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Deep Learning Models for Automatic De-identification of Clinical Text 7th

Deep Learning Models for Automatic De-identification of Cl...

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7th international conference on computer, Communication and Signal Processing, ICCCSP 2023

作者： Sriram, Ravichandra Sundaram, Siva Sathya Sophie, S. LourduMarie Department of Computer Science Pondicherry University Pondicherry India

ISBN: (纸本)9783031398100

In health care, clinical narratives play an essential part in the diagnosis and treatment of patients. In recent times, many organizations have stored these clinical narratives as electronic health records (EHR). the EHR is an organized collection of patient health information in digital format. these EHR’s are an extensive collection of medical information, and they provide novel and rich data for clinical research. EHR also contains the patient’s identities, such as name, address, mobile number, etc. these patient identities must be de-identified before use for clinical research to protect the patient’s privacy. Clinical De-identification intends to detect and eliminate Protected Health Information (PHI) from medical data to facilitate data exchange and publication. this study aims to elucidate how the research community interprets and defines the terms like de-identification and anonymization and the steps involved in the process. A thorough review has been made to provide information on how deep learning approaches are used and how reliable they are. © IFIP international Federation for Information Processing 2023.

关键词： Clinical research

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Multi-camera Enhanced Real-Time Content-Aware Vehicle Detection 7th

Multi-camera Enhanced Real-Time Content-Aware Vehicle Detect...

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7th IFIP TC 12 international conference on Computational Intelligence in Data science, ICCIDS 2024

作者： Mafaz, Mohamed Karthiga, I. Priya, S. Sharon Department of AI and Data Science GST Rd Perungalathur Chennai600063 India Department CSE Grand Southern Trunk Rd Vandalur Chennai603210 India Department Computer Science Engineering Grand Southern Trunk Rd Vandalur Chennai603210 India

ISBN: (纸本)9783031699818

this paper introduces an open-source solution for real-time object awareness in the context of intelligent vehicles, designed to seamlessly adapt to simulation environments. the system transcends traditional vehicle detection, offering a comprehensive framework that includes object classification, precise location, and rotation estimation. Leveraging a multi-camera setup, our approach provides a comprehensive understanding of the surrounding environment, extending beyond vehicle recognition to include various other objects. What sets our solution apart is its capability to efficiently reformat and transition to simulation environments like Blender, enabling integration and testing within virtual contexts. the core components of our system encompass data collection, preprocess-ing, deep learning model selection, Use of Look up table, model training, efficient inference, and post-processing techniques. Designed for open-source collaboration, this solution is positioned for continuous improvement, adaptation to evolving needs, and addressing emerging challenges in the field of intelligent transportation and related domains. this paper represents a foundational step towards establishing an accessible and adaptable real-time object awareness system, encouraging innovation and research in the realm of intelligent vehicles and their applications, both in the real world and in simulated environments. © IFIP international Federation for Information Processing 2024.

关键词： Table lookup

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Detection and Estimation of Diameter of Retinal Vessels 7th

Detection and Estimation of Diameter of Retinal Vessels

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7th international conference on computer, Communication and Signal Processing, ICCCSP 2023

作者： Jamwal, Abhinav Department of Computer Science and Engineering Indian Institute of Technology Roorkee India

ISBN: (纸本)9783031398100

the change in retinal vessel diameter is a crucial measure of disorders, including hypertension, high blood pressure, and diabetes. Abnormalities in the retinal vasculatures can signify a spectrum of diseases. In retinal fundus images, providing an automatically estimated diameter of blood vessels may aid clinicians in diagnosing diseases like hypertensive retinopathy, which can even lead to blindness. therefore, automated diagnosis of these diseases requires accurate measurements of vascular diameters. In this research work, this paper have investigated three methods for the estimation of the diameter of the retinal blood vessel. Two publicly available datasets such as Central Light Reflex Image Set (CLRIS) and Vascular Disease Image Set (VDIS) of the Retinal Vessel Image Set for Estimation of Width (REVIEW) database have been used for the comparison of estimated diameter of the retinal vessel. the performance of these three algorithms is compared against the available ground truth average diameter for both CLRIS and VDIS. It was found that the estimated diameter is closer to the available ground truth using morphological-based and center line-based, respectively. However, the estimated diameter using the line integral-based method is significantly different from the ground truth for both datasets. © IFIP international Federation for Information Processing 2023.

