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Cardiovascular Disease Prediction with Machine Learning Algorithms and Interpretation using Explainable AI methods: LIME & SHAP 3

Cardiovascular Disease Prediction with Machine Learning Algo...

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3rd International Conference for Advancement in Technology, ICONAT 2024

作者： Pratheek, N. Balaji Yashas, D.H. Bhanu Prakash, Y.R. Bharadwaj, Anirudh M Kodapalli, Ashwini Rao, Trupthi Global Academy of Technology Dept. of Information Science & Engineering Karnataka Bengaluru India Global Academy of Technology Dept of Artificial Intelligence & Data Science Karnataka Bengaluru India

ISBN: (纸本)9798350354171

Every year a third of the population dies due to cardiovascular disease and these numbers grow exponentially which is one of the leading cause of deaths. Cardiovascular disease is a group of disorders that affect the heart and blood vessels. These disorders often result from high cholesterol, high blood pressure, smoking, obesity, and many other medical attributes. Therefore, early intervention and diagnosis of cardiovascular diseases improves the patient's quality of life. In this era of AI and ML, the ML techniques integrated with explainable AI are a promising tool for implementing and getting improved accuracies in the detection of cardiovascular disease. With the usage of Binary classification dataset on heart disease this paper explored a comparative study with various machine learning algorithms which includes single classifiers (LR, KNN, DT, SVM), bagging(RF), and boosting(XG, ADA, CAT, GRADIENT), ensemble machine learning model using RF, GRAD, ADA, XG, CAT helped in achieving exceptional accuracy of 89.13%, ensemble model with hyperparameter tuning techniques (GridsearchCV and RandomsearchCV) and implementation XAI methods(lime and sharp). In a comparative analysis of boosting algorithms specifically CAT and ADA boost outperformed other algorithms in identifying cardiovascular disease. The hyperparameter tuning techniques help improve the accuracy of Random forest classifier method to an impressive 88.26%. Additionally, the use of XAI methods such as LIME and SHARP provides global and local insights into model behavior and feature importance and represents it graphically which helps in better *** use of these machine learning models in remote areas where diagnosis and specialized treatment of heart-related disease is limited this model plays a pivotal role by helping in early intervention and diagnosis of cardiovascular disease. © 2024 IEEE.

关键词： Diagnosis

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Internet of Things IoMT data Aggregation Using Machine Learning 2

Internet of Things IoMT Data Aggregation Using Machine Learn...

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2nd IEEE International Conference on Device Intelligence, Computing and Communication Technologies, DICCT 2024

作者： Pulabaigari, Lakshmi Sagar Sivakumar, C. School of Computing Mohan Babu University Dept. of Computer Science and Engineering Andhra Pradesh Tirupati India School of Computing Mohan Babu University Dept. of Data Science Andhra Pradesh Tirupati India

ISBN: (纸本)9798350372847

The Internet of Things (IoT) has revolutionized the way data is handled and collected, allowing for large amounts of information to be revolutionized quickly and efficiently. This has paved the way for Machine Learning (ML) applications to be used on IoT datasets, such as 10MT data aggregation. This paper discusses the use of a ML approach for aggregation 10MT data in order to better understand trends and classify patterns in various sectors that would otherwise go undetected. It discusses the benefits and challenges of such a system, and how it could be used to improve the efficiency of industrial operations. It stresses the importance of data integrity in this process, as wrong or incomplete datasets can significantly reduce the accuracy and reliability of the results obtained. Furthermore, it discusses the need for suitable ML algorithms and models, such as clustering and random forest, to be applied to the collected data in order to produce robust and meaningful output. It observes the security and privacy challenges associated with such ML-based 10MT data aggregation systems, and the potential solutions available. © 2024 IEEE.

关键词： Internet of things

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Sparse Channel Estimation in Massive MIMO Systems Using Compressed Sensing and Neural Networks

Sparse Channel Estimation in Massive MIMO Systems Using Comp...

