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International Conference on Ubiquitous information Management and Communication (IMCOM)

作者： Minh Tuan Luc Roman Senkerik Tran Khanh Dang Dept. of Computer Science Faculty of Electrical Engineering and Computer Science VSB-Technical University of Ostrava Ostrava-Poruba Czech Repulic Center for Applied Information Technology Ton Duc Thang University Ho Chi Minh city Vietnam Institute for Experiential Technology Van Lang University Ho Chi Minh city Vietnam Faculty of Information Technology School of Technology Van Lang University Ho Chi Minh city Vietnam

ISBN: (数字)9798331507817

ISBN: (纸本)9798331507824

The highly volatile nature of cryptocurrencies, particularly Bitcoin, exhibits extreme price volatility, making accurate price prediction a challenging task for traditional forecasting models. This paper proposes an enhanced ARIMA model, called ARIMA_T, which incorporates time-reversed data and optimizes model selection using the Backward Validated information Criterion (BVIC). The ARIMA_T model aims to improve forecasting accuracy by capturing both linear and non-linear patterns in the data that standard ARIMA models often overlook. We apply this model to predict Bitcoin prices over short and medium-term horizons, specifically 1-day, 7-days, and 30-days ahead predictions. The dataset spans from April 2013 to February 2024. Experimental results demonstrate that ARIMA_T consistently outperforms the traditional ARIMA model across all forecast horizons, as evidenced by lower Root Mean Square Error (RMSE) values. The ARIMA_T model provides more accurate and robust predictions, particularly for short-term forecasting, making it a valuable tool for navigating the volatile cryptocurrency market. This study also discusses the limitations of both models in handling longer-term predictions and suggests future work in integrating ARIMA_T with machine learning techniques for further improvements.

关键词： Accuracy Uncertainty Navigation Bitcoin Predictive models Data models Robustness Forecasting Root mean square Standards

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Chaotic Map based Compression Approach to Classification

arXiv

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arXiv 2025年

作者： Harikrishnan, N.B. Vats, Anuja Nagaraj, Nithin Pedersen, Marius Department of Computer Science and Information Systems BITS Pilani K K Birla Goa Campus Goa 403726 India Adjunct Faculty Consciousness Studies Programme National Institute of Advanced Studies Indian Institute of Science Campus Bengaluru560012 India Department of Computer Science Norwegian Institute of Science and Technology Gjøvik Norway Complex Systems Programme National Institute of Advanced Studies Indian Institute of Science Campus Karnataka Bengaluru560012 India

Modern machine learning approaches often prioritize performance at the cost of increased complexity, computational demands, and reduced interpretability. This paper introduces a novel framework that challenges this trend by reinterpreting learning from an information-theoretic perspective, viewing it as a search for encoding schemes that capture intrinsic data structures through compact representations. Rather than following the conventional approach of fitting data to complex models, we propose a fundamentally different method that maps data to intervals of initial conditions in a dynamical system. Our GLS (Generalized Lüroth Series) coding compression classifier employs skew tent maps - a class of chaotic maps - both for encoding data into initial conditions and for subsequent recovery. The effectiveness of this simple framework is noteworthy, with performance closely approaching that of well-established machine learning methods. On the breast cancer dataset, our approach achieves 92.98% accuracy, comparable to Naive Bayes at 94.74%. While these results do not exceed state-of-the-art performance, the significance of our contribution lies not in outperforming existing methods but in demonstrating that a fundamentally simpler, more interpretable approach can achieve competitive results. Copyright © 2025, The Authors. All rights reserved.

关键词： Encoding (symbols)

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Integrating AI-based adaptive learning into the flipped classroom model to enhance engagement and learning outcomes

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computers and Education: Artificial Intelligence 2025年 8卷

作者： Katona, Jozsef Gyonyoru, Klara Ida Katonane Department of Software Development and Application Institute of Computer Engineering University of Dunaujvaros Dunaujvaros Hungary Institute of Electronics and Communication Systems Kandó Kálmán Faculty of Electrical Engineering Obuda University Budapest Hungary GAMF Faculty of Engineering and Computer Science John Von Neumann University Kecskemet Hungary Department of Applied Quantitative Methods Faculty of Finance and Accountancy Budapest University of Economics and Business Budapest Hungary Department of Organizational Development and Communication Science Institute of Social Sciences University of Dunaujvaros Dunaujvaros Hungary

