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2020 3rd International Conference on computer and informatics engineering, IC2IE 2020 2020年 78ADUMMY页

作者： Laya, Mauldy Dept. of Computer and Informatics Engineering

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Cognitive Assessment of Students Using Testing Instruments on computer Networks Courses in informatics and computer engineering Education Study Program at UNJ 8

Cognitive Assessment of Students Using Testing Instruments o...

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8th International Conference on Education and Technology, ICET 2022

作者： Duskarnaen, Muhammad Ficky Sastrawijaya, Yuliatri Ajie, Hamidillah Arrazi, Muhammad Fakhruddin State University of Jakarta Informatics and Computer Engineering Education Study Program Jakarta Indonesia

ISBN: (数字)9781665492140

ISBN: (纸本)9781665492140

This study aims to design an assessment instrument to assess students' cognition in computer networking lessons. The design and design of the test instrument followed the ADDIE model, from the analysis stage to determine student targets and characteristics, then the design stage, determining learning objectives, learning material design, and question grids, at this stage the number of Subject Matter Experts (SME) was also determined as many as 6 people, for 6 Subject Matter Experts (SME), the minimum acceptable Content Validation Ratio (CVR) is 0.99, while the Content Validation Index (CVI) is 0.83. At the development stage, 81 items were designed as a draft assessment instrument. At the implementation stage, the Subject Matter Experts (SME) assessed each item on the draft assessment instrument. This study uses a quantitative content validity approach from the Lawshe model with a 3-point scale as follows: essential, useful but not necessary, and non-essential. Each Subject Matter Expert (SME) was given an instrument to assess each item in the test instrument. The Subject Matter Expert (SME) will determine whether an important question is given, normal or normal, or whether the question is not important given to students. At the evaluation stage, the Subject Matter Expert (SME) assessment of the draft test instrument will be compiled and the Content Validation Ratio (CVR) value and the Content Validation Index (CVI) value calculated to determine content validity. © 2022 IEEE.

关键词： Students

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Synthesis of batik motifs using a diffusion - generative adversarial network

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Multimedia Tools and Applications 2025年第7期84卷 3407-3438页

作者： Octadion, One Yudistira, Novanto Kurnianingtyas, Diva Informatics Engineering Faculty of Computer Science Universitas Brawijaya Malang Indonesia

This paper presents a novel approach for generating intricate Batik motifs using a modified Diffusion-Generative Adversarial Network (Diffusion-GAN) augmented with StyleGAN2-Ada. Motivated by the rich cultural heritage of Indonesian Batik, our research addresses the challenge of synthesizing high-quality, diverse patterns that capture the artistry and complexity of traditional designs. Traditional generative models often struggle with stability and fidelity in artistic synthesis. We integrate StyleGAN2-Ada and Diffusion techniques to overcome these limitations, optimizing model architecture and employing a curated Batik dataset. Evaluation metrics including Frechet Inception Distance (FID), Kernel Inception Distance (KID), precision, recall, and non-redundancy assess the quality and diversity of generated motifs. Our results demonstrate significant advancements in the realism and authenticity of synthesized Batik patterns, leveraging Diffusion-GAN to enhance detail and artistic fidelity. By exploring the intersection of deep learning and cultural art, this research contributes to the preservation and evolution of Batik as a cultural artifact. References to prior works underscore the evolution of Artificial Intelligence (AI) in cultural heritage preservation, highlighting the potential of modern AI techniques to innovate within traditional art forms like Batik. In summary, this study showcases the efficacy of the proposed Diffusion-GAN methodology in synthesizing original and convincing Batik motifs, while identifying opportunities for further refinement and complexity in motif synthesis. The dataset and code can be accessed here: https://***/octadion/diffusion-stylegan2-ada-pytorch © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2025.

