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Dimensional Reduction to Improve Learning Recommender systems Precision Based on Grades 9th

Dimensional Reduction to Improve Learning Recommender System...

引用

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

作者： Bojorque, Rodolfo Plaza, Andrea Moscoso, Fernando Univ Politecn Salesiana Cuenca Ecuador

ISBN: (纸本)9789819733019;9789819733026

The incorporation of optional subjects into higher education curricula is a ubiquitous practice, affording students the latitude to pursue coursework aligned with their interests across diverse knowledge domains such as humanities, arts, sciences, and technology, along with supplementary offerings. Despite this expansive selection, students often grapple with an inundation of information when confronted with selecting optional subjects. Recognizing the extensive dataset encompassing student grades and subject enrollment, this study proposes the implementation of a Learning Recommender System, based on collaborative filtering, a machine-learning technique. This method harnesses students' academic success in particular subjects to proffer judicious recommendations to their counterparts. A challenge encountered by recommender systems is the issue of data sparsity, given that not all students undertake every available course, resulting in a sparse matrix. To surmount this challenge, the study advocates for the application of dimensional reduction techniques, which serve to transform the sparse matrix into a more condensed yet informative representation. This reduction augments the predictive efficacy, and versatility for employing a spectrum of machine-learning techniques. The Matrix Factorization model emerges as the preeminent choice in contemporary recommender systems. The research substantiates the efficacy of these methodologies through an empirical examination of data sourced from Salesian Technical University, comprising records of ten thousand students and one thousand subjects over a five-year duration. In essence, the study conclusively demonstrates that dimensional reduction is indispensable for facilitating the data sparsity predicament in recommender systems, thereby enhancing the efficiency and efficacy of the proposed Learning Recommender System.

关键词： Learning recommender systems Artificial intelligence Machine learning

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Approaches of Safe and Efficient 3D UAM Traffic Management Using VR, AR, and MR with AI;HCI Requirements for Digital Twin Application 18th

Approaches of Safe and Efficient 3D UAM Traffic Management U...

引用

18th International Conference on Intelligent Autonomous systems (IAS)

作者： Oh, Chang-Geun Lee, Mikyoung Hanseo Univ Taean South Korea DfR Consulting Inc Seoul South Korea

ISBN: (纸本)9783031449802;9783031449819

This paper highlights the digital twin (DT) application that utilizes VR/ AR/MR to develop high-level urban air mobility (UAM) operation monitoring systems. UAM operations may require precision 3D maps for planning, monitoring, and debriefing purposes. Recent VR and MR technologies have implemented vivid 3D graphics, and these are subject to the digital twin (DT) for the UAM monitoring system. Digital twin (DT) is a virtual content that visualizes synchronized features of an existing system of interest. DT has two paradigms-functional DT and physio-functional DT for its usage. This paper proceeds with a discussion of six human-DT interaction issues-the reality of 3D graphics, AI functions, DT users' allocentric versus egocentric viewpoints, accuracy of spatial synchronization, and systems engineering process-for safe and efficient UAM operations.

关键词： Virtual reality Augmented reality Mixed reality AI Digital twin Urban air mobility Human-computer interaction

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Computing the Shortest Vector in a Lattice Using the Alternating Direction Method of Multipliers

Computing the Shortest Vector in a Lattice Using the Alterna...

引用

Computing Conference

作者： Al Kendi, Wissam Chretien, Stephane Guyeux, Christophe Univ Franche Comte IUT Belfort Lab DISC FEMTO ST Belfort France

ISBN: (纸本)9783031622830;9783031622816;9783031622809

Computing the shortest vector in a lattice has many applications in computer science and engineering such as in cryptography, coding theory, and quantum computing. One of the best known algorithms for computing such shortest vectors is the Lenstra, Lenstra and Lovasz (LLL) algorithm. Unfortunately the LLL algorithm has a worst-case exponential running time, and its practical performance depends heavily on the lattice structure. In this paper, we propose an efficient implementation of the Alternating Direction Method of Multipliers for finding the shortest vector in a lattice. Our algorithm is an improved stochastic version of the ADMM algorithm proposed by Takapoui, Moehle, Boyd and Bemporad. The performance of our method is illustrated via extensive numerical experiments on various lattice structures. The numerical results show that our method significantly outperforms the LLL algorithm in terms of running time and solution quality.

