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Detection and Treatment of Epilepsy Disease using EEG Signals

Detection and Treatment of Epilepsy Disease using EEG Signal...

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Cybernation and Computation (CYBERCOM), International Conference on

作者： Shiv Ashish Dhondiyal Sushil Chandra Dimri Dept. of Computer Science and Engineering Graphic Era Deemed to be University Dehradun India

ISBN: (数字)9798331542344

ISBN: (纸本)9798331542351

Epilepsy Seizures is a neurological illness that causes repeated seizures and affects millions of people throughout the world. From the studies it has been detected that 70% peoples in the world suffer from epilepsy disease. The prognosis and medical care of epileptic seizure are critical for improving the quality of life of affected individual. Electroencephalography (EEG) is a valuable technique for diagnosing and managing epilepsy because it allows for real-time monitoring of brain electrical activity. Recent advancements in signal processing techniques, such as the Discrete Wavelet Transform (DWT), have shown significant promise in enhancing the diagnosis and treatment of epilepsy. This paper purposed an optimize Random Forest (RF) algorithm by using the DWT application in the analysis of EEG signals for the prognosis of epilepsy disease along with its treatment like medicines, therapies and other treatments methods to cure epilepsy disease. The suggested approach provides clinicians with a promising tool for early and precise identification of epileptic seizures, which is critical for making improvements in patient outcomes and quality of life. The simulation results show that the purposed optimize support vector machine algorithm gives better results with an accuracy of 98% along with the sensitivity, specificity and f1_score of 97%, 96% and 98% respectively.

关键词： Support vector machines Sensitivity Simulation Epilepsy Signal processing algorithms Transforms Signal processing Electroencephalography Discrete wavelet transforms Prognostics and health management

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Identification of Cancer Biomarkers for Multi-class Diagnostics through Network Analysis of RNAseq Data of Tumor-Educated Platelets

Identification of Cancer Biomarkers for Multi-class Diagnost...

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2022 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2022

作者： Toccacieli, Ali Petti, Manuela Sapienza University of Rome MSc Program in Engineering in Computer Science Rome Italy Sapienza University of Rome Dept. of Computer Control and Management Engineering Rome Italy

ISBN: (纸本)9781665468190

Tumor-educated platelets (TEPs) are circulating blood cells with a distinct tumor-driven phenotype and act as carriers and protectors of metastases. To date, some studies have shown that the TEPs transcriptome can be used for cancer diagnostics. The objective of this study is to propose a procedure based on differential gene expression and differential gene co-expression analyses to identify a set of key genes for multi-class cancer diagnostics. To reach this aim, we analyzed RNA-seq data (57736 genes) of 130 subjects, of whom 40 patients with glioblastoma multiforme (GBM), 35 patients with pancreatic adenocarcinoma (PAAD), and 55 healthy donors (HC). We focused our analysis on the subset of differentially expressed genes (DEGs), and we used these genes to build and analyze the differential co-expression networks, identifying the hub genes. With this procedure, we obtained a restricted set of DEGs that maximize the accuracy in classifying patients according to their conditions (GBM, PAAD, or HC). Indeed, we validated our results by comparing the achieved classification accuracy with that resulting from random selections of DEGs and we obtained that genes selected by differential co-expression (DCE) network analysis have greater predictive power than any other set of differentially expressed genes, including using all of them. © 2022 IEEE.

关键词： Platelets

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Design and Optimization of High-Performance Health Monitoring Systems Using Advanced Sensor Networks

Design and Optimization of High-Performance Health Monitorin...

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Advanced Computing & Communication Technologies (ICACCTech), International Conference on

作者： Heena Mehta Mukesh Singla Dept. of Computer Science and Engineering Baba Mastnath University Rohtak Haryana India

ISBN: (数字)9798331519056

ISBN: (纸本)9798331519063

This research elucidates the development and optimization of advanced sensor network-based health monitoring systems. In response to the increasing need for accurate health data in real-time, advanced gadgets capable of continuously monitoring essential indicators have been created. Contemporary sensor technologies, such as biosensors, IoT devices, and WSN s, enable these systems to facilitate data collection and processing with little disturbance. Optimization techniques like PSO, including machine learning algorithms and energy conservation procedures, are used to enhance the system's performance, reliability, and battery longevity. The study focuses on developing robust and scalable systems suitable for various healthcare applications, while also investigating data processing, communication protocols, network architecture, and sensor selection. Advanced sensor networks will be crucial for future health monitoring, as research indicates substantial improvements in monitoring accuracy, system efficiency, and patient outcomes. Present approach is supposed to provide scalable and efficient solution. Moreover, it would support privacy in order to provide secure solution.

关键词： Wireless sensor networks Accuracy Protocols System performance Medical services Network architecture Real-time systems Biosensors Monitoring Optimization

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A Wireless Intrusion Detection System using Feature Selection with Random Forest

A Wireless Intrusion Detection System using Feature Selectio...

