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Data-Driven Digital Twins of Renewable Energy Grids 1

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2nd Serbian International Conference on Applied Artificial Intelligence (SICAAI)

作者： Prodanovic, Danica N. Andrijevic, Nikola N. Lakovic, Bosko R. Stojanovic, Boban S. Univ Kragujevac Fac Sci Radoja Domanovica 12 Kragujevac Serbia Vodena Kralja Milana IV 19B-5 Kragujevac Serbia

ISBN: (数字)9783031608407

ISBN: (纸本)9783031608391;9783031608407

This conference paper presents the concept of data-driven digital twins for renewable energy grids, with a focus on the application of artificial intelligence methods for modeling photovoltaic systems. The proposed data-driven digital twin system utilizes automated machine learning algorithms to generate accurate models of solar collectors, enabling real-time monitoring and forecasting of energy production. A use case of a solar power plant in a company from Cacak, Serbia is presented, demonstrating the potential of this technology to improve energy management and increase the efficiency of renewable energy sources. Results obtained from the use case demonstrate the effectiveness of this approach in optimizing energy management, reducing energy consumption, and improving the reliability of renewable energy systems.

关键词： artificial intelligence renewable energy modeling forecasting digital twins

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Automatic Generation of a Large Multiple-Choice Question-Answer Corpus

Automatic Generation of a Large Multiple-Choice Question-Ans...

引用

Intelligent systems Conference

作者： Kauchak, David Song, Vivien Mishra, Prashant Leroy, Gondy Harber, Phil Rains, Stephen Hamre, John Morgenstein, Nick Pomona Coll Claremont CA 91711 USA Univ Arizona Tucson AZ 85721 USA

ISBN: (纸本)9783031664274;9783031664281

Large corpora with fine-grained metrics for difficulty and understandability are a critical resource for developing algorithms and tools to create more informative content. We introduce a new approach for automatically generating a large corpus of health-related content with associated multiple-choice questions using Google's related questions and ChatGPT, including two new algorithms for generating potential wrong answers. We compare both the question quality as well as the suggested wrong answers using automatedmetrics and user studies. Overall, we find both algorithms generate reasonable questions that are complementary. Google questions use more accessible language and are easier to answer while ChatGPT questions appear easier, but are more difficult to answer and have better coverage over the entire text. For wrong answer generation, we find ChatGPT produces higher quality wrong answers that are more likely to be good distractors and are more closely related to the text content than our corpus-based approaches. We recommend both questions as options for studies with wrong answers generated by ChatGPT.

关键词： Corpus generation Text difficulty Large language model applications

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Anonymization of Bids in Blockchain Auctions Using Zero-Knowledge Proof 1

引用

19th International Conference on Dependability of Computer systems (DepCoS-RELCOMEX)

作者： Broniszewska, Marlena Daszczuk, Wiktor B. Czejdo, Denny B. Warsaw Univ Technol Dept Elect & Informat Technol Nowowiejska St 15-19 PL-00665 Warsaw Poland Fayetteville State Univ Dept Math & Comp Sci Fayetteville NC 28301 USA

ISBN: (数字)9783031618574

ISBN: (纸本)9783031618567;9783031618574

The recent COVID-19 pandemic accelerated the world's digitalization including transformation of traditional auctions. Although buyers cannot examine an item in online auctions, they become a more popular option. They are more convenient since buyers can participate from all around the world. Additional advantage is that the possibility to make auctions more anonymous. Specifically, it may be crucial for the user to keep their interest in the commodity hidden from other buyers and even the seller. This article explores the problem of anonymization in web services by using the example of anonymous online auctions. A system for anonymous, blockchain-based online auctions using zero-knowledge proof is proposed. The system enables a party to attend an auction anonymously digitally isolated from other competitors and the sellers. The system consists of a smart contract on a blockchain and a decentralized web application that facilitates zero-knowledge proof calculation and interactions with the smart contract.

关键词： blockchain zero knowledge proof auction anonymity Zk-SNARK

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Heart Disease Detection Using Deep Learning: A Survey 2nd

Heart Disease Detection Using Deep Learning: A Survey

引用

2nd International Conference on Forthcoming networks and Sustainability in the AIoT Era (FoNeS-AIoT)

作者： Shakir, Sahar Obied, Ali Univ Al Qadisiyah Coll Comp Sci & Informat Technol Dept Comp Sci Al Diwaniyah Iraq

ISBN: (纸本)9783031628801;9783031628818

Heart disease is the leading cause of death worldwide and one of the key problems confronting today's globe. A recent study by the World Health Organization found 17.9 million people die each year from heart-related disorders. In medicine and healthcare, one of the most often used diagnostic instruments is the echocardiogram. Recent developments in machine learning and deep learning applications show that it is possible to identify cardiac illness early on utilizing echocardiogram and patient data. This paper uses a literature review technique to identify the challenges associated with heart disease prediction to provide a more thorough evaluation of the information that is currently available. For this investigation, the kind of heart illness, algorithms, datasets, and methods have all been considered.