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2024 IEEE International Conference on Intelligent Signal Processing and Effective Communication Technologies, INSPECT 2024

作者： Upadhyay, Deepak Sharma, Kunj Bihari Gupta, Mridul Upadhyay, Abhay Venu, Nookala Graphic Era Hill University Dept. of Computer Science and Engineering Dehradun India Graphic Era deemed to be University Dept. of Computer Science and Engineering Dehradun India ABV Indian Institute of Information Technology and Management Data Science and Quantum Computing Gwalior India

ISBN: (纸本)9798350379525

In this paper, based on compressed sensing and neural networks, we present an intelligent sparse channel estimation technique for Massive MIMO systems. We show that our approach improves considerably the estimation accuracy in terms of Mean Squared Error (MSE) and Normalized Mean Squared Error (NMSE) with respect to classical methods. We demonstrate [Figure: ] that our approach is computationally more efficient, requires less time and resources (memory/disk) compared to the existing literature but for a robustness of performance across distal or low Signal-to-Noise Ratios (SNRs). We also confirm our method exhibits fast convergence and efficiently accommodates a variety of sparsity levels. By visualizing it using heatmaps and ROC curves we show that this method is able to effectively differentiate between signal and noise, asserting our approach as a powerful tool for the contemporary set of problems present in wireless communications. © 2024 IEEE.

关键词： Signal to noise ratio

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Predicting CO and NOx Emissions from a Gas Turbine Using Machine Learning Techniques

Predicting CO and NOx Emissions from a Gas Turbine Using Mac...

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AIAA SciTech Forum and Exposition, 2023

作者： Shahadat, Muhammad Rubayat Bin Murillo, Michael S. Jaberi, Farhad A. Michigan State University Dept. of Computational Mathematics Science and Engineering Dept. of Chemical Engineering and Materials Science East LansingMI48824 United States Michigan State University Dept. of Mechanical Engineering East LansingMI48824 United States

ISBN: (纸本)9781624106996

CO and NOx are emitted from various sources among which gas turbine power plants play a significant role. CO and NOx emissions from a gas turbine are dependent on various factors such as ambient temperature, pressure, humidity, turbine inlet temperature, exhaust temperature, compressor pressure, turbine energy yield, etc. The dataset we are using in this study contains these features which are connected to CO and NOx emissions from a gas turbine power plant. This dataset is used here to predict CO and NOx emissions with several machine learning algorithms. Six different machine learning algorithms (deep neural network, random forest, decision tree, support vector regression, lasso regression, and linear regression) have been used to predict these emissions separately. All the machine learning algorithms were optimized and the best algorithm for this dataset has been found based on four performance metrics: testing scores, R2 value, mean square error (MSE), mean absolute error (MAE), and computational efficiency. The deep neural network and support vector regression have the highest scores, whereas linear regression has the lowest testing score and R2 value for CO prediction. However, for NOx prediction, in terms of testing score and R2 value, support vector regression works better than all other algorithms considered in this study. From MSE and MAE analysis, support vector regression is found to be the algorithm with the least error and linear regression has the highest error for both CO and NOx predictions. On the other hand, linear regression is computationally very efficient because of its simplicity and the deep neural network is computationally costly because of its higher number of hyperparameters. Considering all the performance parameters and computational efficiency, support vector regression is found to be the best machine learning algorithm for predicting CO and NOx emissions. © 2023, American Institute of Aeronautics and Astronautics Inc, AIAA. All right

关键词： Deep neural networks

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Firefly Algorithm Approach for Recovering the Mathematical Structure of One-Dimensional Chaotic Maps from Time Series data 3

Firefly Algorithm Approach for Recovering the Mathematical S...

