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An Effective Analysis of Machine Learning Models for Prediction of Heart Disease 2

An Effective Analysis of Machine Learning Models for Predict...

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2nd IEEE International Conference on Networks, Multimedia and Information technology, NMITCON 2024

作者： Patil, Rudragoud Kamkar, Shambahvi C. Goudar, Shwetha Indudhar K.L.S.Gogte Institute of Technology Dept of Computer Science and Engineering Karnataka Belagavi India K.L.S Gogte Institute of Technology Dept.of Master of Computer Applications Karnataka Belagavi India

ISBN: (纸本)9798350372892

Heart failure is a chronic illness that impacts millions of individuals worldwide. In order to forecast heart failure health status early and take the necessary activities to address this global issue an effective machine learning-based technique is required. Globally, heart disease is one of the leading causes of death. Medical professionals find it challenging to forecast since it demands experience and a deeper level of predictive knowledge. The healthcare industry operates in a knowledge-deficient but information-rich environment. Healthcare systems have access to a vast amount of data via the internet, but there aren't many useful tools for analyzing the data to find hidden patterns. Medical efficiency will increase with an automated system, which will also save money and time. This software uses information acquired from Kaggle to forecast the likelihood of a disease developing. To create a prediction model, the following algorithms are used: Random Forest (RF), support vector machine (SVM), K-nearest neighbor (KNN), Decision Tree (DT), and Logistic Regression (LR). Phases for feature selection and data preprocessing were finished before the models were created. When the models were evaluated based on accuracy and the F1-score, the RF model performed the best. © 2024 IEEE.

关键词： Adversarial machine learning

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Enhancing Siamese Neural Network Performance with Custom Local Binary Patterns for Face Matching 8

Enhancing Siamese Neural Network Performance with Custom Loc...

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8th IEEE International Conference on Information and Communication technology, CICT 2024

作者： Koranga, Pawan Singh Indian Institute of Technology Kharagpur Dept. of Computer Science and Engineering Kharagpur India School of Computer Sciences Upes Dehradun248001 India

ISBN: (纸本)9798331531539

In this study, we investigate the efficacy of incorporating custom local binary patterns (LBP) as a preprocessing technique to enhance the performance of Siamese neural networks for face matching tasks. Our research focuses on training Siamese models using the DC Face dataset and comparing their performance when trained with and without LBP integration. The custom LBP function is designed to capture fine-grained texture details from facial images, providing robust feature representations that are invariant to variations in lighting conditions and facial expressions. Through rigorous experimentation and evaluation, we analyze the impact of LBP on the threshold point for image differentiation and compare the matching accuracy of Siamese models trained with and without LBP. Our findings reveal significant improvements in matching accuracy and robustness when utilizing the texture features extracted by the custom LBP function. By encoding local pixel structures and capturing subtle facial characteristics, LBP-enhanced Siamese models demonstrate enhanced performance in accurately matching faces across different images. Further-more, our study highlights the importance of using domain-specific preprocessing techniques, such as LBP, to optimize the capabilities of Siamese neural networks for face matching tasks. The results underscore the effectiveness of customizing feature extraction methods to suit the characteristics of the dataset, leading to improved matching accuracy and robustness. Overall, our research contributes valuable insights into the role of LBP in enhancing the performance of Siamese neural networks for image-based similarity tasks, with implications for various real-world applications in face recognition and biometric authentication. © 2024 IEEE.

关键词： Local binary pattern

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EHRGPT: Leveraging language models for generating synthetic health records

EHRGPT: Leveraging language models for generating synthetic ...

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2024 IEEE International Conference on Big Data, BigData 2024

作者： Pamisetty, Giridhar Batreddy, Subbareddy Verma, Priya Babu, Ch Sobhan Indian Institute of Technology Dept. of Electrical Engineering Hyderabad India Administrative Staff College of India Centre for Management Studies Hyderabad India Indian Institute of Technology Dept. of Computer Science and Engineering Hyderabad India

