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A Deep Learning and Artificial intelligence Technique for COVID-19 Detection in Chest X-rays 2

A Deep Learning and Artificial Intelligence Technique for CO...

引用

2nd international conference on Data Science and Information System, ICDSIS 2024

作者： Prabu, R. Dhinakar, P. Bharath Institute Of Higher Education And Research Department Of Electronics And Communication Engineering Tamil Nadu Chennai India

ISBN: (纸本)9798350381665

The coronavirus (COVID-19) is a serious global health concern in 2019. The recognition of COVID-19 in X-ray images is crucial to diagnosis, assessment, and management of this disease. A radiology report must also communicate diagnostic uncertainty, an unpleasant but necessary task. In this study, chest X-ray images are used to detect COVID-19 infections using artificial intelligence. To provide reliable diagnostic information, a proposed AI model categorizes patients into two classes (COVID and normal). As part of this analysis, we also assess several existing COVID-19 detection techniques, including MobileNetv2 and ResNet50, and compare to recommended model to these techniques. A lot of interest has been shown in deep learning and artificial intelligence techniques for detecting COVID-19 in chest X-rays. The F1score of this suggested model for identifying COVID-19 patients is 98.37 percent on the basis of experimental data. In tests, this proposed method of AI outperforms the most sophisticated existing algorithms. The methodology improves access to test kits and aids in the diagnosis of COVID-19 by helping physicians become more informed. © 2024 IEEE.

关键词： COVID-19

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Analysis of New Distribution Network Planning Using Artificial intelligence Semantic recognition 2

Analysis of New Distribution Network Planning Using Artifici...

引用

2nd international conference on Innovations and Development of Information Technologies and Robotics, IDITR 2023

作者： Liao, He Yang, Jing Li, Hao Tuo, Jianjun Ma, Yarong Feng, Yuxin Fei, Changshun State Grid Gansu Electric Power Company Enterprise Development Department Lanzhou China Accenture Information and Technology Center Co Development Department Beijing SGITG Ltd. Beijing China

ISBN: (纸本)9798350345575

Use artificial intelligence semantic extraction and knowledge calculation to create professional knowledge base, covering professional field standards, key indicators, and evaluation experience, and form a knowledge map and store it in the graph database, and the formed professional key knowledge base can be used as a standard library and algorithm library for professional applications. This paper extracts knowledge from the feasibility study documents in the power grid field to form a knowledge base of key points for grid feasibility study review: Use artificial intelligence OCR recognition technology to structure useful information from feasibility study documents, use artificial intelligence NLP components to extract knowledge from information, and use artificial intelligence knowledge graphs to store knowledge. © 2023 IEEE.

关键词： Semantics

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Research on Robot Image recognition Based on Deep Learning 2

Research on Robot Image Recognition Based on Deep Learning

引用

2nd IEEE international conference on Control, Electronics and Computer Technology, ICCECT 2024

作者： Zhang, He Chen, Lei Tong, Xinyu Li, Yaxuan Guo, Huabo Yang, Yizhou Lu, Shixiang College of Mechanical Engineering Tianjin University of Science & Technology Tianjin China College of Food Science and Engineering Tianjin University of Science & Technology Tianjin China College of Electronic Information and Automation Tianjin University of Science & Technology Tianjin China College of Artificial Intelligence Tianjin University of Science & Technology Tianjin China

ISBN: (纸本)9798350380958

In recent years, the integration of image processing and machine vision technologies to achieve automation and intelligence has become a major focus of research. Intelligent management not only reduces labor and intensity but also enhances the efficiency of planting, harvesting, and picking crops and fruits. Initially, data is preprocessed by checking the dataset, removing erroneous labels and low-quality images, adjusting image size, transforming color space, denoising, and normalizing to enhance feature extraction and increase dataset size and diversity. This paper utilizes the YOLOV8 model to identify apples in images, obtaining bounding boxes and counting them. This count represents the number of apples. Simultaneously, a histogram is created to display the distribution of apple numbers based on these counts. Subsequently, the image dataset is read, images are displayed, and preprocessing and feature extraction functions are defined. The YOLOV8 model is loaded, position transformations are performed, and color and texture features are integrated. Finally, maturity is assessed, statistical analysis is conducted, and results are displayed. Lastly, the image dataset is read and an image is displayed. The paper defines preprocessing functions for binarization and morphological processing, contour extraction functions for apple detection and fitting, and functions for estimating apple area and quality. Statistical analysis and presentation of results are then performed. © 2024 IEEE.

关键词： Feature extraction

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6th international conference on Computational intelligence in pattern recognition, CIPR 2024

6th International Conference on Computational Intelligence i...

