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检索条件"任意字段=Conference on Visual Data Exploration and Analysis V"

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An XOR-based Multiple Medical Image Sharing using visual Secret Sharing 4

An XOR-based Multiple Medical Image Sharing using Visual Sec...

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4th International conference on Electronics and Sustainable Communication Systems, ICESC 2023

作者： Ajmal Mohammed, v.M. Samundiswary, P. Pondicherry University Department of Electronics Engineering Pondicherry India

ISBN: (纸本)9798350300093

On the basis of visual secret sharing, many researchers have proposed numerous techniques for image secret sharing. Most secret sharing techniques convert each secret image into multiple shares. Therefore, multiple shares are required for sharing multiple secret images. Such sharing technique uses large quantity of bandwidth and storage. This paper proposes a revolutionary Multiple Medical Image Sharing (MMIS) technique to reduce bandwidth and storage usage. This technique helps exchange multiple medical images as shares and performs reconstruction without any information loss. MMIS generates secret shares as public and private shares. Public shares are common for multiple images, and private shares differentiate individual secret images. A simple XOR operation is used to perform the lossless reconstruction of the initial secret medical image. As an individual's medical data may contain multiple records, this property makes MMIS efficiently used for medical image secret sharing (each group corresponds to an individual). Compared to existing secret-sharing techniques, the experimental results show that MMIS reconstructs the images with better reconstruction quality. The MMIS security analysis shows that the technique possesses better security with minimum space complexity. Along with lossless reconstruction, MMIS offers different properties such as diversity to improve security and fewer shares to reduce bandwidth and storage. MMIS also ensures user authentication using private shares to prevent share abuse. © 2023 IEEE.

关键词： Cryptography

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Exploiting Large Neuroimaging datasets to Create Connectome-Constrained Approaches for more Robust, Efficient, and Adaptable Artificial Intelligence 5

Exploiting Large Neuroimaging Datasets to Create Connectome-...

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conference on Artificial Intelligence and Machine Learning for Multi-Domain Operations Applications v

作者： Johnson, Erik C. Robinson, Brian S. vallabha, Gautam K. Joyce, Justin Matelsky, Jordan K. Norman-Tenazas, Raphael Western, Isaac villafane-Delgado, Marisel Cervantes, Martha Robinette, Michael S. Reddy, Arun v. Kitchell, Lindsey Rivlin, Patricia K. Reilly, Elizabeth P. Drenkow, Nathan Roos, Matthew J. Wang, I-Jeng Wester, Brock A. Gray-Roncal, William R. Hoffmann, Joan A. Johns Hopkins Univ Res & Exploratory Dev Dept Appl Phys Lab Laurel MD 20723 USA

ISBN: (数字)9781510661936

ISBN: (纸本)9781510661929;9781510661936

Despite the progress in deep learning networks, efficient learning at the edge (enabling adaptable, low-complexity machine learning solutions) remains a critical need for defense and commercial applications. We have pursued multiple neuroscience-inspired AI efforts which may overcome these data inefficiencies, power inefficiencies, and lack of generalization. We envision a pipeline to utilize large neuroimaging datasets, including maps of the brain which capture neuron and synapse connectivity, to improve machine learning approaches. We have pursued different approaches within this pipeline structure, including data-driven discovery in biological networks, augmenting existing computational neuroscience models, investigating the biological structure which enables behaviors, and modifying existing machine learning architectures with insight from biological structure. First, as a demonstration of data-driven discovery, the team has developed a technique for discovery of repeated subcircuits, or motifs. These were incorporated into a neural architecture search approach to evolve network architectures. Second, we have conducted analysis of the heading direction circuit in the fruit fly, which performs fusion of visual and angular velocity features, to explore augmenting existing computational models with new insight. Our team discovered a novel pattern of connectivity, implemented a new model, and demonstrated sensor fusion on a robotic platform. Third, the team analyzed circuitry for memory formation in the fruit fly connectome, enabling the design of a novel generative replay approach. This replay approach resulted in an over 20% accuracy improvement in an incremental class learning scenario, and also demonstrated the ability to utilize large neuroscience datasets to analyze the neural connectivity underlying behavior. Finally, the team has begun analysis of connectivity in mammalian cortex to explore potential improvements to transformer networks. These constraints in

