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23rd IEEE International Conference on Machine Learning and Applications, ICMLA 2024

作者： Sérgio, B. Mastelini, Saulo M. Paula, Marcos B. S. Santos, Moisés R. Guarnier, Ewerton Filho, Donato S. Santos, Maria E. C. M. Fernandes, Thaina R. Rosa, Victor C. V. Volt Robotics Brazil Institute of Mathematics and Computer Science University of São Paulo Brazil University of Porto Faculty of Engineering Portugal Norte Energia S.A. Brazil

ISBN: (纸本)9798350374889

With its vast territory and diverse population distribution, Brazil is undergoing significant developments in its energy sector as it transitions towards a liberalized market. By 2030, the country aims to open the market to all consumers, enhancing competition, and liquidity in energy contracts, and fostering a dynamic trading environment. Effective market price monitoring is essential for understanding its dynamics and guiding decision-making processes in engineering applications. This paper aims to predict medium and long-term price movements in the free energy market. Our analysis of market price curves for different time horizons offers insights into energy market dynamics, enabling energy companies to optimize portfolio strategies and mitigate risks effectively. We consider forecast horizons of one quarter and one semester ahead, providing valuable insights for stakeholders navigating Brazil's evolving energy landscape. We utilize a combination of Machine Learning (ML)-based forecasters enriched by a broad set of exogenous data sources to provide forecasts for energy contracts whose effective date is Y2, Y3, and Y4 years ahead. In this study, we explore a simple yet effective and explainable alternative to using all the exogenous time series directly as input for our models. Namely, multiple related time series are aggregated into a series of histograms. Our proposed ensemble outperforms traditional statistical-based forecasting models, other ML solutions, and strong baselines in terms of resulting financial returns, particularly in the one-quarter prediction horizon, while also achieving competitive results for one semester ahead. © 2024 IEEE.

关键词： Energy Market Ensemble Forecasting Long-term Prices Machine Learning

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Deep Neural Network in Dermatology: Pioneering Skin Lesion Classification 6

Deep Neural Network in Dermatology: Pioneering Skin Lesion C...

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6th International Conference on Sustainable Technologies for Industry 5.0, STI 2024

作者： Fuad, Nasif Rakib, Abdur Al Noman, Abdullah Islam, Md Rafiqul Rahman, Mahbubur Refat, Md Abu Rumman Green University of Bangladesh Department of Computer Science and Engineering Dhaka Bangladesh Bangabandhu Sheikh Mujibur Rahman Digital University Department of Internet of Things and Robotics Engineering Dhaka Bangladesh

ISBN: (纸本)9798331531973

Skin cancer is one of the most common cancer types in the world. Early detection of skin cancer is essential as it significantly improves the treatment outcomes. However, recognizing skin lesions is challenging due to the high degree of similarity in lesion's appearance, location, color, and size. In response to this challenge, we developed a deep learning based method for skin lesion classification by ensembling two pre-trained CNN models called ResNet50 and DenseNet201 using the HAM10000 dataset. With the imbalance in the dataset, we used the Synthetic Minority Over-sampling Technique(SMOTE) and class weighted approach along with the data Augmentation approach. As a result, our study demonstrates that the proposed approach attained a remarkable 96.0% accuracy on the HAM10000 dataset, surpassing other cutting-edge methods. © 2024 IEEE.

关键词： Deep neural networks

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Enhanced Ship Classification in SAR Imagery using Fine-tuned DenseNet and Self-attention 15

Enhanced Ship Classification in SAR Imagery using Fine-tuned...

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15th International Conference on Computing Communication and Networking Technologies, ICCCNT 2024

作者： Chethan, Kosaraju Sai Lokesh, Vadlamudi Reddy, Kunta Vignaj Singh, Rimjhim Padam Srivastava, Smita Amrita Vishwa Vidyapeetham Amrita School of Computing Department of Computer Science and Engineering Bengaluru India Defense Research & Development Organization Centre for Artificial Intelligence & Robotics Bengaluru India

ISBN: (纸本)9798350370249

The categorization of ships in synthetic aperture radar (SAR) imagery is vital for civilian and military applications, including maritime management and reconnaissance. Deep learning techniques, such as Inception, DenseNet, Fusion Transformers, and EfficientNet, are used to extract multiscale information from SAR images, improving classification capabilities. Advanced solutions are necessary to address challenges in SAR imagery, such as speckle noise, varying illumination conditions, and complex sea clutter. The FUSAR-Ship dataset, which utilizes GF-3 SAR technology, offers a comprehensive collection of diverse scenarios including sea, land, coast, river, and islands. With over 5000 ship chips containing AIS messages and examples of various clutter types, FUSAR-Ship is a benchmark dataset for ship classification tasks. Our research involves thorough testing of advanced classification models using the FUSAR dataset. Through meticulous evaluation of a wide range of ship instances, scales, and maritime scenes, we aim to demonstrate the robustness and adaptability of these models. By refining their architectures and optimizing their parameters, we strive to enhance the capabilities of classification models and advance the performance of ship classification in SAR imagery. Our efforts contribute to enhancing maritime surveillance, management, and reconnaissance in both civilian and military domains. © 2024 IEEE.

