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Image Classification of multimedia Platforms With Texture algorithms and Neural Networks

IEEE access 2025年 13卷 46478-46494页

作者： Sauzameda-Gonzalez, Mauricio Alvarez-Borrego, Josue Guerra-Rosas, Esperanza Ctr Invest Cient & Educ Super Ensenada CICESE Ensenada 22860 Baja California Mexico Univ Autonoma Baja California Fac Ingn Arquitectura & Diseno Ensenada 22860 Baja California Mexico

In this paper, a methodology for real-time image classification on multimedia platforms has been developed. For this purpose, six feedforward neural network models were trained with images from two databases, which were preprocessed by three texture extraction methods: local binary pattern-uniform (LBP-U), gray level co-occurrence matrix (GLCM), and wavelet image scattering (WIS). The databases used consist of 157,448 images of the sections with the thumbnails of the platform content (mosaics) representing 14 classes and 38,214 images with the descriptions of the available content (descriptors) representing 11 classes, where all images have a resolution of 1280 x 720 pixels. The six models (three for mosaics and three for descriptors) were validated with images from the databases, which were not part of the training process, to obtain their performance metrics. The training and validation process was performed 30 times, and the average results were compared. The most outstanding models for each database were the neural networks trained with the wavelet image scattering method, with metrics of 99.97 +/- 0.01 % accuracy, 99.99 +/- 0.01 % specificity, 99.84 +/- 0.06 % sensitivity, 99.59 +/- 0.13 % precision and 99.71 +/- 0. 08 % of F1 score with a response time of 0.7349 seconds for the model trained with mosaics and with metrics of 99.90 +/- 0.03 % of accuracy, 99.94 +/- 0.02 % of specificity, 99.58 +/- 0.15 % of sensitivity, 98.63 +/- 0.55 % of precision and 99.09 +/- 0.30 % of F1 score with a response time of 0.6227 seconds for the model trained with descriptor images. The results are very significant due to the high efficiency obtained and confirm the effectiveness of the models with the WIS method for the classification of multimedia platform images with the characteristics of the databases used. It is suggested that the remaining methods be adjusted to improve their performance.

关键词： Gray-scale Mathematical models Symmetric matrices Scattering Real-time systems Image classification Statistical analysis Object recognition multimedia databases Histograms Classification method neural networks texture analysis methods local binary pattern grayscale co-occurrence matrix performance metrics

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Accelerating Video Segment access via Quality-Aware Multi-Source Selection 25

Accelerating Video Segment Access via Quality-Aware Multi-So...

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16th ACM multimedia systems conference, MMSys 2025

作者： Winecki, Dominik Nandi, Arnab The Ohio State University ColumbusOH United States

ISBN: (纸本)9798400714672

Video data can be slow to process due to the size of video streams and the computational complexity needed to decode, transform, and encode them. These challenges are particularly significant in interactive applications, such as quickly generating compilation videos from a user search. We look at optimizing access to source video segments in multimedia systems where multiple separately encoded copies of video sources are available, such as proxy/optimized media in conventional non-linear video editors or VOD streams in content distribution networks. Rather than selecting a single source to use (e.g., "use the lowest-bitrate 720p source"), we specify a minimum visual quality (e.g., "use any frames with VMAF ≥ 85"). This quality constraint and the needed segment bounds are used to find the lowest-latency operations to decode a segment from multiple available sources with diverse bitrates, resolutions, and codecs. This uses higher-quality/slower-to-decode sources if the encoding is better aligned for the specific segment bounds, which can provide faster access than using just one lower-quality source. We provide a general solution to this Quality-Aware Multi-Source Selection problem with optimal computational complexity. We create a dataset using adaptive-bitrate streaming Video on Demand sources from YouTube's CDN. We evaluate our algorithm on simple segment decoding as well as embedded into a larger editing system - -a declarative video editor. Our evaluation shows up to 23% lower latency access, depending on segment length, at identical visual quality levels. © 2025 Copyright is held by the owner/author(s). Publication rights licensed to ACM.

