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Unlocking the Perfect Fit: ML Approach for Apparel Size Recommendation

Unlocking the Perfect Fit: ML Approach for Apparel Size Reco...

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International conference on Applied System Innovation (ICASI)

作者： Shashin Bhaskar Shishir Hebbar Ruthvik v Kumar Poorvi B H R vanamala Department of Electronics and Communication Engineering PES University Bengaluru Karnataka India

ISBN: (数字)9798350394924

ISBN: (纸本)9798350394931

This study is based on the exploration of an e-commerce apparel size recommendation system through machine learning and image processing, evaluating three backbone architectures for YOLOv8—EfficientNet, ResNet, and CSPDarknet—identifying CSPDarknet as the most effective. The novelty of the model is emphasized by its conjunction with a custom-gathered dataset, further optimized by CLAHE for data augmentation and the AdamW optimizer. validated with precise measurements on a set of numerous diverse individuals, the approach integrates C2f and SPPF blocks to enhance gradient flow and model efficiency, significantly advancing the online shopping experience by optimizing the analysis of one single user image.

关键词： Technological innovation Analytical models Image processing Machine learning Predictive models data augmentation User experience

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An Effectual Forecasting of Oil Production Performance Using Advanced Adaptive Recurrent Neural Network Classifier

An Effectual Forecasting of Oil Production Performance Using...

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International conference on Communication Systems and Network Technologies (CSNT)

作者： K. Siva Kumar v. R. Nagalakshmi Nethi Korupula Siva Kumar Yagati Sai Chanikhya Mohammad Yousf Basha B P M Harshavardhan Department of Petroleum Engineering Godavari Institute of Engineering and Technology (A) Rajahmundry Andhra Pradesh

ISBN: (数字)9798331531935

ISBN: (纸本)9798331531942

Oil is the primary source of energy worldwide, accounting for a large percentage of global energy consumption, with significant impacts on economic, industrial, geopolitical, and social dimensions. It enables the movement of transport, driving globalization and economic integration because oil is a backbone of international trade. However, various reasons, including political, technical, natural, economic, and strategic ones, lead to the reduction of oil production. Hence, this paper proposed a comprehensive analysis and prediction of oil production using an advanced adaptive Recurrent Neural Network (RNN). Initially, diverse datasets are collected to ensure a robust foundation for analysis. Following this, preprocessing techniques, including data cleaning and filtering, are employed to enhance data quality and relevance. The exploration phase focuses on evaluating the data, identifying trends, and uncovering underlying patterns. Feature selection is critical to optimize the predictive model's performance. The Adaptive RNN is then utilized to model the relationships within the dataset, leading to the generation of predicted values. Overall, this work is implemented by python language. Finally, predicted the error value, such as the Root Mean Square Error (RMSE) of 72.9% and the Mean Absolute Error (MAE) of 26.1%, are attained. This structured approach facilitates effective data analysis and contributes to the advancement of predictive analytics in various domains.

关键词： Adaptation models Recurrent neural networks Adaptive systems Oils Production Predictive models Feature extraction Reservoirs Topology Python

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LEARNING WHAT AND WHERE: DISENTANGLING LOCATION AND IDENTITY TRACKING WITHOUT SUPERvISION 11

LEARNING WHAT AND WHERE: DISENTANGLING LOCATION AND IDENTITY...

引用

11th International conference on Learning Representations, ICLR 2023

作者： Traub, Manuel Otte, Sebastian Menge, Tobias Karlbauer, Matthias Thümmel, Jannik Butz, Martin v. University of Tübingen Germany University of Tübingen Germany

Our brain can almost effortlessly decompose visual data streams into background and salient objects. Moreover, it can anticipate object motion and interactions, which are crucial abilities for conceptual planning and reasoning. Recent object reasoning datasets, such as CATER, have revealed fundamental shortcomings of current vision-based AI systems, particularly when targeting explicit object representations, object permanence, and object reasoning. Here we introduce a self-supervised LOCation and Identity tracking system (Loci), which excels on the CATER tracking challenge. Inspired by the dorsal and ventral pathways in the brain, Loci tackles the binding problem by processing separate, slot-wise encodings of 'what' and 'where'. Loci's predictive coding-like processing encourages active error minimization, such that individual slots tend to encode individual objects. Interactions between objects and object dynamics are processed in the disentangled latent space. Truncated backpropagation through time combined with forward eligibility accumulation significantly speeds up learning and improves memory efficiency. Besides exhibiting superior performance in current benchmarks, Loci effectively extracts objects from video streams and separates them into location and Gestalt components. We believe that this separation offers a representation that will facilitate effective planning and reasoning on conceptual levels. © 2023 11th International conference on Learning Representations, ICLR 2023. All rights reserved.

