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Product Recommendation Using Collaborative Filtering and K-Means Clustering 5

Product Recommendation Using Collaborative Filtering and K-M...

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5th IEEE International Conference on Computing, Power, and Communication Technologies, IC2PCT 2024

作者： Kumar, M. Ranjith Vishnu, S. Roshen, G. Kumar, D. Niranjan Revathi, P. Baster, David Ricardo Ledo Amrita Vishwa Vidyapeetham Amrita School of Engineering Department of Mathematics Chennai India Amrita Vishwa Vidyapeetham Dept. of Computer Science & Engineering Amrita School of Computing Chennai India C. Abdul Hakeem College of Engineering and Technology Dept. of Computer Science & Engineering Vellore Melvisharam India University of Holguin Dept. of Informatics and Mathematics Cuba

ISBN: (纸本)9798350383522

The growth of e-commerce has altered how consumers shop, providing a digital space where convenience, vast product offerings, and competitive pricing converge. In today's world, e-commerce websites are transitioning from traditional search-driven methods to customized and intuitive approaches via product suggestions. Product recommendation systems are vital in e-commerce, from bringing new business to retaining existing ones. Our three-part recommendation system is designed so that new users have a great and engaging experience as the Product Popularity -Based System shows them carefully chosen products that are in demand. Collaborative Filtering Recommendations are highly personalized recommendations given to people who have already made their first purchases based on their prior actions and preferences. The K-Means Clustering-Based Recommendation System uses textual clustering analysis to deliver contextually relevant recommendations. We use a variety of evaluation metrics, such as click-through rates, user engagement, and the Silhouette Score, to assess the effectiveness and accuracy of our recommendation systems. Our findings show significant increases in user engagement, conversion rates, and relevant recommendations. Our findings demonstrate the transformative power of well-designed recommendation systems, which improve user experiences and retention and provide invaluable solutions for businesses entering the e-commerce space. This paper provides an in-dept. examination of the multifaceted landscape of e-commerce recommendations, shedding light on their far-reaching implications for customer acquisition and retention in this dynamic digital era. © 2024 IEEE.

关键词： clustering collaborative filtering E-commerce systems K-means algorithm recommender system

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Generative Adversarial Networks as Weight Generators in Ensemble Classification 4

Generative Adversarial Networks as Weight Generators in Ense...

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4th International Conference on Artificial Intelligence, Robotics and Control, AIRC 2023

作者： Yehia, Asmaa Fahmy, Sherif Fahmy, Ahmed AASTMT Basic Applied Science Dept. Giza Egypt AASTMT Computer Engineering Dept. Cairo Egypt

ISBN: (纸本)9798350348248

Ensemble classifiers are constructed from various component classifiers by aggregating the results of the individual classifiers in some manner. While there are many ways to do this, one of the least computationally intensive is to use a voting ensemble, where the vote of each classifier is weighted by a number representing its performance. Choosing the appropriate weights is an involved process, that ranges from using trial and error to using sophisticated machine learning algorithms. In this paper, we proposed a novel technique that uses generative adversarial networks to customize the weights used for each individual input to the ensemble. Specifically, we propose to use conditional generative adversarial networks to generate the weights, with the input image acting as the label to the network and the appropriate weight being the output. We test the proposed system on an image classification task and show that the proposed method produces better results than using fixed weights by 2.43 percent. © 2023 IEEE.

关键词： Image recognition

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A Comparative Analysis of Transformer Models in Zero-Shot Text Classification 15

A Comparative Analysis of Transformer Models in Zero-Shot Te...

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15th International Conference on Information, Intelligence, Systems and Applications, IISA 2024

作者： Kyritsis, Konstantinos Liapis, Charalampos M. Spatiotis, Nikolaos Perikos, Isidoros Paraskevas, Michael University of Peloponnese Electrical & Computer Engineering Dept. Patras26334 Greece University of Patras Mathematics Dept. Patras26504 Greece Computer Technology Institute & Press 'Diophantus' Patras26504 Greece University of Patras Computer Engineering & Informatics Dept. Patras26504 Greece

ISBN: (纸本)9798350368833

Ahstract-Zero-shot text classification leverages pre-trained transformer models to categorize texts without the need for task-specific training. This paper analyzes a variety of transformer-based pre-trained methods on the task of zero-shot text classi-fication. Specifically, we present a deep comparative analysis of various transformer models, such as BART, DeBerta, DistiIBART, RoBERTa, and their variants, and evaluate their performance in various zero-shot text classification schemes. Furthermore, we examine the models' generalization capabilities. The findings highlight key strengths and weaknesses of each model, providing insights into their suitability for different text categorization tasks. Our research contributes to the broader understanding of transformer-based approaches and offers guidance for selecting models for zero-shot text classification. © 2024 IEEE.

