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Harnessing the Potential of visual and Audio data using IoT in Multimedia Analytics 1

Harnessing the Potential of Visual and Audio Data using IoT ...

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

作者： Doni, A. Ronald Nirmalrani, v. Franklin, Ramya G Poornima, D. Sathyabama Institute of Science & Technology Department of Computer Science and Engineering Chennai India

ISBN: (纸本)9798350359756

In the cutting-edge digital panorama, the amalgamation of multimedia analytics and the Internet of Things (IoT) has ignited a paradigm shift in information usage. This transformation leverages the abundance of visible and audio records, encompassing photos, films, and sound recordings, facilitated via the interconnected IoT community. This convergence gives challenges, which includes information privacy troubles, bandwidth limitations, and the need for superior information processing algorithms. Multimedia analytics, as a subject, revolves throughout the extraction of significant insights from multimedia records. IoT, then again, constitutes a widespread array of interconnected devices and sensors that generate copious portions of visible and audio facts. This mixture permits the actual-time collection, transmission, and evaluation of multimedia information, thereby empowering decision-makers to make extra informed alternatives and benefit profound insights at some point of various domain names. Hence, this paper proposes harnessing the potential of the Internet of Things in Multimedia Analytics (IoT-MA) to enhance the visible and audio facts. The convergence of IoT and multimedia analytics opens up a realm of exceptional possibilities spanning numerous industries and programs. This synergy has a long way-reaching implication in sectors along side healthcare, production, transportation, agriculture, and safety. IoT in multimedia analytics plays a pivotal feature in protection and surveillance, improving public protection, catastrophe response, and danger control. This article sets the stage for a comprehensive exploration of the potential of visible and audio information harnessed through IoT in multimedia analytics. The studies delves into the technologies, applications, and implications of this integration, shedding light on how it's miles reshaping the interactions with the sector and revolutionizing decision-making in an more and more interconnected and informat

关键词： Internet of things

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Enhancing Facial Expression Synthesis through GAN with Multi-Scale Dilated Feature Extraction and Edge-Enhanced Facial Features 38

Enhancing Facial Expression Synthesis through GAN with Multi...

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38th IEEE Region 10 conference, TENCON 2023

作者： Nimitha, U. Gunasagar, P. Durgaprasad, v.v.S.S. Abhijith, K.S. Sai, R.v.v. Manikantha Ameer, P.M.

ISBN: (纸本)9798350302196

Affective computing aims to facilitate effective communication between humans and machines. Many affective computing systems use machine learning models trained on labeled data, like images and videos, to recognize emotions. Among these, the Generative Adversarial Network (GAN)-based expression GAN (ExprGAN) stands out as it can generate faces displaying various expressions. However, the generated faces often lack clarity in crucial facial features, such as the eyes and lips, which are essential for defining facial expressions. To address this issue, a novel feature extraction block is proposed. This module incorporates two parallel channels with multi-scale dilated convolution to mimic the human visual system and extract multi-scale facial features from the facial images. Additionally, an unsharp masking filter is integrated into the pre-processing stage to enhance the quality of facial expression features, making them sharper and clearer. The proposed model can generate faces with six distinct expressions: Anger, Disgust, Fear, Happiness, Sadness, and Surprise. To evaluate, a Convolutional Neural Network (CNN)-based expression classifier, a Principal Component analysis (PCA)-based face classifier, and the FID (Frechet Inception Distance) score are used for comparison. The results demonstrate that the proposed model outperforms the existing ExprGAN, providing better quality and more expressive faces, which can be beneficial for improving human-machine interaction. © 2023 IEEE.

关键词： Generative adversarial networks

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A non-invasive device for skin cancer diagnosis: First clinical evidence with spectroscopic data enhanced by machine learning algorithms

A non-invasive device for skin cancer diagnosis: First clini...

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2024 IEEE/RSJ International conference on Intelligent Robots and Systems, IROS 2024

作者： Mainardi, v. Carletti, L. Tsiakmakis, D. Dal Canto, M. Melillo, T. Noferi, S. Bagnoni, G. Rubegni, P. Ciuti, G. Noze S.r.l. Pisa56023 Italy Spedali Riuniti Dermatological Department Leghorn57124 Italy Senese Hospital Dermatological Department Siena53100 Italy The BioRobotics Institute Scuola Superiore Sant'Anna Pisa56025 Italy Scuola Superiore Sant'Anna Department of Excellence in Robotics & Ai Pisa56025 Italy Health Science Interdisciplinary Center Scuola Superiore Sant'Anna Pisa56127 Italy

