ISSN: 2790-7511
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2025
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Editor(s)-in-Chief: Shuai (Steven) Li

EAI Endorsed Transactions on AI and Robotics (eISSN: 2790-7511) covers all aspects of robotics and knowledge-based AI systems along with interdisciplinary approaches to computer science, control systems, computer vision, machine learning, electrical engineering, intelligent machines, mathematics, and other disciplines. An important goal of this journal is to extend cutting-edge technologies in the control and learning of both symbolic and sensory robots with regard to smart systems. Our journal contains articles on the theoretical, mathematical, computational, and experimental aspects of robotics and intelligent systems.

INDEXING: Scopus, CrossRef, Google Scholar, ProQuest, EBSCO, CNKI, Dimensions

  • Editor-in-Chief
  • Shuai (Steven) Li, College of Engineering, Swansea University, United Kingdom.
  • Administrative Editor
  • Yiguo Yang, School of Computer Science, Shanghai University, China.
  • Advisory Board
  • Oubay Hassan, Department of Civil Engineering, Swansea University, UK. Fellow of UK Royal Academy of Engineering.
  • Michael Navon, Department of Scientific Computing, Florida State University, USA. Fellow of the American Meteorological Society; Honorary Member of Romanian Academy of Science. 
  • Jianming Shi, School of Mathematical Sciences, Soochow University, China. School of Management, Tokyo University of Science, Japan. Foreign Member, Engineering Academy of Japan (EAJ).   
  • Editors
  • Dunhui Xiao, School of Mathematical Sciences, Tongji University, China. 
  • Lining Sun, School of Mechanical and Electric Engineering, Soochow University, China. Foreign member of Russian Academy of Engineering.
  • Yuhua Zheng, Zhejiang Lab, Hangzhou, China.
  • Xingye Yue, School of Mathematical Sciences, Soochow University, China.
  • Editorial Board Members
  • Ivona Brajević, University Business Academy in Novi Sad, Serbia.
  • Xinwei Cao, School of Management, Shanghai University, China.
  • JinLong Fu，Faculty of Science and Engineering, Swansea University, United Kingdom.
  • Hewa Majeed Zangana, Duhok Polytechnic University and Nawroz University
  • Christian Griffiths, College of Engineering, Swansea University, United Kingdom.
  • Hongliang Guo, Agency for Science, Technology and Research, Singapore. 
  • Wojciech Hunek, Opole University of Technology, Opole, Poland.
  • T. Jarin, Department of Electrical Engineering Jyothi Engineering College India.
  • Chao Jiang, Department of Electrical Engineering, University of Wyoming, USA.
  • Vasilios N. Katsikis, Division of Mathematics and Informatics, National and Kapodistrian University of Athens, Greece.
  • Ameer Hamza Khan, Department of Computing, Hongkong Polytechnic University, Hongkong China.
  • Wenqiang Lu, Chongqing Institute of Green and Intelligent Technology, China.
  • Zhan Li, Department of Computer Sciences, Swansea University, UK.
  • Jianfeng Li, College of Information Science and Engineering, Jishou University, China.
  • Bolin Liao, College of Information Science and Engineering, Jishou University, China.
  • Zhenzhong Liu, Tianjin University of Technology, China.
  • Md. Asraful Haque, Zakir Hussain College of Engineering & Technology, India.
  • Huanfei Ma, School of Mathematical Sciences, Soochow University, China.
  • Ata Jahangir Moshayedi, College of Computing, Jiangxi University of Science and Technology, China.
  • Muhammad Rehan, Department of Electrical Engineering, Pakistan Institute of Engineering and Applied Sciences (PIEAS), Pakistan.
  • Mingsheng Shang, Chongqing Institute of Green and Intelligent Technology, China.
  • Predrag S. Stanimirović, Faculty of Sciences and Mathematics, University of Nis, Serbia.
  • Xuefeng Zhou, Institute of Intelligent Manufacturing, Guangdong Academy of Sciences, China.
  • Weiwei Jiang, Beijing University of Posts and Telecommunications, China.
  • Ameer Tamoor Khan, Computing Department, University of Copenhagen, Denmark.
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  • Enhancing Virtual Reality Experiences in Architectural Visualization of an Academic Environment

    Appears in:
    airo 25(1):