关键词： Image segmentation

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Novel Visual Effects in computer Vision of Animation Film Based on Artificial Intelligence 7th

Novel Visual Effects in Computer Vision of Animation Film Ba...

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7th international conference on Inventive Communication and Computaional Technologies, ICICCT 2023

作者： SivaKrishna Reddy, V. Kathiravan, M. Lokeswara Reddy, V. Hindustan Institute of Technology and Science Kelambaakkam Kelambaakkam India Department of Computer Science Engineering Hindustan Institute of Technology and Science Padur Chengalpattu Kelambaakkam India Krishnapuramu India

ISBN: (纸本)9789819951659

A person appreciates more spiritually fulfilling entertainment as his worldly life advances. Spiritually themed films and TV shows are attracting more viewers. However, the film industry has proliferated, and animated film production has increased yearly. there’s an urgent need to create a mechanism to locate consumers’ favourite anime movies in enormous databases fast and reliably. this work uses artificial intelligence and machine learning (ML) to explore new visual expressions in animation films. this study provides a backdrop for the sensible development and upgrading that has been taking place, which uses ML technology as a basis for researching new approaches and models to improve movie visual representation and suggests that it is crucial to film’s creative development. Convolution Neural Network investigates Hollywood’s "Kung Fu Panda news" visual language. the study found that when the model parameters were identified, the test set’s accuracy remained at 57%. the enhancing film works’ innovative and visual quality and promoting a balanced and long-term film industry expansion are crucial. © 2023, the Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： computer vision

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Neural Networks for Environmental Monitoring: A Web Application for Image Recognition 7

Neural Networks for Environmental Monitoring: A Web Applicat...

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7th international conference of Artificial Intelligence, Medical Engineering, Education, AIMEE 2023

作者： Bernatska, Nataliya Dzhumelia, Elvira Typilo, Iryna Kochan, Orest Salamon, Ivan Department of Physical Analytical and General Chemistry Institute of Chemistry and Chemical Technologies Lviv Polytechnic National University Lviv79000 Ukraine Software Department Institute of Computer Science and Information Technologies Lviv Polytechnic National University Lviv79000 Ukraine Department of Information-Measuring Technologies Institute of Computer Technologies Automation and Metrology Lviv Polytechnic National University Lviv79000 Ukraine School of Computer Science Hubei University of Technology Wuhan430068 China Department of Ecology Faculty of Humanities and Natural Sciences University of Presov Presov080 01 Slovakia

ISBN: (纸本)9781643684901

Environmental monitoring is increasingly reliant on image recognition through neural networks, automating the identification and classification of environmental threats. this automation optimizes resource allocation, enhances monitoring accuracy, and empowers researchers to address climate-related challenges. In response to this, we present a web application built using *** and ***, which enables real-time image recognition. this application facilitates tasks ranging from climate analysis to disaster response and wildlife conservation, offering valuable insights into environmental trends. Moreover, it engages the public, promotes awareness, and contributes to conservation efforts, making it a versatile and cost-effective tool for environmental monitoring. the application leverages advancements in image recognition technology, availability of datasets, open-source frameworks, and cloud computing infrastructure. It supports a wide range of applications, promotes global collaboration, and ensures data security. While challenges exist, the potential of neural networks in environmental monitoring is promising. the web application combines the power of *** and artificial neural networks, enhancing environmental monitoring efficacy and fostering sustainable practices on a global scale. Ongoing research and standardization efforts are essential for refining and expanding this approach. © 2024 the Authors.

关键词： Image recognition

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Advanced Fake News Detection in Chinese Using ECL (ERNIE-CNN-LSTM) Model 7

Advanced Fake News Detection in Chinese Using ECL (ERNIE-CNN...

