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8th International Artificial Intelligence and Data Processing Symposium, IDAP 2024

作者： Daglarli, Evren Aribas, Erke Istanbul Technical University Faculty of Computer and Informatics Engineering Istanbul Turkey

ISBN: (纸本)9798331531492

In this paper, the design of fixed-wing autonomous UAVs is aimed. The goal is to lock onto rival UAVs during the competition, avoid these locks with aggressive maneuvers, and perform the designated tasks autonomously. Autonomous Aircraft System;It consists of a ground control station, ground antenna, controller, and autonomously operating aircraft components. In the declaration, the system's performance features, mechanical and aerodynamic design, flight control systems and communication systems are detailed. In addition, the UAV's autonomous locking, image processing and task performance capabilities are presented, supported by tests and analysis. The final system architecture and performance analysis results show that the UAV meets the design requirements. © 2024 IEEE.

关键词： Antenna grounds

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Fog Computing-based Real-Time Emotion Recognition using Physiological Signals 27

Fog Computing-based Real-Time Emotion Recognition using Phys...

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27th International Conference on Advanced Communications Technology, ICACT 2025

作者： Erzurumluoglu, Omur Fatmanur Ozlem, Kadir Atalay, Asli Tuncay Atalay, Ozgur Ince, Gokhan Faculty of Computer and Informatics Engineering Istanbul Technical University Istanbul Turkey Faculty of Textile Technologies and Design Istanbul Technical University Istanbul Turkey

ISBN: (纸本)9791188428137

Emotion recognition based on physiological signals has become a crucial area of research in affective computing and human-computer interaction, with applications in smart homes, workplaces, educational institutions, healthcare, and entertainment. In this study, a real-time emotion recognition system utilizing fog computing architecture was developed by considering the challenges of latency, total response time, resource usage, and security in IoT environments. The random forest machine learning model was trained with time-based statistical features by using the DREAMER dataset. Even though the model achieved an accuracy of 84.21% with 104 features, to meet real-time performance requirements, the system was optimized to calculate 24 features, maintaining a commendable accuracy of 79.70%. Extensive experiments demonstrated the superior performance of fog computing compared to edge and cloud computing in terms of latency, queuing delay, jitter, and most importantly total response time. The results highlight the proposed system's ability to support multiple users simultaneously. Copyright 2025 Global IT Research Institute (GIRI). All rights reserved.

关键词： Fog computing

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Machine Learning Prediction Based UI for Aircraft Cockpit 14

Machine Learning Prediction Based UI for Aircraft Cockpit

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14th International Conference on Electrical and Electronics engineering, ELECO 2023

作者： Topal, Bilge Çarkacioǧlu, Levent Töreyin, Behçet Uǧur Istanbul Technical University Faculty of Computer and Informatics Engineering Dept. of Computer Engineering Istanbul Turkey Aselsan Inc. Ankara Turkey Istanbul Technical University Informatics Institute Istanbul Turkey

ISBN: (纸本)9798350360493

Pilot vehicle interfaces on aircraft, similar to the automobile digital displays found in automobiles, are used to present the current status of various systems (e.g., navigation, engine information). Aircraft interfaces exhibit deterministic behavior due to safety concerns, ensuring that user input yields predictable outputs. Consequently, integrating text-based pattern recognition or artificial neural network based solutions to aircraft systems are challenging because of their deterministic nature. In this paper, an approach complementary to the existing user interface models is proposed. This approach is based on learning recurring user interactions during flight. It utilizes text-based pattern recognition and prediction models based on user interaction logs and sensor readings. Test results on sample data suggest that the proposed approach yields faster and context-aware user interaction model that decreases the time needed to perform tasks in the aircraft cockpit, where timing is crucial. © 2023 IEEE.

关键词： User interfaces

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Root-zone Soil Moisture Nowcasting using Context Aware Machine Learning 11

Root-zone Soil Moisture Nowcasting using Context Aware Machi...

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11th International Conference on Agro-Geoinformatics, Agro-Geoinformatics 2023

作者： Aktas, Ayda F. Ustundag, Burak Berk Istanbul Technical University Informatics Institute Istanbul Turkey Istanbul Technical University Faculty of Computer and Informatics Engineering Istanbul Turkey

