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检索条件"任意字段=Proceedings of the 2023 4th International Conference on Machine Learning and Computer Application"

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Towards Inclsuive Multimedia Acessibility: A Synergy of Artificial Intelligence & machine earning Technologies 4

Towards Inclsuive Multimedia Acessibility: A Synergy of Arti...

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4th IEEE international conference on Smart Technologies in Computing, Electrical and Electronics, ICSTCEE 2023

作者： Deepa, B.G. Kaur, Sanjeet Hari Sankar, C.A. Reva University School Of Csa Bangalore India

ISBN: (纸本)9798350384628

this paper explores the integration of artificial intelligence (AI) and machine learning (ML) algorithms with multimedia accessibility and inclusivity. the goal is to improve the accessibility of multimedia content for individuals with disabilities, including those with hearing and visual impairments. through the use of artificial intelligence and machine learning, this paper proposes solutions such as automatic captioning, audio descriptions, and sign language interpretation to make multimedia content more inclusive. the paper also discusses the challenges and limitations of these technologies and provides recommendations for future research in this field. Ultimately, this paper highlights the importance of creating a more accessible and inclusive digital environment for all individuals. © 2023 IEEE.

关键词： machine learning

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2023 4th international conference on computer Vision, Image and Deep learning, CVIDL 2023

2023 4th International Conference on Computer Vision, Image ...

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4th international conference on computer Vision, Image and Deep learning, CVIDL 2023

ISBN: (纸本)9798350326444

the proceedings contain 136 papers. the topics discussed include: comparison of image generation methods based on diffusion models;research on image preprocessing and target detection of vehicle-machine cooperative system;a research on traditional tangka image classification based on visual features;research on image recognition based on neural network model learning algorithm;research on image recognition based on reinforcement learning;low-quality image binarization method based on threshold array system;comprehensive study of image processing techniques for low-altitude target recognition;visual cryptography scheme by freeform optics based on optimal mass transport;SPECT bone scan image classification by fusing multi-attention mechanism with deep residual networks;fine-grained image recognition method based on input perception joint probability prediction;deep learning in image classification: an overview;auricular images with annotations for segmenting key-organ mapping regions in auricular diagnosis;insulator image dataset generation based on generative adversarial network;structure-preserving domain adaptation network for generating pencil sketches;underwater image recovery method considering target polarization characteristics;and a dual attention network for multimodal remote sensing image matching.

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Electronic Music Signal Identification Model Based on machine learning Algorithm 4

Electronic Music Signal Identification Model Based on Machin...

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4th international conference for Emerging Technology, INCET 2023

作者： Dongfang, Li University of Jinan School of Music Jinan250002 China

ISBN: (纸本)9798350335750

the main idea of this model is to recognize sound signals and classify them into musical instruments. the working principle of the model is that different sounds have different characteristics and can be used for classification. the algorithm is based on machine learning algorithm, which can learn from data and improve performance over time. the concept of the model is to automatically identify music signals so that they can be used as inputs to other models. the model is based on machine learning algorithm and uses artificial neural network (ANN) and machine learning technology. Artificial neural network is a computer algorithm that simulates the working mode of human brain neurons. machine learning algorithms are more complex than artificial neural networks, which use different types of layers to process data and learn from it. they have been successfully applied in many fields, such as image recognition, speech recognition, text classification and so on. © 2023 IEEE.

关键词： Music

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4th EAI international conference on Data Information in Online Environments, DIONE 2023

4th EAI International Conference on Data Information in Onli...

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4th EAI international conference on Data Information in Online Environments, DIONE 2023

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.

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4th international conference on Recent Trends in machine learning, IoT, Smart Cities, and applications, ICMISC 2023

4th International Conference on Recent Trends in Machine Lea...

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4th international conference on Recent Trends in machine learning, IoT, Smart Cities, and applications, ICMISC 2023

