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GLSI Texture Descriptor Based on Complex Networks for Music Genre Classification

GLSI Texture Descriptor Based on Complex Networks for Music ...

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international Joint conference on Neural Networks (IJCNN)

作者： Coca Salazar, Andres Eduardo Fed Univ Technol Parana UTFPR TD Comp Engn Coordinat COENC Toledo PR Brazil

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 machine learning stage, 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.

关键词： Digital image processing computer vision applications pattern recognition performance evaluation

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machine learning based Detection and Severity Classification of Knee Osteoarthritis 1

Machine Learning based Detection and Severity Classification...

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1st IEEE international conference on Emerging Research in Computational Science, ICERCS 2023

作者： Chandra Bose, Anandh Sam Srinivasan, C. Immaculate Joy, S. AlMaarefa University Faculty of Applied Science Saudi Arabia Saveetha Engineering College Tamilnadu Chennai India

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 machine learning (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

关键词： Neural network models

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1st international conference on pattern Analysis and machine Intelligence, ICPAMI 2024

1st International Conference on Pattern Analysis and Machine...

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1st international conference on pattern Analysis and machine Intelligence, ICPAMI 2024

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 image recognition 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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Epidemiological image Analysis: Insights, Perspectives, and Challenges 1

Epidemiological Image Analysis: Insights, Perspectives, and ...

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1st international conference on Optimization Techniques for learning, ICOTL 2023

作者： Ivanovska, Tatyana Kruger, Markus Daboul, Amro East Bavarian Technical University of Applied Sciences Amberg-Weiden Department of Electrical Engineering Media and Computer Science Amberg Germany University Medicine Greifswald Department of Prosthodontics Gerodontology and Biomaterials Greifswald Germany

ISBN: (纸本)9798350328042

The spotlight on large population-based studies is growing within the research community. Epidemiological studies, amassing extensive data through questionnaires and check-ups, often include imaging data like magnetic resonance imaging (MRI) or ultrasonography from numerous participants. This article offers an exploration of several recent epidemiological studies conducted in Germany, addressing not only the intriguing research tasks but also the multifaceted perspectives and challenges of analyzing image data. This includes potential enhancements in imaging technologies, machine learning applications for data analysis, and collaboration between different research disciplines. Key insights into the methodologies, algorithms, and techniques used for processing and interpreting complex imaging data are detailed. The potential of epidemiological image analysis to guide clinical practices and contribute to personalized medicine is also discussed. © 2023 IEEE.

关键词： Magnetic resonance imaging

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Felis catus disease detection in the digital era: Combining CNN and Random Forest 1

Felis catus disease detection in the digital era: Combining ...

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1st international conference on Artificial Intelligence for Innovations in Healthcare Industries, ICAIIHI 2023

作者： Suryavanshi, Ankita Kukreja, Vinay Srivastava, Prateek Bhattacherjee, Abhishek Rawat, Ramesh Singh Chitkara University Institute of Engineering and Technology Chitkara University Punjab India Graphic Era Hill University Computer Science and Engineering Uttarakhand Dehradun India

ISBN: (纸本)9798350330915

Cat skin illnesses have traditionally been diagnosed manually, which can be laborious and arbitrary. Researchers offer a novel solution to this problem that utilizes Max Pooling layers and neural networks based on convolution (CNNs) for autonomous disease recognition. To extract complex features while boosting generalization, the study focuses on combining two convolutional layers into a CNN architecture, before adding a Max Pooling layer. By combining the effectiveness of Max Pooling and CNNs, this integration accelerates processing while learning significant texture and pattern information from photos. They show the efficacy of the method in correctly detecting a wide range of cat skin illnesses through careful experimentation and performance evaluation. Utilizing a selected dataset of medical photos, data enhancement techniques, and several measures like precision, recall, precision, and F1-score, they test the efficacy of our methodology. The findings demonstrate the potential of combining Max Pooling and convolutional feature extraction for automating and enhancing the accuracy of cat skin disease diagnosis. This study offers a strong tool that improves efficiency and accuracy and advances veterinary dermatology diagnosis. The suggested method follows the current state of digital diagnostics and holds out the prospect of an entirely novel age of streamlined identification of illnesses for feline health and veterinary care. © 2023 IEEE.

