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International Conference on Intelligent Transportation

作者： Benedict Flade Simon Kohaut Julian Eggert Devendra Singh Dhami Kristian Kersting Honda Research Institute Europe GmbH Offenbach Germany Department of Computer Science Artificial Intelligence and Machine Learning Lab TU Darmstadt Darmstadt Germany Department of Mathematics and Computer Science Uncertainty in Artificial Intelligence Group TU Eindhoven Eindhoven MB Netherlands Hessian AI Centre for Cognitive Science German Center for Artificial Intelligence (DFKI)

ISBN: (数字)9798331505929

ISBN: (纸本)9798331505936

The growing complexity of intelligent transportation systems and their applications in public spaces has increased the demand for expressive and versatile knowledge representation. While various mapping efforts have achieved widespread coverage, including detailed annotation of features with semantic labels, it is essential to understand their inherent uncertainties, which are commonly underrepresented by the respective geographic information systems. Hence, it is critical to develop a representation that combines a statistical, probabilistic perspective with the relational nature of geospatial data. Further, such a representation should facilitate an honest view of the data's accuracy and provide an environment for high-level reasoning to obtain novel insights from task-dependent queries. Our work addresses this gap in two ways. First, we present Statistical Relational Maps (StaR Maps) as a representation of uncertain, semantic map data. Second, we demonstrate efficient computation of StaR Maps to scale the approach to wide urban spaces. Through experiments on real-world, crowd-sourced data, we underpin the application and utility of StaR Maps in terms of representing uncertain knowledge and reasoning for complex geospatial information.

关键词： Vocabulary Uncertainty Navigation Semantics Urban areas Stars Probabilistic logic Cognition Geospatial analysis Computational efficiency

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Avoidance of Ship Collision Using Deep Reinforcement learning with Bi-Directional Long Short-Term Memory in Continuous Action Spaces

Avoidance of Ship Collision Using Deep Reinforcement Learnin...

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Data science and Network Security (ICDSNS), International Conference on

作者： Revatthy Krishnamurthy Myasar Mundher Adnan Sunil Kumar V T. Aditya Sai Srinivas Srinivas Aluvala Artificial Intelligence & Machine Learning Department Rajalakshmi Engineering College Chennai India Department of Computers Techniques Engineering College of Technical Engineering The Islamic university Najaf Iraq Department of Artificial intelligence and Machine learning Nitte Meenakshi Institute of Technology Bengaluru India Artificial Intelligence and Machine Learning Jayaprakash Naryan College of Engineering Mahabubnagar India School of Computer Science and Engineering SR University Warangal India

ISBN: (数字)9798350373110

ISBN: (纸本)9798350373127

Recent days, with the globalization of the world economy the ships count are increasing for marine transportation and the Waterways are growing increasingly overcrowded than previous. This situation may leads to problem of collision of ships which may cause losing of life and damage for property and to nature. Many automatically collision models of ships are implemented but many focused only on the ship-ship encounter situation only. By using a grid sensor which is virtual sensor, agents of Deep Reinforcement learning (DRL) classify approach of a multiple ships. This framework introduces an automatically collision detection algorithm for ships using DRL in continuous action spaces. DRL is used for avoidance of collision with a maximum distance of safe passing between ships. A unique method is developed named inside Obstacle Zone by Target (OZT) used to change learning capability that expands the OZT. Using Bi-directional Long Short-Term Memory (BI-LSTM) cell, network is redesigned and continuous action spaces training is carried out to train a model with longer safe distance of ships. In collision detection model that the bow cross range is effective for COLREGs compliant collision avoidance that is proposed in this model. The propose model also validates a scenario that included more ships and have passed that Imazu problem. The proposed BI-LSTM model achieved 80.43 % of accuracy, 95.67% of precision, 85.89% of recall and 93.54% of f1 score values.

关键词： Accuracy Biological system modeling Bidirectional control Power system stability Deep reinforcement learning Stability analysis Vectors Marine vehicles Collision avoidance Long short term memory

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Rescaled Hinge Loss Function based Convolutional Neural Network for Identification of Art Forgery by Image Analysis 1

Rescaled Hinge Loss Function based Convolutional Neural Netw...

