission
  topic: keywords
subject: signal amplitude
  topic: keywords
subject: signal energy
  topic: keywords
subject: von Mises criterion
  topic: keywords
creator: Sergii Filonenko
creator: Anzhelika Stakhova
rights: Copyright (c) 2025 Journal of Automation, Mobile Robotics and Intelligent Systems https://creativecommons.org/licenses/by-nc-nd/4.0
date: 2025-06-26
publication date: 2025-06-26
starting page: 50
ending page: 58
doi: 10.14313/jamris-2025-014

Formation Control of Multi-agent Nonlinear Systems using the State-Dependent Riccati Equation
type: item
description: This work investigates the formation control of a multi-agent robotic system via the state-dependent Riccati equation (SDRE). The system's agents interact with each other and follow a leader in point-to-point motion control (regulation). The number of agents is unlimited in conventional multi-agent formation control considering a complex dynamic for each agent, though the complexities of the algorithms usually result in small-scale simulations. Here a formalism is proposed that considers fully coupled nonlinear dynamics for robotic systems in multi-agent system formation control with a large number of agents. The interaction of the agents with each other and obstacle avoidance are embedded in the design through the weighting matrix of states in the SDRE. The input constraint also limits the actuators to create a more realistic scenario. Two dynamical systems have been modeled and simulated in this work: wheeled mobile robots (WMR) and multirotor unmanned aerial vehicles (UAVs). The simulation results show success in the implementation of a total of 1,089 agents in the desired square formation shape in the UAV case study, and a figure of 45 agents and 1,050 differential wheeled mobile robots in the circular desired shape, considering obstacle avoidance and also collision avoidance between the agents.
subject: Articles
  topic: section
subject: Multi-agent
  topic: keywords
subject: Formation
  topic: keywords
subject: Control
  topic: keywords
subject: SDRE
  topic: keywords
subject: Robotics
  topic: keywords
creator: Saeed Rafee Nekoo
rights: Copyright (c) 2025 Journal of Automation, Mobile Robotics and Intelligent Systems https://creativecommons.org/licenses/by-nc-nd/4.0
date: 2025-03-26
publication date: 2025-03-26
starting page: 17
ending page: 32
doi: 10.14313/jamris-2025-003

Positivity and Stability of Descriptor Discrete-Time Linear Systems with Interval State Matrices
type: item
description: The positivity and stability of descriptor discrete-time linear systems with interval state matrices are addressed. Necessary and sufficient conditions for the positivity of descriptor discrete-time linear systems are established. The stability of descriptor linear systems with interval state matrices is investigated.
subject: Articles
  topic: section
subject: descriptor
  topic: keywords
subject: linear
  topic: keywords
subject: discrete-time
  topic: keywords
subject: interval system
  topic: keywords
subject: positivity
  topic: keywords
subject: stability
  topic: keywords
creator: Tadeusz Kaczorek
rights: Copyright (c) 2025 Journal of Automation, Mobile Robotics and Intelligent Systems https://creativecommons.org/licenses/by-nc-nd/4.0
date: 2025-12-15
publication date: 2025-12-15
starting page: 1
ending page: 6
doi: 10.14313/jamris-2025-030

Monocular 3D Object Localization using 2D Estimates for Industrial Robot Vision System
type: item
description: 3D Object Localization has been emerging as one of the main challenges in Machine Vision tasks. In this paper, we proposed a novel 3D object localization method, leveraging a blend of deep learning techniques primarily rooted in object detection, post-image processing, and pose estimation algorithms. Our approach involves 3D calibration methods tailored for low-cost industrial robotics systems, requiring only a single 2D image input. Initially, object detection is performed using the You Only Look Once (YOLO) model, followed by an R-CNN model for segmenting the object into two distinct parts, i.e., the top face and the remainder. Subsequently, the center of the top face is served as an initialization position, and being refined with a novel calibration algorithm. Experimental results demonstrate a notable reduction in localization error by 87.65% when compared to existing methodologies.
subject: Articles
  topic: section
subject: Object Localization
  topic: keywords
subject: Camera Calibration
  topic: keywords
subject: Machine (Robot) Vision System
  topic: keywords
subject: Industrial robotics
  topic: keywords
creator: Thanh Nguyen Canh
creator: Du Trinh Ngoc
creator: Xiem HoangVan
rights: Copyright (c) 2025 Journal of Automation, Mobile Robotics and Intelligent Systems https://creativecommons.org/licenses/by-nc-nd/4.0
date: 2025-09-09
publication date: 2025-09-09
starting page: 53
ending page: 65
doi: 10.14313/jamris-2025-025

