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Leveraging XAI and Breakthrough Machine Learning Techniques for Trigeminal Neuralgia Severity Classification

Leveraging XAI and Breakthrough Machine Learning Techniques ...

引用

IEEE Region 10 Symposium

作者： Abhijna MB Akul P Ashwini Kodipalli Dept. of Information Science and Engineering Global Academy of Technology Dept. of Computer Science and Engineering Global Academy of Technology Dept. of Artificial Intelligence and Data Science Global Academy of Technology

ISBN: (数字)9798350364866

ISBN: (纸本)9798350364873

Trigeminal Neuralgia (TN) is a debilitating chronic pain disorder that significantly diminishes overall well-being, making diagnosis and therapy more challenging. The quick and precise categorization of TN severity is critical to therapy effectiveness. To assess the severity of TN, this study makes use of advanced machine learning techniques and explainable AI (XAI) approaches. We use a range of bagging and boosting strategies, including AdaBoost, Random Forest, Decision Tree, K-Nearest Neighbours (KNN), Gradient Boosting, and Logistic Regression. Random Forest was 95.2% accurate, whereas AdaBoost and Gradient Boosting were 92% and 97.2% in terms of their accuracy. The Decision Tree classifier obtained an accuracy of 84.62%, while K-Nearest Neighbours and Logistic Regression also achieved 92.31% accuracy. F1- score, precision, and recall metrics were used to assess performance on a dataset that included patient demographics and medical histories. XAI techniques, such as LIME and SHAP values, were used to improve the models' readability and transparency, which helped to progress the creation of cutting-edge diagnostic tools and increase clinician and researcher confidence.

关键词： Logistic regression Accuracy Explainable AI Medical treatment Nearest neighbor methods Boosting Brain modeling Planning Random forests Regression tree analysis

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Strong Baseline with Auto-encoder for Scale-Invariant Person Re-identification 6

Strong Baseline with Auto-encoder for Scale-Invariant Person...

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6th IEEE International Conference on Distributed Computing, VLSI, Electrical Circuits and Robotics, DISCOVER 2022

作者： Bilakeri, Shavantrevva Kotegar, Karunakar Manipal Institute of Technology Manipal Academy of Higher Education Dept. of Data Science and Computer Applications Karnataka Manipal India

ISBN: (数字)9781665487160

ISBN: (纸本)9781665487160

Person Re-identification associates the same person images captured by disjoint cameras. The different training strategies and precise hyper-parameter selection are required for the construction of an effective person re-identification. However, in the current work, the tests are carried out by trial and error, which takes a long time to reach an optimal solution. We experiment with various training strategies with the metric learning model serving as the baseline to address this issue. Existing person re-identification benchmarks have insufficient samples for training the ReID model. Also, the previous approaches fail when the same individual appears in varied sizes. To solve this issue, an auto-encoder module is used to create a single image in three distinct scales to enhance the sample size and tackle the scale variation problem of the person reidentification. In addition, a performance comparison is made between the baseline and the baseline with auto-encoder to demonstrate the influence of an auto-encoder as a data augmentation for the person re-identification. The adoption of auto-encoder module and bestperforming training techniques to a baseline have enhanced the rank1 and mAP on Market1501, DukeMTMC-reID datasets. © 2022 IEEE.

关键词： computer vision

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Multi-modal Underwater Acoustic Target Recognition 5

Multi-modal Underwater Acoustic Target Recognition

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5th International Conference on computer, Big data and Artificial Intelligence, ICCBD+AI 2024

作者： He, Tianxiang Xiang, Rui Zhou, Junlin Chen, Duanbing Feng, Sheng Yang, Jie Chengdu Union Big Data Technology Inc. Chengdu China Uestc School of Computer Science and Engineering Chengdu China Sichuan Jiuzhou Electric Co. Ltd. Dept. 804 Mianyang China

ISBN: (纸本)9798331533991

Underwater acoustic target recognition (UATR) has gained significant attention across various fields in recent years. However, traditional UATR methods, which primarily rely on data from a single sensor, have limitations in fully capturing target characteristics. This constraint ultimately impacts the accuracy and robustness of recognition. This paper presents a multi-modal UATR method that fuses data from two different sensors, thereby improving underwater target recognition accuracy. Additionally, a new training framework is proposed to facilitate the learning of consistent feature representations across diverse data sources, allowing for more effective capture of the intrinsic characteristics of acoustic data. Experimental results demonstrate that the method has positive effectiveness under various Signal-to-Noise Ratio (SNR) conditions and achieves an improvement in recognition accuracy ranging from 0.95% to 8.33%. © 2024 IEEE.

