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On Combining Collective Entity Resolution and Repairing (Extended Abstract) 1

On Combining Collective Entity Resolution and Repairing (Ext...

1st Workshop on AI-Driven Heterogeneous Data Management: Completing, Merging, Handling Inconsistencies and Query-Answering, ENIGMA 2023

作者： Bienvenu, Meghyn Cima, Gianluca Gutiérrez-Basulto, Víctor Univ. Bordeaux CNRS Bordeaux INP LaBRI UMR 5800 Department of Computer Control and Management Engineering Sapienza University of Rome School of Computer Science and Informatics Cardiff University

This work summarizes the salient aspects of our recent work [1], about combining collective entity resolution and repairing. © 2023 Copyright for this paper by its authors. Use permitted under Creative Commons Li... 详细信息

关键词： Repair

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Content Creation for Touristic Portals Using ChatGPT

Content Creation for Touristic Portals Using ChatGPT

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International Conference on Information, Intelligence, Systems and Applications (IISA)

作者： Papantoniou Lampros Voyiatzaki Eleni Hatzilygeroudis Ioannis Computer Engineering and Informatics Dept University of Patras Patras Greece

ISBN: (数字)9798350368833

ISBN: (纸本)9798350368840

Our research presents the development of an innovative web-based application designed to assist hoteliers in creating and managing multilingual content for their tourist accommodations for tourism portals. To address this need, our application leverages the advanced capabilities of OpenAI's ChatGPT for automated content generation and DeepL's API for high-quality translations, integrated within a user-friendly interface. Through an integrated development process, we leveraged the capabilities of combining natural language processing with artificial intelligence to meet the specific needs of the tourism and hospitality industry. Our findings indicate that the application significantly streamlines the content creation and translation process, offering hoteliers a competitive edge in a globally connected marketplace. By automating these tasks, the system not only saves time and resources but also ensures consistency and quality in accommodation listings across different languages. The application's impact is further evidenced by its adaptability to various accommodation types and the potential for enhancing guest satisfaction through improved information accessibility. The benefits of our application are supported by a medium scale evaluation.

关键词： Measurement Machine learning algorithms Databases Tourism industry Chatbots Market research Artificial intelligence Portals Standards Testing

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NAVIGATING THE IMPACT OF HUGE ELECTRICAL DEMAND ON GRID THROUGH INTELLIGENTLY CONTROLLED RENEWABLES AND EMERGING V2G TECHNOLOGY 14

NAVIGATING THE IMPACT OF HUGE ELECTRICAL DEMAND ON GRID THRO...

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14th Mediterranean Conference on Power Generation Transmission, Distribution and Energy Conversion, MEDPOWER 2024

作者： Tariq, Raheel Ali, Zunaib Saleem, Komal Christofides, Nicholas School of Engineering London South bank University London United Kingdom School of Architecture Computing and Engineering University of East London London United Kingdom Electrical Engineering Computer Engineering and Informatics Frederick University Nicosia Cyprus Frederick Research Center Nicosia Cyprus

ISBN: (纸本)9781837242689

Among the biggest initiatives to help the UK reach its Net Zero goal are the conversion of the power grid to renewable energy sources (RESs) and the switch to all-electric vehicles (EVs) by 2050. However, because of the advent of dispersed unconventional energy sources and electric vehicles (EVs), the full switch to renewable energy sources and EVs poses a possible risk to power system stability, power quality, and energy management. To intelligently regulate the flow of energy and enhance the current power system, it is crucial to employ appropriate energy management strategies. This paper aims to establish a microgrid that is grid-connected and utilises multiple energy sources, including solar, battery energy storage, and electric vehicles. The goal is to create an intelligent algorithm that efficiently controls these resources and mainly enable bi-directional EV operation for vehicle-to-grid (V2G) application, resulting in both financial and environmental advantages. The main objective is to maximise the microgrid's overall profitability while minimising carbon emissions and effectively managing energy demand and network stability. It has been shown that utilising EVs as storage units via beneficial V2G technology not only stabilises renewable energy generation but also provides economic advantages. © The Institution of engineering & Technology 2024.

