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Digital Assistant to Improve User Experience on Websites: Innovative Support and Efficient Communication Strategies 12

Digital Assistant to Improve User Experience on Websites: In...

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12th international conference in Software Engineering Research and Innovation, CONISOFT 2024

作者： Cervantes, Leticia Candolfi-Arballo, Norma Mejia-Medina, David A. Ugalde-Garduno, Fernanda Sarraipa, Joao Universidad Autónoma de Baja California Facultad de Ciencias de la Ingeniería y Tecnología Tijuana Mexico Nova School of Science and Technology Cts Uninova Lisboa Portugal

ISBN: (纸本)9798331532116

The integration proposal of an innovative chatbot into the Yachay system highlights its current status as an internal project, yet to be fully integrated into the educational framework. Within Yachay Tech, the initiative named 'intelligent systems for Personalization and Flexibilization' signifies a proactive leap towards improving the quality and accessibility of higher education in Latin America. At the core of the Yachay website lies a pioneering chatbot strategically poised to debut and revolutionize virtual education. Drawing insights from user satisfaction studies and subsequent design iterations, the proposal aims to enhance crucial metrics such as adaptability, user experience, and personalized engagement. While the chatbot's potential benefits remain to be validated against real-world outcomes, the methodology section meticulously outlines its implementation process. This includes developing logic, integrating with Yachay databases, ensuring interactivity, managing exceptions, and conducting trial implementations. Adapted assets from diverse countries within the Yachay project collaborated seamlessly to ensure integration success. Digital assistants envisioned as pivotal components within the Yachay project are set to drive qualitative and quantitative advancements in the educational landscape. Despite the chatbot's current proposal stage, its potential to revolutionize virtual education is evident, offering a glimpse into a future where adaptability, engagement, and innovation converge to redefine the educational experience. It's noteworthy that internal tests preceded the testing phase with various user types. These tests involved diverse phrases and words, assessing metrics like response time, which averaged a swift 2.1 seconds. These internal evaluations are crucial steps towards implementing the proposal effectively within the system for users. © 2024 ieee.

关键词： intelligent systems

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Research on Geospatial Object Recognition Algorithm in Power Transmission and Transformation Engineering Based on FIseg-YOLO 1

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8th international conference on Life system Modeling and Simulation, LSMS 2024 and 8th international conference on intelligent Computing for Sustainable Energy and Environment, ICSEE 2024

作者： Ouyang, Peng Pi, Yufei Zhang, Pengcheng Zhang, Kun School of Electrical Engineering and Automation Nantong University Nantong China Nantong Key Laboratory of Intelligent Control and Intelligent Computing Nantong Institute of Technology Jiangsu Nantong China

ISBN: (数字)9789819603138

ISBN: (纸本)9789819603121

The preliminary work of power transmission and transformation engineering design tasks relies on the precise survey of terrain to ensure the high-quality completion of design tasks. Therefore, developing a technology that can accurately and efficiently survey the construction area has become a hot topic in the field of electrical design research. However, identifying the location and precise boundaries of geospatial targets of different scales and densities from high-resolution natural environment images captured by drones remains a challenge. To address this issue, we improved upon the YOLOv8 algorithm by incorporating the concept of the YOLACT algorithm, proposing a novel instance segmentation algorithm—FIseg-YOLO, specifically designed for recognizing geospatial targets in power transmission and transformation line projects. This algorithm integrates various attention mechanism modules in the backbone network, effectively improving the accuracy of target recognition against complex backgrounds. Simultaneously, we optimized the neck network structure by enhancing the multi-path fusion of the network, achieving multi-level feature fusion between different layers. Additionally, the algorithm adds a multi-scale detection head to the basic network structure to extract shallow information from the input image and combine it with the feature fusion network to detect objects across a broader scale. Experimental validation on the geospatial orthophoto dataset shows that the mAP@.5 of the FIseg-YOLO model reached 67.3%, a 6.6% increase compared to the YOLOv8 base model, indicating a significant improvement in model performance. This improvement is of great significance for enhancing the efficiency of preliminary survey tasks in power transmission and transformation engineering. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Deep learning

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intelligent Funds Assistant Exploiting Hierarchical Text Classification Algorithms

Intelligent Funds Assistant Exploiting Hierarchical Text Cla...

