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An Efficacy analysis of data using Fuzzy Logic and Fractal Encryption Techniques for Cloud Platform data Security

An Efficacy Analysis of Data using Fuzzy Logic and Fractal E...

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2024 International conference on Knowledge Engineering and Communication Systems, ICKECS 2024

作者： Dineshkumar, P. Jeeva, v. Nithiesh, C. Arun, P.J. Kumar, K. Sathish K. S. Rangasamy College of Technology Department of Information Technology Tiruchengode637 215 India Sri Sai Ram Engineering College Computer Science and Business Systems Chennai600044 India

ISBN: (纸本)9798350359688

In the realm of cloud computing, safeguarding data confidentiality is paramount. This paper introduces a novel approach to data security in cloud platforms, merging fuzzy logic and fractal encryption techniques. Fuzzy logic accommodates uncertain data, enabling adaptable encryption strategies tailored to cloud dynamics. Fractal encryption harnesses self-similarity in fractals to create intricate encryption keys, fortifying data protection. Together, they propose a robust solution to mitigate unauthorized access, data breaches, and cyber threats. Through theoretical exploration and practical testing, the efficacy of this approach is demonstrated, highlighting its potential to address cloud security challenges. The scalability and versatility of these methods make them applicable across diverse cloud infrastructures, ensuring comprehensive data protection. This paper advocates for the adoption of fuzzy logic and fractal encryption as proactive measures to enhance cloud platform security, fostering trust among users and stakeholders. © 2024 IEEE.

关键词： Cloud platforms

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Knowledge-based Graph exploration analysis on Customer's Preference of Insurance Services 10

Knowledge-based Graph Exploration Analysis on Customer's Pre...

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10th International conference on Digital Arts, Media and Technology and 8th ECTI Northern Section conference on Electrical, Electronics, Computer and Telecommunications Engineering, ECTI DAMT and NCON 2025

作者： Chen, Yingfan Kongjit, Chalermpon Media and Technology Chiang Mai University College of Arts Chiang Mai Thailand

ISBN: (纸本)9798331543273

With the increasing number of Chinese students in Thailand, the demand for insurance services from these students has gradually shown diversity and personalized characteristics. However, the current research on the insurance demand of overseas students is relatively insufficient. Based on text analysis and knowledge graph technology, this study focuses on the needs and preferences of Chinese students in Thailand in terms of insurance choices, and presents the research results in a visual form. The researchers collected chat history content of some target groups, as well as public discussion data on social media platforms and forums frequently used by Chinese students in Thailand. The researchers used the text analysis method to extract high-frequency keywords related to international student insurance, and carried out sentiment analysis to obtain the main concerns of current international students in insurance selection. Then, a knowledge graph centered on insurance demand is constructed to visualize key nodes and relationships such as insurance categories, budget preferences, and demand characteristics. © 2025 IEEE.

关键词： Knowledge graph

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Image Understanding Through visual Question Answering: A Review from Past Research 23th

Image Understanding Through Visual Question Answering: A Rev...

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23rd International conference on Intelligent Systems Design and Applications, ISDA 2023

作者： Yanda, Nagamani Tagore Babu, J. Aswin Kumar, K. Taraka Rama Rao, M. Ranjith varma, K.v. Rahul Babu, N. GMR Institute of Technology Rajam532127 India

ISBN: (纸本)9783031648465

visual Question Answering (vQA) lies at the crossroads of computer vision, natural language processing, and deep learning, captivating researchers across various AI domains. This dynamic field involves processing an image alongside a corresponding textual question, generating, or selecting an answer from provided options. The past five years have witnessed substantial advancements in vQA and visual reasoning, fueled by deep learning and extensive annotated datasets. This study presents a comprehensive literature review, delving into the current state-of-the-art from four perspectives: problem definition, existing datasets, literature review, and evaluation metrics. Through a critical analysis, we address dataset limitations and scrutinize contemporary algorithms. Here we use multimodal Fusion, which achieves the state of the art compared to existing methodologies. Moreover, we explore potential future research directions to inspire innovative solutions and applications in this evolving domain, aiming to propel vQA into new realms of exploration and practical utility. This project will allow users to input an image and image-related text so that it will aid the question-answering system. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2024.