ISBN: (纸本)9789819994410

the proceedings contain 69 papers. the special focus in this conference is on Recent Trends in machine learning. the topics include: Implementation of Dual-Band Dielectric Resonator Antenna for 5G applications;defect Detection in Metal Surfaces Using computer Vision;liver Cirrhosis Prediction Using machine learning Classification Techniques;a Recent Survey on Risk Factors Affecting the Blood Pressure in India;Real-Time Monitoring System for Breakdown Analysis and OEE in the Wire Drawing Industry;tooth Sensitivity Device—Detection and Diagnosing of Sensitivity in the Dental Pulp;recognition of Skin Cancer;ioT-Based Smart Street Lighting Surveillance System;rock Segmentation of Real Martian Scenes Using Dual Attention Mechanism-Based U-Net;IAAS: IoT-Based Automatic Attendance System with Photo Face Recognition in Smart Campus;hardware Implementation of Moving Object Detection Using Background Subtraction Algorithm;crime Pattern Identification and Prediction Using machine learning;IMICE: An Improved Missing Data Imputation Using machine learning;analyzing Students’ Opinion on E-learning—Indian Students’ Perspective;rash Driving Detection and Alerting System;Logistic-Based OVA-CNN Model for Alzheimer’s Disease Detection and Prediction Using MR Images;Comparative Study of CNNs for Camouflaged Object Detection;3D Avatar Reconstruction Using Multi-level Pixel-Aligned Implicit Function;Helmet Detection Using YOLO-v5 and Paddle OCR for Embedded Systems;defogNet: A Residual Network for Removal of Fog Using Weighted Combination Loss;Text-to-Image Generation Model with DNN Architecture and computer Vision for Embedded Devices Using Quantization Technique;one-Shot learning for Archaeological Site Data Using Deep Neural Network on Embedded Systems;enhanceNet: A Deep Neural Network for Low-Light Image Enhancement with Image Restoration;intelligent Prediction of Cardiac Abnormality.

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Advancements in Sarcasm Detection: A Review of Accurate Models for Short Text Data 4

Advancements in Sarcasm Detection: A Review of Accurate Mode...

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4th international conference on Advancement in Electronics and Communication Engineering, AECE 2024

作者： Srivastava, Amit Kumar Srivastava, Reena Bbd University School of Computer Application Lucknow India

ISBN: (纸本)9798350364729

this review paper explores advancements in sarcasm detection in short text data, crucial for sentiment analysis in digital communication. It traces the evolution from rule-based and statistical methods to advanced machine learning and deep learning models, particularly transformer-based models like BERT, which excel in understanding nuanced contexts. the paper evaluates these models' accuracy, efficiency, and real-world applicability, highlighting their use in social media monitoring and automated customer service. It also addresses ethical considerations and emphasizes the need for models that better handle contextual and cultural nuances, along with more diverse datasets. the goal is to enhance digital communication by improving sarcasm detection accuracy and reliability. © 2024 IEEE.

关键词： Deep learning Models Digital Communication Sarcasm Detection Sentiment Analysis Transformer-based Models

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Non-Invasive Skin Disease Diagnostic System Using Deep learning 4

Non-Invasive Skin Disease Diagnostic System Using Deep Learn...

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4th international conference on Technological Advancements in Computational Sciences, ICTACS 2024

作者： Tomar, Vivek Bisht, Kamlesh Singh Panwar, Ayush Dimri, Reetesh Kumar Negi, Mayank Singh Sharma, Swati Computer Science & Engineering Department Uttarakhand Dehradun India Kiet Group of Institutions Computer Science & Engineering Department U.P. Ghaziabad India

ISBN: (纸本)9798350387490

Human skin is an important part of the body that needs attention. Skin diseases are quite common in a country like India with varied climate and high pollution levels. the usual diagnosis of skin diseases is based on visual examination and certain tests performed by a dermatologist. But this is a time-consuming process and can also end up in the wrong diagnosis. Skin disease diagnosis has undergone a revolution because of the significant development in the field of machine learning, notably convolutional neural networks (CNN). CNNs have the potential to completely change the way skin illnesses are diagnosed, tracked, and treated. this study investigates the application of various machine learning techniques in skin problem identification. By using CNN analysis of skin disease images, identification of patterns and making accurate predictions can be done with an unprecedented accuracy and consistency. In addition to diagnosis, machine learning models can treat different skins individually according to their type, thus simplifying the doctor's role. this study aims to use CNN to improve dermatology practices by introducing efficiency and accuracy. this study includes a comprehensive literature review examining existing CNN models and performance metrics. Goals include collecting health data, using CNN's predefined models, comparing their performance, recommending the best models for diagnosing skin diseases, and improving the user interface. It presents a benchmark-based neural network model that may contribute to advances in dermatology in identifying skin diseases through machine learning using data from HAM10000 and DERMNET. the effectiveness of the model is determined by comparing it with other existing models by evaluating quality indicators such as accuracy, precision, and F1 score. © 2024 IEEE.

