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A Transfer learning Approach to the Development of an Automation System for Recognizing Guava Disease Using CNN Models for Feasible Fruit Production 22nd

A Transfer Learning Approach to the Development of an Automa...

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22nd international conference on Hybrid intelligent Systems, HIS 2022 and the 18th international conference on Information Assurance and Security, IAS 2022

作者： Shakil, Rashiduzzaman Akter, Bonna Rajbongshi, Aditya Sara, Umme Barman, Mala Rani Dhali, Aditi Department of CSE Daffodil International University Dhaka Bangladesh Department of CSE National Institute of Textile Engineering and Research Dhaka Bangladesh Department of CSE Sheikh Hasina University Dhaka Bangladesh Department of CSE Jahangirnagar University Dhaka Bangladesh

ISBN: (纸本)9783031274084

Guava (Psidium guava) is one of the most popular fruits which plays a vital role in the world economy. To increase guava production and sustain economic development, early detection and diagnosis of guava disease is important. As traditional recognition systems are time-consuming, expensive, and sometimes their predictions are also inaccurate, farmers are facing a lot of losses because of not getting the proper diagnosis and appropriate cure in time. In this study, an automatic system based on Convolution Neural Networks (CNN) models for recognizing guava disease has been proposed. To make the dataset more efficient, image processing techniques have been employed to boost the dataset which is collected from the local Guava Garden. For training and testing the applied models named InceptionResNetV2, ResNet50, and Xception with transfer learning technique, a total of 2,580 images in five categories such as Phytophthora, Red Rust, Scab, Stylar end rot, and Fresh leaf are utilized. To estimate the performance of each applied classifier, the six-performance evaluation metrics have been calculated where the Xception model conducted the highest accuracy of 98.88% which is good enough compared to other recent relevant works. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Diagnosis

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An Automatic Detection of Heart Block from ECG Images Using YOLOv4 22nd

An Automatic Detection of Heart Block from ECG Images Using ...

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22nd international conference on Hybrid intelligent Systems, HIS 2022 and the 18th international conference on Information Assurance and Security, IAS 2022

作者： Das, Samar Hasan, Omlan Chowdhury, Anupam Aslam, Sultan Md Minhaz Hossain, Syed Md. Premier University Chattogram4000 Bangladesh International Islamic University Chittagong Chattogram Bangladesh

ISBN: (纸本)9783031274084

Cardiovascular diseases are one of the world’s significant health issues. It is becoming a major health issue in Bangladesh and other poor nations, particularly heart block. It is a condition in which the heart beats too slowly (bradycardia). The electrical impulses that command the heart to contract are partly or completely blocked between the top chambers (atria) and the lower chambers in this situation (ventricles).Therefore, computer-assisted diagnosis techniques are urgently needed to aid doctors in making more informed decisions. In this study, a deep learning model, you only look once (YOLOv4) backbone with CSPDarkNet53 is proposed to detect four classes including three types of heart blocks, such as, 1st degree block (A-V block), left bundle branch block (LBBB) and right bundle branch block (RBBB), and no block. We prepared a novel dataset of the patient’s electrocardiogram (ECG) images. This dataset contains 271 images of Bangladeshi patients. The model’s mAP@0.5 on test data was 97.65%. This study may also find application in the diagnosis and classification of block and heart diseases in ECG images. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Electrocardiograms

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ChartReader: Automatic Parsing of Bar-Plots 22

ChartReader: Automatic Parsing of Bar-Plots

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22nd IEEE international conference on Information Reuse and Integration for data Science (IRI)

作者： Rane, Chinmayee Subramanya, Seshasayee Mahadevan Endluri, Devi Sandeep Wu, Jian Giles, C. Lee Univ Buffalo Comp Sci & Engn Buffalo NY 14260 USA Penn State Univ Dept Chem Engn University Pk PA 16802 USA Texas A&M Univ Comp Sci & Engn College Stn TX USA Old Dominion Univ Dept Comp Sci Norfolk VA 23529 USA Penn State Univ Coll Informat Sci & Technol University Pk PA 16802 USA