关键词： Deep learning

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Assessing the Potential of Three Assets: From exploration to Development, a Multidisciplinary Integration Case

Assessing the Potential of Three Assets: From Exploration to...

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2023 SPE/AAPG/SEG Unconventional Resources Technology conference, URTC 2023

作者： Tomassini, Federico Gonzalez Fernandez, Juan Jose Frydman, Marcelo Lazzari, victoria Palacio, Juan Pablo vítolo, Martín Moler, Alfredo Noguera, Ivan Lanusse Phoenix Global Resources

This work in the vaca Muerta Formation examines three assets along 420 km (260 miles) from southeast to northwest in the oil window in completely different geological settings. These settings include a southern block in development phase, an exploration area in the fold and thrust belt, and a third asset where mudstones facies are intruded by magmatic sills. This manuscript contributes with a complete evaluation process from exploration to pilot stages and when conditions are appropriate to a development phase of unconventional reservoir from a multidisciplinary team standpoint. This work incorporates available data, evaluation parameter cut-offs based on well performance in the vaca Muerta Formation, and considerations for unconventional play analysis. Copyright 2023, Unconventional Resources Technology conference (URTeC).

关键词： Resource valuation

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data-driven visual analytics of Human Mobility data and green cover using Image Processing for Smart Cities 3

Data-driven visual analytics of Human Mobility data and gree...

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3rd IEEE International conference on Intelligent Technologies, CONIT 2023

作者： Ramanathan, Sivasubramanian Syed, Kulsoom Chavan, Tejaswini B. Tech Computer Science & Business Systems SVKMs NMIMS Navi Mumbai Mumbai India School of Technology Management & Engineering SVKMs NMIMS Navi Mumbai Mumbai India

ISBN: (纸本)9798350338607

This research paper presents an approach to data-driven visual analytics of human mobility data using Kernel Density Estimation visualized through heatmaps, highlighting the need for exploration of forecasting methods and intuitive visualizations using the ARIMA model. A specific geographic area is chosen for the demonstration of the scope of the proposed system. The system is a web application developed using Streamlit, an open-source python framework. To effectively implement the smart city concept, it is crucial to integrate diverse IoT systems, data sources, data streams, and analytical tools into a unified and seamless platform that facilitates the collection, analysis and presentation of information related to urban systems and subsystems. We propose taking into consideration several attributes when analyzing human mobility patterns, such as vehicle/traffic density, modes of transport, transport data, demographics, latitude, and longitude. Additionally, the system utilizes image processing as an efficient method for calculating urban green cover using morphological operations that are computationally cheaper and easy to use as compared to traditional surveys that are time and resource intensive. This information is used to develop smart plans to sustain or increase green cover in select areas, leading to the creation of sustainable and green smart cities. © 2023 IEEE.

关键词： Smart city

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Audio Perception in Robotic Assistance for Human Space exploration: A Feasibility Study

Audio Perception in Robotic Assistance for Human Space Explo...

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IEEE Aerospace conference

作者： Sewtz, Marco Friedl, Werner Bauer, Adrian Koepken, Anne Lay, Florian Bechtel, Nicolai Schmaus, Peter Triebel, Rudolph Lii, Neal Y. German Aerosp Ctr DLR Inst Robot & Mechatron Muenchener Str 20 D-82234 Wessling Germany