关键词： Machine learning continual learning SWaP Computational Neuroscience Connectomics

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Exclusion vis: A visual Interactive System for Exclusion analysis in Life Insurance Using Machine Learning Techniques

Exclusion Vis: A Visual Interactive System for Exclusion Ana...

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International conference on Behavior, Economic and Social Computing (BESC)

作者： Md Rafiqul Islam Lam Nguyen Thanh Thao Shanjita Akter Prome Md. Kowsar Hossain Sakib Cesar Sanin Guandong Xu Weidong Huang Information Systems Australian Institute of Higher Education Sydney Australia College of Business & Management UinUniversity Vietnam School of Computer Science Taylors University Malaysia Data Science Institute University of Technology Sydney Australia TD School University of Technology Sydney Australia

ISBN: (数字)9798331531904

ISBN: (纸本)9798331531911

Exclusion analysis in life insurance stands out as a crucial task in minimizing customer risks. Numerous insur-ance companies have implemented artificial intelligence (AI) solutions to streamline processes and mitigate customer-related risks, specifically leveraging machine learning (ML) technolo-gies. While some studies have acknowledged its significance, there needs to be more to deliver comprehensive research on exclusion analysis utilizing advanced techniques. Thus, this paper focuses on exclusion classification blending with visual interactive systems (vis), aiming to comprehend data obtained from customer disclosure information. To tackle this challenge, 1) we initiate the investigation by employing various multi-label classifiers, 2) apply several ML techniques, utilizing data from a prominent Australian insurance company, where support vector classification (SvC) and random forest (RF) emerges as the superior performer, and 3) we introduce a visual interactive system called Exclusion vis, meticulously crafted to facilitate in-depth exploration of the obtained results. The evaluation of Exclusion vis further underscores its superiority compared to existing systems.

关键词： Industries visualization Social computing Interactive systems Insurance Static vAr compensators vectors Classification algorithms Risk management Random forests

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The Application of Machine Learning to Natural Language Processing: Modern Advances in the Study of Human Language

The Application of Machine Learning to Natural Language Proc...

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2024 International conference on Cybernation and Computation, CYBERCOM 2024

作者： Kumar, S. Pradeep Diwakar, Meenakshi Jaishika, S. Arthi, P. Pandi, v. Samuthira Shobana, D. Vel Tech Rangarajan Dr. Sagunthala RandD Institute of Science and Technology Department of Information Technology Avadi Chennai India Panimalar Engineering College Department of IT Chennai India Aarupadai Veedu Institute of Science and Technology Department of Electronics and Communication Engineering Avadi Chennai India GRT Institute of Engineering and Technology Department of ECE Tiruttani India Chennai Institute of Technology Centre for Advanced Wireless Integrated Technology Tamil Nadu Chennai India Rajalakshmi Engineering College Department of Mechatronics Engineering Tamil Nadu Chennai India

ISBN: (纸本)9798331542344

The field of natural language processing (NLP) is that of artificial intelligence and computer science, which investigates how computers influence human language. Speech recognition, translation, and sentiment analysis have all been improved because to the application of machine learning in natural language processing. It has become more difficult for computers to comprehend, interpret, and synthesize human language. Recent developments in machine learning for natural language processing (NLP) have centered on the creation and implementation of intricate algorithms and models that assist machines in deciphering and producing human language in a more speedy and effective manner. First, we will go over the fundamentals of natural language processing and machine learning. NLP will continue to develop, passing through rule-based systems, statistical models, and most recently, deep learning. Having a strong understanding of context, semantics, and syntax, these models make it possible to develop increasingly complex applications for language analysis. In this paper, the confines and restrictions that are associated with NLP machine learning approaches are discussed. An example of a problem is the necessity for computational resources, as well as interpretability and data bias. When we talk about continuing efforts to address these problems, we talk about things like research into transfer learning and few-shot learning, as well as the development of models that are more efficient and resilient. Our final discussion examines natural language processing research's future. Multimodal data (text, audio, and visual) and interactive and customizable NLP systems are conceivable. To conclude, the study emphasizes machine learning's revolutionary impact on human language research and applications. Further innovation and growth in this area are possible. © 2024 IEEE.