关键词： Heart Disease Echocardiogram Deep Learning Transfer Learning

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Estimating Alighting Stops and Transfers from AFC Data: The Case Study of Porto 1

引用

11st World Conference on Information systems and Technologies (WorldCIST)

作者： Hora, Joana Marta, Campos Ferreira Camanho, Ana Galvao, Teresa Univ Porto Fac Engn R Dr Roberto Frias P-4200465 Porto Portugal INESC TEC R Dr Roberto Frias P-4200465 Porto Portugal

ISBN: (数字)9783031456510

ISBN: (纸本)9783031456503;9783031456510

This study estimates alighting stops and transfers from entry-only Automatic Fare Collection (AFC) data. The methodology adopted includes two main steps: an implementation of the Trip Chaining Method (TCM) to estimate the alighting stops from AFC records and the subsequent application of criteria for the identification of transfers. For each pair of consecutive AFC records on the same smart card, a transfer is identified considering a threshold for the walking distance, a threshold for the time required to perform an activity, and the validation of different boarding routes. This methodology was applied to the case study of Porto, Portugal, considering all trips performed by a set of 19999 smart cards over one year. The results of this methodology allied with visualization techniques allowed to study Origin-Destination (OD) patterns by type of day, seasonally, and by user frequency, each analyzed at the stop level and at the geographic area level.

关键词： Origin-Destination Trip Chaining Method Public Transportation Automatic Fare Collection

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Identification and Interpretation of Significant Factors Influencing Client Defaults in Microfinance Institutions Using Machine Learning Methods 1

引用

13th Computer Science Online Conference

作者： Frolov, Yury V. Zhidkova, Elizaveta S. Bosenko, Timur M. Zhidkov, Andrey L. Moscow City Pedag Univ Sheremetyevskaya St 28 Moscow 129594 Russia

ISBN: (数字)9783031703003

ISBN: (纸本)9783031702990;9783031703003

One of the problems of business analytics is the lack of understanding by decision makers of the process of obtaining results presented by machine learning methods. The paper discusses the background and results of a study aimed at discovering factors that significantly affect the risk of non-repayment of loans by clients of microfinance institutions. The issues of application of gradient bousting and SHAP methods to identify factors characterizing microcredit borrowers are considered. The results of the research show that the interpretation of complex models using the SHAP method makes it possible to apply advanced machine learning methods to obtain analytical recommendations in the field of credit risk management in microfinance institutions. #CSOC1120.

关键词： Microfinance Institution Credit Risks Gradient Bousting Method SHAP Visualization of the Influence of Factors on Default Risk Analytics of Research Results

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Prototype App Mobile for Real Time American Sign Language Recognition Based on Deep Learning 1

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Intelligent systems Conference (IntelliSys)

作者： Ramirez, Sergio A. Ramirez, Maria del Pilar L. Tito, Euler Apaza, Honorio Univ Nacl Moquegua Ingenieria Sistemas Informat Ilo Peru Univ Nacl San Agustin Arequipa Ciencia Computac Arequipa Peru

ISBN: (数字)9783031477249

ISBN: (纸本)9783031477232;9783031477249

The recognition of sign language alphabet through computer vision techniques has been widely studied. As a result of multiple studies, the CNN algorithm has demonstrated a very high learning rate. Other researchers have created and published a dataset consisting of approximately 200 images of static gestures of the American Sign Language alphabet. For this research work, the aforementioned dataset was used, and the gestures were captured and trained with CNN. Consequently, a significantly good recognition rate was achieved and migrated to a mobile application capable of recognizing the gestures. The ultimate goal is to recognize and transcribe words through a mobile device, which could be very useful in the practical teaching of the sign language alphabet, providing a significant breakthrough for a more complete learning of sign language.

关键词： American Sign Language Computer Vision Deep Learning

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Maritime Vessel Detection and Classification in Harbor Environment Using Deep Learning 20th

Maritime Vessel Detection and Classification in Harbor Envir...