Multi-Input Melanoma Classification using MobileNet-V3-Large Architecture
type: item
description: All over the globe there exists a serious problem with skin cancer, most especially melanoma; a malignancy which is known to behave aggressively and able to metastasize. Detecting this early is the key to saving life. This study introduces a new method of classifying melanoma using an advanced model known as MobileNet-V3-Large. This technique differs from others in that it considers both the images of the skin lesion and tabular data including factors consisting of patient’s approximate age, gender and the location of the lesion on the body of the patient is considered here. Such an approach empowers the predictions of whether skin lesion may be malignant or benign. Tested on huge collection consisting of skin images combined with the tabular data, it was established that the method outperforms others already existing. The results of this study showed that a high accuracy of 99.56% was achieved using the proposed model. The study indicates that utilizing multi-input method will substantially enhance diagnosis for melanoma hence reducing mortalities in the future.
subject: Articles
  topic: section
subject: Melanoma cancer
  topic: keywords
subject: benign
  topic: keywords
subject: malignant
  topic: keywords
subject: Convolution neural network (CNN)
  topic: keywords
subject: Deep learning (DL)
  topic: keywords
subject: Pretrained model
  topic: keywords
creator: Serra Aksoy
rights: Copyright (c) 2025 Journal of Automation, Mobile Robotics and Intelligent Systems https://creativecommons.org/licenses/by-nc-nd/4.0
date: 2025-03-26
publication date: 2025-03-26
starting page: 73
ending page: 84
doi: 10.14313/jamris-2025-008

Enhancing Efficiency and Security in Healthcare IoT: a Novel Approach for Fog Computing Resource Optimization Using TGA-RNN
type: item
description: Fog computing, a new computing paradigm that has gained popularity, brings calculations closer to data sources from healthcare facilities. The health care industry is the driving force behind the growth of Internet of Things (IoT) driven Fog computing that improves network performance and efficiency, particularly when it comes to the safe and effective aggregation and transmission of healthcare data. This requires optimizing resource allocation and addressing overflow issues. This study introduces a novel approach that combines Task Group Aggregation (TGA) with a Recurrent Neural Network (RNN) to assess Quality of Service (QoS) characteristics and detect overloaded servers. The TGA method is utilized to effectively manage data movement to Virtual Machines (VMs), thereby alleviating congestion and improving system stability. Furthermore, it uses Chaotic Fruit Fly Optimization Algorithm (CFOA), a neural computing system, to optimize service and user separation based on individual qualities in the context of secure healthcare data aggregation and transmission within IoT networks. The integration of TGA with CFF enhances the detection of overflow problems within the RNN framework, enabling proactive management of resource allocation. The proposed work is evaluated by using the Java programming language and the results demonstrate the effectiveness of the Fog computing overflow control model in mitigating congestion and optimizing resource scheduling, thereby facilitating the efficient and secure aggregation and transmission of healthcare data within IoT networks.
subject: Articles
  topic: section
subject: Fog Computing
  topic: keywords
subject: Internet of Things
  topic: keywords
subject: Task Group Aggregation
  topic: keywords
subject: Recurrent Neural Network
  topic: keywords
subject: Chaotic Fruit Fly Optimization Algorithm
  topic: keywords
creator: Rahul Jaywantrao Shimpi
creator: Vibha Tiwari
rights: Copyright (c) 2025 Journal of Automation, Mobile Robotics and Intelligent Systems https://creativecommons.org/licenses/by-nc-nd/4.0
date: 2025-12-15
publication date: 2025-12-15
starting page: 82
ending page: 93
doi: 10.14313/jamris-2025-037

