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3rd International Conference on Hybrid Human-Artificial Intelligence, HHAI 2024

作者： Fabris, Bertilla Belfrage, Michael Lorig, Fabian Department of Computer Science and Media Technology Malmö University Sweden Internet of Things and People Research Center Malmö University Sweden

ISBN: (纸本)9781643685229

Understanding the potential impact of policy changes before implementation is vital, and can be achieved through modelling and simulation. To adequately model stakeholders and regulative constraints, we propose the use of Institutional Grammar to facilitate institutional modelling in Agent-based Social Simulations. We present an early-stage case study exploring the Swedish organ donation system. © 2024 The Authors.

关键词： Agent-Based Social Simulation Model Formalisation Policy Support

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Strategies to Improve LoRaWAN Performance in Multi Communication Use Listen Before Talk 12

Strategies to Improve LoRaWAN Performance in Multi Communica...

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12th Electrical Power, Electronics, Communications, Controls and Informatics Seminar, EECCIS 2024

作者： Adi, Puput Dani Prasetyo Maulana, Fairuz Iqbal Purwandoko, Pradeka Brilyan Adiprabowo, Tjahjo Maulana, Yudi Yulius Prasetyo, Sofyan Mufti Wahyu, Yuyu Satyawan, Arief Suryadi Yasir, Muhammad Research Center for Telecommunication BRIN Bandung Indonesia Bina Nusantara University Computer Science Department School of Computer Science Jakarta Indonesia Research Center for Appropriate Technology BRIN Subang Indonesia Research Center for Artificial Intelligence and Cybersecurity BRIN Bandung Indonesia Institut Teknologi dan Bisnis Nobel Department of System and Information System Makassar Indonesia

ISBN: (纸本)9798350379914

LoRaWAN (Long Range Wide Area Network) is a wireless communication technology widely used in intelligent transportation and supply chain sectors. Despite its advantages, such as wide range and low power consumption, LoRaWAN faces significant challenges, including high packet loss and inefficient spectrum usage. This research examines the Listen Before Talk (LBT) mechanism to address these issues by comparing its performance against other mechanisms such as Adaptive Data Rate (ADR) and Additive Links On-line Hawaii Area (ALOHA). LBT allows devices to check the channel before transmission, reducing the risk of interference and data collisions. The results show that LBT significantly reduces the collision rate compared to ADR and ALOHA, with a collision rate of 10% for LBT, 20% for ADR, and 40% for ALOHA. These findings reinforce the potential of LBT in improving the performance of LoRaWAN, especially in applications that require reliable data delivery without data loss. © 2024 IEEE.

关键词： Data transfer rates

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Systematic Literature Review on Statistics and Machine Learning Predictive Models for Rice Phenotypes 8

Systematic Literature Review on Statistics and Machine Learn...

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8th International Conference on computer science and Computational Intelligence, ICCSCI 2023

作者： Dominic, Nicholas Cenggoro, Tjeng Wawan Pardamean, Bens Bioinformatics and Data Science Research Center Bina Nusantara University Jakarta11480 Indonesia Computer Science Department School of Computer Science Bina Nusantara University Jakarta11480 Indonesia Computer Science Department BINUS Graduate Program - Master of Computer Science Bina Nusantara University Jakarta11480 Indonesia

Predicting the best-quality of rice phenotypes is the priority among agricultural researchers to fulfill worldwide food security. Trend development of predictive models from statistics to machine learning is the subject of this review. Gathered from the Google Scholar database, 14 appropriate papers (2016-2020) related to the rice phenotypes prediction were selected through title and abstract content filtering. The outputs show that Support Vector Machine, Multi-layer Perceptron, and regression are the most used models, while yield is the priority prediction point besides tiller, panicle, and 1000-grain weight of rice. However, finding the accurate predictor is invariably challenging due to distinct rice varieties in the world and high confounding factors. Thus, developing an advanced deep learning model that accommodates these needs is worth considering further. © 2023 The Authors. Published by Elsevier B.V.