ISBN: (纸本)9798350377705

Skin cancer represents a significant global health concern, with melanoma alone accounting for thousands of deaths annually. Early diagnosis is crucial for improving survival rates and reducing healthcare costs. While traditional diagnostic approaches involve visual inspection followed by biopsy, emerging technologies offer less invasive options with improved precision. In this study, a novel non-invasive device was designed, developed, and validated to employ near-infrared reflectance spectroscopy for skin lesion analysis. Furthermore, this work presents a machine learning approach aimed at classifying different types of skin lesions, as well as a new sequential approach to distinguish benign from malignant lesions based on spectral data and exploring the impact of anamnestic features. The device was used in two independent hospitals in Italy to collect data from 69 patients in total, including various types of skin lesions, all of whom followed the standard protocol for screening and diagnosis intervention. The implemented model achieved a recall of 93.8% and an accuracy of 75% for melanoma and benign classification, and a recall of 100% and an accuracy of 98.6% in distinguishing non-melanoma cancer from benign lesions, demonstrating promising results for skin cancer diagnosis utilizing spectral and anamnestic data. In summary, this study contributes to the development of allied non-invasive diagnostic tools and underscores the potential of machine learning in dermatology using spectroscopic data. © 2024 IEEE.

关键词： Oncology

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Ensemble Deep Learning Framework for Classification of Skin Lesions

Ensemble Deep Learning Framework for Classification of Skin ...

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2023 International conference on Circuit Power and Computing Technologies, ICCPCT 2023

作者： Patil, Shantakumar B Ramana Rao, H.v. Chatrapathy, K. Kiran, Ajmeera Kumar, A Shiva Shaker Reddy, Pundru Chandra Sai Vidya Institute of Technology Computer Science and Engineering Bengaluru Karnataka Rajanukunte India Hyderabad India School of Computing &information Technology REVA University Karnataka Bengaluru India MLR Institute of Technology Computer Science and Engineering Hyderabad Telangana Dundigal India CMR College of Engineering & Technology Computer Science and Engineering Telangana Hyderabad India School of Computer Science and Engineering Lovely Professional University Punjab Phagwara India

ISBN: (纸本)9798350333244

Skin cancer is one of the majority prevalent malignancies that can be diagnosed visually and then further investigated with dermoscopic analysis and other procedures. Several skin-lesion (SL) classification methods using deep learning (DL) based on convolution-neural-network (CNN) and annotated skin photos illustrate modified results because visual observation provides the opportunity to use artificial-intelligence (AI) to intercept the various skin images at an early stage. In this regard, the research proposes a trustworthy method for identifying skin cancer using dermoscopy images, with the goal of bettering the visual perception and diagnostic abilities of medical experts. SL region of interest (RoI) segmentation from dermoscopy pictures was performed using swarm intelligence (SI) algorithms, and the RoI marked as the best segmentation oucome was subjected to feature extraction using the Grasshopper Optimization Algorithm (GOA). Using CNN and the ISIC-2017, ISIC-2018, and PH-2 data sets, the SLs are divided into two categories. Classification accuracy-sensitivity,-specificity-F -measure, precision, MCC, dice coefficient, and Jaccard index are used to evaluate the results of the proposed segmentation and classification strategies, with an average of 98.42%, 977.33%, and 0.97040%, respectively. Our proposed model outperforms the state-of the-art in every metric we considered © 2023 IEEE.

关键词： Learning algorithms

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Using Deep Learning for Building Stock Classification in Seismic Risk analysis 22nd

Using Deep Learning for Building Stock Classification in Sei...

引用

22nd EPIA conference on Artificial Intelligence (EPIA)

作者： Lopes, Jorge Gouveia, Feliz Silva, vitor Moreira, Rui S. Torres, Jose M. Guerreiro, Maria Reis, Luis Paulo Univ Fernando Pessoa IRIS Porto Portugal Univ Porto LIACC Porto Portugal Univ Aveiro Aveiro Portugal

ISBN: (纸本)9783031490101;9783031490118

In the last decades most efforts to catalog and characterize the built environment for multi-hazard risk assessment have focused on the exploration of census data, cadastral datasets, and local surveys. The first approach is only updated every 10 years and does not provide building locations, the second type of data is only available for restricted urban centers, and the third approach requires surveyors with an engineering background, which is cost-prohibitive for large-scale risk studies. It is thus clear that methods to characterize the built environment for large-scale risk analysis at the asset level are currently missing, which hampers the assessment of the impact of natural hazards for the purposes of risk management. Some recent efforts have demonstrated how deep learning algorithms can be trained to recognize specific architectural and structural features of buildings, which is needed for earthquake risk analysis. In this paper we describe how convolutional neural networks can be combined with data from OpenStreetMap and Google Street view to help develop exposure models for multi-hazard risk analysis. This project produced an original comprehensively annotated (15 characteristics) dataset of approximately 5000 images of buildings from the parish of Alvalade (Lisbon, Portugal). The dataset was used to train and test different deep learning networks for building exposure models. The best results were obtained with ResNet50v2, Inceptionv3 and DenseNet201, all with accuracies above 82%. These results will support future developments for assessing exposure models for seismic risk analysis. The novelty of our work consists in the number of characteristics of the images in the dataset, the number of deep learning models trained and the number of classes that can be used for building exposure models.