关键词： Microgrids

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AI-Driven Predictive Analytics for CRM to Enhance Retention Personalization and Decision-Making

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International Journal of Advanced computer Science and Applications 2025年第4期16卷 552-563页

作者： Gaidhani, Yashika Ramesh, Janjhyam Venkata Naga Singh, Sanjit Dagar, Reetika Rao, T. Subha Mastan Godla, Sanjiv Rao Baker El-Ebiary, Yousef A. Department of Electronics Engineering Yeshwantrao Chavan College of Engineering Maharashtra Nagpur India Department of CSE Graphic Era Hill University Dehradun248002 India Department of CSE Graphic Era Deemed to Be University Uttarakhand Dehradun248002 India Department of MBA-Sanjivani College of Engineering Savitribai Phule Pune University Pune India GD Goenka University Haryana Gurugram India Department of Computer Science and Engineering Koneru Lakshmaiah Education Foundation Vaddeswaram Andhra Pradesh Guntur India Department of Computer Science and Engineering Aditya University Andhra Pradesh Surampalem India Faculty of Informatics and Computing UniSZA University Malaysia

The advent of Artificial Intelligence (AI) has dramatically altered Customer Relationship Management (CRM) by allowing organizations to anticipate customer behavior, customize interactions and automate service delivery. This research introduces an extensive AI-based predictive analytics framework aimed at improving customer engagement, retention and satisfaction using advanced Machine Learning (ML) and Natural Language Processing (NLP) methodologies. By using XGBoost for churn prediction and BERT-based models for sentiment analysis, the system efficiently handles both structured and unstructured customer data. The methodology involves sophisticated feature engineering, customer segmentation via K-Means clustering, and Customer Lifetime Value (CLV) prediction to aid data-driven business strategies. An NLP-driven chatbot offers real-time, personalized support, response time and improving user experience. Evaluation metrics such as accuracy, precision, recall and F1-score demonstrate the better performance of the proposed system compared to conventional CRM approaches. This work also addresses important issues such as data privacy compliance, algorithmic bias and explainability of AI decision-making. Ethical deployment and transparency of AI are emphasized for building confidence in automated CRM systems. Future evolution will tackle the use of reinforcement learning to facilitate learning-based interaction schemes and federated learning for trusted, decentralized management of data. This architecture does not only provide better CRM functionality but also builds a platform towards intelligent, responsible and scalable solutions for customer relations across industries. © (2025), (Science and Information Organization). All rights reserved.

关键词： Reinforcement learning

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A Personalized Fitness Application for Injury Rehabilitation Using Artificial Neural Networks

A Personalized Fitness Application for Injury Rehabilitation...

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International Workshop on Semantic Media Adaptation and Personalization (SMAP)

作者： Nikolaos Kalofonos Christos Troussas Akrivi Krouska Phivos Mylonas Cleo Sgouropoulou Department of Informatics and Computer Engineering University of West Attica Greece

ISBN: (数字)9798331504502

ISBN: (纸本)9798331504519

This paper presents a personalized fitness mobile application with the use of Artificial Intelligence (AI), namely Artificial Neural Networks (ANN), which contributes to injury rehabilitation. It automatically generates customized workout plans on a weekly basis, taking into consideration user-supplied data on issues such as injury type, fitness level, body mass index, and available equipment. The system continuously updates the workout plans with the help of feedback provided by the user to achieve the best recovery process. Built with React Native for the frontend and Django for the backend, this application guarantees access anytime, anywhere, with smooth data treatment. The paper further reflects on the general implications of using AI in e-fitness, stresses intuitive user interfaces, and furthers possibilities such technologies could undergo.

关键词： Training Wearable Health Monitoring Systems Artificial neural networks Medical services Transforms Muscles User interfaces Real-time systems Injuries Stress

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Congestion Probabilities in a Mobile Hotspot Supporting Quasi-Random Traffic 5

Congestion Probabilities in a Mobile Hotspot Supporting Quas...

