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Deep Learning based Intelligent System for Skin Disease Detection 10

Deep Learning based Intelligent System for Skin Disease Dete...

引用

10th International Conference on Communication and Signal Processing, ICCSP 2024

作者： Dhanush, K.B. Arul Xavier, V.M. Shyni, S.S. Karunya Institute of Technology and Sciences Division of Computer Science and Engineering Tamilnadu Coimbatore India Karunya Institute of Technology and Sciences Division of Data Science and Cyber Security Tamilnadu Coimbatore India

ISBN: (纸本)9798350353068

Malignant and benign skin diseases significantly impact public health and personal well-being. Traditional machine learning models encounter difficulties in processing image datasets, which is crucial for early and accurate diagnosis. This work employed two pre-trained deep learning models, ResNet50 and VGG19, known for their precise and swift image processing capabilities. These models underwent training utilizing an extensive dataset of images from patients battling benign keratosis, Melanoma, and Melanocytic nevus (Mole). The models were then tasked to distinguish between these three conditions. The results were remarkable, with the models achieving accuracies of 91.3 % and 95.6 %, respectively. A comparative analysis revealed that the VGG19 model outperformed in accurately identifying these three diseases, surpassing traditional AI and machine learning methods. The trained VGG19 model was subsequently incorporated into an Android application to enhance user experience. © 2024 IEEE.

关键词： Diagnosis

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Slow Convergence of Interacting Kalman Filters in Word-of-Mouth Social Learning 60

Slow Convergence of Interacting Kalman Filters in Word-of-Mo...

引用

60th Annual Allerton Conference on Communication, Control, and Computing, Allerton 2024

作者： Krishnamurthy, Vikram Rojas, Cristian R. School of Electrical and Computer Engineering Cornell University United States School of Electrical Engineering and Computer Science KTH Royal Institute of Technology Division of Decision and Control Systems Stockholm Sweden

ISBN: (纸本)9798331541033

We consider word-of-mouth social learning involving m Kalman filter agents that operate sequentially. The first Kalman filter receives the raw observations, while each subsequent Kalman filter receives a noisy measurement of the conditional mean of the previous Kalman filter. The prior is updated by the m-th Kalman filter. When m=2, and the observations are noisy measurements of a Gaussian random variable, the covariance goes to zero as k-1/3 for 7kappa;observations, instead of O(k-1) in the standard Kalman filter. In this paper we prove that for m agents, the covariance decreases to zero as k-(2m-1), i.e, the learning slows down exponentially with the number of agents. We also show that by artificially weighing the prior at each time, the learning rate can be made optimal as k-1. The implication is that in word-of-mouth social learning, artificially re-weighing the prior can yield the optimal learning rate. © 2024 IEEE.

关键词： Kalman filters

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Cyber-Physical Vulnerabilities and NIST Mitigation Framework for Power Sector 2

Cyber-Physical Vulnerabilities and NIST Mitigation Framework...

引用

2nd International Conference on Cyber Physical Systems, Power Electronics and Electric Vehicles, ICPEEV 2024

作者： Verma, Dheeraj Agrawal, Praveen Kumar Niazi, K.R. Gupta, Nikhil Pandey, Vipin Chandra Department of Electrical Engineering Malaviya National Institute of Technology Jaipur India Computer Electrical and Mathematical Sciences and Engineering Division KAUST Saudi Arabia

ISBN: (纸本)9798350388664

As power systems evolve and increasingly rely on digital technology, they become more vulnerable to both cyber and physical threats. This paper explores the growing challenges faced by modern power grids, particularly regarding the cybersecurity risks. It takes a closer look at the National Institute of Standards and technology (NIST) Cybersecurity Framework (CSF) and how it helps address these issues. By guiding organizations through steps such as identifying risks, protecting systems, detecting attacks, responding effectively, and recovering quickly, the NIST CSF provides a strong foundation for securing critical power infrastructure. In particular, this paper highlights the improvements from NIST CSF 1.0 to 2.0, including stronger focus on zero trust security, supply chain risks, and emerging technologies like the Internet of Things (IoT) and distributed energy resources (DERs). The analysis shows how these frameworks are essential for maintaining the stability, reliability, and security of critical systems against evolving threats". © 2024 IEEE.

关键词： Cyber attacks

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Conjectural Online Learning with First-order Beliefs in Asymmetric Information Stochastic Games 63

Conjectural Online Learning with First-order Beliefs in Asym...