关键词： cat health feline welfare image processing precision veterinary care

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Facial Emotion Detection Using Deep learning: A Survey 1

Facial Emotion Detection Using Deep Learning: A Survey

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1st IEEE international conference on Contemporary Computing and Communications, InC4 2023

作者： Ansari, Saif Kulkarni, Praveen Rajesh, Tm Gurudas, V.R. Department of Computer Science and Engineering Bangalore India Dayanand Sagar University Department of Computer Science and Engineering Bangalore India

ISBN: (纸本)9798350335774

The long history of facial expression analysis has influenced current research and public interest. The scientific study and comprehension of emotion are credited to Charles Darwin's 19th-century publication The Representation of the Sentiment in Man and Animals (originally published in 1872). As recognition of human emotions from images is one of the utmost important and difficult societal connection study assignments. One advantage of using a deep learning strategy is its independence from human intervention while undertaking feature engineering. This approach involves an algorithm that scans the data for features that connect, then combines them to promote quicker learning without being explicitly told to. Deep learning (DL) based emotion detection outperforms traditional image processing methods in terms of performance. In this analytical study, the creation of an artificial intelligence (AI) system that can recognize emotions from facial expressions is presented. It discusses the various techniques for doing so, which generally involve three steps: face uncovering, feature extraction, and sentiment categorization. This study describes the various existing solutions and methodologies used by the researchers to build facial landmark interpretation. The Significance of this survey paper is to analyze the recent works on facial expression detection and distribute better insights to novice researchers for the upgradation in this domain. © 2023 IEEE.

关键词： Emotion recognition

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international conference on Applied Intelligence and Sustainable Computing, ICAISC 2023

International Conference on Applied Intelligence and Sustain...

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1st IEEE international conference on Applied Intelligence and Sustainable Computing, ICAISC 2023

ISBN: (纸本)9798350323795

The proceedings contain 245 papers. The topics discussed include: a comprehensive analysis on deep learning based image retrieval;research on the influences of network characteristics of digital economy industry based on big data intelligent analysis algorithm;plant leaf disease classification using modified SVM with post processing techniques;a study of logistics teaching based on multimodal information input;feature extraction strategy of translated text based on neural network;localization recommendation algorithm of online translation course based on deep neural network;performance analysis of load balancing mechanism in cloud computing;pending interest table control management with optimal forwarder selection in named data network;big data analytics in small and medium enterprises;investigation and analyses of data processing through sports using scripting language: a novel paradigm;application of artificial neural network technology in electricity marketing;an accurate detection of drowsiness using a graph-based neural network;investigation on multi-objective optimization system of wastewater treatment process based on artificial intelligence;and ensemble machine learning techniques for pancreatic cancer detection.

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A Robust Convolutional Neural Network Approach for Classifying Skin Cancer 1

A Robust Convolutional Neural Network Approach for Classifyi...

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1st international conference on Artificial Intelligence for Innovations in Healthcare Industries, ICAIIHI 2023

作者： Ranjith Kumar, M. Kumar, G V Krishna Nikhil, V. Ishwariya, R. Govindan, Vediyappan Amrita School of Engineering Amrita Vishwa Vidyapeetham Department of Mathematics Chennai India Amrita Vishwa Vidyapeetham Dept. of Computer Science & Engineering Amrita School of Computing Chennai India DMI-St. John the Baptist University Mangochi Department of Mathematics Malawi

ISBN: (纸本)9798350330915

Early detection identified by dermoscopy images significantly decreases the mortality rate from skin cancer. However, the accuracy of the system diagnosis is impacted by multiple factors. A significant issue in this procedure arises during the process of acquiring images. The image quality in medical photography is frequently affected by unanticipated circumstances such as noises and brightness variations, initial digitalization, and sampling. In this work, we suggest a method to reduce the possibility of erroneous diagnosis. The proposed approach begins with preprocessing the data set through CNN and deep learning techniques for data visualization, pre-processing, and augmentation. This preprocessing aids in accelerating the rate of reorganization by eliminating all irrelevant textures. The outcomes identified a 96% accuracy rate with a 4% margin of error. This scheme demonstrated a high degree of accuracy in determining the images, as evidenced by its Precision and F1 Scores of 85% and 87%, respectively. © 2023 IEEE.