Partitioning of Complex Discrete Models for Highly Scalable Simulations
type: item
description: The need for more and more accurate simulations of groups of autonomous beings directs the attention of researchers towards the ways of parallelizing simulation algorithms. Parallel execution of discrete simulation models update methods requires their division between workers. Existing methods used for grid division aim at providing equal areas of fragments and minimizing the length of created borders. However, in real life simulations, other factors also have to be considered. In this paper we present a method for grid partitioning, which also allows defining indivisible areas, considers complex shapes of real-life environments and supports division suitable for defined architecture of nodes and cores. The method is evaluated using several scenarios, providing satisfactory results.
subject: Articles
  topic: section
subject: Simulation
  topic: keywords
subject: Model Partitioning
  topic: keywords
subject: Scalability
  topic: keywords
subject: High Performance Computing
  topic: keywords
creator: Jakub Ziarko
creator: Mateusz Najdek
creator: Wojciech Turek
rights: Copyright (c) 2025 Journal of Automation, Mobile Robotics and Intelligent Systems https://creativecommons.org/licenses/by-nc-nd/4.0
date: 2025-12-15
publication date: 2025-12-15
starting page: 26
ending page: 34
doi: 10.14313/jamris-2025-033

Optimization of the Nominal Capacity of the Energy Storage System for Ensuring Security of Electrical Energy Supply
type: item
description: This paper presents an optimization model for determining the nominal capacity of an energy storage system is presented, which transfers excess amounts of electrical energy from solar power plants as part of a group of distributed generation power plants, based on the criterion of the minimum cost of supplying electricity to end consumers. The use of the developed optimization model allows to determine the optimal parameters of the energy storage system to achieve an economic balance between the excess amounts of electric energy of solar power plants that are transferred and the amount of restrictions on inverters that are subject to payment. Based on the developed method, based on the forecast level of electric energy consumption and statistical data on the level of electric energy production at wind and solar power plants for the territory of Ukraine, the installed capacity of the wind, solar, reserve power plant and the nominal capacity of the two-hour energy storage system were determined, which will provide the minimum levelized cost of energy of supplying to consumers during the entire period of commercial operation of a group of distributed generation power plants.
subject: Articles
  topic: section
subject: Optimization model
  topic: keywords
subject: Distributed generation
  topic: keywords
subject: Solar power plant
  topic: keywords
subject: Wind power plant
  topic: keywords
subject: Backup power plant
  topic: keywords
subject: Energy storage system
  topic: keywords
creator: Ihor Buratynskyi
creator: Artur Zaporozhets
rights: Copyright (c) 2025 Journal of Automation, Mobile Robotics and Intelligent Systems https://creativecommons.org/licenses/by-nc-nd/4.0
date: 2025-09-09
publication date: 2025-09-09
starting page: 75
ending page: 81
doi: 10.14313/jamris-2025-027

Health Recommender System for Sleep Apnea using Computational Intelligence
type: item
description: In health care, there is a growing interest on building recommendation systems for sleep apnea management. These systems use data from a variety of sources, including patient-reported outcomes and electronic health records, to assess sleep quality, breathing patterns, and medical treatment adherence. Leveraging artificial intelligence (AI), machine learning (ML), the Internet of Things (IoT), and cloud platforms, the system analyzes these data to uncover patterns and correlations. It then creates individualized patient profiles that incorporate details about diet, medical history, and sleep habits. Based on these profiles, customized recommendations are generated to enhance sleep apnea management. These recommendations may encompass treatment options and lifestyle adjustments, Yoga, exercise, etc. to improve treatment effectiveness and overall well-being for individuals with sleep apnea. This review article discusses available literature on sleep apnea, its diagnosis, and the role played by ML and deep learning classifiers in the prediction and classification of the disease. The article also presents a comparative analysis on performance measures for these methods. This article highlights the research scope for incorporating technologies such as AI, the IoT, and computational intelligence in improving the diagnosis, remote monitoring, and treatment of sleep apnea.
subject: Articles
  topic: section
subject: Sleep apnea
  topic: keywords
subject: Health recommender system
  topic: keywords
subject: Internet of Things
  topic: keywords
subject: Sleep-disordered breathing
  topic: keywords
subject: Deep learning
  topic: keywords
subject: Machine learning
  topic: keywords
creator: Mubashir Khan
creator: Yashpal Singh
creator: Harshit Bhardwaj
rights: Copyright (c) 2025 Journal of Automation, Mobile Robotics and Intelligent Systems https://creativecommons.org/licenses/by-nc-nd/4.0
date: 2025-09-09
publication date: 2025-09-09
starting page: 89
ending page: 103
doi: 10.14313/jamris-2025-029