引用

63rd IEEE Conference on Decision and Control, CDC 2024

作者： Li, Tao Hammar, Kim Stadler, Rolf Zhu, Quanyan New York University Department of Electrical and Computer Engineering United States Kth Royal Institute of Technology Division of Network and Systems Engineering Sweden

ISBN: (纸本)9798350316339

Asymmetric information stochastic games (AISGs) arise in many complex socio-technical systems, such as cyberphysical systems and IT infrastructures. Existing computational methods for AISGs are primarily offline and can not adapt to equilibrium deviations. Further, current methods are limited to particular information structures to avoid belief hierarchies. Considering these limitations, we propose conjectural online learning (COL), an online learning method under generic information structures in AISGs. COL uses a forecaster-actorcritic (FAC) architecture, where subjective forecasts are used to conjecture the opponents' strategies within a lookahead horizon, and Bayesian learning is used to calibrate the conjectures. To adapt strategies to nonstationary environments based on information feedback, COL uses online rollout with cost function approximation (actor-critic). We prove that the conjectures produced by COL are asymptotically consistent with the information feedback in the sense of a relaxed Bayesian consistency. We also prove that the empirical strategy profile induced by COL converges to the Berk-Nash equilibrium, a solution concept characterizing rationality under subjectivity. Experimental results from an intrusion response use case demonstrate COL's faster convergence over state-of-the-art reinforcement learning methods against nonstationary attacks. © 2024 IEEE.

关键词： Cost functions

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Risk-Aware Real-Time Task Allocation for Stochastic Multi-Agent Systems under STL Specifications 63

Risk-Aware Real-Time Task Allocation for Stochastic Multi-Ag...

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63rd IEEE Conference on Decision and Control, CDC 2024

作者： Engelaar, M.H.W. Zhang, Z. Vlahakis, E.E. Dimarogonas, D.V. Lazar, M. Haesaert, S. Netherlands Kth Royal Institute of Technology Division of Decision and Control Systems School of Electrical Engineering and Computer Science Sweden

ISBN: (纸本)9798350316339

This paper addresses the control synthesis of heterogeneous stochastic linear multi-agent systems with realtime allocation of signal temporal logic (STL) specifications. Based on previous work, we decompose specifications into subspecifications on the individual agent level. To leverage the efficiency of task allocation, a heuristic filter evaluates potential task allocation based on STL robustness, and subsequently, an auctioning algorithm determines the definitive allocation of specifications. Finally, a control strategy is synthesized for each agent-specification pair using tube-based model predictive control (MPC), ensuring provable probabilistic satisfaction. We demonstrate the efficacy of the proposed methods using a multi-shuttle scenario that highlights a promising extension to automated driving applications like vehicle routing. © 2024 IEEE.

关键词： Stochastic systems

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Deep Fake Detection using Inception ResNetV2 15

Deep Fake Detection using Inception ResNetV2

引用

15th International Conference on Advances in Computing, Control, and Telecommunication Technologies, ACT 2024

作者： Chemparathy, Alex B. Kavitha, T. Division of Computer Science Engineering Karunya Institute of Technology and Science Karunya Nagar Tamil Nadu Coimbatore641114 India

ISBN: (纸本)9798331300579

The increasing prevalence of DeepFake technology poses significant threats to various industries and public trust, making the development of robust detection methods crucial. In this study, we propose a novel approach for DeepFake detection using the InceptionResNetV2 architecture, leveraging its advanced capabilities in extracting features from images. Our method utilizes a deep learning framework to train the model on a diverse dataset of authentic and DeepFake videos, enabling it to learn distinct patterns and discrepancies between the two types of content. Through extensive experimentation and evaluation, we demonstrate the effectiveness of the proposed approach in accurately identifying DeepFake videos with high precision and recall rates. Furthermore, our method exhibits robustness against various manipulation techniques, showcasing its potential for real-world applications in combating the spread of misinformation and fraudulent content. The implementation of InceptionResNetV2 for DeepFake detection presents a promising solution to the growing challenges posed by synthetic media, providing a reliable tool for safeguarding the integrity of visual information in digital environments. © Grenze Scientific Society, 2024.

关键词： Deep learning

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Constructive Function Approximation with Local Models 32

Constructive Function Approximation with Local Models

引用

32nd Mediterranean Conference on Control and Automation, MED 2024

作者： Mavridis, Christos N. Johansson, Karl Henrik School of Electrical Engineering and Computer Science KTH Royal Institute of Technology Division of Decision and Control Systems Stockholm Sweden

ISBN: (纸本)9798350395440

We introduce a constructive function approximation approach as a general tool, particularly useful in adaptive and data-driven methods for perception and control. The key idea is to estimate of a collection of simple local models as opposed to a single and complex regression model trained in the entire input space. We use principles from the Online Deterministic Annealing (ODA) optimization framework to construct an adaptive partition of the input space, which enables the introduction of local function approximation models within each subset of the partition. We show that both the partitioning and the local model training algorithms are stochastic approximation algorithms that operate online, and with the same observations, as part of a two-timescale stochastic approximation scheme. This process constitutes a heuristic method to gradually increase the complexity of the function approximation framework in a task-agnostic manner, giving emphasis to regions of the input space where the regression error is high. As a result this framework has inherent explainability properties, and is suitable for continuous learning applications where regression improvement without retraining from scratch is crucial. Simulation results illustrate the properties of the proposed approach. © 2024 IEEE.

