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Deep Learning based Brain Tumor Classification for MR Images using ResNet50 12

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Deep Learning based Brain Tumor Classification for MR Images...

12th International Symposium on Digital Forensics and Security, ISDFS 2024

作者： Kokcam, Omer Mirac Boyaci, Aytug Colak, Muhammed Emre Firat University Faculty of Technology Department of Software Engineering Elaziǧ Turkey National Defence University Department of Computer Engineering Air Force Academy İstanbul Turkey Firat University Faculty of Engineering Department of Artificial Intelligence and Data Science Engineering Elaziǧ Turkey

ISBN: (纸本)9798350330366

Brain tumors are abnormal cell growths that occur in various parts of the brain, and the accurate classification of these tumors plays a critical role in determining treatment methods. Classification and diagnosis of brain tumors based on artificial intelligence and deep learning models have been made possible due to advances in medical image processing technologies. For instance, such technologies facilitate quick detection of tumors by health experts thereby improving early diagnosis rate and hence enhancing treatment outcomes. Importantly, however, one considers improvement or breakthroughs in artificial intelligence-based classification systems as a crucial step forward towards brain tumor diagnosis as well as its treatment strategy. In this study we use Google Research's ImageNet-21k pre-trained ResNet50 model for classifying brain tumor cases. This model is a deep learning-based image classification tool and is capable of learning from large data sets. The model is specifically trained for high-resolution image recognition and is designed to achieve high accuracy rates. The results showed a classification accuracy of 99.9 percent, which is an exceptional accuracy rate for this model. This accuracy rate indicates that the model is highly effective in detecting and classifying brain tumors. High accuracy rates allow for early diagnosis of patients' diseases and, as it is often said in medicine, 'Early Diagnosis Saves Lives'. This result is one of the many proofs of how effective artificial intelligence and deep learning are in medical image analysis. © 2024 IEEE.

关键词： Medical imaging

ASQ-IT: Interactive Explanations for Reinforcement-Learning Agents

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arXiv 2023年

作者： Amitai, Yotam Avni, Guy Amir, Ofra Faculty of Data & Decision Science Technion - I.I.T Faculty of Computer Science University of Haifa

As reinforcement learning methods increasingly amass accomplishments, the need for comprehending their solutions becomes more crucial. Most explainable reinforcement learning (XRL) methods generate a static explanation depicting their developers’ intuition of what should be explained and how. In contrast, literature from the social sciences proposes that meaningful explanations are structured as a dialog between the explainer and the explainee, suggesting a more active role for the user and her communication with the agent. In this paper, we present ASQ-IT – an interactive tool that presents video clips of the agent acting in its environment based on queries given by the user that describe temporal properties of behaviors of interest. Our approach is based on formal methods: queries in ASQ-IT’s user interface map to a fragment of Linear Temporal Logic over finite traces (LTLf), which we developed, and our algorithm for query processing is based on automata theory. User studies show that end-users can understand and formulate queries in ASQ-IT, and that using ASQ-IT assists users in identifying faulty agent behaviors. © 2023, CC BY-SA.

关键词： User interfaces

Pattern Discovery in an EEG database of Depression Patients: Preliminary Results 14

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Pattern Discovery in an EEG Database of Depression Patients:...

14th International Conference on Measurement, MEASUREMENT 2023

作者： Hlavackova-Schindler, Katerina Pacher, Christina Plant, Claudia Lazarenko, Mykola Palus, Milan Hlinka, Jaroslav Kathpalia, Aditi Brunovsky, Martin University of Vienna Data Mining and Machine Learning Research Group Faculty of Computer Science Vienna Austria Czech Academy of Sciences Institute of Computer Science Department of Complex Systems Prague Czech Republic National Institute of Mental Health Clinical Research Programme Klecany Czech Republic

ISBN: (纸本)9788097262976

The ability to predict response to medication treatment of depressed patients, either early in the course of therapy or before treatment even begins can avoid trials of ineffective therapy and save patients from prolonged intervals of suffering. Symptom alleviation requires 4-6 weeks after starting current antidepressive medication. Based on the data basis of the patients and their EEG before and on the 7th day of treatment we apply data mining, causal discovery and machine learning approaches to discover interactive patterns between patient's brain regions to separate the treatment responders from non-responders. In this paper we report the preliminary results of our international project "Learning Synchronization Patterns in Multivariate Neural Signals for Prediction of Response to Antidepressants"ongoing at the University of Vienna, the Czech Academy of sciences and the National Institute of Mental Health in the Czech Republic. © 2023 Institute of Measurement science, Slovak Academy o.

关键词： data mining

Navigating Energy: Unveiling Power Consumption Patterns in Autonomous Vehicles 1

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Navigating Energy: Unveiling Power Consumption Patterns in A...