ISBN: (纸本)9798350362480

Electronic health records (EHRs) are the comprehensive digital records containing patient health information, which help in various domains like public health monitoring, predictive modeling, data-driven research, etc. However, there are strict regulations about data sharing due to the sensitivity of the EHR data. So, there is an increasing demand for generating high-quality synthetic EHR data that mitigates privacy concerns. Most of the existing approaches to EHR generation are limited as they predominantly generate binary decisions about affecting a disease or count-based summaries of disease occurrences for a patient, failing to capture the full patient trajectory contained in the real data and hence reducing the downstream applications. With the success of language models in generative approaches, we propose a language modeling approach using transformers to generate the complete health record of a patient for each admission. We propose to group the patient's EHRs as it enhances the model to capture temporal coherence and the medical history of the corresponding patient. The proposed model can generate high-fidelity privacy preserved EHR data with the statistical properties of the real data. © 2024 IEEE.

关键词： Electronic health record

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Adaptive Neighborhood Sampling and KAN-Based Edge Features for Enhanced Fraud Detection

Adaptive Neighborhood Sampling and KAN-Based Edge Features f...

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2024 IEEE International Conference on Big Data, BigData 2024

作者： Batreddy, Subbareddy Pamisetty, Giridhar Kothuri, Manish Verma, Priya Babu, Ch Sobhan Indian Institute of Technology Dept. of Electrical Engineering Hyderabad India Indian Institute of Technology Dept. of Computer Science and Engineering Hyderabad India Administrative Staff College of India Centre for Management Studies Hyderabad India

ISBN: (纸本)9798350362480

Fraud detection models have shown immense improvement in performance with the incorporation of graph neural networks (GNNs), as they are naturally capable of modeling relational data and capturing complex relations between users. However, real-world data has a significant class imbalance with very few fraudulent labels, resulting in a bias toward predicting the majority class as the decision boundaries are skewed towards it. To alleviate this class imbalance, we propose adaptive neighborhood sampling, an effective and optimal neighborhood sampling approach that uses sampling probability comprising of class frequency and global importance of the node through the page rank to reduce the dilution of the fraud nodes. We also propose a novel Kolmogorov-Arnold Network (KAN) based edge feature extraction approach, which treats incoming and outgoing edges separately to effectively capture the directional patterns that are crucial in distinguishing the patterns of both labels. In addition to this, we employed topology-aware margin loss to further improve the class imbalanced node classification. Incorporating these proposed approaches in the GNN-based fraud detection model helped outperform the state-of-the-art fraud models on open-source and complex real-world datasets. © 2024 IEEE.

关键词： Graph neural networks

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Device Identification using Network Traces in a Smart Home IoT Network 17

Device Identification using Network Traces in a Smart Home I...

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17th IEEE International Conference on Advanced Networks and Telecommunications Systems, ANTS 2023

作者： Brahma, Jwngfu Matam, Rakesh Choudhury, Nikumani Indian Institute of Information Technology Dept. of Computer Science & Engineering Guwahati India Birla Institute of Technology & Science Dept. of Computer Science & Information Systems Hyderabad India

ISBN: (纸本)9798350307672

Device identification is a crucial aspect of securing networks, particularly in the context of the Internet of Things (IoT), where a vast variety of devices are interconnected. Recently, there has been significant research on developing techniques for identifying IoT devices based on their unique characteristics, called as device fingerprints. These techniques use machine learning algorithms, which can effectively learn and classify devices based on their features. However, device identification remains a challenging task due to the diversity of IoT devices and the constant evolution of their characteristics. This paper presents our attempt at device identification by analyzing distinct device characteristics observed during network communication and using different machine learning techniques. © 2023 IEEE.

关键词： Internet of things

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A Survey on Eliminating Botnet and Intrusion Attacks through Machine Learning

A Survey on Eliminating Botnet and Intrusion Attacks through...

