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Emerging Smart Computing and informatics (ESCI), Conference on

作者： Zainab Malik Aisha Sir Elkhatem Gokhan Bilgin Dept. of Computer Engineering Yildiz Technical University Istanbul Turkey Dept. of Control and Automation Engineering Yildiz Technical University Istanbul Turkey

ISBN: (数字)9798331515683

ISBN: (纸本)9798331515690

Land use and land cover (LULC) classification is essential for understanding the impact of both human activities and natural processes on the Earth’s surface. This classification plays a critical role in deforestation, urban planning, and damage assessment. However, traditional machine learning techniques often face challenges in achieving consistent accuracy in diverse datasets. In this study, we implement transfer learning and Bayesian learning approaches to enhance the accuracy and robustness of LULC classification. Transfer learning leverages knowledge from previous classification problems, while Bayesian learning addresses uncertainties in the classification process. Using the Remote Sensing Image Classification Benchmark (RSI-CB128) dataset, the study evaluates the performance of Bayesian Convolutional Neural Networks (CNN), Mobile Neural Networks (MobileNet), Inception Neural Networks (InceptionNet), Densely Connected Convolutional Networks (DenseNet) and Efficient Neural Networks (EfficientNet). The study also investigates the impact of varying the Bayesian layers on model performance, finding that a medium number of layers optimally captures model uncertainty. The results indicate that the transfer learning models, particularly MobileNet, achieved the highest accuracy of 97. 48% compared to 90. 33% for Bayesian CNN. These findings suggest that transfer learning techniques are highly effective for LULC classification, providing a reliable method for practical applications in remote sensing.

关键词： Deforestation Accuracy Uncertainty Computational modeling Transfer learning Neural networks Urban planning Land surface Bayes methods Convolutional neural networks

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Analyzing the concept of technical debt in the context of agile software development: A systematic literature review

arXiv

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arXiv 2024年

作者： Behutiye, Woubshet Nema Rodriguez, Pilar Oivo, Markku Tosun, Ayşe University of Oulu Oulu Finland Faculty of Computer Engineering and Informatics Istanbul Technical University Turkey

Context: technical debt (TD) is a metaphor that is used to communicate the consequences of poor software development practices to non-technical stakeholders. In recent years, it has gained significant attention in agile software development (ASD). Objective: The purpose of this study is to analyze and synthesize the state of the art of TD, and its causes, consequences, and management strategies in the context of ASD. Research Method: Using a systematic literature review (SLR), 38 primary studies, out of 346 studies, were identified and analyzed. Results: We found five research areas of interest related to the literature of TD in ASD. Among those areas, "managing TD in ASD" received the highest attention, followed by "architecture in ASD and its relationship with TD". In addition, eight categories regarding the causes and five categories regarding the consequences of incurring TD in ASD were identified. "Focus on quick delivery" and "architectural and design issues" were the most popular causes of incurring TD in ASD. "Reduced productivity", "system degradation" and "increased maintenance cost" were identified as significant consequences of incurring TD in ASD. Additionally, we found 12 strategies for managing TD in the context of ASD, out of which "refactoring" and "enhancing the visibility of TD" were the most significant. Conclusion: The results of this study provide a structured synthesis of TD and its management in the context of ASD as well as potential research areas for further investigation. © 2024, CC BY-NC-ND.

关键词： Software design

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Classifying Turkish Trade Registry Gazette Announcements 7

Classifying Turkish Trade Registry Gazette Announcements

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

作者： Demirtas, Irem Nur Arslan, Secil Eyigit, Gulsen Istanbul Technical University Faculty of Computer and Informatics Engineering Istanbul Turkey Data Science and Artificial Intelligence Prometeia Spa Istanbul Merkez Şubesi Istanbul Turkey

ISBN: (数字)9781665470100

ISBN: (纸本)9781665470100

Turkish Trade Registry Gazette is an important source of information in many sectors such as banking and telecommunication. Although the newspaper is publicly available, the data is hard to acquire, and announcements are offered in image format. It is possible to search for a specific announcement a company has, but there exist many other unrelated announcements in the image returned. This poses multiple challenges in the way of information extraction. Due to the structure of the documents in these images, it is hard to perform OCR directly. Moreover, even in the case where the text is extracted, the announcement boundaries must be detected to split the announcements within the page. Once the announcements are extracted, the announcement of the searched company should be matched. Since no information regarding the surrounding announcements is given as a result of the query, these announcements should also be categorized to detect any events of interest other companies may have. In this work, we address all of these problems and present a pipeline that includes image processing, OCR, announcement splitting, and document classification steps. © 2022 IEEE.

关键词： Commerce

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Root-zone Soil Moisture Nowcasting using Context Aware Machine Learning

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

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International Conference on Agro-Geoinformatics

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

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

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Modeling Hot Rolling Process Forces Using Bio-Inspired Metaheuristic Search Algorithm 2

Modeling Hot Rolling Process Forces Using Bio-Inspired Metah...

