In recent times, the image sensor has become a crucial part of smartphones and have always been critical part of professional cameras, telescopes used in the astronomical observatory and space telescopes. Th...
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Four-dimensional computed tomography (4DCT) is a time-resolved, multi-modal imaging method that captures respiratory signals synchronised with the CT scan in order to track the movement of the lung. It is routinely us...
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The construction of a strong machinelearning model for accurate categorization of different ovarian cancer subtypes is the main goal, approaches, results, and implications of this research work, which are summarized ...
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The proceedings contain 214 papers. The topics discussed include: disease detection based on nail color analysis using imageprocessing;various algorithms and techniques for traffic density estimation;determination of...
ISBN:
(纸本)9781665476553
The proceedings contain 214 papers. The topics discussed include: disease detection based on nail color analysis using imageprocessing;various algorithms and techniques for traffic density estimation;determination of gout disease using machinelearning;detection of emotions using a boosted machinelearning approach;predicting Parkinson’s disease progression using machinelearning ensemble methods;quantified symptomatic analysis of psychological disorders;sentiment analysis for headlines categorization in newspaper industry Malayala Manorama company limited;flood prediction in the area of Tamil Nadu and Andhra Pradesh using machinelearning technique;hippocampus region's volume-based Alzheimer’s stages detection using a deep learning model;a IoT based residential smart energy meter with power saving methodology;adoption of extended reality as a teaching tool;a comprehensive analysis of techniques used for dimensional reduction of HSI images;and an analysis comparing traditional and digital marketing (advertising) using chi square test and linear regression model.
In March 2020, World Health Organization (WHO) recognized COVID-19 as a pandemic and urged governments to exert maximum efforts to prevent its spreading through political decisions together with public awareness campa...
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There are over 200 distinct date fruit varieties around the globe. Physical characteristics such as size and structure (collectively known as morphological attributes), colour, and other shape characteristics are used...
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If you read that quantum machinelearning applications solve some traditional machinelearning problems at an amazing speed, be sure to check if they return quantum results. Quantum outputs, such as magnitude-encoded ...
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The texture classification of an image is related to an important musical attribute, the music genre. This relationship is depicted in the visual representation of the audio signal, called as spectrogram. In this pape...
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ISBN:
(纸本)9781665488679
The texture classification of an image is related to an important musical attribute, the music genre. This relationship is depicted in the visual representation of the audio signal, called as spectrogram. In this paper, we propose a new Music Genre Classification (MGC) system that processes the spectrogram texture using the Gray Level and structural Information (GLSI) descriptor, and represents the interconnection between the descriptor codes through complex networks. The GLSI descriptor is an improvement of the CLBP (Completed Local Binary pattern) descriptor, which quantifies the texture of an image with three codes: signal (CLBP-S), magnitude (CLBP-M), and central (CLBP-C). By transforming the CLBP-C code, GLSI adds macro-structural information. The network nodes represent the descriptor codes, and the respective edges, the relationship according to the horizontal and vertical consecutive condition. We defined two representations for the nodes: 1) individual code node, obtaining the G(s), G(m) and G(g) networks, and 2) triple code node, obtaining the G(smg) network. For the experimental stage, we used the GTZAN dataset, three types of spectrograms: conventional, mel-spectrogram and gammatonegram;and mining with network topological measures. For each type of spectrogram, we performed three experiments according to feature vector combinations, such as measures of: 1) G(s), G(m) and G(g), 2) G(smg), and 3) all networks. In the machinelearningstage, we used the ensemble classifier Bagging with Random Forest, and 10fold cross-validation repeated 100 times. The experiment using all measures and all spectrograms revealed a satisfactory result, indicating that the MGC proposed is promising. We also propose a new equation to calculate the GLSI code, which proved to be much faster and with more intuitive encoding.
Knee Osteoarthritis (KOA) is a common musculoskeletal condition that has a significant worldwide influence on patient well-being. Despite its importance, manually detecting KOA poses tremendous obstacles. Traditional ...
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ISBN:
(纸本)9798350359756
Knee Osteoarthritis (KOA) is a common musculoskeletal condition that has a significant worldwide influence on patient well-being. Despite its importance, manually detecting KOA poses tremendous obstacles. Traditional diagnostic methodologies heavily depend on the visual interpretation of radiographic images by certified radiologists. This approach leads to laborious assessments and introduces the potential for discrepancies among observers. Effective manual identification is further complicated by the intricate structure of the KOA grading system, which evaluates joint space narrowing, osteophyte production, and overall degeneration. This paper presents a method for addressing these challenges by integrating the capabilities of Deep learning (DL) and machinelearning (ML) models. The dataset utilized in this research was acquired from Kaggle and comprises image samples that have been assigned KOA grades between 0 to 4. The data source is utilized to generate two distinct datasets. The initial dataset is structured as a binary classification task, wherein images are categorized as 'KOA' (grades 2 to 4) or 'healthy' (grades 0 and 1). The second dataset is the same as the original dataset, with five classes for each KOA grade. An important objective of this research is to assess the performance of the model in both binary and multi-class classification. The datasets go through pre-processing phases such as segmentation and normalization before being fed to a CNN (Convolutional Neural Network) model to extract significant features. Following this, the gathered features are utilized to train and validate numerous ML models. The assessment is predicated on several critical metrics that give information about the effectiveness of the model. The results of our study indicate that the Support Vector machine (SVM) demonstrates outstanding efficiency in the area of binary classification. In contrast, the Random Forest (RF) model performs exceptionally well in multi-class class
The proceedings contain 28 papers. The special focus in this conference is on pattern Analysis and machine Intelligence. The topics include: Development of a Low Cost 3D LiDAR Using 2D LiDAR and Servo Motor;the Design...
ISBN:
(纸本)9789819633487
The proceedings contain 28 papers. The special focus in this conference is on pattern Analysis and machine Intelligence. The topics include: Development of a Low Cost 3D LiDAR Using 2D LiDAR and Servo Motor;the Design of machine Vision-Based Waste Sorting System;ECLNet: Efficient Convolution with Lite Transformer for 3D Medical image Segmentation;exploring High-Performance 3D Object Detection with Partial Depth Completion;full-Scale Network for Remote Sensing Object Detection;Detection of Pedestrian Movement Poses in High-Speed Autonomous Driving Environments Using DVS;city-Scale Multi-Camera Vehicle Tracking System with Improved Self-Supervised Camera Link Model;an Efficient Transformer-Based Network for Remote Sensing image Change Detection;the Method for Three-Dimensional Visual Measurement of Circular Markers Based on Active Fusion Technology;intelligent imagerecognition and Classification Technology in Digital Media;Indoor Visible Light Positioning System Based on the image Sensor and CNN-GRU Fusion Neural Network;stock Investor Sentiment Analysis Based on NLP;Novel Audiobook System Based on BERT;student Enrollment Consultation Q&A Robot Based on Large Language Model;family Doctor Model Training Based on Large Language Model Tuning;composite Awareness-Based Knowledge Distillation for Medical Anomaly Detection;Improved CNN-GRU RF Fingerprint Feature recognition Method Based on Comb Filter;emotional state recognition of English Learners Based on Deep learning;Application of Classification Framework Based on CDR and CNN in Ophthalmic Prediagnosis;visual recognition and Recommendation System for Cultural Tourism Attractions Based on Deep learning;quadruped Robot System Based on Proprioceptive Vision and Complex Ground Mobility Capabilities;a Simulated Dataset to Evaluate the Visual-Inertial Odometry Algorithms.
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