关键词： Zero-shot learning

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Real-time Detection of Submerged Debris in Aquatic Ecosystems using YOLOv8 26

Real-time Detection of Submerged Debris in Aquatic Ecosystem...

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26th International Conference on computer and Information Technology, ICCIT 2023

作者： Sinthia, Afsana Kabir Alim Rasel, Annajiat Haque, Munima Brac University Dept. of Computer Science and Engineering Bangladesh Brac University Dept. of Mathematics and Sciences Bangladesh

ISBN: (纸本)9798350359015

The current circumstances, it is imperative to prioritize the implementation of effective strategies to mitigate the negative Water contamination is a prominent environmental concern in contemporary society. The lack of standardized benchmarks and measurements, as well as the absence of widely acknowledged standards for data, make it challenging to compare current research efforts in the field of automatic waste identification. This article addresses the aforementioned issues by offering a comprehensive examination of current underwater waste datasets and a concise yet insightful evaluation of existing waste detection algorithms based on Deep Learning. This article compiles and synthesizes prior research and presents the findings of the authors' own tests conducted on the provided datasets. The objective is to provide a first reproducible benchmark for detecting waste using YOLOv8. These datasets are collected from open-source datasets. They include comprehensive annotations that encompass all conceivable waste categories, such as bio, glass, metal and plastic, non-recyclable, other, paper, and unknown. The technique provided in this study demonstrates a maximum average accuracy of 92% on the dataset. The findings indicated that the enhanced model successfully achieved the detection and identification of waste present in underwater. © 2023 IEEE.

关键词： Underwater Object detection waste detection YOLO YOLOv8

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Machine Learning-Based Context Space Theory

Machine Learning-Based Context Space Theory

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2023 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2023

作者： D'Aniello, Giuseppe Gaeta, Matteo Policastro, Pasquale University of Salerno Dept. Information and Electrical Engineering and Applied Mathematics SA Fisciano Italy

ISBN: (纸本)9798350337020

Situation awareness of human and artificial agents can be improved by the recognition and adequate representation of real-life situations. The lack of easy-understandable, easy-to-use, and effective computational models of situations hindered the adoption and diffusion of situation awareness approaches in modern human-machine systems. Context Space Theory is a context awareness approach that uses a geometric metaphor to provide integrated mechanisms for representing contexts and situations. A drawback of this approach is the expert-based definition of context and situation spaces. This process can be time-consuming and expensive. In this paper, we propose a novel approach, namely Machine Learning-based Context Space Theory, which adopts machine learning techniques and, in particular, decision trees, to semi-automatically define context spaces and situation spaces with a data-driven approach. A case study related to the monitoring and control of the Covid-19 pandemic in Italy is proposed to demonstrate the feasibility and benefits of the proposed approach. © 2023 IEEE.

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Automatic transformation of UML-RT models into nuXmv model checker 7

Automatic transformation of UML-RT models into nuXmv model c...

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7th International Symposium on Innovative Approaches in Smart Technologies, ISAS 2023

作者： Schorr, Ruth Frankfurt Unviersity of Applied Sciences Dept. of Computer Science and Engineering Frankfurt Germany

ISBN: (纸本)9798350383065

The use of diagram-based development approach especially Model Driven Development (MDD), where the executable code can be generated directly from the diagrammatic system model, has gained much popularity in the recent years. The use of automated code generation allows to focus primarily on the core design aspects of system design without having to deal with the developmental challenges. Moreover, the approach also avoids any gap between the system design and the developed system, which can be very common when code development is manual. However, at present, these generated programs have to be manually checked for semantics correctness and hence, the possibility of an automated model level verification of such MDD artifacts is the next big step in this domain. There are multiple tools today that implement MDD and are being used by the research world for exploring the possible verification alternatives. In this paper we look closely into the verification of one such standardized profile, UML for Real-Time (UML-RT), developed particularly for MDD of Real-Time systems. Eclipse Papyrus-RT is an open-source modeling tool that supports UML-RT for specifying software systems. nuXmv is a model checker, that can verify system models against a set of properties defined using temporal logics. This paper presents a system that automatically translates Papyrus-RT models into a behaviorally equivalent nuXmv model. © 2023 IEEE.

关键词： Real time systems

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EV Allocation and Charging within Parking Lots Using a Locational Marginal Pricing Mechanism

EV Allocation and Charging within Parking Lots Using a Locat...

