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Diagnosis of Glaucoma Through the analysis of Saccadic Eye Movements Employing Machine Learning Methods

Diagnosis of Glaucoma Through the Analysis of Saccadic Eye M...

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International conference on Computing, Communication, and Intelligent Systems (ICCCIS)

作者： R v B S Prasanth Kumar Amudha J Sajitha Krishnan Department of Computer Science & Engineering Amrita School of Computing Amrita Vishwa Vidyapeetham Bengaluru India

This study addresses the significant public health concern of glaucoma, a collection of eye conditions resulting in optic nerve damage and progressive vision loss. To conduct our research, we employed a dataset obtained from collaborative efforts between the Amrita School of Engineering in Bangalore and Narayana Nethralaya Hospital during visual field examinations. The dataset, named EXGP, comprises Humphrey visual Field tests conducted on a sample of 111 patients. Our research investigates the impact of variations in saccadic eye movements on glaucoma performance. This hypothesis is tested through an examination of diverse metrics associated with saccadic eye movements during visual search tasks. The summarizing parameter, visual Field Index (vFI), is employed. However, for its widespread acceptance, further characterization and comparison with conventional indices used in glaucoma diagnosis, specifically Mean Deviation (MD), are required. Our objective is to define the applicability of vFI in advanced glaucoma cases by contrasting it with MD and the criteria for establishing blindness. Through statistical analysis of confidential eye data, we generated data samples from the provided dataset. Subsequently, a range of machine learning algorithms, including Support vector Machine (SvM), K nearest neighbors (Knn), and Logistic Regression (LR), were utilized to predict the presence of glaucoma or normality in individuals. Classification accuracy for the various machine learning models was assessed using appropriate evaluation metrics. Among the three models, Logistic Regression demonstrated the highest accuracy of 0.94 in performing the classification task.

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v2X based vehicle Environment Perception and Occupancy analysis for Dynamic Pedestrian Behaviour

V2X based Vehicle Environment Perception and Occupancy Analy...

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European Control conference (ECC)

作者： Swaraj Tendulkar Mayank Yogesh Khandelwal Frank Schrödel Faculty of Mechanical Engineering Schmalkalden University of Applied Sciences Schmalkalden Germany

ISBN: (数字)9783907144107

ISBN: (纸本)9798331540920

This research paper addresses the topics of the environment perception domain to realise the solution for connected autonomous mobility by using simulation softwares and real life sensors in tandem. The camera and lidar sensor simulation is performed for a specific scenario arising in the environment and the simulation results, sensor readings and the binary occupancy grids received from sensor simulations are analysed. The RealSense Depth Camera is used to perform visual mapping of the static environment for static obstacle perception and map generation whereas the SICK 2D lidar is used to create a dynamic probabilistic occupancy grid for perceiving the dynamic obstacles in the environment. The results from the sensors in simulation and real life are compared, analysed and validated. The dynamic probabilistic occupancy is used as a foundation to further develop an occupancy prediction model which predicts the future occupancy of any dynamic obstacle based on its velocity and direction of motion. Furthermore, a framework is conceptualised for the vehicle to Everything (v2X) communication system which includes the identification and determination of the essential communication infrastructure, type of data, recipients, rate of data transfer and ROS communication nodes.

关键词： visualization Laser radar vehicle-to-infrastructure Dynamics vehicular ad hoc networks Predictive models Probabilistic logic

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Enhancing Battery Management Systems in the Industrial Internet of Things (IIoT): An In-depth exploration of Energy Efficiency, Predictive Analytics, and Intelligent Maintenance Approaches 3

Enhancing Battery Management Systems in the Industrial Inter...

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3rd International conference on Trends in Electrical, Electronics and Computer Engineering, TEECCON 2024

作者： Kumar, N vinoth Sujatha, S. Pandi, v. Samuthira Priya, J Lakshmi Maheswari, M.Uma Arumugam, Deepak Kumaraguru College of Technology Department of Electrical and Electronics Engineering Chinnapedampatti PO India Sri Sairam College of Engineering Department of Eee Bangalore Karnataka Anekal India Chennai Institute of Technology Centre for Advanced Wireless Integrated Technology Tamil Nadu Chennai India Saveetha Engineering College Department of Ece Chennai India Rajalakshmi Engineering College Department of Mechatronics Engineering Thandalam Chennai India Saveetha University Saveetha Institute of Medical and Technical Sciences Saveetha School of Engineering Chennai India

