作者：
Warbhe, Mohan K.Verma, Prateek
Faculty of Engineering and Technology Department of Computer Science and Design Sawangi Maharashtra Wardha442001 India
Faculty of Engineering and Technology Department of Artificial Intelligence and Machine Learning Sawangi Maharashtra Wardha442001 India
In healthcare, Natural Language Processing (NLP) is a technique to improve clinical decision support, patient care, and medical research. By automating clinical recording, NLP enhances the calibre of medical records a...
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Computer vision plays an important role in this technology-driven world. Using computer vision, the images can be acquired and processed to achieve the high dimensional data of the images. In late 1960s computer visio...
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ISBN:
(数字)9798331540364
ISBN:
(纸本)9798331540364
Computer vision plays an important role in this technology-driven world. Using computer vision, the images can be acquired and processed to achieve the high dimensional data of the images. In late 1960s computer vision was start emerging which is considered as the colonist for the latest technology Artificial intelligence. the machinelearning and deep learning comes under the artificial intelligence as the subsets. the object detection falls under the category of computer vision. Tracking of each license plate is considered as a most difficult task as the population increases massively day by day. Also, to identify whether a vehicle is authorized or unauthorized in distinct environment is a challenging one. When the population becomes higher and higher leads to many complications such as vehicle theft, misusing vehicles, and unauthorized accessing of vehicles. To overcome these issues many algorithms are handled and the best method is done withthe deep learning algorithm. the project aims at designing a system by training the model using deep learning which allows to detect the number plate accurately using open cv module and then the text of number plate is extracted using optical character recognition (OCR) and thus fixed database is compared withthe code written in python and thus the vehicle can be detected whether it is authorized or unauthorized. By implementing this project, the complications associated withthe detection of vehicles can be overcome. this project aims at designing a most efficient and conceded way for detecting the license plates. For this purpose, the deep learning method is handled which follows the basic algorithm CNN (convolution neural networks) and the model created using the deep learning method follows the YOLOv8, the latest version used for the object detection. In this process the YOLOv8 (you only look once) is handled to make the detection easier since it follows the single neural network to identify the boundary boxes and the c
the proceedings contain 21 papers. the special focus in this conference is on data Information in Online Environments. the topics include: Deep Image Inpainting Incorporating Texture Prior Based on Gabor Filter;the Im...
ISBN:
(纸本)9783031807121
the proceedings contain 21 papers. the special focus in this conference is on data Information in Online Environments. the topics include: Deep Image Inpainting Incorporating Texture Prior Based on Gabor Filter;the Improved XdeepFM Algorithm Based on Attention Mechanism and Factorization machine;deep learning-Based Glaucoma Diagnostic Assistance System on Mobile Devices;using Alignment Chain to Boost Genetic Sequence Alignment Process;analysis of the Industrial Internet Industry Chain and Supply Capability Analysis of Key Links Within Jiangxi Province;a Lithium-Ion Battery Cathode Material Literature Entity recognition Method Based on Deep learning;design and Implementation of Improved Multi-objective Genetic Algorithm Based on Uniform Distribution;Wavelet and Kalman Filter-Empowered Traffic Detection for Secure QUIC Network Communication;hyFed: A Hybrid Blockchain Empowered Federated learning Privacy Fair Framework;research on the Capability Status of Industrial Internet Security Supervision Platform;solar System Dynamics with Jet Propulsion Laboratory Ephemeris;elderly Health Care data Integration Framework: Design and Implementation;challenges and Prospects of Power Network Security Protection in the Context of a New Power System: A Case Study of Jiangxi;enhancing Sequence Alignment Efficiency through Concurrent Utilization of Multiple Arm Processors in a Sequential Processing Framework;Fusion of Multiscale Convolution and LSTM for Stock Price Prediction;development of a Web Application for the Sociocultural Diffusion of the Municipality of Lamas, Peru;A Solution Against Selective Jamming Attack in IEEE 802.15.4e Wireless Networks;TDFM and TAFM: Time-Aware and Feature Fusion-Based Deep Recommendation Models for Short Videos;blockchain Based Access Control: A Review and Future Perspectives.
Pigmented skin lesions must be identified in order to detect hazardous skin-related disorders, especially cancer. To improve the correctness of skin cancer recognition, image detection strategies and classification ab...
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this work presents a novel region-based layout analysis (LA) method for Optical Music recognition (OMR) systems, aimed at overcoming the data scarcity challenge. Contemporary OMR techniques, grounded in machine learni...