关键词： Deep Learning Convolutional Neural Networks Computer vision Building Exposure Models Seismic Risk analysis

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Logarithmic Lenses: Exploring Log RGB data for Image Classification

Logarithmic Lenses: Exploring Log RGB Data for Image Classif...

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conference on Computer vision and Pattern Recognition (CvPR)

作者： Bruce A. Maxwell Sumegha Singhania Avnish Patel Rahul Kumar Heather Fryling Sihan Li Haonan Sun Ping He Zewen Li Northeastern University Boston USA

ISBN: (数字)9798350353006

ISBN: (纸本)9798350353013

The design of deep network architectures and training methods in computer vision has been well-explored. However, in almost all cases the images have been used as provided, with little exploration of pre-processing steps beyond normalization and data augmentation. virtually all images posted on the web or captured by devices are processed for viewing by humans. Is the pipeline used for humans also best for use by computers and deep networks? The human visual system uses logarithmic sensors; differences and sums correspond to ratios and products. Features in log space will be invariant to intensity changes and robust to color balance changes. Log RGB space also reveals structure that is corrupted by typical pre-processing. We explore using linear and log RGB data for training standard backbone architectures on an image classification task using data derived directly from RAW images to guarantee its integrity. We found that networks trained on log RGB data exhibit improved performance on an unmodified test set and invariance to intensity and color balance modifications without additional training or data augmentation. Furthermore, we found that the gains from using high quality log data could also be partially or fully realized from data in 8-bit sRGB-JPG format by inverting the sRGB transform and taking the log. These results imply existing databases may benefit from this type of pre-processing. While working with log data, we found it was critical to retain the integrity of the log relationships and that networks using log data train best with meta-parameters different than those used for sRGB or linear data. Finally, we introduce a new 10-category 10k RAW image data set (RAW10) for image classification and other purposes to enable further the exploration of log RGB as an input format for deep networks in computer vision.

关键词： Training Computer vision Image color analysis Pipelines Transforms visual systems Sensor systems

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An Improvement on exploration Step of Whale Optimization Algorithm with Levy Distribution for Classification Problems

An Improvement on Exploration Step of Whale Optimization Alg...

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Big data Analytics and Practices (IBDAP), International conference on

作者： Sakkayaphop Pravesjit Krittika Kantawong Natdanai Kamkhad Saksit Sabaiporn Jantawan Monchanuan Duangjai Jitkongchuen Arit Thammano Panchit Longpradit School of Information and Communication Technology University of Phayao Phayao Thailand Big Data Instutite Bangkok Thailand Facluty of Information Technology King Mongkut's Institute of Technology Ladkrabang Ladkrabang Thailand Faculty of Social Sciences and Humanities Mahidol University Nakhon Pathom Thailand

ISBN: (数字)9798350391749

ISBN: (纸本)9798350391756

The proposed system represents an enhanced movement in search of food of Whale Optimization Algorithm (WOA), based on Levy distribution for image classification of grape leaf disease. In the preprocessing, the proposed system uses convolution kernels to transform images into input data within the range of (0,1). Thereafter, the Levy distribution is incorporated into the WOA model as an exploration search mechanism. A grape leaf dataset from the Plant village project (***), consisting of 4062 labeled images with dimensions of 256 by 256 pixels and divided into four classes -healthy, Black Rot, Black Measles, and Isariopsis leaf spot -is used to evaluate the performance of the proposed system. Experimental results show that the proposed system is better than visual Geometry Group (vGG16), Gray Level Co-occurrence Matrix (GLCM) with SvM, Low contrast haze reduction-neighborhood component analysis with SvM, and whale optimization algorithm (WOA).

关键词： Support vector machines Convolution Pipelines data preprocessing Measles Transforms Whale optimization algorithms

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Enhancing Customer Experience in Retail for Adaptive Store Layouts with Reinforcement Learning

Enhancing Customer Experience in Retail for Adaptive Store L...