关键词： multimedia systems

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VV-DASH: A Framework for Volumetric Video DASH Streaming 25

VV-DASH: A Framework for Volumetric Video DASH Streaming

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16th ACM multimedia systems conference, MMSys 2025

作者： Heidarirad, Hadi Wang, Mea Department of Computer Science University of Calgary CalgaryAB Canada

ISBN: (纸本)9798400714672

With the increasing demand for immersive experiences, volumetric video has emerged as a critical technology, offering users six degrees of freedom (6DoF) to fully explore three-dimensional scenes. However, despite significant advancements, there remains a lack of a comprehensive and flexible adaptive streaming framework capable of delivering volumetric video over dynamic network conditions. To address this gap, we present VV-DASH, an end-to-end framework for adaptive volumetric video streaming over DASH (Dynamic Adaptive Streaming over HTTP). Our framework covers the entire streaming pipeline, from video source to video playback. We propose a codec-agnostic DASH Volumetric Video (DVV) segment format that consolidates compressed video content into DASH-ready segments. This segmentation improves achievable streaming throughput by 13.2%, effectively reduces bandwidth demand, and enhances the achievable streaming bitrate by up to 37.8%. In summary, VV-DASH provides a practical, high-performance framework for scalable and adaptive volumetric video streaming. © 2025 Copyright is held by the owner/author(s). Publication rights licensed to ACM.

关键词： Code division multiple access

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FLoRaSat 2: Simulating Cross-Linked Direct-to-Satellite IoT LEO Constellations 12

FLoRaSat 2: Simulating Cross-Linked Direct-to-Satellite IoT ...

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12th Advanced Satellite multimedia systems conference and the 18th Signal Processing for Space Communications Workshop, ASMS/SPSC 2025

作者： Choquenaira-Florez, Alexander Y. Ohs, Robin Fraire, Juan A. Rivano, Hervé Saarland University Saarbrücken66123 Germany Inria INSA Lyon CITI Villeurbanne69621 France Universidad Nacional de Córdoba CONICET Córdoba Argentina

ISBN: (纸本)9798331522353

Direct-to-Satellite IoT (DTS-IOT) represents a promising solution for data transmission in remote regions where terrestrial infrastructure deployment is unfeasible. In DTS-IOT scenarios, Low-Earth Orbit (LEO) satellites function as in-orbit gateways. Addressing the need for practical simulation tools, we present FLORASAT 2, an open-source, event-driven, end-to-end simulation tool leveraging OMNET++. The original FLO-RASAT met many DTS-IOT Medium access Control (MAC) requirements. Still, this enhanced version introduces advanced Inter-Satellite Link (ISL) communication modules, including a helper for constellation creation, dynamic ISL topology control, routing, and analytics, facilitating the thorough evaluation of constellation-grade DTS-IOT networks. These new features allow detailed simulation scenario configuration, flexible support for developing and including diverse routing algorithms, and the tooling to perform automated data analysis from parametric simulations. Overall, the simulator enables the analysis of complex behaviors in DTS-IOT environments, optimizing performance and enhancing connectivity and efficiency in large-scale satellite IoT constellation networks. © 2025 IEEE.

关键词： access control

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LAYAMRITAM: A Scalable and Secure multimedia Delivery Platform for Devotional Songs 5th

LAYAMRITAM: A Scalable and Secure Multimedia Delivery Platfo...

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5th International conference on Computing and Network Communications, CoCoNet 2023

作者： Guntha, Ramesh Amrita Vishwa Vidyapeetham Amritapuri Vallikavu India

ISBN: (纸本)9789819747108

The creation of a platform for delivering multimedia content poses distinct challenges, especially when focusing on devotional music. In contrast to text-based platforms, multimedia delivery necessitates meticulous management of latency, augmented storage space, and bandwidth needs. Although the market is saturated with mainstream multimedia streaming services like Spotify, Apple Music, and Amazon Music, there is a noticeable absence of a platform dedicated to devotional music, despite its deep cultural and spiritual importance. This paper introduces Layamritam, a platform thoughtfully crafted to meet the specific needs of devotional music enthusiasts and artists. Layamritam distinguishes itself through its dedication to providing an immersive musical experience, complemented by sing-along lyrics in 26 languages. We delve into the distributed server architecture of Layamritam, which utilizes adaptive and resilient algorithms to offer a scalable music listening experience while safeguarding user privacy and digital content security. Investigating the user experience of Layamritam, this paper emphasizes its intuitive interface, which organizes songs by deity, mood, speed, tune, and beat style, ensuring a frictionless experience for users across all age groups and digital proficiency levels. Not only does this paper reveal Layamritam, but it also scrutinizes its potential to transform the preservation and distribution of devotional music in the digital age. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Distributed computer systems

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Exploring Decentralized Ledger Technology to Ensure the Integrity and Origin of Visual Data Using ML algorithms 5th

Exploring Decentralized Ledger Technology to Ensure the Inte...

