<abstract xmlns="http://www.w3.org/1999/xhtml">

Artificial swarms have the potential to provide robust, efficient solutions for a broad range of applications from assisting search and rescue operations to exploring remote planets. However, many fundamental obstacles still need to be overcome to bridge the gap between theory and application. In this characterization work, we demonstrate how a human rescuer can leverage minimal local observations of emergent swarm behavior to locate a lone survivor in a maze-like environment. The simulated robots and rescuer have limited sensing and no communication capabilities to model a worst-case scenario. We then explore the impact of fundamental properties at the individual robot level on the utility of the emergent behavior to direct swarm design choices. We further demonstrate the relative robustness of the simulated robotic swarm by quantifying how reasonable probabilistic failure affects the rescue time in a complex environment. These results are compared to the theoretical performance of a single wall-following robot to further demonstrate the potential benefits of utilizing robotic swarms for rescue operations.
</abstract>

Quantifying Swarm Resilience with Simulated Exploration of Maze-Like Environments

<abstract xmlns="http://www.w3.org/1999/xhtml">

Recently, time series forecasting modelling in the Consumer Price Index (CPI) has attracted the attention of the scientific community. Several research projects have tackled the problem of CPI prediction for their countries using statistical learning, machine learning and deep neural networks. The most popular approach to CPI in several countries is the Autoregressive Integrated Moving Average (ARIMA) due to the nature of the data. This paper addresses the Cuban CPI forecasting problem using Transformer with attention model over univariate dataset. The fine tuning of the lag parameter shows that Cuban CPI has better performance with small lag and that the best result was in p = 1. Finally, the comparative results between ARIMA and our proposal show that the Transformer with attention has a very high performance despite having a small data set.
</abstract>

Universidad de las Ciencias Informáticas

Cuban Consumer Price Index Forecasting Through Transformer with Attention

<abstract xmlns="http://www.w3.org/1999/xhtml">

This paper presents a new aggregation operator technique that uses the ordered weighted average (OWA), heavy aggregation operators, Hamming distance, and moving averages. This approach is called heavy ordered weighted moving average distance (HOWMAD). The main advantage of this operator is that it can use the characteristics of the HOWMA operator to under-or overestimate the results according to the expectations and the knowledge of the future scenarios, analyze the historical data of the moving average, and compare the different alternatives with the ideal results of the distance measures. Some of the main families and specific cases using generalized and quasi-arithmetic means are presented, such as the generalized heavy moving average distance and a generalized HOWMAD. This study develops an application of this operator in forecasting the sales growth rate for a commercial company. We find that it is possible to determine whether the company’s objectives can be achieved or must be reevaluated in response to the actual situation and future expectations of the enterprise.
</abstract>

Universidad Católica de la Santísima Concepción

Universidad Pedagógica y Tecnológica de Colombia

Heavy Heavymoving Average Distances In Sales Forecasting

<abstract xmlns="http://www.w3.org/1999/xhtml">

This paper presents a complete simulation and reinforcement learning solution to train mobile agents’ strategy of route tracking and avoiding mutual collisions. The aim was to achieve such functionality with limited resources, w.r.t. model input and model size itself. The designed models prove to keep agents safely on the track. Collision avoidance agent’s skills developed in the course of model training are primitive but rational. Small size of the model allows fast training with limited computational resources.
</abstract>

A Compact Dqn Model for Mobile Agents with Collision Avoidance

<abstract xmlns="http://www.w3.org/1999/xhtml">

During the past few years, the number of drones (unmanned aerial vehicles, or UAVs) manufactured and purchased has risen dramatically. It is predicted that it will continue to spread, making its use inevitable in all walks of life. Drone apps are therefore expected to overrun the app stores in the near future. The UAV’s software is not being studied/researched despite several active research and studies being carried out in the UAV’s hardware field. A large-scale empirical analysis of Google Play Store Platform apps connected to drones is being done in this direction. There are, however, a number of challenges with drone apps because of the lack of formal and specialized app development procedures. In this paper, eleven drone app issues have been identified. Then we applied the DEMATEL (Decision Making Trial and Evaluation Laboratory) method to analyze the drone app issues (DIs) and divide these issues into cause and effect groups. First, multiple experts assess the direct relationships between influential issues in drone apps. The evaluation results are presented in spherical fuzzy numbers (SFN). Secondly, we convert the linguistic terms into SFN. Thirdly, based on DEMATEL, the cause-effect classifications of issues are obtained. Finally, the issues in the cause category are identified as DI’s in drone apps. The outcome of the research is compared with the other variants of DEMATEL, like rough-Z-numberbased DEMATEL and spherical fuzzy number, and the comparative results suggest that spherical fuzzy DEMATEL is the most fitting method to analyze the interrelationship of different issues in drone apps. The findings revealed that highest influenced values feature request (DI9) 3.12, Customer support (DI6) 2.91, Connection/Sync ((DI4) 2./72, Cellular Data Usage ((DI3) 2.51, Battery (DI2) 2.31, Advertisements ((DI1) – 0.3, Cost (DI5) – 0.5, Additional cost (D11) – 0.5, Device Compatibility (DI7) – 0.96, and Functional Error (DI10) – 1.2. The outcome of this work definitely assists the software industry in the successful identification of the critical issues where professionals and project managers could really focus.
</abstract>