关键词： Shortest vector Lattice LLL algorithm ADMM algorithm

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Adaptive Bayesian Optimization for Robotic Pushing of Thin Fragile Deformable Objects 6th

Adaptive Bayesian Optimization for Robotic Pushing of Thin F...

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6th Iberian Robotics Conference (Robot)

作者： Herguedas, Rafael Sundaram, Ashok M. Lopez-Nicolas, Gonzalo Roa, Maximo A. Saguees, Carlos Univ Zaragoza Inst Invest Ingn Aragon Zaragoza Spain German Aerosp Ctr DLR Inst Robot & Mech D-82234 Wessling Germany Inst Tecnol Aragon Zaragoza Spain

ISBN: (纸本)9783031586750;9783031586767

Robotic manipulation of deformable objects is challenging due to the great variety of materials and shapes. This task is even more complex when the object is also fragile, and the allowed amount of deformation needs to be constrained. For the goal of driving a thin fragile deformable object to a target 2D position and orientation, we propose a manipulation method based on executing planar pushing actions on the object edges with a robotic arm. Firstly, we obtain a probabilistic model through Gaussian process regression, which represents the time-varying deformation properties of the system. Then, we exploit the model in the framework of an Adaptive Bayesian Optimization (ABO) algorithm to compute the pushing action at each instant. We evaluate our proposal in simulation.

关键词： Manipulation of deformable objects robotic pushing Bayesian optimization

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Enhancing Decision Support systems for the Energy Sector with Sustainable Artificial Intelligence Solutions 1

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Intelligent systems Conference (IntelliSys)

作者： Mouzakitis, Spiros Markaki, Ourania Papapostolou, Katerina Karakolis, Evangelos Pelekis, Sotiris Psarras, John Natl Tech Univ Athens Decis Support Syst Lab Athens Greece

ISBN: (数字)9783031477249

ISBN: (纸本)9783031477232;9783031477249

Artificial intelligence (AI) can significantly enhance the decisionmaking processes in a plethora of key energy-related challenges such as energy supply and demand prediction, grid flexibility, asset maintenance and operation, and load forecasting. As such, it is expected to play a crucial role in the digitalisation and sustainability of the energy industry. Despite the potential benefits of AI and the wealth of research in the related fields, several factors, including a lack of in-house AI expertise, data interoperability issues, unclear regulations, and ethical concerns, prevent stakeholders from the Electric Power and Energy systems (EPES) sector from taking a universal approach to the EPES value chain and fully utilizingAI's potential. This paper presents ourwork that aims to address the former challenges by proposing a holistic approach to utilizing, developing, and deploying state-of-the-art AI and data analytic services for the decision makers of the energy sector. The framework will be seamlessly integrated with the AI-on-Demand platform, i.e. Europe's one-stop-shop for AI assets. The paper introduces the I-NERGY framework and describes its bidirectional relation to the AIoD platform under the light of contributing to the EPES value chain optimal management, particularly for SMEs and non-tech industries.