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5th International conference on Recent Trends in Machine Learning, IoT, Smart Cities, and Applications, ICMISC 2024

作者： Ranaveer, Indur Srinivasa Rao, Katakam Renuka, Kummari Pachimatla, Divya Kilari, Rampriya Ranitha, I.B. CMR Institute of Technology Telangana Hyderabad 501401 India

ISBN: (纸本)9789819788644

This research investigates the synergies between Decentralized Ledger Technology (DLT) and Machine Learning (ML) algorithms to enhance the security and traceability of visual data. In an era of increasing reliance on visual information, ensuring the integrity and origin of such data is crucial for applications ranging from digital forensics to multimedia content verification. Decentralized ledger technologies like blockchain offer a decentralized and tamper-resistant framework for maintaining transparent and immutable records. The study explores how DLT can be integrated with ML algorithms to create a robust system for validating visual data authenticity. ML algorithms are employed for real-time analysis of visual content, detecting anomalies and inconsistencies that may indicate tampering or manipulation. These algorithms contribute to an intelligent layer that complements the decentralized nature of DLT, reinforcing the trustworthiness of visual data. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Blockchain content verification DLT ML Visual data

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Universal access to education based on Software Defined Radio (SDR): The case of Mali 27

Universal access to education based on Software Defined Radi...

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27th International conference on Advanced Communications Technology, ICACT 2025

作者： Coulibaly, Souhahebou Ba, Mamadou Ngom, Ibrahima Kora, Ahmed Dooguy Ouya, Samuel Laboratory LITA Higher Polytechnic School University Cheikh Anta Diop of Dakar Senegal

ISBN: (纸本)9791188428137

This study presents an innovative solution to improve access to education in Mali using a private 4G/5G mobile network based on software-defined radio (SDR) technology. Faced with the persistent challenges of the Malian education system, including insecurity, recurrent strikes, and lack of infrastructure, our approach integrates cutting-edge technologies to offer a resilient and flexible alternative. The proposed architecture combines 5G Non-Standalone (NSA), SDR, and an IP multimedia Subsystem (IMS) to create a comprehensive educational ecosystem, including an online learning platform. Our experiments demonstrate the technical feasibility of this solution, with successful 5G connectivity and educational content streaming tests. The results show that this approach can effectively extend network coverage in underserved areas and provide educational services even in challenging conditions. While challenges remain in terms of scalability and security, this research opens new perspectives to reduce the digital divide in education in developing countries facing similar obstacles. Copyright 2025 Global IT Research Institute (GIRI). All rights reserved.

关键词： 5G mobile communication systems

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Optical Remote Sensing and Machine Learning Algorithm Used to access the Leaf Area Index and Canopy Water content 3rd

Optical Remote Sensing and Machine Learning Algorithm Used t...

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3rd International conference on Intelligent systems and Applications, ICISA 2024

作者： Vinta, Surendra Reddy Kambala, Gireesh Ahmad, Anzar Patro, Pramoda School of Computer Science and Engineering VIT-AP University Andhra Pradesh Amaravati India Teach For America 2140 Hillside Derby Run CummingGA30040 United States Department of Electronics and Communication Graphic Era to be deemed University Dehradun India Department of Mathematics Koneru Lakshmaiah Education Foundation Hyderabad India