关键词： Generative adversarial networks

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Forecasting stock market volatility using social media sentiment analysis

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Neural Computing and Applications 2025年第17期37卷 10771-10794页

作者： Saravanos, Christina Kanavos, Andreas Department of Computer Engineering and Informatics University of Patras Patras26504 Greece Department of Informatics Ionian University Corfu49100 Greece

In the era where social media significantly influences public sentiment, platforms such as Twitter have become vital in predicting stock market trends. This paper presents a cutting-edge predictive model that integrates historical stock market data, Twitter sentiment analysis, and an extensive array of tweet-related features. Utilizing advanced regression techniques and deep neural networks, our model forecasts the daily closing prices of the U.S. stock market indices with notable accuracy and demonstrates a strong link between market values, sentiment scores, and social media activities. Our analysis particularly emphasizes the importance of tweet diffusion and the influence of prominent Twitter users in refining prediction accuracy. Contrary to conventional wisdom, we discovered that incorporating a wide range of tweet-derived features significantly improves the model’s performance without leading to sparsity challenges. This study not only questions established paradigms but also underscores the potential of social media analytics in financial market forecasting, with substantial implications for investors, market analysts, and policy makers. © The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature 2024.

关键词： Tweets

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Throughput and Delay Performance of Slotted Aloha in SmartBANs Under Saturation Conditions

IEEE Networking Letters

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IEEE Networking Letters 2024年第3期6卷 168-172页

作者： Politis, Anastasios C. Hilas, Constantinos S. Department of Computer Informatics and Telecommunications Engineering Serres621 24 Greece

This letter evaluates the performance of the slotted Aloha protocol defined by the European Telecommunication Standard Institute (ETSI) SmartBAN specification, under saturation conditions. For this purpose, we develop a two-dimensional discrete time Markov chain (DTMC) to model the operational details of the protocol and assess its performance in terms of saturation throughput and average end-to-end delay. The accuracy of the proposed model is validated by means of simulation which reveals a very good match among theoretical and simulation results. The model can be used for protocol performance prediction and optimization purposes. © 2019 IEEE.

关键词： Markov chains

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Enhancing demand forecasting through combination of anomaly detection and continuous improvement

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Soft Computing 2025年第2期29卷 1243-1258页

作者： Jahani, Meysam Zojaji, Zahra Raji, Fatemeh Faculty of Computer Engineering University of Isfahan Isfahan Iran School of Computer Science and Informatics De Montfort University Leicester United Kingdom

Demand forecasting has emerged as a crucial element in supply chain management. It is essential to identify anomalous data and continuously improve the forecasting model with new data. However, existing literature fails to comprehensively cover both aspects of anomaly detection and continuous improvement in demand forecasting. This study proposes an enhanced model to improve accuracy in the demand forecasting. The proposed model introduces a novel data handling method that incorporates an anomaly detection autoencoder, improved with anomaly correction mechanisms. The data handling approach simultaneously detects data anomalies, distinguishes between expected and unexpected anomalies, and corrects anomalous data, ensuring cleaner input for demand forecasting. Then, the proposed model employs a long short-term memory architecture for demand forecasting, enhanced with a continuous improvement method. Thus, the model not only forecasts demand but also retrains the model when the anomaly data surpasses the predetermined threshold, thereby improving the accuracy of forecasting. The results show that the proposed model outperforms other models in detecting data anomalies, achieving an average precision-recall of 0.922, a receiver operating characteristic value of 0.739, and a significance level of less than 0.05. Finally, the model exhibits superior performance in demand forecasting, with average mean squared error, root mean squared error, and mean absolute error values of 33.167, 4.347, and 1.509, respectively, all with a significance level of less than 0.05. © The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2025.

关键词： Demand response

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Development of a machine-learning-based method for early fault detection in photovoltaic systems

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Journal of engineering and Applied Science 2023年第1期70卷 1-17页

作者： Voutsinas, Stylianos Karolidis, Dimitrios Voyiatzis, Ioannis Samarakou, Maria Department of Informatics and Computer Engineering University of West Attica Athens Greece

In the process of the decarbonization of energy production, the use of photovoltaic systems (PVS) is an increasing trend. In order to optimize the power generation, the fault detection and identification in PVS is significant. The purpose of this work is the study and implementation of such an algorithm, for the detection as many as faults arising on the DC side of a photovoltaic system. A machine learning technique was chosen. The dataset used to train the algorithm was based on a year’s worth of irradiance and temperature data, as well as data from the PV cell used. The method uses logistic regression with cross validation as a new approach to detect and identify faults in PVS. It is applied to smart PV arrays, that can transmit voltage and current measurements from each PV cell of the array individually. The results are satisfactory since the algorithm can detect the majority of faults that occur on the DC side of a photovoltaic (open-circuit fault, short-circuit fault, mismatch faults). The accuracy of the algorithm (97.11%) is comparable to other methods presented by the literature. Moreover, the computational cost of the proposed method is significantly lower than the methods presented in the literature. In summary, the performance of the implemented algorithm is considered particularly satisfactory and can be easily applied to PVS. © 2023, The Author(s).