    Authors:
    Alireza Hajfathalian, Abiodun Durojaye, Amin Kolahdooz

    Published:
    11th Apr 2025

    Abstract:
    Virtual Reality (VR) technology possesses the capability to transport users into immersive, alternative environments, providing them with a convincing sense of presence within a simulated world. This project leverages VR to develop an interactive, educational system centered around the De Montfort University (DMU) Queens Building, simulating key facilities and infrastructure through the integration of 360-degree imagery and Adobe Captivate software. Designed in response to contemporary challenges, such as the COVID-19 pandemic, which underscored the need for flexible and innovative learning methodologies, the VR system offers an immersive educational platform enriched with essential information to enhance student engagement and learning outcomes. A comprehensive literature review explored the expanding applications of VR across diverse sectors, including education, healthcare, robotics, and manufacturing. The findings of this review underscored VR's transformative potential in enhancing educational engagement and facilitating a deeper understanding of complex concepts. The project methodology involved meticulously mapping the physical layout of the Queens Building, capturing targeted 360-degree scenarios using a Ricoh Theta V camera, and subsequently transforming these into immersive VR scenarios enriched with interactive hotspots, meticulously synchronized with the building's design layout. The VR system successfully achieved the project's objectives by simulating key educational and informational use cases. It provides students with an alternative learning medium, offering interactive insights into the functionalities of equipment and facilities within the building. Furthermore, the system enables a virtual tour of the DMU campus, facilitating familiarization with the university environment. Findings from the VR application highlight its potential as a dynamic educational tool, positioning it as a valuable complement to traditional learning methods. This innovative approach demonstrates the capacity of VR to enhance student understanding, support academic and research pursuits, and ultimately enrich the overall student experience.

  • Apple Disease Detection and Classification using Random Forest (One-vs-All)

    Appears in:
    airo 25(1):

    Authors:
    Muhammad Asim Khan, Zengming Wen, Hong Lan

    Published:
    11th Apr 2025

    Abstract:
    Fruit diseases detection and recognition are a common problem worldwide. Fruit Disease detection is a hot topic among researchers and is very complex due to structure and color similarity factors. In this research we proposed a new model to detect and classify the apple disease with the help of digital image processing and machine learning. First, image processing techniques were applied to enhance the image contrast and remove the noise, which helped to segment the region of interest accurately also help to extract feature without garbage data. Then K-means clustering technique with fuzzy C-mean method was implemented to segment the images. GLCM feature extraction was used after the segmentation. Real images of apple disease with multi-disease regions were used in the research method. These features were preprocessed with methods like LDA. K-Fold cross validation was used for training and testing, with combination of random forest machine learning method. The result showed high accuracy with comparison of existing techniques.

  • Advancing Food Security through Precision Agriculture: YOLOv8’s Role in Efficient Pest Detection and Management

    Appears in:
    airo 25(1):

    Authors:
    Noman Khan, Ameer Tamoor Khan, Sign Marie Jensen

    Published:
    11th Apr 2025

    Abstract:
    In response to the growing global population and the consequent need for sustainable food security, effective pest management is critical for enhancing agricultural productivity. This research presents YOLOv8, a state-of-the-art deep learning model optimized for pest detection in agricultural environments, contributing to modern food security efforts. Evaluated using the complex IP102 dataset, YOLOv8 demonstrated notable improvements in pest detection accuracy, achieving scores of 66.9 mAP@0.5 and 42.1 mAP@[0.5:0.95]. These results underscore YOLOv8’s robust performance across diverse detection scenarios, enabling more precise pest control and reducing crop loss. However, in-depth dataset analysis revealed a bias towards larger pests, likely due to bounding box size variations, which presents an opportunity for model improvement. Future work will focus on addressing data imbalances, enhancing sensitivity to smaller pests, and validating YOLOv8 in varied real-world agricultural settings. These advancements are expected to significantly improve pest management practices, ultimately boosting agricultural productivity and supporting global food security through the application of modern agricultural technologies.