关键词： Self-supervised learning

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Deep Convolutional Neural Network Based Covid-19 Classification From Radiology X-Ray Images For IoT Enabled Devices 8

Deep Convolutional Neural Network Based Covid-19 Classificat...

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8th International Conference on Advanced Computing and Communication Systems, ICACCS 2022

作者： Bhosale, Yogesh H. Zanwar, Shrinivas Ahmed, Zakee Nakrani, Mahendra Bhuyar, Devendra Shinde, Ulhas Dept. of Computer Science and Engineering Mesra Ranchi India Chh. Shahu College of Engineering Dept. of Artificial Intelligence and Data Science Csmss Aurangabad India WeAgile Software Solution Pvt. Ltd Dept. of Computer Vision Engineering Pune India

ISBN: (纸本)9781665408165

The Coronavirus Disease 2019 (COVID19) epidemic, which erupted at the end of 2019, continued rapidly throughout the nations from Wuhan, China. This highly contagious infectious disease is rapidly spreading among the public. Early research on COVID-19-affected patients has revealed distinctive anomalies in chest radiography images. As a result, it is now necessary to identify various risk factors that can move an infected person from a mild to a serious stage of sickness. In Deep Learning (DL), strategies as a subset of Artificial Intelligence (AI) are used to deal with many real-life glitches. This paper introduces a Deep Convolutional Neural Network (DCNN) to perform multiclass classification for COVID-19, Pneumonia, and Normal Patients from radiological imaging of the chest. Also, the work is implemented with an IoT framework, used for communicating user and DCNN model. This Deep Convolutional Neural Network (DCNN) classification mechanism achieved a perfect test accuracy of 94.95% for COVID-19. The used datasets are acquired from Kaggle and GitHub. © 2022 IEEE.

关键词： COVID-19

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BBQ: A Hand-Built Bias Benchmark for Question Answering 60

BBQ: A Hand-Built Bias Benchmark for Question Answering

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Findings of the Association for Computational Linguistics: ACL 2022

作者： Parrish, Alicia Chen, Angelica Nangia, Nikita Padmakumar, Vishakh Phang, Jason Thompson, Jana Htut, Phu Mon Bowman, Samuel R. New York University Dept. of Linguistics United States New York University Center for Data Science United States New York University Dept. of Computer Science United States

ISBN: (纸本)9781955917254

It is well documented that NLP models learn social biases, but little work has been done on how these biases manifest in model outputs for applied tasks like question answering (QA). We introduce the Bias Benchmark for QA (BBQ), a dataset of question sets constructed by the authors that highlight attested social biases against people belonging to protected classes along nine social dimensions relevant for U.S. English-speaking contexts. Our task evaluates model responses at two levels: (i) given an under-informative context, we test how strongly responses refect social biases, and (ii) given an adequately informative context, we test whether the model's biases override a correct answer choice. We fnd that models often rely on stereotypes when the context is under-informative, meaning the model's outputs consistently reproduce harmful biases in this setting. Though models are more accurate when the context provides an informative answer, they still rely on stereotypes and average up to 3.4 percentage points higher accuracy when the correct answer aligns with a social bias than when it conficts, with this difference widening to over 5 points on examples targeting gender for most models tested. © 2022 Association for Computational Linguistics.

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Enhancing the Coexistence of 802.11 WLAN and 5G Networks using a Multi-Task Learning with Convolutional Neural Network Model

Enhancing the Coexistence of 802.11 WLAN and 5G Networks usi...