ISBN: (纸本)9798331505714

Battery management systems (BMS) are essential in the fast developing IIoT domain for guaranteeing the efficiency, lifetime, and dependability of energy storage solutions. With the proliferation of IIoT devices across many industries, there is a growing need for sophisticated BMS systems that can optimise energy usage, anticipate battery performance, and enable intelligent maintenance. Improvements in energy efficiency, predictive analytics, and intelligent maintenance approaches are the primary foci of this research as it pertains to the IIoT ecosystem and BMS. Our first step is to investigate ways that IIoT devices can be made more energy efficient. This includes things like optimising the charging and discharging operations of batteries and using adaptive power management techniques. We want to keep IIoT devices running longer and lessen their impact on the environment by making the most of BMS's energy efficiency. Our second topic is the function of predictive analytics in predicting the state of a battery's health and output. Models are created using machine learning algorithms and big data analytics to forecast battery deterioration patterns, identify impending failures, and plan proactive maintenance to reduce maintenance expenses and downtime. We conclude by exploring intelligent maintenance approaches that can deliver BMS valuable insights through the use of real-time data and advanced analytics. A combination of these methods - which include automated maintenance procedures, remote monitoring, and self-diagnostic devices - improves the performance and dependability of IIoT battery systems. This paper examines the present and future of BMS in the IIoT through a thorough literature analysis, case studies, and experimental validation. The insights it provides are quite significant. In light of these results, data-driven methods have the ability to transform battery management, guaranteeing that IIoT devices can continue to withstand the increasing demands of

关键词： Adaptive Power Management Big data Analytics Energy Efficiency Intelligent Maintenance Predictive Analytics Proactive Maintenance Remote Monitoring Self-Diagnostic Systems

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A Comprehensive analysis on Refined BiLSTM-RNN Models for Accurate Forecasting of Bottom Hole Pressure in Managed Pressure Drilling Operations

A Comprehensive Analysis on Refined BiLSTM-RNN Models for Ac...

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International conference on Communication Systems and Network Technologies (CSNT)

作者： v.R. Nagalakshmi Nethi Md Ahammad Sharif Bora Hymavathi Nitish Raj Banka Akhil Muppidi Daniel Ashirvad Department of Petroleum Engineering Godavari Institute of Engineering and Technology (A) Rajahmundry Andhra Pradesh

ISBN: (数字)9798331531935

ISBN: (纸本)9798331531942

Managed Pressure Drilling (MPD) is a crucial technique in the oil and gas industry, ensuring optimal pressure conditions to reduce risks like bore instability and blowouts. Accurate Bottom Hole Pressure (BHP) forecasting is essential for real-time decision-making, but traditional methods often fail to capture the complex dynamics of drilling environments. This paper introduces a Bidirectional Long Short-Term Memory Recurrent Neural Network (BiLSTM-RNN) model for BHP forecasting in MPD. The process includes data exploration, cleaning, pattern identification, feature engineering, and dimensionality reduction to enhance computational efficiency. The BiLSTM-RNN model addresses the vanishing gradient problem and passes in time-series analysis. Simulations in Python achieved reliable error metrics, including MSE of 31942.15, MAE of 136.71, MAPE of 13.02, RMSE of 178.72, and an R2-score of 0.6, demonstrating its effectiveness in improving drilling performance and safety.

关键词： Drilling Analytical models Accuracy Atmospheric measurements Computational modeling Atmospheric modeling Time series analysis Predictive models Security Forecasting

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Single-Channel EEG Artifact Identification with the Spectral Slope

Single-Channel EEG Artifact Identification with the Spectral...

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2023 IEEE International conference on Bioinformatics and Biomedicine, BIBM 2023

作者： Fasol, Melissa C. M. Escudero, Javier Gonzalez-Sulser, Alfredo University of Edinburgh Centre for Discovery Brain Sciences Edinburgh United Kingdom University of Edinburgh School of Engineering Edinburgh United Kingdom