ISBN: (纸本)9781665490320

Future crewed missions beyond low earth orbit will greatly rely on the support of robotic assistance platforms to perform inspection and manipulation of critical assets. This includes crew habitats, landing sites or assets for life support and operation. Maintenance and manipulation of a crewed site in extraterrestrial environments is a complex task and the system will have to face different challenges during operation. While most may be solved autonomously, in certain occasions human intervention will be required. The telerobotic demonstration mission, Surface Avatar, led by the German Aerospace Center (DLR), with partner European Space Agency (ESA), investigates different approaches offering astronauts on board the International Space Station (ISS) control of ground robots in representative scenarios, e.g. a Martian landing and exploration Site. In this work we present a feasibility study on how to integrate auditory information into the mentioned application. We will discuss methods for obtaining audio information and localizing audio sources in the environment, as well as fusing auditory and visual information to perform state estimation based on the gathered data. We demonstrate our work in different experiments to show the effectiveness of utilizing audio information, the results of spectral analysis of our mission assets, and how this information could help future astronauts to argue about the current mission situation.

关键词： Robotics

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Neural Networks and Emotions: A Deep Learning Perspective 9

Neural Networks and Emotions: A Deep Learning Perspective

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9th IEEE International conference for Convergence in Technology, I2CT 2024

作者： Madhura, M. Meghana, S. varshitha, v.S. Kodipalli, Ashwini Rao, Trupthi Global Academy of Technology Dept. of Artificial Intelligence & Data Science Karnataka Bangalore India

ISBN: (纸本)9798350394474

Emotion recognition represents a critical facet of human-centric artificial intelligence systems. This paper delves into the forefront of emotion detection by leveraging cutting-edge deep learning models across three distinct modalities: textual, visual, and auditory. Our text-based emotion detection model harnesses the potency of Bidirectional Long Short-Term Memory (BiLSTM) networks, adept at capturing intricate semantic relationships and contextual nuances within textual data. Simultaneously, we employ Convolutional Neural Networks (CNNs) in the domain of image-based emotion detection, effectively extracting discriminative spatial features to discern emotional states within visual content. For speech-based emotion recognition, we employ Long Short-Term Memory (LSTM) networks, capitalizing on their ability to capture temporal dependencies within acoustic signals. These three modalities converge to offer comprehensive insights into multimodal emotion recognition, where the fusion of textual, visual, and auditory information enhances emotion classification accuracy. This research not only underscores the importance of deep learning in emotion analysis but also holds great potential for applications in human-computer interaction, sentiment analysis, mental health diagnostics, and multimedia content understanding. By elucidating the strengths and synergies of these modalities, this paper contributes significantly to the burgeoning field of emotion-aware AI systems, promising a more nuanced understanding of human emotions in an increasingly digital world. © 2024 IEEE.

关键词： Long short-term memory

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Enhancing visual Question Answering through Bi-Modal Feature Fusion: Performance analysis 24

Enhancing Visual Question Answering through Bi-Modal Feature...

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6th International conference on Image Processing and Machine vision (IPMv)

作者： Mao, Keyu Fudan Univ Sch Data Sci Shanghai Peoples R China

ISBN: (纸本)9798400708473

With the significant development in deep learning within the domains of computer vision and natural language processing, the research involving the multimodal aspects of visual Question Answering (vQA) has also reached a pivotal turning point in recent years. Throughout prior investigations, scholars have consistently emphasized feature extraction from images and text. Numerous models have been applied in this context, ranging from the initial breakthroughs of Recurrent Neural Network (RNN) and Convolutional Neural Network (CNN), to the momentary prominence of Dynamic Memory Networks, and subsequently, the rise of transformers in recent times. Nonetheless, it is imperative to recognize that beyond the ambit of feature extraction models, the fusion of bi-modal features assumes pivotal significance. This paper builds upon SOAT model from the previous work, serving as the baseline, and meticulously scrutinizes its performance across distinct fusion methodologies. various notable fusion strategies, such as MUTAN and BLOCK, are considered. Notably, the most adept model achieves an impressive 65.74% accuracy on the vQA v2 dataset, outperforming established benchmarks. This outcome robustly substantiates the premise that fusion techniques exert tangible influence over the ultimate research outcomes.