This study explores the integration of the AI-powered adaptive learning system within the flipped classroom model and discusses programming education. The increasing demand for the acquisition of programming skills in the digital era generates the development of innovative pedagogical approaches to help students overcome the difficulties in mastering programming concepts. The study investigates how AI-based adaptive feedback influences student engagement, motivation, and learning outcomes. This study was conducted for 13 weeks, with two groups of undergraduate students. In one of the groups, tshe traditional flipped classroom model was applied, while in the other group, AI-driven adaptive learning tools were introduced. In this research methodology, a mixed-methods approach was adopted, integrating quantitative analyses with qualitative data. These showed that the experimental group demonstrated significant improvements in learning results and motivation, thereby realiszing the value of real-time, personalized feedback provided through the AI system. The qualitative findings revealed that with the AI system, there was an increase in student autonomy;thus, there is increased responsibility among students to collaborate in in-class participation, which helps enhance a more dynamic and cooperative learning environment. On the whole, these findings reveal important implications of the integration of AI into flipped classrooms, while at the same time, they reveal how to make the modernization of programming education achieve a better overall experience in student learning. © 2025 The Authors

关键词： Contrastive Learning

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A Tamper-Proof Smart Contract Metamodel for Blockchain to Optimise Computational Latency 3rd

A Tamper-Proof Smart Contract Metamodel for Blockchain to Op...

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3rd International Conference on Computing and Communication Networks, ICCCN 2023

作者： Sengupta, Ratul Srivastava, Ruchika Mishra, Sushruta Abualigah, Laith Kalinga Institute of Industrial Technology Deemed to be University Bhubaneswar India Computer Science Department Prince Hussein Bin Abdullah Faculty for Information Technology Al-Albayt University Mafraq25113 Jordan Hourani Center for Applied Scientific Research Al-Ahliyya Amman University Amman19328 Jordan MEU Research Unit Middle East University Amman11831 Jordan School of Computer Sciences Universiti Sains Malaysia Pulau Pinang Gelugor11800 Malaysia School of Engineering and Technology Sunway University Malaysia Petaling Jaya27500 Malaysia

ISBN: (纸本)9789819726707

In recent years, blockchain technology has garnered considerable interest on account of its peer-to-peer, distributed consensus, and anonymity functionalities. A smart contract functions as a verifiable, self-executing, and self-validating computing layer. This study seeks to identify the key concepts and associated future research goals pertaining to smart contracts based on the blockchain. Smart contracts enable the implementation of programmable assets, including money, and the automation of business logic processes that were previously executed manually. In response to this disclosure, a comprehensive examination of smart contracts employing blockchain technology is undertaken. In this article, we propose an innovative and practical metamodel for ensuring the integrity of protocols within smart contracts facilitated by blockchain technology. This article begins with an explanation of smart contracts’ operational models, functionalities, and applications. Analysis of the results reveals that the utilisation of the model alleviates the workload associated with numerous measurements. It was determined that the mean response latency, computation time, and overhead were each 1.47, 1.19, and 3.04 s. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Smart contract

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applied intelligence with deep learning assisted automated sarcasm recognition in twitter data

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Alexandria Engineering Journal 2025年 128卷 79-91页

作者： Muhammad Swaileh A. Alzaidi Taghreed Ali Alsudais Majdy M. Eltahir Shouki A. Ebad Asma Alshuhail Mohammed Alshahrani Sultan Alanazi Department of English Language College of Language Sciences King Saud University P. O. Box 145111 Riyadh Saudi Arabia Department of Teaching and Learning College of Education and Human Development Princess Nourah Bint Abdulrahman University P.O. Box 84428 Riyadh 11671 Saudi Arabia Department of Information Systems Applied College at Mahayil King Khalid University Saudi Arabia Center for Scientific Research and Entrepreneurship Northern Border University Arar 73213 Saudi Arabia Department of Information Systems College of Computer Sciences & Information Technology King Faisal University Saudi Arabia Computer Department Applied College Najran University Najran 66462 Saudi Arabia Department of Computer Science College of Computer Engineering and Sciences Prince Sattam bin Abdulaziz University Al-Kharj 16273 Saudi Arabia

