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Artificial Intelligence in Renewable Energy: Bibliometric Review of Current Trends and Collaborations 1

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

作者： Kut, Pawel Pietrucha-Urbanik, Katarzyna Zelenakova, Martina Abd-Elhamid, Hany F. Rzeszow Univ Technol Fac Civil Environm Engn & Architecture Al Powstancow Warszawy 6 PL-35959 Rzeszow Poland Tech Univ Kosice Fac Civil Engn Vysokoskolska 4 Kosice 04200 Slovakia Zagazig Univ Fac Engn Zagazig 44519 Egypt

ISBN: (数字)9783031618574

ISBN: (纸本)9783031618567;9783031618574

The article presents a bibliometric analysis of publications on the use of artificial intelligence (AI) in the area of renewable energy sources (RES). The study is based on data downloaded from the Web of Science database, using the keywords "artificial intelligence" and "renewable energy". The aim of the analysis is to identify the main trends, research areas, as well as leading institutions and authors in this interdisciplinary field. The CiteSpace tool was used to process and visualize the data. The analysis covers publications from the last two decades, enabling understanding of the evolution and development directions of the combination of AI and renewable energy. Particular attention was paid to interactions between various branches of science, which allows for the identification of potential new research areas and interdisciplinary cooperation. The results of the analysis show how AI contributes to the development of renewable energy efficiency, management and integration, also highlighting challenges and opportunities for future research in this field.

关键词： CiteSpace Bibliometric Artificial Intelligence Renewable Energy

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Automatic People Detection Based on RGB and Thermal Imagery for Military Applications 6th

Automatic People Detection Based on RGB and Thermal Imagery ...

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6th Iberian Robotics Conference (Robot)

作者： Matos, Joao Pedro Machado, Artur Ribeiro, Ricardo Moutinho, Alexandra Univ Lisbon Inst Super Tecn Lisbon Portugal Exerc Portugues Lisbon Portugal Univ Lisbon Inst Super Tecn ISR Lisbon Portugal Univ Lisbon Inst Super Tecn IDMEC Lisbon Portugal

ISBN: (纸本)9783031591662;9783031591679

Automatic detection of people in military applications offers numerous benefits that enhance situational awareness, operational efficiency, and overall security. RGB images offer high resolution and color information, making them suitable for detailed visualization, but they are limited by lighting conditions and can struggle to detect people in low-light or nighttime scenarios. Additionally, camouflage and adverse weather can hinder their effectiveness. On the other hand, thermal images detect heat signatures, enabling people detection in darkness and adverse conditions, but they may lack detail, and their cost is higher. Both technologies have their merits, and a combined approach can provide a more comprehensive solution for people detection in various military and security applications. This research contributes to the field of perception systems for military applications, by harnessing the potential of artificial intelligence (AI) and deep learning technologies. This paper presents a comparison of performance of three people detectors based on the YOLOv8 architecture, a computer vision model, namely using 1) RGB images, 2) thermal images, and 3) RGB and thermal images combined. A quantitative analysis of performance of the three models allows to compare their performance in realistic and challenging scenarios. Additionally, a qualitative assessment is conducted to identify specific limitations and advantages associated with each approach, providing valuable insights for further improvement and optimization of these detection systems.

关键词： Perception Deep learning YOLOv8 People detection RGB imagery Thermal imagery Military application Intelligence Surveillance and Reconnaissance

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Using Machine Learning to Predict Additional Taxi-Out Time as a Airport Key Performance Indicator in the Eurocontrol Zone 10th

Using Machine Learning to Predict Additional Taxi-Out Time a...