引用

20th International Workshop on Combinatorial Image Analysis (IWCIA)

作者： Waleed, Khalid Farooq, Muhammad Umar Raza, Rana Hammad Natl Univ Sci & Technol NUST Pakistan Navy Engn Coll PNEC Karachi Pakistan

ISBN: (纸本)9783031665936;9783031665943

Automatic surveillance of docks, harbors, and seaports is considered crucial for ensuring smooth port operations. The identification and tracking of maritime vessels in these environments are generally facilitated by Radar and Automatic Identification System (AIS). However, radar faces challenges in detection of small non-metallic vessels, as well as there are other issues such as radar signal clutter, high electromagnetic radiation, and higher costs. Similarly, AIS also face challenges such as device malfunctioning or illegal manipulation etc. Consequently, cameras have been increasingly used in maritime traffic management systems. In recent years, Convolutional Neural networks (CNNs) have shown significant progress in object detection and classification tasks. Though, obtaining large scale maritime datasets with focus on harbor environment still remains a challenge. In this study, experimentation with various pre-trained state-of-the-art CNN models were conducted on images captured from harbor environments in Karachi and Grand Canal in Venice. The dataset presented several challenges such as high-density traffic, occlusions, shadows, background infrastructure, wave motion, boat wakes and reflection on water surface. The pre-trained CNNs were finetuned using a combination of small datasets to obtain both detector and classifier which outperformed previously published results on the MarDCT dataset. Specifically, the proposed model achieved a DR (Detection Rate) of 0.81, FAR (False Alarm Rate) of 0.07 and an average accuracy of 98.78% on the MarDCT dataset.

关键词： Maritime Traffic Vessel Detection Vessel Classification Machine Learning Deep Learning Convolutional Neural networks

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PLATERA: A Comprehensive Web-Based System and Sensor Dataset for Activity Recognition in Households Environments 1

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16th International Conference on Ubiquitous Computing and Ambient Intelligence

作者： Diaz-Jimenez, David Luis Lopez, Jose Gaitan-Guerrero, Juan F. Espinilla, Macarena Univ Jaen Jaen Spain

ISBN: (数字)9783031775710

ISBN: (纸本)9783031775703;9783031775710

In this work we present a web-based system, PLATERA, and an extensive dataset, Living Dataset PLATERA, designed to be used for activity recognition and monitoring in households environments. Each user of the platform can access different functions depending on their role, including activity monitoring, data management, sensor visualization, home creation, and sensor management. The dataset contains information gathered from a variety of sensors, including motion sensors that use passive infrared (PIR) technology to detect movement, anchor devices that measure Received Signal Strength Indicator (RSSI) to determine location, and activity bands that record physical activity data. Temperature and humidity sensors provide accurate measurements of environmental conditions, while magnetic technology powers the opening and closing sensors that operate doors. Through this work we hope to encourage the development of sensor-based activity recognition systems.

关键词： Activity Recognition Smart Homes Sensor Data Web-Based System Internet of Things

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Literature Survey: Complaint Management System Using Blockchain Technology 8th

Literature Survey: Complaint Management System Using Blockch...

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8th Smart Trends in Computing and Communications (SmartCom)

作者： Shetye, Isha Morwadkar, Suchitra Chavan, Bhakti Rajole, Samruddhi Kulkarni, Sanika MKSSSs Cummins Coll Engn Women Dept Informat Technol Pune 411052 Maharashtra India

ISBN: (纸本)9789819713127;9789819713134

In a world where technological advancements are rapidly evolving, the need for secure and trustworthy digital processes is very important. As digital interactions play a significant role in daily lives, issues like data breaches, unauthorized access, and fraudulent activities have raised profound concerns. To address these real world problems, Blockchain technology emerges as a transformative solution. The paper highlights the unique characteristics of Blockchain which includes decentralization, security, immutability, transparency, and anonymity revolutionizing the management of police complaints, particularly through First Information Reports in Complaint Management System. The paper addresses the various challenges faced by the traditional Complaint Management System which includes manual intervention, security concerns, lack of transparency, and delays. The paper proposes Blockchain technology as a solution to surmount these challenges by providing a decentralized, tamper-proof, and transparent platform for complaint registration, tracking, investigation, and resolution. The literature survey encompasses various research studies that propose and explore Blockchain-based solutions for complaint management and outlines a need for shift from traditional Complaint Management System to Blockchain-based Complaint Management System.

关键词： Blockchain Complaint Management System (CMS) Decentralization First Information Report (FIR) Immutability Security Traditional Complaint Management System (t-CMS)

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