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Study of Detection Object and People with Radar Technology 11th

Study of Detection Object and People with Radar Technology

引用

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

作者： Nogueira, Hugo Duraes, Dalila Novais, Paulo Univ Minho Algorithm Ctr Braga Portugal LASI Intelligent Syst Associate Lab Guimaraes Portugal

ISBN: (纸本)9783031456473;9783031456480

Street monitoring can be used as an excellent tool to decrease the number of incidents by, for example, giving information to street users (pedestrians, vehicles, cyclists, etc.) about the position of other street users and therefore helping prevent any possible harmful situation. Today, with the growing concern about privacy and data protection issues, the use of video and audio has become problematic in terms of street monitoring, so there is a need to find a solution to this problem. With that in mind, using radar is a possible solution since the data retrieved from it doesn't contain anything considered personal and could violate people's privacy. This paper presents a systematic review of pedestrian, vehicle and cyclist detection. The objective is to identify the main methods of radar target detection and the algorithms. With that in mind, a search in the SCOPUS repository identified thirteen papers as relevant to include in the review.

关键词： Radar Pedestrian Detection Cyclist Detection Vehicle Detection Machine Learning Deep Learning

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State of Charge Estimation for 18650 Lithium-Ion Batteries: A Comparative Analysis Using Technical Specifications from Three Leading Manufacturers 4th

State of Charge Estimation for 18650 Lithium-Ion Batteries: ...

引用

4th International Conference on Digital Technologies and Applications (ICDTA)

作者： Khiati, Wassim El Habchi, Ali Zerrouk, Ilham Aissi, Mohammed Moumen, Younes Berrich, Jamal Bouchentouf, Toumi Mohammed 1st Univ AIRES Team ENSAO LaRSA Lab Oujda Morocco ATLAN Space Casablanca Morocco

ISBN: (纸本)9783031686597;9783031686603

This paper addresses the crucial challenge of optimizing battery management for electric Unmanned Aerial Vehicles (UAVs), with a specific focus on 18650 lithium-ion (Li-Ion) batteries. Drawing upon extensive knowledge and insights gained from experimental tests and real-world missions, we aim to establish a foundational understanding of their application in UAVs and address inventory issues related to State of Charge (SoC) battery estimation. To enhance our analysis, we delve into the technical specifications of leading Li-Ion cell manufacturers-LG, Samsung, and Panasonic-to extract discharge behavior data, forming our training dataset. Employing the Four Parameter Logistic (4PL) regression model, we then introduce three distinct models, each tailored to one of the three manufacturers' cells, demonstrating their capability to accurately estimate the SoC. To validate our approach, we apply these models to a real-world flight log, achieving satisfactory results.

关键词： State of Charge (SoC) estimation Li-Ion battery Li-Po battery UAV Battery Management Unmanned Aerial Vehicles (UAVs)

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Legal Challenges of Using Artificial Intelligence in Corporate Governance in Post-Soviet Countries 9th

Legal Challenges of Using Artificial Intelligence in Corpora...

引用

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

作者： Jamilya, Panabergenova Karligash, Umarova Karakalpak State Univ Nukus Uzbekistan

ISBN: (纸本)9789819733040;9789819733057

This paper analyzes the legal challenges associated with the use of artificial intelligence technologies in corporate governance in post-Soviet countries. It examines issues such as the lack of comprehensive legislation regulating AI, uncertainty regarding the status and responsibility of autonomous AI agents, insufficient protection of data and privacy, absence of tailored liability regimes, and limitations of judicial redress. The paper argues that addressing these legislative gaps and adapting regulation to the specific characteristics of AI are crucial to fully realize the potential benefits of using AI in corporate governance while safeguarding the rights of stakeholders. The analysis is grounded in a review of relevant academic literature, international frameworks, and national laws in post-Soviet jurisdictions. The study concludes that extensive legal reform and further research are required in this rapidly evolving domain.

关键词： Artificial intelligence Corporate governance Post-Soviet countries Legal regulation Autonomous systems Liability Data protection

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A Transfer Learning Approach to Mango Image Classification 4th

A Transfer Learning Approach to Mango Image Classification

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4th Congress on Intelligent systems (CIS)

作者： Ballo, Abou Bakary Diaby, Moustapha Mamadou, Diarra Coulibaly, Adama Univ Felix Houphouet Boigny LaMI Abidjan Cote Ivoire Univ Peleforo Gon Coulibaly LMI Korhogo Cote Ivoire Ecole Super Africaine Technol Informat & Commun Last Abidjan Cote Ivoire

ISBN: (纸本)9789819990368;9789819990375

Cote d'Ivoire is one of West Africa's leading mango exporters, significantly contributing to its economy. With an annual production of around 180,000 tons, fresh mango is the third most exported fruit from Cote d'Ivoire. The country is the third biggest supplier to the European market, with 32,400 tons a year. However, to maximize profits, farmers must accurately assess mangoes' ripening stage. This paper proposes a CNN-based system using transfer learning to accurately detect mango ripeness. Our study focuses on developing a system using renowned CNN algorithms such as VGG19, ResNet101, and DenseNet121. The model was first trained on a dataset of 996 mango images. Next, it was refined on a smaller dataset specific to the task. Finally, it was evaluated on a test dataset. The DenseNet121 model performed best, with an accuracy of 97.50%. These machine learning techniques were chosen because they can improve the accurate identification of mango maturity. The contributions of our research are manifold: not only have we proposed an accurate solution for detecting mango ripeness, but we have also evaluated the performance of different models of CNN algorithms. This system will strengthen Cote d'Ivoire's position as a leader in the African mango industry by reducing product losses associated with mango harvesting and increasing export volumes.

