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2024 IEEE International Conference on Electronic Systems and Intelligent Computing, ICESIC 2024

作者： Umamageswari, A. Pratishwaran, R.J. Poojitha Reddy, M. Yuvan Sankar Raj, R. SRM Institute of Science and Technology Department of Computer Science and Engineering Ramapuram Chennai India SRM Institute of Science and Technology Department of Computer Science and Engineering With Specialization in Big Data Analytics Ramapuram Chennai India

ISBN: (纸本)9798331522988

The goal of this project is to draw a deeper understanding of the subjective nature behind online product reviews, largely by examining a large dataset received from Amazon that contains numerous star ratings and comments. Since there is such a massive amount of data on the Amazon platform, it was perfect for the analysis. Our methodology is platform-agnostic and can be easily extended to other comparable datasets from other web platforms with comparable guarantees in achieving wide applicability. Our primary motivation is improving the dependability of online shopping. Since product reviews are currently widely used by many consumers as guides in making decisions to buy products, such reviews ought to be fair representations of the actual dependability and quality of the products. The goal of our effort is to identify patterns in the language and ratings of such reviews that would directly reflect the real customer sentiment and also the reliability of the product. Our approach has yielded already a rate of about 92%, which shows it has the potential greatly to raise the legitimacy of online reviews. We'd like to make our customers feel more confident and better equipped with information to make better and safer purchases on a website like Amazon and others. In the final analysis, we strive to make online shopping for our customers worldwide safer and more trustworthy. © 2024 IEEE.

关键词： data reliability

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Fast Moving Consumer Goods Object Detection Using YOLOV8

Fast Moving Consumer Goods Object Detection Using YOLOV8

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2024 IEEE International Conference on Electronic Systems and Intelligent Computing, ICESIC 2024

作者： Umamageswari, A. Mukund, P.U. Abhishek, V.B. Devi Vaishnavi, Y. SRM Institute of Science and Technology Department of Computer Science and Engineering Ramapuram Chennai India SRM Institute of Science and Technology Department of Computer Science and Engineering With Specialization in Big Data Analytics Ramapuram Chennai India

ISBN: (纸本)9798331522988

The basis of this project is to investigate whether the YOLO, an object detection algorithm where 'You Only Look Once' constitutes the name, could be applied to develop FMCG management;together with the management of FMCG, it becomes the gateway to increasing retail precision, efficiency, and agility. Under real-time processing, the system detects and counts FMCG products on retail shelves with an accuracy rate of 92.5%. This accuracy ensures that the optimum stock levels are maintained and there will be no discrepancies. The system processes video feeds at 0.08 seconds per frame, thus allowing inventory data to be updated in real-time to enable quick, data-driven decisions. YOLO's flexibility ensures consistent performance across different store layouts and product types. This system integrates with other retail management tools and allows integrating inventory data in the real time. This means that it would be made available with invaluable insights which would prevent stock depletion while helping to curtail costs. This project takes YOLO ahead of just being a game changer in the FMCG inventory management while operating to reduce expenses and customer satisfaction due to product availability. © 2024 IEEE.

关键词： Efficiency

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Customized Movie Recommendation for Enhanced Entertainment

Customized Movie Recommendation for Enhanced Entertainment

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2024 International Conference on Cybernation and Computation, CYBERCOM 2024

作者： Minu, M.S. Talapalli, Srikar Chevuri, Saahithi Medhasri, T.S. SRM Institute of Science and Technology Department of Computer Science Engineering Ramapuram Chennai India SRM Institute of Science and Technology Department of Computer Science Engineering with Specialization in Big Data Analytics Ramapuram Chennai India

ISBN: (纸本)9798331542344

The surging popularity of online movie databases has created a challenge for viewers: choosing a film from a massive library can be overwhelming. In this paper, it proposes to design a new hybrid movie recommendation powered by machine learning techniques as a solution to improve user experience. Among those techniques, two are combined into this paper: content-based filtering, using the real features of a movie such as genre and a director, and collaborative filtering, which uses the rating provided by users. This hybrid approach thus improves the intrinsic weaknesses that reside in both these methods individually. On the other hand, content-based filtering tends to reduce exploration to movies of the exact type one is doing well at, whereas, inversely, collaborative filtering settings often mean that a user will be exposed only to films within his or her already-recognized taste profile. It is expected that the proposed hybrid system will solve both problems by balancing this line between exploring familiar cinematic territory and making new recommendations based on both user preferences and the entire collective intelligence of the user base. The approach would aim to enrich user satisfaction by facilitating the selection of movies and providing a richer and more diversified movie-viewing experience. © 2024 IEEE.