1st International Conference on Emerging Technologies for Dependable Internet of Things, ICETI 2024

作者： Saif, Abdu Shah, Nor Shahida Mohd Ameen, Ali Alnoamani, Aiman Al-Qatf, Majjed Alsamhi, Saeed Hamood Department of Communication and Computer Engineering Faculty of Engineering and IT Taiz University P.O. Box 6803 Taiz Yemen Faculty of Engineering Technology Universiti Tun Hussein Onn Malaysia Johor Malaysia Faculty of Computer Science Lincoln University College Kuala Lumpur Malaysia Insight Center for Data Analytics University of Galway Galway Ireland

ISBN: (纸本)9798331533557

Energy consumption is critical in self-driving cars and is set to change how transportation works. In this paper, we address the challenges of vehicle energy consumption by examining how the location of self-driving cars affects their energy use over time, looking at two- and three-dimensional aspects. Using models and real-world data, we examine the energy usage patterns of vehicle systems within MATLAB to reveal insights into their design and operation. In this paper, the power dynamics of cars that significantly contribute to the discussions of practical and sustainable self-driving transportation are introduced. The main goal is to demonstrate how power management may be revolutionized for self-driving car systems, ensuring maximum performance and dependability in today’s dynamic mobility scene. The findings show that enhancing autonomous vehicles’ design and operational strategies ensures their reliability and optimizes performance in today’s rapidly evolving transportation landscape. © 2024 IEEE.

关键词： Autonomous vehicles

Large Language Model Federated Learning with Blockchain and Unlearning for Cross-Organizational Collaboration

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arXiv 2024年

作者： Zuo, Xuhan Wang, Minghao Zhu, Tianqing Yu, Shui Zhou, Wanlei Faculty of Data Science City University of Macau China School of Computer Science University of Technology Sydney Ultimo2007 Australia

Large language models (LLMs) have transformed the way computers understand and process human language, but using them effectively across different organizations remains still difficult. When organizations work together to improve LLMs, they face several main challenges. First, organizations hesitate to share their valuable data with others. Second, competition between organizations creates trust problems during collaboration. Third, new privacy laws require organizations to be able to delete specific data when requested, which is especially difficult when multiple organizations are learning from shared data. Traditional federated learning approaches do not address these interconnected challenges, particularly in scenarios where participants cannot fully trust each other or the central aggregator. To overcome these limitations, we propose a hybrid blockchain-based federated learning framework that uniquely combines public and private blockchain architectures with multiagent reinforcement learning. Our framework enables transparent sharing of model update through the public blockchain while protecting sensitive computations in private chains. Each organization operates as an intelligent agent, using Q-learning to optimize its participation strategy and resource allocation, thus aligning individual incentives with collective goals. Notably, we introduce an efficient unlearning mechanism based on Low-Rank Adaptation (LoRA) that enables selective removal of specific data contributions without compromising the model’s overall performance. Through extensive experimentation on real-world datasets, we demonstrate that our framework effectively balances privacy protection, trust establishment, and regulatory compliance while maintaining high model performance. Case studies in healthcare and education sectors validate our approach’s practical applicability in sensitive domains where data privacy and trust are paramount. Copyright © 2024, The Authors. All rights reserved.

关键词： Federated learning

Federated Learning with Blockchain-Enhanced Machine Unlearning: A Trustworthy Approach

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arXiv 2024年

作者： Zuo, Xuhan Wang, Minghao Zhu, Tianqing Zhang, Lefeng Yu, Shui Zhou, Wanlei Faculty of Data Science City University of Macau China School of Computer Science University of Technology Sydney Ultimo2007 Australia

With the growing need to comply with privacy regulations and respond to user data deletion requests, integrating machine unlearning into IoT-based federated learning has become imperative. Traditional unlearning methods, however, often lack verifiable mechanisms, leading to challenges in establishing trust. This paper delves into the innovative integration of blockchain technology with federated learning to surmount these obstacles. Blockchain fortifies the unlearning process through its inherent qualities of immutability, transparency, and robust security. It facilitates verifiable certification, harmonizes security with privacy, and sustains system efficiency. We introduce a framework that melds blockchain with federated learning, thereby ensuring an immutable record of unlearning requests and actions. This strategy not only bolsters the trustworthiness and integrity of the federated learning model but also adeptly addresses efficiency and security challenges typical in IoT environments. Our key contributions encompass a certification mechanism for the unlearning process, the enhancement of data security and privacy, and the optimization of data management to ensure system responsiveness in IoT scenarios. Copyright © 2024, The Authors. All rights reserved.

关键词： Blockchain

So Far Yet So Near: Time Series data Augmentation with Exploring non-Semantic Boundaries based on Reinforcement Learning

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So Far Yet So Near: Time Series Data Augmentation with Explo...

