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International Symposium on Applied Machine Intelligence and informatics (SAMI)

作者： Pouria Maleki Abbas Ramazani Hassan Khotanlou Sina Ojaghi Milad Mousavi Alexey Kalinin Amir Mosavi Department of Electrical Engineering Bu-Ali Sina University Hamedan Iran Department of Computer Engineering Bu-Ali Sina University Hamedan Iran School of Computer and Electrical Engineering University of Tehran Tehran Iran K. N. Toosi University of Technology Tehran Iran Abylkas Saginov Karaganda Technical University Kazakistan John von Neumann Faculty of Informatics Obuda University Budapest Hungary Ludovika University of Public Service Budapest Hungary

The rapid urban population growth has intensified the challenges associated with urban and suburban traffic, necessitating effective traffic control and management. The efficient movement of emergency vehicles, particularly ambulances and fire trucks, has emerged as a critical concern. This article presents the Vehicle Dataset, a comprehensive benchmark for object detection, encompassing seven vehicle classes, including cars, motorcycles, buses, trucks, vans, ambulances, and fire trucks. The dataset, includes 29,759 meticulously labeled images obtained from freely available online sources, enables the identification of traffic patterns through deep neural networks. Notably, the dataset emphasizes the facilitation of emergency vehicle movement. The Vehicle Dataset in this study is divided into three subsets, with 25,369 images assigned for training, 2,896 for validation, and 1,494 for testing. Through the utilization of the dataset, object detection algorithms based on YOLO versions 5, 6, and 7 have been trained. Remarkably, YOLO version 7 has yielded outstanding results, achieving a final precision of 85% and a mAP of 85% at an IoU threshold of 0.5. Moreover, at IoU thresholds ranging from 0.5 to 0.9, a mAP of 64% has been attained. The Vehicle Dataset represents significant resource for researchers and practitioners in the transportation and traffic management field. Its inclusion of emergency vehicles such as ambulances and fire trucks contribute to its unique value. This article presents a detailed exploration of the dataset, underscoring its significance in advancing object detection methodologies.

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Design of an ice detector for electric tramways 15

Design of an ice detector for electric tramways

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15th IEEE International Workshop of Electronics, Control, Measurement, Signals and their application to Mechatronics, ECMSM 2021

作者： Bilek, Petr Horak, Marcel Jandura, Pavel Mach, Ondrej Richter, Ales Institute of Mechatronics and Computer Engineering Technical University of Liberec Faculty of MechatronicsInformatics and Interdisciplinary Studies Liberec Czech Republic Technical University of Liberec Faculty of Engineering Department of Glass Producing Machines and Robotics Liberec Czech Republic

ISBN: (纸本)9781538617571

The article describes design and development of an ice detection device that uses a real 1 m part of an electric copper trolley wire for its operation. The wire part is oriented in the same direction as the trolley wire in the given place and thus a high compliance of the ice sensor with real conditions (spatial dimensions, orientation and heat capacity) is achieved. An ice meter equipped with this sensor can determine the robustness and duration of ice phenomena on trolley wires much more accurately and uses this information to deduce the risk of endangering public transport. The ice detector works on the principle of strain gauge load cell weighing. For its own diagnostics, the device can also measure temperature and relative humidity and is equipped with heating wires so that the movable parts of the device do not freeze. © 2021 IEEE.

关键词： Wire

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Implementation of Preprocessing in Text Summarization Techniques for Indonesian Language Documents Using the Flax T5 Approach

Implementation of Preprocessing in Text Summarization Techni...

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International Conference on Cyber and IT Service Management (CITSM)

作者： Arif Ridho Lubis Habibi Ramdani Safitri Irvan Muharman Lubis Al-Khowarizmi Okvi Nugroho Department of Computer Engineering and Informatics Politeknik Negeri Medan Medan Indonesia Department of Mathematic Education Universitas Muhammadiyah Sumatera Utara Medan Indonesia School of Industrial Engineering Telkom University Bandung Indonesia Department of Information Technology Universitas Muhammadiyah Sumatera Utara Medan Indonesia Kulkas IT Medan Indonesia