关键词： Location

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Method of Coding video Images Based on Meta-Determination of Segments

Method of Coding Video Images Based on Meta-Determination of...

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IEEE lnternational conference on Advanced Trends in Radioelectronics, Telecommunications and Computer Engineering, TCSET 2024

作者： Barannik, vladimir Barannik, valeriy Babenko, Yurii Kolesnyk, vitalii Zeleny, Pavlo Pasynchuk, Kirill Ushan, vladyslav Yermachenkov, Andrii Savchuk, Maksym V. N. Karazin Kharkiv National University Kharkiv Ukraine Kharkov National University of Radio Electronics Kharkiv Ukraine Taras Shevchenko National University of Kyiv Kyiv Ukraine State Research Institute of Armament and Military Equipment Testing and Certification Kyiv Ukraine National University of Civil Defense of Ukraine Kharkiv Ukraine Ivan Kozhedub Kharkiv National Air Force University Kharkiv Ukraine Heroes of Kruty Military Institute of Telecommunications and Informatization Kyiv Ukraine

ISBN: (纸本)9783031612206

The given chapter is devoted to the scientific problem of the increasing video availability level while ensuring its integrity. To solve the formulated problem, a direction related to the coding methods is used. Such methods enable reducing the video traffic intensity. various standardized platforms for encoding video data analysis are carried out. Thus, a conclusion is made regarding the need to modify the standardized coding process. The following requirements are substantiated. The first is the use of the mechanism for establishing the segments’ informative load regarding their integrity preservation. The direction is implemented based on the segments’ meta-determination technology using components’ intellectual analysis. The second is the creation of methods for segment-differentiated processing depending on their informative load level. A method for segment-differentiated coding based on structural-statistical (SS) saturation is developed. video frame segment differential processing is carried out considering their preliminary typification, depending on the significance of the video information resource (vIR) semantic integrity (SI) preservation position. This is achieved through the syntactic description correction technologies used according to the visual perception model. The main creating model stages for assessing the vIR bit volume reduction level in ensuring its availability process have been described. The created method main differences are: redundancies and their types eliminating process is selected depending on the microsegment’s presence with different SS levels;spectral component areas’ (ASC) permissible correction chains (CPC) codegrams properties regarding the bits excess number presence are considered;the bits excess number filling in the ASC CPC codegrams is carried out depending on the segments influence importance on the vIR SI preservation. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2024.

关键词： Redundancy

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visualizing Results of Promoting Campaigns 14

Visualizing Results of Promoting Campaigns

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14th International conference Management of Large-Scale System Development, MLSD 2021

作者： vladova, Alla Yu. vladov, Yu. R. Yakimov, A.I. V.A. Trapeznikov Institute of Control Sciences of RAS Financial University Government of the Russian Federation Laboratory of Identification of Systems Moscow Russia Orenburg Federal Research Center of the Ural Branch of RAS Laboratory of Environmental Management Technologies Orenburg Russia Belorussian-Russian University Department of Automation Systems Mogilev Belarus

ISBN: (纸本)9781665412308

Literature review related to the data exploration methods in marketing showed that the number of publications and software tools in the field of exploration data analysis of business processes has been increasing since 2015. The contribution of the given research lies in the hypothesis of the possibility to construct a quantitative model of a business process for big data containing fraud. At the first stage, we performed data cleaning and reduction, examined data distributions, and identified issues such as extreme values that supplement the previously developed visualization approach. The descriptive analysis helped us to describe the significant number of categorical features. We emphasized the data outliers that should be marked for further research. © 2021 IEEE.

关键词： data visualization

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PassFlow: Guessing Passwords with Generative Flows 52

PassFlow: Guessing Passwords with Generative Flows

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52nd Annual IEEE/IFIP International conference on Dependable Systems and Networks (DSN)

作者： Pagnotta, Giulio Hitaj, Dorjan De Gaspari, Fabio Mancini, Luigi, v Sapienza Univ Rome Dipartimento Informat Rome Italy

ISBN: (数字)9781665416931

ISBN: (纸本)9781665416931

Recent advances in generative machine learning models rekindled research interest in the area of password guessing. data-driven password guessing approaches based on GANs, language models, and deep latent variable models have shown impressive generalization performance and offer compelling properties for the task of password guessing. This paper proposes PassFlow, a flow-based generative model approach to password guessing. Flow-based models allow for precise log-likelihood computation and optimization, which enables exact latent variable inference. Additionally, flow-based models provide meaningful latent space representation, which enables operations such as exploration of specific subspaces of the latent space and interpolation. We demonstrate the applicability of generative flows to the context of password guessing, departing from previous applications of flow-networks which are mainly limited to the continuous space of image generation. We show that PassFlow is able to outperform prior state-of-the-art GAN-based approaches in the password guessing task while using a training set that is orders of magnitudes smaller than that of prior art. Furthermore, a qualitative analysis of the generated samples shows that PassFlow can accurately model the distribution of the original passwords, with even non-matched samples closely resembling human-like passwords.

