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A novel approach to predicting liquefaction-induced settlements using Kolmogorov–Arnold Networks (KANs)

Discover Geoscience 2024年第1期2卷 1-14页

作者： Karakaş, Seyidcem Garanti BBVA Payment Systems İstanbul Turkey Faculty of Computer and Informatics Engineering Istanbul Technical University Istanbul Turkey

Soil liquefaction refers to the loss of strength and bearing capacity of soil under dynamic loads. Structures built on liquefied soils experience settlement. In this study, the newly developed Kolmogorov–Arnold Networks (KAN) method, an innovative artificial neural network technique, was utilized to predict liquefaction-induced settlement. The KAN method, which extends beyond the traditional Multi-Layer Perceptron (MLP) approach, was compared with the Random Forest method for benchmarking purposes. Using data derived from laboratory and field studies, models were constructed using both methods, and their performances were analyzed. According to the results, the KAN model outperformed the RF model in terms of R2, MAE, MSE, and RMSE metrics. Additionally, the feature importance analysis on the KAN model identified cyclic stress ratio (csr) and corrected SPT blow count (n1_60) as the most significant variables. These results underscore the potential of the KAN method in enhancing predictive accuracy and reliability in geotechnical applications, paving the way for its broader acceptance and implementation in real-world scenarios.

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

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

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International Conference on Advanced Communication Technology (ICACT)

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

ISBN: (数字)9791188428137

ISBN: (纸本)9798331507602

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.

关键词： Emotion recognition Accuracy Computational modeling Smart homes Signal processing Brain modeling Real-time systems Physiology Time factors Edge computing

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Machine Learning Prediction Based UI for Aircraft Cockpit

Machine Learning Prediction Based UI for Aircraft Cockpit

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International Conference on Electrical and Electronics engineering, ELECO

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

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.

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I-MCM: IoT Malware Counter Measures for Cross-Architecture IoT Malware Detection

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IEEE Access 2025年 13卷 95524-95534页

作者： Ibrahim Gulatas H. Hakan Kilinc A. Halim Zaim M. Ali Aydin Department of Computer Engineering National Defense University Istanbul Türkiye Department of Research and Development Orion Innovation Türkiye Istanbul Türkiye Faculty of Computer and Informatics Istanbul Technical University Istanbul Türkiye Department of Computer Engineering Istanbul University-Cerrahpaşa Istanbul Türkiye

The recent attacks initiated by malware-infected IoT devices illustrate that these attacks have tremendous impacts not only on the targeted systems but also on the entire internet infrastructure. Due to the vast number of infected IoT devices, the Quality of Service of the target system can be severely hampered by DDoS attacks. Despite the severity of the threat, the security measures against IoT malware are highly limited, and mostly, users are unaware that their devices are infected by malware. Due to the constrained nature of IoT devices, high-resource-demanding anti-malware tools cannot be run on edge devices, and lightweight malware detection systems are highly needed to secure those devices against IoT malware. In this regard, we propose the I-MCM framework for IoT malware detection on IoT devices by utilizing Tiny ML techniques based on static malware analysis data. Our proposed I-MCM framework is capable of detecting IoT malware in a Raspberry Pi Node deployed with Random Forest Classifier in 7.87 s. (including file analysis and data preprocessing) with 99.8% accuracy and in an Arduino Nano 33 BLE Sense microcontroller deployed with a Tiny ANN model in 8.16 s. (including file analysis and data preprocessing) with 97.1% accuracy. Besides, the I-MCM framework can detect IoT malware regardless of CPU architecture with only one ML model by utilizing our Opcode Purification Technique. Last but not least, this research presents a cross-architecture IoT Malware dataset that includes both malware and benignware samples.