  • Deep Learning Based Optical Character Recognition Model for Old Turkic

    Appears in:
    airo 25(1):

    Authors:
    Isabel Adrover Gallego, Nedasadat Taheri, Houman Kosarirad, Seyed Hossein Taheri

    Published:
    10th Apr 2025

    Abstract:
    This study presents the development and evaluation of a deep learning-based optical character recognition (OCR) model specifically designed for recognizing Old Turkic script. Utilizing a convolutional neural network (CNN), the project aimed to achieve high classification accuracy across a dataset comprising 38 distinct Old Turkic characters. To enhance the model’s robustness and generalization capabilities, sophisticated data augmentation techniques were employed, generating 760 augmented images from the original 38 characters. The model was rigorously trained and validated, achieving an overall ac- curacy of 96.34%. Evaluation metrics such as precision, recall, and F1-scores were systematically analyzed, showing superior performance in most classes while identifying areas for further optimization. The results underscore the effectiveness of CNN architectures in specialized OCR tasks, demonstrating their potential in preserving and digitizing historical scripts. This study not only advances the field of document analysis and OCR but also contributes to the digital preservation and accessibility of ancient scripts.

  • Simulation and Control of the KUKA KR6 900EX Robot in Unity 3D: Advancing Industrial Automation through Virtual Environments

    Appears in:
    airo 25(1):

    Authors:
    Amin Kolahdooz, Alireza Hajfathalian, Mohammadreza Jafari, Anand Ajayakumar Sujatha

    Published:
    20th Mar 2025

    Abstract:
    This study presents the development of a virtual simulation of a KUKA robot within the Unity 3D platform, focusing on its ability to execute pick-and-place operations in an industrial setting. The research emphasizes the importance of digital simulations as cost-effective and safe alternatives to physical prototypes in industrial automation. By replicating robotic tasks in a virtual environment, organizations can mitigate wear and tear on expensive machinery and minimize safety hazards inherent in real-world operations. The simulation process commenced with the creation of a detailed 3D model of the KUKA robot utilizing Creo CAD software. This model was subsequently imported into the Unity 3D environment, where an interactive and realistic simulation environment was constructed. A manual control system was implemented through custom C# scripts, enabling precise joint manipulation via keyboard inputs. While the current control mechanism remains manual, this study provides a foundational framework for the future integration of advanced algorithms for trajectory planning and autonomous control. The simulation successfully demonstrates the feasibility of performing industrial robotic tasks within a virtual environment. It serves as a platform for further research, including the automation of robotic movements and the integration of virtual reality and digital twin technologies. These advancements have the potential to significantly enhance real-time monitoring, operator training, and overall operational efficiency in industrial applications. This work underscores the growing significance of virtual simulation technologies in industrial automation, presenting a scalable and flexible solution for prototyping, testing, and training within complex industrial ecosystems.

  • Lightweight Keyword Spotting with Inter-Domain Interaction and Attention for Real-Time Voice-Controlled Robotics

    Appears in:
    airo 25(1):

    Authors:
    Thuy Phuong Vu, Hien Vu Pham, Minhhuy Le, Huong Thi Nguyen

    Published:
    18th Mar 2025

    Abstract:
    This study introduces a novel lightweight Keyword Spotting (KWS) model optimized for deployment on resource-constrained microcontrollers, with potential applications in robotic control and end-effector operations. The proposed model employs inter-domain interaction to effectively extract features from both Mel-frequency cepstral coefficients (MFCCs) and temporal audio characteristics, complemented by an attention mechanism to prioritize relevant audio segments for enhanced keyword detection. Achieving a 93.70% accuracy on the Google Command v2-12 commands dataset, the model outperforms existing benchmarks. It also demonstrates remarkable efficiency in inference speed (0.359 seconds) and resource utilization (34.9KB peak RAM and 98.7KB flash memory), offering a 3x faster inference time and reduced memory footprint compared to the DS-CNN-S model. These attributes make it particularly suitable for real-time voice command applications in low-power robotic systems, enabling intuitive and responsive control of robotic arms, end-effectors, and navigation systems. In this work, however, the KWS model is demonstrated in a simple non-destructive testing system for controlling sensor movement. This research lays the groundwork for advancing voice-activated robotic technologies on resource-limited hardware platforms.