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1st International Conference on Software, Systems and Information Technology, SSITCON 2024

作者： Sundravadivelu, K. Karthika, S. Al-Fatlawy, Ramy Riad Monisha, Jothi R. Sundari, Mandapati Venkata Rama Madurai Kamaraj University School of Information Technology Department of Computer Science Madurai India Bannari Amman Institute of Technology Department of Artificial Intelligence and Machine Learning Erode India The Islamic University College of Technical Engineering Department of Computers Techniques Engineering Najaf Iraq New Prince Shri Bhavani College of Engineering and Technology Department of Computer Science and Engineering Chennai India Hyderabad India

ISBN: (纸本)9798350352931

The rapid growth of Information Technology (IT) as well as computer science becomes an important evolution in marketable painting applications. The systematic authentication of paintings embraces the implication into the monarchy of art gathering. Performing the Convolutional Neural Network (CNN) approach for the identification of the reliable as well as forged painting arts according to the colour images is a feasible but trivial selection. However, this is challenging due to variations in artistic styles, textures, and subtle details which ambiguous forgery detection. Hence, this research proposes the Rescaled Hinge Loss Function based CNN (RHLF-CNN) approach for the identification of art forgery. The RHLF improves the model's capability to discriminate between genuine and forged artworks, leads to more precise classification. The high-definition and hyperspectral image dataset of Chinese characters is utilized to estimate the effectiveness of the RHLF-CNN approach. The Local Binary Pattern (LBP) approach is used for the feature extraction process. The Chi-square approach is utilized for extracting the relevant features from the extraction process. The experimental results demonstrates that the proposed RHLF-CNN approach attains the better accuracy of 93.16% as compared to the existing approaches like Pulse-Coupled Neural Network (PCNN). © 2024 IEEE.

关键词： Convolution

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Genomic Insights into the Classification and Identification of Plasmodium Parasites 1

Genomic Insights into the Classification and Identification ...

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1st International Conference on Innovations in Communications, Electrical and computer Engineering, ICICEC 2024

作者： Senthil, K. Yuktha Varshika, J. Gopinath, M. Pooja Sri, V. Vishwa, S. Monikaa, R. Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Department of Computer Science and Engineering Chennai602105 India Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Department of Artificial Intelligence and Machine Learning Chennai602105 India Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Department of Information Technology Chennai602105 India Easwari Engineering College Department of Computer Science and Engineering Chennai602105 India

ISBN: (数字)9798350376517

ISBN: (纸本)9798350376517

Malaria is a contagious disease caused by plasmodium parasites transmitted to humans through infected female anopheles mosquito bites. The parasites invade Red Blood Cells (RBCs), impairing oxygen transport and potentially leading to respiratory distress and serious complications. Accurate and timely detection of plasmodium-infected cells is crucial for effective treatment and disease management. This study explores the application of Feed Forward Neural Network (FFNN) for the classification of plasmodium parasite-infected cells versus uninfected cells. Our Work Leverages a dataset comprising 27,558 colored images. We preprocess the data using Bilateral filtering, Histogram equalization, and Sobel filters to enhance model robustness. Our FFNN model, characterized by multiple hidden layers, and activation functions, is trained to discern subtle differences between infected and uninfected cells. Gaussian filter is used to highlight and detect the parasitized regions. The model's performance is evaluated using classification rate, precision, Log Loss etc. Initial results demonstrate that our model classifies the parasitized and uninfected cells with a classification rate of 97.5%, precision of 97.62, and log loss of 1.857. © 2024 IEEE.

关键词： Wiener filtering

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Sample complexity of posted pricing for a single item 24

Sample complexity of posted pricing for a single item

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Proceedings of the 38th International Conference on Neural Information Processing Systems

作者： Billy Jin Thomas Kesselheim Will Ma Sahil Singla Cornell University School of Operations Research and Information Engineering University of Bonn Institute of Computer Science and Lamarr Institute for Machine Learning and Artificial Intelligence Columbia University Graduate School of Business and Data Science Institute Georgia Tech School of Computer Science

ISBN: (纸本)9798331314385

Selling a single item to n self-interested buyers is a fundamental problem in economics, where the two objectives typically considered are welfare maximization and revenue maximization. Since the optimal mechanisms are often impractical and do not work for sequential buyers, posted pricing mechanisms, where fixed prices are set for the item for different buyers, have emerged as a practical and effective alternative. This paper investigates how many samples are needed from buyers' value distributions to find near-optimal posted prices, considering both independent and correlated buyer distributions, and welfare versus revenue maximization. We obtain matching upper and lower bounds (up to logarithmic factors) on the sample complexity for all these settings.