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2023 Innovations in Power and Advanced Computing Technologies, i-PACT 2023

作者： Faseela, M.H. Manjith, B.C. Dept. Computer Science and Engineering Indian Institute of Information Technology Kerala Kottayam India

ISBN: (纸本)9798350325188

With the rising linkage and dependence on computer networks, the threat of intrusions and botnets has become a critical concern for organizations and those involved. Malicious actors are in control of unauthorized access or actions that harm the security and integrity of computer systems and insecure devices. The identification of attacks and botnets is of utmost importance in ensuring the security of networks and safeguarding confidential information. In recent years, there has been a growing interest among researchers and organizations in developing unique evaluation methods that can adapt to emerging threats and apply intelligent algorithms such as sparse modelling, transfer learning, distributed computing, or meta-learning capable of detecting, classifying, and predicting previously unknown attack patterns, which can result in machine learning and deep learning models that are more accurate and efficient. This review paper compares the performance of machine learning and deep learning approaches such as Naïve Bayes, Support Vector Machines (SVM), and Logical Regression for intrusion and botnet attacks in various network platforms. This paper provides a comprehensive comparison of machine-learning approaches used in the cyber realm for botnet and intrusion detection and encompasses an analysis of the datasets utilized, the specific cyber issues addressed, and the resultant outcomes. Furthermore, it offers valuable insights into the challenges and advancements within the realm of cybersecurity, thereby facilitating the development of novel approaches and technologies aimed at enhancing digital safety. © 2023 IEEE.

关键词： Botnet & Intrusion detection Cybersecurity Machine learning & Deep learning Techniques

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Photoplethysmography Signal Quality Assessment Using Neighbour Edge Restricted Horizontal Visibility Graph and Machine Learning Classifiers 16

Photoplethysmography Signal Quality Assessment Using Neighbo...

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16th International Conference on Electronics, computers and Artificial Intelligence, ECAI 2024

作者： Khan, Zahir Manikandan, M. Sabarimalai Mahmud, Mufti Indian Institute of Technology Palakkad Dept. of Computer Science and Engineering Kerala Palakkad678623 India Indian Institute of Technology Palakkad Dept. of Electrical Engineering Kerala Palakkad678623 India Nottingham Trent University Dept. of Computer Science Nottingham Clifton Lane United Kingdom

ISBN: (纸本)9798350371154

Photoplethysmography (PPG) signals are vital for monitoring pulse rate, blood pressure, and more, but they are prone to motion artefacts and noise, leading to unreliable data. Assessing PPG signal quality is crucial for reliable healthcare and accurate medical diagnoses. By transferring the PPG time domain signal to a Horizontal Visibility Graph (HVG) network and combining extracted features from HVG with machine learning algorithms, we can classify the PPG signal into clean (or high quality) and noisy. We have proposed a new version of HVG called Neighbour Edge Restricted Horizontal Visibility Graph (NERHVG) by invoking some extra conditions for joining edges in HVG for PPG signal quality assessment (SQA). We have used the average degree (AD) of graphs extracted from HVG, and NERHVG algorithms as features in 3 different machine learning classifiers such as Random Forest (RF), Gaussian Naive Bayes (GNB), Decision Tree (DT) to classify 4 standard untrained PPG datasets (DS). The classifier models DT, RF, GNB associated with graph feature AD of HVG, NERHVG algorithms are named as: DT-HVG, DT-NERHVG, RF-HVG, RF-HVG, RF-NERHVG, GNB-HVG and GNB-NERHVG. After all the performance of HVG and NERHVG algorithms using AD feature are compared over the mentioned classifier models. It is observed that the NERHVG algorithm outperformed the HVG algorithm with the AD feature in all 4 datasets with a maximum accuracy of: 99.09%, 95.03%, 96.56 % and 84.63% using the GNB classifier. © 2024 IEEE.

关键词： Decision trees

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Text Transmission Through Li-Fi in WSN Using Smart Based Secure Morse Code Algorithm

Text Transmission Through Li-Fi in WSN Using Smart Based Sec...

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2025 International Conference on Multi-Agent Systems for Collaborative Intelligence, ICMSCI 2025

作者： Vasudevan, I. Malathi, P. Siddik, S. Therasa, P.R. Nasurudeen, M.Mohamed Charaan, R.M.Dilip Vel Tech Rangarajan Dr.Sagunthala R&d Institute of Science and Technology Dept.of Cse Chennai India Saveetha College of Liberal Arts and Sciences Simats Dept of Computer Science India Pavaai Engineering College Dept.of Eee Namakkal India R.M.K Engineering College Dept.of Cse Kavaraipettai India V.S.B Engineering College Dept.of Cse Karur India