关键词： Semantics

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Deep Learning Strategies for Multiclass Skin Disease Classification 5th

Deep Learning Strategies for Multiclass Skin Disease Classi...

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5th EAI International conference on Cognitive Computing and Cyber Physical Systems, IC4S 2024

作者： venkat, Nakka Lokeswara Satya Tejasri, Masina Naga vijaya Uppalapati, Padma Jyothi Rao, v. v. R. Maheswara Sripada, v. S. S. Lakshmi Murty, P. Sita Rama Department of Artificial Intelligence and Data Science Vishnu Institute of Technology Andhra Pradesh Bhimavaram534201 India Department of Computer Science and Engineering Vishnu Institute of Technology Andhra Pradesh Bhimavaram534201 India Department of CSE Shri Vishnu Engineering College for Women Andhra Pradesh Bhimavaram534201 India Andhra Pradesh Bhimavaram534201 India

ISBN: (纸本)9783031770746

Skin diseases represent a significant global health challenge, necessitating accurate and timely diagnosis for effective treatment. In this paper, we delve into the intricate realm of skin disease classification, leveraging advanced deep learning techniques. Skin diseases, ranging from common conditions like eczema and atopic dermatitis to potentially life-threatening melanoma, pose complex diagnostic challenges due to their varied visual characteristics. Accurate classification demands the ability to discern subtle patterns, textures, and features within medical images. Our paper introduces a novel methodology that includes the integration of our proposed model into the existing deep learning frameworks. Unlike conventional approaches involving the replacement of output layers, our model maintains the integrity of pre-trained structures. It achieves this by utilizing a blend of vGG16 and EfficientNet B2, incorporating dense activations for improved regularization. This novel approach seeks to improve the model’s flexibility to various skin disease patterns while maintaining accurate classification without changing the pre-trained models’ fundamental design. The model’s performance is evaluated in-depth using a variety of criteria, including accuracy, precision, recall, and F1 score. The outcomes of this analysis unveil distinct F1 scores for each disease class across different models, thereby illuminating the models’ strengths and limitations in the context of disease classification. © ICST Institute for Computer Sciences, Social Informatics and Telecommunications Engineering 2025.

关键词： Dermatitis

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visual analysis of Heterogenous Air Pollution data 21

Visual Analysis of Heterogenous Air Pollution Data

引用

4th International conference on Computer Science and Software Engineering, CSSE 2021

作者： Yang, Xudong Peng, Hui Zhang, Qingming School of Computer Science and Technology Southwest University of Science and Technology China

ISBN: (纸本)9781450390675

Some studies have shown that air pollution is one of the important risk factors for human health. In order to find out the transmission mode, space-Time patterns, main pollution causes and potential mode of air pollution, we analyzed the multi-granularity and multi-dimensional data, and designed a visual analysis scheme of air pollution data, which integrated time, space and relevant factors, and used many kinds of visual charts, including map, calendar chart, single axis scatter chart and so on. Based on these, a data-driven visual analysis system is designed and implemented, and a variety of visual analysis models are established. The interactive methods such as filtering, scaling and exploration can assist users to carry out the data analysis tasks such as data overview, key data tracking, comparing of correlation, analytic reasoning, hypothesis testing and potential mode discovery, and realize the analysis of key causes, spatiotemporal evolution and transmission mode of air pollution. The system is simple and efficient, which can provide valuable reference for in-depth analysis of air pollution data and evaluation of air pollution control measures. © 2021 ACM.