  • A Multi-Channel Spam Detection System Utilizing Natural Language Processing and Machine Learning

    Appears in:
    airo 25(1):

    Authors:
    Shivam Kumar Yadav, Pradeep Kumar Singh, Sanjay Kumar Soni, Mohini Tyagi

    Published:
    18th Mar 2025

    Abstract:
    As digital communication rapidly expands, the issue of unsolicited and unwanted messages, commonly known as spam, has become a major concern. This paper introduces an advanced spam detection system that integrates Natural Language Processing (NLP) and Machine Learning (ML) techniques. The system differentiates between spam and legitimate messages by employing a hybrid model that combines Naive Bayes, Support Vector Machines (SVM), and deep learning models like Bidirectional Encoder Representations from Transformers (BERT). The model demonstrates high effectiveness across various communication platforms, including emails, SMS, and social media, achieving an accuracy exceeding 98.5%.

  • From Social Media Reactions to Grades: A Machine Learning-Based SocialNet Analysis for Academic Performance Prediction

    Appears in:
    airo 25(1):

    Authors:
    Muhammad Ramzan, Naeem Ahmed

    Published:
    17th Mar 2025

    Abstract:
    The impact of social media on student academic performance has garnered significant research interest in recent years. The pervasive use of social networking sites (SNS) among college and university students, both in and outside classrooms, has raised concerns about its potential effects on academic achievement. This study investigates the relationship between social media usage and academic performance through a dataset of 550 participants. Machine learning models, including Random Forest, Decision Trees, and Long Short-Term Memory (LSTM), were employed to analyze and predict the impact of social media on students' academic outcomes. The models were trained using clean and well-engineered data. The results indicate a moderate influence of social media usage on academic performance, with the LSTM model outperforming traditional approaches in predictive accuracy. These findings highlight the importance of considering sequential usage patterns in understanding the academic implications of social media.

  • Comparison of the Performance of Six Machine Learning Algorithms for Fake News

    Appears in:
    airo 24(1):

    Authors:
    Hasan Abdulkader, Rafah H Al-Furaiji

    Published:
    4th Dec 2024

    Abstract:
    INTRODUCTION: This research focuses on the increasing importance of social media websites as versatile platforms for entertainment, work, communication, commerce, and accessing global news. However, it emphasizes the need to use this power responsibly. OBJECTIVES: The objective of the study is to evaluate the performance of artificial intelligence algorithms in detecting fake news. METHODS: Through a comparison of six machine learning algorithms and the use of natural language processing techniques, RESULTS: The study identifies four algorithms with a 99% accuracy rate in detecting fake news. CONCLUSION: The results demonstrate the effectiveness of the proposed method in enhancing the performance of artificial intelligence algorithms in addressing the problem of fake news detection.

  • Prediction of short circuit current of wind turbines based on artificial neural network model

    Appears in:
    airo 24(1):

    Authors:
    Ebrahim Aghajari, Ali AbdulKarim AbdulRahim

    Published:
    4th Dec 2024

    Abstract:
    The growth of renewable energy on a global scale is making significant strides in power plants. This is due to the increasing concern about climate change, the rising demand for electricity, and the necessity to reduce reliance on fossil fuels. Ensuring the successful integration of new energy resources into the existing network is just as crucial as it requires the system to be reliable and adaptable. For instance, wind energy, which is one of the renewable sources, has an intermittent nature that necessitates the ability to synchronize its actions to achieve the desired system performance. The objective of this study is to utilize a new neural network system to calculate the short circuit current of power plants. Specifically, the focus is on identifying and categorizing the short circuit faults that occur between the stator coils of the squirrel cage induction generator used in wind power generation. To achieve this, a system was developed to simulate turbine data. Subsequently, four feature extraction techniques and machine learning algorithms were employed to enable early detection of short circuit faults. The numerical results obtained from the simulation demonstrated the high efficiency and accuracy of the proposed model.

  • Deep Learning Application Pros And Cons Over Algorithm Deep Learning Application Pros And Cons Over Algorithm

    Appears in:
    airo 22(1): 7

    Authors:
    Atanu Shuvam Roy, Yang Shuxin, Ata Jahangir Moshayedi, Amin Kolahdooz