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ieee international conference on Fuzzy systems (FUZZ-ieee) / ieee World Congress on Computational Intelligence (ieee WCCI) / international Joint conference on Neural Networks (IJCNN) / ieee Congress on Evolutionary Computation (ieee CEC)

作者： Saraiva, Ines Moniz, Daniela Almeida, Alexandre Sousa, Joao Vieira, Susana Axians Digital Consulting Funds & Investment Lisbon Portugal Axians Digital Consulting Coinnovat Grp Lisbon Portugal Inst Super Tecn IDMEC Lisbon Portugal

ISBN: (数字)9781728186719

ISBN: (纸本)9781728186719

The need to ensure that all funding projects made available by the European Union are fully exploited by each country leads to a robust investigation of the main difficulties of applying to these projects. In this context, Portugal has verified that these funds are not being fully used and detected that one of the main setbacks in the European Union funding application system is the identification of the best call for application. Therefore, this work aims at developing a funds assistant framework to suggest the most suitable set of available calls for application based on the written description of the beneficiaries' projects, via the use of Natural Language Processing and text classification algorithms. Moreover, this problem is addressed as a hierarchical multi-class text classification, taking advantage of the hierarchical structure inside the European funds and aiming to improve the overall performance of the proposed framework. In this context, four models are chosen to compare the classification performance: Naive Bayes, Support Vector Machines, Random Forest and k-Nearest Neighbors. Simulation results highlight that the Support Vector Machine outperforms the rest of the algorithms for almost all classification scenarios. In order to enhance the results, the models are also calibrated to return the second prediction when the likelihood of the first prediction is small, achieving even higher performances for all levels of the hierarchy.

关键词： Machine Learning Natural Language Processing Hierarchical Text Classification Support Vector Machines TF-IDF Vectorization

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An Ensemble Learning Approach for Wind Power Forecasting 3

An Ensemble Learning Approach for Wind Power Forecasting

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3rd international conference on Electronic Information Engineering and Computer, EIECT 2023

作者： Huang, Yuesheng Chen, Sida Wu, Qiling Xiao, Jiapeng Ren, Yongqi Wang, Leijun Chen, Rongjun Li, Jiawen Guangdong Polytechnic Normal University School of Computer Science Guangzhou China Wuhan University of Science and Technology Hubei Province Key Laboratory of Intelligent Information Processing and Real-Time Industrial System Wuhan China

ISBN: (纸本)9798350357707

With the growing global energy demand and requirement for environmental protection, renewable energy is attracting attention as a vital development direction. Particularly, wind power is rapidly developing as a clean and sustainable form of energy. Nonetheless, wind speed uncertainty and system dynamics make wind power prediction and optimization complex and challenging. Accurate wind power prediction is essential to improve system reliability and performance. In this regard, traditional forecasting methods have limitations in some cases. In this study, based on XGBoost and decision tree with Bagging methods in ensemble learning, wind power data is predicted. The results indicated that better performances than the traditional linear regression and ordinary decision tree models are achieved, revealing the efficiency of the proposed approach. © 2023 ieee.

关键词： Decision trees

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Disentangling Client Contributions: Improving Federated Learning Accuracy in the Presence of Heterogeneous Data 21

Disentangling Client Contributions: Improving Federated Lear...

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21st ieee international Symposium on Parallel and Distributed Processing with Applications, 13th ieee international conference on Big Data and Cloud Computing, 16th ieee international conference on Social Computing and Networking and 13th international conference on Sustainable Computing and Communications, ISPA/BDCloud/SocialCom/SustainCom 2023

作者： Liu, Chunming Alghazzawi, Daniyal M Cheng, Li Liu, Gaoyang Wang, Chen Zeng, Cheng Yang, Yang Hubei University Key Laboratory of Intelligent Sensing System and Security Ministry of Education School of Artificial Intelligence Wuhan China King Abdulaziz University Jeddah Saudi Arabia Huazhong University of Science and Technology Wuhan China

ISBN: (纸本)9798350329223

Federated Learning (FL) is a promising paradigm that leverages distributed data sources to train machine learning models, thereby offering significant privacy advantages. However, the inherent statistical heterogeneity among clients, characterized by distinct data distributions and varying model performance, poses a substantial challenge. Such heterogeneity adversely affects both the convergence and overall performance of the global model within the FL framework. To address this issue, we introduce a novel FL algorithm, FedVa, which considers both the local data volume and model accuracy to determine client contributions, assigning respective weights. This consideration facilitates greater engagement from key clients during the aggregation process, thereby enhancing their influence on the global model refinement. Experimental results indicate that FedVa surpasses prevailing methods in managing statistical heterogeneity and enhancing global model accuracy, without incurring additional communication costs. Specifically, FedVa achieves a convergence speed 3.9 times faster than FedAvg on the MNIST dataset, and on the CIFAR-10 dataset, it results in a 2.3% increase in final model accuracy compared to FedAvg. Our code is publicly available at https://***/ChunmingLiu23/FedVa. © 2023 ieee.