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ISBN:
(纸本)9783031705458;9783031705465
this work presents a novel region-based layout analysis (LA) method for Optical Music recognition (OMR) systems, aimed at overcoming the data scarcity challenge. Contemporary OMR techniques, grounded in machinelearning principles, have a critical requirement: a labeled dataset for training. this presents a practical challenge due to the extensive manual effort required, coupled withthe fact that the availability of suitable data for creating training sets is not always guaranteed. Unlike other approaches, our method focuses on adapting the training and sample extraction processes within an existing neural network framework. Our approach incorporates a labeled data-driven oversampling technique, a masking layer to enable training with partial labeling, and an adaptive scaling process to improve results for varying score sizes. through comprehensive experimentation, we established the minimal labeled data necessary for an effective model and demonstrated that our method could achieve a performance comparable withthe state-of-the-art with just 8 to 32 labeled samples. the implications of our research extend beyond improving LA, providing a scalable and practical solution for digitizing and preserving musical documents.
Expert human tutors can observe learner mistakes to understand their misconceptions and procedural errors. Highly capable, but opaque large language models have shown remarkable abilities across numerous domains, and ...
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ISBN:
(纸本)9783031606083;9783031606090
Expert human tutors can observe learner mistakes to understand their misconceptions and procedural errors. Highly capable, but opaque large language models have shown remarkable abilities across numerous domains, and may be useful for adaptive instruction in a variety of ways. Working with publicly available data from the National Assessment of Educational Progress, (388 questions selected from 4th, 8th and 12th grade math and science) we examined these three questions: 1) Do language models find the same problems difficult as students do? We found statistically significant, but small similarities in performance that differ somewhat by model. 2) Do language models have the same pattern of errors as students? Our findings reveal that, under the "minimal " prompts, the models often mirror students in choosing the same incorrect answers. However, this alignment decreases when prompt models used "chain of thoughts". 3) Can language models interpret and explain students' wrong answers? We presented frequently-chosen wrong answers toNAEP items to GPT-4 and an experienced science teacher, and compared their explanations. there was a good correspondence between these explanations, with 81% being fully or partially in agreement. Discussion focuses on how these capabilities can be used for test design and adaptive instruction.
Brain signal data are records of the electrical activity of the brain made with electroencephalography (EEG). EEG data mining is one of the key topics which has received lots of attention. State-of-art machine learnin...
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this paper details the conception of an electronic nose tailored for the precise detection of Volatile Organic Compounds (VOCs) emitted by cannabis and tobacco. Leveraging the Grove—Gas Sensor V2, equipped with four ...
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this paper aims to improve the reliability and safety of cables, prolong the service life of cables and reduce the operating cost of power systems. the paper examines in detail how partial discharge (PD) patterns are ...
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the proceedings contain 42 papers. the special focus in this conference is on Dynamic data Driven Applications Systems. the topics include: Physics-Aware machinelearning for Dynamic, data-Driven Radar Target Recognit...
ISBN:
(纸本)9783031526695
the proceedings contain 42 papers. the special focus in this conference is on Dynamic data Driven Applications Systems. the topics include: Physics-Aware machinelearning for Dynamic, data-Driven Radar Target recognition;DDDAS for Optimized Design and Management of 5G and Beyond 5G (6G) Networks;DDDAS-Based learning for Edge Computing at 5G and Beyond 5G;monitoring and Secure Communications for Small Modular Reactors;data Augmentation of High-Rate Dynamic Testing via a Physics-Informed GAN Approach;unsupervised Wave Physics-Informed Representation learning for Guided Wavefield Reconstruction;passive Radio Frequency-Based 3D Indoor Positioning System via Ensemble learning;deep learning Approach for data and Computing Efficient Situational Assessment and Awareness in Human Assistance and Disaster Response and Battlefield Damage Assessment Applications;SpecAL: Towards Active learning for Semantic Segmentation of Hyperspectral Imagery;generalized Multifidelity Active learning for Gaussian-process-based Reliability Analysis;Multimodal IR and RF Based Sensor System for Real-Time Human Target Detection, Identification, and Geolocation;learning Interacting Dynamic Systems with Neural Ordinary Differential Equations;Relational Active Feature Elicitation for DDDAS;explainable Human-in-the-Loop Dynamic data-Driven Digital Twins;transmission Censoring and Information Fusion for Communication-Efficient Distributed Nonlinear Filtering;distributed Estimation of the Pelagic Scattering Layer Using a Buoyancy Controlled Robotic System;towards a data-Driven Bilinear Koopman Operator for Controlled Nonlinear Systems and Sensitivity Analysis;tracking Dynamic Gaussian Density with a theoretically Optimal Sliding Window Approach;dynamic data-Driven Digital Twins for Blockchain Systems;Adversarial Forecasting through Adversarial Risk Analysis Within a DDDAS Framework;essential Properties of a Multimodal Hypersonic Object Detection and Tracking System;power Grid Resilience: data Gaps for Da
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