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Enhancement of Outdated Vision systems in SMEs with Artificial Intelligence Powered Solutions 1

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

作者： Pajic, Nemanja Pavlovic, Lazar Zivic, Fatima Aleksic, Jovana Univ Kragujevac Fac Engn Sestre Janjic 6 Kragujevac Serbia ZF Friedrichshafen AG ZF Serbia doo 7 Nova Pancevo Serbia Sch Elect & Comp Engn Appl Studies Belgrade Serbia Fac Mech Engn Belgrade Serbia

ISBN: (数字)9783031608407

ISBN: (纸本)9783031608391;9783031608407

This paper deals with the application of Artificial Intelligence (AI) based software solutions to the quality assurance in manufacturing of the printed circuit boards (PCBs). We have used Convolutional Neural Network (CNN) and YOLOv4 for image defect detection at older vision systems that are based on predefined pixel detection. We realised three case studies of using AI with old camera setup and low computer resources, since it is often the case in real industrial systems: 1) simple image recognition, 2) recognition of the simple true or false object parameters and 3) more advanced recognition of certain defect types at the observed object. The results showed that it is possible to use AI/based solutions with older camera and computer setups, even though the time needed was significantly longer that in the case of up-to-date visual and computer resources. Application of an AI-based systems for image defect detection with older visual and computer systems reduced pseudo failures from 10% to 3%, thus making significant financial savings.

关键词： Machine Vision Artificial Intelligence Quality Assurance Small and Medium Enterprise

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Innovative Approaches to User Expectation Classification in E-commerce Mobile Apps Using Fuzzy Kano Model Approach

Innovative Approaches to User Expectation Classification in ...

引用

International Conference on Intelligent and Fuzzy systems (INFUS)

作者： Karakurt, Necip Corlu, Selcuk Tekirdag Namik Kemal Univ Corlu Engn Fac Ind Engn Dept Tekirdag Turkiye Yildiz Tech Univ Dept Ind Engn TR-34349 Besiktas Turkiye Azerbaijan State Univ Econ UNEC Ind Data Analyt & Decis Support Syst Ctr Baku 1001 Azerbaijan

ISBN: (纸本)9783031671944;9783031671951

This study aims to determine the design features that affect mobile application design and to classify these features according to customer perception. The methodology involved an exhaustive examination of Turkish mobile applications, consultations with an expert academic, scrutiny of forum pages, and an extensive literature review. Subsequently, the categorization of these features based on customer satisfaction was pursued through a survey conducted with 525 participants. The suggested fuzzy methods play a crucial role in mitigating information loss associated with crispness, presenting a promising avenue to enhance the accuracy of user expectation classification in e-commerce mobile applications. The findings of this study are expected to provide insights into how mobile application features differ across various demographic group. The "Attractive" features identified include One-Step Registration, Augmented Reality, Wishlist, Personalized Customer Experience, Fast Payment, Offline Features, Sale Progress Bar, Easy Browsing, Share Button, Loyalty Program, Product Comparison, Shipping Flexibility, and Barcode Scanning.

关键词： User experience Mobile Apps Fuzzy Kano Model

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Volume of Imbalance Container Prediction using Kalman Filter and Long Short-Term Memory 1

引用

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

作者： Kim, Geunjeong Block, Brit-Maren Mercorelli, Paolo Leuphana Univ Luneburg D-21335 Luneburg Germany

ISBN: (数字)9789819984763

ISBN: (纸本)9789819984756;9789819984763

This paper develops a hybrid prediction model KALSTM that is a combination of a Kalman filter and a well-known neural network-based prediction model, Long Short-Term Memory (LSTM), to generate prediction results from the data, more specifically the weekly number of imbalance 20 general (GE) dry cargo container at Hamburg depots in Germany. The given data has been split into training and testing set. The training set has been used as input for the LSTM model to forecast the future weekly number of imbalance 20 GE containers. Even if the result from the LSTM model normally outperforms other existing forecasting models, it is still an approximation to the future because there are factors such as international political situations, overseas economic trends, and any kind of unexpected cases which increase the uncertainty of the prediction. Therefore, the output of LSTM has been used as container model xk, ik of Kalman filter which filters the noise out with container measurement zk applying other forecasting models. Thus, a Kalman filter has been applied to improve the output of LSTM by correcting the noise of it. The prediction results of KALSTM demonstrate improved predictive performance based on common metrics such as mean squared error (MSE), root mean squared error (RMSE), and R2. Thus, the results can help decision-makers to improve tracking the volume of imbalance containers and to better prepare for further investigation to deal with the uncertainty of imbalance container volume.