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7th international conference on Machine Learning and Natural Language Processing, MLNLP 2024

作者： Yu, Bo Shan, Yiyu Abdalla, Hemn Barzan Ihnaini, Baha School of Mathematical Sciences College of Science Mathematics and Technology Wenzhou-Kean University Wenzhou China College of Science Mathematics and Technology Wenzhou-Kean University IAINLP Department of Computer Science Wenzhou China

ISBN: (纸本)9798350354973

With the proliferation of Internet technology and social media platforms, information can be easily exchanged across regions at unprecedented speeds, but this can also reduce the quality of information - especially in an era when false and misleading content spreads like wildfire on social media. False news spreads among people, misleads individuals and causes great harm in politics, economy, public health, and other fields. Despite the challenges of fake news detection and intervention, excellent technical support is provided in this regard thanks to recent advances in big data, natural language processing (NLP), deep learning paradigms, and more. Currently, however, the technology that can be used to determine whether news is true or false has been developed primarily in English - and there is an urgent need to optimize doing this in China at a non-minimal scale and complexity. the motivation and purpose of this study is to achieve the same high accuracy of fake news detection in the Chinese context. this article suggests a high-precision Chinese fake news detection system that innovatively combines the ERNIE 3.0, TextCNN, and BiLSTM models. Each model has a distinct role in our system: ERNIE 3.0 collects sophisticated semantic information using its robust pre-trained language model, which is particularly useful for comprehending Chinese text;TextCNN is good at extracting local features from text and identifying essential patterns. BiLSTM excels in identifying long-distance dependencies and contextual information in text. We employ a progressive training technique, initially training and optimizing the performance of each model independently, and then in the integrated model by weighting the outputs of these independent models so that they learn from one another, hence enhancing overall predictive power. As a result, our model can effectively integrate the benefits of each, improve the complete analysis capability of the complex characteristics of fake news, and i

关键词： Semantics

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Proceedings of the 7th international conference on advances in Computing and Data sciences, ICACDS 2023

Proceedings of the 7th International Conference on Advances ...

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Proceedings of the 7th international conference on advances in Computing and Data sciences, ICACDS 2023

ISBN: (纸本)9783031379390

the proceedings contain 47 papers. the special focus in this conference is on advances in Computing and Data sciences. the topics include: Optimal Perfect Phylogeny Using ILP and Continuous Approximations;a Novel Method for Near-Duplicate Image Detection Using Global Features;VSMAS2HN: Verifiably Secure Mutual Authentication Scheme for Smart Healthcare Network;Optimal KAZE and AKAZE Features for Facial Similarity Matching;modified InceptionV3 Using Soft Attention for the Grading of Diabetic Retinopathy;Comprehensive Study of Cyber Security in AI Based Smart Grid;sentiment Classification of Diabetes-Related Tweets Using Transformer-Based Deep Learning Approach;assessment and Prediction of a Cyclonic Event: A Deep Learning Model;technology Enabled Self-directed Learning: A Review and Framework;Energy Preserving ABE-Based Data Security Scheme for Fog Computing;Detecting Toxic Comments Using FastText, CNN, and LSTM Models;UEye: Insights on User Interface Design Using Eye Movement Visualizations;code Mixed Information Retrieval for Gujarati Script News Articles;Plant Disease Classification Using VGG-19 Based Faster-RCNN;classification of Real and Deepfakes Visual Samples with Pre-trained Deep Learning Models;deep Learning Based Speech Synthesis with Emotion Overlay;An Ensemble Deep Learning Algorithm to Predict PM2.5 Concentration Levels in Bengaluru’s Atmosphere;Deep Learning Model Based on a Transformers Network for Sentiment Analysis Using NLP in Sports Worldwide;An Effective Framework for Sentiment Analysis Using RNN and LSTM-Based Deep Learning Approaches;spatial Domain Method for Image Analysis: A Grey-Level Computation Approach;an Approach for Effective Object Detection;crop Yield Prediction for Smart Agriculture with Climatic Parameters Using Random Forest;Implementation of XGBoost Regression for Calories Burnt Prediction Using R;machine Learning-Based Temperature Monitoring and Prediction.

