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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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Ocular Cataract Identification Using Deep Convolutional Neural Networks 6

Ocular Cataract Identification Using Deep Convolutional Neur...

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6th International Conference on Artificial Intelligence, Big data, Computing and data Communication Systems, icABCD 2023

作者： Manuel, Feliciana M. E. Saide, Saide Manuel Ali, Felermino D. M. A. Lotfi, Sanae Lurio University Faculty of Engineering Dept. of Computer Engineering Pemba Mozambique New York University Center for Data Science New York United States

ISBN: (纸本)9798350314809

Ocular cataract is among diseases that result in blindness if not treated in time. It affects people worldwide, primarily in underdeveloped countries. This health problem affects the quality of patients' lives. However, early diagnosis avoids blindness and allows the patient to have appropriate treatment. Developing countries, especially those with low income, have a precarious health system, even in the ophthalmology sector, where equipment is lacking. This research aims to develop a deep learning-based model to detect ocular cataracts based on retinal images. We collect 1000 retinal images from Kaggle, which are then equally divided into two classes: with and without cataracts. We then use several neural architectures to correctly classify these images, including ResNet18, ResNet34, InceptionResNetV2, and InceptionV4. We demonstrate that ResNet18 outperforms the other architectures, reaching 95.5% accuracy score. Our results suggest that deep convolutional neural networks can achieve a significant performance in ocular cataracts classification using retinal images. © 2023 IEEE.

关键词： Convolution

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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...

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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 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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A genetic niching algorithm with self-adaptating operator rates for document clustering

A genetic niching algorithm with self-adaptating operator ra...

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2012 8th Latin American Web Congress, LA-WEB 2012

作者： León, Elizabeth Gómez, Jonatan Nasraoui, Olfa Midas and Alife Research Groups Dept. of Computer Systems Engineering Universidad Nacional de Colombia Colombia Knowledge Discovery and Web Mining Lab. Dept. of Computer Engineering and Computer Science University of Louisville United States

ISBN: (纸本)9780769548395

We propose a Genetic algorithm for document clustering, where an evolutionary multimodal optimization algorithm evolves candidate cluster representative solutions to search for dense regions in the sparse high dimensional vector space of text documents. The evolution affects not only the document cluster representatives but also the genetic operator rates which are evolved simultaneously with the document cluster representative solutions. The evolving population consists of candidate document cluster representatives that are encoded in the form of a sparse index and sparse index/frequency variable length vectors. In addition, specialized sparse genetic operators are defined for this special representation. The proposed specialized genetic operators achieve different degrees of exploitation and exploration in searching for the optimal document cluster prototypes, in particular the most specialized operator for the document clustering problem is the Sparse Top-K-Addition operator, which can be seen as an incentive towards a more aggressive exploitation of the local context in a small subset of documents, whereas the simple Sparse Real Addition operator works more in an exploratory manner. As shown in our experiments on two well-known document data sets, taking into account associated terms within a local context adds the benefit of an explicit latent semantic consideration in the search for optimal term lists to describe the cluster prototypes. © 2012 IEEE.

关键词： Cluster analysis

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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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Research Concept Link Prediction via Graph Convolutional Network 8

Research Concept Link Prediction via Graph Convolutional Net...

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8th International Conference on Big data and Information Analytics, BigDIA 2022

作者： Wang, Xiangyu Chen, Lyuzhou Lyu, Derui Ban, Taiyu Guan, Yifeng Chen, Qiuju School of Data Science Ustc Hefei China School of Computer Science and Technology Ustc Hefei China Dept. Electronic Engineering and Information Science Ustc Hefei China

ISBN: (数字)9781665487962

ISBN: (纸本)9781665487962

As interdisciplinary research develops, numerous publications are uploaded to the research community. This makes researchers spend a lot of time searching for valuable information. Some methods have been proposed to provide more accurate recommendations, such as domain terminology-based or graph pattern-based methods. However, due to the continuous emergence of new research concepts and new research fields, it is difficult to apply it to practical tasks. To tackle these issues, this paper constructs the research keywords of different publications into a topological network, and then predicts the possibility of connection between different keywords. To analyze the associations between research concepts, graph convolution technology are employed to design method. It could provide domain-independent recommendations for the relevance of research concepts. Experiments demonstrate that the proposed method can predict associations between research concepts more effectively than traditional methods. © 2022 IEEE.

关键词： Convolution

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