关键词： Mango CNN Ripeness

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Estimating Function Points of Development Project in IoT systems Case Study 13th

Estimating Function Points of Development Project in IoT Sys...

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13th Computer Science Online Conference

作者： Ho Le Thi Kim Nhung Silhavy, Radek Silhavy, Petr Tomas Bata Univ Zlin Fac Appl Informat Zlin 76001 Czech Republic

ISBN: (纸本)9783031705946;9783031705953

Function point analysis (FPA) is a commonly used approach for measuring software size and evaluating software functionality from the end user's perspective, usually based on a requirements specification. This paper explores a methodology for estimating function points in development projects using a structured model with the data flow diagram based on the Internet of Things (IoT) system specifications. The motivation for this research stems from the increasing development of IoT systems that require quantification of their size and effort. However, IoT systems pose a particular challenge as they contain hardware components, distinguishing them from traditional software solutions. To validate the methodology, a case study was conducted on the IoT infrastructure of a hydroponic farm in the Philippines to demonstrate the applicability and effectiveness of the approach for IoT systems.

关键词： Internet of Things function point analysis size estimation methods

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A Simple Way to Predict Heart Disease Using AI 1

引用

4th Congress on Intelligent systems (CIS)

作者： Kanrar, Soumen Shit, Suman Chakrarbarti, Subhadeep Amity Univ Jharkhand Ranchi Bihar India

ISBN: (数字)9789819990375

ISBN: (纸本)9789819990368;9789819990375

Early diagnosis of cardiovascular diseases in high-risk patients will help them make decisions about lifestyle changes and, in turn, minimise their complications. Due to asymptomatic illnesses like cardiovascular diseases, healthcare costs are exceeding the average national medical treatment cost and corporate budgets. The need for early identification and treatment of such diseases is critical. One of the developments in machine learning is the technology that has been used for disease prediction in many fields around the world, including the healthcare industry. Analysis has been attempted to classify the most influential heart disease causes and to reliably predict the overall risk using homogeneous techniques of data mining. This paper uses machine learning algorithms and selects the best one based on its classification report to find a simple way to predict heart disease. It helps to develop cost-effective software to predict heart disease for the betterment of mankind.

关键词： Heart disease Chest pain Cardiovascular Machine learning AdaBoost Random forest

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Industry 4.0 in Micro, Small and Medium Sized Enterprises of the Sarajevo Canton 10th

Industry 4.0 in Micro, Small and Medium Sized Enterprises of...

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

作者： Car, Mirha Bico Karabegovic, Isak Sestic, Munira Sahic, Elvir Hodzic, Selma Stupar, Savo Univ Sarajevo Sch Business & Econ Sarajevo 71000 Bosnia & Herceg Acad Sci & Arts Bosnia & Herzegovina Sarajevo 71000 Bosnia & Herceg Uniqa Osiguranje Sarajevo 71000 Bosnia & Herceg

ISBN: (纸本)9783031662706;9783031662713

Industry 4.0, along with its fundamental technologies, has been fundamentally changing the landscape around us for the last 15 years. From the company's point of view, these changes seem ambivalent: in addition to opening up numerous opportunities, they also present companies with the challenges of effectively implementing these technologies and successfully mastering them in order to preserve their competitive positions and achieve their goals. Unfortunately, the implementation processes are not simple, but require both sophisticated knowledge, as well as large financial expenditures and commitment to the implementation of these processes. The challenges faced by micro, small and medium-sized enterprises are even greater. The usual lack of resources puts these companies in front of additional difficulties in how to survive in turbulent times and turn the changes to their advantage. The paper examines the practice of implementing Industry 4.0 in micro, small and medium-sized enterprises, with a special focus on the representation of these technologies in Bosnia and Herzegovina, Sarajevo Canton. The research conducted in 2023 showed that micro, small and medium-sized enterprises in Sarajevo Canton first adopt those I 4.0 technologies that are more affordable and easy to master the knowledge for their use. Further implementation of I4.0 technologies in micro, small and medium-sized enterprises in Sarajevo Canton is essentially determined by the support of institutions through appropriate educational programs, as well as the establishment of an appropriate cyber-physical environment that will support this ecosystem.