ISBN: (纸本)9781665438759

Scientific figures such as bar graphs are a critical part of scientific research and a predominant method used to represent trends and relationships in data. However, manually interpreting and extracting information from graphs is often tedious. Since data consumption has exponentially evolved over the past few decades, there is a need for automated data inference from these bar graphs. ChartReader presents a fully automated end-to-end framework that extracts data from bar graphs in scientific research papers focusing on process engineering and environmental science journals. ChartReader uses a deep learning-based classifier to determine the chart type of a given chart image. We then develop novel heuristic methods for analyzing scientific figures (text detection, pixel grouping, object detection) and address prime challenges like axis detection, legend parsing, and label detection. Our framework achieves 98% and 68% accuracy in parsing x-axis and y-axis ticks, respectively. It achieves 83% accuracy in parsing legends and 42% accuracy in parsing data values in the testing corpus. We compare the proposed method with state-of-the-art methods and address its limitations.

关键词： data Extraction Chart Classification Text Detection Computer Vision

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Value Conditional State Entropy Reinforcement learning for Autonomous Driving Decision Making

Value Conditional State Entropy Reinforcement Learning for A...

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IEEE international conference on Industrial Informatics (IndIN)

作者： Yiyi Han Lulu Guo Hong Chen Department of Control Science and Engineering Tongji University Shanghai China Tongji University School of Automotive Studies Shanghai China

ISBN: (数字)9798331527471

ISBN: (纸本)9798331527488

Compared with traditional pipelined frameworks, end-to-end autonomous driving integrates the problems of perception, decision-making, planning, and control, which can be more adaptive to complex scenarios and more generalizable. However, in end-to-end automated driving, especially when in complex urban roads with high-dimensional observation data, reinforcement learning training is difficult and inefficient, and intelligence exploration is difficult to find the best driving strategy. To address the above problems, in this paper, we propose to use maximized value conditional state entropy(VCSE) reinforcement learning for automatic driving strategy learning. The method estimates the conditional value of each state, also known as the conditional state entropy, and then maximizes their average value, increasing the state entropy to encourage the intelligent body to explore the environment, and at the same time, limiting the state entropy to prevent the excessive pursuit of state entropy from causing the intelligent body to prefer exploring the low-value state. In this paper, the value-conditional state entropy is used as the intrinsic reward, which is weighted and summed with the task reward, so as to encourage the intelligent body to explore the environment and improve training efficiency. We use the exact same environment and traffic flow for training and evaluation in the Carla simulator, and our method shows a large performance improvement in all aspects and better exploration utilization of the environment compared to the baseline method.

关键词： Training Roads Decision making Urban areas Reinforcement learning Entropy Autonomous agents Safety Planning Autonomous vehicles

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Accelerating Real-Time Imaging for Radiotherapy: Leveraging Multi-GPU Training with PyTorch 22

Accelerating Real-Time Imaging for Radiotherapy: Leveraging ...

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22nd IEEE international conference on Machine learning and Applications, ICMLA 2023

作者： Obe, Ruth Kaufmann, Brandt Baird, Kaelen Kadel, Sam Soltani, Yasmin Cham, Mostafa Gobbert, Matthias K. Barajas, Carlos A. Jiang, Zhuoran Sharma, Vijay R. Ren, Lei Peterson, Stephen W. Poif, Jerimy C. U. of Houston-Clear Lake Dept. of Computer Science and of Software Engineering United States U. of San Francisco Dept. of Mathematics and Statistics United States Skidmore College Dept. of Computer Science and of Mathematics United States Mount Holyoke College Dept. of Computer Science and of Psychology United States U. of Houston Dept. of Biomedical Engineering United States U. of Maryland Dept. of Information Systems Baltimore County United States U. of Maryland Dept. of Mathematics and Statistics Baltimore County United States Medical Physics Graduate Program Duke University United States U. of Maryland School of Medicine Dept. of Radiation Oncology United States U. of Maryland School of Medicine Department of Radiation Oncology United States U. of Cape Town Dept. of Physics South Africa H3D Inc. United States