关键词： DenseNet201 EfficientNetB1 InceptionV3 MobilenetV3Small RestNet50 Self Attention Ship small object detection Xception

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Comparative Study Between CNN and LSTM Approaches for Sign Language Recognition 6

Comparative Study Between CNN and LSTM Approaches for Sign L...

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6th IEEE Novel Intelligent and Leading Emerging sciences Conference, NILES 2024

作者： Edward, Phoebe Sameeh, Bola Alexan, Wassim Universities of Canada in Egypt Computer Science Dept. Egypt German International University Robotics and Automation Engineering Germany The German University in Cairo Communications Dept. Egypt

ISBN: (纸本)9798350378511

Image recognition technology has advanced to the point where it can help address challenges such as communication barriers faced by individuals with hearing impairments. Due to the societal rarity of learning and practicing Sign Languages (SLs), Machine Learning (ML) techniques have come to light to ease and automate this process through Sign Language Recognition (SLR) systems. This paper compares two SLR approaches;namely: Convolutional Neural Network (CNN) and Long Short-Term Memory (LSTM). These two ML approaches are used to accurately identify the English alphabet, numbers, and specific words in American Sign Language (ASL). Results show that both techniques achieve very good accuracy values using test dataset, however in real-time tests CNN has outperformed LSTM. © 2024 IEEE.

关键词： Convolutional neural networks

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Deep Learning Based FOD Detection For Runway Health Monitoring 1

Deep Learning Based FOD Detection For Runway Health Monitori...

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1st International Conference on AIML-Applications for Engineering and Technology, ICAET 2025

作者： Kumar, Amarjeet Mittal, Aanya Dixit, Abhishek Agrawal, Pooja School of Robotics Defence Institute of Advanced Technology Pune India Manipal University Jaipur Department of Computer Science Rajasthan India Defence Institute of Advanced Technology Department of Aerospace Pune India

ISBN: (纸本)9798350355611

This paper focuses on the safety of airport runways, which is crucial for aviation industries. Foreign Object Debris (FOD) i.e. objects like nails or bolts can cause significant harm to aircraft and potentially lead to catastrophic incidents and fatalities. This study delves into detecting objects on runways by using versions of the YOLO (You Only Look Once) model. Further, it also presents the differences in detecting objects and comparing the accuracy across a range of objects, paying attention to their ability to identify even the smallest things that have a potential danger. In order to identify the model variation and enable real-time runway surveillance, the proposed methodology considers the trade-off between detection speed and precision. The results illustrate the effectiveness of the latest YOLO model in object recognition over existing models, this can support the improvement of runway safety and the development of automated systems that monitor runway conditions. © 2025 IEEE.

关键词： Aircraft accidents

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Vision Transformers for Lung Segmentation on CXR Images

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SN computer science 2023年第4期4卷 414页

作者： Ghali, Rafik Akhloufi, Moulay A. Perception Robotics and Intelligent Machines (PRIME) Department of Computer Science Université de Moncton Moncton E1A 3E9 NB Canada

Accurate segmentation of the lungs in CXR images is the basis for an automated CXR image analysis system. It helps radiologists in detecting lung areas, subtle signs of disease and improving the diagnosis process for patients. However, precise semantic segmentation of lungs is considered a challenging case due to the presence of the edge rib cage, wide variation of lung shape, and lungs affected by diseases. In this paper, we address the problem of lung segmentation in healthy and unhealthy CXR images. Five models were developed and used in detecting and segmenting lung regions. Two loss functions and three benchmark datasets were employed to evaluate these models. Experimental results showed that the proposed models were able to extract salient global and local features from the input CXR images. The best performing model achieved an F1 score of 97.47%, outperforming recent published models. They proved their ability to separate lung regions from the rib cage and clavicle edges and segment varying lung shape depending on age and gender, as well as challenging cases of lungs affected by anomalies such as tuberculosis and the presence of nodules. © 2023, The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd.

关键词： Chest X-rays Deep learning Lung segmentation Medical image analysis Vision transformers

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Network Parameter Control in Cellular Networks through Graph-Based Multi-Agent Constrained Reinforcement Learning 19

Network Parameter Control in Cellular Networks through Graph...