关键词： Decision Support Artificial Intelligence Big Data Energy Machine Learning Analytics

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SmartCrop: An IoT-Powered Crop Health Monitoring System 1

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8th Smart Trends in Computing and Communications (SmartCom)

作者： Sai, P. Sarvan Sri Sunil, Abhinav Mukesh, Koraganji Matcha, Mukhesh Balaji Valsan, Vipina Pandi, V. Ravikumar Sathyan, Soumya Suresh, Kavya Amrita Vishwa Vidyapeetham Dept Comp Sci & Engn Amrita Sch Comp Amritapuri India Amrita Vishwa Vidyapeetham Dept Elect & Elect Engn Amritapuri India

ISBN: (数字)9789819713233

ISBN: (纸本)9789819713226;9789819713233

SmartCrop is an innovative IoT-based crop health monitoring system that integrates crucial sensors and cutting-edge technology to transform modern agriculture. It excels in monitoring environmental conditions and crop health, employing advanced algorithms for precision irrigation and rapid threat detection while providing real-time data on temperature, humidity, and soil moisture. Farmers can access this valuable information through a user-friendly cloud platform, enabling informed decisions for sustainable and resource-efficient farming. SmartCrop not only boosts crop quality and yields but also aligns with eco-conscious farming practices, conserving water, and reducing pesticide use. In a world marked by climate uncertainty and rising food demand, SmartCrop represents a beacon of hope, enhancing food security, reducing environmental impact, and promoting agricultural sustainability. Moreover, SmartCrop aligns with several United Nations Sustainable Development Goals (SDGs). It contributes to SDG 2 (Zero Hunger) by optimizing crop yields and quality, SDG 13 (Climate Action) by enhancing agricultural resilience, SDG 15 (Life on Land) through biodiversity-friendly practices, SDG 12 (Responsible Consumption and Production) by promoting resource-efficient farming, and SDG 9 (Industry, Innovation, and Infrastructure) as a testament to technology-driven agricultural innovation. SmartCrop showcases the power of technology to shape a more environmentally responsible and economically viable future for agriculture while exemplifying innovation for a more sustainable planet.

关键词： IoT agriculture Crop health monitoring Sensor integration Precision farming Sustainable agriculture Smart farming

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Hybrid Algorithm of Adhesive Joint Shape Optimization

Hybrid Algorithm of Adhesive Joint Shape Optimization

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

作者： Kurennov, Serhii Barakhov, Kostiantyn Taranenko, Igor Poliakov, Olexandr G. Barakhova, Hanna Vernadska, Kristina Natl Aerosp Univ Kharkiv Aviat Inst Kharkiv Ukraine

ISBN: (纸本)9783031614149;9783031614156

The subject of this article is the algorithm for optimization of symmetrical adhesive lapped joint. The goal of the paper is development of the method for optimization problem solving, which allows to unite high rate of calculations with stability of obtained results. This goal can be reached by means of two algorithms of optimization - genetic algorithms implemented on the original stage and swarm of particles algorithm - on the last stage of optimization. The problem of optimization is in finding optimal shape of doublers, i.e. doubler length and function of thickness variation along joint length. To describe stress-strain state of a joint modified Holland-Reissner model is used. To solve direct problem of structural stress state estimation the finite elements method is used. For optimization problem solving combination of multi-population model of genetic algorithm and swarm of particles algorithm are used. Introduction of individuals from other populations to the considered one allows to escape of homogenization of genotypes in separate population and premature breakage of optimization process. To describe doubler shape thickness variation function development of Fourier series are used. Above-mentioned implemented methods allow to create algorithm for topologic optimization which unites advantages of both methods and find solution of considered problem quite quickly. Duration of algorithm realization on Python language requires several minutes only to find optimal parameters.

关键词： Glued Joint Optimization Genetic Algorithm Particle Swarm Optimization

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Simplified Extended Belief-Rule-Based System for Classification Problems 1

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16th International Conference on Ubiquitous Computing and Ambient Intelligence

作者： McHugh, Orla Liu, Jun Noriega, Jaime Solano Jordan, Phil Browne, Fiona Ulster Univ Sch Comp Belfast Campus Belfast Antrim North Ireland Univ Autonoma Occidente Los Mochis Sinaloa Mexico Ulster Univ Sch Geog & Environm Sci Coleraine Campus Coleraine Londonderry North Ireland Datactics Belfast Antrim North Ireland