关键词： vQA Natural language processing Computer vision Fusion Bilinear models

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An exploration of Machine Learning Methods for Gait analysis of Potential Guide Dogs 10

An Exploration of Machine Learning Methods for Gait Analysis...

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10th International conference on Animal-Computer Interaction (ACI)

作者： Wu, Yifan Nichols, Colt Foster, Marc Martin, Devon Dieffenderfer, James Enomoto, Masataka Lascelles, B. Duncan X. Russenberger, Jane Brenninkmeyer, Gerald Bozkurt, Alper Roberts, David L. NC State Univ Dept Elect & Comp Engn Raleigh NC 27695 USA NC State Univ Coll Vet Med Raleigh NC USA Int Working Dog Registry San Antonio TX USA Guiding Eyes Blind Director Training Yorktown Hts NY USA NC State Univ Dept Comp Sci Raleigh NC USA

ISBN: (纸本)9798400716560

Gait analysis is important for guide dog organizations, as ideal guide dogs have a smooth and efficient gait, where they can also easily shift between and maintain various gaits. Gait quality and natural traveling speed are two of the multiple factors important in matching a guide dog to its visually impaired handler. Gait evaluation typically includes subjective visual observation of the dog or objective assessments obtained from special-designed equipment. Guide dog organizations need a method to easily collect and analyze objective data of gait information. In this work, we explored how various machine learning models could learn and analyze gait patterns from inertial measurements data that were collected during two different data collection experiments using a wearable sensor device. We also evaluated how well each machine learning model could generalize behavior patterns from various dogs under different environments. Additionally, we compared how sensor placement locations could affect gait prediction performance by attaching the sensor device to the dog's neck and back area respectively. The tested machine learning models were able to classify different gaits in the range of 42% to 91% in terms of accuracy, and predict various gait parameters with an error rate ranging from 14% to 29% depending on the setup. Furthermore, we also observed that using behavior data collected from the neck region contains more movement information than the back area. By performing a cross-dataset generalization test on the machine learning models, we found that even with performance drop, the models were able to learn gait-specific behavior patterns that are generalizable for different dogs. Although the results were preliminary, the proposed gait analysis exploration still showed promising potential for studying behavior patterns of candidate guide dogs.

关键词： Guide Dog Canine Behavior analysis Canine Gait analysis IMU Machine Learning

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Is it safe to cross? Interpretable Risk Assessment with GPT-4v for Safety-Aware Street Crossing 21

Is it safe to cross? Interpretable Risk Assessment with GPT-...

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21st International conference on Ubiquitous Robots (UR)

作者： Hwang, Hochul Kwon, Sunjae Kim, Yekyung Kim, Donghyun Univ Massachusetts Amherst Manning Coll Informat & Comp Sci 140 Governors Dr Amherst MA 01002 USA

ISBN: (纸本)9798350361087;9798350361070

Safely navigating street intersections is a complex challenge for blind and low-vision individuals, as it requires a nuanced understanding of the surrounding context - a task heavily reliant on visual cues. Traditional methods for assisting in this decision-making process often fall short, lacking the ability to provide a comprehensive scene analysis and safety level. This paper introduces an innovative approach that leverages vision-language models (vLMs) to interpret complex street crossing scenes, offering a potential advancement over conventional traffic signal recognition techniques. By generating a safety score and scene description in natural language, our method supports safe decision-making for blind and low-vision individuals. We collected crosswalk intersection data that contains multiview egocentric images captured by a quadruped robot and annotated the images with corresponding safety scores based on our predefined safety score categorization. Grounded on the visual knowledge, extracted from images and text prompts, we evaluate a vLM for safety score prediction and scene description. Our findings highlight the reasoning and safety score prediction capabilities of the vLM, activated by various prompts, as a pathway to developing a trustworthy system, crucial for applications requiring reliable decision-making support.

关键词： Decision making

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