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visual Analytics and data visualization (ICvADv), International conference on

作者： P. Suresh T Sivakumar N. Revathy M. Pandi Department of Computer Science and Engineering Saveetha School of Engineering SIMATS Chennai Tamil Nadu India Department of Computer Science and Engineering Sri Eshwar College of Engineering Coimbatore Tamil Nadu India Department of Master of Computer Applications Hindustan College of Arts and Science Coimbatore Tamil Nadu India Department of Information Technology SRM Madurai College for Engineering and Technology Sivagangai Tamil Nadu India

ISBN: (数字)9798331521394

ISBN: (纸本)9798331521400

In the competitive retail industry, improving customer experience is essential for increasing sales and cultivating brand loyalty. A crucial determinant of consumer happiness is the shop layout, which impacts customers' navigation, exploration, and interaction with merchandise. Traditional store layouts are often inflexible and do not adjust to evolving customer behaviour, hence diminishing their efficacy in enhancing the shopping experience. This paper examines the use of Reinforcement Learning (RL) to create adaptable shop layouts that adjust based on real-time consumer interactions. Through the analysis of customer behaviour and feedback, RL algorithms may dynamically modify the layout of merchandise, aisles, and promotional areas, therefore optimizing the retail environment for both consumers and merchants. The RL algorithm analyses historical data, progressively optimizing store layouts to correspond with consumer preferences, so improving the entire shopping experience. It provides substantial advantages, such as enhanced consumer involvement, more operational efficiency, and elevated revenue designs. It examines the potential of RL in revolutionizing retail settings in optimizing adaptable shop layouts and its effects on consumer happiness and corporate performance.

关键词： Technological innovation IEEE merchandise Heuristic algorithms visual analytics Layout Reinforcement learning Real-time systems Internet of Things Artificial intelligence Optimization

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Current Status of the Application of Metaverse in Education: visual analysis Based on Knowledge Graphs

Current Status of the Application of Metaverse in Education:...

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Educational Technology (ICET), IEEE International conference on

作者： Xiaochun Zhou Ruotian Jia Xing Li Minmin Tang Liu Qi Institute of Education Jianghan University Wuhan China Luzhou Normal Affiliated Primary School Chengxi School Luzhou China

ISBN: (数字)9798350376944

ISBN: (纸本)9798350376951

Based on the relevant papers and citation data on the application of the metaverse in education published in SSCI and SCI-E in Web of Science from 2011 to 2024, this study uses vOSviewer and CiteSpace for visual analysis. From the perspectives of literature authors, countries, research hotspots, and research trend evolution, the mainstream research hotspots in this field are summarized to grasp the development pulse of this field. Research has found that: (1) The application of the metaverse in education has received increasing attention in the academic community. Countries/affiliated institutions located in the same region are close partners in scientific cooperation; (2) There is a clear evolutionary path in the research focus, shifting from understanding the educational potential of the metaverse to understanding its application value and its impact on learner performance and perception; (3) The application of the metaverse in education requires further exploration of future directions to address challenges.

关键词： Training visualization Metaverse Education Focusing Knowledge graphs Educational technology Market research Problem-solving Protection

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analysis and visual exploration of prediction algorithms for public bicycle sharing systems 15

Analysis and visual exploration of prediction algorithms for...

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International conference on Computer Graphics, visualization, Computer vision and Image Processing 2021, CGvCvIP 2021, Connected Smart Cities 2021, CSC 2021 and Big data Analytics, data Mining and Computational Intelligence 2021, BIGDACI 2021 - Held at the 15th Multi-conference on Computer Science and Information Systems, MCCSIS 2021

作者： Ordoñez, Alexandra Cortez vázquez, Pere-Pau ViRVIG Universitat Politècnica de Catalunya Carrer Llorens I Artigas 4-6 Barcelona08028 Spain

ISBN: (纸本)9789898704320

Public bicycle sharing systems have become an increasingly popular means of transportation in many cities around the world. However, the information shown in mobile apps or websites is commonly limited to the system's current status and is of little use for both citizens and responsible planning entities. The vast amount of data produced by these managing systems makes it feasible to elaborate and present predictive models that may help its users in the decision-making process. For example, if a user finds a station empty, the application could provide an estimation of when a new bicycle would be available. In this paper, we explore the suitability of several prediction algorithms applied to this case of bicycle availability, and we present a web-based tool to visually explore their prediction errors under different time frames. Even though a quick quantitative analysis may initially suggest that Random Forest yields a lower error, our visual exploration interface allows us to perform a more thorough analysis and detect subtle but relevant differences between algorithms depending on variables such as the station's behavior, hourly intervals, days, or types of days (weekdays and weekends). This case illustrates the potential of visual representation together with quantitative metrics to compare prediction algorithms with a higher level of detail, which can, in turn, assist application designers and decision-makers to dynamically adjust the best model for their specific scenarios. © 2021 International conference on Computer Graphics, visualization, Computer vision and Image Processing 2021, CGvCvIP 2021, Connected Smart Cities 2021, CSC 2021 and Big data Analytics, data Mining and Computational Intelligence 2021, BIGDACI 2021 - Held at the 15th Multi-conference on Computer Science and Information Systems, MCCSIS 2021. All rights reserved.

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