ISBN: (数字)9783031775710

ISBN: (纸本)9783031775703;9783031775710

Extended Belief Rule-Based (EBRB) system, as an advanced rule-base system, has attracted great attention recently due to its nature of data-knowledge integration, transparency, uncertainty handling, as well as overcoming the limit of combinational explosion of traditional rule-base systems. Despite their strengths, EBRB systems face challenges about computational efficiency and predication accuracy. This study aims to address these challenges by simplifying the EBRB structure through the removal of referential sets of values in implementation, thus enhancing computational efficiency and predication accuracy. Empirical study shows that this approach achieves competitive results compared with benchmarks from other state-of-the-art methods.

关键词： belief rule base extended belief rule base classification data-driven

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Using Natural Language Processing and Data Mining for Forecasting Consumer Spending Through Social Media

Using Natural Language Processing and Data Mining for Foreca...

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Intelligent systems Conference (IntelliSys)

作者： Mostafa, Noha Abdelazim, Kholoud Grida, Mohamed British Univ Egypt Cairo 11837 Egypt Zagazig Univ Zagazig 44519 Sharkia Egypt

ISBN: (纸本)9783031477171;9783031477188

With the considerable rise in social media users, the platforms are no longer limited to sharing blog posts and emotions and making online friends. It is currently used for quite larger purposes which have created what is called the Big Data. This research paper covers one of the most recent studies in forecasting, which is forecasting through social media. Social media forecasting is a very promising yet controversial technique that numerous researchers have been quite interested to investigate. With the Big Data challenges, social media forecasting requires a few advanced machine learning and social media analytic techniques such as: data mining and sentimental analysis. The objective of this research covers the study of forecasting consumer intentions and understanding whether it can be possible to make future predictions about consumers' spending from the enormous data shared every day. Data used in the experimental procedures of this research was collected in two methods, public surveys, and data mining techniques. The survey included in this research was publicly shared for people on a social media platform to anonymously participate and share their experience. For data mining techniques, some algorithms are proposed such as Part-of-speech Tagging, word2vec and semantic vectors. Forecasting techniques are applied as Time series analysis and SARIMA. The results discuss the different errors with each method to conclude which is the most accurate forecasting technique in our model.

关键词： Purchase intentions Sentiment analysis Big data analytics Social media NLP Time series analysis Regression models Quantitative forecasting

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Performance Analysis of Synthetic Events via Visual Object Trackers

Performance Analysis of Synthetic Events via Visual Object T...

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Computing Conference

作者： Alansari, Mohamad AlRemeithi, Hamad Alansari, Sara Werghi, Naoufel Javed, Sajid Khalifa Univ Dept Elect Engn & Comp Sci Abu Dhabi U Arab Emirates Tawazun Technol & Innovat Abu Dhabi U Arab Emirates Khalifa Univ Ctr Autonomous Robot Syst Abu Dhabi U Arab Emirates Khalifa Univ Ctr Cyber Phys Syst Abu Dhabi U Arab Emirates

ISBN: (纸本)9783031622717;9783031622694;9783031622687

This study investigated the effectiveness of synthetic events in enhancing the accuracy of visual object trackers, particularly in scenarios where conventional RGB data encounters difficulties, such as with rapidly moving objects, motion blur, and varying lighting conditions, thereby highlighting the potential of event cameras in tracking applications. Synthetic events were generated from RGB Visual Object Tracking (VOT) datasets using the v2e toolbox and post processed in Inivation Dynamic Vision (DV) software. This post processed data was subsequently fused with traditional RGB data. Evaluation was conducted through the Pytracking library to measure potential tracking improvements. The results showed a notable increase in tracking efficacy upon the integration of post processed synthetic events with RGB data. Conclusively, synthetically generated events have the capacity to augment current state-of-the-art (SOTA) VOT frameworks with minimal Neural Network (NN) adjustments.

关键词： Event cameras Fusion technique Neuromorphic vision Visual object tracking (VOT)

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