As information develops, many individuals or communities change their habits in obtaining information, which is usually consumed in large quantities, so that sometimes it is difficult to understand, it becomes easier to get information in summary form, so this research applies the text summarization technique to Indonesian language documents using the Flax T5 model. is the development of transformer-based google. The results of this study are the use of parameters from the Flax T5 model that can affect the performance of text summaries, evidenced by the number of epochs 20, batch size 64 producing ROUGE-1 of 0.24. the ROUGE-2 value is 0.3 and the ROUGE-L value is 0.31, while the epoch value is 60, batch size 128 makes ROUGE-1 0.35, ROUGE-2 value is 0.38 and the ROUGE-L value is 0.36. while the epoch value of 100 and the batch size value of 256 produce a ROUGE-1 value of 0.5, a ROUGE-2 value of 0.54, and a ROUGE-L value of 0.55. However, this model has limitations in text lengths of more than 150 because it will make the training time so long that it is highly recommended not to use text lengths that exceed 150 words.

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Interpretable temporal graph neural network for prognostic prediction of Alzheimer's disease using longitudinal neuroimaging data

Interpretable temporal graph neural network for prognostic p...

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2021 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2021

作者： Kim, Mansu Kim, Jaesik Qu, Jeffrey Huang, Heng Long, Qi Sohn, Kyung-Ah Kim, Dokyoon Shen, Li Catholic University of Korea Department of Artificial Intelligence Bucheon Korea Republic of Ajou University Department of Computer Engineering Suwon Korea Republic of University of Pennsylvania School of Engineering and Applied Sciences Philadelphia United States University of Pittsburgh Department of Electrical and Computer Engineering Pittsburgh United States University of Pennsylvania Epidemiology Informatics Department of Biostatistics Philadelphia United States Ajou University Department of Artificial Intelligence Suwon Korea Republic of

ISBN: (纸本)9781665401265

Alzheimer's disease (AD) is a progressive neurodegenerative brain disorder characterized by memory loss and cognitive decline. Early detection and accurate prognosis of AD is an important research topic, and numerous machine learning methods have been proposed to solve this problem. However, traditional machine learning models are facing challenges in effectively integrating longitudinal neuroimaging data and biologically meaningful structure and knowledge to build accurate and interpretable prognostic predictors. To bridge this gap, we propose an interpretable graph neural network (GNN) model for AD prognostic prediction based on longitudinal neuroimaging data while embracing the valuable knowledge of structural brain connectivity. In our empirical study, we demonstrate that 1) the proposed model outperforms several competing models (i.e., DNN, SVM) in terms of prognostic prediction accuracy, and 2) our model can capture neuroanatomical contribution to the prognostic predictor and yield biologically meaningful interpretation to facilitate better mechanistic understanding of the Alzheimer's disease. Source code is available at https://***/JaesikKim/temporal-GNN. © 2021 IEEE.

关键词： Forecasting

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AlexNet Architecture Based Convolution Neural Network for Realtime Audio to Text Translator of Bisindo Hand Sign

AlexNet Architecture Based Convolution Neural Network for Re...

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International Seminar on Application for Technology of Information and Communication (ISemantic)

作者： Dhiki Sujatmiko Christy Atika Sari Eko Hari Rachmawanto Andi Danang Krismawan Bilal R. Altamer Mohamed Ayad Alkhafaji Study Program in Informatics Engineering University of Dian Nuswantoro Semarang Indonesia Study Program in Animation University of Dian Nuswantoro Semarang Indonesia Department of Computer Science Mosul University Presidency University of Mosul Mosul Iraq Department of Computer Science National University of Science and Technology DhiQar Iraq

Deafness is a condition that results in the loss of hearing function, hindering the reception of information such as oral communication that relies on auditory senses. Consequently, individuals with hearing impairment experience communication barriers and may have limited or no ability to respond. One solution is the use of sign language. In Indonesia, there are two known sign languages: Sibi and Bisindo. Both serve the same function but differ in their style of movement and expression. Bisindo is considered more flexible as it conveys meaning based on the Indonesian language. However, the universal understanding of this language solution is still limited among many people. Therefore, a program is needed to facilitate translation between deaf individuals who use sign language and their counterparts who do not communicate through sign language. CNN (Convolutional Neural Network) is a deep learning algorithm used for training visual input data recognition by computer systems. There are various CNN-based architectures, and one of them is AlexNet. Based on the author's testing, the AlexNet architecture proves to be suitable for real-time sign language translation. The evaluation of the system involved 7,800 datasets and 520 testing instances, with an average accuracy of 468 correct translations. When averaged, the system achieved a 90% accuracy rate, representing a 100% increase in accuracy compared to previous approaches.