Identification of Keywords for Legal Documents Categories using SOM
type: item
description: This study aims to use the decision-making process in categorizing legal documents by identifying keywords characterizing each legal domain class. The study utilizes the Kohonen Self-Organizing Map method and the Global Vectors for Word Representation (GloVe) model to create an efficient document classification system. As a result, a satisfactory classification accuracy of 71.69% was achieved. The article also discusses alternative approaches implemented to improve classification accuracy, such as the use of Named Entity Recognizer (NER) tools and the RoBERTa model, along with a comparison of their effectiveness. Challenges related to the uneven distribution of categories in the dataset are also mentioned, and potential directions for further research to enhance the classification results of legal documents are presented.
subject: Articles
  topic: section
subject: Document classification
  topic: keywords
subject: RoBERTa
  topic: keywords
subject: NLP
  topic: keywords
subject: 20 GloVe
  topic: keywords
subject: NER
  topic: keywords
subject: SOM
  topic: keywords
creator: Paulina Puchalska
creator: Kacper Krzemiński
creator: Maksymilian Lis
creator: Rafal Scherer
creator: Paweł Drozda
creator: Kajetan Komar-Komarowski
creator: Konrad Szałapak
creator: Andrzej Sobecki
creator: Tomasz Zymkowski
creator: Julian Szymański
rights: Copyright (c) 2025 Journal of Automation, Mobile Robotics and Intelligent Systems https://creativecommons.org/licenses/by-nc-nd/4.0
date: 2025-03-26
publication date: 2025-03-26
starting page: 33
ending page: 41
doi: 10.14313/jamris-2025-004

The Design of 3D-Printed Open Bearings for Human Assisting Robots
type: item
description: Wearable technologies, including exoskeletons, significantly impact supporting the motion of people with disabilities. However, activating internal/external rotations in extremity segments requires placing body segments inside bearings, as their rotation axes must overlap. This hinders the exoskeleton mounting process and even excludes some medical cases from using the devices. For this reason, the design of innovative 3D-printed open bearings was presented in this paper. It consists of anthropometric modelling, computer-aided mechanical design, multibody dynamics simulations, strength analysis, and parametric optimisation to obtain minimal mass while complying with the strength requirements. The design process resulted in reducing the overall mass of the design by 40%. Moreover, it proved that the powder additive manufacturing techniques combined with the thin sliding layers printed with FFF/FDM technology are more appropriate for the intended use than monoliths manufactured with FFF/FDM technology. The presented methodology is universally applicable to other robots interacting with humans, which require the use of open bearings without drives, also if manufactured subtractively.
subject: Articles
  topic: section
subject: additive manufacturing
  topic: keywords
subject: exoskeleton
  topic: keywords
subject: finite elements methods
  topic: keywords
subject: multibody simulation
  topic: keywords
subject: open bearings
  topic: keywords
subject: rehabilitation robotics
  topic: keywords
subject: strength simulation
  topic: keywords
creator: Piotr Falkowski
creator: Bazyli Leczkowski
rights: Copyright (c) 2025 Journal of Automation, Mobile Robotics and Intelligent Systems https://creativecommons.org/licenses/by-nc-nd/4.0
date: 2025-09-09
publication date: 2025-09-09
starting page: 13
ending page: 24
doi: 10.14313/jamris-2025-021