关键词： Collaborative filtering

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Lifelong Graph Learning for Graph Summarization

Lifelong Graph Learning for Graph Summarization

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IEEE WIC ACM International Conference on Web Intelligence (WI)

作者： Jonatan Frank Marcel Hoffmann Nicolas Lell David Richerby Ansgar Scherp Data Science and Big Data Analytics Ulm University Germany University of Essex Colchester UK

ISBN: (数字)9798331504946

ISBN: (纸本)9798331504953

Summarizing web graphs is challenging due to the heterogeneity of the modeled information and its changes over time. We investigate the use of neural networks for lifelong graph summarization. After observing the web graph at a point in time, we train a network to summarize graph vertices. We apply this trained network to summarize the vertices of the changed graph at the next point in time. Subsequently, we continue training and evaluating the network to perform lifelong graph summarization. We use the GNNs Graph-MLP and GCN, as well as an MLP baseline, to summarize the temporal graphs. We compare 1-hop and 2-hop summaries. We investigate the impact of reusing parameters from a previous snapshot by measuring backward and forward transfer as well as forgetting rate. Our extensive experiments are on two series of ten weekly snapshots, from 2012 and 2022, of a web graph with over 100M edges. They show that all networks predominantly use 1-hop information to determine the summary, even when performing 2-hop summarization. Due to the heterogeneity of web graphs, in some snapshots, the 2-hop summary produces up to ten times as many vertex classes as the 1-hop summary. When using the network trained on the last snapshot from 2012 and applying it to the first snapshot of 2022, we observe a strong drop in accuracy. We attribute this drop over the ten-year time warp to the strongly increased heterogeneity of the web graph in 2022. The source code and additional resources are available at https://***/jofranky/Lifelong-Graph-Summarization-with-Neural-Networks.

关键词： Training Accuracy Current measurement Source coding Time measurement Resource description framework Graph neural networks Intelligent agents

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Multi-View Structural Graph Summaries

Multi-View Structural Graph Summaries

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IEEE WIC ACM International Conference on Web Intelligence (WI)

作者： Jonatan Frank Andor Diera David Richerby Ansgar Scherp Data Science and Big Data Analytics Ulm University Germany University of Essex Colchester UK

ISBN: (数字)9798331504946

ISBN: (纸本)9798331504953

A structural graph summary is a small graph representation that preserves structural information necessary for a given task. The summary is used instead of the original graph to complete the task faster. We introduce multi-view structural graph summaries and propose an algorithm for merging two summaries. We conduct a theoretical analysis of our algorithm. We run experiments on three datasets, contributing two new ones. The datasets are of different domains (web graph, source code, and news) and sizes. The interpretation of multi-view depends on the domain: pay-level domains on the web, control vs. data flow of the code, and the output of different news broadcasters. We experiment with three graph summary models: attribute collection, class collection, and their combination. We observe that merging two structural summaries has an upper bound of quadratic complexity; but under reasonable assumptions, it has linear-time worst-case complexity. The running time of merging has a strong linear correlation with the number of edges in the two summaries. Therefore, the experiments support the assumption that the upper bound of quadratic complexity is not tight and that linear complexity is possible. Furthermore, our experiments show that always merging the two smallest summaries by the number of edges is the most efficient strategy for merging multiple structural summaries. The source code and additional resources are available at https://***/jofranky/Multi-View-Structural-Graph-Summaries.

关键词： Upper bound Correlation Codes Source coding Merging Knowledge graphs Resource description framework Complexity theory Intelligent agents

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big OLAP data Cube Compression Algorithms in Column-Oriented Cloud/Edge data Infrastructures 9

Big OLAP Data Cube Compression Algorithms in Column-Oriented...

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9th IEEE International Conference on Multimedia big data, bigMM 2023

作者： Cuzzocrea, Alfredo University of Calabria The Big Data Engineering and Analytics Laboratory Rende Italy University of Paris City Dept. of Computer Science Paris France

This paper provides a brief overview on the innovative problem of devising and implementing big OLAP data cube compression algorithms in column-oriented Cloud/Edge data infrastructures, an emerging need for next-gener... 详细信息

ISBN: (纸本)9798350360004

关键词： data analytics

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Multi-View Structural Graph Summaries

arXiv

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arXiv 2024年

作者： Frank, Jonatan Diera, Andor Richerby, David Scherp, Ansgar Data Science and Big Data Analytics Ulm University Germany University of Essex Colchester United Kingdom