ISBN: (纸本)9798350303513

Estimation of root-zone soil moisture (SM) is crucial for effective agricultural management and water resource planning. However, current methods for soil moisture estimation exhibit several limitations that hinder their practical application. This study introduces a novel nowcasting model, which integrates in-situ and remote sensing data through a Predictive Error Compensated wavelet Neural NETwork (PECNET), addressing the drawbacks of existing *** SM estimation techniques often suffer from limited accuracy, inadequate contextual information, and no real-time monitoring capabilities. Although remote sensing technologies offer promising advantages, such as wide spatial coverage and frequent data acquisition, they are not immune to limitations. Vegetation coverage and density present challenges in accurately estimating root-zone SM using remote sensing techniques. These factors can introduce uncertainties and errors in the estimation process, thereby impacting the reliability of the *** overcome these limitations and enhance the accuracy of root-zone SM estimation, this study proposes the integration of remote sensing data with in-situ measurements. Specifically, Normalized Difference Vegetation Index (NDVI) calculations from Landsat 7 and Landsat 8 satellites are fused with evapotranspiration and rainfall data obtained from agrometeorological stations. Combining these datasets generates an 8-day time series for the target parcels, leveraging the contextual information provided by NDVI and seasonality to improve the accuracy of root-zone soil moisture *** develop a robust and efficient model, we introduce PECNET, which ensures the orthogonality of input features and facilitates the learning of non-linear relationships between variables. Notably, PECNET addresses the challenge of limited labeled training data, minimizing the risk of overfitting and enabling accurate estimation with fewer labeled samples. In addition, this study employs

关键词： Time series

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Internal Validity Index for Fuzzy Clustering Based on Relative Uncertainty

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computers, Materials & Continua 2022年第8期72卷 2909-2926页

作者： Refik Tanju Sirmen Burak Berk Üstündag Graduate School of Science Engineering&Technology Istanbul Technical UniversityIstanbul34469Turkey Faculty of Computer&Informatics Istanbul Technical UniversityIstanbul34469Turkey

Unsupervised clustering and clustering validity are used as essential instruments of data *** clustering being realized under uncertainty,validity indices do not deliver any quantitative evaluation of the uncertainties in the suggested ***,validity measures may be biased towards the underlying clustering ***,neglecting a confidence requirement may result in *** the absence of an error estimate or a confidence parameter,probable clustering errors are forwarded to the later stages of the ***,having an uncertainty margin of the projected labeling can be very fruitful for many applications such as machine ***,the validity issue was approached through estimation of the uncertainty and a novel low complexity index proposed for fuzzy *** involves only uni-dimensional membership weights,regardless of the data dimension,stipulates no specific distribution,and is independent of the underlying similarity *** tests and comparisons returned that it can reliably estimate the optimum number of partitions under different data distributions,besides behaving more robust to over ***,in the comparative correlation analysis between true clustering error rates and some known internal validity indices,the suggested index exhibited the highest strong *** relationship has been also proven stable through additional statistical acceptance *** the provided relative uncertainty measure can be used as a probable error estimate in the clustering as ***,it is the only method known that can exclusively identify data points in dubiety and is adjustable according to the required confidence level.

关键词： Machine learning data science clustering validity fuzzy clustering uncertainty intelligent systems data analytics

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Neural End-to-End Coreference Resolution using Morphological Information 6

Neural End-to-End Coreference Resolution using Morphological...

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CRAC Shared Task on Multilingual Coreference Resolution at 6th Workshop on Computational Models of Reference, Anaphora and Coreference, CRAC 2023

作者： Arslan, Tugba Pamay Acar, Kutay Eryigit, Gulsen ITÜ NLP Research Group Department of AI&Data Engineering Faculty of Computer&Informatics Istanbul Technical University Turkey ITÜ NLP Research Group Department of Computer Engineering Faculty of Computer&Informatics Istanbul Technical University Turkey

ISBN: (纸本)9781955917025

In morphologically rich languages, words consist of morphemes containing deeper information in morphology, and thus such languages may necessitate the use of morpheme-level representations as well as word representations. This study introduces a neural multilingual end-to-end coreference resolution system by incorporating morphological information in transformer-based word embeddings on the baseline model. This proposed model participated in the Sixth Workshop on Computational Models of Reference, Anaphora and Coreference (CRAC 2023). Including morphological information explicitly into the coreference resolution improves the performance, especially in morphologically rich languages (e.g., Catalan, Hungarian, and Turkish). The introduced model outperforms the baseline system by 2.57 percentage points on average by obtaining 59.53% CoNLL F-score. © CRAC 2023. All Rights Reserved.

关键词：

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Exploring the Semantic Complexity of Adjective-Noun Collocations between Uzbek and English for Improved Machine Translation 9

Exploring the Semantic Complexity of Adjective-Noun Collocat...