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3rd International Conference on Computer Technologies, ICCTech 2024

作者： Galvez, Akemi Fister, Iztok Fister, Iztok Iglesias, Andres University of Cantabria Dept. of Applied Mathematics & Computational Sciences Santander Spain University of Maribor Faculty of Electrical Engineering & Computer Science Maribor Slovenia

ISBN: (纸本)9798350395419

Uncovering the mathematical structure of unknown chaotic systems from limited time series data poses a significant challenge in the field of dynamical systems, with broad applications across various domains. In this paper, we propose a swarm intelligence approach to tackle this issue, specifically focusing on one-dimensional discrete maps. Our approach leverages the firefly algorithm, a popular nature-inspired metaheuristic, known for its optimization capabilities. We assess the performance of the method by applying it to two illustrative examples of time series derived from one-dimensional maps: the Hénon map and the Burger map. The results show that this approach is promising for recovering the equations of motion of one-dimensional chaotic maps, even in situations where no additional information is available beyond the provided data points. © 2024 IEEE.

关键词： Time series

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Deep Learning for Channel Prediction in Non-Stationary Wireless Fading Environments

Deep Learning for Channel Prediction in Non-Stationary Wirel...

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2024 IEEE International Conference on Intelligent Signal Processing and Effective Communication Technologies, INSPECT 2024

作者： Upadhyay, Deepak Sharma, Kunj Bihari Gupta, Mridul Upadhyay, Abhay Venu, Nookala Graphic Era Hill University Dept. of Computer Science and Engineering Dehradun India Graphic Era deemed to be University Dept. of Computer Science and Engineering Dehradun India Graphic Era deemed to be University Dept. of Electronics and Communication Engineering Dehradun India Data Science and Quantum Computing ABV Indian Institute of Information Technology and Management Gwalior India

ISBN: (纸本)9798350379525

In non-stationary wireless fading channels deep learning models are effective in predicting channel states as indicated by this study. This work compared the LSTM and GRU models with autoregressive methods or Kalman filter, implying that deep learning is indeed able to capture temporal dependence between variables and was likely more adapted than linear method when dealing with many changes in dynamics. From the perspective of pre-processing data, keys like attention mechanism and transfer learning were decisive elements in helping them to improve prediction scores and make models more robust. These results showed that these enhanced procedures allow the models to generalise over a wide range of non-stationary situations, generating consistent performance in various diverse environments. This study indicates the promise of deep learning for enhancing wireless network operation, which could lead to greater self-awareness and utilization features in emerging future-generation wireless systems. © 2024 IEEE.

关键词： Prediction models

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Secrecy Capacity Optimization in RF/FSO Systems: Impact of Mixed Rayleigh and Log-Normal Fading with Atmospheric Turbulence

Secrecy Capacity Optimization in RF/FSO Systems: Impact of M...

引用

2024 IEEE International Conference on Intelligent Signal Processing and Effective Communication Technologies, INSPECT 2024

作者： Upadhyay, Deepak Sharma, Kunj Bihari Gupta, Mridul Upadhyay, Abhay Venu, Nookala Graphic Era Hill University Dept. of Computer Science and Engineering Dehradun India Graphic Era deemed to be University Dept. of Computer Science and Engineering Dehradun India Graphic Era deemed to be University Dept. of Electronics and Communication Engineering Dehradun India ABV Indian Institute of Information Technology and Management Data Science and Quantum Computing Gwalior India

ISBN: (纸本)9798350379525

This paper investigates the secrecy capacity optimization in RF/FSO systems with mixed Rayleigh and log-normal fading, while masking eavesdroppers channel state information () as well as suffering from atmospheric turbulence. We explore the impact of several parameters including beamforming angles, modulation schemes and hybrid RF/FSO link configurations through a combination of simulations as well as analytical models. Secrecy capacity is enhanced for an improved SNR and vice versa under higher turbulence conditions and cooler influents eavesdropped scenarios. Performance of the hybrid RF/FSO systems dominates considerably over single-link solutions, especially at longer distances. All these mechanisms appreciably increase the secrecy capacity, while beamforming and advanced modulation techniques offer most of the improvement. The study also demonstrates the interdependencies of system parameters, providing guidelines for designing secure communication systems under different environmental and operational conditions. © 2024 IEEE.

关键词： Beamforming

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Image Segmentation Survey: Classical and Deep Learning Methods 4

Image Segmentation Survey: Classical and Deep Learning Metho...