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Information Technology and Digital Innovation (ISITDI), International Symposium on

作者： Dody Ichwana Putra Shelvi Ekariani Dept. of Computer Engineering Universitas Andalas Padang Indonesia Dept. of Mathematics and Data Science Universitas Andalas Padang Indonesia

ISBN: (数字)9798350389241

ISBN: (纸本)9798350389258

The increasing need for high-speed internet for wireless devices has driven the development of 5G New Radio (NR) technology. The 5G NR operates in various spectrums such as sub-1 GHz, sub-6 GHz, and millimeter wave (mmWave) in the 26–28 GHz range. Conversely, the new standard Wi-Fi 6E operates on the unlicensed band and currently utilizes 2.4 GHz, 5 GHz, and 6 GHz. The coexistence of 5G NR sub-6 GHz and Wi-Fi 6E networks can result in interference and reduced performance for both technologies. To address this challenge, we introduce a Multi-Task Learning (MTL) approach using Convolutional Neural Networks (CNN) to improve the coexistence of 5G and Wi-Fi 6E networks. This proposes identifying and classifying packets transmitted and its modulation over both types of networks. The MTL-CNN model is trained to classify packets based on their type (e.g., 5G or Wi-Fi) and to identify the specific protocol used within each packet. We confirm the accuracy of this method with Wi-Fi and 5G signals produced by the MATLAB waveform simulator and subsequently deploy it on a hardware testbed using SDR. Our experimental results show that this method improves the accuracy of packet identification and classification, thus enhancing the coexistence between 5G and Wi-Fi 6E networks. MTL-CNN potentially leads to more efficient use of the wireless spectrum and improves the overall performance of wireless communication systems.

关键词： Wireless communication Accuracy 5G mobile communication Modulation Multitasking Mathematical models Convolutional neural networks Millimeter wave communication Wireless fidelity IEEE 802.11 Standard

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A Blockchain-Based Secure and Transparent Global Radioactive Source Lifecycle Management System

A Blockchain-Based Secure and Transparent Global Radioactive...

引用

IEEE Southeastcon

作者： Susan Poudel Firdous Kausar Debashis Das Pushpita Chatterjee Sajid Hussain Uttam Ghosh Dept. of Mathematics and Computer Science Fisk University Nashville TN USA Department of Computer Science and Data Science Meharry Medical College Nashville TN USA

ISBN: (数字)9798331504847

ISBN: (纸本)9798331504854

The management of radioactive sources is a critical process that ensures the safe and responsible handling of radioactive materials throughout their lifecycle. These sources require careful management from production to disposal, such as real-time tracking and radiation monitoring to follow everyone's safety rules and protect people and the environment. However, these sources present significant global challenges, especially regarding safety, security, and transparency. Current systems face limitations such as fragmented oversight, lack of accountability, and risks of unauthorized access. To address these limitations, this paper proposes a blockchain-based radioactive source lifecycle management system to manage the lifecycle of radioactive sources. Blockchain's decentralized and tamper-proof ledger secures data throughout the entire lifecycle of radioactive materials. By using smart contracts and access controls, the system ensures that only authorized parties can monitor and verify transactions in real-time, which reduces human error and prevents unauthorized changes to the data. Users can perform key operations such as retrieving source details, transferring ownership, updating source locations, and adding observers to our proposed system. Our experiment in designing and testing a blockchain application has proven the potential for a secure and transparent system that enhances global cooperation in managing radioactive sources. Overall, the proposed system not only addresses current challenges in radioactive source management but also enhances the tracking and monitoring of radioactive materials.

关键词： Access control Radioactive materials Smart contracts Supply chains Production Position measurement Real-time systems Blockchains Safety Testing

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Max-Min Fair 3D Trajectory Planning for Solar-Powered UAV-Assisted data Collection

Max-Min Fair 3D Trajectory Planning for Solar-Powered UAV-As...

