In the context of the COVID-19 pandemic that occurred in the past year, people have had to adjust their daily lives to minimize physical contact, including the shift towards online learning, as traditional classroom s...
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The proceedings contain 71 papers. The topics discussed include: an innovative time domain graph convolutional network enables effective tracking of multiple objects;a spatiotemporal mask autoencoder for one-shot vide...
ISBN:
(纸本)9798400709777
The proceedings contain 71 papers. The topics discussed include: an innovative time domain graph convolutional network enables effective tracking of multiple objects;a spatiotemporal mask autoencoder for one-shot video object segmentation;real-time vehicle pedestrian detection and tracking algorithm based on computer vision;real-time license plate detection and recognition in unconstrained scenarios;research on the application of image element extraction technology based on improved faster R-CNN in the communication of Hubei traditional culture;direct position determination of quasi-stationary sources based on virtual array synthesis of distributed nested arrays;analysis of sea clutter characteristics of digital array multi-channel ubiquitous radar;characterization and extraction of sea and air maneuvering targets using passive radar;advancing multi-modal beam prediction with multipath-like data augmentation and efficient fusion mechanism;and an overview study of automated inland waterway topographic contour mapping.
The proceedings contain 18 papers. The special focus in this conference is on Information Technologies and Intelligent Decision Making Systems. The topics include: Comparative Analysis of Traditional machine Learning ...
ISBN:
(纸本)9783031603174
The proceedings contain 18 papers. The special focus in this conference is on Information Technologies and Intelligent Decision Making Systems. The topics include: Comparative Analysis of Traditional machine Learning Approaches for Time Series Clustering Under Colored Noise;on the Open Transport Data Analysis Platform;investigation of the Characteristics of a Frequency Diversity Array Antenna;comparative Analysis of Fuzzy Controllers in a Truck Cruise Control System;implementation of a Blockchain-Based Software Tool to Verify the Authenticity of Paper Documents;development of Methods and Algorithms for Dimension Reduction of Space Description for patternrecognition Problem;service for Checking Students’ Written Work Using a Neural Network;implementing a Jenkins Plugin to Visualize Continuous Integration Pipelines;elimination of Optical Distortions Arising from In Vivo Investigation of the Mouse Brain;quantum Fourier Transform in Image Processing;choosing an Information Protection Mechanism Based on the Discrete Programming Method;application of machine Learning Methods for Annotating Boundaries of Meshes of Perineuronal Nets;diagnostics of Animals Diseases Based on the Principles of Neutrosophic Sets and Sugeno Fuzzy Inference;the Technique of Processing Non-Gaussian Data Based on Artificial intelligence;development of Automation and Control System of Waste Gas Production Process Based on Information Technology;machine Learning and Data Mining.
The proceedings contain 11 papers. The topics discussed include: assessing appropriate reliance: a framework for evaluating AI influence on user decision-making;AI to detect Parkinson’s disease symptoms via wearables...
The proceedings contain 11 papers. The topics discussed include: assessing appropriate reliance: a framework for evaluating AI influence on user decision-making;AI to detect Parkinson’s disease symptoms via wearables: from detection to management to treatment;AI personalized models based on subjective data;the role of physiological signals in human-machine interaction;co-design of scenarios for interacting with a NAO robot in treating autism spectrum condition;digital biomarkers of mood states from speech in bipolar disorder;a protocol to evaluate the impact of visual distractors on driving attention using a virtual reality simulator;rule enforcement in LLMs: a parameter efficient fine-tuning approach with self-generated training dataset;and a multi-source deep learning model for music emotion recognition.
The proceedings contain 76 papers. The special focus in this conference is on machine Vision and Augmented intelligence. The topics include: Survey on Robustness of Deep Learning Techniques on Adversarial Attacks in W...
ISBN:
(纸本)9789819743582
The proceedings contain 76 papers. The special focus in this conference is on machine Vision and Augmented intelligence. The topics include: Survey on Robustness of Deep Learning Techniques on Adversarial Attacks in WBAN;synergizing Collaborative and Content-Based Filtering for Enhanced Movie Recommendations;exploring Transformer-Based Approaches for Hyperspectral Image Classification: A Comparative Analysis;deep Learning for Cognitive Task and Seizure Classification with Hilbert–Huang Transform and Variational Mode Decomposition;tracking of Ship and Plane in Satellite Videos Using a Convolutional Regression Network with Deep Features;Tumor Detection and Analysis from Brain MRI Images Using Deep Learning;software Maintenance Prediction Using Stack Ensemble Deep Learning Algorithms;resource Allocation in 6G Network for High-Speed Train Using D2D Outband Communication;controlling the Band-to-Band Tunneling Effect in Charge Plasma Based Dopingless Transistor;Comparison of Different CIC Filter Architectures on the Basis of a Novel Parameter Called Noise Factor for Sigma-Delta Based ADCs;the Scientific Analysis on Effective Yoga Posture recognition Techniques;impact of Gamma Rays on Emerging Devices for Photonic Applications;shaft Rotation Monitoring Using Radar Signal Processing and Wavelet Transform;gysel Power Divider Miniaturization Using an Inter-Digital Capacitor-Based Slow-Wave Structure;noise Estimation and Removal in Fundus Images Using Pyramid Real Image Denoising Network;evaluation of Hybrid Encryption Method to Secure Healthcare Data;multimodal Face recognition System Using Hybrid Deep Learning Feature;Classification of Copy and Move Image by Using HELM-FSK Method: An Efficient Approach;analysis of Energy Efficient Smart Home Based on IoT System;role of Explainable Artificial intelligence Approaches in Cybersecurity.