关键词： machine learning rice phenotypes prediction statistics

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Multi-CNN Model for Multi-Classification of Cultural Heritage Monuments 6

Multi-CNN Model for Multi-Classification of Cultural Heritag...

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6th International Conference on Pattern Analysis and Intelligent Systems, PAIS 2024

作者： Djelliout, Toufik Aliane, Hassina University of Bejaia Faculty of Exact Sciences Department of Computer Science Algeria Research Center For Scientific And Technical Information Algiers Algeria

ISBN: (纸本)9798350350265

The use of convolutional neural networks (CNN) in the preservation of cultural heritage monuments, especially in conflict-affected regions such as Gaza, Ukraine, Iraq and others, represents a significant advancement in heritage conservation efforts. This paper presents an approach that uses a Multi-CNN model to classify images of cultural heritage monuments into various categories, encompassing period, monument type and location. By leveraging the capabilities of CNNs, this model demonstrates a high level of accuracy in categorizing heritage monuments based on multiple attributes. The study highlights the superior performance of the Multi-CNN model compared to other popular models such as DenseNet169, GoogleNet and MnasNet, highlighting its effectiveness in accurately classifying images of cultural heritage monuments in various dimensions. According to the evaluation results, the top-performing multi-CNN model achieves a classification accuracy of 94.52%, outperforming the single CNN models. The DenseNet196 model achieves 93.70% accuracy, the MnasNet model achieves 92.80% accuracy, and the GoogleNet model achieves 88.18% accuracy. © 2024 IEEE.

关键词： Neural network models

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AAD-LLM: Adaptive Anomaly Detection Using Large Language Models

AAD-LLM: Adaptive Anomaly Detection Using Large Language Mod...

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2024 IEEE International Conference on Big Data, BigData 2024

作者： Russell-Gilbert, Alicia Sommers, Alexander Thompson, Andrew Cummins, Logan Mittal, Sudip Rahimi, Shahram Seale, Maria Jaboure, Joseph Arnold, Thomas Church, Joshua Mississippi State University Computer Science & Engineering United States Engineer Research and Development Center Department of Defence United States

ISBN: (纸本)9798350362480

For data-constrained, complex and dynamic industrial environments, there is a critical need for transferable and multimodal methodologies to enhance anomaly detection and therefore, prevent costs associated with system failures. Typically, traditional PdM approaches are not transferable or multimodal. This work examines the use of Large Language Models (LLMs) for anomaly detection in complex and dynamic manufacturing systems. The research aims to improve the transferability of anomaly detection models by leveraging Large Language Models (LLMs) and seeks to validate the enhanced effectiveness of the proposed approach in data-sparse industrial applications. The research also seeks to enable more collaborative decision-making between the model and plant operators by allowing for the enriching of input series data with semantics. Additionally, the research aims to address the issue of concept drift in dynamic industrial settings by integrating an adaptability mechanism. The literature review examines the latest developments in LLM time series tasks alongside associated adaptive anomaly detection methods to establish a robust theoretical framework for the proposed architecture. This paper presents a novel model framework (AAD-LLM) that doesn't require any training or finetuning on the dataset it is applied to and is multimodal. Results suggest that anomaly detection can be converted into a "language"task to deliver effective, context-aware detection in data-constrained industrial applications. This work, therefore, contributes significantly to advancements in anomaly detection methodologies. © 2024 IEEE.