Jaypee University of Engineering and Technology

Application of the Spherical Fuzzy Dematel Model for Assessing the Drone Apps Issues

<abstract xmlns="http://www.w3.org/1999/xhtml">

Alzheimer’s disease is the most common form of dementia that can cause a brain neurological disorder with progressive memory loss as a result of brain cell damage. Prevention and treatment of disease is a key challenge in today’s aging society. Accurate diagnosis of Alzheimer’s disease plays an important role in patient management, especially in the early stages of the disease, because awareness of risk allows patients to undergo preventive measures even before irreversible brain damage occurs. Over the years, techniques such as statistical modeling or machine learning algorithms have been used to improve understanding of this condition. The objective of the work is the study of the methods of detection and progression of Alzheimer’s disease through artificial intelligence techniques that have been proposed in the last three years.
The methodology used was based on the search, selection, review, and analysis of the state of the art and the most current articles published on the subject. The most representative works were analyzed, which allowed proposing a taxonomic classification of the studied methods and on this basis a possible solution strategy was proposed within the framework of the project developed by the Cuban Center for Neurosciences based on the conditions more convenient in terms of cost and effectiveness and the most current trends based on the use of artificial intelligence techniques.
</abstract>

Update on the Study of Alzheimer’s Disease Through Artificial Intelligence Techniques

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The contemporary international pandemic proved that a flexible approach towards work, trade and healthcare is not only favorable but a must. Hence, the devices enabling home-rehabilitation became one of the urgent needs of the medical market. The following overview is a part of an R&amp;D project aimed at designing an exoskeleton and developing methods enabling effective home rehabilitation. It contains a comparison of current devices in terms of their kinematics, applications, weights, sizes, and integration with selected ICT technologies. The data is analyzed regarding conclusions from qualitative research, based on in-depth interviews with physiotherapists and questionnaires organized beforehand. The investigation assesses whether commercial and developed devices enable feedback from a patient by all possible means; hence, if they could allow effective telerehabilitation. Moreover, their capabilities of increasing engagement and accelerating improvements by supervising techniques and measuring biomechanical parameters are evaluated. These outcomes are a base to set the constraints and requirements before designing an exoskeleton dedicated to home treatment.
</abstract>

Al. Jerozolimskie 202, Warsaw Warsaw University of Technology

Analyzis of Rehabilitation Systems in Regards to Requirements Towards Remote Home Rehabilitation Devices

<abstract xmlns="http://www.w3.org/1999/xhtml">

Solar energy is an essential factor in Moroccan sustainable development, especially in solar pumping in the agricultural sector. It is therefore difficult to dissociate the energy system of a society from its economic development and social development. Solar radiation prediction is useful in giving us a global overview on maintaining the integrity of solar systems. Access to database use makes this process more flexible. Solar forecasts can be generated using various available data sources. There are two major pillars of this data: the exploitation of historical solar radiation data, and the exploitation of other meteorological factors. On the other hand, the choice of data can have an impact on the choice of the model and the approach employed. In this paper we suggest an idea that aims to monitor in real time the situation of solar radiation in Morocco, using Long Short-Term Memory for deep learning models compared with Artificial Neural Networks and Deep Neural Networks to predict the solar radiation with regard to solar pumping in the Moroccan agricultural sector.
</abstract>

Sidi Mohamed Ben Abdellah University, Faculty of Sciences

New Model of Photovoltaic System Adapted By a Digital Mppt Control and Radiation Predictions Using Deep Learning in Morocco Agricultural Sector

AHEAD OF PRINT

Volume 18 (2024): Issue 1 (March 2024)

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Journal of Automation, Mobile Robotics and Intelligent Systems

Journal of Automation, Mobile Robotics and Intelligent Systems (ISSN 1897-8649 (Print) / ISSN 2080-2145 (Online) publishes high-quality, state-of-the-art technical and position papers in the fields of theory, design and applications of broadly perceived intelligent technologies, notably based on artificial intelligence and computational intelligence, in particular for the analysis and solution of scientific and engineering problems and systems.  JAMRIS is an international peer-reviewed, open-access, scientific journal, disseminating the highest quality research in the following　 fields:    Automation, Systems and Control,  Autonomous Systems,  Multiagent Systems,  Decision-making and Decision Support,  Robotics,  Mechatronics,  Data Sciences,  New Computing Paradigms, etc.   Supporting the realization of Open Science, all scientific articles published in the Journal of Automation, Mobile Robotics and Intelligent Systems are free of charge.   The journal is an open-access journal that allows free unlimited access to all its contents by all users without any restrictions upon publication.  Journal of Automation, Mobile Robotics and Intelligent Systems has been published 4 times a year since 2007.  Journal of Automation, Mobile Robotics and Intelligent Systems (JAMRIS) is published in printed and electronic version by Sciendo in cooperation with £ukasiewicz - Industrial Research Institute for Automation and Measurements PIAP, located in Warsaw, Poland.  Archiving  Sciendo archives the contents of this journal in Portico - digital long-term preservation service of scholarly books, journals and collections.  Plagiarism Policy  The editorial board is participating in a growing community of Similarity Check System's users in order to ensure that the content published is original and trustworthy. Similarity Check is a medium that allows for comprehensive manuscripts screening, aimed to eliminate plagiarism and provide a high standard and quality peer-review process. 