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2023 IEEE PES Innovative Smart Grid Technologies Europe, ISGT EUROPE 2023

作者： Arias, Nataly Bañol Giraldo, Juan S. López, Juan Camilo Hoogsteen, Gerwin Hurink, Johann Dept. of Electrical Engineering Mathematics and Computer Science University of Twente Netherlands Netherlands

ISBN: (纸本)9798350396782

Electrifying parking lots (PLs) is becoming increasingly challenging as the number of electric vehicle (EV) chargers operating within PLs increases, putting a strain on the internal electricity grid. Reinforcing the local grid can be an expensive solution. Therefore, an alternative approach is to design a mechanism that optimizes the distribution of incoming EVs along the PL infrastructure, minimizing operating costs for the PL owner and the charging costs for EV users. This paper proposes a user-aware pricing mechanism for EV charging in PLs based on locational marginal prices (LMPs), which consists of a base tariff and a location-dependent tariff. A mixed-integer second-order programming model is developed to determine the optimal charging spot for each EV upon arrival at the PL. Results show that the proposed LMP-based allocation mechanism distributes the strain more evenly throughout the local grid, increasing the EV hosting capacity of the PL, while minimizing charging costs for users. Compared to a random allocation approach, the proposed mechanism resulted in 30.9% less energy not served and 17.5% lower charging costs. © 2023 IEEE.

关键词： Electric vehicles locational marginal prices optimization parking lots pricing mechanism

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Robust and Predictive Charging of Large Electric Vehicle Fleets in Grid Constrained Parking Lots 14

Robust and Predictive Charging of Large Electric Vehicle Fle...

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14th IEEE International Conference on Communications, Control, and Computing Technologies for Smart Grids, SmartGridComm 2023

作者： Hoogsteen, Gerwin Winschermann, Leoni Nijenhuis, Bart Arias, Nataly Banol Hurink, Johann L. University of Twente Dept. of Electrical Engineering Mathematics and Computer Science Enschede Netherlands

ISBN: (纸本)9781665455541

Grid congestion forms an obstacle for the adoption of electric vehicles (EVs) by companies for their sustainability goals. This paper presents a robust approach to smart charging of large fleets of EVs that only utilizes currently available technologies and data, such that it can be applied in practice. The method relies only on available data at charge point operators. The presented approach uses offline aggregated planning to determine a robust fill-level. Subsequently, this fill-level is used in online control to maximise the quality of service to users while being able to provide flexibility to the energy system. Simulation results using real data show that the self-consumption of PV energy can be improved from 72.0% to 85.4%. Furthermore, the energy not served is reduced from 5.9% to 2.8%. © 2023 IEEE.

关键词： Charging (batteries)

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Combining image and point cloud segmentation to improve heritage understanding 10

Combining image and point cloud segmentation to improve heri...

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10th International Workshop 3D-ARCH "3D Virtual Reconstruction and Visualization of Complex Architectures"

作者： Bassier, Maarten Mazzacca, Gabriele Battisti, Roberto Malek, Salim Remondino, Fabio Dept. of Civil Engineering Geomatics KU Leuven Faculty of Engineering Technology Ghent Belgium Trento Italy Dept. Mathematics Computer Science and Physics University of Udine Italy

Current 2D and 3D semantic segmentation frameworks are developed and trained on specific benchmark datasets, often rich of synthetic data, and when they are applied to complex and real-world heritage scenarios they offer much lower accuracy than expected. In this work, we present and demonstrate an early and late fusion of methods for semantic segmentation in cultural heritage applications. We rely on image datasets, point clouds and BIM models. The early fusion utilizes multi-view rendering to generate RGBD imagery of the scene. In contrast, the late fusion approach merges image-based segmentation with a Point Transformer applied to point clouds. Two scenarios are considered and inference results show that predictions are primarily influenced by whether the scene has a predominantly geometric or texture-based signature, underscoring the necessity of fusion methods. © 2024 International Society for Photogrammetry and Remote Sensing. All rights reserved.

关键词： Semantic Segmentation

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Video Classification Using CNN and Ensemble Learning 9

Video Classification Using CNN and Ensemble Learning

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9th International Conference on Advanced Computing and Communication Systems, ICACCS 2023

作者： Duvvuri, Kavya Kanisettypalli, Harshitha Jaswanth, Kunisetty Murali, K. Amrita School of Computing Dept. of Computer Science and Engineering Bengaluru India Amrita School of Engineering Dept. of Mathematics Bengaluru India

ISBN: (纸本)9798350397376

Video classification is the task of producing an index or a label that is relevant to the given video. For this classification input can be in frames, heatmaps of respective videos, etc., depending on the application. Video classification is more than just simple image classification, it can be done using the concept of Convolutional Neural Network (CNN). Based on the application, and dataset limitations there are many extensions to this method such as CNN- Long Short-Term Memory (CNN-LSTM), Convolutional 3D network, Pose detection and LSTM, etc. In this project, we used the UCF11 (YouTube Action) dataset and classified them based on the action. A CNN model has been developed and trained with the dataset and the tested results are improved with a better approach called Ensemble Learning. Using this approach, the same CNN model having the same architecture gave better prediction results. © 2023 IEEE.

关键词： Convolutional neural networks

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