关键词： Carcinoma Convolutional Neural Networks Deep learning machine learning Skin Cancer

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Building a Heroin Abuse Prediction Model: A Generalized machine learning Approach 6th

Building a Heroin Abuse Prediction Model: A Generalized Mach...

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6th international conference on Recent Trends in image processing and pattern recognition, RTIP2R 2023

作者： Kumari, Divya Swetapadma, Aleena New Horizon College of Engineering Bangalore560103 India KIIT University Odisha Bhubaneswar751024 India

ISBN: (纸本)9783031530814

Aims: In the modern era, substance abuse is a global problem. CDC’s (Centre for Disease Control) National Center for Health statistics reported in July 2021 that, more than 93000 drug overdose deaths occurred alone in the United states. Design: A cohort study. Setting: The drug consumption data is collected online in United Kingdom. Total 2000 records are present, out of which, 1885 records are considered for the work after initial data processing. The dataset has the drug consumption output outcome of 18 drugs, heroin is one of them. Measurements: In this work, an intelligent approach has been proposed for detecting abuse of one of the most illicit substances - heroin. A random forest-based machine learning model is proposed which can predict heroin abused individuals with very high accuracy. For the abuse prediction, various supervised machine learning methods are applied, and their performance is compared. These algorithms are applied to different sets of features. Among the applied five algorithms, feature importance score is calculated for logistic regression, ensemble learning (gradient boosting) and random forest. The feature importance score for each feature is calculated for all applied algorithms k-nearest neighbour and naïve bayes. The ranking of all features is done based on the obtained score. Findings: It is found in the study that the heroin-abuse dataset collected from UCI, two-classification based on random forest (RF) and gradient boosting (GB) achieved more than 90% accuracy as well as more than 90% sensitivity, specificity, precision, and f-score. The proposed model gave the best accuracy 94.697% for heroin-use detection. Conclusions: The assessment of the proposed framework on all possible feature sets shows that the framework works well for heroin abuse prediction as different give satisfactory results. © 2024, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Demography

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1st international conference on Complex Computational Ecosystems, CCE 2023

1st International Conference on Complex Computational Ecosys...

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1st international conference on Complex Computational Ecosystems, CCE 2023

ISBN: (纸本)9783031443541

The proceedings contain 16 papers. The special focus in this conference is on Complex Computational Ecosystems. The topics include: Modeling Ecosystem Management Based on the Integration of image Analysis and Human Subjective Evaluation - Case studies with Synecological Farming;on the Provision of Network-Wide Cyber Situational Awareness via Graph-Based Analytics;metrics for Evaluating Interface Explainability Models for Cyberattack Detection in IoT Data;machine learning Model Applied to Higher Education;modeling El Niño and La Niña Events Using Evolutionary Algorithms;estimation of Seismic Phase Delays Using Evolutionary Algorithms;new Siamese Neural Networks for Text Classification and Ontologies Alignment;theorizing, Modeling and Visualizing Business Ecosystems. What Should Be Done?;A Comparative study of YOLO V4 and V5 Architectures on Pavement Cracks Using Region-Based Detection;evolutionary Reduction of the Laser Noise Impact on Quantum Gates;short Time Series Forecasting Method Based on Genetic Programming and Kalman Filter;performance Upgrade of Sequence Detector Evolution Using Grammatical Evolution and Lexicase Parent Selection Method;fuzzy pattern Trees with Pre-classification;models for the Computational Design of Microfarms.

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