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International Conference on New Technologies, Development and Application - Advanced Production Processes and Intelligent systems

作者： Simic, Edvin Begovic, Muhamed Sabic, Muharem Muharemovic, Ermin Univ Sarajevo Fac Traff & Commun Sarajevo 71000 Bosnia & Herceg

ISBN: (纸本)9783031662706;9783031662713

The need to expand the air transport system capacity has increased rapidly in the last few years. European airports are located in urban areas and have limited opportunities for expansion. In addition, there is high pressure on air traffic and is associated with reducing the negative impact on the environment. Therefore, it is important to optimize and transform the system from what already exists into a more efficient and greener one. This paper will evaluate how to use machine learning methods to predict a key performance indicator for airside operation, taxi-out time. Statistical correlation methods will be used to establish the dependence between attributes and the output variable. This will help decision makers with airport optimization measures and ease the decision-making process at the macroscale level.

关键词： Pearson correlation airport capacity prediction taxi-out time

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Leaky Democracy: Third Parties in Voting Advice Applications 8th

Leaky Democracy: Third Parties in Voting Advice Applications

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8th Smart Trends in Computing and Communications (SmartCom)

作者： Heino, Timi Rauti, Sampsa Laato, Samuli Carlsson, Robin Leppanen, Ville Univ Turku Turku 20014 Finland

ISBN: (纸本)9789819713127;9789819713134

An important part of democracy is the premise of ballot secrecy, where each individual can vote privately without interference, and without anyone getting to say what they vote for. Legislation such as GDPR also indicates that a person's political opinion is sensitive personal data. Yet, in the modern technological era, there are countless ways through which people's political views can be probed, stored and in the worst cases, manipulated. One avenue through which this can happen are voting advice applications, online tools designed for helping people in selecting optimal candidates during elections. In this work, we performed a network traffic analysis of Finnish voting advice applications during the parliamentary election of Spring 2023, to see whether any sensitive information regarding, for example, the persons' political opinions, is leaked to third parties through the available voting advice applications. The results revealed that in 7 out of 11 analyzed applications, this was the case. Our work offers empirical evidence showing that while there may still be secrecy in the voting booth, the steps leading to the booth are paved with data leaks. We conclude the study by discussing the assurance of democratic processes and systems in our modern socio-digital society.

关键词： Voting advice applications Third-party services Online privacy

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Industry 4.0 Within Postgraduate Business Education in Bosnia and Herzegovina: Overview and Recommendations 10th

Industry 4.0 Within Postgraduate Business Education in Bosni...

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International Conference on New Technologies, Development and Application - Advanced Production Processes and Intelligent systems

作者： Sestic, Munira Car, Mirha Bico Donlagic, Sabina Alibegovic Kulovic, Dzenan Univ Sarajevo Sch Econ & Business Sarajevo 71000 Bosnia & Herceg Univ Tuzla Fac Econ Tuzla 75000 Bosnia & Herceg Univ Zenica Fac Econ Zenica 72000 Bosnia & Herceg

ISBN: (纸本)9783031662706;9783031662713

New models of business education strive to harmonize the demands of the labor market with business development trends. Industry 4.0 has irreversibly changed our context and continues to change it, and therefore educational policymakers and lecturers are permanently facing the challenges of creating and implementing curricula that will prepare business students for jobs within the Industry 4.0, both now and in the future. The aim of this paper is to determine the degree of current inclusion of Industry 4.0 in the business postgraduate curricula at three public universities in Bosnia and Herzegovina. In accordance with the findings of the analysis, an overview of the current situation will be given, as a starting point for recommendations to educational policy stakeholders - to more widely accept changes in the context of I4.0, and above all to further improve learning outcomes for postgraduate business students within the Bosnia and Herzegovina.

关键词： Industry 4.0 business education postgraduate curricula Bosnia and Herzegovina

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How Can I Grow!!: Analysis of Co-authorship Network of Early Career Stage Researchers 8th

How Can I Grow!!: Analysis of Co-authorship Network of Early...

