The aim of the study is to examine cognitive methods for document matching in a chatbot modeling framework by utilizing Euclidean Distance, Cosine Similarity, and BERT methodologies. Five primary indications are used to carry out evaluation in testing: document matching accuracy, document matching execution time, document search efficiency, consistency of document matching results, and the quality of the document representation in the matrix. Document matching accuracy is evaluated by precision; document matching execution time is measured from the beginning to the end of the document matching process; document search efficiency is measured through evaluation of execution time and matching accuracy; the consistency of document matching results is assessed by comparing method results when tested against the same or similar queries and the quality of document representation is assessed based on the method's ability to represent documents in a matrix or vector. The test findings offer a comprehensive understanding of how well the three approaches operate and exhibit their capacity to address the unique requirements of chatbot users. These results may contribute to the advancement of language technology applications, making it possible for chatbots to deliver pertinent information more rapidly and precisely. There are 1,755 labeled question samples in the dataset, which were split up into two sets: 60% for training (1,053 pieces), and 40% for testing (702 samples) to evaluate the model's performance. The test results show the accuracy of the three methods based on five measured evaluation indications, namely Euclidean Distance 0,45%, Cosine similarity 0,59%, and BERT 0,91%. By comprehending the benefits and drawbacks of each approach, this research strengthens contributions to the growth of chatbot systems to better serve user demands and opens the door for the creation of more complex human-machine

pdf/cognition-based-document-matching-within-the-chatbot-3yozwa1lig.pdf

Cognition-Based Document Matching Within the Chatbot Modeling Framework

Research on Product Recommendation Algorithm Based on Neural Network for User Interests

This study investigates the effectiveness of various missing data imputation techniques on the performance of deep learning models for time-series forecasting using the Beijing PM2.5 dataset. Imputation methods including Mean, Mode, Median, Multiple Imputation by Chained Equations (MICE), Last Observation Carried Forward (LOCF), and K-Nearest Neighbors (KNN) are compared across different deep learning architectures: Recurrent Neural Networks (RNN), Long Short-Term Memory Networks (LSTM), Bidirectional LSTM Networks (Bi-LSTM), and Gated Recurrent Units (GRU). Performance is evaluated using metrics such as Mean Absolute Percentage Error (MAPE), Root Mean Squared Error (RMSE), and Coefficient of Determination (R2). Results indicate that the KNN imputation method consistently outperforms other techniques across all models, achieving lower MAPE (0.186) and RMSE (18.495) values. Furthermore, KNN-imputed models exhibit higher R2 values, indicating better model fit than other imputation methods. These findings underscore the importance of preprocessing in time-series forecasting and highlight the potential of KNN imputation to enhance predictive accuracy in real-world applications. Robust imputation strategies like KNN promise to improve forecasting accuracy and reliability, particularly in domains where accurate predictions are crucial for decision-making. Future research may focus on refining and integrating imputation techniques with advanced deep-learning methodologies to enhance forecasting capabilities in diverse application scenarios.

Improving Time-Series Forecasting Performance Using Imputation Techniques in Deep Learning

This research explores the vital role of ontology in learning forecasting in electrical engineering and informatics. As formally defined models of knowledge, ontologies are critical in organizing concepts for predictive learning. More than just an inquiry, our research reveals complex interconnections centered on Internet of Things (IoT) design, the semantic web, and knowledge modeling. Applications demonstrate the practical significance of ontologies in fostering intelligent connections, advancing information production, and improving interactions between computers, devices, and humans. This research introduces a comprehensive forecasting learning ontology to highlight the importance of ontologies in education, scientific inquiry, and developing systems for predictive analysis. Ontologies provide a structured framework for understanding the essence of predictive learning, encompassing key elements such as ideas, terminology, methodology, algorithms, data preprocessing, assessment, validation, data sources, application environments, interactions with technology, and learning processes. Emphasizing ontologies as indispensable instruments that drive technological development, our work underscores structured representation, semantic interoperability, and knowledge integration. In summary, this research improves the understanding of ontologies in forecasting by explaining practical applications and revealing new perspectives. Its unique contribution lies in its specific applications and natural consequences, laying the foundation for the future progress of ontology and learning forecasting, especially in educational contexts.