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Implementation of K-Means Algorithm for Clustering Corn Planting Feasibility Area in South Lampung Regency 3

Implementation of K-Means Algorithm for Clustering Corn Plan...

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3rd International Conference on Applied Sciences Mathematics and informatics, ICASMI 2020

作者： Aldino, A.A. Darwis, D. Prastowo, A.T. Sujana, C. Department of Informatics Faculty of Engineering and Computer Science Universitas Teknokrat Indonesia Jl. ZA. Pagar Alam No.9-11 Bandar Lampung Indonesia Department of Accounting Information System Faculty of Engineering and Computer Science Universitas Teknokrat Indonesia Jl. ZA. Pagar Alam No.9-11 Bandar Lampung Indonesia Department of Computer Engineering Faculty of Engineering and Computer Science Universitas Teknokrat Indonesia Jl. ZA. Pagar Alam No.9-11 Bandar Lampung Indonesia

South Lampung is a regency with the capital of Kalianda which has an area of 2,007.01 km2 that dominates the agricultural area. Based on the data of corn crops in the South Lampung Regency Agriculture Office through BPS (Central Bureau of Statistics), showing several areas with corn crops that vary in number. Therefore, a grouping of potential corn-producing regions is required to know which areas produce large or small amounts of corn. The distribution of crops is usually done based on the name of the corn-producing sub-district. The K-Means clustering method is one of the data mining methods that is non-hierarchical clustering that groups data in the form of one or more clusters. Data that have the same characteristics are grouped in one cluster and the remaining is grouped into another cluster so that the data that is in one cluster has a small degree of variation. So the authors tried to apply the K-Means clustering method from the corn crop data of the last 2 years to produce feasibility information from each sub-district. © Published under licence by IOP Publishing Ltd.

关键词： Data mining

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A Methodology for Drones to Learn How to Navigate and Avoid Obstacles Using Decision Trees 11

A Methodology for Drones to Learn How to Navigate and Avoid ...

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11th International Conference on Information, Intelligence, Systems and Applications, IISA 2020

作者： Daramouskas, Ioannis Perikos, Isidoros Hatzilygeroudis, Ioannis Lappas, Vaios J. Kostopoulos, Vasilios University of Patras Department of Computer Engineering and Informatics Patras Greece University of Patras Department of Mechanical Engineering and Aeronautics Patras Greece

ISBN: (纸本)9781665422284

Over the last decade, drones and UAVs have attracted great research interest mainly due to their abilities and their potential to be used in various applications and domains. One of the most important operations that Drones must perform efficiently concerns the navigation in real-world environments. This typically includes the ability of path planning and obstacle avoidance. It is crucial that drones have the ability to perform automatically and efficiently procedures related to the avoidance of objects while navigating in environments. In this work, we present a methodology for assisting a drone to navigate in unknown environments and avoid obstacles. The methodology is based on a training-by-human concept where the drone learns how to avoid obstacles by example cases that are provided to it and it is trained on them. The results are quite interesting and indicate that the methodology is efficient and can assist drones and robotics systems to learn how to navigate and avoid obstacles in environments. © 2020 IEEE.

关键词： Navigation

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Exploring Relationships Between Cryptocurrency News Outlets and Influencers’ Twitter Activity and Market Prices

arXiv

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arXiv 2024年

作者： Alizadeh, Meysam Asgari, Yasaman Samei, Zeynab Yari, Sara Dehghani, Shirin Kubli, Mael Zare, Darya Bermeo, Juan Diego Batzdorfer, Veronika Gilardi, Fabrizio Digital Democracy Lab University of Zurich Zurich Switzerland Digital Society Initiative Department of Mathematical Modeling and Machine learning University of Zurich Zurich Switzerland Department of Computer Science IPM Tehran Iran Department of Computer Engineering Allameh Tabataba’i University Tehran Iran Department of Informatics University of Zurich Zurich Switzerland Institute for Sociology and Computational Social Science Karlsruhe Institute of Technology Karlsruhe Germany