ISBN: (纸本)9789819798384

Precise evaluation of crop canopy characteristics requires optical remote sensing (RS) equipped with solid procedures. Despite much research on the subject, algorithm performance employing RS still needs improvement. In a short amount of time, five distinct algorithms partial-least-squares regression (PLSR), support vector regression (SVR), and random forest regression (RFR) with feature selection were used to evaluate the LAI and CWC of rice canopy data. Results from the visible to short-wave electromagnetic spectrum of canopy reflectance were input into the algorithms. Researchers used two sets of repeated field trials in the 2014–15 and 2015–16 rice growing seasons to gather data on leaf area index (LAI) (600) and Canopy water content (CWC) (480). The coefficient of determination (R2) was used to assess the efficiency of each method. Compared to other algorithms, PLSRLW outperformed them with R2 values of 0.77 for LAI and 0.66 for CWC. In addition, for every model, created a kernel concentration estimator of the root mean formed error values using a bootstrapping strategy. By giving calibration samples, which have the same canopy assembly as the test samples, more weight, the findings indicated that an appropriate method might be used to increase the forecast accuracy of LAI and CWC. Calibration of the model should be done using an entire season's worth of canopy spectral data since sub-setting it leads to high error values in the test dataset. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Optical remote sensing

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Cryptography machine: Encryption and decryption model for multimedia data 1st

Cryptography machine: Encryption and decryption model for mu...

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1st International conference on Applications of AI in 5G and IOT, ICAAI5GI 2024

作者： Prakash, Vijay Dudeja, Deepak Chhabra, Gunjan Garg, Lalit Department of Computer Information Systems University of Malta Msida Malta Department of Computer Science and Engineering MMEC MMDU Haryana Mullana India Department of Computer Science & Engineering Graphic Era Hill University Dehradun India Department of Computer Science & Engineering Graphic Era Deemed to be University Dehradun India

ISBN: (纸本)9781032874036

The digital era has brought a surge in the amount of data generated, increasing the need for data security across individuals, organizations, and governments. Protecting sensitive information from unauthorized access is a top priority in today’s digital world. Cryptography is used to ensure secure communication over the internet, including email, instant messaging, and web browsing. Our paper’s primary goal is to develop a cryptography machine capable of encrypting and decrypting different data types, such as plain text, images, audio and videos using the cryptography of the symmetric key. The proposed model will be implemented using Python and Java. Developing the cryptography machine will significantly protect sensitive information and ensure its confidentiality and integrity. The machine’s ability to encrypt and decrypt different data types using symmetrickey cryptography will provide an added layer of security to individuals, organizations, and governments in the digital era. © 2025 the Author(s).

关键词： algorithms Cryptography decryption encryption keys machine

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Social Media-Based Travel Planning: Integrating content-Based and Collaborative Filtering for Personalized Suggestions

Social Media-Based Travel Planning: Integrating Content-Base...

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Multi-Agent systems for Collaborative Intelligence (ICMSCI), International conference on

作者： S. Naveen G. Vinoth Raikumar R. Santhana Krishnan R. Umesh A. Alice Blessie Kruthika Paulraj Department of Mechanical Engineering Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Chennai India Department of Electronics and Communication Engineering J.P. College of Engineering Tenkasi India Department of Electronics and Communication Engineering SCAD College of Engineering and Technology Cheranmahadevi India Department of Information Technology Velammal College of Engineering and Technology Madurai India Department of Electronics and Communication Engineering Government College of Engineering Tirunelveli India Department of Information Technology Sona College of Technology Salem India

ISBN: (数字)9798331509828

ISBN: (纸本)9798331509835

This research presents a data-driven approach for providing personalized travel and stay recommendations by integrating social media data., machine learning models., and time-series analysis. Data is collected from social media platforms using APIs., including textual data (captions., hashtags., and comments)., multimedia data (images and videos)., and engagement metrics (likes.,shares., and comments). Textual data is preprocessed using advanced NLP techniques such as BERT for keyword extraction and TextBlob for sentiment analysis., while multimedia data is classified using a pre-trained ResNet model. A comprehensive user travel history is built from geotags and timestamps., and seasonal travel patterns are analyzed using Prophet. User preferences are derived by combining engagement metrics., sentiment scores., and multimedia classification results. Unexplored destinations are identified using K-Means clustering., and similarity scores are computed using cosine similarity between user preferences and destination features. Recommendations for destinations and accommodations are generated using content-based filtering and collaborative filtering with Singular Value Decomposition (SVD). This integrated system ensures context-aware., relevant., and personalized travel and stay suggestions based on user interests and travel history.

关键词： Measurement Sentiment analysis Social networking (online) multimedia systems Clustering algorithms Streaming media Market research Classification algorithms Recommender systems Singular value decomposition

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