关键词： Malware Internet of Things Accuracy computer architecture Security Purification Microprogramming Image edge detection Electronic mail Data preprocessing

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Deriving Uzbek Words with Derivational Suffixes 9

Deriving Uzbek Words with Derivational Suffixes

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

作者： Adali, Esref Mirdjonovna, Khamroeva Shahlo Computer Eng. and Informatics Faculty Istanbul Technical University Istanbul Turkey Tashkent State University of Uzbek Language and Literature named Alisher Navo'i Dept.of Computational Linguistics and Digital Technologies Tashkent Uzbekistan

ISBN: (纸本)9798350365887

Uzbek language is an agglutinative language and has many derivational suffixes. New words can be derived by adding derivational suffixes to nouns, adjectives, verbs and adverb roots. In this paper, it is shown which derivational suffix or suffixes can be used to produce words with a certain meaning. When adding a derivational suffix to a root word, the phonologic features of Uzbek are taken into consideration;Necessary sound changes are made. The aim of this paper is to show which derivational suffix or suffixes can be used to derive new words in a certain sense and to create the algorithm and flow diagrams required for the computer program to perform this process. © 2024 IEEE.

关键词： derivational suffix phonology Uzbek language word derivation

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Arabic Sentiment Analysis of Food Delivery Services Reviews

Arabic Sentiment Analysis of Food Delivery Services Reviews

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2023 International Symposium on Networks, computers and Communications, ISNCC 2023

作者： Mustafa, Dheya Khabour, Safaa M. Shatnawi, Ahmed S. Taqieddin, Eyad The Hashemite University Faculty of Engineering Dept. of Computer Engineering Zarqa13133 Jordan Yarmouk University Faculty of Information Technology and Computer Sciences Dept. of Information Systems Irbid21163 Jordan Jordan University of Science & Technology Dept. of Software Engineering Irbid21110 Jordan School of Computing and Informatics Dept. of Cyber Security Al-Hussein Technical University Amman Jordan

ISBN: (纸本)9798350335590

Customer reviews on online platforms have grown to become an important source of insight into a company's performance. Food delivery services (FDS) companies aim to effectively use customers' feedback to identify areas for improvement of customer satisfaction. Although Arabic is becoming one of the most widely used languages on the Internet, only a few studies have focused on Arabic sentiment analysis to date. The present study conducts an extensive emotion mining and sentiment analysis on FDS-related reviews in Arabic, exploiting natural language processing, and machine learning techniques to extract information that reflects customers' subjective viewpoints, recognize their feelings, and determine polarity in the FDS domain. This work begins with collecting the FDS dataset (Talabat), and then extracting and creating a dialects lexicon for Arabic dialects, followed by walking the reader through detailed steps of cleaning and pre-processing a manually annotated dataset. Finally, we examined classification algorithms including Decision Tree (DT), and Support Vector Machine (SVM). We achieved a maximum accuracy of about 82% using the SVM classifier. © 2023 IEEE.

关键词： Sentiment analysis

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Towards a Multilingual Platform for Gamified Morphology Learning 7

Towards a Multilingual Platform for Gamified Morphology Lear...

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

作者： Bektas, Fatih Dereli, Bihter Hayta, Furkan Sahin, Erkin Ali, Ubey Eryigit, Gulsen Istanbul Technical University Department of Artificial Intelligence & Data Engineering Istanbul Turkey Istanbul Turkey Nlp Research Group Istanbul Technical University Faculty of Computer & Informatics Istanbul Turkey

ISBN: (数字)9781665470100

ISBN: (纸本)9781665470100

Mobile-assisted language learning is an emerging trend in language education. Recently, the use of gamification for complex morphology learning has been perceived positively by morphologically rich language learners (namely, Turkish as a foreign language learners). Generating automatic explicit grammar exercises with the use of finite-state transducers (FST) in a gamified environment is a new and practical approach in this field. The integration of such platforms into new languages should be easily implementable in order to reduce the development efforts. This study is a first attempt towards this goal. The paper introduces an architecture for a multilingual platform supporting gamified morphology learning. As a test case, the French language module is developed in addition to Turkish. Experiences with these languages are explained and the stages for adding additional languages are described. The study reveals that morphological complexity is an important issue to consider, and the morphological differences of the target language should be considered while designing a mobile language learning application. © 2022 IEEE.