The proceedings contain 12 papers. The topics discussed include: research on lip recognition method based on 3D ResNet;player position binary classification model;towards in x-ray induced acoustic imaging for nasophar...
ISBN:
(纸本)9798331539795
The proceedings contain 12 papers. The topics discussed include: research on lip recognition method based on 3D ResNet;player position binary classification model;towards in x-ray induced acoustic imaging for nasopharyngeal carcinoma radiotherapy with a multidimensional information fusion network;complex project production mode and man-machine interaction exploration;using ArcGIS to analyze spatiotemporal distribution pattern of intangible cultural heritages in Huaihai economic zone;research on predicting public opinion event heat levels based on large language models;enhancing cold chain logistics efficiency: improved ga for time-window-constrained routing;and industrial IoT big data platforms based on 5G and BeiDou technologies.
An incredibly resilient and destructive pest, Varroa destructor has proved to be the greatest biological threat to the Western honeybee and the bee-based economy as a whole. Current methods of controlling varroa mite ...
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ISBN:
(纸本)9798350372977;9798350372984
An incredibly resilient and destructive pest, Varroa destructor has proved to be the greatest biological threat to the Western honeybee and the bee-based economy as a whole. Current methods of controlling varroa mite populations are becoming increasingly insufficient, leading to the necessity for more effective acaricides. Aiding the effort for efficient testing of acaricides, we developed the first AI-based semantic segmentation model to track and analyze a bee's full flight path. Named BeeTrack, this model can monitor bee decision-making processes and bee behavior quickly and cost-effectively. Using BeeTrack and our training setup and methods, we evaluated commercial and experimental acaricides and their effects on bee memory. The memory training utilized three quinine-coated yellow and three sucrose-coated blue landing platforms. A blanket covered the training box and LEDs were stored in the platforms to reduce phototaxis. After 2 training trials, 45 bees in total were exposed to each chemical and continued to the testing phase, where platforms contained water, and the resulting footage of bee landing patterns was analyzed. Lactic acid (LA), 2 essential oil mixtures (EO1 and EO2), and capric acid (CA) were our experimental chemicals, with untreated bees and commercial acaricides Apiguard and FormicPro being our controls. BeeTrack analyzed each bee's direction, speed, and path. Untreated bees performed with 81.82% landing accuracy, measured by their landings on blue platforms over their total landings. Of our chemicals, EO2-exposed bees performed the best (75% accuracy), followed by EO1 (71.4%), and CA (70%), outperforming FormicPro and Apiguard (45%, 55.5%). Thus, we have designed a free-flying training environment and procedure to train bees with a minimal number of trials. Using our BeeTrack system, we were able to analyze high-speed bee flight paths while bypassing much of the manual analysis and find chemicals with high potential as acaricides.
In modern automated production, the efficiency and quality of the cable winding process are crucial. In traditional methods, automatic reversing and edge detection are difficult to achieve, which can lead to reduced p...
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ISBN:
(纸本)9798400709777
In modern automated production, the efficiency and quality of the cable winding process are crucial. In traditional methods, automatic reversing and edge detection are difficult to achieve, which can lead to reduced production efficiency. To solve the problem of low efficiency, research has introduced fuzzy PID and AI technology in this field, established models and algorithms, automated reversing and edge detection. Research on subtracting its length through the difference algorithm of the filtering program, and under the drive of the sensor, search for the edge of the wire near the side plate of the take-up reel. The results show that the output characteristics of the sensor on the metal surface are better than those on wooden boards, with an average error of 0.10mm. After filtering, it approximates a straight line. Research on the introduction of fuzzy PID and AI technology, proposing solutions to improve the efficiency and quality of cable collection and arrangement process, in order to reduce cable damage and improve production efficiency.
The proceedings contain 157 papers. The topics discussed include: a self-scaling dynamic blockchain model for IoT;advanced generative ai methods for academic text summarization;data augmentation for entity resolution:...
ISBN:
(纸本)9798350372977
The proceedings contain 157 papers. The topics discussed include: a self-scaling dynamic blockchain model for IoT;advanced generative ai methods for academic text summarization;data augmentation for entity resolution: a comparative evaluation;mitigation of user-prompt bias in large language models: a natural language processing and deep learning based framework;evaluating the effectiveness of an object detection pipeline to support surveillance of unintended passage;automated scripting for real-time responses to suspicious user actions;highway merging control using multi-agent reinforcement learning;using machine learning to predict student success in undergraduate engineering programs;NeuroAqua: developing an optimized artificial intelligence and Internet of Things-based aquaponics system;and precision fish farming to mitigate pond water quality through IoT.
Significant progress has been made in the field of autonomous vehicles. Object detection and tracking are the primary tasks for any autonomous vehicle. The task of object detection in autonomous vehicles relies on a v...
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ISBN:
(纸本)9798350372977;9798350372984
Significant progress has been made in the field of autonomous vehicles. Object detection and tracking are the primary tasks for any autonomous vehicle. The task of object detection in autonomous vehicles relies on a variety of sensors like cameras, and LiDAR. Although image features are typically preferred, numerous approaches take spatial data as input. Exploiting this information we present an approach wherein, using a novel encoding of the LiDAR point cloud we infer the location of different classes near the autonomous vehicles. This approach does not implement a bird's eye view approach, which is generally applied for this application and thus saves the extensive pre-processing required. After studying the numerous networks and approaches, we have implemented a novel model intending to inculcate their advantages and avoid their shortcomings. The output is predictions about the location and orientation of objects in the scene in the form of 3D bounding boxes and labels of scene objects.
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