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2nd International Conference of Intelligent Methods, Systems and Applications, IMSA 2024

作者： Sheta, Alaa Braik, Malik Oznergiz, Ertan Elashmawi, Walaa H. Rausch, Peter Othman, Emad S. Southern Connecticut State University Department of Computer Science New HavenCT United States Al-Balqa Applied University Computer Science Department Salt Jordan Istanbul Technical University Mechanical Engineering Faculty Istanbul Turkey Misr International University Dept of Computer Science Cairo Egypt George Simon Ohm Univ. of Applied Sciences Computer Science Department Nuremberg Germany AL-Shorouk Academy Management Information System Dept. Cairo Egypt

ISBN: (纸本)9798350362633

This research introduces the application of an innovative bio-inspired metaheuristic technique, termed the Crow Search Algorithm (CSA), to model a crucial industrial process - hot rolling manufacturing. Inspired by the foraging patterns of crows, the CSA algorithm has demonstrated its prowess in solving diverse optimization challenges. In the context of this study, the CSA algorithm is harnessed to fine-tune the parameters of a simulation model focused on predicting the force exerted during a hot rolling procedure. The proposed model takes into consideration a range of influential factors, including the initial temperature (Ti), width (Ws), carbon equivalent (Ce), gauge (hi), draft (i), and roll diameter (R). The findings underscore the CSA's capability to deliver an exceptional modeling performance characterized by swift convergence and high solution quality. By getting along very well with the proposed model with the CSA algorithm, a robust and efficient avenue to optimize the hot rolling process emerges, with the potential for expansion into other manufacturing domains. The computational and simulation results demonstrated that the proposed approach-based CSA outperformed different meta-heuristic search algorithms, such as the Salp Swarm Algorithm (SSA), Dandelion Optimizer (DO), Particle Swarm Optimization (PSO), Gray Wolf Optimizer (GWO), and Moth-Flame Optimization (MFO), in all test cases. The CSA has achieved the highest coefficient of determination (R2), equal to 0.97244, and the lowest mean squared error (MSE), equal to 1904.97, compared to its opponent algorithms. © 2024 IEEE.

关键词： Particle swarm optimization (PSO)

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Real time detection of acoustic anomalies in industrial processes using sequential autoencoders

Real time detection of acoustic anomalies in industrial proc...

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作者： Bayram, Barış Duman, Taha Berkay Ince, Gökhan Faculty of Computer and Informatics Engineering Istanbul Technical University Istanbul Turkey

Development of intelligent systems with the pursuit of detecting abnormal events in real world and in real time is challenging due to difficult environmental conditions, hardware limitations, and computational algorithmic restrictions. As a result, degradation of detection performance in dynamically changing environments is often encountered. However, in the next-generation factories, an anomaly detection system based on acoustic signals is especially required to quickly detect and interfere with the abnormal events during the industrial processes due to the increased cost of complex equipment and facilities. In this study we propose a real time Acoustic Anomaly Detection (AAD) system with the use of sequence-to-sequence Autoencoder (AE) models in the industrial environments. The proposed processing pipeline makes use of the audio features extracted from the streaming audio signal captured by a single-channel microphone. The reconstruction error generated by the AE model is calculated to measure the degree of abnormality of the sound event. The performance of Convolutional Long Short-Term Memory AE (Conv-LSTMAE) is evaluated and compared with sequential Convolutional AE (CAE) using sounds captured from various industrial manufacturing processes. In the experiments conducted with the real time AAD system, it is shown that the Conv-LSTMAE-based AAD demonstrates better detection performance than CAE model-based AAD under different signal-to-noise ratio conditions of sound events such as explosion, fire and glass breaking. © 2020 John Wiley & Sons, Ltd

关键词： Anomaly detection

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LEGALTURK OPTIMIZED BERT FOR MULTI-LABEL TEXT CLASSIFICATION AND NER

arXiv

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arXiv 2024年

作者： Zeidi, Farnaz Amasyali, Mehmet Fatih Erol, Çigdem Department of Informatics Istanbul University Istanbul Turkey Department of Computer Engineering Yildiz Technical University Istanbul Turkey Faculty of Science Department of Computer Sciences Istanbul University Istanbul Turkey

The introduction of the Transformer neural network, along with techniques like self-supervised pretraining and transfer learning, has paved the way for advanced models like BERT. Despite BERT's impressive performance, opportunities for further enhancement exist. To our knowledge, most efforts are focusing on improving BERT's performance in English and in general domains, with no study specifically addressing the legal Turkish domain. Our study is primarily dedicated to enhancing the BERT model within the legal Turkish domain through modifications in the pre-training phase. In this work, we introduce our innovative modified pre-training approach by combining diverse masking strategies. In the fine-tuning task, we focus on two essential downstream tasks in the legal domain: name entity recognition and multi-label text classification. To evaluate our modified pre-training approach, we fine-tuned all customized models alongside the original BERT models to compare their performance. Our modified approach demonstrated significant improvements in both NER and multi-label text classification tasks compared to the original BERT model. Finally, to showcase the impact of our proposed models, we trained our best models with different corpus sizes and compared them with BERTurk models. The experimental results demonstrate that our innovative approach, despite being pre-trained on a smaller corpus, competes with BERTurk. © 2024, CC BY-NC-SA.