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Custom Transformer-Based Approach for Enhanced Bengali Automatic Speech Recognition 1

Custom Transformer-Based Approach for Enhanced Bengali Autom...

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1st International Conference on Innovations in Communications, Electrical and computer Engineering, ICICEC 2024

作者： Deepa, R. Jayalakshmi, V. Thilakavathy, P. Manikandan, G. Surendran, R. SRM Institute of Science and Technology School of Computing Department of Computing Technologies Chennai India SRM Institute of Science and Technology Department of Computer Science and Applications Ramapuram Chennai India Vels Institute of Science and Engineering Vels Institute of Science Technology and Advanced Studies Chennai India Anna University St. Joseph's College of Engineering Department of Artificial Intelligence and Machine Learning Chennai India Saveetha Institute of Medical and Technical Sciences Saveetha School of Engineering Department of Computer Science and Engineering Chennai India

ISBN: (数字)9798350376517

ISBN: (纸本)9798350376517

Automated speech recognition (ASR) systems struggle with Bengali, which is the fifth most spoken language. Bengali has a varied morphology, many dialects, and limited high-quality annotated voice data. Traditional voice recognition techniques work well however struggle with Bengali phonetics and syntax. This method eliminates the need for large amounts of tagged data and the difficulties of differentiating sounds in different languages, both issues are addressed by this procedure. The research in this paper offers an Intellectual Bengali Speech Recognition System using Deep learning Techniques (IBSRS-DLT). The model in this system uses transformer architecture and attention processes to manage Bengali speech pattern long-range dependencies. The IBSRS-DLT outperforms standard models in recognition accuracy using a pre-trained transformer model modified for phonetic and lexical correctness and fine-tuned on Bengali datasets. After training on a vast Bengali speech corpus, real-world datasets and rigorous simulations confirm the system. IBSRS-DLT can be used for automatic transcription, voice-controlled devices, and Bengali virtual assistants, such applications are efficient and accessible. The simulation showed a considerable boost in recognition speed and a decrease in keyword mistakes compared to current methods. This paper shows that deep learning can solve linguistic issues in ASR systems, enabling scalable regional language detection. © 2024 IEEE.

关键词： Deep learning

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Sentiment Analysis Using machine learning Methods

Sentiment Analysis Using Machine Learning Methods

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2024 International Conference on Intelligent and Innovative Practices in Engineering and Management, IIPEM 2024

作者： Badholia, Abhishek Diwan, Tarun Dhar Narooka, Preeti Khatkale, Pravin B Vishnoi, Ankit Kaushik, Keshav Shri Shankaracharya Institute of Professional Management and Technology Department of Data Science Raipur India Atal Bihari Vajpayee University Bilaspur India Manipal University Artificial Intelligence and Machine Learning School of Computer and Engineering Jaipur India Sanjivani University Maharashtra Kopargaon India Graphic Era Deemed to Be University Department of Computer Science and Engineering Uttarakhand Dehradun India Amity University Punjab Amity School of Engineering and Technology Mohali India

ISBN: (纸本)9798350390049

machine learning (ML) will be utilized to evaluate sentiment analysis in this project. This study aims to do this. Sentiment analysis is a popular natural language processing area. This is a natural language processing subfield. This section identifies textual senses. Due to the growing availability of digital text across platforms, sentiment analysis is now vital for many applications. This is because sentiment analysis has become essential. This technology might be used for market research, political analysis, and consumer feedback. A few instances. This study analyzes several machine learning methods, to name a few. This category includes SVM, Naive Bayes, Random Forest, and RNNs. To clarify, several datasets are used to assess these methodologies. To get more information. Preprocessing and feature engineering are crucial to sentiment analysis model construction, as this paper shows. The research emphasises these two phases. In addition, it provides insights into how these approaches are used in constantly changing digital situations. © 2024 IEEE.