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5th International Workshop on Microwave Theory and Technology in Wireless Communications, MTTW 2023

作者： Vlasakis, Marinos Moscholios, Ioannis Sarigiannidis, Panagiotis Logothetis, Michael University of Peloponnese Dept. Informatics & Telecommunications Tripolis Greece University of W. Macedonia Dept. Electrical & Computer Engineering Kozani Greece University of Patras Dept. Electrical & Computer Engineering Patras Greece

ISBN: (纸本)9798350393491

In this paper, we consider a vehicle that has an access point of fixed capacity. The vehicle accommodates a finite number of users who generate calls (quasi-random process). Each call requires a single bandwidth unit to be serviced for an exponentially distributed service time. The applied call admission policy depends on which phase the vehicle is. Two phases are taken into account: a moving and a stop one. During the moving phase, only new calls are serviced under the complete sharing policy. During the stop phase, both new and handover calls are being serviced, while the latter are being prioritized utilizing a probabilistic bandwidth reservation policy. The analytical determination of the various performance metrics of the system presented in this paper is accurate and is based on three-dimensional Markov chains. © 2023 IEEE.

关键词： Vehicles

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PhishGuard: A Convolutional Neural Network-Based Model for Detecting Phishing URLs with Explainability Analysis 3

PhishGuard: A Convolutional Neural Network-Based Model for D...

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3rd International Conference on Artificial Intelligence For Internet of Things, AIIoT 2024

作者： Islam, Md Robiul Islam, Md Mahamodul Afrin, Mst. Suraiya Antara, Anika Tabassum, Nujhat Amin, Al Uttara University Dept. of Computer Science and Engineering Dhaka Bangladesh University of Oslo Dept. of Informatics Oslo Norway Dept. of Building Engineering and Construction Management Khulna Bangladesh BRAC University Dept. of Electrical and Electronics Engineering Dhaka Bangladesh North South University Dept. of Electrical and Computer Engineering Dhaka Bangladesh Dept. of Computer Science American International Dhaka Bangladesh

ISBN: (纸本)9798350372120

Cybersecurity is one of the global issues because of the extensive dependence on cyber systems of individuals, industries, and organizations. Among the cyber attacks, phishing is increasing tremendously and affecting the global economy. Therefore, this phenomenon highlights the vital need for enhancing user awareness and robust support at both individual and organizational levels. Phishing URL identification is the best way to address the problem. Various machine learning and deep learning methods have been proposed to automate the detection of phishing URLs. However, these approaches often need more convincing accuracy and rely on datasets consisting of limited samples. Furthermore, these black-box intelligent models' decision to detect Suspicious URLs needs proper explanation to understand the features affecting the output. To address the issues, we propose a 1 D Convolutional Neural Network (CNN) and trained the model with extensive features and a substantial amount of data. The proposed model outperforms existing works by attaining an accuracy of 99.85 %. Additionally, our explainability analysis highlights certain features that significantly contribute to identifying the phishing URL. © 2024 IEEE.

关键词： Convolutional neural networks

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How Lasers Exploit Photoacoustic and Photoelectric Phenomena to Inject Signals into MEMS Microphones

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Journal of Hardware and Systems Security 2025年 1-17页

作者： Cyr, Benjamin Sumaria, Vedant Long, Yan Tadigadapa, Srinivas Sugawara, Takeshi Fu, Kevin Computer Science and Engineering University of Michigan Ann Arbor USA Electrical & Computer Engineering Northeastern University Boston USA Department of Informatics The University of Electro-Communications Tokyo Japan