关键词： Learning systems

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Development of the Concept and Architecture of an Automated system for Updating Physical Knowledge for Information Support of Search Design

Development of the Concept and Architecture of an Automated ...

引用

2023 international Russian Smart Industry conference, SmartIndustryCon 2023

作者： Bobunov, Artyom Korobkin, Dmitriy Fomenkov, Sergey Volgograd State Technical University CAD Department Volgograd Russia

ISBN: (纸本)9781665464291

For developing an automated system for updating physical knowledge for information support of search design, it is necessary to choose a technology stack that would meet the implementation requirements. In view of the sanctions currently imposed on the Russian Federation, it is worth considering mainly open projects and/or domestic developments. We will highlight the main criteria that it is desirable to take into account when designing the architecture of an automated system to support the synthesis of new technical systems and technologies: (a) ability to store and process large amounts of data;(b) unification access for all data analysis procedures;(c) maximum automation of all stages;(d) modularity of the structure, focus on the expansion of functionality;(e) focus on open source solutions and software of domestic manufacturers, excluding rigid binding to paid foreign solutions. As a result of the work done, various aspects of the implementation of the required automated system were analyzed. A review of various software systems and cloud products showed that the concept of building data lakes (Data Lake) in conjunction with the distributed processing tools of the Apache Hadoop ecosystem is used for big data processing. An architecture framework based on a centralized data warehouse and Hadoop components is proposed. It will be possible to increase the functionality of the platform by adding new microservices that connect to the storage and distributed processing tools via the API, as well as using a single web service for managing and displaying data analysis results from these microservices. © 2023 ieee.

关键词： Data warehouses

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Enhancing Photovoltaic Maintenance with Real-Time Data Integration: A Sensorless and Scalable Approach Validated by intelligent Correlation Analysis 3

Enhancing Photovoltaic Maintenance with Real-Time Data Integ...

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3rd international conference on Embedded systems and Artificial Intelligence, ESAI 2024

作者： Mohamed, Karom Bekkaye, Chaieb El Mounim, Chikri Hajar, Chadli Sara, Chadli Khalid, Salmi Abdechafik, Derkaoui Abdelouahad, Tahani Mohammed First University Electronics and Systems Laboratory Faculty of Sciences Oujda Morocco Mohammed First University Laboratory of Physics Matter and Radiation Faculty of Sciences Oujda Morocco

ISBN: (纸本)9798331522056

This study introduces an innovative and cost-effective approach to gathering, storing and managing photovoltaic and meteorological data, avoiding the use of traditional sensors, wires and cables. Conventionally, systems for data collection often require costly tools as well as sophisticated cabling networks;they revolve around the same methodologies with difficult setups and complicated maintenance needs, which can limit their application, long-Term reliability and scalability in countries with budget constraints and challenging environmental conditions such as Morocco. The presented solution leverages built-in functionalities of photovoltaic inverters to collect crucial data such as real-Time generated power, current, voltage and consumed energy. Regarding weather parameters, a set of online open-source techniques has been explored to compile all necessary data required by the researchers, especially those working merely with simulated metrics, to successfully carry out their projects. Machine learning models have been developed to forecast PV power and verify the accuracy of the data, showing a high degree of correlation with inverter-based information. Particularly, decision tree regression and support vector regression were employed. The results on the test sets were as follows: The decision tree regression model achieved a mean squared error of 0.2199 and anR}^{2 score of 0.7769, while the support vector regression model presented a mean squared error of 0.0936 and a higherR}^{2 score of 0.9051. In the end, an online accessible dashboard that showcases important graphs and includes a notification center to alert users as the need arises has been designed, representing a significant step forward in the quest for photovoltaic system maintenance excellence. © 2024 ieee.