In-Flight Solar Radiation Intensity Measurement Using a Small Unmanned Aerial Vehicle
type: item
description: Recently, the Small Low-Altitude Long Endurance (LALE) solar-powered aircraft gained significant attention in the community. A reliable mathematical model of the aircraft and solar radiation should be available to design such a plane. This paper addresses the problem of measuring solar radiation in flight and validating its model. The configuration of the measurement system was described in detail. Moreover, the Multiplex Funcub NG was used as the intermediate research platform. The results of flight tests in two different geographic locations in Poland confirmed that the proposed models allow for the precise prediction of the amount of harvested energy. The maximum solar radiation intensity measured in September at around noon was 829,3 W/m2. Achieving perpetual flight functionality is possible in these conditions, provided that the aircraft has very low power consumption and a high wing aspect ratio. The obtained data can be used in the design process of the AZ-5 solar-powered plane.
subject: Articles
  topic: section
subject: solar-powered UAV
  topic: keywords
subject: solar energy model
  topic: keywords
subject: flight tests
  topic: keywords
subject: mathematical modeling
  topic: keywords
subject: solar radiation measurement
  topic: keywords
creator: Rafał Ożóg
creator: Mariusz Jacewicz
creator: Robert Głębocki
creator: Juliusz Hanke
rights: Copyright (c) 2025 Journal of Automation, Mobile Robotics and Intelligent Systems https://creativecommons.org/licenses/by-nc-nd/4.0
date: 2025-12-15
publication date: 2025-12-15
starting page: 52
ending page: 69
doi: 10.14313/jamris-2025-035

Preface
type: item
subject: Preface
  topic: section
creator: Piotr Kowalski
creator: Szymon Łukasik
rights: Copyright (c) 2024 Journal of Automation, Mobile Robotics and Intelligent Systems https://creativecommons.org/licenses/by-nc-nd/4.0
date: 2024-09-03
publication date: 2024-09-03
doi: 10.14313/

A 2‐Tuple Linguistic Dynamic OWAWA Aggregation Operator and its Application to Multi‐Attribute Decision Making
type: item
description: A linguistic dynamic decision making problem reveals situations in which the decision data gathered in multiple periods is represented by means of linguistic values. To deal with linguistic variables in linguistic dynamic decision making problems, the 2-tuple linguistic model stands out among computational models because of its accuracy and interpretability. The selection of a suitable time-dependent 2-tuple linguistic aggregation operator is relevant due to its properties can highly modify the computing cost as well as results themselves and their accuracy and interpretability. This paper proposes a new 2-tuple linguistic dynamic hybrid weighted aggregation operator which is suitable to model different attitudes in decision making by simultaneously weighting the given arguments as well as their ordered positions. The novel 2-tuple Linguistic Dynamic Ordered Weighted Averaging–Weighted Average operator weights not only the importance of a particular time period, but also the importance of non-dynamic evaluations in such a time period. Eventually, a practical example is provided to illustrate the developed approach and to demonstrate its practicality and effectiveness.
subject: Articles
  topic: section
subject: linguistic decision making
  topic: keywords
subject: dynamic decision making
  topic: keywords
subject: 2-tuple linguistic model
  topic: keywords
subject: aggregation operator
  topic: keywords
creator: Yeleny Zulueta-Véliz
creator: Carlos Rafael Rodríguez Rodríguez
creator: Aylin Estrada Velazco
rights: Copyright (c) 2025 Journal of Automation, Mobile Robotics and Intelligent Systems https://creativecommons.org/licenses/by-nc-nd/4.0
date: 2025-12-15
publication date: 2025-12-15
starting page: 18
ending page: 25
doi: 10.14313/jamris-2025-032