An amplitude-modulated laser can be used to generate false, yet coherent acoustic signals on the outputs of MEMS microphones. While this vulnerability has ramifications on the security of cyber-physical systems that trust these microphones, the physical explanation of this effect remained a mystery. Without an understanding of the physical phenomena contributing to this signal injection, it is difficult to design effective and reliable defenses. In this work, we show the degree to which the mechanisms of thermoelastic bending, thermal diffusion, and photocurrent generation are used to inject signals into MEMS microphones. We provide models for each of these mechanisms, develop a procedure to empirically determine their relative contributions, and highlight the effects on eight commercial MEMS microphones. We accomplish this with a precise setup to isolate each mechanism using several laser wavelengths and a vacuum chamber. The results indicate that the injected signal on the microphone is dependent on the wavelength of the incoming light. Shorter wavelengths (such as a 450 nm blue laser) exploit photoacoustic effects, and the periodic heating and expansion of air is the dominant factor in seven of eight sample microphones. Longer wavelengths (such as a 904 nm infrared laser) exploit photoelectric effects on the sensitive ASIC, generating signals that are between 2x and 100x stronger than photoacoustic signals in six of eight sample microphones. This understanding of the physical causality of laser signal injection leads to recommendations for future laser-resistant microphone designs. These include adding light-blocking structures at the system or device level, improving to glob top application, and adding simple light or temperature sensors for injection detection. Based on the fundamental causality, we also suggest potential vulnerabilities within other sensors with similar characteristics to MEMS microphones, such as conventional microphones, ultrasonic

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DO DEEP NEURAL NETWORKS UTILIZE THE WEIGHT SPACE EFFICIENTLY?

arXiv

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arXiv 2024年

作者： Koyun, Onur Can Töreyin, Behçet Ugur Department of Computer Engineering Faculty of Computer and Informatics Istanbul Technical University Istanbul Turkey Department of Artificial Intelligence and Data Engineering Faculty of Computer and Informatics Istanbul Technical University Istanbul Turkey

Deep learning models like Transformers and Convolutional Neural Networks (CNNs) have revolutionized various domains, but their parameter-intensive nature hampers deployment in resource-constrained settings. In this paper, we introduce a novel concept utilizes column space and row space of weight matrices, which allows for a substantial reduction in model parameters without compromising performance. Leveraging this paradigm, we achieve parameter-efficient deep learning models.. Our approach applies to both Bottleneck and Attention layers, effectively halving the parameters while incurring only minor performance degradation. Extensive experiments conducted on the ImageNet dataset with ViT and ResNet50 demonstrate the effectiveness of our method, showcasing competitive performance when compared to traditional models. This approach not only addresses the pressing demand for parameter efficient deep learning solutions but also holds great promise for practical deployment in real-world scenarios. © 2024, CC BY-NC-ND.

关键词： Deep neural networks

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Simulation of optimal methodologies for channel estimation in a MISO RIS-aided system 23

Simulation of optimal methodologies for channel estimation i...

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Proceedings of the 27th Pan-Hellenic Conference on Progress in Computing and informatics

作者： Stamatia Drampalou Nikolaos Miridakis Panagiotis Karkazis Department of Informatics and Computer Engineering University of West Attica Greece

ISBN: (纸本)9798400716263

Extensive research in telecommunications and especially in wireless systems assisted by reconfigurable intelligent surfaces (RIS) has emerged at the forefront of cutting-edge wireless communications nowadays. RISs are composed of several arrays of passive elements, where their purpose is to receive the transmitted signal and send it to the corresponding receiver. One of the main disadvantages of RIS, due to the various processes performed in the RIS controller, is the channel estimation time. Therefore, research has focused on optimized channel estimation algorithms in multiple input- multiple output (MIMO) systems to reduce channel overhead and estimation time. In a previous publication, we presented the methodologies that have been used for channel estimation and our goal in this research is to simulate the proposed algorithms on a common system and common parameters. The presentation of the operation mode of the optimal channel estimation methodologies is implemented in a multiple input- single output (MISO) system and in a Base Station (BS) -RIS-user channel. Then we will present in detail the results of the comparison of the methods and mention future scenarios that can be tested.

关键词： Katri-Rao product Kronecker product Least Square Method MISO systems

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