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9th International Conference on computer Science and engineering, UBMK 2024

作者： Abdurakhmonova, Nilufar Shamieva, Nargiza Adali, Eşref National University of Uzbekistan Dept. of Computational and applied linguistics Tashkent Uzbekistan National Univ. of Uzbekistan Dept. of Foreign language and Literature Tashkent Uzbekistan Istanbul Technical University Computer Engineering and Informatics Faculty Istanbul Turkey

ISBN: (纸本)9798350365887

The bilingual thesaurus of Uzbek-English adjectives is a field that has not been studied well explored yet for machine translation. Several websites and electronic dictionaries created nearby show that not enough semantically close adjectives are included in them. Despite extensive research on the meaning the adjectives in both languages, there remains a lack of understanding about the relationship between the adjective itself and the noun, which has an absolutely new connotation in collaboration. Our result shows that a recent bilingual thesaurus demonstrates how one adjective could acquire numerous new synonyms while combining with different nouns. The meanings of words, in both languages have been extensively studied, Little is understood about the dynamic connection between these words and the nouns they describe. The complexity of how adjectives pair with nouns is evident in how this relationship leads to meanings and connotations. Our study aims to bridge this gap by demonstrating how a bilingual thesaurus can capture the meanings and variations of adjectives when combined with nouns. The findings indicate that depending on the noun it accompanies a contemporary bilingual thesaurus can generate synonyms, for a single adjective. © 2024 IEEE.

关键词： Machine translation

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One-Class Classification of 3D Point Clouds Using Dynamic Graph CNN 7

One-Class Classification of 3D Point Clouds Using Dynamic Gr...

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7th International Conference on computer Science and engineering, UBMK 2022

作者： Gencer, Zeki Sahin, Yusuf H. Unal, Gozde Istanbul Technical University Faculty of Computer and Informatics Engineering Istanbul Turkey

ISBN: (数字)9781665470100

ISBN: (纸本)9781665470100

Anomaly detection is defined as a binary classification of normal and abnormal samples in a given data. In this task, anomalous samples are considered very rare and thus they are not used in training the deep learning model. Anomaly detection is thoroughly studied in 2D images, but 3D applications are not common yet. In this work, we combine studies in the field of 3D point cloud and anomaly detection and present a one-class classification method for 3D data. This method uses a pretrained Dynamic Graph Convolutional Neural Network (DG-CNN) to extract features from 3D point cloud data, and then fits a separate multivariate Gaussian distribution for each class. In the test phase, features are extracted from the test samples, and the distances to the distribution obtained in training indicate how anomalous the sample is for that class. Our method achieves 94.2% AUROC accuracy for the given setup. This is the first attempt to handle 3D point cloud classification as an anomaly detection case, and thus our findings and methods serve as a baseline for further studies. © 2022 IEEE.

关键词： Anomaly detection

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Measuring Bug Reporter's Reputation and Its Effect on Bug Resolution Time Prediction 7

Measuring Bug Reporter's Reputation and Its Effect on Bug Re...

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7th International Conference on computer Science and engineering, UBMK 2022

作者： Yucel, Muhammed Kadir Tosun, Ayse Istanbul Technical University Faculty of Computer and Informatics Engineering Istanbul Turkey

ISBN: (数字)9781665470100

ISBN: (纸本)9781665470100

In software projects, bug reports remain open and get updates from team members during the related bug's lifetime. It is an important task to predict when a bug would be resolved so that managers plan timeline and allocate team resources accordingly. Prior works show that reporter information is the most effective feature for predicting resolution time. Previous work only considers bug reporting activity to define reporter reputation and misses other activities. In this paper, we propose a new reputation calculation method that considers all activities of a reporter within a bug tracking system. We collected bug reports of Chromium and WebRTC projects and calculated reputations of each bug reporter within the dataset using our weighted, activity based calculation method as well as other methods from previous work in order to compare performance. We trained a Doc2Vec model to utilize textual information in bug reports to build a base model for comparing different reputation methods. Bug reports are classified into two categories as FAST and SLOW according to their resolution time. Stochastic Gradient Descent (SGD) and Extreme Gradient Boost (XGB) classifier algorithms are employed. XGB classifier resulted with 55 % - 72 % F -Scores for FAST and SLOW respectively. The results also show that data characteristics of project affects the effectiveness of the reputation calculation method on bug resolution time prediction. © 2022 IEEE.

关键词： Forecasting

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Risk Assessment of Linkedin Profiles 8

Risk Assessment of Linkedin Profiles

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8th International Conference on computer Science and engineering, UBMK 2023

作者： Aydin, Arzum Bahtiyar, Serif Istanbul Technical University Cyber Security and Privacy Research Lab SPFLab. Faculty of Computer and Informatics Engineering Maslak Istanbul Turkey

ISBN: (纸本)9798350340815

Fake or fraudulent accounts are a significant problem in social networking that is often addressed by the users of various social networking platforms involving Linkedin. Fraudulent accounts generally try to convince people about their identity that they are authentic. Adversaries use fake profiles or they impersonate a real identity on social networks to obtain more privileges. Therefore, it is critical to understand problems regarding fraudulent profiles. In this paper, we propose new sustainable framework to assess risks about profiles in social networking platforms. The framework is implemented as a web extension and it is analyzed with ITU alumni profiles in Linkedin. The analyses results show that there are different profiles within one community in Linkedin that may be significant threat for social network users. © 2023 IEEE.

关键词： Risk assessment

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