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8th Smart Trends in Computing and Communications (SmartCom)

作者： Gayen, Avijit Chakraborty, Somyajit Chakraborty, Nilotpal Jana, Angshuman Indian Inst Informat Technol Guwahati Gauhati India

ISBN: (纸本)9789819713196;9789819713202

Identifying and providing assistance to young researchers in the early stages of their careers is crucial to foster their development and promote the advancement of scientific knowledge within their specific fields. Within this document, we introduce a machine learning-driven approach designed to pinpoint ECRs within extensive bibliographic datasets. Our suggested framework capitalizes on the set of features selected in a dataset, encompassing details like publication history, citations, academic associations, and collaborative author networks. In our models, we make use of the comprehensive and varied AMiner Author dataset. By employing a novel approach, we have achieved a notable level of accuracy in distinguishing ECRs, effectively separating them from established researchers, with "h-index" distributions as a key discriminator.

关键词： Social network Citation network Data analysis

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A Knowledge Sharing Approach to Foster Interdisciplinary Pedestrian Dynamics and Epidemiological Modeling Research and Practice 9th

A Knowledge Sharing Approach to Foster Interdisciplinary Ped...

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9th International Congress on Information and Communication Technology (ICICT)

作者： Coffey, John W. Pahle, Robert Srinivasan, Ashok Namilae, Sirish Scotch, Matthew Univ West Florida Pensacola FL 32514 USA NYU New York NY 10012 USA Embry Riddle Aeronaut Univ Daytona Beach FL 32114 USA Arizona State Univ Tempe AZ 85281 USA

ISBN: (纸本)9789819735556;9789819735563

A significant challenge to interdisciplinary computational science arises from the difficulty of using models from diverse domains. We show that combining the concepts of knowledge sharing-a component of knowledge management-and recommender systems, which have traditionally been viewed as separate undertakings, can help address the above limitation. In particular, we describe the VIPRA Recommender System (VRS), which enables transformative interdisciplinary science using pedestrian dynamics for epidemiological modeling. VRS provides a venue through which researchers can share capabilities of modeling systems and practitioners can receive assistance in identifying and using systems that meet their modeling needs, such as recommendations of suitable models and their input parameters. We present a usability study to establish its usefulness as a tool to empower interdisciplinary science using models from a variety of domains.

关键词： Knowledge sharing Recommender systems Pedestrian dynamics Epidemiological modeling Decision support

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Machine Learning-Driven Job Recommendations: Harnessing Genetic Algorithms 9th

Machine Learning-Driven Job Recommendations: Harnessing Gene...

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9th International Congress on Information and Communication Technology (ICICT)

作者： Aziz, Mohammad Tarek Mahmud, Tanjim Uddin, Mohammad Kamal Hossain, Samien Naif Datta, Nippon Akther, Sharmin Hossain, Mohammad Shahadat Andersson, Karl Rangamati Sci & Technol Univ Rangamati 4500 Bangladesh Univ Chittagong Chittagong Bangladesh Univ Greenwich London England Noakhali Sci & Technol Univ Noakhali 3814 Bangladesh Lulea Univ Technol Pervas & Mobile Comp Lab S-93187 Skelleftea Sweden

ISBN: (纸本)9789819733040;9789819733057

In the present day, employers are discussing difficulties with educated people. The web system posts positions in the authority and recommends various career categories based on skills and qualifications. Those who meet the qualifications for jobs at different organizations or income institutions around the globe are paid more and are promoted over time. Thus, learning about a web-based job suggestion system is crucial in real life. It is necessary to have a crucial dataset with both textual and numerical information in order to classify people who are suggested for jobs and who are not. In this work, we employed a hybrid Kaggle dataset to forecast the job recommendation system through the application of machine learning-based versions of the genetic algorithm. With the help of 11 characteristics and 1000 records overall in the used dataset, a genetic algorithm can identify the top applicants for a job suggestion. The parameters of the fitness function enabled the model to generate the most accurate results. The core components of the suggested system were natural selection approaches, crossover, and mutation tasks. In light of our primary objective is to produce people who are highly qualified for the position and have the highest fitness value. In this work, we proposed a job recommendation system that categorizes candidates into those who are and are not qualified for the position using a genetic algorithm. The model with the best fitness percentage accuracy generates the best schedules.

关键词： Job recommendation Genetic algorithm Natural selection Fitness function Best candidates

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Comparative Analysis of Face Recognition Models for Criminal Detection

Comparative Analysis of Face Recognition Models for Criminal...