关键词： Budget control

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IoT-Aided intelligent Irrigation system Using Edge Computing 2

IoT-Aided Intelligent Irrigation System Using Edge Computing

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2nd international conference on Multidisciplinary Engineering and Applied Science, ICMEAS 2023

作者： Ayoade, Idowu Adetona Nwakanma, Cosmas Ifeanyi Alimi, Sheriff Akinde, Olusola Kunle Adebisi, Oluwadare Oladeji, Dolapo Michael Ajayi, Dolapo Boluwatife Kayode, Michael Ife First Technical University Department of Mechatronics Engineering Ibadan Nigeria Kumoh National Institute of Technology ICT Convergence Research Center Gumi Korea Republic of Babcock University Ilishan Department of Computer Science and Artificial Intelligence Remo Nigeria First Technical University Department of Electrical and Electronics Engineering Ibadan Nigeria

ISBN: (纸本)9798350358834

Farmers in developing nations are faced with limited technology for smart and economical ways of irrigation. This is worsened by the high cost of imports since most of these nations are import-dependent. Increasing agricultural yield to meet the growing population in most of these nations has led their governments to pursue cost-effective ways of managing water and other resources required for optimum agricultural yield. In this study, the Internet of Things (IoT) and edge computing are proposed to provide an intelligent irrigation system to bridge the irrigation gap. This research was carried out using a greenhouse with a controlled environment i.e., the ambient temperature, air humidity, ultraviolet radiations (UV), soil temperature, and soil moisture content were all monitored and measured in real-Time within the controlled environment. These data were collected using field sensors processed using IoT-enabled microcontrollers (Arduino kits). The data were processed via Amazon Web Services (A WS) for ontological capabilities of the system consisting of computational capabilities of IoT server and Edge Server. Internet-enabled mobile devices were used to monitor and adopt preferential yield rates for cultivation. Also, an irrigation sequencing algorithm was developed, which uses predictive machine learning K-Mean Clustering Algorithm to predict a more accurate soil moisture content with minimum standard error and a lower mean square error of 0.11 to the predicted soil content compared to when only SVR with mean square error of 0.12. © 2023 ieee.

关键词： Internet of things

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IoT Based Smart Garbage Management system

IoT Based Smart Garbage Management System

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2022 international conference on Communication, Computing and Internet of Things, IC3IoT 2022

作者： Rifat, Rifatul Islam Haque, Farah Binte Jahan, Tashreef Zahir, Zunayeed Bin Khan, Riasat North South University Department of Electrical and Computer Engineering Dhaka Bangladesh

ISBN: (纸本)9781665479950

The internet of things (IoT) is integrated to provide accountability, interrelation, and data transfer features to a vast number of different end systems. intelligent cities combine numerous sensors, electronic devices, and IoT resolutions to assemble a restful human living. One of these keys is to nourish an environmentally affable, efficient, and helpful waste managing system. However, waste management is one of the most neglected matters in developing countries, creating an urgency to address this problem. In this paper, we will develop an IoT-based intelligent waste management system for developing countries like Bangladesh, which ensures the proper collection of waste, disposal, and transportation with a minimum amount of resources. This paper reflects a capable architecture of the waste management system that ensures the proper storage of the waste within a limited period. Arduino Mega microcontroller, USR-C215 IoT WiFi, GPS and GSM modules, ultrasonic and IR sensors have been utilized to design the proposed device. When waste is dumped in the bin, if it reaches its maximum capacity, it will alarm the collectors that the trash can is complete so that the time will be saved and the trash can will not be spilled. The main concern of this paper is to develop a highly functional garbage management system that will be cost-efficient and shall assemble the collection of waste as conveniently as possible. © 2022 ieee.

关键词： Internet of things

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A New Theory on Temporary systems that Explains Project Behavior and Performance

A New Theory on Temporary Systems that Explains Project Beha...

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2022 Portland international conference on Management of Engineering and Technology, PICMET 2022

作者： Sperry, Richard C. Portland State University Dept. of Engineering and Technology Management Portland United States

ISBN: (数字)9781890843410

ISBN: (纸本)9781890843410

Although adaptive approaches to managing complex projects have shown performance improvement, projects are still not controlling the scope of work and exceed schedule and budget. Therefore, these performance problems must be caused by other factors affecting the project's behavior. This paper proposes a new system's theory that explains project behavior and performance. A project is a temporary system of resources that possess the knowledge and skills to fulfill the output scope (i.e., product, service, or result). The natural variation in project behavior and performance is the uncertainty within the project resources to fulfill the complexity of the project output. The theory is based on the following principles. First, there is natural variance is in all systems. Second, the more natural variance in a system, the more chaotic a system will behave. Third, improving the system's performance can only be achieved by reducing the natural variance in the system. The theory was developed from studying the behavior and performance of ten longitudinal IT projects that ranged in complexity over a period of seven years. © 2022 PICMET.

关键词： intelligent systems

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