关键词： Heuristic methods

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Federated Learning Approach for Pneumonia Detection 15

Federated Learning Approach for Pneumonia Detection

引用

15th International Conference on Advances in Computing, Control, and Telecommunication Technologies, ACT 2024

作者： Jacob, Alan Kavitha, T. Division of Computer Science Engineering Karunya Institute of Technology and Science Karunya Nagar Tamil Nadu Coimbatore641114 India

ISBN: (纸本)9798331300579

By using deep learning methods, our study aims to identify pneumonia in chest X-ray pictures using VGG16 convolutional neural networks (CNNs). Our technique ensures data privacy and security by using federated learning to allow collaborative training across distant datasets without centralized data aggregation. We surpass other algorithms, such as ResNet50, in terms of pneumonia diagnosis accuracy. Pneumonia may be easily diagnosed with the use of the Streamlit-based online application. Patients may submit chest X-rays, and the model will indicate whether or not pneumonia is present, along with the likelihood of each diagnosis. By using deep learning with an emphasis on data protection and accessibility, we want to improve healthcare diagnoses via this initiative. © Grenze Scientific Society, 2024.

关键词： Federated learning

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Analyzing Stress Levels in IT Professionals using Machine Learning 15

Analyzing Stress Levels in IT Professionals using Machine Le...

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15th International Conference on Advances in Computing, Control, and Telecommunication Technologies, ACT 2024

作者： Joseph, Jehosh Kavitha, T. Division of Computer Science Engineering Karunya Institute of Technology and Science Karunya Nagar Tamil Nadu Coimbatore641114 India

ISBN: (纸本)9798331300579

Stress management is a growing concern in today's high-pressure tech industry, particularly for those working in information technology. Long hours, strict deadlines, and high expectations are common in the information technology workplace, all of which may contribute to an already stressful situation. Professionals' health, happiness, and productivity are all negatively impacted by unmanaged stress. In order to help with proactive stress management, this project intends to use machine learning methods to forecast the stress levels of IT workers. Heart rate, skin conductivity, hours worked, number of emails sent, and meetings attended are some of the characteristics that we use to identify job stress. Together, these characteristics illuminate the many ways in which stress manifests in the workplace, both physiologically and otherwise. Here, machine learning provides a fresh perspective on a problem that is becoming more pressing. This model's goal is to help people and businesses by using data analytics to deliver them actionable insights. Both individuals and organizations may benefit from these forecasts. The former can use them for self-monitoring and early intervention, while the latter can use them to understand which positions or settings cause the most stress and allocate resources accordingly. Our first findings show that the selected variables are significantly correlated with stress levels, suggesting that machine learning might be useful for predicting stress in IT workers. When it comes to workplace mental health and wellness, this report is a major step in the right direction. © Grenze Scientific Society, 2024.

关键词： Machine learning

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A Multi-Dimensional Deep Learning Approach for Enhanced Cardiovascular Disease Diagnosis using ECG Signals 8

A Multi-Dimensional Deep Learning Approach for Enhanced Card...

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8th International Conference on I-SMAC (IoT in Social, Mobile, Analytics and Cloud), I-SMAC 2024

作者： Evangelin Sonia, S.V. Nedunchezhian, R. Rajalakshmi, M. Kavya, S.P. Parveen, H. Summia Jerusha Miraclin Dulcie, B. Karunya Institute of Technology and Sciences Division of Cse Coimbatore India Coimbatore Institute of Technology Computer Science and Engineering Coimbatore India Coimbatore Institute of Technology Information Technology Coimbatore India Kpr Institute of Engineering and Technology Department of Artificial Intelligence and Data Science Coimbatore India Sri Eshwar college of Engineering Computer Science and Engineering Coimbatore India Karunya Institute of Technology and Sciences Computer Science and Engineering Tamil Nadu Coimbatore India

ISBN: (纸本)9798350376425

The diagnosis of cardiovascular disease is essential for timely clinical intervention and patient prognose. Although electrocardiogram is performed frequently for cardiovascular disease diagnosis, its interpretation requires experts. This study first introduces a Multi-Dimensional Deep Neural Network aimed to improve the accuracy and efficiency for cardiovascular disease diagnosis from electrocardiogram data. The Multi-Dimensional Deep Neural Network architecture of our electrocardiogram signal model, which coalesces convolutional and recurrent neural networks (layers) to learn spatial/temporal features. We developed the model on a large annotated dataset of electrocardiogram recordings using training and validation datasets, yielding personal performance compared to baseline methods as well as known deep learning models. We summarize the various statistics-sensitivity, specificity;overall accuracies-that are calculated for our Multi-Dimensional Deep Neural Network indicating that our model is both robust and reliable in terms of diagnostics. This research could lead to more advanced neural networks in medical diagnostics, which may provide clinicians with an effective tool for the clinical diagnosis of cardiovascular diseases. © 2024 IEEE.

关键词： Convolutional neural networks

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