关键词： Adversarial machine learning

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Life Expectancy Prediction using machine learning Technique – KNN

Life Expectancy Prediction using Machine Learning Technique ...

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Applied intelligence and Computing (AIC), IEEE World Conference on

作者： Preeti Narooka Aarushi Vashistha Priyanka Mitra Gulisha Derashri Department of Artificial Intelligence and Machine Learning School of Computer Science Engineering Manipal University Jaipur Rajasthan India Department of Computer Science and Engineering Jaipur Engineering College and Research Centre Jaipur India

ISBN: (数字)9798350384598

ISBN: (纸本)9798350384604

In the modern world, life expectancy turns out to be the most valuable factor in determining population’s health. Today, it is more crucial to predict the future because life is more precarious due to a variety of circumstances, including social, economic, environmental, educational, lifestyle, and health conditions. The machine learning algorithm KNN for life expectancy is presented in this work. The dataset gathered for this study is separated into a training set (70%) and a test set (30%), and the forecasts precision is examined. To avoid misclassification, KNN needs a larger sample size, and the best K are chosen more in accordance with cross-validation experience than with strong statistical theory.

关键词： Training Support vector machines Logistic regression machine learning algorithms Pandemics Biological system modeling Medical services Nearest neighbor methods Socioeconomics Random forests

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Factorised Neural Relational Inference for Particle Decay Reconstruction

Factorised Neural Relational Inference for Particle Decay Re...

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artificial intelligence, Networking and Information Technology (AINIT), International Seminar on

作者： Junjian Lu Siwei Liu Shangsong Liang School of Computer Science and Engineering Sun Yat-sen University China Department of Machine Learning Mohamed bin Zayed University of Artificial Intelligence The United Arab Emirates

ISBN: (数字)9798350385557

ISBN: (纸本)9798350385564

Investigating the interactions among particles in high-energy physics is essential for various tasks, such as reconstructing particle decays. The neural relational inference encoder model, capable of capturing inter-entity dependencies and system dynamics, has been recently introduced for this purpose. However, it faces limitations in accurately capturing diverse relations between complex particles. To address this, we propose the factorised neural relational inference (fNRI) encoder model, which factorises the inferred interaction graph into a multigraph, with each layer encoding a distinct relation. Additionally, we present a simplified version of our model that offers improved efficiency without compromising performance. Moreover, we explore three different inference strategies to leverage the learned relation representations effectively. We demonstrate on a simulated particle physics decay dataset that fNRI improves the reconstruction of the decay tree structure.

关键词： Training Seminars Representation learning High energy physics System dynamics Encoding Task analysis

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TAMBRIDGE: Bridging Frame-Centered Tracking and 3D Gaussian Splatting for Enhanced SLAM

arXiv

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arXiv 2024年

作者： Jiang, Peifeng Liu, Hong Li, Xia Wang, Ti Zhang, Fabian Buhmann, Joachim National Key Laboratory of General Artificial Intelligence Peking University Shenzhen Graduate School China Institute for Machine Learning Department of Computer Science ETH Zurich Switzerland

The limited robustness of 3D Gaussian Splatting (3DGS) to motion blur and camera noise, along with its poor real-time performance, restricts its application in robotic SLAM tasks. Upon analysis, the primary causes of these issues are the density of views with motion blur and the cumulative errors in dense pose estimation from calculating losses based on noisy original images and rendering results, which increase the difficulty of 3DGS rendering convergence. Thus, a cutting-edge 3DGS-based SLAM system is introduced, leveraging the efficiency and flexibility of 3DGS to achieve real-time performance while remaining robust against sensor noise, motion blur, and the challenges posed by long-session SLAM. Central to this approach is the Fusion Bridge module, which seamlessly integrates tracking-centered ORB Visual Odometry with mapping-centered online 3DGS. Precise pose initialization is enabled by this module through joint optimization of re-projection and rendering loss, as well as strategic view selection, enhancing rendering convergence in large-scale scenes. Extensive experiments demonstrate state-of-the-art rendering quality and localization accuracy, positioning this system as a promising solution for real-world robotics applications that require stable, nearreal-time performance. Our project is available at https://***/TAMBRIDGE/ Copyright © 2024, The Authors. All rights reserved.

关键词： Rendering (computer graphics)

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