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International Conference on Computing Communication Control and Automation (ICCUBEA)

作者： Abhijit Sharad Warhekar Deepak D. Kshirsagar Dept. of Computer Science and Engineering COEP Technological University Pune Maharashtra India

ISBN: (数字)9798350391770

ISBN: (纸本)9798350391787

Wireless networks have become integral to modern communication infrastructure, facilitating seamless connectivity in various domains. However, their presence everywhere also makes them vulnerable to various attacks. Those can cause failure in network services. Indeed, a robust Wireless Intrusion Detection System (WIDS) is essential for identifying and mitigating attacks on wireless networks. The key objective of WIDS is to detect wireless traffic and classify it as an attack or normal. In this situation, machine learning (ML) algorithms are used to detect attacks or intrusions in wireless systems. However, these algorithms need to accurately detect attacks early. Feature selection (FS) techniques are used to reduce this detection time and increase accuracy. These allow our system to identify attacks against local nodes. The proposed system is implemented, experimented and tested on AWID3 dataset. The performance of the system is determined by using a random forest (RF) algorithm and FS selection techniques. The experimentation conducted on AWID3 shows that the proposed methodology achieved an accuracy of 99.9990% for the binary model using 143 significant features and an accuracy of 99.9985% for multilabel classification using 144 significant features

关键词： Radio frequency Accuracy Machine learning algorithms Wireless networks Intrusion detection Telecommunication traffic Feature extraction Communication system security Security Random forests

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Decentralized Finance and Cross-Chain Interoperable Automated Market Maker - Using BlockChain

Decentralized Finance and Cross-Chain Interoperable Automate...

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Emerging Trends in Information Technology and engineering (ic-ETITE), Conference on

作者： Sarumathi S Apoorv Raj Saxena Yash Raj Saxena Mehreen S Md Sana Saritha Verma Sneha Roy Dept. of Computer Science Engineering HKBK College of Engineering Bangalore India ClayStack Bangalore India

Decentralized Finance (DeFi) represents an alternative paradigm in financial infrastructure, operating a top the Ethereum Blockchain. DeFi leverages Automated Market Makers (AMMs) to facilitate the exchange of coins/tokens within Liquidity Pools (LPs). However, the current framework limits transactions to a single chain, resulting in prolonged processing times for cross-chain transactions. The principal drawbacks of existing AMMs include protracted transaction confirmation periods and constraints in single-chain functionality. This paper introduces Mixta, a web-based multi-chain interoperable AMM, aiming to address these limitations. Mixta empowers users to seamlessly exchange assets across different chains, ultimately reducing transaction confirmation times.

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eSeiz 2.0: An IoMT Framework for Accurate Low-Latency Seizure Detection using Pulse Exclusion Mechanism 20

eSeiz 2.0: An IoMT Framework for Accurate Low-Latency Seizur...

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20th OITS International Conference on Information Technology, OCIT 2022

作者： Sayeed, Md Abu Nasrin, Fatahia Mohanty, Saraju P. Kougianos, Elias Electronic Engineering Technology Eastern New Mexico University United States Computer Science Eastern New Mexico University United States University of North Texas Dept. of Computer Science and Engineering United States University of North Texas Dept. of Electrical Engineering United States

ISBN: (纸本)9781665493482

Epilepsy is a neurological disorder marked by recurrent seizures. At least 3 million Americans and 1% of the global population have epilepsy, requiring a low-latency seizure detection system necessary for effective epilepsy treatment. In this paper, a pulse exclusion mechanism (PEM) based novel seizure detection system has been presented in the internet of medical things (IoMT), which uses a PEM to eliminate unnecessary features or channels and allocate desired pulses in a time frame. An optimized deep neural network (DNN) algorithm is used for feature classification. The proposed approach has been evaluated using CHB-MIT Scalp database. The results of the experiments indicate that the proposed eSeiz 2.0 offers a high specificity of 100% and a low latency of 1.05 sec, which can be useful for wearable biomedical applications as well as real-world epilepsy treatment. © 2022 IEEE.

关键词： Feature extraction

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Analysis of Deep Learning Approaches to Predict Cardiac Arrhythmia in ECG Signals

Analysis of Deep Learning Approaches to Predict Cardiac Arrh...

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IEEE Region 10 Humanitarian Technology Conference (R10-HTC)

作者： Subham Kumar Padhy Saurav Mahalik Himanshu Nayak Sabyasachi Patra Anjali Mohapatra Dept. of Computer Science and Engineering DST-FIST Bioinformatics Lab IIIT Bhubaneswar India Dept. of Computer Science and Engineering IIIT Bhubaneswar India

Abnormal heart rhythm or irregular heartbeat, often known as arrhythmia. It is a kind of cardiovascular illness that necessitates a precise and fast diagnosis. Because of its simplicity and non-invasive nature, an electrocardiogram (ECG) that detects the electric activity of the heart has been frequently used to identify cardiac disorders. Each heartbeat's electrical signal, the peak of action impulse waveforms produced by various specialised cardiac tissues, can be used to diagnose various heart defects. Deep learning has evolved as a significant technique in recent decades due to its ability to handle vast amounts of data. The use of hidden layers in the convolution layer have enhanced pattern recognition performance. Deep learning has aided in the automation of medical image analysis and can aid in detecting of any anomalies in the medical images. In this work, ECG-based automated irregular heartbeat prediction is conducted to determine to which arrhythmia class it belongs with greater accuracy and less data loss. This study is based on convolutional neural networks, which are used to evaluate ECG images. For the normal case and cases impacted by various arrhythmias and my-ocardial infarction, the signals correspond to electrocardiogram (ECG) forms of ***-CNN, ResNet34, ResNet50, vgg16, and vgg19 models are utilised to predict of cardiac arrhythmia. vgg16 performed the best and is chosen to be further tweaked to improve accuracy to 99.79 percent.