关键词： Convolutional neural networks

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Overfitting Identification in machine learning Models with the Person-Fit Indicator 4

Overfitting Identification in Machine Learning Models with t...

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2023 4th international conference on computer Engineering and Intelligent Control, ICCEIC 2023

作者： He, Ruihang Xu, Yongze Beijing Normal University Faculty of Psychology Guangdong Province Zhuhai China Beijing Normal University College of Arts and Sciences Guangdong Province Zhuhai China

ISBN: (纸本)9798350308877

Overfitting is a phenomenon that can affect the generalization ability of machine learning models, but there is currently no clear criterion for identification. Overfitting models exhibit similar behavior to examinees with item preknowledge. Item preknowledge (IP) describes a situation in which a group of examinees have access to a subset of items from an administered test before the exam. One common method for identifying examinees with IP is based on the person-fit indicators, which are calculated using Item Response theory (IRT). this study attempts to utilize the person-fit indicator to identify overfitting models. It is discovered that when the models adhere to the fundamental assumptions of IRT, the person-fit indicator exhibits the best recognition performance. © 2023 IEEE.

关键词： machine learning

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Plant Pathologist- A machine learning Diagnostician for the Plant Disease 4

Plant Pathologist- A Machine Learning Diagnostician for the ...

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4th international conference for Emerging Technology, INCET 2023

作者： Yadav, Vidushi Pangaonkar, Satyajit Gunjan, Reena Rokade, Prakash Avantika University School of Computer Science and Engineering Madhya Pradesh Ujjain India Design and Technology University MIT School of Computing MIT Art Maharashtra Pune India Cloud and Infrastructure Services Infosys Ltd Maharashtra Pune India

ISBN: (纸本)9798350335750

Crop infection in terms of bacteria, fungus and other viruses is an unrelenting issue and loos of food due to this is a key challenge faced by farmers for many decades. Proper identification of this disease may prevent this loss. this study describes precision agriculture in terms of a mobile application called 'plant pathologist' for detecting plant diseases that forecast their occurrence using machine learning and artificial intelligence. A combination of computer vision and deep learning algorithms were used to evaluate images of plant leaves and compared them to a database of known diseases in order to make a quick and accurate diagnosis. the development process involved the identification of the research issue and the objectives, the gathering and pre-processing of data, the choice and training of the model, and the model evaluation through this quantitative methodology. the convolutional neural network (CNN) was trained on a sizable dataset of annotated plant disease photos as part of the machine learning model utilized in the mobile app. the CNN was trained to recognise the visual characteristics of photos of healthy and diseased plants and classify them according to several disease types. To ensure adaptability and precise identification of plant diseases, the app has been trained on a specific type. With hyperparameter tuning and cross-validation, the best-performing CNN model was chosen for the model selection procedure. the mobile application, which offers thorough information on plant disease diagnosis is then updated with the chosen model. this offers farmers, botanists, and plant enthusiasts an invaluable resource to accurately identify plant diseases through machine learning. © 2023 IEEE.

关键词： Convolutional neural networks

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Classification and identification of degraded alpine m eadows based on machine learning techniques 4

Classification and identification of degraded alpine m eadow...

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4th international conference on computer Vision, Image and Deep learning, CVIDL 2023

作者： Cui, Lulu Wang, Lu Su, Jinyu Song, Zihan Li, Xilai Qinghai University Department of Computer Technology and Application Qinghai Xining China College of Agriculture and Animal Husbandry Qinghai University Qinghai Xining China

ISBN: (纸本)9798350326444

Aim to address the intelligent recognition problem of different types of degraded grasslands in the alpine region of t he Qinghai-Tibet Plateau. the study takes the grassland sample p lots in Henan County, Qinghai Province as the research object an d constructs three machine learning models for classifying and id entifying degraded alpine meadows using the random forest, mult ilayer perceptron neural network, and support vector machine m ethods. the performance and effectiveness of the models are eval uated from five different perspectives, namely classification accur acy, F-measure, precision, recall, and the time taken to identify ea ch image. the experimental results demonstrate that all three alg orithms can efficiently identify different types of degraded alpine meadows, with the support vector machine algorithm being the o ptimal model, achieving the best performance across all evaluatio n metrics. this study provides a theoretical foundation and techn ical support for monitoring and restoring degraded grasslands in high-altitude regions. © 2023 IEEE.

关键词： Image classification

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