A structural graph summary is a small graph representation that preserves structural information necessary for a given task. The summary is used instead of the original graph to complete the task faster. We introduce multi-view structural graph summaries and propose an algorithm for merging two summaries. We conduct a theoretical analysis of our algorithm. We run experiments on three datasets, contributing two new ones. The datasets are of different domains (web graph, source code, and news) and sizes. The interpretation of multi-view depends on the domain: pay-level domains on the web, control vs. data flow of the code, and the output of different news broadcasters. We experiment with three graph summary models: attribute collection, class collection, and their combination. We observe that merging two structural summaries has an upper bound of quadratic complexity;but under reasonable assumptions, it has linear-time worst-case complexity. The running time of merging has a strong linear correlation with the number of edges in the two summaries. Therefore, the experiments support the assumption that the upper bound of quadratic complexity is not tight and that linear complexity is possible. Furthermore, our experiments show that always merging the two smallest summaries by the number of edges is the most efficient strategy for merging multiple structural summaries. The source code and additional resources are available at https://***/jofranky/ Multi-View-Structural-Graph-Summaries. Copyright © 2024, The Authors. All rights reserved.

关键词： Knowledge graph

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Using Reinforcement Learning for Per-Instance Algorithm Configuration on the TSP

Using Reinforcement Learning for Per-Instance Algorithm Conf...

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2023 IEEE Symposium Series on Computational Intelligence, SSCI 2023

作者： Vinzent Seiler, Moritz Rook, Jeroen Heins, Jonathan Ludger Preub, Oliver Bossek, Jakob Trautmann, Heike University of Twente Data Management and Biometrics Enschede Netherlands Tu Dresden Big Data Analytics in Transportation Dresden Germany Aachen University Chair for Ai Methodology Rwth Aachen Germany University of Münster Data Science: Statistics and Optimization Münster Germany University of Twente Data Science: Statistics and Optimization Enschede Netherlands

ISBN: (纸本)9781665430654

Automated Algorithm Configuration (AAC) usually takes a global perspective: it identifies a parameter configuration for an (optimization) algorithm that maximizes a performance metric over a set of instances. However, the optimal choice of parameters strongly depends on the instance at hand and should thus be calculated on a per-instance basis. We explore the potential of Per-Instance Algorithm Configuration (PIAC) by using Reinforcement Learning (RL). To this end, we propose a novel PIAC approach that is based on deep neural networks. We apply it to predict configurations for the Lin-Kernighan heuristic (LKH) for the Traveling Salesperson Problem (TSP) individually for every single instance. To train our PIAC approach, we create a large set of 100 000 TSP instances with 2 000 nodes each - currently the largest benchmark set to the best of our knowledge. We compare our approach to the state-of-the-art AAC method Sequential Model-based Algorithm Configuration (SMAC). The results show that our PIAC approach outperforms this baseline on both the newly created instance set and established instance sets. © 2023 IEEE.

关键词： Deep Reinforcement Learning Per-Instance Algorithm Configuration Traveling Salesperson Problem

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Lifelong Graph Learning for Graph Summarization

arXiv

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arXiv 2024年

作者： Frank, Jonatan Hoffmann, Marcel Lell, Nicolas Richerby, David Scherp, Ansgar Data Science and Big Data Analytics Ulm University Germany University of Essex Colchester United Kingdom

关键词： Graph neural networks

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Harnessing Medical big data: Integrating Computational Insights for Enhanced Patient Outcomes 24

Harnessing Medical Big Data: Integrating Computational Insig...

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7th International Conference on Digital Medicine and Image Processing, DMIP 2024

作者： Syed Masood, Jafar Ali Ibrahim Vinayagam, Jai Kumar Onteru, Ranjit Kumar Kopparapu, Umamaheswararao Parabathini, Bulah Pushpa Rani Department of IoT School of Computer Science and Engineering Vellore Institute of Technology Tamil Nadu Vellore632014 India Centre for Big Data Analytics QIS College of Engineering and Technology Andhra Pradesh Ongole India

ISBN: (纸本)9798400709586

The rapid proliferation of medical big data has opened unprecedented opportunities for enhancing patient outcomes through advanced computational analysis. This paper explores the integration of big data analytics with clinical practices to deliver personalized medicine, optimize treatment protocols, and improve patient care. Our findings demonstrate that a synergistic approach between computational insights and clinical decision-making can significantly advance healthcare outcomes. This study provides a comprehensive framework for harnessing medical big data, addressing key challenges, and proposing solutions to integrate these insights into everyday clinical practice. Copyright © 2024 held by the owner/author(s). Publication rights licensed to ACM.

关键词： Personalized medicine

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