Academics increasingly acknowledge the predictive power of social media for a wide variety of events and, more specifically, for financial markets. Anecdotal and empirical findings show that cryptocurrencies are among the financial assets that have been affected by news and influencers’ activities on Twitter. However, the extent to which Twitter crypto influencer’s posts about trading signals and their effect on market prices is mostly unexplored. In this paper, we use LLMs to uncover buy and not-buy signals from influencers and news outlets’ Twitter posts and use a VAR analysis with Granger Causality tests and cross-correlation analysis to understand how these trading signals are temporally correlated with the top nine major cryptocurrencies’ prices. Overall, the results show a mixed pattern across cryptocurrencies and temporal periods. However, we found that for the top three cryptocurrencies with the highest presence within news and influencer posts, their aggregated LLM-detected trading signal over the preceding 24 hours granger-causes fluctuations in their market prices, exhibiting a lag of at least 6 hours. In addition, the results reveal fundamental differences in how influencers and news outlets cover cryptocurrencies. © 2024, CC BY.

关键词： Tweets

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Quality Grading of Beef using Deep Convolutional Neural Network 9

Quality Grading of Beef using Deep Convolutional Neural Netw...

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9th International Conference on Information Technology Trends, ITT 2023

作者： Sharma, Aarav Choudhury, Tanupriya Sarkar, Tanmay Mohanty, Madhusmita Bansal, Nikunj Mohanty, Sachi Nandan Informatics Cluster School of Computer Science Uttarakhand Dehradun2480007 India Cse Dept. Uttarakhand Dehradun India Graphic Era Hill University Cse Dept. Uttarakhand Dehradun248002 India Malda Polytechnic West Bengal State Council of Technical Education Govt. of West Bengal Department of Food Processing Technology Malda732102 India Andhra Pradesh Amaravati India VIT-AP University School of Computer Science & Engineering Andhra Pradesh Amaravati5222237 India

ISBN: (纸本)9798350327502

Beef is one of the most widely consumed meat, being an organic substance it is prone to degradation over time. In our paper we have proposed a Convolutional Neural Network model to grade a given sample of Beef and predict its freshness as good or bad. We have proposed a CNN model architecture along with a data (image) pre-processing pipeline that can be used to classify a given Beef sample. We have done comparative analysis between different pre-processing pipelines as well as other CNN based classification models and after training have achieved 99.63% test accuracy. © 2023 IEEE.

关键词： computer vision

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Energy Landscape Shaping for Robust Control of Atoms in Optical Lattices

arXiv

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arXiv 2025年

作者： Weidner, C.A. O'Neil, S.P. Jonckheere, E.A. Langbein, F.C. Schirmer, S.G. Quantum Engineering Technology Laboratories H. H. Wills Physics Laboratory Department of Electrical and Electronic Engineering University of Bristol BristolBS8 1FD United Kingdom Department of Electrical Engineering and Computer Science United States Military Academy West PointNY10996 United States Department of Electrical and Computer Engineering University of Southern California Los AngelesCA90007 United States School of Computer Science and Informatics Cardiff University CardiffCF24 4AG United Kingdom Faculty of Science & Engineering Physics Swansea University SwanseaSA2 8PP United Kingdom

Robust quantum control is crucial for realizing practical quantum technologies. Energy landscape shaping offers an alternative to conventional dynamic control, providing theoretically enhanced robustness and simplifying implementation for certain applications. This work demonstrates the feasibility of robust energy landscape control in a practical implementation with ultracold atoms. We leverage a digital mirror device (DMD) to shape optical potentials, creating complex energy landscapes. To achieve a desired objective, such as efficient quantum state transfer, we formulate a novel hybrid optimization approach that effectively handles both continuous (laser power) and discrete (DMD pixel activation) control parameters. This approach combines constrained quasi-Newton methods with surrogate models for efficient exploration of the vast parameter space. Furthermore, we introduce a framework for analyzing the robustness of the resulting control schemes against experimental uncertainties. By modeling uncertainties as structured perturbations, we systematically assess controller performance and identify robust solutions. We apply these techniques to maximize spin transfer in a chain of trapped atoms, achieving high-fidelity control while maintaining robustness. Our findings provide insights into the experimental viability of controlled spin transfer in cold atom systems. More broadly, the presented optimization and robustness analysis methods apply to a wide range of quantum control problems, offering a toolkit for designing and evaluating robust controllers in complex experimental settings. Copyright © 2025, The Authors. All rights reserved.

关键词： Robust control

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