关键词： Predictive maintenance

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Optimization of Intelligent Classroom based on CAD Multi-Valued Color Image Segmentation Algorithm

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computer-Aided Design and Applications 2023年第S3期20卷 16-28页

作者： Wang, Nanlan Li, Jianqi Balaji, Prasanalakshmi Vaiyapuri, Thavavel Verma, Amit School of Computer and Electrical Engineering Hunan university of Arts and Science Hunan Changde415000 China International Education Center Philippine Christian University Manila1772 Philippines Department of Computer Science King Khalid University Saudi Arabia Department of Computer Sciences College of Computer Engineering and Sciences Prince Sattam Bin Abdulaziz University Saudi Arabia Department of CSE University Centre for Research & Development Chandigarh University Punjab Mohali140413 India

In order to solve the problem that the efficiency of smart classroom is not high enough, a multi valued color image segmentation algorithm based on CAD is proposed to optimize the smart classroom. This paper explores the notion of smart education as well as a conceptual framework to enable children to receive excellent education and to spark their interest through creative learning. Firstly, CAD software is used to improve software design efficiency and optimize product technology;Secondly, the core technology of the smart classroom is improved by using the national value color image segmentation algorithm, and compared with the smart classroom without the multi value color image segmentation algorithm;Finally, the utilization efficiency of the improved smart classroom is counted. It is proved that the smart classroom based on CAD multi valued color image segmentation algorithm can give greater play to the role of smart classroom, reduce the teaching pressure of teachers by 20% and improve the learning efficiency of students by 20%. The intelligent classroom has a higher degree of informatization and more powerful functions, which is of great help to promote classroom communication and improve communication efficiency. The smart classroom is easier to run and adapt thanks to the optimization of the CAD multi threshold color picture segmentation algorithm, which considerably improves the system's work efficiency and capability. However, the smart classroom also has its own shortcomings, which are mainly reflected in the high investment cost, the complex design of the system and the high maintenance cost in the later stage. With the optimization of CAD multi valued color image segmentation algorithm, the smart classroom is easier to operate and adjust, which greatly enhances the work efficiency and ability of the system. © 2023 CAD Solutions, LLC.

关键词： computer aided design

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A scale conjugate neural network approach for the fractional schistosomiasis disease system

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computer Methods in Biomechanics and Biomedical Engineering 2025年第5期28卷 614-627页

作者： Sabir, Zulqurnain Bhat, Shahid Ahmad Raja, Muhammad Asif Zahoor Baleanu, Dumitru Amin, Fazli Wahab, Hafiz Abdul Department of Computer Science and Mathematics Lebanese American University Beirut Lebanon LUT Business School LUT University Lappeenranta Finland Future Technology Research Center National Yunlin University of Science and Technology Yunlin Taiwan Department of Mathematics and Statistics Hazara University Mansehra Pakistan

This study presents the numerical solutions of the fractional schistosomiasis disease model (SDM) using the supervised neural networks (SNNs) and the computational scaled conjugate gradient (SCG), i.e. SNNs-SCG. The fractional derivatives are used for the precise outcomes of the fractional SDM. The preliminary fractional SDM is categorized as: uninfected, infected with schistosomiasis, recovered through infection, expose and susceptible to this virus. The accurateness of the SNNs-SCG is performed to solve three different scenarios based on the fractional SDM with synthetic data obtained with fractional Adams scheme (FAS). The generated data of FAS is used to execute SNNs-SCG scheme with 81% for training samples, 12% for testing and 7% for validation or authorization. The correctness of SNNs-SCG approach is perceived by the comparison with reference FAS results. The performances based on the error histograms (EHs), absolute error, MSE, regression, state transitions (STs) and correlation accomplish the accuracy, competence, and finesse of the SNNs-SCG scheme. © 2023 The Author(s). Published by Informa UK Limited, trading as Taylor & Francis Group.

关键词： Biological systems

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Multivariate Data Augmentation for Predictive Maintenance using Diffusion

Multivariate Data Augmentation for Predictive Maintenance us...