Forecasting learning in electrical engineering and informatics: An ontological approach

This paper aims to explore the relationship between deep learning and forecasting within the context of the Sustainable Development Goals (SDGs). The primary objective is to systematically review 38 articles published between 2019 and 2023, following PRISMA guidelines, to understand the current landscape of deep learning forecasting for SDGs. Using data from 2019-2023 allows capturing the latest developments in deep learning forecasting for Sustainable Development Goals (SDGs), while excluding data before 2019 and after 2023 is based on the desire to avoid including potentially less relevant or unpublished research and to maintain focus on the most current and contextually relevant literature. The methodological approach involves analyzing the application of deep learning methods for forecasting within various SDG fields and identifying trends, challenges, and opportunities. The literature review results reveal the popularity of LSTM models, challenges related to data availability, and the interconnected nature of SDGs. Additionally, the study demonstrates that deep learning models enhance forecast accuracy and computational performance, as measured by Mean Absolute Percentage Error (MAPE), Root Mean Square Error (RMSE), and R-squared (R2). The findings underscore the importance of advanced data preparation techniques and the integration of deep learning with SDGs to improve forecasting outcomes. The novelty of this research lies in its comprehensive overview of the current landscape and its valuable insights for researchers, policymakers, and stakeholders interested in advancing sustainable development goals through deep learning forecasting. Finally, the paper suggests future research directions, including exploring the potential of hybrid forecasting models and investigating the impact of emerging technologies on SDG forecasting methodologies. Innovative methods for imputing missing values in deep learning forecasting models could be further explored to enhance predictive accuracy and robustness.

Exploring the Role of Deep Learning in Forecasting for Sustainable Development Goals: A Systematic Literature Review

Energy use is an essential aspect of many human activities, from individual to industrial scale. However, increasing global energy demand and the challenges posed by environmental change make understanding energy use patterns crucial. Accurate predictions of future energy consumption can greatly influence decision-making, supply-demand stability and energy efficiency. Energy use data often exhibits time-series patterns, which creates complexity in forecasting. To address this complexity, this research utilizes Deep Learning (DL), Convolutional Neural Networks (CNN), Recurrent Neural Networks (RNN), Long Short-term Memory (LSTM), Bidirectional LSTM (Bi-LSTM), and Gated Recurrent Unit (GRU) models. The main objective is to improve the accuracy of energy usage forecasting by optimizing the alpha value in exponential smoothing, thereby improving forecasting accuracy. The results showed that all DL methods experienced improved accuracy when using optimum alpha. LSTM has the most optimal MAPE, RMSE, and R2 values compared to other methods. This research promotes energy management, decision-making, and efficiency by providing an innovative framework for accurate forecasting of energy use, thus contributing to a sustainable and efficient energy system.

pdf/deep-learning-approaches-with-optimum-alpha-for-energy-usage-2eep4xyp9n.pdf

Deep Learning Approaches with Optimum Alpha for Energy Usage Forecasting

Humans are facing multiple disruptions caused by the development of the industrial revolution 4.0 and the covid-19 pandemic. This changes the work pattern of the people who are required to carry out all online-based activities, including the implementation of seminars or conferences. The amount of training and socialization carried out by webinars requires a good webinar management system. The purpose of this paper is to develop a web-based information system for webinar activity management under the name webiplan. The method used in system development is the waterfall method. In the waterfall method, there are 5 stages, including requirements, design, implementation, verification, and maintenance. The subjects of this research are prospective end users of the system, namely lecturers and students who are accustomed to participating in webinars. The design and development stages are tailored to the user's needs obtained in the previous phase and are described as product backlog items. At the verification stage, three stages are used to evaluate the feasibility level of the information system, including: unit testing using whitebox testing, validation testing using the blackbox testing method, and compatibility testing. The results of whitebox testing and blackbox testing showed that every feature and user task could run well so as to provide convenience for the organizers and participants in the implementation of the webinar. This Webiplan is also compatible with various web browsers so as to give users flexibility in using it.

Webiplan as a Webinar Management Information System for Optimizing Online Seminars During the COVID-19 Pandemic

The growing demand for energy, especially in urban and densely populated areas, has driven the need for smarter and more efficient approaches to energy resource management. One of the main challenges in energy management is fluctuations in energy demand and production. To overcome this challenge, accurate and careful forecasting of hourly energy fluctuations is required. One method that has proven effective in time series forecasting is using deep learning. The research phase uses the CRISP-DM data mining methodology as a common problem solver for business and research. The scenarios tested in the study used 5 attribute selection scenarios based on correlation values based on target attributes and 2 normalization scenarios. Then, the deep learning model used is Bi-LSTM with hyperparameter tuning grid search. Performance measurement evaluation is performed with MAPE, RMSE, and R2. Based on the tests conducted, it was found that the Bi-LSTM model produced the best MAPE of 7.7256%. RMSE of 0.1234. and R2 of 0.6151 at min-max normalization. In comparison, the results on the z-score normalization are lower with the best MAPE value produced at 10.5525%. RMSE of 0.7627. and R2 of 0.4186.

Bidirectional Long Short-Term Memory (Bi-LSTM) Hourly Energy Forecasting

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Deep Learning Approaches with Optimum Alpha for Energy Usage Forecasting

Webiplan as a Webinar Management Information System for Optimizing Online Seminars During the COVID-19 Pandemic

Bidirectional Long Short-Term Memory (Bi-LSTM) Hourly Energy Forecasting