ISBN: (纸本)9798331509828

Cyber security is one the most challenging task in today's world. Most of the existing Li-Fi technology in wireless sensor network is to detect the data within the short range of distance less accuracy. In this research, we proposed a Smart based Secure Morse Code (SSMC) technique. Here there are two part, first is encoding and next is decoding. This proposed encoding technique takes user input in text based form and convert these text to standard morse code pattern and decoding does in the opposite direction. The transmitter parts encode the user input and send the pattern through laser module. The receiver part receives the pattern sends to microcontroller and then decodes and displays the data. In our experiment results shows that the proposed technique can transmit data over long distance with help of morse code encoding decoding algorithm. This technique takes 5ms for the whole transmission and process the transmission accurately. © 2025 IEEE.

关键词： Encoding (symbols)

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Enhancing urban thermal understanding via digital twin integration of thermal radiance mapping and geospatial analysis 9

Enhancing urban thermal understanding via digital twin integ...

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Remote Sensing Technologies and Applications in Urban Environments IX 2024

作者： Halder, Dyutisree Harshit, Harshit Garg, Rahul Dev Geomatics Engineering Group Department Indian Institute of Technology Roorkee India Dept. of Computer Science IT University Copenhagen Denmark

ISBN: (纸本)9781510681040

Urban environments are increasingly experiencing heat-related challenges due to climate change and rapid urbanization. To address these challenges, it is essential to improve our understanding of urban thermal dynamics. Digital twin technologies provide an innovative way to integrate multiple data sources and generate high-fidelity, real-time models of urban landscapes, allowing for deeper insights into urban heat distribution. In this study, we showcase three distinct workflows for generating digital twin environments for urban settings with varying degrees of complexity and visualization fidelity, focusing on thermal radiance mapping and geospatial analysis. The first workflow presents a low-level integration utilizing OpenStreetMap (OSM) building footprints to create a fundamental digital twin. Here, OSM data is leveraged to map urban geometries, providing the basic framework for thermal radiance analysis by associating building shapes and layouts with thermal data. This workflow is ideal for lightweight, accessible applications that focus on simple 2D urban heat mapping with readily available open-source tools. In the second workflow, we integrate Cesium Ion into the QGIS environment to create an enhanced 3D urban digital twin. Cesium Ion's 3D tiling capabilities are used to visualize urban geometries in three dimensions, enabling more detailed geospatial analysis. Combined with QGIS's robust spatial data processing, this workflow facilitates advanced urban thermal analysis, including the impact of building heights and materials on heat distribution. Finally, the third workflow demonstrates a cutting-edge approach utilizing NVIDIA Omniverse's implementation of Open Universal Scene Description (OpenUSD) to create highly detailed and realistic 3D urban environments. This state-of-the-art framework allows for the development of photorealistic urban digital twins, capable of supporting complex simulations of thermal dynamics and interactions. With high-definition re

关键词： Thermal modeling

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Detection of Lung Inflammation using Chest X-ray with Deep Learning Algorithm 7

Detection of Lung Inflammation using Chest X-ray with Deep L...

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7th International Conference on Communication and Electronics Systems, ICCES 2022

作者： Singh, K. Purushotham Kumar, B. Niranjan Anil, A. Rajendran, Selvi Dept.of Computer Science and Engineering Chennai India Hindustan Institute of Technology and Science India

ISBN: (数字)9781665496346

ISBN: (纸本)9781665496346

Lung Inflammation causes the passing of around 700,000 youngsters consistently and influences 7% of the worldwide populace. Chest X-ray images are principally utilized for the analysis of this infection. Notwithstanding, in any event, for a prepared radiologist, it is a moving errand to look at chest X-ray. There is a need to further develop the determination precision. In this work, an effective model is developed for the recognition of pneumonia from the computerized chest X-ray pictures is proposed. Several methods are utilized to expand the preparation of dataset in a decent manner. Consequently, it is more advantageous to fabricate a computerized indicator to anticipate the pneumonia utilizing the large information profound learning strategies. Among the wide range of various procedures, Convolutional Neural Networks stand tall and high in this forecast alongside different classifiers. Convolutional brain network comprising of convolution and pooling layers and completely associated Softmax layers toward the finish to give the last forecast. © 2022 IEEE.

关键词： Forecasting

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