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19th IEEE International Conference on Automation science and Engineering, CASE 2023

作者： Forsberg, Albin Larsson Nikou, Alexandros Feljan, Aneta Vulgarakis Tumova, Jana Stockholm Sweden Kth Division of Robotics Perception and Learning Department of Electrical Engineering and Computer Science Stockholm Sweden Digital Futures United States

ISBN: (纸本)9798350320695

Cellular networks are growing in complexity at increasing speed and the geographical locations in which they are deployed in are getting denser. Traditional control methods fall short in providing a scalable and dynamic way of adapting the network to new conditions. Distributed multiagent reinforcement learning successfully addresses scalability problems seen in centralized approaches. The question of achieving learning with constraint satisfaction in distributed systems is still left unanswered in the state-of-the-art. In this work, we aim to perform distributed multi-agent constrained reinforcement learning in order to learn a policy online while satisfying imposed constraints. We use a coordination graph to model the interactions between agents and decompose the global value function. A conservative safety critic is trained in parallel to evaluate the safety of proposed actions. Our method allows for separate training of both the critic and the value network independently of each other, and hence offers flexibility in how and when to train the different models. The results are compared to a baseline using no safety critic. Simulations show that the agents succeed in learning a policy that can satisfy the constraints, while still maximizing the objective. © 2023 IEEE.

关键词： Graphic methods

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Solving Large-Scale Pursuit-Evasion Games Using Pre-trained Strategies 37

Solving Large-Scale Pursuit-Evasion Games Using Pre-trained ...

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37th AAAI Conference on Artificial Intelligence, AAAI 2023

作者： Li, Shuxin Wang, Xinrun Zhang, Youzhi Xue, Wanqi Černý, Jakub An, Bo School of Computer Science and Engineering Nanyang Technological University Singapore Centre for Artificial Intelligence and Robotics Hong Kong Institute of Science & Innovation Chinese Academy of Sciences Hong Kong

ISBN: (纸本)9781577358800

Pursuit-evasion games on graphs model the coordination of police forces chasing a fleeing felon in real-world urban settings, using the standard framework of imperfect-information extensive-form games (EFGs). In recent years, solving EFGs has been largely dominated by the Policy-Space Response Oracle (PSRO) methods due to their modularity, scalability, and favorable convergence properties. However, even these methods quickly reach their limits when facing large combinatorial strategy spaces of the pursuit-evasion games. To improve their efficiency, we integrate the pre-training and fine-tuning paradigm into the core module of PSRO – the repeated computation of the best response. First, we pre-train the pursuer’s policy base model against many different strategies of the evader. Then we proceed with the PSRO loop and fine-tune the pre-trained policy to attain the pursuer’s best responses. The empirical evaluation shows that our approach significantly outperforms the baselines in terms of speed and scalability, and can solve even games on street maps of megalopolises with tens of thousands of crossroads – a scale beyond the effective reach of previous methods. Copyright © 2023, Association for the Advancement of Artificial Intelligence (***). All rights reserved.

关键词： Scalability

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Archive Video Stream Using AI

Archive Video Stream Using AI

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International Conference on Industrial Engineering, Applications and Manufacturing (ICIEAM)

作者： Anastasia P. Kostyukova Regina I. Mukhametyanova Institute of Computer Science Mathematics and Robotics Ufa University of Science and Technology Ufa Russian Federation

ISBN: (数字)9798331512194

ISBN: (纸本)9798331512200

The article is devoted to the use of artificial intelligence in the development of a video stream archiving system, which allows you to effectively organize the storage of data received from the ESP32Cam camera, as well as their recording in a database for subsequent viewing and processing. The work substantiates the relevance of creating such a system, with a detailed description of the video stream archiving process in the form of a functional model, which allows for a better understanding of all stages of this process. The development of client-server architecture, as well as individual system components, such as modules for data processing and storage, is considered. Particular attention is paid to the implementation of an Android application that provides convenient access to the video stream archiving system, making the use of technology more accessible and practical for the end user, as well as significantly simplifying interaction with the archive.

关键词： Systems architecture Prototypes Streaming media Cameras Web servers Real-time systems Recording Security Reliability Artificial intelligence

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Digital Transformation of a Scientific and Technical Library at a University: Analysis of Tasks Using Decision Tree

Digital Transformation of a Scientific and Technical Library...

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International Conference on Control Systems, Mathematical Modelling, Automation and Energy Efficiency (SUMMA)

作者： Anastasia Karamzina Svetlana Silnova Institute of Computer Science Mathematics and Robotics UFA University of Science and Technology Ufa Russia

ISBN: (数字)9798331532178

ISBN: (纸本)9798331532185

The paper proposes an approach to analyzing the tasks of digital transformation of scientific and technical libraries in universities based on a decision tree. The results of applying this approach to identifying groups of tasks that allow for the formation of a well-reasoned roadmap taking into account the specifics of the object of digital transformation are presented. An example of building a roadmap for integrating information library systems of the university into a single information space and transforming information resource management processes is given.

关键词： Information resources Digital transformation Process control Organizations Libraries Mathematical models Energy efficiency Decision trees Object recognition Testing

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