    Downloads:
    2826

    Abstract:
    Deep learning is a new area of machine learning research. Deep learning technology applies the nonlinear and advanced transformation of model abstraction into a large database. The latest development shows that deep learning in various fields and greatly contributed to artificial intelligence so far. This article reviews the contributions and new applications of deep learning. The main target of this review is to give the summarize points for scholars to have the analysis about applications and algorithms. Then review tries to investigate the main applications and uses algorithms. In addition, the advantages of using the method of deep learning and its hierarchical and nonlinear functioning are introduced and compared to traditional algorithms in common applications. The following three criteria should be taken into consideration when choosing the area of application. (1) expertise or knowledge of the author; (2) the successful application of deep learning technology has changed the field of application, such as voice recognition, chat robots, search technology and vision; and (3) deep learning can have a significant impact on the application domain and benefit from recent research with natural language and text processing, information recovery and multimodal information processing resulting from multitasking deep learning. This review provides a general overview of a new concept and the growing benefits and popularity of deep learning, which can help researchers and students interested in deep learning methods. Deep learning is a new area of machine learning research. Deep learning technology applies the nonlinear and advanced transformation of model abstraction into a large database. The latest development shows that deep learning in various fields and greatly contributed to artificial intelligence so far. This article reviews the contributions and new applications of deep learning. The main target of this review is to give the summarize points for scholars to have the analysis about applications and algorithms. Then review tries to investigate the main applications and uses algorithms. In addition, the advantages of using the method of deep learning and its hierarchical and nonlinear functioning are introduced and compared to traditional algorithms in common applications. The following three criteria should be taken into consideration when choosing the area of application. (1) expertise or knowledge of the author; (2) the successful application of deep learning technology has changed the field of application, such as voice recognition, chat robots, search technology and vision; and (3) deep learning can have a significant impact on the application domain and benefit from recent research with natural language and text processing, information recovery and multimodal information processing resulting from multitasking deep learning. This review provides a general overview of a new concept and the growing benefits and popularity of deep learning, which can help researchers and students interested in deep learning methods.

  • The Object Detection, Perspective and Obstacles In Robotic: A Review

    Appears in:
    airo 22(1): e13

    Authors:
    Ata Jahangir Moshayedi, Ge Xu, Amir Sohail Khan, Yang Shuxin, Xiaohong Zhang

    Downloads:
    1572

    Abstract:
    A few years back, when the image processing hardware and software were created, it was limited, and most of the time, object detection would fail., But as with time, the advancement in technology has significantly changed the scenario. A lot of researchers worked in this field to carry out a solution through which they can detect objects in any field, especially in the robotic domain [1]. In today's world, with so much research in the field of deep learning, it is very easy to identify and detect any object using computer vision. This paper focuses on the various deep learning technologies and algorithms through which object detection can be done. A new and advanced deep learning technology known as salient object detection has been discussed. Also, the 3D object detection and the end-to-end approach for object detection are discussed. The existing methods of deep learning through which object detection can be done. The applications in which object detection is being used and the importance of object detection. It also reports; what the predecessors have done, what problems have been solved by them, how they solved these problems, the characteristics of the predecessors' methods and their future work.

  • The Role of Biometric in Banking: A Review

    Appears in:
    airo 23(1):

    Authors:
    Masoumeh Soleimani, Mehdi Marani, Atajahangir Moshayedi, Mina Bahadori

    Downloads:
    1363

    Abstract:
    Biometrics plays a pivotal role in enhancing security, ensuring accurate identification, and offering convenient solutions across diverse industries. Its uniqueness, reliability, and potential for future advancements establish it as a crucial and valuable field in today's digital landscape. Fingerprint authentication in ATMs presents primary advantages such as heightened security through distinctive identification and user convenience by eliminating the reliance on PINs or passwords. This research paper focuses on conducting a comprehensive review and comparative analysis of various approaches for fingerprint identification, aiming to contribute to the understanding of effective and efficient methods in the context of ATM authentication.

  • Review On: The Service Robot Mathematical Model

    Appears in:
    airo 22(1): 8

    Authors:
    Liefa Liao, Ata Jahangir Moshayedi, Atanu Shuvam Roy, Yangwan Zhong, Sithembiso Khaya Sambo

    Downloads:
    1347

    Abstract:
    After nearly 30 years of development, service robot technology has made important achievements in the interdisciplinary aspects of machinery, information, materials, control, medicine, etc. These robot types have different shapes, and mainly in some are shaped based on application. Till today various structure are proposed which for the better analysis’s need to have the mathematical equation that can model the structure and later the behaviour of them after implementing the controlling strategy. The current paper discusses the various shape and applications of all available service robots and briefly summarizes the research progress of key points such as robot dynamics, robot types, and different dynamic models of the differential types of service robots. The current review study can be helpful as an initial node for all researchers in this topic and help them to have the better simulation and analyses. Besides the current research shows some application that can specify the service robot model over the application.