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Automatic Detection of Hypernasality in Cleft Palate Speech Using Phase Feature

Automatic Detection of Hypernasality in Cleft Palate Speech ...

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International Conference on Confluence The Next Generation Information Technology Summit (Confluence)

作者： Kanuri Naga venkata sai Hashreta Duggaraju Sri Lalitha Maddula Lalitha Lahari Pittala Deepthi Sri Akhilesh Kumar Dubey Dept. of computer science and engineering Koneru Lakshmaiah Education Foundation Guntur India

The detection of hypernasality, a speech disorder common in children with cleft palate, typically involves analyzing vowels within speech stimuli. An essential acoustic cue for hypernasality detection is the presence of a nasal peak near the first formant $(F_{1})$ . Traditionally, a two-step process is employed, where vowel selection from stimuli is initially performed through manual annotation, followed by automatic feature extraction and classification. This work introduces a novel method for hypernasality detection, automating the entire process, including vowel region selection, feature extraction, and classification. The method leverages vowel onset points for the automatic identification of the vowel region. Subsequently, a modified group delay cepstral coefficient feature is extracted from this automatically selected vowel region. The feature is extracted from modified group delay spectrum, offering enhanced resolution to distinguish closely spaced nasal peaks and the $F_{1}$ , thus improving the capture of nasality evidence. The proposed method demonstrates promising results when applied to hypernasality detection using a support vector machine classifier with the accuracy of 83.50% for /a/ vowel and 93.21% for /i/ vowel.

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Data Augmentation's Effect on Machine Learning Models when Learning with Imbalanced Data

Data Augmentation's Effect on Machine Learning Models when L...

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International Conference on Data science and Advanced Analytics (DSAA)

作者： Damien A. Dablain Nitesh V. Chawla Dept. Computer Science and Engineering University of Notre Dame Notre Dame USA

ISBN: (数字)9798350364941

ISBN: (纸本)9798350364958

Real-world data is often imbalanced, such that the number of training instances varies by class. Data augmentation (DA) of under-represented classes is commonly used to improve model generalization in the face of class imbalance. Despite its ubiquity, the impact of data augmentation on machine learning (ML) models is not clearly understood. Here, we undertake a holistic examination of the effect of DA on under-represented classes. Unlike other studies, which focus on a single ML model type, we examine three different classifier families: convolutional neural networks, support vector machines, and logistic regression models; five different DA techniques and two different data modalities - image and tabular. Our research indicates that DA, when applied to imbalanced data, produces substantial changes in model weights, support vectors and front-end feature selection. These changes occur with respect to all classes, not just the ones that DA is applied to. Further, our empirical analysis shows that data augmentation's positive influence on generalization does not necessarily occur as a result of reducing weight norms. Rather, weight and support vector specialization play important roles in generalization. The specialization process may be a form of memorization that is spawned by variances introduced by augmented data. We investigate the seeming contradiction between improved generalization versus weight and support vector specialization.

关键词： Training Support vector machines Logistic regression Neural networks Machine learning Data augmentation Feature extraction Data models Vectors Faces

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Optimization of ARIA Block Cipher Implementation on RISC-V Systems Through Instruction Extension

Optimization of ARIA Block Cipher Implementation on RISC-V S...

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International Conference on Platform Technology and Service (PlatCon)

作者： Dongwook Yun Muhammad Iqbal Howon Kim Dept. of Computer Science&Engineering Pusan National University Busan Korea

ISBN: (数字)9798350367874

ISBN: (纸本)9798350367881

Cryptographic modules in Internet of Things (IoT) devices have had limited use, primarily to meet key requirements such as efficiency and lightweight. However, as IoT devices fulfill more complex roles and handle sensitive information, the need for efficient and lightweight cryptographic modules is increasing. However, traditional software- or hardware-based implementations to provide cryptographic modules result in a tradeoff between efficiency and lightweight. The emergence of the RISC-V architecture presents a new strategy to unify these distinct implementation approaches. RISC-V provides a modular Instruction Set Architecture (ISA) and support for user-extended instructions to enable the configuration of optimal system architectures. Therefore, a new cryptographic algorithm implementation method that integrates existing implementation methods can be presented. This paper presents various strategies for implementing ARIA block cipher, a Korean standard cryptographic algorithm, on a lightweight RISC-V system.

关键词： Ciphers Instruction sets Systems architecture Internet of Things Standards Optimization

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