引用

6th international conference on Computational intelligence in pattern recognition, CIPR 2024

ISBN: (纸本)9789819780891

The proceedings contain 45 papers. The special focus in this conference is on Computational intelligence in pattern recognition. The topics include: Vietnamese Text Summarization Using Transformer Approach;DQN Trader: Reinforcement Learning for Automated Trading;indirect Feature Extraction for Efficient Fingerprint recognition;A Novel and Intelligent Neuroimage Analysis for Tumor Classification with Thermo-Color Segmentation Using VGG-16;a Deep Feature Ensemble Methodology for 2D Biomedical Image Classification;detection of Hardware Trojan in Nanocrossbar-Based Cryptographic Circuit;convolutional Neural Network Segmentation for Satellite Imagery Data to Identify Landforms Using U-Net Architecture;performance Comparison of Different machine Learning Classifiers for Diabetes Prediction;Intelligent Anti-tamper Systems: Safeguarding Critical Infrastructure in the Era of Proliferating Interconnected Devices and IoBT;contrasting Various Cryptographic Algorithms for Storage and Cloud Computing Services;A New Technique of Cellular-Automata-Based PUF Design in Nanoscale Crossbar Circuits;CrimeScene2Graph: Generating Scene Graphs from Crime Scene Descriptions Using BERT NER;a Squeeze and Excitation Framework Utilizing ResNet-152 for Alzheimer’s Disease Dementia Classification;Identification of Real or Fake Currency Using Semi-supervised Generative Adversarial Networks (SSGANs);efficient Detection of Fake Twitter Profiles on Social Media Through Hybrid Deep Learning Model;religious Sentiment Analysis and Detection on Social Media;Visualizing NASDAQ Composite Index Historical Data Using Tableau;typeface Identification for English Using Convolutional Neural Networks;Adaptive Ensemble of ML Regressors and LSTM for Stock Price Prediction;PolyNMT: Neural machine Translation Model with Seq2Seq Encoder-Decoder System;a Rectangular Slotted Sierpinski Fractal Bowtie Antenna for Multiband Wireless Communications;enhancing Cardiac Health: machine Learning in Coronary Artery Disease Predi

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Artificial intelligence for Enhanced Anti-Money Laundering and Asset Recovery: A New Frontier in Financial Crime Prevention 2

Artificial Intelligence for Enhanced Anti-Money Laundering a...

引用

2nd international conference on Intelligent Cyber Physical Systems and Internet of Things (ICoICI)

作者： Balaji, K. Christ Univ Business & Management Bengaluru India

ISBN: (纸本)9798331540661;9798331540678

The incorporation of artificial intelligence (AI) into asset recovery and anti-money laundering (AML) procedures signifies a revolutionary change in the handling of financial crime. This article investigates the use of AI techniques to improve AML compliance, detect suspicious activity, and improve transaction monitoring. Financial institutions can now analyze massive volumes of transaction data in real-time, find anomalies, and lower false positives thanks to AI-based solutions, which include machine learning algorithms and predictive modeling. The research also outlines the difficulties and advantages of implementing AI, such as enhancing the effectiveness and caliber of suspicious activity reports (SARs) while resolving security and privacy issues with data. The study makes the case that AI's capacity to offer collaborative analytics and dynamic risk assessments is essential for the development of AML frameworks and the overall effectiveness of financial crime prevention.

关键词： Artificial intelligence (AI) Anti-Money Laundering (AML) Transaction Monitoring machine Learning Algorithms Suspicious Activity Reports (SARs)

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ACI 2023 - Workshop on Advances in Computational intelligence, co-located with the 2nd international conference on Artificial intelligence and Data Science, ICAIDS 2023

ACI 2023 - Workshop on Advances in Computational Intelligenc...

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2023 Workshop on Advances in Computational intelligence, ACI 2023

The proceedings contain 25 papers. The topics discussed include: development of a knowledge-based system for diagnosis and treatment of obesity;real-time hand gesture recognition system;enhanced detection of epileptic seizure using supervised and unsupervised algorithms;an anomaly-based network intrusion detection system using ensemble feature integration with deep learning techniques;advancements in text-to-image generation: a comparative study of model architectures, datasets and performance metrics;survey of early melanoma symptoms in medical dermoscopy using deep learning methods;a survey on underwater image segmentation and image quality enhancements using deep learning models;deepfake image detection & classification using Conv2D neural networks;and integrating language models and machine learning for crop disease detection for farmer guidance.

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Determining the Underlying Factors of Chronic Liver Disease through Factor Analysis 2

Determining the Underlying Factors of Chronic Liver Disease ...