  • Review of Image Classification Algorithms Based on Graph Convolutional Networks

    Appears in:
    airo 23(1):

    Authors:
    Wenhao Tang

    Downloads:
    1090

    Abstract:
    In recent years, graph convolutional networks (GCNs) have gained widespread attention and applications in image classification tasks. While traditional convolutional neural networks (CNNs) usually represent images as a two-dimensional grid of pixels when processing image data, the classical model of graph neural networks (GNNs), GCNs, can effectively handle data with graph structure, such as social networks, recommender systems, and molecular structures. In this paper, we will introduce the problems that graph convolutional networks have had, such as over-smoothing, and the methods to solve them, and suggest some possible future directions.

  • Peaks Detector Algorithm after CFAR for Multiple Targets Detection

    Appears in:
    airo 22(1): 11

    Authors:
    Syed Waqar Hameed

    Downloads:
    925

    Abstract:
    The constant false alarm rate (CFAR) algorithm is a strong technique to detect and track dynamic targets in an environment of an unknown noise floor. Multiple reflections of a pulse from a target and different signal processing techniques applied to the received pulse, make it spread along the range and/or Doppler axis. Spreading of a pulse results in a cluster of targets detection for a single target when the CFAR technique is applied to it. This causes difficulties in calculating those target’s parameters which require only a single maximum peak for a target, such as Radar cross-section (RCS), relative phase, etc. This manuscript proposes a solution, which extracts a single independent peak for a target that had clusters of peaks after CFAR. The novelty of the algorithm is that it works well to extract a single peak for each of all targets in the multiple targets environment, as compared to the conventional global maxima finding techniques which outputs only one target of the maximum amplitude while suppressing the rest of the small targets. The algorithm is basically a local maxima finder algorithm termed as peaks detector algorithm. An attractive feature of this algorithm is that it neither disturbs the Probability of false alarm rate (Pfa) of CFAR nor it affects the probability of detection (Pd) of a target. The algorithm is tested and its performance is evaluated in a multiple targets environment on the output of 1D and 2D CFAR.

  • A Brief Analysis of “ChatGPT” – A Revolutionary Tool Designed by OpenAI

    Appears in:
    airo 23(1): e15

    Authors:
    Md. Asraful Haque

    Downloads:
    838

    Abstract:
    The ChatGPT, a powerful conversational tool trained by OpenAI is considered to be a revolutionary model in the field of artificial intelligence and natural language processing. It has received a lot of attention because of its potential to automate a variety of tasks and possibly have an impact on sectors like translation, customer service, and content creation. It uses GPT-3 (Generative Pre-training Transformer 3) language model to process user queries. GPT-3 has been trained on a very large dataset, which includes a wide range of texts from the internet and other sources. This has given it a broad knowledge base and has allowed it to generate responses to a wide range of prompts that are coherent and human-like. GPT-3 is one of the largest and most powerful language models to date, and it has the ability to perform a wide range of natural language processing tasks. After its release, ChatGPT has become a trending tool for the public to experiment and explore what it is capable of. In this article, we want to clarify what ChatGPT is. How does it work? What makes it different from other chatbots or search engines like Google? What are the major challenges and future prospects for it?

  • LQG, PID controller, ANN for single axis gimbal actuator

    Appears in:
    airo 22(1): e12

    Authors:
    Nguyen Cong Danh

    Downloads:
    724

    Abstract:
    Gimbal or other stable platforms have structures that move according to its functions. This is for the purpose of keeping track of the goals to the fullest. Tracking targets can become difficult as the subject moves further and further away and they are out of the gimbal’s allowable viewing range. Besides, under the influence of noise signals form outside space, it becomes even more difficult to observe the gimbal’s targets. To overcome above disadvantages, this paper is presented an adjustment method to limit above risks. Adjusting Linear Quadratic Gaussian (LQG) for expensive gimbal systems, noise signals are processed purely by Kalman filters to improve the function of observing targets. In addition, proportional- integral-derivative (PID) controller, artificial neural network in this case is also considered to verify the effectiveness of  control methods listed below. In particular, ANN is the most effective control method today to deal with unwanted signals. These unwanted signals can cause worsening conditions during the operation of systems.Therefore, artificial network (ANN) is a solution to information and communication security problems. Simulation is done by Matlab. Novelty of the work: no previous research has been published for this  genre. The study of this genre with the use of artificial intelligence is suggestive of the study of artificial intellligence technologies at a higher level. This category is also a suggestion for studying a smoother control method based on existing data.