关键词： Machine learning State-observation model Hybrid prediction model

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The Role of Artificial Intelligence in the Process of Improving Communication systems of Strategic Objects 1

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

作者： Manvelidze, Irakli Varshanidze, Tamta Varshanidze, David Batumi Shota Rustaveli State Univ Batumi Georgia Erasmus Univ Maritime & Transport Law Rotterdam Netherlands

ISBN: (数字)9789819735624

ISBN: (纸本)9789819735617;9789819735624

Interests toward strategic units of Georgia are sharply increased due to its geopolitical status and transit function;accordingly, it is of the highest priority to regulate the communication systems of the mentioned institutions, both inside the country and outside of it. Equipping the communication system of strategic units using modern technologies and especially artificial intelligence will increase the interest in Georgia as a favorable country for investment. Considering the above-mentioned, in the research process, we aimed to determine what impact technological development, the pandemic and the ongoing Russian-Ukrainian war had on the activities of strategic units. Furthermore, we determined what was the role and purpose of the development of the communication systems for strategic units, how they were modified, and what effect did it had for the economic development. Proportion of persons with high digital skills differs among European countries. It is necessary to evaluate strategic units of economic purpose of Georgia and search for possible development ways;this is because utilization of the potential of strategic units of the country and its future development is intimately correlated with the nation's overall social and economic development. The study is based on statistical data processing, as well as quantitative and qualitative research, which was conducted directly through interviews in the Batumi harbor. In this study, the role of artificial intelligence in the process of perfecting and refining the communication systems of strategic purpose units. The Batumi port, in particular, is examined and investigated for the first time in terms of economic effectiveness.

关键词： Communications systems Digitalization Artificial intelligence Strategic units

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Machine Learning-Powered Car Recommendation System: A Content-Based and Collaborative Approach 8th

Machine Learning-Powered Car Recommendation System: A Conten...

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8th Smart Trends in Computing and Communications (SmartCom)

作者： Reddy, Kummathi Uday Kiran Sai, Kondapaneni Lohith Rao, R. Nithin MS Ramaiah Univ Appl Sci Comp Sci & Engn Bangalore 560054 Karnataka India

ISBN: (纸本)9789819713257;9789819713264

This research introduces an advanced car recommendation system designed to provide tailored guidance to consumers in the car purchasing process. By incorporating user input and preferences expressed in natural language, the system offers personalized recommendations based on vehicle body types. Comparative analysis reveals the multiclass random forest algorithms' superior performance in content-based filtering and item-item collaborative filtering, surpassing neural networks for multiclass classification, logistic regression, and SVM algorithms. The hybrid approach, combining these techniques, demonstrates its effectiveness in delivering high-quality, personalized suggestions. This research not only contributes to the field of recommendation systems but also offers a practical tool to assist individuals in navigating the process of car selection.

关键词： Recommendation system Content-based filtering Collaboration-based filtering Multiclass random forest algorithm

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Prediction of Ionospheric TEC Using RNN During the Indonesia Earthquakes Based on GPS Data and Comparison with the IRI Model 4th

Prediction of Ionospheric TEC Using RNN During the Indonesia...

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4th Congress on Intelligent systems (CIS)

作者： Mukesh, R. Dass, Sarat C. Kiruthiga, S. Mythili, S. Vijay, M. Shree, K. Likitha Abinesh, M. Ambika, T. Pooja ACS Coll Engn Dept Aerosp Engn Bangalore India Heriot Watt Univ Sch Math Comp Sci Putrajaya Malaysia Saranathan Coll Engn Dept ECE Trichy India PSNA Coll Engn & Technol Dept ECE Dindigul India ACS Coll Engn Dept Aeronaut Engn Bangalore India

ISBN: (纸本)9789819990368;9789819990375

Total electron content (TEC) is a significant descriptive measure for the ionosphere of the earth. Due to either the sun's activity like solar flare or the positive hall effect caused during earthquake (EQ), the oxygen atoms of the ionosphere split into oxygen ions and electrons increasing the electron content in the ionosphere which causes a rise in the TEC value, thus causing the delay in the signals coming from the satellite to the earth. TEC is associated with the Sun's parameter and geomagnetic indices. In this research, parameters such as planetary K and A-index (Kp and Ap), Radio flux at 10.7 cm (F10.7), Sunspot number (SSN), and IONOLAB true TEC values were collected for the BAKO IGS network station situated in Indonesia (- 6.45 degrees N, 106.85 degrees E) for predicting TEC variations during EQ days occurred in the years 2004 and 2012. A total of three months of TEC data from the BAKO station during the years 2004 and 2012 were used for the developed Recurrent Neural Network (RNN) model in order to predict the TEC before and after the EQ days. For the year 2004, the model has an average Root Mean Square Error (RMSE) and Correlation Coefficient (CC) of 6.79 and 0.90. Also, for the year 2012, during April it has the average RMSE and CC of 8.90 and 0.94. For the same year in August month, the model has the average RMSE and CC of 8.70 and 0.94. The performance of the model is also evaluated using linear regression scatter plot. The Pearson's R value calculated from the scatter plot is 0.92, shows that the model has good correlation with the true TEC.