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2nd international conference on Artificial intelligence and machine Learning Applications, AIMLA 2024

作者： Balaji, V.S. Mahi, A.R. Aashif, M. Manju, M. Indira Priya, P. Rajalakshmi Engineering College Department of Artificial Intelligence and Machine Learning Chennai India

ISBN: (数字)9798350349221

ISBN: (纸本)9798350349221

Chronic liver disease is a significant health concern, necessitating a comprehensive understanding of its underlying causes. The work aims to identify the key factors contributing to chronic liver disease using Factor Analysis, a statistical technique that explores the inter-relationships among a set of variables. The dataset, comprising various biochemical parameters and demographic details, is subjected to rigorous preprocessing, including imputation and standardisation. The suitability of data is determined using KMO measure value. The factors are represented by combinations of things that include the biochemical parameters protein, aminotransferase, globulin, phosphatase and *** research provides valuable insights, potentially aiding in early diagnosis and effective treatment strategies for chronic liver disease. This research underscores the utility of Factor Analysis in medical research, particularly in understanding complex diseases like chronic liver disease. The overview of Factor Analysis would include the loadings of each factor for each variable on each factor, which indicate how much each variable contributes to each factor and further help to identify which variables are most strongly associated with each underlying factor. The communalities and specific variances of the factors are *** could potentially aid in early diagnosis and effective treatment strategies for chronic liver disease. The complex relationship among various attributes of the dataset is examined thereby providing the contribution of a vital parameter to liver disease. © 2024 IEEE.

关键词： Factor analysis

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Designing the Built Environment Through Hybrid intelligence 2

Designing the Built Environment Through Hybrid Intelligence

引用

2nd international conference on Hybrid Human-Artificial intelligence (HHAI)

作者： Homayounirad, Amir Delft Univ Technol TU Delft Elect Engn Math & Comp Sci EEMCS Interact Intelligence Grp AiBLE Lab Delft Netherlands

ISBN: (纸本)9781643683942;9781643683959

There is a lack of an intelligent platform that supports continuous deliberation and captures diverse views and stakeholders' values during the architectural design process in the early stages. Using hybrid intelligence, this study proposes a method that integrates value, and design pattern theories, to support deliberation during the design process. Three steps comprise the method: eliciting value, extracting design patterns, and designing through deliberation with AI agents using natural language processing through hybrid intelligence. The final set of design patterns reflects the participants' values and ideas, facilitating informed consensus and collaboration between stakeholders supported by AI agents. By integrating diverse perspectives into the loop through continuous deliberation, the proposed method incorporates stakeholders' value for extracting design patterns that address primary design goals and challenges such as energy transition in the built environment.

关键词： Hybrid intelligence Built environment Design pattern Value Deliberation

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Automated Detection of Tree-Cutting Activities in Forest Environments using Convolutional Neural Networks and Advanced Audio Feature Extraction Techniques 2

Automated Detection of Tree-Cutting Activities in Forest Env...

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2nd international conference on Self Sustainable Artificial intelligence Systems, ICSSAS 2024

作者： Mohmmad, Sallauddin Sanampudi, Suresh Kumar Jntu Telangana Hyderabad India Sr University School of Computer Science and Artificaial Intelligence Telangana Warangal506371 India Jntuh University College of Engineering Department of Information Technology Nachupally Kondagattu Telangana Jagtial India

ISBN: (纸本)9798350368413

Detecting tree cutting is essential in preventing illegal logging and also conserving the forest's natural resources. This research aimed to present a solution for illegal logging by developing a sound detection system capable of identifying tree-cutting activities in forest environments. Based on concepts of machine learning and deep learning techniques, this research introduced a customized Convolutional Neural Network (CNN) model that is used to classify different sound events related to tree cutting. The model is trained using a dataset of four classes, and each class of 1200 samples are chainsaws, axe cutting, and handsaw sounds, along with other sounds of the forest environment. In this model features extracted from sound samples such as MFCC, Mel-Spectrogram, and Tonnetz are to enhance the model's classification accuracy. The proposed model with customized CNN architecture provided the classification accuracy of 82% over the 4800 sample of the dataset. © 2024 IEEE.

关键词： Spectrographs

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From Sci-Fi to Reality: The Evolution of Human-Computer Interaction with Artificial intelligence 2

From Sci-Fi to Reality: The Evolution of Human-Computer Inte...

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2nd international conference on Applied Artificial intelligence and Computing, ICAAIC 2023

作者： Sharma, Astha Gupta, Ayush Bhargava, Arti Rawat, Aditya Yadav, Pradeep Gupta, Deepak Itm Computer Science and Engineering Gwalior India

ISBN: (纸本)9781665456302

Artificial intelligence (AI) has transformed human computer interaction (HCI) by providing unprecedented levels of personalization and adaptivity. This research study discusses about the potential of AI in HCI, including advanced natural language processing, seamless integration with Internet of Things (IoT) devices, and the use of emotion recognition. However, the increasing utilization of AIresult in various challenges in terms of data privacy, bias, and job displacement. As such, it is essential for researchers and developers to prioritize ethical, transparent, and accessible AI-powered systems. The impact of AI on HCI is far-reaching and transformative, and it willcontinue to shape the future of technology and Human Computer Interaction (HCI). © 2023 IEEE.

关键词： Human computer interaction

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