  • The Potential Use of ChatGPT for Debugging and Bug Fixing The Potential Use of ChatGPT for Debugging and Bug Fixing

    Appears in:
    airo 23(1):

    Authors:
    Md. Asraful Haque, Shuai Li, Shuai Li, MD ASRAFUL HAQUE

    Downloads:
    718

    Abstract:
    ChatGPT is a cutting-edge language model that has been making waves in the field of natural language processing. However, its capabilities extend far beyond language-based applications. ChatGPT can also be used as a powerful tool for debugging software code. As software applications become increasingly complex, the need for efficient and accurate debugging tools has become more pressing. ChatGPT's ability to analyze and understand code makes it a promising solution to this challenge. Debugging is a critical part of the software development process. Bugs, or errors in code, can have serious consequences for the functionality and security of software applications. Identifying and fixing bugs can be a time-consuming and labor-intensive process, requiring the expertise of experienced developers. ChatGPT has the potential to streamline this process and make it more accessible to a wider range of developers, regardless of their experience level. In this article, we will explore the capabilities of ChatGPT as a debugging tool, the advantages and limitations of using it, and best practices for integrating it into the software development workflow. ChatGPT is a cutting-edge language model that has been making waves in the field of natural language processing. However, its capabilities extend far beyond language-based applications. ChatGPT can also be used as a powerful tool for debugging software code. As software applications become increasingly complex, the need for efficient and accurate debugging tools has become more pressing. ChatGPT's ability to analyze and understand code makes it a promising solution to this challenge. Debugging is a critical part of the software development process. Bugs, or errors in code, can have serious consequences for the functionality and security of software applications. Identifying and fixing bugs can be a time-consuming and labor-intensive process, requiring the expertise of experienced developers. ChatGPT has the potential to streamline this process and make it more accessible to a wider range of developers, regardless of their experience level. In this article, we will explore the capabilities of ChatGPT as a debugging tool, the advantages and limitations of using it, and best practices for integrating it into the software development workflow.

  • Immersive Horizons: Exploring the Transformative Power of Virtual Reality Across Economic Sectors

    Appears in:
    airo 23(1): e6

    Authors:
    Abdullah Nawaz, Amin Kolahdooz, Ata Jahangir Moshayedi, Abiodun Durojaye

    Downloads:
    672

    Abstract:
    The scholarly discourse surrounding the manifold advantages, applications, and limitations of implementing Virtual Reality (VR) in the contemporary milieu has burgeoned over time. VR holds immense potential, attracting fervent interest from governmental and private entities alike. Nevertheless, the existing body of literature pertaining to the expanding utilization of VR in diverse economic sectors remains scant. Therefore, the primary objective of this study is to furnish a comprehensive literature review encompassing VR applications across various economic domains while elucidating concerns surrounding its integration within engineering education. A total of 108 publications were extracted from prominent databases such as Scopus, Elsevier, Science Direct, and Google Scholar, with a subsequent review of 51 relevant works. These scrutinized journals were published between 2015 and 2022 and were predominantly authored in English. The reviewed publications encompassed VR applications in education, robotics, healthcare, transportation, sports, agriculture, governance, security, and media. The study’s findings unveiled significant advancements in VR implementation within engineering education, medical training, cognitive augmentation, aircraft assembly, governance, and diverse other spheres. Notwithstanding these achievements, impediments to VR deployment were identified, stemming from financial exigencies, cultural and conventional norms, with scant evidence of VR’s prevalence in underdeveloped nations, given that all the assessed research originated from developed economies. Additionally, the limitations of this review encompassed a small sample size and a narrowly focused demographic in the examined articles. Nevertheless, despite these constraints, the research highlights substantial progress in VR utilization over the preceding decade.
Publisher
EAI
ISSN
2790-7511
Number of Volumes
4
Last Published
2025-01-22
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