International Conference on Acoustics, Speech, and Signal Processing (ICASSP)

作者： Haoran Li Zhibo Zhang Jiarong Kang Xun Jiang Xiaoli Gong Jin Zhang Zhe Sun Andrzej Cichocki College of Computer Science Nankai University Tianjin China School of Computer Science and Engineering University of Electronic Science and Technology of China Chengdu China Faculty of Medicine and Faculty of Health Data Science Juntendo University Japan Polish Academy of Science Systems Research Institute Warsaw Poland

ISBN: (数字)9798350368741

ISBN: (纸本)9798350368758

data augmentation effectively expands feature distribution in time series classification, enhancing downstream task performance. However, existing techniques often fail to maintain semantic consistency between augmented and original time series data, causing label noise and thereby degrading downstream task performance. We argue that data augmentation should preserve time series semantic consistency and expand the non-semantic information space. In this paper, we reformulate data augmentation as a semantic path planning problem between original data and augmented data, modeled as a Markov Decision Process (MDP). We propose a reinforcement learning-based algorithm (RL) named FreqSYN, where the action space is defined by a set of learnable Gaussian kernels that perturbs the frequency domain of the original data to generate augmented samples. The confidence coefficients of augmented data in semantically relevant classification tasks are used as a reward to iteratively refine the FreqSYN. Our method is validated across four datasets, achieving state-of-the-art performance, with a 2% improvement in F1 score over the SimPSI method. The code and models are available at https://***/NKU-EmbeddedSystem/FreqSYN.

关键词： Semantics Time series analysis Signal processing algorithms Reinforcement learning data augmentation Prediction algorithms Path planning data models Kernel Speech processing

IMGTP: A Unified Framework for Improving and Measuring the Generalizability of Trajectory Prediction Models

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IEEE Transactions on Intelligent Vehicles

IEEE Transactions on Intelligent Vehicles 2024年第10期9卷 1-15页

作者： Ye, Luyao Zhou, Zikang Wang, Jianping Li, Yung-Hui Jan, Nien-Yi Lin, Yi-Rong Lin, Yen-Cheng National Engineering Research Center of Educational Big Data and the Faculty of Artificial Intelligence in Education Central China Normal University China Department of Computer Science City University of Hong Kong and City University of Hong Kong Shenzhen Research Institute China AI Research Center of Hon Hai Research Institute China

Accurately predicting nearby agents' future trajectories is fundamental for ensuring the safety and efficiency of autonomous driving. However, existing learning-based trajectory prediction models struggle with poor generalization to out-of-distribution (OOD) scenes, potentially leading to failed predictions in real-world applications. To tackle this challenge, we introduce IMGTP, a unified framework for systematically evaluating, improving, and measuring the generalization performance of trajectory prediction models in OOD scenes. IMGTP consists of three primary components: the Argoverse-Shift dataset, the FrenÃ©t+ strategy, and an approach for OOD measurement. First, the Argoverse-Shift is a cross-domain dataset that highlights the significant differences in data distributions across domains, serving as a testing platform for models' generalization ability. Second, the FrenÃ©t+ strategy leverages map centerlines and the FrenÃ©t Frame to enhance the generalizability of baseline models by mitigating the domain gap resulting from road geometry variations. Third, we provide a comprehensive evaluation methodology for OOD measurement, quantifying the range of data that can be effectively handled by prediction models. This measurement approach employs the accuracy rate of the correctly predicted samples and their corresponding mean distance as well as standard deviation to the training set center as indicators of the model's generalization ability. The mean distance also serves as a valid criterion for OOD detection and uncertainty quantification. Experimental results demonstrate that our IMGTP framework enhances the generalization capability of mainstream trajectory prediction models by substantially improving their prediction performance in unseen domains. This advancement contributes to developing more robust and safer prediction models for autonomous driving in the real world. The code can be found at https://***/XIAOYEJIAYOU/Frenet-Strategy. IEEE

关键词： Autonomous vehicles

MPT: A Large-scale Multi-Phytoplankton Tracking Benchmark

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arXiv 2024年

作者： Yu, Yang Li, Yuezun Sun, Xin Dong, Junyu College of Computer Science and Technology Ocean University of China Qingdao China Faculty of Data Science City University of Macau China

Phytoplankton are a crucial component of aquatic ecosystems, and effective monitoring of them can provide valuable insights into ocean environments and ecosystem changes. Traditional phytoplankton monitoring methods are often complex and lack timely analysis. Therefore, deep learning algorithms offer a promising approach for automated phytoplankton monitoring. However, the lack of large-scale, high-quality training samples has become a major bottleneck in advancing phytoplankton tracking. In this paper, we propose a challenging benchmark dataset, Multiple Phytoplankton Tracking (MPT), which covers diverse background information and variations in motion during observation. The dataset includes 27 species of phytoplankton and zooplankton, 14 different backgrounds to simulate diverse and complex underwater environments, and a total of 140 videos. To enable accurate real-time observation of phytoplankton, we introduce a multi-object tracking method, Deviation-Corrected Multi-Scale Feature Fusion Tracker(DSFT), which addresses issues such as focus shifts during tracking and the loss of small target information when computing frame-to-frame similarity. Specifically, we introduce an additional feature extractor to predict the residuals of the standard feature extractor’s output, and compute multi-scale frame-to-frame similarity based on features from different layers of the extractor. Extensive experiments on the MPT have demonstrated the validity of the dataset and the superiority of DSFT in tracking phytoplankton, providing an effective solution for phytoplankton monitoring. Copyright © 2024, The Authors. All rights reserved.

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