关键词： password guessing neural networks generative flows

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Disinformation analysis and Tracking Dashboard

Disinformation Analysis and Tracking Dashboard

引用

International conference on Military Communications and Information Systems (ICMCIS)

作者： Justina Mandravickaitė Milita Songailaitė Eglė Kankevičiūtė Anton volčok Tomas Krilavičius Faculty of Informatics Vytautas Magnus University / CARD Lithuania Lingo data Lingo data / CARD Lithuania

The importance of disinformation identification and tracking in the information age is not questionable. War in Ukraine, information attacks in Baltic states, and a number of other disinformation attacks worldwide show that it is becoming a conventional tool in information space. In this paper, we present a disinformation analysis and tracking dashboard. It has 3 main components: Disinformation Analyzer, Match disinformation cases and Model Accuracy. The first component consists of a fined-tuned RoBERTa model for classifying textual input into fake and neutral. The second component contains semantic matching functionality for checking the semantic similarity of input text to the set of known disinformation cases. The third component includes the confusion matrix and model accuracy information. All 3 components have been combined into visual analytics platform for easy inspection and exploration of potential and known disinformation cases. The majority of the work was done during TIDE Hackathon 2023 as a part of the Disinformation Analyzer Challenge.

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A New Branch and Bound Algorithm for Mining Frequent Conceptual Links in Social Networks 27

A New Branch and Bound Algorithm for Mining Frequent Concept...

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27th International conference on Knowledge Based and Intelligent Information and Engineering Sytems, KES 2023

作者： Djahnit, Hadjer Bessedik, Malika BP 68M Oued Smar Alger16 270 Algeria

The frequent conceptual links is a descriptive data mining task which aims at describing a social network in term of the most connected type of nodes. This is done by grouping nodes into clusters or groups according to their attributes and checking the number of links between the nodes of each couple of groups, if this number is greater than a predefined threshold, the set of links is referred to as a frequent conceptual link (FCL). Although relatively recent, this task has received a number of research, chiefly in order to optimize the exploration of the search process. Indeed, the problem is defined as NP-hard, where the search process depends on the size of the network, the number of attributes and the set of their possible values whose combination can explode quickly. In this paper, we propose a new algorithm for mining the frequent conceptual links in a social network based on the technique of the branch and bound. In addition to defining an upper bound for the potential patterns in the search space, the algorithm implements other techniques which improve significantly the performance of the search process and allows to fix the shortcomings constated in the previous implementations. © 2023 The Authors. Published by Elsevier B.v. This is an open access article under the CC BY-NC-ND license (https://***/licenses/by-nc-nd/4.0)

关键词： Branch and Bound data mining Frequent conceptual links frequent pattern mining social network analysis

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visual Analytics for Profiling Land Use Changes

Visual Analytics for Profiling Land Use Changes

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Brazilian Symposium on Computer Graphics and Image Processing (SIBGRAPI)

作者： Claudio Santos Maryam Hosseini João Rulff Fabio Miranda Luc Wilson Claudio Silva Nivan Ferreira Marcos Lage Universidade Federal Fluminense Massachusetts Institute of Technology New York University University of Illinois Chicago Universidade Federal de Pernambuco Kohn Pedersen Fox Associates PC

The growth of cities calls for regulations and zoning rules on how each piece of urban space will be used. Tracking land use can reveal a wealth of information about urban development. For that matter, cities have been releasing data sets describing the historical evolution of the shape and the attributes of land units. The complex nature of land-use data, however, makes the analysis of such data challenging and time-consuming. To address these challenges, we propose Urban Chronicles, a visual analytics system that enables interactive exploration of land-use changes. Using New York City's Primary Land Use Tax Lot Output (PLUTO), we show the system's capabilities to explore the data from several years at different scales. UrbanChron-iclessupports on-the-fly aggregation and filtering operations that leverage the hierarchical nature of the data set to index the shape and attributes of geographical regions that change over time. Finally, we demonstrate the system's utility through case studies that analyze the impact of Hurricane Sandy on land use attributes and the effects of rezoning plans in Brooklyn.

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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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Knowledge Engineering and Communication Systems (ICKECS), International conference on

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

ISBN: (数字)9798350359688

ISBN: (纸本)9798350359695

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.

关键词： Fuzzy logic Knowledge engineering Cloud computing Scalability Merging data protection Fractals

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