ISBN: (纸本)9798350345346

Proton beam therapy is an advanced form of cancer radiotherapy that uses high-energy proton beams to deliver precise and targeted radiation to tumors. This helps to mit-igate unnecessary radiation exposure in healthy tissues. Real-time imaging of prompt gamma rays with Compton cameras has been suggested to improve therapy efficacy. However, the camera's non-zero time resolution leads to incorrect interaction classifications and noisy images that are insufficient for accurately assessing proton delivery in patients. To address the challenges posed by the Compton camera's image quality, machine learning techniques are employed to classify and refine the generated data. These machine-learning techniques include recurrent and feedforward neural networks. A PyTorch model was designed to improve the data captured by the Compton camera. This decision was driven by PyTorch's flexibility, powerful capabilities in handling sequential data, and enhanced G PU usage. This accelerates the model's computations on large-scale radiotherapy data. Through hyperparameter tuning, the validation accuracy of our PyTorch model has been improved from an initial 7 % to over 60 %. Moreover, the PyTorch Distributed data Parallelism strategy was used to train the RNN models on multiple G PU s, which significantly reduced the training time with a minor impact on model accuracy. © 2023 IEEE.

关键词： Clas-sification Compton camera Distributed data Parallelism Proton beam therapy PyTorch Recurrent neural network

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Meta-feature Extraction Strategies for Active Anomaly Detection 22nd

Meta-feature Extraction Strategies for Active Anomaly Detect...

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22nd international conference on intelligent data engineering and automated learning

作者： Angiulli, Fabrizio Fassetti, Fabio Ferragina, Luca Papaleo, Prospero Univ Calabria DIMES I-87036 Arcavacata Di Rende CS Italy

ISBN: (纸本)9783030916077;9783030916084

Active learning is a machine learning scenario in which methods are trained by iteratively submitting a query to a human expert and then taking into account his feedback for the following computations. The application of such paradigm to the anomaly detection task takes the name of Active Anomaly Detection (AAD). Reinforcement learning describes a family of algorithms that aim to teach an agent to determine a policy to deal with external factors, and are based on the maximization of a reward function. Recently some AAD methods, based on the training of a meta-policy with Deep Reinforcement learning have been very successful because, after the training, the methods simply work on a small number of meta-features that can be directly applied to any new dataset without further tuning. For these approaches a central question is the selection of good meta-features: actually, the most common choice is to define these metafeatures in terms of the distances with the points that the expert has already labelled as either anomaly or normal. In this work we explore different strategies for selecting effective meta-features. Specifically, we take into account both direct and reverse nearest-neighbor rankings in order to build meta-features, since they are less sensitive to the specific distance distribution characterizing the training data, and experiment the combination also with related base detectors. The experiments show that there are scenarios in which our approach offers advantages over the standard technique.

关键词： Anomaly detection Active learning Meta-feature extraction Reinforcement learning

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Max Fusing Gated Recurrent Units and Ensemble Classifier for intelligent Acoustic Classification 22

Max Fusing Gated Recurrent Units and Ensemble Classifier for...

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22nd IEEE/ACIS international conference on Computer and Information Science, ICIS 2022

作者： Chin, Cheng Siong See, Simon Newcastle University Singapore Faculty of Science Agriculture and Engineering Singapore599493 Singapore Singapore038988 Singapore

ISBN: (数字)9781665494632

ISBN: (纸本)9781665494632

The paper presents a wavelet scattering feature extraction using an averaged data augmentation to include unseen devices in training. The multiple classifiers are applied to the extracted features. The outputs from Gated Recurrent Units (GRUs), and the ensemble classifiers are maximally fused to classify sound coming from different devices and scenes. The average device-wise accuracy has more than 5.4% improvement with the proposed GRUs network than its counterpart Long short-term memory (LSTM) in device-wise classification. The device-wise classification accuracy for the proposed max-fusion exhibits approximately 19.1% better than the baseline results in DCASE2020-Task1A. The proposed max-fusion also demonstrates around 22.9% higher classification accuracy in scene-wise classification. Lastly, the proposed max-fusion method produces comparative results with the Snapshot ensembles that won the DCASE2020 Challenges-Task 1A. © 2022 IEEE.

关键词： Long short-term memory

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A Handy Diagnostic Tool for Early Congestive Heart Failure Prediction Using Catboost Classifier 22nd

A Handy Diagnostic Tool for Early Congestive Heart Failure P...