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4th IEEE International Conference on Electrical, Computer, and Energy Technologies, ICECET 2024

作者： Bachani, Viren Roychowdhury, Anish Dasgupta, Abhijit Ei-Alfy, Hazem BSc. Data Science SP Jain School of Global Management Sydney Australia SP Jain School of Global Management Data Science Dept. Sydney Australia SP Jain School of Global Management Data Science Dept. Mumbai India School of Computer Science University of Sydney Sydney Australia Alexandria University Faculty of Engineering Alexandria Egypt

ISBN: (纸本)9798350395914

Image segmentation is a prerequisite to almost all computer vision applications. It enables the extraction of meaningful information from visual inputs by partitioning images into segments with shared features. This review paper begins by outlining different image segmentation tasks. It then surveys a broad spectrum of segmentation techniques, ranging from classical frameworks to deep-learning (DL) architectures. We perform this through a systematic literature analysis across a handful of applications as part of our methodology. A key contribution of our survey is that it elaborates on a few situations where classical approaches outperform DL-based methods in accuracy, along with the added advantage of computational efficiency. In some cases, these findings also provoke interest in revisiting classical methods, prompting strategies that leverage the strengths of both paradigms. © 2024 IEEE.

关键词： Markov processes

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Early Cancer Detection Through Deep Learning Analysis of CT Scan Imagery 5

Early Cancer Detection Through Deep Learning Analysis of CT ...

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5th International Conference on Image Processing and Capsule Networks, ICIPCN 2024

作者： Koreddi, Venkatesh Ravi Kumar, A.V. Bai, Kshatriya Vinaya Sree Sani, Siva Ramakrishna Dept.of Artificial Intelligence and Data Science Andhra Pradesh Vijayawada India dept. of Elecrical and Electronics Engineering Andhra Pradesh Vijayawada India

ISBN: (纸本)9798350367171

Early cancer detection is crucial for enabling effective treatment but the current methods like manually analyzing CT scan images by hand, have some significant challenges. It takes huge time and sometimes error occurs, which can mean people don't get the right treatment when they need it. Also, there are so many scans and not enough experts to look at them all quickly, which makes things even harder. To fix this, medical practitioners are getting assistance from computers. Recently, the computers are trained to analyze CT scans and find cancer automatically using Convolutional Neural Networks (CNNs). The idea of this research work is to make a tool that doctors can use easily and quickly to detect cancer. This study has considered different methods, like EfficientNet and DenseNet to make a computer program even better at detecting cancer. With these improvements, doctors can find cancer faster and more accurately, which enables better chances for peoplegetting the right treatment on time. © 2024 IEEE.

关键词： Convolutional neural networks

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The Role of Syntactic Span Preferences in Post-Hoc Explanation Disagreement 30

The Role of Syntactic Span Preferences in Post-Hoc Explanati...

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Joint 30th International Conference on computational Linguistics and 14th International Conference on Language Resources and Evaluation, LREC-COLING 2024

作者： Kamp, Jonathan Beinborn, Lisa Fokkens, Antske Computational Linguistics and Text Mining Lab Vrije Universiteit Amsterdam Netherlands Institute of Computer Science Campus Institute Data Science University of Göttingen Germany Dept. of Mathematics and Computer Science Eindhoven University of Technology Netherlands

ISBN: (纸本)9782493814104

Post-hoc explanation methods are an important tool for increasing model transparency for users. Unfortunately, the currently used methods for attributing token importance often yield diverging patterns. In this work, we study potential sources of disagreement across methods from a linguistic perspective. We find that different methods systematically select different classes of words and that methods that agree most with other methods and with humans display similar linguistic preferences. Token-level differences between methods are smoothed out if we compare them on the syntactic span level. We also find higher agreement across methods by estimating the most important spans dynamically instead of relying on a fixed subset of size k. We systematically investigate the interaction between k and spans and propose an improved configuration for selecting important tokens. © 2024 ELRA Language Resource Association: CC BY-NC 4.0.

关键词： Syntactics

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