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2022 IEEE/CIC International Conference on Communications in China, ICCC 2022

作者： Sun, Chao Xiong, Xinxuan Ni, Wei Ohtsuki, Tomoaki Wang, Xin Fudan University Dept. of Communication Science and Engineering Shanghai China Data61 Business Unit Sydney Australia Keio University Dept. of Information and Computer Science Yokohama Japan

ISBN: (数字)9781665484800

ISBN: (纸本)9781665484800

This paper presents a new solar-powered fixed-wing unmanned aerial vehicle (UAV)-assisted data collection technique, where a fixed-wing UAV harvests solar energy to fly and collect data from smart devices (SDs). The minimum of the data upload-ed from any of the SDs is maximized by optimizing the UAV's three-dimensional (3D) trajectory. The key idea is that we develop variable substitution and successive convex approximation (SCA) techniques to convexify the nonconvex data transmission, UAV energy consumption and mobility constraints, as well as the nonconvex energy harvesting constraint. The proposed algorithm guarantees a locally optimal solution satisfying the Karush-Kuhn-Tucker (KKT) conditions. Another important aspect is that an effective set of approximation parameters are identified for accurate approximation of the solar energy harvesting at relatively low altitudes, contributing to the design of efficient 3D trajectories balancing energy harvesting and data collection. Numerical results show that the 3D flight increases uploaded data by 59% as compared to the two-dimensional (2D) flight. © 2022 IEEE.

关键词： Unmanned aerial vehicles (UAV)

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Vaffel - a tool for forecasting transmission line failures based on Bayesian updating, renalysis data and medium range weather forecasts

Vaffel - a tool for forecasting transmission line failures b...

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International Conference on Probabilistic Methods Applied to Power Systems (PMAPS)

作者： Øystein Rognes Solheim Ingeborg Ligaarden Boye Annfelt Høverstad Thomas Trøtscher Markus Andersen Magnus Korpås Dept. of Data Science Statnett SF Oslo Norway Dept. of Electric Power Engineering NTNU Oslo Norway Dept. of Data Science Statnett SF Trondheim Norway Dept. of Computer Science NTNU Trondheim Norway Dept. of Electric Power Engineering NTNU Trondheim Norway

ISBN: (数字)9798350372786

ISBN: (纸本)9798350372793

This paper presents a method for forecasting the probability of weather-related transmission line failures. The method combines failure statistics, a Bayesian updating scheme and reanalysis weather data to produce fragility curves that model the relationship between weather exposure for a component and the probability of failure. The fragility curves are combined with medium range weather forecasts extracted at each span of the individual transmission lines to obtain a forecast of the hourly probability of failure for each line. A tool that implements the method has been developed and is currently being tested operationally within the Norwegian TSO Statnett. We show the data requirements and demonstrate the use of a dashboard that expose the forecasts to an end-user.

关键词： Power transmission lines Weather forecasting Probabilistic logic data models Power system reliability Bayes methods Reliability

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Efficient Augmentation for Imbalanced Deep Learning 39

Efficient Augmentation for Imbalanced Deep Learning

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39th IEEE International Conference on data Engineering, ICDE 2023

作者： Dablain, Damien A. Bellinger, Colin Krawczyk, Bartosz Chawla, Nitesh V. University of Notre Dame Lucy Family Institute for Data & Society Dept. Computer Science and Engineering Notre DameIN46556 United States National Research Council of Canada OttawaK1A 0R6 Canada Virginia Commonwealth University Dept. Computer Science RichmondVA23824 United States

ISBN: (纸本)9798350322279

Deep learning models may not effectively generalize across under-represented or minority classes. We empirically study a convolutional neural network's (CNN) internal representation of imbalanced image data and measure the generalization gap between a model's feature embeddings in the training and test sets, showing that the gap is wider for minority classes. This insight enables us to design an efficient three-phase CNN training framework for imbalanced data. The framework involves training the network end-to-end on imbalanced data to learn feature embeddings, performing data augmentation in the learned embedding space to balance the training data distribution, and fine-tuning the classifier head on the embedded balanced training data. We develop Expansive Over-Sampling (EOS) as a data augmentation technique to utilize in the training framework. EOS forms synthetic training instances as convex combinations between the minority class samples and their nearest adversaries in the embedding space to reduce the generalization gap. The proposed framework improves the accuracy over leading cost-sensitive and resampling methods commonly used in imbalanced learning. Moreover, it is more computationally efficient than standard data pre-processing methods, such as SMOTE and GAN-based over-sampling, as it requires fewer parameters and less training time. The source code for the proposed framework is available at: https://***/dd1github/EOS. © 2023 IEEE.

关键词： Embeddings

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