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International Conference on Artificial Intelligence: Theory and Applications (AITA)

作者： Goyal, Gouri Kumar, Vaibhav Aggarwal, Piyush Chugh, Gunjan Lamba, Tripti Maharaja Agrasen Inst Technol Dept Artificial Intelligence & Machine Learning New Delhi India

ISBN: (纸本)9789819984787;9789819984794

Facial recognition technology has gained significant attention in recent years for its potential in improving criminal detection and enhancing security systems. This paper compares and contrasts different facial recognition technologies which are being implemented in video datasets for criminal detection. Law enforcement organizations are vigilantly monitoring facial recognition technologies as an approach to finding and apprehending criminals. The objective is to evaluate and compare how different deep learning techniques perform in real criminal detecting circumstances. A comprehensive video dataset that includes footage from law enforcement archives and surveillance camera footage is curated. To ensure a representative sample for the study, the dataset consists of a group of people with an assortment of physical attributes and notoriety. Each facial recognition technology is used and refined through transfer learning on the curated video dataset. To examine the performance of the systems, accuracy, precision, and error rate are employed as performance assessment variables. The comparative analysis highlights both the advantages and disadvantages of each approach. VGGFace, FaceNet, OpenFace, and DeepFace models are being assessed where the VGGFace model depicted the highest accuracy. The findings offer valuable insights into the performance and applications of various facial recognition systems. The findings expand facial recognition technology and demonstrate the value of law enforcement tools in strengthening public safety.

关键词： Deep learning VGGFace FaceNet OpenFace DeepFace Facial recognition Criminal identification

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Touches to the Portrait of the Aviation Designer and Military Pilot Kostyantyn Kalinin

Touches to the Portrait of the Aviation Designer and Militar...

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International Scientific and Technical Conference on Integrated Computer Technologies in Mechanical Engineering - Synergetic Engineering (ICTM)

作者： Oleksandr, Kryvokon Natalia, Kryvokon Kharkiv State Aviat Enterprise Hist Sci Kharkiv Ukraine Natl Acad Natl Guard Ukraine Dept Psychol & Pedag Psychol Sci Kharkiv Ukraine

ISBN: (纸本)9783031614149;9783031614156

The article is dedicated to the exploration of the lesser-known aspects of the life of the prominent designer and aviator of the 20th century, Kostyantyn Oleksiyovych Kalinin. The purpose of the study is to develop existing notions about Kalinin's personality and life path through historical-psychological analysis. The relevance of the publication is determined by the need to revive historical memory and form a more objective picture (free of ideological overlays) of the life activities of the famous aircraft designer. This will help professionals in the field understand the reasons that prevented the realization of Kalinin's creative potential and the implementation of his most progressive and forward-looking aviation discoveries and developments. The subject of the study is biography of K.O. Kalinin, particularly the events between 1916 and 1919, which later led to political repression, his arrest, and execution. As a result of the analysis of newly introduced documents and data, it was revealed that from December 1918, K.O. Kalinin was the commander of the 1st Volyn (Zhytomyr) Aviation Division, and from February 1919, he commanded the air squad of the Army of the Ukrainian People's Republic (UPR) and the Ukrainian Galician Army (UGA), and was also an inspector of the UGA. In the summer and autumn of 1919, he served as the head of the aviation warehouse of the Active Army of the UPR. Later, Kalinin voluntarily agreed to cooperate with the Bolsheviks, concealing some facts of his biography. Describing in 1938 the events that occurred from 1918-1919, Kostyantyn Oleksiyovych tried to avoid repression and save his life. He aimed to realize himself as an aviation designer in his homeland. Partially, he succeeded: from 1923 to 1935, Kalinin designed a series of aircraft, including experimental ones. However, the Bolshevik authority neglected the talent, design skills, and achievements of this extraordinary inventor, aviator, and engineer. On October 22, 1938, after int

关键词： KO Kalinin aviation industry historical memory history of technology

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