关键词： Machine learning

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Explainable Emotion Recognition in Social Networks with Transformers 24

Explainable Emotion Recognition in Social Networks with Tran...

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24th IEEE/ACIS International Conference on computer and Information Science, ICIS 2024

作者： Perikos, Isidoros Pylarinou, Chara University of Patras Computer Technology Institute and Press 'Diophantus' Computer Engineering and Informatics Department Patras Greece University of Patras Computer Engineering and Informatics Department Patras Greece

ISBN: (纸本)9798350376647

Emotion recognition in text has become an essential research area within artificial intelligence and natural language processing due to its applications in sentiment analysis, human-computer interaction, and social media analysis. This paper introduces an approach for emotion recognition in social networks using transformer models enhanced with explainability techniques. By leveraging the advanced capabilities of transformers to analyze and classify emotions in written content, our finetuned models achieves high accuracy in identifying emotions according to the Ekman emotional model. The integration of LIME and SHAP provides transparency and interpretability to our model, making it more trustworthy for practical applications. Our findings indicate that models like XLNET report an exceptional performance in emotion recognition tasks. The explainability techniques illustrate the transformer model decision-making processes and the way that they utilize words and their continual information in sentences. © 2024 IEEE.

关键词： Distribution transformers

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Enhancing Aviation Efficiency Through Big Data and Machine Learning for Flight Delay Prediction 4th

Enhancing Aviation Efficiency Through Big Data and Machine ...

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4th International Conference on Novel and Intelligent Digital Systems, NiDS 2024

作者： Vonitsanos, Gerasimos Gounaridis, Ioannis Kanavos, Andreas Mylonas, Phivos Computer Engineering and Informatics Department University of Patras Patras Greece Department of Informatics Ionian University Corfu Greece Department of Informatics and Computer Engineering University of West Attica Athens Greece

ISBN: (纸本)9783031733437

Flight delays pose significant challenges to the aviation industry, leading to increased operational costs and passenger dissatisfaction. This paper explores the use of machine learning (ML) and big data analytics to enhance the accuracy and efficiency of flight delay predictions. Utilizing data from the Federal Aviation Administration (FAA) covering the period from 2018 to 2022, we analyze critical factors influencing delays and develop predictive models employing techniques such as Random Forest, Gradient Boosting Machines, Decision Trees, and k-Nearest Neighbors. Our analysis demonstrates that these ML techniques significantly outperform traditional models, improving the accuracy of delay predictions and thereby supporting airlines in optimizing operational efficiency and enhancing passenger satisfaction. The paper also discusses the practical implementation of these findings in real-time airline operations and outlines future research directions to further improve predictive accuracy. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2024.

关键词： Adversarial machine learning

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Evaluating Machine Learning Techniques for Enhanced Prediction of Building Energy Consumption 9

Evaluating Machine Learning Techniques for Enhanced Predicti...

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9th South-East Europe Design Automation, computer engineering, computer Networks and Social Media Conference, SEEDA-CECNSM 2024

作者： Vonitsanos, Gerasimos Kanavos, Andreas Mylonas, Phivos University of Patras Computer Engineering and Informatics Department Patras Greece Ionian University Department of Informatics Corfu Greece University of West Attica Department of Informatics and Computer Engineering Athens Greece

ISBN: (纸本)9798350342482

Accurate prediction of energy usage is crucial for optimizing resource allocation, enhancing energy efficiency, and reducing environmental impact, pivotal for sustainable development. This study examines electricity consumption in three Cornell University buildings, utilizing advanced machine learning techniques to tackle the challenges of sustainable energy management effectively. We specifically evaluated the performance of Support Vector Machine (SVM), Random Forest, Decision Tree, and K-Nearest Neighbors (KNN) in forecasting electricity usage. Our findings reveal that SVM consistently outperforms the other models across various performance metrics, including accuracy and efficiency. These results provide vital insights into the efficacy of these algorithms in predicting energy consumption, thereby supporting strategic energy management decisions in educational institutions and potentially other similar settings. © 2024 IEEE.

关键词： Sustainable building

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