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2024 IEEE International Conference on Big Data, BigData 2024

作者： Thompson, Andrew Sommers, Alexander Russell-Gilbert, Alicia Cummins, Logan Mittal, Sudip Rahimi, Shahram Seale, Maria Jaboure, Joseph Arnold, Thomas Church, Joshua Mississippi State University Computer Science & Engineering United States Engineer Research and Development Center Department of Defence United States

ISBN: (纸本)9798350362480

Predictive maintenance has been used to optimize system repairs in the industrial, medical, and financial domains. This technique relies on the consistent ability to detect and predict anomalies in critical systems. AI models have been trained to detect system faults, improving predictive maintenance efficiency. Typically there is a lack of fault data to train these models, due to organizations working to keep fault occurrences and down time to a minimum. For newly installed systems, no fault data exists since they have yet to fail. By using diffusion models for synthetic data generation, the complex training datasets for these predictive models can be supplemented with high level synthetic fault data to improve their performance in anomaly detection. By learning the relationship between healthy and faulty data in similar systems, a diffusion model can attempt to apply that relationship to healthy data of a newly installed system that has no fault data. The diffusion model would then be able to generate useful fault data for the new system, and enable predictive models to be trained for predictive maintenance. The following paper demonstrates a system for generating useful, multivariate synthetic data for predictive maintenance, and how it can be applied to systems that have yet to fail. © 2024 IEEE.

关键词： Predictive maintenance

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StanceEval 2024: The First Arabic Stance Detection Shared Task 2

StanceEval 2024: The First Arabic Stance Detection Shared Ta...

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2nd Arabic Natural Language Processing Conference, ArabicNLP 2024

作者： Alturayeif, Nora Luqman, Hamzah Alyafeai, Zaid Yamani, Asma Department of Computer Science College of Computer Science and Information Technology Imam Abdulrahman Bin Faisal University Dammam Saudi Arabia Information and Computer Science Department King Fahd University of Petroleum and Minerals Dhahran Saudi Arabia SDAIA-KFUPM Joint Research Center for Artificial Intelligence KFUPM Saudi Arabia

ISBN: (纸本)9798891761322

Recently, there has been a growing interest in analyzing user-generated text to understand opinions expressed on social media. In NLP, this task is known as stance detection, where the goal is to predict whether the writer is in favor, against, or has no opinion on a given topic. Stance detection is crucial for applications such as sentiment analysis, opinion mining, and social media monitoring, as it helps in capturing the nuanced perspectives of users on various subjects. As part of the ArabicNLP 2024 program, we organized the first shared task on Arabic Stance Detection, StanceEval 2024. This initiative aimed to foster advancements in stance detection for the Arabic language, a relatively underrepresented area in Arabic NLP research. This overview paper provides a detailed description of the shared task, covering the dataset, the methodologies used by various teams, and a summary of the results from all participants. We received 28 unique team registrations, and during the testing phase, 16 teams submitted valid entries. The highest classification F-score obtained was 84.38. ©2024 Association for Computational Linguistics.

关键词： Social networking (online)

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LLMs for Autism Treatment: Current Trends and Emerging Strategies

LLMs for Autism Treatment: Current Trends and Emerging Strat...

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2024 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2024

作者： Ciobanu, Madalina G. Tucci, Cesare Fasano, Fausto University of Salerno Department of Computer Science SA Fisciano Italy University of Molise Mosaic Research Center - DiBT Campobasso Italy

ISBN: (纸本)9798350386226

This paper reviews the current role of Large Language Models (LLMs) in the treatment and support of people with autism spectrum disorder (ASD). We explore applications of LLM-based systems designed to improve communication, social skills, and emotional learning for individuals with ASD, highlighting their ability to generate personalized conversational interactions and simulate social scenarios. Current research demonstrates promising results in different domains, such as dialogue interventions, emotional recognition training, and work-related communication assistance. However, significant challenges remain, including ethical concerns about overreliance on AI, personalization, and privacy. This review synthesizes recent findings, highlights gaps in the existing literature, and proposes directions for future investigations. It particularly underscores the need for real-world applications and long-term studies to assess the efficacy of LLM efficacy in interventions for ASD. © 2024 IEEE.

关键词： Diseases

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