关键词： Morphology

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Comparison of Uzbek-Turkish Inflection Suffixes 7

Comparison of Uzbek-Turkish Inflection Suffixes

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

作者： Adali, Ecref Rajabovich, Mengliyev Bakhtiyor Mirdjonovna, Khamroyeva Shahlo Istanbul Technical University Computer Engineering and Informatics Faculty Istanbul Turkey Tashkent State University Uzbek Language and Literature Tashkent Uzbekistan

ISBN: (数字)9781665470100

ISBN: (纸本)9781665470100

Creating a parallel corpus is one of the necessary resources for machine translation and can he considered as the first translation dictionary. However, it is clear that a successful translation cannot he made with a translation dictionary alone;The morphological features of the languages should also he added to the compilation. Therefore, in this paper, we present comparatively the morphology of the Uzbek and Turkish languages, which we have prepared especially in order to create a parallel corpus of Uzhek-Turkish languages and make machine translations, in terms of inflectional suffixes. © 2022 IEEE.

关键词： Computational linguistics

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Chaotic Dynamics in the 2D System of Nonsmooth Ordinary Differential Equations

Iraqi Journal for Computer Science and Mathematics

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Iraqi Journal for computer Science and Mathematics 2022年第2期3卷 8-17页

作者： Rahman, Zain-Aldeen S.A. Jasim, Basil H. Al-Yasir, Yasir I.A. Dept. of Electrical Techniques Technical Institute/Qurna Southern Technical University Basra Iraq Dept. of Electrical Engineering College of Engineering University of Basrah Basra Iraq School of Electrical Engineering and Computer Science Faculty of Engineering and Informatics University of Bradford BradfordBD7 1DP United Kingdom

Over the last decade, the chaotic behaviors of dynamical systems have been extensively explored. Recently, discovering or developing a 2D system of ordinary differential equations (ODEs) capable of exhibiting chaotic dynamical behaviors is an attractive research topic. In this study, a chaotic system with a 2D system of nonsmooth ODEs has been developed. This system is can exhibit chaotic dynamical behaviors. Its main dynamical behaviors, including time-series trajectories, phase portraits of attractors, and equilibria and their stability, have been investigated. The developed system has been verified by an excessive variety of fascinating chaotic behaviors, such as chaotic attractor, symmetry, sensitivity to initial conditions (ICs), fractal dimension, autocorrelation, power spectrum, Lyapunov exponent, and bifurcation diagram. Analytical and numerical simulations are used to study the dynamical behaviors of such a system. The developed system has extreme sensitivity to ICs, a fractal dimension of more than 1.8 and less than 2.05, an autocorrelation fluctuating randomly about an average of zero, a broadband power spectrum, and one positive Lyapunov exponent. The obtained numerical simulation results have proven the capability of the developed 2D system for exciting chaotic dynamical behaviors. © 2022 Autoctonía. Revista de Ciencias Sociales e Historia. All rights reserved.

关键词： Fractal dimension

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Real Time Sound Source Localization Using von-Mises ResNet

Real Time Sound Source Localization Using von-Mises ResNet

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2024 IEEE/SICE International Symposium on System Integration, SII 2024

作者： Bozkurtlar, Mert Yen, Benjamin Itoyama, Katsutoshi Nakadai, Kazuhiro School of Engineering Tokyo Institute of Technology Department of Systems and Control Engineering 2-12-1 Ookayama Meguro-ku Tokyo152-8552 Japan Istanbul Technical University Faculty of Management Department of Industrial Engineering Maçka/Beşiktaş/Harbiye Istanbul34367 Turkey Istanbul Technical University Faculty of Computer and Informatics Engineering Department of Computer Engineering Sariyer/Ayazaga Istanbul34485 Turkey The Nakadai Lab Japan Honda Research Institute Japan Co. Ltd 8-1 Honcho Saitama Wako351-0188 Japan

ISBN: (纸本)9798350312072

This paper addresses the task of learning periodic information using deep neural networks to achieve real-time, environment-independent sound source localization. Previous papers showed phase data is the most significant cue in sound source localization tasks and the proposed vM-B DNN was validated to be able to handle such periodic information using on synthesized data. However, they haven't shown its effectiveness and robustness in realistic use cases. This paper introduces a more complex model based on residual networks and adapts vM-B activation function for convolutional layers for use cases that require real-time predictions in dynamically changing environments. © 2024 IEEE.

关键词： Deep neural networks

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