Implementation of Enzyme Family Classification by using Autoencoders in a Study Case with Imbalanced and Underrepresented Classes
type: item
description: In the field of Bioinformatics, the scientific community is fully aware of the challenges associated with enzyme classification. In this study, a novel strategy is proposed based on the use of Anomalous Autoencoders to characterize chitinases belonging to glycoside hydrolases. Python and TensorFlow programming technologies were employed to conduct this analysis. The designed classifier consists of two levels that determine both the enzymatic nature of an amino acid sequence and its corresponding chitinase enzyme family. These levels considered class imbalance and the underrepresentation of those enzyme families in the CAZy.org database. Furthermore, a comprehensive comparison was made with other available software in the field. To represent the amino acid sequences, embeddings generated from the ProtFlash model were used. The results obtained in this study confirm the effectiveness of the proposed implementation compared to the methods EzyPred, ECPred, and Proteinfer.
subject: Articles
  topic: section
subject: Autoencoders
  topic: keywords
subject: Bioinformatics
  topic: keywords
subject: Embeddings
  topic: keywords
subject: Enzyme classification
  topic: keywords
creator: Darian Fernández Gutiérrez
creator: Ariadna Arbolaez Espinosa
creator: Deborah Raquel Galpert Cañizares
creator: María Matilde García Lorenzo
rights: Copyright (c) 2025 Journal of Automation, Mobile Robotics and Intelligent Systems https://creativecommons.org/licenses/by-nc-nd/4.0
date: 2025-03-26
publication date: 2025-03-26
starting page: 42
ending page: 48
doi: 10.14313/jamris-2025-005

Construction Automation with Bio-inspired Hierarchical Extremely Modular Systems
type: item
description: This paper presents the concept of hierarchical extremely modular systems (EMS). The biology-inspired nomenclature, genetic encoding, and operations for this class of structures is introduced and illustrated with various examples. Four mutation types are introduced and briefly analyzed. A relatively good convergence of the evolution strategy-based algorithm applied for optimization of EMS is shown.
subject: Articles
  topic: section
subject: evolution strategy
  topic: keywords
subject: discrete structure encoding
  topic: keywords
subject: Extremely Modular System
  topic: keywords
subject: multi-branch structure
  topic: keywords
creator: Ela Zawidzka
creator: Machi Zawidzki
rights: Copyright (c) 2025 Journal of Automation, Mobile Robotics and Intelligent Systems https://creativecommons.org/licenses/by-nc-nd/4.0
date: 2025-06-26
publication date: 2025-06-26
starting page: 59
ending page: 64
doi: 10.14313/jamris-2025-015

Controllability, Observability and Transfer Matrix Zeroing of the 2D Roesser Model
type: item
description: The article considers the fundamental properties of the two-dimensional (2D) system described by the Roesser model. The controllability and observability are analyzed and the sufficient conditions under which the transfer matrix is zero are given. It is shown that if the matrix of the state equation A and B or A and C of the Roesser model has full row rank (respectively, full column rank) then there exists a nonsingular matrix of transformation such that the new pair of new matrices is controllable (observable). The numerical examples are given to show the correctness of the obtained conditions.
subject: Articles
  topic: section
subject: Controllability
  topic: keywords
subject: Observability
  topic: keywords
subject: Roesser model
  topic: keywords
subject: 15 Transfer matrix
  topic: keywords
subject: Zeroing
  topic: keywords
creator: Tadeusz Kaczorek
creator: Krzysztof Rogowski
rights: Copyright (c) 2025 Journal of Automation, Mobile Robotics and Intelligent Systems https://creativecommons.org/licenses/by-nc-nd/4.0
date: 2025-03-26
publication date: 2025-03-26
starting page: 1
ending page: 6
doi: 10.14313/jamris-2025-001