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22nd international conference on Hybrid intelligent Systems, HIS 2022 and the 18th international conference on Information Assurance and Security, IAS 2022

作者： Mythili, S. Pousia, S. Kalamani, M. Hindhuja, V. Nimisha, C. Jayabharathi, C. Department of ECE Bannari Amman Institute of Technology Sathyamangalam India Department of ECE KPR Institute of Engineering and Technology Coimbatore India UG scholar Department of ECE Bannari Amman Institute of Technology Sathyamangalam India Department of E&I Erode Sengunthar Engineering College Perundurai India

ISBN: (纸本)9783031274084

In this world 33% of deaths are due to the Cardiovascular diseases (CVDs) which affects the people globally irrespective of ages. Based on the saying "Prevention is better than cure" there is a necessity for early detection of heart failures. By addressing the behavioural risk factors such as tobacco use, obesity and harmful use of alcohol there is a possibility to circumvent it. But the people with disorders or more risk factors like hypertension, diabetes, hyperlipidaemia etc., need early detection and management wherein a machine learning model is of great help. The analyzed dataset contains 12 features that may be accustomed to predict the heart failure. Various algorithms such as SVM, KNN, LR, DT, Cat boost algorithms are taken into consideration in the aspect of accurate heart failure prediction. Analysed Cat boost classifier algorithm proves that it suits for earlier heart failure prediction with high accuracy level. It is further deployed in real time environment as a handy tool by integrating the trained accurate model with a User Interface for the heart failure prediction. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： User interfaces

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Federated learning-Based Intrusion Detection on Non-IID data 1

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22nd international conference on Algorithms and Architectures for Parallel Processing, ICA3PP 2022

作者： Liu, Yongfei Wu, Guangjun Zhang, Wenyuan Li, Jun Institute of Information Engineering Chinese Academy of Sciences Beijing100093 China School of Cyber Security University of Chinese Academy of Sciences Beijing100049 China

ISBN: (数字)9783031226779

ISBN: (纸本)9783031226762

Intrusion detection is an effective means to deal with network attacks. Currently, the commonly used detection methods are based on machine learning. However, traditional machine learning-based methods are centralized architectures that require uploading data to cloud servers, which face serious latency and data security issues. Federated learning (FL) can collaboratively train a machine learning model with good performance while the data is kept locally on the client, which can effectively make up for the shortcomings of the centralized architecture. Most of the current research on using FL methods in machine learning-based intrusion detection ideally consider the data to be independent and identically distributed (IID), which doesn’t conform to real scenarios. In the real world, due to the different environment of the client, the types of attacks contained in the data owned by each client may be different. Therefore, we study the effects of various non-independent and identically distribution (non-IID) settings on FL in detail and give specific partitioning methods. In addition, we also propose a FL data rebalancing method based on auxiliary classifier generative adversarial networks (ACGAN), which is experimentally validated on the UNSW-NB15 dataset. Experiments show that the proposed data augmentation method can well improve the impact of non-IID data on FL. © 2023, Springer Nature Switzerland AG.

关键词： Intrusion detection

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Research on the Visual Representation of Autonomous Complex Transportation System Architecture 22

Research on the Visual Representation of Autonomous Complex ...

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22nd COTA international conference of Transportation Professionals (CICTP) - Intelligence, Connectivity, and Mobility

作者： Li, Meng'en Wang, Ping Wang, Yu Liu, Wenchao Bai, Siyuan Changan Univ Sch Elect Control Engn Xian Peoples R China Minist Nat Resources Technol Innovat Ctr Land Spatial Data Engn Informat Ctr Beijing Peoples R China

ISBN: (数字)9780784484265

ISBN: (纸本)9780784484265

The architecture of the future autonomous transportation system (ATS) has more comprehensive and complex characteristics, but there is still a lack of a scientific and effective method for the expression of this complex architecture. This paper builds a new visualization platform based on the ATS architecture, and completes the clear and efficient expression of the architecture. This new visualization platform is driven by data flow between systems and information flow between physical elements, combined with the operation logic of perception-learning-decision-response, to realize the dynamic simulation of ATS architecture. Finally, the effectiveness of the theoretical method is demonstrated by taking the scenario of "passing pedestrians passing zebra crossings to avoid vehicles at intersections without signal lights" as an example.

关键词： data visualization

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