关键词： Industry 4.0 basic technologies micro small and medium-sized enterprises Sarajevo Canton Bosnia and Herzegovina

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Mortars with Marble Powder as Partial Replacement for Cement 10th

Mortars with Marble Powder as Partial Replacement for Cement

引用

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

作者： Brescic, Armina Cecez, Marko Sahinagic-Isovic, Merima Catovic, Fuad Dzemal Bijedic Univ Mostar Fac Civil Engn Mostar 88000 Bosnia & Herceg

ISBN: (纸本)9783031662706;9783031662713

The use of industrial by-products as replacement for cement and/or sand in mortar and concrete production can lead to sustainable development in civil engineering. Cement industry contributes with 8% of total world CO2 emission and is considered as one of the greatest polluters. Marble powder has great potential for application in the construction industry, but there is still lack of sufficient relevant research. Material used in this study originates from sawing of marble blocks and is a potential environmental threat due to high calcium oxide content. The chemical composition of marble powder also includes silicon, aluminum and iron oxides. Mortar properties with marble powder as partial replacement for cement were analyzed and are presented in this paper. Marble powder replaced cement in ratios 0%, 5%, 10%, 15% and 20% by weight. The aim of this paper is to investigate waste marble powder as partial replacement for cement, possibilities, advantages, and disadvantages. Fresh and certain hardened mortar properties are tested and presented in this paper.

关键词： sustainable development industrial waste marble powder mortar

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Exploring the Trade-Off Between Performance and Cost in Facial Recognition: Deep Learning Versus Traditional Computer Vision

Exploring the Trade-Off Between Performance and Cost in Faci...

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Intelligent systems Conference (IntelliSys)

作者： Chen, Jan-Jo Husnain, Ali Cheng, Wei-Wei Chicago State Univ Chicago IL 60628 USA Northrop Grumman Chicago IL 60606 USA

ISBN: (纸本)9783031477232;9783031477249

During the late 20th century, the rise of computers and technology led to collaborations between mathematicians and computer scientists to develop systems that could solve various problems using traditional approaches. One such problem was facial recognition, which enables the identification of human faces without interference. Traditional machine learning methods were used to build these systems until recently, when deep neural networks trained with massive datasets replaced them. Deep learning has pushed the boundaries of what is achievable in facial recognition. However, traditional computer vision methods, which had been improving for years prior to the emergence of deep learning, still have room for improvement. This study aims to compare the accuracy of facial recognition algorithms, specifically FaceNet and Eigenfaces, from deep learning and traditional computer vision domains. The availability of physical machine power and dataset scale creates a tradeoff between performance and cost. Traditional computer vision techniques may be suitable for low-power applications, while deep neural networks provide better accuracy results when high-powered machines are available. The performance benefits of deep learning outweigh the data scale and training execution time required.

关键词： Deep learning Face recognition Eigenfaces FaceNet

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Artificial Intelligence - Insights into the Mechanics of Biomaterials: Predicting the Compressive Load of Composite Sandwich Structures 1

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3rd International Symposium on Industrial Engineering and Automation

作者： Dashtgoli, Danial Sheini Taghizadeh, Seyedahmad Macconi, Lorenzo Concli, Franco Free Univ Bozen Bolzano Fac Engn Piazza Univ 5 I-39100 Bolzano Italy Univ Trieste Dept Math Informat & Geosci Trieste Italy Natl Inst Oceanog & Appl Geophys OGS I-34010 Sgonico Italy

ISBN: (数字)9783031704628

ISBN: (纸本)9783031704611;9783031704628

Environmentally friendly sandwich structures made of composite materials are the key to sustainable innovation. They offer lightweight, durable solutions that reduce emissions in numerous industries. Machine learning (ML) streamlines the prediction of the mechanical behavior of materials, drastically reducing both the time and cost of material design and development. This study investigates the mechanical behavior of novel environmentally friendly composite sandwich structures under quasi-static out-of-plane compression loading using ML algorithms. The research focuses on assessing how the geometric dimensions affect the strength of these panels when used as load-bearing structures. Random forest (RF) and ridge regression methods were employed to assess the linearity and nonlinearity relationships within a database of 42,280 data points, focusing on the compression load as the target variable. The results show for the test data set that the RF model has a higher accuracy with an R-squared value of 0.9990, root mean square error of 0.1532 and a mean absolute error of 0.7030, which underlines the non-linearity of the relationship. Conversely, linear regression was found to be unable to effectively capture complexity, which is reflected in its lower performance. Furthermore, the uncertainty analysis shows that the RF model has a minimal bias and higher precision and clearly outperforms the ridge regression in terms of predictive accuracy and consistency. The analysis of the importance of the feature contribution reveals that the number of cupules has the greatest influence and the mechanical properties of the balsa wood have the least influence on the compressive strength of the composite panels. This study highlights the effectiveness of ML in predicting the mechanical behavior of environmentally friendly composite materials and structures, emphasizing the crucial role of geometric parameters and material properties in improving prediction accuracy and reliability.

关键词： Machine learning analysis Bio-composite structures Random Forest Ridge regression Mechanical property

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