关键词： Air pollution

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Multi-Modal Latent variables for Cross-Individual Primary visual Cortex Modeling and analysis 39

Multi-Modal Latent Variables for Cross-Individual Primary Vi...

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39th Annual AAAI conference on Artificial Intelligence, AAAI 2025

作者： Zhu, Yu Lei, Bo Song, Chunfeng Ouyang, Wanli Yu, Shan Huang, Tiejun Institute of Automation Chinese Academy of Sciences China School of Artificial Intelligence University of Chinese Academy of Sciences China Shanghai Artificial Intelligence Laboratory China Beijing Academy of Artificial Intelligence China

ISBN: (纸本)157735897X

Elucidating the functional mechanisms of the primary visual cortex (v1) remains a fundamental challenge in systems neuroscience. Current computational models face two critical limitations, namely the challenge of cross-modal integration between partial neural recordings and complex visual stimuli, and the inherent variability in neural characteristics across individuals, including differences in neuron populations and firing patterns. To address these challenges, we present a multi-modal identifiable variational autoencoder (mivAE) that employs a two-level disentanglement strategy to map neural activity and visual stimuli into a unified latent space. This framework enables robust identification of cross-modal correlations through refined latent space modeling. We complement this with a novel score-based attribution analysis that traces latent variables back to their origins in the source data space. Evaluation on a large-scale mouse v1 dataset demonstrates that our method achieves state-of-the-art performance in cross-individual latent representation and alignment, without requiring subject-specific fine-tuning, and exhibits improved performance with increasing data size. Significantly, our attribution algorithm successfully identifies distinct neuronal subpopulations characterized by unique temporal patterns and stimulus discrimination properties, while simultaneously revealing stimulus regions that show specific sensitivity to edge features and luminance variations. This scalable framework offers promising applications not only for advancing v1 research but also for broader investigations in neuroscience. © 2025, Association for the Advancement of Artificial Intelligence (***). All rights reserved.

关键词： Modal analysis

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visual analysis of Tobacco Leaf Recognition Research Based on CiteSpace

Visual Analysis of Tobacco Leaf Recognition Research Based o...

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International conference on Software Technology and Engineering (ICSTE)

作者： Yanting Dai Hua Zhou Southwest Forestry University College of Big Data and Intelligent Engineering Kunming China

ISBN: (数字)9798350378955

ISBN: (纸本)9798350378962

This study utilizes CiteSpace software to conduct a visual analysis of the literature in the field of tobacco leaf recognition technology in recent years, aiming to reveal the research hotspots, development trends, and contributions of key institutions and scholars in this domain. By analyzing the changes in the number of publications, keyword co-occurrence networks, author collaboration patterns, and keyword burst situations, this paper demonstrates the in-depth exploration and innovation in both basic research and practical applications of tobacco leaf recognition technology. Firstly, through the analysis of the annual publication volume, we found that research on tobacco leaf recognition has shown a significant growth trend over the past decade, indicating the increasing importance of this field in both academia and industrial applications. Secondly, the analysis of research institutions and authors reveals the dominant positions of institutions such as the Chinese Academy of Agricultural Sciences, the Chinese Academy of Sciences, and N anjing Agricultural University, as well as the outstanding contributions of scholars like Wang Ying, Wang Lu, and Guo Yongfeng in terms of publication volume and collaboration relationships. Furthermore, the keyword co-occurrence network and cluster analysis show that the research focus of tobacco leaf recognition technology covers multiple keyword groups, including gene expression, transcription factors, plant resistance, and biosynthesis, reflecting researchers' multifaceted efforts in theoretical exploration and practical applications. Finally, the keyword burst analysis reveals the rise and fall of keywords in different periods. For instance, the period from 2014 to 2017 was mainly concentrated on “gene expression” and “transgenic tobacco,” while from 2018 to 2020, emerging research hotspots such as “functional analysis” and “cell death” became prominent. This study provides profound insights and significant references for the

关键词： Industries visualization Technological innovation Collaboration Market research Software Functional analysis Pattern recognition Gene expression Immune system

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Genetic Privacy Shields: A DNA Steganography Approach for Multi-Level Text Encryption : iling the Future of Genetic data Protection 1

Genetic Privacy Shields: A DNA Steganography Approach for Mu...