关键词： TEC BAKO IONOLAB RNN

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Big Data and Management Control in Tourist Destinations 4th

Big Data and Management Control in Tourist Destinations

引用

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

作者： Salmi, Kacem Hmioui, Aziz Sidi Mohamed Ben Abdellah Univ Fez Natl Sch Business & Management Fes Morocco

ISBN: (纸本)9783031686597;9783031686603

Big Data has become a determining factor in the day-to-day and strategic management of organizations. It is a new technology that offers many advantages to businesses. It enables patterns and connections in data to be analyzed. These analyses enable good decisions to be made based on exhaustive information and data. In this way, Big Data enables the analyses used by organizations to optimize their business processes and, as a result, become more efficient. Thus, management control is associated with the role of supporting data, which constitutes its solid foundation, its relevance, its reliability and can influence the main objectives of management control. This paper aims to show whether Big Data can influence management control so that it functions well and achieves the main objectives of the destination while improving its performance. The study also shows that Big Data is used at all levels;strategic and operational;of the destination for the determination of tourism strategies and the operational management of the tourism activity.

关键词： Big Data management control performance tourism

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FST-GNN: A Fuzzy-Based Spatial-Temporal Graph Neural Network for Traffic Flow Prediction During Emergent Epidemics

FST-GNN: A Fuzzy-Based Spatial-Temporal Graph Neural Network...

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International Conference on Intelligent and Fuzzy systems (INFUS)

作者： Aw, Jiyao Zhang, Xuan Fang, Ju Liu, Qingqin Hunan Univ Coll Comp Sci & Elect Engn Changsha Peoples R China

ISBN: (纸本)9783031671944;9783031671951

Traffic flow prediction is very essential in traffic cognition management of modern cities, especially in the case of emergent epidemics. Due to epidemics having a huge impact on citizen travel and government control, some emergencies make traffic data increasingly dynamic, irregular, uncertain, etc., which brings a significant challenge for accurate traffic estimation or forecasting. In this study, a novel fuzzy-based spatial-temporal graph neural network approach called FST-GNN is proposed for traffic flow prediction during emergent epidemics. The model is based on emergent epidemics represented by COVID-19 and Omicron which introduces a fuzzy inference mechanism to obtain the fuzzy representation and extract features of emergent epidemic-related data such as confirmed cases, confirmed deaths, and government response indicators. And, graph neural networks are constructed to capture the dynamic spatial and temporal correlation of traffic data. At last, experiments on the two real-world datasets (bikes and taxis) in New York City are developed to show that our model achieves better effectiveness and superiority compared to other existing models.

关键词： Traffic flow prediction Emergent epidemic data Fuzzy inference mechanism Graph neural networks Spatiotemporal traffic features

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Social Media Emotion Detection and Analysis System Using Cutting-Edge Artificial Intelligence Techniques 9th

Social Media Emotion Detection and Analysis System Using Cut...

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

作者： Rayhan, Tapu Siddika, Ayesha Hasan, Mehedi Elme, Nafisa Sultana World Univ Bangladesh Dhaka 1230 Bangladesh

ISBN: (纸本)9789819732883;9789819732890

This paper presents an in-depth analysis of the Social Media Emotion Detection and Analysis System (SM-EDS) using cutting-edge artificial intelligence (AI) techniques. The study explores the effectiveness of AI in text-based emotion recognition models to detect emotions from social media data. The research involves state-of-the-art natural language processing algorithms, deep learning models, and sentiment analysis techniques, using diverse social media data sets for training and validation. The critical aspects of the Social Media Emotion Detection system (SM-EDS), including data prepossessing, model architecture, and evaluation metrics, are investigated. Ethical implications and societal impact are discussed, highlighting the potential of AI-driven emotion detection systems to revolutionize social media analytics. The paper emphasizes responsible deployment and identifies challenges and future research opportunities in this promising field.

关键词： Artificial intelligence Emotion detection Machine learning Sentiment analysis Social media

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Feature Selection for the Shear Stress Classification of Hip Implant Surface Topographies 2nd

Feature Selection for the Shear Stress Classification of Hip...

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

作者： Vulovic, Aleksandra Geroski, Tijana Filipovic, Nenad Univ Kragujevac Fac Engn Sestre Janjic 6 Kragujevac 34000 Serbia Bioengn Res & Dev Ctr BioIRC Prvoslava Stojanovica 6 Kragujevac 34000 Serbia

ISBN: (纸本)9783031608391;9783031608407

In order to determine the optimal hip implant surface topography that minimizes the shear stress during everyday activities, a large number of models need to be created and analyzed. To understand how different model parameters affect the shear stress values and distributions, the parameters are varied during the model creation process. Depending on the complexity of the model and the number of elements for the finite element simulation, the time needed to obtain the results can vary from a few minutes to a few hours. The aim of this study was to analyze the application of feature selection algorithms as a way to understand which parameters have the highest impact on the shear stress results. Two algorithms were considered - a tree-based model and Principal Component Analysis. The used dataset consisted of 64 models, previously analyzed using the finite element method. There were 11 input parameters and a single target variable. This approach can reduce the number of numerical models that need to be created and analyzed, thus saving time and resources. The obtained results indicated that the most important parameters can be extracted even when working with a small dataset such as the one considered in this paper.

关键词： feature selection hip implant surface topographies

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