ISBN: (纸本)9798350337488

Electroencephalogram (EEG) signals are a valuable recording technique to diagnose neurological disorders and identify noninvasive biomarkers for clinical application, however, they are vulnerable to various artifacts. It is difficult to define exact parameters which efficiently distinguish artifacts from neural activity, and thus cleaning EEG data often relies on labor-intensive visual scoring methods. While signal processing techniques to remove artifacts exist, many state-of-the-art techniques are designed for multivariate signals, which can be challenging to implement in recording setups with few electrodes. We demonstrate how the spectral slope - a method previously used to distinguish between conscious states by linear regression of the logarithmic EEG power spectra - can also be used to identify epochs contaminated by recording artifacts in rat EEG recordings and propose this as a first pass artifact detection method. We computed the mean spectral slope for both 'clean' and 'noisy' epochs and compared the distributions among individual recordings to determine whether the decision threshold should be dynamic or fixed. We found no significant difference between the mean of these distributions and determined that a spectral slope threshold of -8 μv2/Hz was effective at identifying noisy epochs across all recordings. The accuracy of our method was evaluated against visually scored recordings and obtained an average accuracy, ROC AUC, F1 and Cohen Kappa score of 94.2%, 92%, 86.4%, and 83%, respectively, across all epochs. Our study contributes to the automation of EEG artifact detection by presenting a straightforward initial method for identifying contaminated epochs based on the spectral slope of a single EEG channel in rodent recordings. © 2023 IEEE.

关键词： Artifact Detection EEG Preprocessing Spectral analysis

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Usage of machine learning Algorithms for structural boundaries reconstruction using the non-seismic methods data with feature selection 2

Usage of machine learning Algorithms for structural boundari...

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2nd EAGE data Science in Oil and Gas conference 2021, DSOG 2021

作者： Zaycev, S.v. Ahmetsafin, R.D. Budennyj, S.A. Zhuravlev, S.D. Kiselev, K.v. Orlov, R.v. Smelov, A.S. Grigorev, G.S. Gulin, v.D. Ananev, v.v. LLC MIPT Engineering Center for Minerals Russia PJSC Gazprom Neft Russia LLC Gazpromneft Science and Technology Centre Russia

ISBN: (纸本)9789462823600

Non-seismic methods NSMin geophysics are a crucial addition to classical seismic information. It helps to make decisions at early stages of geological exploration in case of limited information value conditions and provide a new knowledge about geological structure. While seismic exploration remains as the most spreading technique in field geophysics, non-seismic methods predominantly play a role of auxiliary methods, more often particular cases advocate self-sufficiency of NSM in application to exploration geophysical problems. The restoration of structural boundaries is especially important to restore structural boundaries in the space between seismic survey profiles. A simple solution in the form of interpolation does not provide the necessary prediction accuracy, and requires the creation of a complex, often nonlinear model, which is possible using machine learning MLmethods. There is a large number of features at one measurement point - the values of the geophysical fields and their transformations derivatives, filters in a window of different widths. The analysis of the importunateness of each feature before training the ML algorithm allows you to increase the accuracy of the constructed model. © 2021 data Science in Oil and Gas 2021, DSOG 2021. All rights reserved.

关键词： Restoration

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American Sign Language Recognition System for Numerical and Alphabets

American Sign Language Recognition System for Numerical and ...

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2023 IEEE International conference on Research Methodologies in Knowledge Management, Artificial Intelligence and Telecommunication Engineering, RMKMATE 2023

作者： Sathyanarayanan, D. Reddy, T. Srinivasa Sathish, A. Geetha, P. Arunkumar, J.R. Deepak, S. Prem Kumar Department of Computer Science and Engineering B V Raju Institute of Technology Telangana Narsapur India Department of Computer Science and Engineering Potti Sriramulu Chalivudi Mallikarjuna Rao College of Engineering and Technology vijayawada India Department of Computer Science and Engineering Roever Engineering College Perambalur621220 India Department of Artificial Intelligence and Data Science K.Ramakrishnan College of Engineering Samayapuram Trichy India Department of Computer Science and Engineering Modern Institute of Technology and research centre Alwar Rajasthan India

ISBN: (纸本)9798350305708

Sign language is a helpful visual technique that can be utilized by individuals who have difficulty communicating verbally. This language is used frequently by people as well. However, the vast majority of individuals don't understand sign language very well, and as a result, they frequently struggle when attempting to communicate with someone who is fluent in it. The community of people who have trouble speaking or hearing will benefit from an initiative called the Sign Language Recognition System. It is extremely challenging for deaf mutes to communicate, which is why we require a linguistic framework to understand what it is that they are attempting to express. In light of this, we present in this research an American Sign Language (ASL) handwriting translator that is built on convolutional neural networks. Instructing computers to recognize specific characters is one way to accomplish this goal. The Support vector Machine (SvM) and the Convolutional Neural Network are two types of artificial neural networks that are being utilized in our project we have suggest the identification of American Sign Language. In addition, the Decision Tree and voting Classifier are applied to the dataset in order to do an analysis. We have demonstrated the design and execution of an American Sign Language (ASL) translator that also incorporates fingerspelling as a direct consequence of this. © 2023 IEEE.