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1st International conference on Advances in Electrical, Electronics and Computational Intelligence, ICAEECI 2023

作者： Harshita, S. Jayanth Adhitya, C.M. Geethika Naidu, P. Bashpika, T. Manikumar, v.S.S.S.R. Oviya, I.R. Divya, S. Kumar, Rajender Chennai India KTH Royal Institute of Technology Alba Nova University Center Division of Glycoscience Department of Chemistry School of Engineering Sciences in Chemistry Biotechnology and Health Stockholm10691 Sweden

ISBN: (纸本)9798350342796

In the current era genetic sequencing has emerged as an indispensable methodology for examining diverse DNA profiles across domains encompassing healthcare, agriculture, and forensic science. To enhance precision, a range of techniques, including high-throughput shotgun sequencing, have evolved over time. Recent strides in next-generation sequencing methodologies have facilitated the embedding of data within DNA using synthesized oligonucleotides. Notably, significant efforts have been dedicated to embedding extensive volumes of information within living organisms to safeguard intellectual property. When it comes to DNA steganography, traditional detection methods, exemplified by frequency analysis-based approaches, often overlook crucial signals and are susceptible to newly emerging steganography techniques. This study undertakes a comprehensive analysis of inherent allocations, distribution variance computation, and the classification of sequences as coding or non-coding. Our research endeavors to devise an advanced security strategy employing DNA steganography. Central to our proposition is the utilization of DNA steganography to forge a robust security framework. In this study, we introduce the DNA-Genetic Encryption (D-GET) mechanism to augment security. The D-GET technique involves the binarization of digital data followed by its conversion into DNA sequences. Subsequently, a sequence of operations, including reshaping, encryption, crossover, and mutation, is performed iteratively to heighten the robustness and unpredictability of the technique. The core processes of D-GET are iterated at least three times. Encrypted data is transmitted in either text or image file formats. Upon reception, the D-GET approach is employed to decode and restore the acquired data to its original form. A distinguishing aspect of our approach is the transformation of textual content into visual representations and vice versa, augmenting security measures. Multiple key sequences are

关键词： bit exchange DNA steganography Genome sequencing Mutation Next generation sequencing style Sequence analysis

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AI Powered Ayurvedic Leaf Prediction and Disease Classification: A Comprehensive analysis

AI Powered Ayurvedic Leaf Prediction and Disease Classificat...

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

作者： R. Sathya M. Suganthi M. Trisha E. Janani S. Dhivya R.v. viswanathan Department of IT Kongunadu College of Engineering and Technology Trichy Tamil Nadu India

ISBN: (数字)9798331521394

ISBN: (纸本)9798331521400

Ayurvedic medicine, a fundamental component of India's traditional healthcare framework, is heavily dependent on the quality and efficacy of medicinal plants. Nevertheless, the medicinal properties of these plants can be adversely affected by diseases stemming from pathogens and environmental factors. This research explores the utilization of artificial intelligence (AI) methodologies, such as deep learning (DL), machine learning (ML), and technologies, for the prompt identification and diagnosis of plant diseases. The review emphasizes recent developments in this field, evaluates their success in disease detection, and pinpoints areas where further research is needed. By merging AI technologies with traditional practices, the integrity of medicinal plants can be preserved, promoting sustainable agricultural practices and improved disease management

关键词： Medicinal plants Deep learning Plant diseases Explainable AI visual analytics Environmental factors data models Robustness Sustainable development Medical diagnostic imaging

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