关键词： Classification (of information)

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Obstacle Identification and Ellipsoidal Decomposition for Fast Motion Planning in Unknown Dynamic Environments

Obstacle Identification and Ellipsoidal Decomposition for Fa...

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IEEE International Conference on Robotics and Automation (ICRA)

作者： Mehmetcan Kaymaz Nazım Kemal Ure Faculty of Aeronautics and Astronautics Istanbul Technical University Istanbul Turkey Faculty of Computer and Informatics Engineering Istanbul Technical University Istanbul Turkey

Collision avoidance in the presence of dynamic obstacles in unknown environments is one of the most critical challenges for unmanned systems. In this paper, we present a method that identifies obstacles in terms of ellipsoids to estimate linear and angular obstacle velocities. Our proposed method is based on the idea of any object can be approximately expressed by ellipsoids. To achieve this, we propose a method based on variational Bayesian estimation of Gaussian mixture model, the Kyachiyan algorithm, and a refinement algorithm. Our proposed method does not require knowledge of the number of clusters and can operate in real-time, unlike existing optimization-based methods. In addition, we define an ellipsoid-based feature vector to match obstacles given two timely close point frames. Our method can be applied to any environment with static and dynamic obstacles, including ones with rotating obstacles. We compare our algorithm with other clustering methods and show that when coupled with a trajectory planner, the overall system can efficiently traverse unknown environments in the presence of dynamic obstacles.

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A New Segmentation Framework for Arabic Handwritten Text Using Machine Learning Techniques

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computers, Materials & Continua 2021年第8期68卷 2727-2754页

作者： Saleem Ibraheem Saleem Adnan Mohsin Abdulazeez Zeynep Orman Department of Information Technology Technical Informatics College of AkreDuhok Polytechnic UniversityDuhok42004Kurdistan RegionIraq Department of Computer Engineering Faculty of EngineeringIstanbul University-CerrahpasaIstanbul34320Turkey

The writer identification(WI)of handwritten Arabic text is now of great concern to intelligence agencies following the recent attacks perpetrated by known Middle East terrorist *** is also a useful instrument for the digitalization and attribution of old text to other authors of historic studies,including old national and religious *** this study,we proposed a new affective segmentation model by modifying an artificial neural network model and making it suitable for the binarization stage based on *** modified method is combined with a new effective rotation model to achieve an accurate segmentation through the analysis of the histogram of binary ***,propose a new framework for correct text rotation that will help us to establish a segmentation method that can facilitate the extraction of text from its *** projections and the radon transform are used and improved using machine learning based on a co-occurrence matrix to produce binary *** training stage involves taking a number of images for model *** images are selected randomly with different angles to generate four classes(0–90,90–180,180–270,and 270–360).The proposed segmentation approach achieves a high accuracy of 98.18%.The study ultimately provides two major contributions that are ranked from top to bottom according to the degree of *** proposed method can be further developed as a new application and used in the recognition of handwritten Arabic text from small documents regardless of logical combinations and sentence construction.

关键词： Writer identification handwritten Arabic biometric systems artificial neural network segmentation skew detection model

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A Multivocal Review of MLOps Practices, Challenges and Open Issues

arXiv

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arXiv 2024年

作者： Eken, Beyza Pallewatta, Samodha Tran, Nguyen Khoi Tosun, Ayse Babar, Muhammad Ali Faculty of Computer and Information Sciences Sakarya University Turkey CREST - The Centre for Research on Engineering Software Technology The University of Adelaide Australia Faculty of Computer and Informatics Engineering Istanbul Technical University Turkey

With the increasing trend of Machine Learning (ML) enabled software applications, the paradigm of ML Operations (MLOps) has gained tremendous attention of researchers and practitioners. MLOps encompasses the practices and technologies for streamlining the resources and monitoring needs of operationalizing ML models. Software development practitioners need access to the detailed and easily understandable knowledge of MLOps workflows, practices, challenges and solutions to effectively and efficiently support the adoption of MLOps. Whilst the academic and industry literature on the MLOps has been growing rapidly, there have been relatively a few attempts at systematically synthesizing and analyzing the vast amount of existing literature of MLOps for improving ease of access and understanding. We conducted a Multivocal Literature Review (MLR) of 150 relevant academic studies and 48 gray literature to provide a comprehensive body of knowledge on MLOps. Through this MLR, we identified the emerging MLOps practices, adoption challenges and solutions related to various areas, including development and operation of complex pipelines, managing production at scale, managing artifacts, and ensuring quality, security, governance, and ethical aspects. We also report the socio-technical aspect of MLOps relating to diverse roles involved and collaboration practices across them through the MLOps lifecycle. We assert that this MLR provides valuable insights to researchers and practitioners seeking to navigate the rapidly evolving landscape of MLOps. We also identify the open issues that need to be addressed in order to advance the current state-of-the-art of MLOps. Copyright © 2024, The Authors. All rights reserved.

关键词： Machine learning

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