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Energy Preserving ABE-Based Data Security Scheme for Fog Computing 1

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Proceedings of the 7th international conference on advances in Computing and Data sciences, ICACDS 2023

作者： Kumar, Sandeep Garg, Ritu Department of Computer Engineering NIT Kurukshetra Kurukshetra India

ISBN: (数字)9783031379406

ISBN: (纸本)9783031379390

A cutting-edge technology called fog computing makes it possible to offer services and applications for IoT/end devices at the network's edge quickly and effectively. As the fog computing paradigm is driven from the cloud computing paradigm, the security and privacy concerns have been also passed down to fog. But fog and cloud differ in terms of architecture and capabilities, thus the available cloud solutions are irrelevant to the fog. Moreover, fog is resource constrained in nature as well as it has limited energy as it runs over batteries. therefore there is a need to develop an energy preserving lightweight security scheme to secure the data as well as to provide a fine grained access control mechanism. thus, in this article we have developed a cryptosystem based on Chosen-Ciphertext Attribute-based encryption (ABE) with dynamic key change in order to secure the data and to prohibit unauthorized access in an energy efficient manner. We have evaluated and analyzed the sturdiness and the security of our proposed cryptosystem considering the energy consumption and mitigating attacks as primary aspects. Additionally, our cryptosystem is effective and energy efficient compared to other techniques. © 2023, the Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Energy utilization

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Comparison of Malware Detection Techniques Using Machine Learning Algorithms 7th

Comparison of Malware Detection Techniques Using Machine Lea...

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7th international conference on Advanced Computing and Intelligent Engineering, ICACIE 2022

作者： Kumar, Vishal Srivastava, Mitali Srivastava, Atul Kumar Kumar, Adarsh School of Computing DIT Uiversity Uttarakhand Dehradun India Capgemini India Pvt. Limited Pune India School of Computer Science and Engineering Technology Bennett University Greater Noida India

ISBN: (纸本)9789819950140

With the intricacy of malware evolving as quickly as innovation advances, the conflict between security researchers and malware writers is never-ending. Cyber-attacks and therefore the use of malware are increasingly ubiquitous these days. States or a publicly traded firm is the examples of targets. Malware analysis has become a critical component of incident response for computer security issues. During forensics investigations, organizations are frequently presented with strange records gathered by their antiviral and security monitoring systems. Most arrangements suspiciously leak multiple tactical files through various strategies involving both inactive and active procedures in order to identify malware. However, these mechanisms have numerous limitations that give rise to an unused inquire about track. the point of this paper is to handle the utilization of machine learning calculations to analyze malware and uncover how information science is utilized to detect malware. Attack-detection training systems enable the development of better protection tools that can detect attacks and unprecedented campaign. this research demonstrates that a variety of models can be utilized to determine their detect ability. the findings of our example show that malware can be analyzed using a variety of machine learning techniques (ML) algorithm to compare them. On a short dataset, the result shows that the decision tree algorithm has the best detection accuracy when compared to other classifiers, with 99 and 0.021% false positive rate (FPR). © the Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Malware

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Intra-Hour Solar Irradiance Estimation Using Infrared Sky Images and MobileNetV2-Based CNN Regression 7

Intra-Hour Solar Irradiance Estimation Using Infrared Sky Im...

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7th IEEE conference on Energy Internet and Energy System Integration, EI2 2023

作者： Nijhum, Ifran Rahman Kenny, Paul Dev, Soumyabrata The Adapt Sfi Research Centre Ireland School of Computer Science University College Dublin Dublin Ireland School of Architecture Planning and Environmental Policy University College Dublin Dublin Ireland School of Computer Science University College Dublin Beijing-Dublin International College Dublin Ireland

ISBN: (纸本)9798350345094

In recent years, the rapid growth in both the development and use of solar energy has spurred intensive research in solar power estimation and forecasting in energy system integration. Solar irradiance, the sun's energy incident on Earth, is subject to fluctuations caused by dynamic atmospheric particulates such as clouds, significantly impacting energy pro-duction. Ground-based sky imagers, such as the Total Sky Imager (TSI), have substantially improved forecasting accuracy by cap-turing essential cloud data. Deep learning models, particularly Convolutional Neural Networks (CNNs), have become pivotal in this field. this paper introduces a novel method for intra-hour solar irradiance estimation using infrared sky images and a CNN-regression model. the primary objective of this research is to achieve real-time accuracy, addressing the critical need for precise solar energy forecasting. the proposed method advances the state-of-the-art by reducing the Root Mean Square Error (RMSE) to an impressive 16.18 W/m2. In our study, we leveraged a pre-trained and lightweight MobileNetV2 model, underlining its exceptional effectiveness in enhancing our approach for solar irradiance estimation from infrared sky images. this utilization of advanced pre-trained CNN models underscores the potential for accelerating progress in the field of solar energy forecasting. © 2023 IEEE.

关键词： Solar energy

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