Sarcasm is essential in human interaction, particularly on social networking, where individuals share their feelings through criticism, humour, and satire. Sarcasm detection is vital in understanding the context of communication and the sentiment on platforms such as Twitter. This presents sarcasm detection as a complex problem in natural language processing (NLP). The challenges and importance upsurge, particularly in languages such as Arabic and Urdu, where resources for NLP are inadequate. The classical rule-based techniques need to improve performance owing to sarcasm's context-based and subtle nature. On the other hand, the current developments in deep learning (DL) and NLP offer efficient solutions. Therefore, this study presents an applied Intelligence with Deep Learning Assisted Automated Sarcasm Recognition Approach (AIDL-ASRA) on Twitter Data. The presented AIDL-ASRA technique concentrates on identifying sarcastic and non-sarcastic contexts in tweets. In the AIDL-ASRA technique, the initial pre-processing stage is executed with different subprocesses. Next, Global Vectors for Word Representation (GloVe)-based word embedding process occurs. The AIDL-ASRA technique utilizes the Discriminative Deep Belief Networks (DDBN) method for sarcasm recognition. The DBN model's effectiveness is improved using the cat swarm optimization (CSO) model. A set of experiments were involved in evaluating the performance of the AIDL-ASRA method under the Arabic sarcastic dataset comprising 10,000 tweets. The experimental validation of the AIDL-ASRA method attained a superior accuracy value of 91.25 % over existing techniques under distinct measures.

关键词： Sarcasm Recognition Sentiment Analysis Twitter Data Deep Learning Cat Swarm Optimization Word Embedding

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External-Wrench Estimation for Aerial Robots Exploiting a Learned Model

External-Wrench Estimation for Aerial Robots Exploiting a Le...

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International Conference on Unmanned Aircraft systems (ICUAS)

作者： Ayham Alharbat Gabriele Ruscelli Roberto Diversi Abeje Mersha Smart Mechatronics and Robotics research group Saxion University of Applied Science Enschede The Netherlands Robotics and Mechatronics research group Faculty of Electrical Engineering Mathematics & Computer Science University of Twente Enschede The Netherlands Department of Electrical Electronic and Information Engineering University of Bologna Bologna Italy

ISBN: (数字)9798331513283

ISBN: (纸本)9798331513290

This paper presents an external wrench estimator that uses a hybrid dynamics model consisting of a firstprinciples model and a neural network. This framework addresses one of the limitations of the state-of-the-art modelbased wrench observers: the wrench estimation of these observers comprises the external wrench (e.g. collision, physical interaction, wind); in addition to residual wrench (e.g. model parameters uncertainty or unmodeled dynamics). This is a problem if these wrench estimations are to be used as wrench feedback to a force controller, for example. In the proposed framework, a neural network is combined with a first-principles model to estimate the residual dynamics arising from unmodeled dynamics and parameters uncertainties, then, the hybrid trained model is used to estimate the external wrench, leading to a wrench estimation that has smaller contributions from the residual dynamics, and affected more by the external wrench. This method is validated with numerical simulations of an aerial robot in different flying scenarios and different types of residual dynamics, and the statistical analysis of the results shows that the wrench estimation error has improved significantly compared to a model-based wrench observer using only a firstprinciples model.

关键词： Uncertain systems Analytical models Statistical analysis Atmospheric modeling Dynamics Neural networks Observers Autonomous aerial vehicles Numerical simulation Numerical models

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External-Wrench Estimation for Aerial Robots Exploiting a Learned Model

arXiv

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arXiv 2025年

作者： Alharbat, Ayham Ruscelli, Gabriele Diversi, Roberto Mersha, Abeje Smart Mechatronics and Robotics research group Saxion University of Applied Science Enschede Netherlands Robotics and Mechatronics research group Faculty of Electrical Engineering Mathematics & Computer Science University of Twente Enschede Netherlands Department of Electrical Electronic and Information Engineering University of Bologna Bologna Italy

This paper presents an external wrench estimator that uses a hybrid dynamics model consisting of a first-principles model and a neural network. This framework addresses one of the limitations of the state-of-the-art model-based wrench observers: the wrench estimation of these observers comprises the external wrench (e.g. collision, physical interaction, wind);in addition to residual wrench (e.g. model parameters uncertainty or unmodeled dynamics). This is a problem if these wrench estimations are to be used as wrench feedback to a force controller, for example. In the proposed framework, a neural network is combined with a first-principles model to estimate the residual dynamics arising from unmodeled dynamics and parameters uncertainties, then, the hybrid trained model is used to estimate the external wrench, leading to a wrench estimation that has smaller contributions from the residual dynamics, and affected more by the external wrench. This method is validated with numerical simulations of an aerial robot in different flying scenarios and different types of residual dynamics, and the statistical analysis of the results shows that the wrench estimation error has improved significantly compared to a model-based wrench observer using only a first-principles model. © 2025, CC BY-NC-ND.