关键词： Convolution Neural Network Decision Tree Sign Language Support vector Machine voting Classifier

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visualization Research of Big data in Higher Education Based on the Web of Science

Visualization Research of Big Data in Higher Education Based...

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International conference on Electronics, Communications and Information Technology (CECIT)

作者： Linwei She Liqi Lai International Business School Jinan University Zhuhai P.R. China GBA and B&R International Joint Research Center for Smart Logistics Jinan University Zhuhai P.R. China

With the rapid development of information technology such as artificial intelligence and cloud computing, big data of higher education has received a lot of attention. This article uses the visual software Citespace from 2013 to 2022 to research higher education in big data in the Web of Science database. Through visual analysis, and uses of the keyword co-occurrence function and cluster analysis function, this article clarifies the main research directions, key documents, hotspots, and limitations in the field of big data in higher education, education big data technology, learning behavior analysis, learning style exploration, education resource management, and personalized teaching reform are the development direction and research hotspots. This article can provide readers and other researchers with some references and help.

关键词： visualization Education Systems architecture Big data Software Complexity theory Behavioral sciences

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Exploring the mechanism of adjustment formula related to Siwu Decoction in the treatment of polycystic ovary syndrome based on network pharmacology

Exploring the mechanism of adjustment formula related to Siw...

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IEEE International conference on Big data

作者： Tongxuan Ouyang Yu Chen Yan Wang Ying Xie Haotian Li Min Zhao School of Basic Medical Sciences Hubei University of Chinese Medicine Wuhan China School of Traditional Chinese Medicine Hubei University of Chinese Medicine Wuhan China Information Engineering College Hubei University of Chinese Medicine Wuhan China

ISBN: (数字)9798350362480

ISBN: (纸本)9798350362497

OBJECTIvE: Polycystic ovary syndrome (PCOS) is regarded as one of the most common endocrine-metabolic disorders among women of reproductive age, characterized by the complexity of its etiology, the interplay of multiple factors, and its polygenic nature. This paper utilizes the rigorous research methodology of traditional Chinese medicine network pharmacology to conduct an in-depth exploration of the potential mechanisms of action of modified Siwu Decoction in the treatment of PCOS. METHODS:In this study, we first identified the key components of Siwu Decoction using the TCMSP database, and subsequently conducted a meticulous screening of potential targets utilizing the SwissTargetPrediction database. Relevant targets for polycystic ovary syndrome (PCOS) were comprehensively collected from multiple authoritative databases including GeneCards, Therapeutic Target database, OMIM, DisGeNET, and The Pharmacogenomics Knowledgebase. Subsequently, the overlapping portion of these two datasets was visually presented through a venn diagram and imported into the String database to construct a protein-protein interaction network. Using the plugin function of Cytoscape 3.10.1 software, we conducted an in-depth analysis of this network and successfully identified ten core targets. Furthermore, we employed tools provided by the DAvID database and the Microbiotics website to perform an extensive Gene Ontology (GO) enrichment analysis and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analysis on the overlapping genes. Additionally, to validate the specific interaction mechanism between drug components and targets, we conducted molecular docking experiments using AutoDockTools 1.5.6 ***: After analysis, we found that there are 290 common points between drug targets and disease-related targets. Further analysis using molecular docking technology revealed that the top ten crucial targets exhibit excellent intermolecular binding energy with drug active i

关键词： Drugs Proteins Genomics Encyclopedias Ontologies Pharmacology Software Complexity theory visual databases Diseases

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A Numerical real time web tracking and scrapping strategy applied to analysing covid-19 datasets 7

A Numerical real time web tracking and scrapping strategy ap...

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7th Annual International conference on Network and Information Systems for Computers, ICNISC 2021

作者： Ez-Zahout, Abderrahmane Chakouk, Said Mitouilli, Salwa El Bouni, Mohamed Amine Mohammed v University in Rabat Faculty of Sciences Computer Sciences Department Morocco Mohammed v University in Rabat Faculty of Letters and Human Sciences Communication Department Morocco Al Akhawayn University in Ifrane School of Science and Engineering Morocco

ISBN: (纸本)9781665402323

With the huge amount of data collected from the web, it is hard to manually analyze and extract useful incites from tables, matrices, or rows of data. Therefore, we need a way to represent these data (maps or graphs) to analyze/interpret them. data visualization makes it easier to identify trends, patterns, and outliers with large datasets. data Analytics is used to analyze raw data to make conclusions about the information. In this paper, we tackled web scrapping strategies to get COvID-19 data from the web, and then we went through some data analysis and exploration using EDA. © 2021 IEEE.

关键词： data visualization

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