Comparison of Open Source SDN Controllers and Cloud Platforms in Terms of Performance, Stability, and Infrastructure Flexibility
type: item
description: The IT industry is advancing rapidly, with virtually every branch of modern computing experiencing swift development. Concepts such as Cloud Computing and Artificial Intelligence no longer surprise anyone. Recently, Software Defined Networks (SDN) have been gaining significant popularity. This innovative approach to computer networks allows for greater flexibility and is, therefore, much more well-known in the world of cloud computing than in traditional network implementations. This paper introduces the concept of SDN and Network Functions Virtualization (NFV) and outlines all the challenges and security issues associated with the cloud environment. The dynamic nature of the IT landscape requires constant adaptation to emerging technologies, and SDN represents a noteworthy evolution in the realm of computer networking. Platforms such as SDN and open-source tools enabling the creation of private cloud environments such as OpenStack or OpenNebula were compared. At the same time, aspects like security, network performance, flexibility, and scalability were analyzed. Based on the prior analysis, a comprehensive cloud environment was built using the OpenStack solution and SDN - OpenDaylight was deployed. Additional tests conducted on the OpenStack cloud, both with and without SDN, demonstrated the superiority of SDN implementation in the cloud.
subject: Articles
  topic: section
subject: Software-Defined Network
  topic: keywords
subject: NFV
  topic: keywords
subject: Cloud Computing
  topic: keywords
subject: OpenDaylight
  topic: keywords
subject: OpenStack
  topic: keywords
subject: OpenNebula
  topic: keywords
creator: Andrzej Mycek
rights: Copyright (c) 2024 Journal of Automation, Mobile Robotics and Intelligent Systems https://creativecommons.org/licenses/by-nc-nd/4.0
date: 2024-12-02
publication date: 2024-12-02
starting page: 62
ending page: 70
doi: 10.14313/JAMRIS/4-2024/31

Segmentation of E-Commerce Users on Cart Abandonment and Product Recommendation Using Double Transformer Residual Super-Resolution Network
type: item
description: A novel approach named "Segmentation of E-commerce users on Cart Abandonment with Product Recommendation using Double Transformer Residual Super-resolution Network (SEC-CAPR-DTRSRN)" is proposed. This study commences with the collection of e-commerce data from a diverse multi-category store. The pre-processing phase uses Fairness-aware Collaborative Filtering (FACF) to suggest personalized items or content to users based on their preferences with behavior. Following pre-processing, the data undergoes segmentation using the Generalized Intuitionistic Fuzzy c-means Clustering (GIFCMC) technique to categorize users to target-based customer groups. To enhance cart transactions, Double Transformer Residual Super-resolution Network (DTRSN) is introduced for product recommendations. The exploration of cart abandonment identifies potential discord stemming from a misalignment between consumer perceptions and digital site experiences, potentially leading to cart abandonment due to user annoyance. While the DTRSN lacks an explicit adoption of optimization systems for calculating optimal parameters, the manuscript proposes the integration of Polar Coordinate Bald Eagle Search Algorithm (PCBESA). PCBESA is introduced to optimize Polar Coordinate Bald Eagle Search Algorithm DTRSN, ensuring precise product recommendations. The proposed (SEC-CAPR-DTRSRN) method is implemented, and their performances is rigorously evaluated using key metrics, including mean square error, standard deviation, and mean reciprocal rank (MRR). The proposed method gives 12.78%, 29.85% and 17.45% lower mean square error and 23.67%, 28.86% and 16.45% higher MRR with existing techniques like segmentation of E-commerce users depend on cart abandonment with product recommendation using collaborative filtering: moderating effect of exorbitant pricing (SECU-CAPR-CF), new top-n recommendation technique for multi-criteria collaborative filtering (PR-MCCF-EC) and reinforcement learning E-commerce cart targeting to…
subject: Articles
  topic: section
subject: E‐commerce
  topic: keywords
subject: cart abandonment
  topic: keywords
subject: product recommendation
  topic: keywords
subject: double transformer residual super‐ resolution network
  topic: keywords
subject: polar coordinate bald eagle search algorithm
  topic: keywords
creator: Praveen Kumar P
creator: Suguna R
rights: Copyright (c) 2025 Journal of Automation, Mobile Robotics and Intelligent Systems https://creativecommons.org/licenses/by-nc-nd/4.0
date: 2025-09-09
publication date: 2025-09-09
starting page: 45
ending page: 52
doi: 10.14313/jamris-2025-024