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Simulation of Random LR Fuzzy Intervals

arXiv

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arXiv 2025年

作者： Romaniuk, Maciej Parchami, Abbas Grzegorzewski, Przemyslaw Systems Research Institute PAS Newelska 6 Warszawa01-447 Poland Department of Statistics Faculty of Mathematics and Computer Shahid Bahonar University of Kerman Kerman Iran Faculty of Mathematics and Information Science Warsaw University of Technology Koszykowa 75 Warszawa00-662 Poland

Random fuzzy variables join the modeling of the impreciseness (due to their "fuzzy part") and randomness. Statistical samples of such objects are widely used, and their direct, numerically effective generation is therefore necessary. Usually, these samples consist of triangular or trapezoidal fuzzy numbers. In this paper, we describe theoretical results and simulation algorithms for another family of fuzzy numbers – LR fuzzy numbers with interval-valued cores. Starting from a simulation perspective on the piecewise linear LR fuzzy numbers with the interval-valued cores, their limiting behavior is then considered. This leads us to the numerically efficient algorithm for simulating a sample consisting of such fuzzy values. Copyright © 2025, The Authors. All rights reserved.

关键词： Fuzzy models

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Health Misinformation Detection: A Chunking Strategy Integrated to Retrieval-Augmented Generation 2

Health Misinformation Detection: A Chunking Strategy Integra...

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2nd Workshop on Artificial Intelligence for Perception and Artificial Consciousness, AIxPAC 2024

作者： Taib, Walid Saadallah, Idriss Ichou, Abdelali Hamdi, Abderrahmene Bruno, Alessandro Mazzeo, Pier Luigi Chetouani, Aladine Tliba, Marouane Kerkouri, Mohamed Amine Higher National School of Telecommunications and Information Technologies and Communications Es Senia Oran Algeria University of Orleans Orleans France Higher National School of Computer Science Oued Smar Algiers Algeria IULM University Department of Business Law Economics Consumer Behaviour -"Carlo A. Ricciardi" Via Carlo Bo 1 Milan20143 Italy ISASI Institute of Applied Sciences and Intelligent Systems-CNR Lecce73100 Italy University Sorbonne - Paris Nord Villetaneuse France

Generative AI (GenAI) and Natural Language Processing (NLP) have advanced significantly in recent years, exhibiting breakthroughs and pushing the bar of accuracy rates in text mining. Cascade effects have been observed in many application domains, spanning text analysis, question answering, classification, and new textual content generation. The latter has allowed many end-users to perceive AI as ready-to-go solutions to optimise their daily workflow. However, dark and bright sides lurk behind textual content generation, as trustworthy and unverified content can be effortlessly generated. That has fuelled a significant challenge in our society: fake news. Although fake news has existed for a while, it remains an unsolved issue. Generative AI has brought it to a new level by enabling the automated production of large volumes of high-quality, individually targeted fake content. Our work is part of the HeReFaNMi (Health-Related Fake News Mitigation) project, which focuses on health-related fake news mitigation by using NLP, Language Models, and a Retrieval-Augmented Generation (RAG) system. We propose a new chunking mechanism that streamlines the overall RAG framework pipeline. BERT and BERT+RAG have been compared on the health-related fake news classification task on a dataset of 2000 health-related articles equally split into two categories (’fake’ and’credible’). Preliminary experimental results reveal improvements in Accuracy, Recall, and F1-score. © 2024 Copyright for this paper by its authors.

关键词： Natural language processing systems

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Distributed Prescribed-Time Observer for Nonlinear systems

arXiv

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arXiv 2025年

作者： de Heij, Vincent Niazi, M.B. Umar Johansson, Karl H. Ahmed, Saeed Faculty of Science and Engineering University of Groningen Groningen9747 AG Netherlands Division of Decision and Control Systems Digital Futures KTH Royal Institute of Technology StockholmSE-100 44 Sweden Laboratory for Information and Decision Systems Department of Electrical Engineering and Computer Science Massachusetts Institute of Technology CambridgeMA02139 United States

This paper proposes a distributed prescribed-time observer for nonlinear systems representable in a block-triangular observable canonical form. Using a weighted average of neighbor estimates exchanged over a strongly connected digraph, each observer estimates the system state despite limited local sensor measurements. The proposed design guarantees that distributed state estimation errors converge to zero at a user-specified convergence time, irrespective of observers’ initial conditions. To achieve this prescribed-time convergence, distributed observers implement time-varying local output injection gains that asymptotically approach infinity as the prescribed time is approached. The theoretical convergence is rigorously proven and validated through numerical simulations. Copyright © 2025, The Authors. All rights reserved.

关键词： Nonlinear simulations

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